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Chapter 2An evaluation of three processing methods and the effect

of reduced culture times for faster direct identification of

pathogens from BacT/ALERT blood cultures by MALDI-TOF MS

Anne J.M. Loonen1,2,3,5 , Petra F.G. Wolffs3, Arjan Jansz1, Jeandery N.B. Bergland1, Herman Kreeftenberg4, Cathrien A. Bruggeman3, and Adriaan J.C. van den Brule1,2,5

1 PAMM Laboratory, Department of Medical Microbiology, Eindhoven/Veldhoven, The Netherlands2 Fontys University of Applied Science, Department of Medical Molecular Diagnostics, Eindhoven, The Netherlands3 Maastricht University Medical Centre, CAPHRI, Department of Medical Microbiology, Maastricht, The Netherlands

4 Catharina Hospital, Department of Intensive Care, Eindhoven, The Netherlands5 Jeroen Bosch Hospital, Department of Molecular Diagnostics, ‘s-Hertogenbosch, The Netherlands

Submitted

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Abstract

Bloodstream infections (BSI) are often nosocomial infections that are frequently caused by Gram-positive cocci and are associated with high morbidity and mortality rates. Although culture techniques remain the gold standard for pathogen identification they are hampered by the lengthy time-to-result. To accelerate the diagnostic procedure, two multiplex real-time PCRs were developed to detect these pathogens.The PCRs were evaluated using 100 bacterial strains to determine the specificity. Additionally, 317 blood cultures and 30 whole blood samples were analysed, to study the potential clinical value of this multiplex assay. Five bacterial DNA isolation methods were compared to determine the optimal sensitivity of this assay on remnant whole blood samples.The analytical sensitivity of this real-time PCR is 0.4 CFU for Staphylococcus aureus and 4 CFU for Enterococcus faecalis. Both the S. aureus and the Enterococcus spp. probes indicated to be 100% sensitive and specific. The PCRs showed 100% sensitivity and specificity on positive blood culture bottles containing Gram-positive cocci. The optimal DNA detection limit from 1 ml whole blood was 10 CFU/ml, obtained with the MolYsis Complete kit. This method was therefore used on 1 ml EDTA blood of 30 patients suspected of having BSI, to show proof-of-principle. Of the whole blood samples analysed, identical results between culture and PCR were found in 81.8% (18/22) of the cases. All CoNS and S. aureus samples were confirmed with PCR. In conclusion, sensitive and specific real-time PCR assays were developed enabling accurate and fast detection of pathogens from clinical samples indicative for Gram-positive cocci. The pilot study on remaining EDTA blood of suspected sepsis patients supports further evaluation in a larger patient group.

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Development and clinical evaluation of a tuf gene based real-time PCR | 3

Introduction

Bloodstream infection (BSI) is a serious medical condition. Gram-positive microorganisms, especially Staphylococcus aureus, coagulase-negative staphylococci (CoNS), and Enterococcus species, account for the majority of episodes of bacteremia in critically ill patients in the intensive care unit (ICU) [1, 2]. CoNS are increasingly recognized as causative agents for both community-acquired and nosocomial bloodstream infections worldwide, mainly due to increased usage of medical devices [3]. A rapid and reliable method to detect these pathogens is therefore of clinical importance. Early detection and subsequent adequate treatment of bloodstream infections is critical for patient outcomes [4].Blood cultures are still considered as the gold standard for pathogen detection from blood. However, this technique is slow, taking 24-72 hours, and can result in false-negative results (30%) even when the patient shows clear signs of systemic infection [5]. This is frequently thought to be the result of previous administration of antibiotics [1].Nucleic acid based techniques offer a rapid and sensitive option for detecting pathogens directly from clinical specimens [6-11]. However, blood consists of many cells containing genomic DNA. This highly abundant genomic DNA might decrease the sensitivity of a PCR specifically detecting bacterial DNA. An optimal sample processing method should diminish human DNA, concentrate pathogen DNA and wash out inhibitory factors present in clinical samples. Several studies have investigated both automated and manual methods for isolation of bacterial DNA from several clinical specimens [12-16]. Sample preparation is of crucial importance when a sensitive test result needs to be obtained. This study aims to develop a fast and sensitive PCR to detect S. aureus, CoNS, and Enterococcus spp, based on the tuf gene, which has been described as a reliable target for detection of staphylococci [17, 18]. Furthermore, this assay will be clinically evaluated using blood culture materials and whole blood specimens of sepsis suspected patients, using a combined sensitive pathogen DNA isolation method and in-house PCR.

Materials and Methods

Bacterial strainsBacterial strains (n=100), commonly encountered in our laboratory were selected (Table 1). The bacteria tested were mainly clinical isolates (n=95), although a few reference strains (ATCC 33591 MRSA, ATCC 25923 MSSA, ATCC 12228 S. epidermidis, RIVM 122 S. hominis, and ATCC 29212 E. faecalis) were used (derived from the American Type Culture Collection and the National Institute for Health and Environment (RIVM, Bilthoven, the Netherlands)). All clinical isolates were identified by standard culture and biochemical methods used in our certified diagnostic laboratory (PAMM laboratories, Veldhoven, The Netherlands).

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Clinical samplesThree hundred and seventeen positive blood cultures were used, which consisted of BacT/ALERT Standard Aerobe (SA) and Standard Anaerobe (SN) bottles (bioMérieux, Marcy L’Etoile, France). Bottles were handled according to manufacturer’s instructions. After being flagged positive by the BacT/ALERT 3D system (bioMérieux), Gram staining was performed. Positive blood cultures containing either Gram-positive cocci or Gram-negatives were included in the study. All positive blood cultures were further evaluated by routine culture and biochemical methods. The negative culture bottles had been incubated for 1 week and remained negative before inclusion in this study. Whole blood was obtained from patients admitted to the Intensive Care Unit (ICU) of the Catharina Hospital, Eindhoven, The Netherlands. Patients were included in the study when showing ≥ 2 signs of systemic inflammatory response syndrome (SIRS) and were suspected of having a bloodstream infection [19]. Duration of hospital stay was not taken into account. Patients (n=30) were selected by the intensive care physician. For routine diagnostic purposes both blood cultures (n=22) for pathogen detection and EDTA whole blood were collected. After routine diagnostics, 1 ml remnant whole blood of these patients was obtained after blood culture was drawn. Whole blood was used for pathogen DNA isolation. Laboratory and clinical data were anonimised for analyses and no informant consent was obtained. This is in agreement with the code for proper use of human tissue as formulated by the Dutch Federation of Medical Scientific Societies.

DNA isolationDNA from bacterial strains was obtained by 10 min boiling of 1McF bacterial suspensions in Tris-HCL 20mM pH 8.3. Blood culture DNA was prepared by diluting positive blood culture material 1:100 in Tris-HCl 20 mM pH 8.3. This was centrifuged for 5 min at maximum speed and the pellet was subsequently dissolved in 100 µl aquadest. When the DNA sample of blood culture material showed to be red/brownish of colour, it was additionally diluted 10 fold before real-time PCR analysis.

EDTA whole blood sample preparationFor spiking experiments, randomly taken EDTA blood (remaining after diagnostics) of the same blood type (i.e. O+), was pooled and subsequently spiked with a 2 McF MRSA (ATCC 33591), to obtain a ten-fold dilution series. Bacterial DNA was isolated with 5 different methods: EasyMAG generic protocol, EasyMAG specific A off-board lysis protocol, EasyMAG specific B off-board lysis protocol (bioMérieux, Marcy L’Etoile, France), MolYsis Complete kit (1 and 5 ml) according to manufacturer’s instructions (Molzym GmbH, Bremen, Germany), and the MolYsis Basic kit (Molzym GmbH, Bremen, Germany) followed by the EasyMAG spec B protocol. Input for each isolation method was 200 µl, except for the Molzym Complete



kit isolations for which the recommended volumes of 1 ml and 5 ml were used. Samples isolated with one of the EasyMAG protocols were eluted in 70 µl elution buffer provided; all other samples were eluted in 100 µl elution buffer provided with the isolation system. From each sample of the dilution series, 100 µl was plated on bloodagar to determine CFU/ml. DNA samples were analysed with the tuf LightCycler assay described below. Each isolation was at least performed in three independent experiments. Of the EDTA blood samples derived from sepsis suspected patients 1 ml was processed with the best performing protocol (see Results section).

Tuf real-time PCR assayTwo multiplex real-time PCRs were developed to detect S. aureus, CoNS, and Enterococcus spp., as schematically shown in Figure 1. PCR 1 detects and differentiates both Staphylococcus spp. and S. aureus specifically, and PCR 2 detects both Enterococcus spp. and Phocine herpesvirus (PhHV, gB polymerase), which is used as an internal amplification control and to check for inhibitory factors. Primers and probes are described in Table 2. All Staphylococcus spp. and S. aureus primers and probe positions are based on S. aureus tuf gene sequence (GenBank accession no AF298796). Enterococcus probe position is based on E. faecium and E. faecalis tuf gene sequences (GenBank accession no AF124222 and no AF124221). Oligonucleotide primers were obtained from Eurogentec (Liège, Belgium). XS probes were obtained from Biolegio (Nijmegen, The Netherlands). PhHV-1 primers and VIC labeled probe [20] were obtained from Applied Biosystems (Nieuwerkerk aan den IJssel, The Netherlands). In silico analysis (BLAST) of the probes revealed cross-reactivity of the Staphylococcus spp. probe with Enterococcus spp, and the Enterococcus spp. probe detects besides E. faecalis and E. faecium 9 other Enterococcus spp. (E. saccharolyticus, E. mundtii, E. hirae, E. urans, E. dispar, E. columbae, E. cecorum, E. casseliflavus, E. qallinarum). PCR mixtures consisted of 5x LightCycler TaqMan Master mixture (Roche Diagnostics, Almere, The Netherlands), 0.9 µM concentration of each primer, 0.2 µM concentration of each probe, and aquadest in a volume of 15 µl to which 5 µl DNA sample was added, leading to a final reaction volume of 20 µl in each capillary. Primers and probe targeting PhHV had a concentration of 0.1 µM each. All samples were amplified on the LightCycler v2.0 (Roche Diagnostics) according to the following cycling parameters: an initial incubation step for 10 min at 95ºC, subsequently 45 cycli of 15 sec 95 ºC followed by 1 min 60ºC. Results were analyzed using LightCycler Software version 4.05 (Roche Diagnostics).

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Two realtime PCR duplex tests are performed on each sample. By using duplex 1 (left), two

fragments of the gene are amplified detecting both spp. (339bp) and

(115bp) specifically. Duplex 2 is used to detect spp, based on the gene,

and PhHV (gB polymerase) which is added to each eluate as amplification and inhibition control. The

figure is not presented on correct scale.

Figure 1. Schematic overview of tuf real-time PCR duplex assays.Two real-time PCR duplex tests are performed on each sample. By using duplex 1 (left), two fragments of the tuf gene are amplified detecting both Staphylococcus spp. (339bp) and S. aureus (115bp) specifically. Duplex 2 is used to detect Enterococcus spp, based on the tuf gene, and PhHV (gB polymerase) which is added to each eluate as amplification and inhibition control. The figure is not presented on correct scale.

Results

Analytical specificity of tuf real-time PCR Specificity of the assay was determined using a representative panel of bacteria (Table 1). The S. aureus and Enterococcus spp. probe showed correct results in the tuf real-time PCR assay with all tested bacterial strains. The Staphylococcus spp. probe showed cross-hybridisation with E. faecalis, E. faecium, and L. monocytogenes, although in silico analysis indicated at least 1 to 3 mismatches with the primers.

Analytical sensitivity of tuf real-time PCR The analytical sensitivity of the tuf real-time PCR was determined using 10-fold serial dilutions in Tris-HCl buffer in combination with MolYsis Complete DNA isolation (Molzym), and was shown to be 0.4 CFU per PCR reaction for S. aureus and 4 CFU per PCR for E. faecalis. Additionally, the sensitivity of the tuf PCR was analysed on serial dilutions of S. aureus spiked EDTA whole blood samples. From whole blood bacterial DNA was isolated according to 5 different methods as described in Materials and Methods (Table 3). All isolations were performed in 3 independent experiments. The automated extractor EasyMAG did not result in sensitive detection from whole blood material. The generic protocol resulted in a detection limit of 105 CFU/ml, however in one run 104 CFU/ml was detected. The most sensitive detection limit using the EasyMAG was 103 CFU/ml; this was reached by using the off-board lysis



combined with the specific B protocol. Bacterial DNA isolation with the MolYsis Complete kit (Molzym) resulted in the most sensitive detection of 10 CFU/ml for S. aureus from 1 ml EDTA blood. This equals a sensitivity of 0.5 CFU/PCR. Occasionally, 1 CFU/ml could be detected when bacterial DNA was isolated from 5 ml spiked EDTA blood with the MolYsis Complete kit (1 out of 3 isolations). Inhibiting factors were not present since the internal control, i.e. PhHV gave comparable signals (Cp±0.5) for all samples in one run.

Table 2. Primers and probes used for tuf real-time PCR assays.

Primers and Probes Sequence 5’-3’ Position ReferenceStaphylococcus FW ccaatgccacaaactcgtga 32-51 [25]Staphylococcus RV cacgaccagtgattgagaatacg 371-349 [25]Staphylococcus probe VIC-ccattcatgatgccagttg-BHQ1 326-344 [25]S. aureus FW tcctggttcaattacaccacatactg 583-608 [25]S. aureus RV ggaaatagaattgtggacgatagtttga 698-671 [25]S. aureus probe FAM-tgataatacrtawacttctgc-BHQ1 637-617 This studyEnterococcus probe FAM-gctgcagttgacgaata-BHQ1 281-297 This studyPhHV-1 FW gggcgaatcacagattgaatc [20]PhHV-1 RV gcggttccaaacgtaccaa [20]PhHV-1 probe VIC-tttttatgtgtccgccaccatctggatc-BHQ1 [20]

Clinical evaluation

Blood culturesThe tuf assay was clinically evaluated on 317 blood cultures which were either culture negative or showed presence of Gram-positive cocci or Gram-negative bacteria (Table 4). Conventional culture showed that 79 bottles contained S. aureus, 78 bottles CoNS, 5 bottles showed Micrococcus spp., 22 bottles Enterococcus spp. (E. faecalis or E. faecium), 97 bottles indicated Gram negatives to be present, 30 bottles were culture negative and 6 bottles contained double infections. All 79 blood cultures containing S.aureus were correctly identified by this PCR assay. CoNS containing bottles correctly only showed a positive signal with the Staphylococcus spp. probe. Bottles containing Micrococcus spp. were all negative with this assay, and the Enterococcus spp. were all positive with the Enterococcus spp. probe. As described before, Enterococcus spp. also showed a false positive signal with the Staphylococcus spp. probe. All double infections found (n=6) during this study were correctly identified by PCR. In addition to the microorganisms detected by culture, the tuf assay detected CoNS in 13/97 Gram-negative bottles (13.4%), and in 5/30 culture negative bottles (16.6%). In 5/13 Gram-negative bottles, which showed the unexpected presence of CoNS by PCR, a high load was detected as Cp values of less than 36 were found. In the culture negative bottles the CoNS detected by PCR showed a Cp value higher than 40, and this was also found for 8/13 Gram-negative bottles that indicated presence of CoNS (Cp>39). The PhHV signals were comparable for all samples, as well as the negative (water) control, tested in one run (Cp±0.5).

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EDTA blood samplesIn addition to blood cultures, whole blood samples from a total of 30 patients with suspected bloodstream infection were evaluated in a small proof-of-principle pilot study. The MolYsis Complete kit was used to isolate bacterial DNA from 1 ml residual EDTA blood after completion of all other routine diagnostics. Available blood cultures data of these patients were compared to tuf real-time PCR data. Of 22/30 patients it was possible to compare PCR data to blood cultures results (Table 5). From 8/30 patients no blood cultures were drawn. In 18 out of the 22 samples (81.8%) of which blood culture and PCR data were available, identical results were found. Fourteen culture negative samples were also negative in PCR, whereas S. aureus (sample 5 and 26) and CoNS (sample 1) positivity was confirmed in PCR. In 4/22 (18.2%) of the blood samples different results were obtained using either blood culture technique or PCR (Table 5). Cp values of positive samples were found in the range of 32-40. PhHV signals were comparable for all samples in one run (CP±0.5), indicating that none of the samples contained inhibiting factors.

Discussion

In this study, we developed and evaluated the potential clinical value of a real-time PCR assay for detection of commonly encountered Gram-positive cocci from clinical samples. The analytical sensitivity of this assay has shown to be 0.4 CFU/PCR for S. aureus, and 4 CFU/PCR for E. faecalis. Some clinical specimens (i.e. blood) contain many cells containing genomic DNA. This highly abundant genomic DNA might decrease the sensitivity of a PCR specifically detecting bacterial DNA [21]. An optimal sample processing method should diminish human DNA, concentrate pathogen DNA, and wash out inhibitory factors present in clinical specimens. As specimen preparation is of crucial importance when a sensitive test result needs to be obtained, the analytical sensitivity of the developed real-time PCR was investigated from S. aureus spiked EDTA whole blood. By using MolYsis complete (Molzym) a detection limit of 0.5 CFU/PCR was obtained, which is similar as the detection limit obtained from spiked Tris-buffer. The S. aureus and Enterococcus spp. probe are shown to be 100% specific. The cross-hybridisation observed of the Staphylococcus spp. probe with L. monocytogenes is a limitation of this assay. However, as this assay is proposed to be used in combination with Gram staining, the observed aspecific PCR detection of Listeria is considered less important due to the fact that this pathogen shows a different Gram stain. Additionally, Listeria is a food-borne pathogen and is not commonly described to cause bloodstream infections as staphylococci and enterococci do [3, 22]. A potential limitation of this assay, as previously discussed by Sakai et al. [10], is that diseases resulting from mixed infections of both S. aureus and CoNS



will not be correctly identified by this assay. If both probes show a positive signal, one would automatically conclude the presence of S. aureus bacteria and not the presence of CoNS. This also holds true for mixed infections of CoNS with Enterococcus spp. Several clinical applications of the tuf gene based PCR assay were investigated in which the detection of S. aureus, CoNS, and enterococci are of importance; 1) blood cultures containing Gram-positive cocci, and 2) whole blood from patients of the ICU.Firstly, the multiplex PCRs described were evaluated using 317 blood culture bottles of the BacT/ALERT system. All positive cultures containing Gram-positive cocci were detected with 100% sensitivity and specificity. In addition, all double infections were correctly identified with this assay. CoNS were detected in blood culture negative bottles (16.6%) as well as in positive bottles containing Gram negatives (13.4%). Both Kocuglu et al. [23] and Shigei et al. [24] describe a 3-6% false negative rate in culture negative blood culture bottles. These false negative bottles mostly result, when subcultured, in Gram positives (83.3%) [23]. However, no DNA detection was used to investigate false negative rates in those studies, indicating that more false negatives might have been detected when PCR based methods were used due to the fact that this is a more sensitive method. When taking into account the Cp levels obtained in the present study, CoNS contamination might be indicated in cases where the Cp value is higher than 39 and true false negatives in culture when the Cp value is lower than 39. This needs to be further investigated in more detail using a larger sample group to draw definitive conclusions. Although the PCR described is potentially of use for determination of S. aureus, CoNS, and Enterococcus spp. from positive blood cultures, the new technology matrix assisted laser desorption ionisation-time of flight mass spectrometry (MALDI-TOF MS) is an even faster alternative for pathogen identification from positive cultures. This relatively new technique has shown its value in the microbiology laboratory for identification of bacterial strains from positive blood cultures. However, not every diagnostic laboratory can afford a MALDI-TOF MS system. Due to the high sensitivity of this tuf based PCR assay it can be applied directly on positive blood culture bottles, but, also on blood cultures with reduced incubation times [25]. MALDI-TOF MS can thus only be applied on positive blood cultures, because >107 CFU needs to be used for this technique [26].In addition to blood cultures, the application of the assay for whole blood samples was studied. When using the MolYsis Complete kit on EDTA whole blood samples of patients suspected of having a bloodstream infection, a similar result between blood culture results and PCR data was found in 81.8% of the samples. For one patient it took 6 days for the culture bottle to become positive for S. aureus, whereas PCR results were available within 4 hours (including pathogen DNA isolation). It cannot be excluded that CoNS are causative agents of disease as this group of microorganisms is increasingly detected in nosocomial infections [27-29]. Bacterial loads in these samples can be low and antibiotics could be administered to patients before blood cultures were taken resulting in negative culture results and positive

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PCR signals. Although strict regulations and precautions are part of standard procedures in our hospital, contamination from skin during blood sample taking cannot be excluded. Double infections can also explain differences between culture and PCR data. We are able to detect double infections in a sensitive way in reconstruction experiments (data not shown) and in blood cultures. However, in this whole blood pilot study only single infections were detected by PCR as well as in blood cultures. A positive blood culture for K. pneumoniae could not be confirmed by this PCR (Table 1); however PCR showed the presence of CoNS, which might indicate a double infection in this patient. Commercially available sepsis tests for whole blood, for example SepsiTest (Molzym), SeptiFAST (Roche) and MagicPlex Sepsis Test (Seegene), are currently under investigation in several laboratories. Preliminary results obtained with these assays [30-33] showed that real-time multiplex PCR has potential value in addition to blood culture. This is in agreement with the outcome in this small pilot study. However, molecular testing still needs further optimization and detection of more clinical relevant pathogens besides S. aureus, CoNS, and Enterococcus spp. needs to be added in blood based diagnostics. Furthermore, a more extended clinical evaluation, using a larger group of patients, is of the essence. In summary, sensitive and specific tuf gene based real-time multiplex PCR assays were developed for S. aureus, CoNS, and Enterococcus spp., which enabled fast and accurate detection from positive blood cultures. In a small pilot study using whole blood samples of ICU patients suspected of having bloodstream infection, the developed PCR assays combined with a sensitive bacterial DNA isolation method have shown potential use in detecting frequently encountered Gram-positive cocci. These results justify further clinical evaluation of this PCR assay in a larger group of patients.

AcknowledgementsWe thank Jitske Stalpers, Mariëlle Bierma, Christel van Herk, Jan Michielse, Paul van Kaathoven and Gitta Stienen for their technical support.



References

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2. Bearman, G.M. and R.P. Wenzel, Bacteremias: a leading cause of death. Arch Med Res, 2005. 36(6): p. 646-59.

3. von Eiff, C., et al., Infections associated with medical devices: pathogenesis, management and prophylaxis. Drugs, 2005. 65(2): p. 179-214.

4. Kumar, A., et al., Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med, 2006. 34(6): p. 1589-96.

5. Annane, D., E. Bellissant, and J.M. Cavaillon, Septic shock. Lancet, 2005. 365(9453): p. 63-78.6. Klaschik, S., et al., Real-time PCR for detection and differentiation of gram-positive and gram-

negative bacteria. J Clin Microbiol, 2002. 40(11): p. 4304-7.7. Kilic, A., et al., Triplex real-time polymerase chain reaction assay for simultaneous detection of

Staphylococcus aureus and coagulase-negative staphylococci and determination of methicillin resistance directly from positive blood culture bottles. Diagn Microbiol Infect Dis, 2010. 66(4): p. 349-55.

8. Riyaz-Ul-Hassan, S., V. Verma, and G.N. Qazi, Evaluation of three different molecular markers for the detection of Staphylococcus aureus by polymerase chain reaction. Food Microbiol, 2008. 25(3): p. 452-9.

9. Klouche, M. and U. Schroder, Rapid methods for diagnosis of bloodstream infections. Clin Chem Lab Med, 2008. 46(7): p. 888-908.

10. Sakai, H., et al., Simultaneous detection of Staphylococcus aureus and coagulase-negative staphylococci in positive blood cultures by real-time PCR with two fluorescence resonance energy transfer probe sets. J Clin Microbiol, 2004. 42(12): p. 5739-44.

11. Casalta, J.P., et al., Evaluation of the LightCycler(R) SeptiFast test in the rapid etiologic diagnostic of infectious endocarditis. Eur J Clin Microbiol Infect Dis, 2009. 28(6): p. 569-73.

12. Horz, H.P., et al., Selective isolation of bacterial DNA from human clinical specimens. J Microbiol Methods, 2008. 72(1): p. 98-102.

13. Rantakokko-Jalava, K. and J. Jalava, Optimal DNA isolation method for detection of bacteria in clinical specimens by broad-range PCR. J Clin Microbiol, 2002. 40(11): p. 4211-7.

14. Smith, K., M.A. Diggle, and S.C. Clarke, Comparison of commercial DNA extraction kits for extraction of bacterial genomic DNA from whole-blood samples. J Clin Microbiol, 2003. 41(6): p. 2440-3.

15. Hansen, W.L., C.A. Bruggeman, and P.F. Wolffs, Evaluation of new preanalysis sample treatment tools and DNA isolation protocols to improve bacterial pathogen detection in whole blood. J Clin Microbiol, 2009. 47(8): p. 2629-31.

16. Loens, K., et al., Evaluation of NucliSens easyMAG for automated nucleic acid extraction from various clinical specimens. J Clin Microbiol, 2007. 45(2): p. 421-5.

17. Heikens, E., et al., Comparison of genotypic and phenotypic methods for species-level identification of clinical isolates of coagulase-negative staphylococci. J Clin Microbiol, 2005. 43(5): p. 2286-90.

18. Martineau, F., et al., Development of a PCR assay for identification of staphylococci at genus and species levels. J Clin Microbiol, 2001. 39(7): p. 2541-7.

19. Bone, R.C., et al., Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest, 1992. 101(6): p. 1644-55.

20. van Doornum, G.J., et al., Diagnosing herpesvirus infections by real-time amplification and rapid culture. J Clin Microbiol, 2003. 41(2): p. 576-80.

21. Sachse, S., et al., Truncated human cytidylate-phosphate-deoxyguanylate-binding protein for improved nucleic acid amplification technique-based detection of bacterial species in human samples. J Clin Microbiol, 2009. 47(4): p. 1050-7.

22. Neely, A.N. and M.P. Maley, Survival of enterococci and staphylococci on hospital fabrics and plastic. J Clin Microbiol, 2000. 38(2): p. 724-6.

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23. Kocoglu, M.E., A. Bayram, and I. Balci, Evaluation of negative results of BacT/Alert 3D automated blood culture system. J Microbiol, 2005. 43(3): p. 257-9.

24. Shigei, J.T., et al., Value of terminal subcultures for blood cultures monitored by BACTEC 9240. J Clin Microbiol, 1995. 33(5): p. 1385-8.

25. Loonen, A.J., et al., Acceleration of the direct identification of Staphylococcus aureus versus coagulase-negative staphylococci from blood culture material: a comparison of six bacterial DNA extraction methods. Eur J Clin Microbiol Infect Dis, 2011. 30(3): p. 337-42.

26. Christner, M., et al., Rapid identification of bacteria from positive blood culture bottles by use of matrix-assisted laser desorption-ionization time of flight mass spectrometry fingerprinting. J Clin Microbiol, 2010. 48(5): p. 1584-91.

27. Kloos, W.E. and T.L. Bannerman, Update on clinical significance of coagulase-negative staphylococci. Clin Microbiol Rev, 1994. 7(1): p. 117-40.

28. Piette, A. and G. Verschraegen, Role of coagulase-negative staphylococci in human disease. Vet Microbiol, 2009. 134(1-2): p. 45-54.

29. Stefani, S. and P.E. Varaldo, Epidemiology of methicillin-resistant staphylococci in Europe. Clin Microbiol Infect, 2003. 9(12): p. 1179-86.

30. Lehmann, L.E., et al., A multiplex real-time PCR assay for rapid detection and differentiation of 25 bacterial and fungal pathogens from whole blood samples. Med Microbiol Immunol, 2008. 197(3): p. 313-24.

31. Tsalik, E.L., et al., Multiplex PCR to diagnose bloodstream infections in patients admitted from the emergency department with sepsis. J Clin Microbiol, 2010. 48(1): p. 26-33.

32. Wallet, F., et al., Preliminary clinical study using a multiplex real-time PCR test for the detection of bacterial and fungal DNA directly in blood. Clin Microbiol Infect, 2010. 16(6): p. 774-779.

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Calibri

5Ecological effects of selective decontamination on

resistant gram-negative bacterial colonization

Evelien A.N. Oostdijk, Anne Marie G.A. de Smet, Hetty E. Blok,

Emily S. Thieme Groen, Gerard J. van Asselt GJ, Robin F. Benus,

Alexandra T. Bernards, Ine H. Frénay, Arjan R. Jansz, Bartelt M. de Jongh,

Jan A. Kaan, Maurine A. Leverstein-van Hall, Ellen M. Mascini, Wouter Pauw,

Patrick D. Sturm, Steven F. Thijsen SF, Jan A. Kluytmans and Marc J.M. Bonten

Published in the American Journal of Respiratory and Critical Care Medicine 2010,

181:452-457

Chapter 5

2

ABSTRACT

Rationale: Selective Digestive tract Decontamination (SDD) and Selective

Oropharyngeal Decontamination (SOD) eradicate Gram-negative bacteria (GNB)

from the intestinal en respiratory tract in intensive-care-unit (ICU) patients, but its

effect on antibiotic resistance remains controversial.

Objectives: We quantified the effects of SDD and SOD on bacterial ecology in 13 ICUs

that participated in a study, in which SDD, SOD or standard care was used during

consecutive periods of 6 months (NEJM 2009;360:20).

Methods: Point prevalence surveys of rectal and respiratory samples were

performed once monthly in all patients in ICU (receiving or not receiving SOD/SDD).

Effects of SDD on rectal and of SDD/SOD on respiratory tract carriage with GNB were

determined by comparing results from consecutive point prevalence surveys during

intervention (6 months for SDD and 12 months for SDD/SOD) to consecutive point

prevalence data in the pre- and post-intervention periods.

Measurements and Main Results: During SDD average proportions of patients

with intestinal colonization with GNB resistant to either ceftazidime, tobramycin

or ciprofloxacin were 5%, 7% and 7%, and increased to 15%, 13% and 13% post-

intervention (p<0.05). During SDD/SOD resistance levels in the respiratory tract

were ≤6% for all three antibiotics, but increased gradually (for ceftazidime; p<0.05

for trend) during intervention and to levels ≥10% for all three antibiotics post-

intervention (p<0.05).

Conclusion: SOD and SDD have marked effects on the bacterial ecology in an ICU

with rising ceftazidime resistance prevalence rates in the respiratory tract during

intervention and a considerable rebound effect of ceftazidime resistance in the

intestinal tract after discontinuation of SDD.

Ecological effects of selective decontamination on resistant gram-negative bacterial colonization

3

5

InTRoduCTIon

Selective decontamination of the digestive tract (SDD) and Selective Oropharyn-

geal Decontamination (SOD) are powerful infection prophylaxis regimens for patients

in intensive care units (ICU). First introduced in 19841, both interventions have been

associated with reduced incidences of Ventilator Associated Pneumonia (VAP)2-4 and

improved patient survival.5;6

Both SDD and SOD consist of non-absorbable antimicrobial agents with activity

against yeasts, Staphylococcus aureus and aerobic Gram-negative bacteria, including

Enterobacteriaceae and Pseudomonas aeruginosa. SOD is applied in the oropharynx

only, while in SDD antibiotics are, in addition to oropharyngeal application, also

administered to the gastrointestinal tract combined with systemic prophylaxis with

a third generation cephalosporin during the first four days. This systemic prophylaxis

aims to treat infections caused by commensal respiratory tract flora, such as

Streptococcus pneumonia and Haemophilus influenza already incubating at the time

of ICU admission.7 The aim of the intestinal component is to selectively eradicate

potentially pathogenic micro-organisms, while sparing the anaerobic flora. The latter

presumably protects against colonization and overgrowth with potential pathogens,

the so-called concept of “colonization resistance”.8

The effects of SDD on antibiotic resistance have been the subject of intense

controversy. In theory, SDD may select micro-organisms already intrinsically resistant

to the regimen, such as Gram-positive bacteria, or those with acquired resistance

for the antibiotics used.7;9;10 Up till now, SDD and SOD have not been associated with

increased resistance in settings with low endemicity of antibiotic resistance. Actually,

in such settings SDD and SOD were associated with lower incidences of carriage

and infections with antibiotic resistant Gram-negative bacteria 5;6;11-13 However,

emergence of plasmid mediated extended spectrum ß-lactamase (ESBL)14 and other

pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA) 15;16, have

been reported as well.

The question to what extent and in what time SDD and SOD affect the bacterial

ecology in an ICU ward remains unanswered. Most trials performed so far focussed

on antibiotic resistance rates in individual patients. Yet, since SDD, or SOD, is usually

Chapter 5

4

administered to patients with an expected ICU-stay of at least two days only, there

will always remain ICU patients not receiving SDD or SOD. In a Dutch multi-center

SDD-SOD study such patients accounted, on average, for about 70% of all admitted

patients, representing about 20% of all patient days in ICU.5 In that study, surveillance

cultures (rectal and oropharyngeal swabs) were obtained once monthly in all patients

(including short-stay patients) present in the ICU, during SDD, SOD and standard care,

to determine point-prevalence rates of antibiotic resistant Gram-negative bacteria.5

In the current study, we determined the ecological effects of SDD and SOD.

MethOds

Patients and design

Microbiological data were used from monthly point prevalence surveys performed

as part of an open clustered group-randomized cross over study in 13 Intensive Care

Units (ICU) in the Netherlands between May 2004 and July 2006 comparing SDD and

SOD to standard care.5 Two six-month intervention periods (SDD and SOD) and one

standard care period of six months were conducted in each ICU, with the study order

of regimens randomly assigned. Between each period a one month wash-in/wash

out period was carried out, during which the new treatment (either SDD or SOD) was

implemented, but patient data were not used for analysis.

The study interventions, SDD and SOD, were described previously.5 In short,

both consisted of the non-absorbable anti-microbial agents tobramycin, polymyxin

E and amphotericin B. During the use of SDD a 2% mixture of these antibiotics was

applied on the buccal mucosa and a suspension (respective doses 80 mg, 100 mg

and 500 mg) was administered in the gastrointestinal tract four times a day via a

nasogastric tube. Furthermore, for the first four days after ICU admission 1000 mg

cefotaxime was administered intravenously four times a day. SOD consisted only of

the oropharyngeal application of the 2% mixture of these antibiotics.

An expanded microbiological methods description can be found in the online

supplement.


5

5

data analysis

Since the order of study regimens was randomized in each ICU, there were six

possible study orders (table 1A). For the analysis of the effects of SDD on rectal

colonization, the results of the six point prevalence studies during the SDD period

were compared to the consecutive point prevalence results in the periods before

and after the SDD period (table 1B). In this analysis, SOD and standard care were

combined, since SOD was found to have no effect on rectal colonization.5 This is in

accordance with previous findings.2 Data from monthly point prevalence cultures

from different centres were pooled according to study period and specific time

point. Similarly, for the analysis of respiratory tract colonization SDD and SOD were

combined, as both intended similar effects on respiratory tract colonization (Table

1C).

In this way the results for rectal colonization from the six consecutive point-

prevalence surveys during SDD in 13 units were compared to the results from

eight periods (four SOD and four standard care) in the six months before SDD and

four periods (two SOD and two standard care) in the period between twelve and

six months before SDD. Similarly, SDD was compared to equal periods following

SDD as shown in table 1B. For respiratory tract colonization 12 consecutive point

prevalence results (for SDD and SOD, interrupted by one month wash-in/wash-out)

were compared to six and seven periods of standard care preceding and following

the SDD/SOD periods, respectively. In this analysis, four SOD periods were not used,

as standard care followed SOD but preceded SDD (Table 1C). Data obtained during

wash in-wash out periods were not used.

statistical analysis

Proportions of patients colonized with Gram-negative bacteria for every time point

were calculated by determining the numerator and denominator of the prevalence.

95% Confidence intervals for the proportions were derived from the standard error of

proportion. Differences in proportions between subsequent periods were analyzed

with Poisson regression analysis. Non-segmented Poisson regression was performed

to quantify changes in time trend within the periods.17 Poisson regression is preferred

over more common statistical methods, such as linear regression, because counts are

Chapter 5

6

not normally distributed. We adjusted for differences between centers as unit-level

observations, in contrast to individual-level observations, could lead to within unit

correlation18. An alpha value of P < 0.05 was set to define statistical significance. Data

were analyzed using SPSS version 12.0 (SPSS, Chicago, IL. USA) and R version 2.9.0.

ReSulTS

Microbiology

During the monthly point prevalence surveillance studies a total of 2963 rectal and

2304 respiratory tract samples were obtained and processed for specific antimicrobial

susceptibility testing. The mean (SD) number of patients sampled at each time point

was 99 (38) (median, 89; range, 55 – 165) for rectal samples and 96 (24) (median, 91;

range, 64 – 134) for respiratory tract samples. Adherence to obtaining cultures was

estimated to be 87% for rectal swabs (ranging from 67% to 98% per center) and 82%

for respiratory samples (ranging from 69% to 95% per center).

For rectal swabs, growth of any pathogen on the selective media was highest

during standard care (44.5 +1.5%; range 42% - 51%) and 6.1% lower during SOD

(38.3 +1.6%; range 37% - 50%) and 19.6% lower during SDD (24.9 +1.4%; range 22% -

33%). In the respiratory tract, growth was lower during both SDD (17.7 +1.3%; range

15% - 27%) and SOD (22.1 +1.5%; range 13% - 26%), as compared to standard care

(42.5 +1.7%; range 32% - 48%). Because of the small differences between SOD and

standard care for rectal swabs and between SDD and SOD for respiratory samples,

these groups were analyzed simultaneously (for separate analysis see Table E1 in the

online data supplement).

Rectal colonization

Average prevalence rates of colonization with ceftazidime-resistant bacteria were

6% (95% CI, 4.7% - 7.5%) in the pre-SDD period, 5% (95% CI, 3.9%-6.7%) during the

SDD, and 15% (95% CI, 12.4%-17.0%) in the period after SDD (table 2). Before SDD

there was a decline in the prevalence rates of ceftazidime-resistance (Beta-coefficient

= -0.07 ; p = 0.038), with stable resistance levels during the entire period of SDD

(figure 1), followed by an increase from 2.3% in the last month of SDD to 11.1% in the


7

5

first month after SDD (p<0.05). Resistance levels remained stable in the subsequent

twelve months.

Average rates of ciprofloxacin resistance were 12% (95% CI, 9.7%-13.5%) in the

pre-SDD periods, which decreased to 7% (95% CI, 5.1%-8.2%) during SDD, and then

increased to 13% (95% CI, 10.8%-15.2%) after discontinuation of SDD. Differences

between the subsequent periods were statistically significant. Similar trends were

observed for tobramycin resistance; the average resistance prevalence was 9% (95%

CI, 7.7%-11.2%) before, 7% (95% CI, 5.5%-8.7%) during and 13% (95% CI, 10.4%-

14.7%) after SDD.

In all 263, 244 and 228 Gram-negative micro-organisms were isolated from

rectal samples with resistance for ceftazidime, ciprofloxacin and tobramycin,

respectively (table 3). Among those being resistant to ceftazidime 39.9% (n=105)

were Enterobacter cloacae. Strains resistant to ciprofloxacin and tobramycin most

frequently were Escherichia coli: 122 (50.0%) for ciprofloxacin and 110 (48.2%) for

tobramycin.

Respiratory tract colonization

The prevalence rates of antibiotic resistant micro-organisms in respiratory

tract samples decreased significantly after introduction of SDD or SOD (Figure 1).

Before intervention, average prevalence rates of resistance were 10%, 10% and

14% for ceftazidime, tobramycin and ciprofloxacin, respectively, which dropped

to levels below 7% for all antibiotics (p<0.05) after introduction of either SDD or

SOD. This immediate drop was followed by a gradual increase during SDD/SOD (for

ceftazidime resistance; p<0.05 for time trend). There was a slight but statistically

significant increase in prevalence of resistant bacteria for all three antibiotics after

the intervention period. As mentioned, in streamlining the three studies periods

we excluded four SOD periods. We, therefore, also repeated our analysis with the

inclusion of these four periods, while now excluding the four SDD periods. The

interpretation did not change, although the gradual increase in ceftazidime during

SDD/SOD fell short of statistical significance.

In all 131, 113 and 108 Gram-negative micro-organisms were isolated from

respiratory samples with resistance for ceftazidime, ciprofloxacin and tobramycin,

Chapter 5

8

respectively (table 3). Among those being resistant to ceftazidime 38.2% consisted of

E. cloacae (n=50) and 33.6% of P. aeruginosa (n=44). Strains resistant to ciprofloxacin

and tobramycin most frequently were P. aeruginosa: more specific 49.6% (n=56) for

ciprofloxacin and 43.5% (n=47) for tobramycin.

Proportions of ciprofloxacin resistant Gram-negative bacteria (both from rectal swabs

and respiratory samples) with co-resistance for both ciprofloxacin and ceftazidime

were 30.2%, 22.2% and 35.0% during standard care, SOD and SDD, respectively

(p = 0.08). Co-resistance for both ciprofloxacin and tobramycin was documented

in 37.4%, 44.4%, and 35.0% of the isolates during standard care, SOD and SDD,

respectively (p = 0.26).

All samples were also analyzed for vancomycin-resistant enterococci (VRE) and

MRSA. MRSA was not detected in any sample and VRE was isolated from eight rectal

swabs, none during SDD.


9

5

Figure 1: Percentage of patients colonized with Gram- negative rods before, during and after the use of SDD during monthly point prevalence surveillance studies of (1) rectal samples and (2) respiratory samples. (●) pre intervention period; (□) intervention period; (▲) post intervention period for three types of antibiotic agents (A) ceftazidime; (B) tobramycin; (C) ciprofloxacin.

Chapter 5

10

Rectal Respiratory tractCFT CIP TOB CFT CIP TOB

Total 263 244 228 131 113 108

E. coli 69 (26,2%) 122 (50,0%) 110 (48,2%) 13 (9,9%) 15 (13,3%) 16 (14,8%)

Klebsiella. pneumoniae

38 (14,4%) 51 (20,9%) 50 (21,9%) 24 (18,3%) 26 (23,0%) 24 (22,2%)

P. aeruginosa 51 (19,4%) 38 (15,6%) 28 (12,3%) 44 (33,6%) 56 (49,6%) 47 (43,5%)E. cloacae 105 (39,9%) 33 (13,5%) 40 (17,5%) 50 (38,2%) 16 (14,2%) 21 (19,4%)

table 3: Number and percentage of Gram-negative isolates and species with conferred resistance to ceftazidime, tobramycin or tobramycin obtained from rectal en respiratory tract samples during all monthly point prevalence surveys. Ceftazidime (CFT), ciprofloxacin (CIP), tobramycin (TOB).

dISCuSSIon

This longitudinal study demonstrates that SOD and SDD both have marked effects

on the bacterial ecology in an ICU. The ecological effects were most obvious in the

respiratory tract, with large reductions in resistance prevalence rates of Gram-

negative bacteria after the start of SDD or SOD, a trend towards increasing rates

during the interventions, followed by a rapid return to pre-intervention resistance

levels after the interventions. In the intestinal tract, the reduction in resistance

prevalence was less pronounced during SDD as compared to respiratory tract

colonization, but with a considerable rebound effect of ceftazidime resistance after

SDD, with significantly increased prevalence rates as compared to prevalence rates

during the intervention and even before intervention. Ceftazidime resistant isolates

in rectal samples mainly included E. cloacae whereas tobramycin and ciprofloxacin

resistant isolates predominantly included E. coli.

To the best of our knowledge this is the first study to determine the ecological

effects of SDD and SOD on a ward-level in a longitudinal and multi-center study

design. Furthermore, based upon the determined adherence to study protocol,

the microbiological screening results can be considered to represent “whole-ward

ecology” as completeness of sampling was estimated to be 82% for the respiratory

and 87% of the rectal cultures. Previous studies focussed on the effects of SDD and

SOD in individual patients. 2;6;15;19;20


11

5

To guarantee uniform data collection in 13 microbiology laboratories, a pragmatic

and relatively simple study design was needed. We have, therefore, used three

antibiotic-containing media as a first selection to detect antibiotic-resistant aerobic

Gram-negative micro-organisms, followed by susceptibility testing using automated

susceptibility testing systems. The selection of marker antibiotics was based on

resistance profiles of Gram-negative bacteria in Dutch ICUs and the antibiotics used

in SDD and SOD.

Intestinal carriage with antibiotic resistant bacteria markedly increased after

discontinuation of SDD. This association was most obvious for ceftazidime resistance.

During SDD intravenous cephalosporin use increased with 87% 5. We hypothesize that

the increase in cephalosporin use selected for cephalosporin resistant isolates, which

were suppressed by enterally administered antibiotics without complete eradication,

and their growth emerged after discontinuation of these topical antibiotics then

facilitates emergence of such strains. Indeed, SDD has been associated with

emergence of intestinal carriage with multidrug-resistant Gram-negative bacteria

before14 and prior use of third-generation cephalosporins has been stated as a major

risk factor for subsequent cephalosporin resistance.21 The increase of ciprofloxacin

resistance, however, cannot be explained by this scenario, as ciprofloxacin was

not part of SDD and its systemic use was 31% lower during SDD.5 Yet, antibiotic

resistance is frequently present for multiple classes of antibiotics.22 However in the

present study the proportions of ciprofloxacin resistant Gram-negative pathogens

not susceptible to both ciprofloxacin and either ceftazidime or tobramycin were

comparable in all three periods. Unfortunately, it was not feasible to investigate the

genetic mechanisms of resistance in this study. The overall use of systemic antibiotics

has been reported previously 5 but are also provided in the supplementary data.

There was a tendency towards a reduction in rectal colonization during the pre-

intervention period, which might reflect a so-called “Hawthorne-effect”. This implies

that nurses and physician’s behavior might have been affected by the fact that a

trial was executed.23;24 As part of the study, specific attention was paid to hygiene

measurements, which might have reduced the occurrence of cross transmission.

During SDD and SOD, proportions of patients carrying resistant Gram-negative

bacteria in the respiratory tract gradually increased, again most prominently for

Chapter 5

12

ceftazidime resistance. Opposite to the effects of SDD on intestinal resistance rates,

the trend line increased until the last point prevalence survey, suggesting that its

maximum level had not been reached after twelve months.

There are several limitations of this study that must be addressed. The adherence

to study protocol (i.e., the completeness of cultures obtained) and inclusion rates were

based on estimates determined during regular controls in the units by our research

nurses. Therefore, exact proportions of patients receiving either SDD or SOD at each

specific time point and the exact proportion of cultures obtained were not available.

In addition, cultures were processed anonymously, and it was therefore not possible

to link carriage to received medications, and isolates were not genotyped precluding

determination of the relevance of clonal spread. And as there were no “control” ICUs

with similar point prevalence measurements but without interventions, we could not

determine time effects of resistance levels in the absence of interventions.

Furthermore, in our statistical analysis we assumed that prevalence points were

independent from each other. The duration of ICU-stay at the time of sampling is

unknown due to the anonimyzed nature of sample taking. However, the average

duration of ICU-stay for patients included in the trial (with an expected ICU-stay

>48 hours) was nine days during all periods (inter-quartile range: standard care

3-17; SOD 4-15; SDD 4-15) and the average proportion of patients with a length of

ICU-stay of more than 30 days was 9% during all periods (standard care 8.7%; SOD

8.9%; SDD 8.6%)5, which implies that almost all patients were included only once

in a point prevalence study. Yet, the clear differences in prevalence rates between

and uniformity within study periods, suggests at least some data-dependency.25

We have calculated average prevalence for each time point and for whole periods

(i.e., multiple time points) as numerator and denominator of the prevalence and

performed Poisson regression analysis to control for within unit correlation. Although

we would have preferred to perform a time series analysis to adjust for any (auto)

correlation over time, the number of point prevalence surveys per time period was

insufficient to achieve an acceptable level of variability 26 as well as the number of

time points to estimate complex correlation structures.18


13

5

In the current cluster-randomized cross-over study SDD and SOD were associated

with an 13% and 11% mortality reduction at day 28, which provides strong evidence

for a beneficial effect of both regimens in ICU settings with low endemic levels

of antibiotic resistance. This ecological analysis provides detailed insights in the

ecological changes induced by both regimens. SDD and SOD are both associated with

a gradual increase in antibiotic resistance in the respiratory tract, which is magnified

after discontinuation of both regimens, most prominent for ceftazidime resistance.

Therefore, emergence of antibiotic resistance remains a major concern associated

with these infection control measures. Future studies should compare the long-term

effects of both regimens on antibiotic regimens, systematic as well as the effects of

less cephalosporin use during SDD, in order to determine the most “cost-beneficial”

infection control measure from an ecological perspective for ICU patients.

Acknowledgements:

We thank Heidi S.M. Ammerlaan and Marieke J.A. de Regt for their assistance with

the statistical analyzes.

Chapter 5

14

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1. Stoutenbeek, C. P., H. K. van Saene, D. R. Miranda, and D. F. Zandstra. 1984. The effect of selective decontamination of the digestive tract on colonisation and infection rate in multiple trauma patients. Intensive Care Med. 10:185-192.

2. Bergmans, D. C., M. J. Bonten, C. A. Gaillard, J. C. Paling, van der Geest S., F. H. van Tiel, A. J. Beysens, P. W. de Leeuw, and E. E. Stobberingh. 2001. Prevention of ventilator-associated pneumonia by oral decontamination: a prospective, randomized, double-blind, placebo-controlled study. Am.J.Respir.Crit Care Med. 164:382-388.

3. D’Amico, R., S. Pifferi, C. Leonetti, V. Torri, A. Tinazzi, and A. Liberati. 1998. Effectiveness of antibiotic prophylaxis in critically ill adult patients: systematic review of randomised controlled trials. BMJ 316:1275-1285.

4. Collard, H. R., S. Saint, and M. A. Matthay. 2003. Prevention of ventilator-associated pneumonia: an evidence-based systematic review. Ann.Intern.Med. 138:494-501.

5. de Smet, A. M., J. A. Kluytmans, B. S. Cooper, E. M. Mascini, R. F. Benus, T. S. van der Werf, J. G. van der Hoeven, P. Pickkers, D. Bogaers-Hofman, N. J. van der Meer, A. T. Bernards, E. J. Kuijper, J. C. Joore, M. A. Leverstein-van Hall, A. J. Bindels, A. R. Jansz, R. M. Wesselink, B. M. de Jongh, P. J. Dennesen, G. J. van Asselt, L. F. te Velde, I. H. Frenay, K. Kaasjager, F. H. Bosch, I. M. van, S. F. Thijsen, G. H. Kluge, W. Pauw, J. W. de Vries, J. A. Kaan, J. P. Arends, L. P. Aarts, P. D. Sturm, H. I. Harinck, A. Voss, E. V. Uijtendaal, H. E. Blok, E. S. Thieme Groen, M. E. Pouw, C. J. Kalkman, and M. J. Bonten. 2009. Decontamination of the digestive tract and oropharynx in ICU patients. N.Engl.J.Med. 360:20-31.

6. de Jonge E., M. J. Schultz, L. Spanjaard, P. M. Bossuyt, M. B. Vroom, J. Dankert, and J. Kesecioglu. 2003. Effects of selective decontamination of digestive tract on mortality and acquisition of resistant bacteria in intensive care: a randomised controlled trial. Lancet 362:1011-1016.

7. Bonten, M. J., B. J. Kullberg, R. van Dalen, A. R. Girbes, I. M. Hoepelman, W. Hustinx, J. W. van der Meer, P. Speelman, E. E. Stobberingh, H. A. Verbrugh, J. Verhoef, and J. H. Zwaveling. 2000. Selective digestive decontamination in patients in intensive care. The Dutch Working Group on Antibiotic Policy. J.Antimicrob.Chemother. 46:351-362.

8. Donskey, C. J. 2004. The role of the intestinal tract as a reservoir and source for transmission of nosocomial pathogens. Clin.Infect.Dis. 39:219-226.

9. Kollef, M. H. 2003. Selective digestive decontamination should not be routinely employed. Chest 123:464S-468S.

10. Ebner, W., A. Kropec-Hubner, and F. D. Daschner. 2000. Bacterial resistance and overgrowth due to selective decontamination of the digestive tract. Eur.J.Clin.Microbiol.Infect.Dis. 19:243-247.

11. de Jonge E. 2005. Effects of selective decontamination of digestive tract on mortality and antibiotic resistance in the intensive-care unit. Curr.Opin.Crit Care 11:144-149.

12. Krueger, W. A., F. P. Lenhart, G. Neeser, G. Ruckdeschel, H. Schreckhase, H. J. Eissner, H. Forst, J. Eckart, K. Peter, and K. E. Unertl. 2002. Influence of combined intravenous and topical antibiotic prophylaxis on the incidence of infections, organ dysfunctions, and mortality in critically ill surgical patients: a prospective, stratified, randomized, double-blind, placebo-controlled clinical trial. Am.J.Respir.Crit Care Med. 166:1029-1037.


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13. Silvestri, L., H. K. van Saene, A. Casarin, G. Berlot, and A. Gullo. 2008. Impact of selective decontamination of the digestive tract on carriage and infection due to Gram-negative and Gram-positive bacteria: a systematic review of randomised controlled trials. Anaesth.Intensive Care 36:324-338.

14. Al Naiemi, N., E. R. Heddema, A. Bart, E. de Jonge, C. M. Vandenbroucke-Grauls, P. H. Savelkoul, and B. Duim. 2006. Emergence of multidrug-resistant Gram-negative bacteria during selective decontamination of the digestive tract on an intensive care unit. J.Antimicrob.Chemother. 58:853-856.

15. Lingnau, W., J. Berger, F. Javorsky, M. Fille, F. Allerberger, and H. Benzer. 1998. Changing bacterial ecology during a five-year period of selective intestinal decontamination. J.Hosp.Infect. 39:195-206.

16. Verwaest, C., J. Verhaegen, P. Ferdinande, M. Schetz, G. Van den Berghe, L. Verbist, and P. Lauwers. 1997. Randomized, controlled trial of selective digestive decontamination in 600 mechanically ventilated patients in a multidisciplinary intensive care unit. Crit Care Med. 25:63-71.

17. Kianifard, F. and P. P. Gallo. 1995. Poisson regression analysis in clinical research. J.Biopharm.Stat. 5:115-129.

18. Shardell, M., A. D. Harris, S. S. El-Kamary, J. P. Furuno, R. R. Miller, and E. N. Perencevich. 2007. Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies. Clin.Infect.Dis. 45:901-907.

19. Hammond, J. M. and P. D. Potgieter. 1995. Long-term effects of selective decontamination on antimicrobial resistance. Crit Care Med. 23:637-645.

20. Leone, M., J. Albanese, F. Antonini, A. Nguyen-Michel, and C. Martin. 2003. Long-term (6-year) effect of selective digestive decontamination on antimicrobial resistance in intensive care, multiple-trauma patients. Crit Care Med. 31:2090-2095.

21. Livermore, D. M. and N. Woodford. 2006. The beta-lactamase threat in Enterobacteriaceae, Pseudomonas and Acinetobacter. Trends Microbiol. 14:413-420.

22. Denton, M. 2007. Enterobacteriaceae. Int.J.Antimicrob.Agents 29 Suppl 3:S9-S22. 23. Braunholtz, D. A., S. J. Edwards, and R. J. Lilford. 2001. Are randomized clinical trials

good for us (in the short term)? Evidence for a “trial effect”. J.Clin.Epidemiol. 54:217-224.

24. McCarney, R., J. Warner, S. Iliffe, R. van Haselen, M. Griffin, and P. Fisher. 2007. The Hawthorne Effect: a randomised, controlled trial. BMC.Med.Res.Methodol. 7:30.

25. Harris, A. D., E. Lautenbach, and E. Perencevich. 2005. A systematic review of quasi-experimental study designs in the fields of infection control and antibiotic resistance. Clin.Infect.Dis. 41:77-82.

26. Wagner, A. K., S. B. Soumerai, F. Zhang, and D. Ross-Degnan. 2002. Segmented regression analysis of interrupted time series studies in medication use research. J.Clin.Pharm.Ther. 27:299-309.

Calibri Light

2Localization of breast cancer resistance protein in endocrine organs

and inhibition of its transport activity by steroid hormones

Anita C.A. Dankers1, Fred C.G.J. Sweep2, Jeanne C.L.M. Pertijs1, Vivienne Verweij1, Jeroen J.M.W. van den Heuvel1, Jan B. Koenderink1, Frans G.M. Russel1 and

Rosalinde Masereeuw1

1 Department of Pharmacology and Toxicology, Radboudumc, Nijmegen, the Netherlands 2 Department of Laboratory Medicine, Radboudumc, Nijmegen, the Netherlands

Cell Tissue Res. 2012 Aug;349(2):551-63.

30

Chapter 2

Abstract

Breast cancer resistance protein (BCRP) is known for its protective function against the toxic effects of exogenous compounds. In addition to this, a role in the transport of endogenous compounds has also been described. Since BCRP in the plasma membrane was shown to be regulated by sex steroids, we investigated the presence and possible role of BCRP in steroid hormone producing organs. Therefore, the presence and localization of Bcrp was investigated in endocrine organs of wild type mice. Furthermore, the interaction of various steroid hormones with human BCRP activity was studied. Quantitative PCR revealed Bcrp mRNA in the pituitary and adrenal glands, pancreas, ovary, testis and adipose tissue. Immunohistochemistry revealed the presence of Bcrp in the cortex of the adrenal gland and in plasma membranes of adipocytes. In the pituitary gland, pancreas, ovary and testis, Bcrp was mainly located in the capillaries. The interaction between BCRP and twelve steroid hormones was studied using membrane vesicles of HEK293-BCRP cells. Estradiol, testosterone, progesterone and androstenedione inhibited BCRP-mediated uptake of 3H-estrone sulfate (E1S) most potently, with calculated inhibitory constant (Ki) values of 5.0 ± 0.2 µM, 36 ± 14 µM, 14.7 ± 1.3 µM and 217 ± 13 µM, respectively. BCRP function was attenuated non-competitively, which implies an allosteric inhibition of BCRP-mediated E1S transport by these steroids. In conclusion, localization of Bcrp in endocrine organs together with the efficient allosteric inhibition of the efflux pump by steroid hormones are suggestive for a role for BCRP in steroid hormone regulation.

31

Localization of Bcrp in endocrine organs and inhibition of its transport activity by steroid hormones

2

Introduction

Breast Cancer Resistance Protein (BCRP; ABCG2) is a member of the ATP Binding Cassette (ABC) superfamily of transmembrane proteins involved in the transport of a variety of molecules against steep concentration gradients at the expense of ATP. BCRP is a half transporter that requires dimerization to become a fully active efflux pump and was originally discovered in a breast cancer cell line resistant to chemotherapeutics.1-3 Nowadays, BCRP is recognized as a xenobiotic transporter that plays a major role in multidrug resistance.2,4,5 The efflux pump is present in various tissues with a barrier function, including the placenta, prostate, small intestine, brain, colon, liver, mammary gland and kidney.4,6-9 Besides the protective role of BCRP against accumulating xenobiotic compounds, a role in the transport of endogenous compounds has also been described, such as the transport of folic acid10, heme11-13, urate14,15 and uremic toxins16. Moreover, BCRP transports conjugated steroids, such as dehydroepiandrosterone sulfate (DHEAS)17, estrone sulfate (E1S) and estradiol glucuronide (E217βG)10,18,19.The localization of BCRP in the plasma membrane is shown to be sex specific, which is suggested to be due to the suppressive effect of estradiol and the inductive effect of testosterone.20-23 Also, progesterone has been shown to regulate BCRP transcription in cancer cell lines, including BeWo cells, a cell line derived from human placental choriocarcinoma.24 Moreover, a role for BCRP in transport of androgens was suggested in prostate stem cells25 and transport of estradiol was demonstrated in membrane vesicles from Lactococcus lactis containing functional human BCRP26, indicating that the efflux pump might also play a role in steroid action. In this respect, the presence of BCRP was described for some hormone producing organs7,23,27, but for some organs, only BCRP mRNA content or presence of the protein by Western blot was shown. In the present study, we investigated murine organs, including the pituitary and adrenal glands, pancreas, kidney, ovary, testis and adipose tissue for the presence and localization of Bcrp. Furthermore, we evaluated the effects of several sex steroids on BCRP-mediated substrate transport using membrane vesicles of baculovirus transduced HEK293 cells. Our results show presence of the efflux pump in steroid producing organs. In addition, the sex steroids estradiol, testosterone, progesterone and androstenedione inhibited BCRP function in a concentration-dependent manner.

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Materials and methods

RNA isolation and quantitative PCRAll procedures involving animals were approved by the Animal Experimental Committee of the Radboudumc. Bcrp mRNA was assessed in the adrenal gland, pituitary gland, epididymal fat pad, abdominal fat pad, pancreas, testis, ovary and the kidney of Friend leukemia virus B (FVB) mice (Charles River Laboratories, Germany). Isolated organs were immediately snap frozen in liquid nitrogen until further analysis. Fat pads, adrenal glands and pituitaries were homogenized using micropestles. Other organs were homogenized in frozen state using a Mikro-dismembrator U (Sartorius B. Braun Biotech Int., Melsungen, Germany). To avoid RNA degradation, the metal cylinders were washed with 0.5 M NaOH prior to use. Subsequently, total RNA was isolated using a NucleoSpin® RNA II kit (Macherey-Nagel, Düren, Germany) according to manufacturer’s instructions. Immediately, a reverse transcriptase reaction was performed with 250 ng RNA using random primers (Invitrogen, Breda, the Netherlands) and an Omniscript® RT kit (Qiagen, Hilden, Germany), following manufacturer’s recommendations. Synthesized cDNA was used for quantitative PCR, performed in a StepOnePlus™ Real-Time PCR system by means of the TaqMan® protocol (Applied Biosystems, Warrington, UK). Bcrp mRNA concentration was normalized to the mRNA concentration of the housekeeping gene β-actin. The primer-probe sets were obtained from Applied Biosystems (β-actin; 4352933E, Bcrp; Mm00496364_m1). ImmunohistochemistryThe localization of Bcrp was assessed by immunohistochemistry. Organs from Bcrp-/- mice were used as negative controls. The knockout mice were kindly provided by Dr. A. Schinkel (Netherlands Cancer Institute, Amsterdam, the Netherlands) and were bred and housed at the Central Animal Laboratory of the Radboudumc. The organs were fixed by cardiac perfusion. Briefly, the mice were anesthetized with a single, lethal i.p. injection of 100 mg/kg pentobarbital (Nembutal, 60 mg/ml). The hearts of anesthetized mice were exposed and the right atrium was clipped with surgical scissors. A 23-gauge needle was inserted into the left ventricle and phosphate buffered saline (PBS) containing heparin (300 ml/h) was administered. Subsequently, the mice were perfused with a 4% w/v formaldehyde solution, freshly prepared from paraformaldehyde. The organs were removed and fixed in a 4% w/v formaldehyde solution for 24 h and embedded in paraffin, except for fat pads, which were fixed in Bouin’s fixative immediately after the perfusion fixation. Sections of 5 µm were mounted on 3-aminopropyltriethoxysilane (APES) coated slides and dried for 2 h at 57 °C.

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After deparaffinization with xylene and rehydration, the slides were heated in sodium citrate buffer (pH 6) at 100 °C for 15 min. Endogenous peroxidase was blocked with 1.5% v/v H2O2 for 30 min. After blocking with nonimmune rabbit serum, the slides were incubated with primary antibody against mouse Bcrp (BXP-9, 1:20; Kamiya Biomedical Company, Seattle, WA, USA) overnight at 4 °C. The biotinylated secondary antibody (rabbit-anti-rat, 1:500; Acris Antibodies GmbH, Herford, Germany) was incubated for 30 min followed by a 30 min incubation with standard avidin-biotin complex (ABC; Brunschwig Chemie, Amsterdam, the Netherlands). Next, DAB chromogene was used for visualization. Slides were counterstained with Mayer’s hematoxylin, dehydrated, and mounted with DPX. Sections were evaluated by means of a light microscope (Leica DM 6000 B) and digitized using a Leica DFC480 digital camera (Leica Microsystems, Wetzlar, Germany).

Transduction of Human Embryonic Kidney (HEK293) cells and isolation of membrane vesiclesIncreased synthesis of human BCRP and MRP3 in HEK293 cells was established using baculoviruses, which were produced using the Bac-to-Bac system (Invitrogen, Breda, the Netherlands) with BacMamVSV-EX-hBCRP (pENTR221-hBCRP; HsCD00044371; Harvard Institute of Proteomics, Harvard Medical School, Boston, MA, USA) and BacMamVSV-EX-hMRP3 (sequence of MRP3 was equal to GenBank accession number NM_003786), as described previously by Wittgen et al.28. As a control, the enhanced yellow fluorescent protein (eYFP) was introduced into the baculoviruses as well. Crude membranes of HEK293-BCRP, -MRP3, -eYFP cells were isolated, resuspended in TS buffer (10 mM Tris-HEPES and 250 mM sucrose, pH 7.4) and membrane vesicles were prepared according to a previously described method.29 Total protein concentration was determined by a Bio-Rad protein assay kit (Bio-Rad Laboratories, Veenendaal, the Netherlands). Crude membrane vesicles were dispensed in aliquots, snap frozen in liquid nitrogen, and stored at -80 °C until further use.

Western blot analysisMembrane vesicles were prepared for gel electrophoresis by incubation with Laemmli sample buffer (consisting of 0.5 M tris-HCl pH 6.8, 8% w/v sodium dodecyl sulfate (SDS), 40% w/v glycerol, 0.08% w/v bromophenol blue and 0.4 M β-mercapto-ethanol) for 10 min at 65 °C. Proteins (15 µg total protein per sample) were separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) using a 10% gel and blotted onto a nitrocellulose membrane using a dry blot system (iBlot; Invitrogen, Breda, the Netherlands). The membrane was incubated overnight at 4 °C with mouse-anti-hBCRP antibody (BXP-21, 1:200; Kamiya Biomedical

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Company, Seattle, WA, USA) or mouse-anti-hMRP3 antibody (M3II-21, 1:200; Abcam, Cambridge, UK). Primary antibodies were detected using fluorescently labelled goat-anti-mouse secondary antibody (IRdye800; 1:10,000 Rockland Immunochemicals, Boyertown, PA, USA). Signals were visualized using the Odyssey imaging system (Li-Cor Biosciences, Lincoln, NE, USA).

BCRP-mediated estrone sulfate (E1S) uptake and inhibition by steroidsUptake of [3H]-E1S into HEK293-BCRP and -eYFP membrane vesicles was performed using an assay which was well established in our lab.16,28,29 A reaction mix consisting of TS buffer supplemented with 4 mM ATP/AMP, 10 mM MgCl2 and various concentrations of [3H]-E1S at pH 7.4 was added to 7.5 µg of membrane vesicles (based on total protein content). After an incubation of 60 sec at 37 ºC to enable ATP-dependent uptake, the reaction was stopped by placing the samples on ice and by addition of ice-cold TS buffer. Reaction mix was removed and the vesicles were washed by means of a rapid filtration technique using glass fiber filter plates (Millipore, Etten-Leur, the Netherlands). Scintillation fluid was added to the filters and the amount of radioactivity was determined using a scintillation counter (Tri-Carb® 2900TR; Perkin Elmer, Waltham, MA, USA). Reference samples were measured to calculate the amount of transported E1S. ATP-dependent transport was calculated by subtracting values measured in the presence of AMP from those measured in the presence of ATP. Net BCRP-mediated E1S transport was calculated by subtracting ATP-dependent E1S uptake in HEK293-eYFP vesicles from that of HEK293-BCRP vesicles. Linearity of E1S uptake in time was determined using 250 nM E1S. The effects of several androgens, estrogens and progestagens (all obtained from Sigma-Aldrich, St. Louis, MO, USA or Steraloids Inc., Newport, RI, USA) on vesicular E1S uptake were assessed by performing the above-mentioned transport assay in the presence of 50 µM of the steroid. All steroid hormones were dissolved in ethanol. Maximum ethanol concentrations of 1.3% v/v were used in uptake experiments and solvent controls were included to exclude non-specific effects. The inhibition by the steroid hormones estradiol, testosterone, progesterone and androstenedione on vesicular E1S uptake were studied in more detail. Therefore, the membrane vesicles were incubated in the presence of increasing concentrations of the steroid at three different concentrations of [3H]-E1S (50, 100, and 250 nM). In studies with androstenedione, the glass fiber filter plate was pre-incubated with 50 mg/ml BSA for one hour at 37 °C and the reaction mix was supplemented with 0.2 mg/ml BSA, to reduce the background which is caused by adhesion to the filter plate. The sidedness of the membrane vesicles was not determined as ATP-dependent uptake can occur only in inside-out vesicles. The relative inhibition by steroids was expressed as percentage of maximum uptake.

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Inhibition of MRP3-mediated estradiol glucuronide (E217βG) uptake by steroidsThe effects of estradiol, testosterone, progesterone and androstenedione on MRP3 activity was determined by the same method as described above, using E217βG as a substrate. HEK293-eYFP and -MRP3 membrane vesicles were incubated with 80 nM [3H]-E217βG with or without the steroids, for 3 min at 37 °C. The washing steps were performed using PVDF filter plates (Millipore, Etten-Leur, the Netherlands). These parameters were adapted from Wittgen et al.28. Unlabeled E217βG (100 µM) was used as a positive control for inhibition of MRP3 activity.

Data analysisRelative Bcrp mRNA concentration was normalized for the cycle threshold (Ct) value of the endogenous reference gene β-actin (delta Ct; dCt) and depicted as the reciprocal of dCt (mean ± SEM). Differences in mRNA concentration between male and female organs were assessed by means of a Student’s t-test, considering P < 0.05 significant. Fold differences in Bcrp mRNA levels between the organs were calculated using 2(dCt organ1-dCt organ2). Uptake experiments were performed in triplicate in three independent batch isolations. Michaelis-Menten analysis was used to study transport kinetics. Inhibition curves are depicted as mean ± SEM of three independent experiments. Curve fitting was performed by non-linear regression analysis. The mode of inhibition was determined using Dixon’s method combined with linear regression analysis to estimate the inhibitory constant (Ki). A representative of one experiment is depicted. Differences in MRP3-mediated E217βG uptake were assessed by means of a one-way ANOVA test followed by Dunnett’s post hoc test. All analyses were performed with GraphPad Prism software (version 5.02, GraphPad Software Inc., San Diego, CA, USA).

Results

Bcrp mRNA analysis in murine organs Bcrp mRNA levels in murine endocrine organs were determined by means of quantitative PCR (figure 1). For comparison, mRNA levels in kidneys were evaluated, which are known to be very high.8 The relative Bcrp mRNA concentration was normalized to the mRNA concentration of the endogenous reference gene β-actin. The variation in cycle threshold (Ct) values of β-actin indicated that the qPCR reactions were reproducible (SEM ≤ 0.92). Bcrp mRNA was present in all organs tested. The relative mRNA concentration in the adrenal gland, pituitary gland, epididymal and abdominal fat, the pancreas and in the ovary was comparable and

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found to be more than 30 times lower as compared to mRNA levels in the kidney. Bcrp mRNA levels in the testis was about four times higher than in the other organs, but 9-fold lower as compared to the kidney. Interestingly, mRNA levels of Bcrp in male mice were slightly higher than in female mice (P < 0.01 for the kidney and P < 0.05 for other organs), while there was no difference in β-actin mRNA concentration between samples from males and females.

Figure 1. Relative Bcrp mRNA concentration in wild type mouse organs, normalized for the cycle threshold (Ct) value of the endogenous reference gene β-actin (delta Ct; dCt). For all experiments, tissues from six mice were individually analyzed, except for the adrenal gland and the pituitary gland. For those, three samples of two pooled organs were analyzed. Bars represent the reciprocal of mean dCt ± SEM of independent experiments (N=3-6). * P < 0.05; ** P < 0.01 compared to Bcrp mRNA concentration in males by Student’s t-test. There was no difference in β-actin mRNA concentration between males and females. The SEM of β-actin Ct values was ≤ 0.92.

Localization of Bcrp in murine organsImmunohistochemical analysis revealed localization of Bcrp protein in numerous murine organs. Organ sections of Bcrp-/- mice were used to determine nonspecific staining. Both male and female organ sections were evaluated, but no differences in localization of the protein between male and female were found (data not shown). Therefore, only organs from female mice are shown here, except for the testis. Figure 2 depicts Bcrp localization in organs with a barrier function, viz. kidney, brain and liver. In the kidney, Bcrp staining was most prominently observed in the proximal tubular lumen, the brush border. Also in Bowman’s capsule brush border, which is present in murine nephrons, Bcrp was localized. The distal tubular lumen did not contain Bcrp. No Bcrp staining was found in the negative control sections (Bcrp-/-).

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Figure 2. Representative immunohistochemical images of Bcrp distribution in wild type (left panels) and Bcrp-/- (negative control; right panels) mouse kidney, brain tissue and liver. Because of an absence of gender difference in localization, only organs from female mice are shown. Perfusion fixed, paraffin embedded organ-sections were incubated with a primary antibody against mouse Bcrp (BXP-9). DAB chromogene staining (brown) visualizes Bcrp localization. The sections were counterstained with Mayer’s hematoxylin. Bars 100 µm. Inserts represent details of Bcrp positivity and corresponding negative controls. Bcrp was present in the brush border of proximal tubule and in the brush border of Bowman’s capsules (arrow) of the kidney and in the capillaries of the brain and liver.

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Furthermore, Bcrp was localized in the capillaries of the brain and liver. Besides presence of Bcrp in organs with a barrier function, the protein was found in endocrine organs (figure 3 and 4). In the sinusoidal capillaries in adrenal gland cortex, Bcrp was clearly localized. In addition to this, some cells showed intracellular Bcrp staining in the zona glomerulosa and zona reticularis, but neither in the zona fasciculata nor in the medulla of the adrenal gland intracellular Bcrp staining was found. In the capillaries of the anterior pituitary gland, Bcrp was localized at the apical surface of the endothelial cells. The hormone producing cells of the pituitary gland showed no Bcrp-specific staining. The adipocytes in both epididymal and abdominal fat pads showed Bcrp positivity, which was absent in Bcrp-/- sections. No differences in localization of the pump were found between epididymal and abdominal fat pads, therefore, only micrographs of epididymal fat pads were shown in figure 3. Bcrp was not located in the endocrine and exocrine cells of the pancreas (figure 4), but endothelial cells lining the lumen of the capillaries in the pancreas showed a very subtle Bcrp staining, when comparing wild type organs with the negative control organs. Blood vessels of the testis, located between the seminiferous tubules, were Bcrp positive. On the other hand, the interstitial cells of Leydig and the Sertoli cells, which were located within the seminiferous tubules, did not show Bcrp staining. Furthermore, the transport protein was localized in endothelial cells lining the lumen of ovarian blood vessels. Interestingly, not all vessels were Bcrp positive, but Bcrp was present in all small capillaries.

Presence and activity of BCRP in isolated membrane vesiclesIncreased synthesis of human BCRP in HEK293 cells was established by transduction with baculovirus containing the recombinant bacmide DNA. Western blot analysis performed on isolated membrane vesicles demonstrated that the transduction was successful, as indicated by the band at ~75 kD (figure 5a). The negative control, consisting of membrane vesicles isolated from HEK293-eYFP cells, showed no presence of BCRP. Transport activity was determined by incubating the membrane vesicles with [3H]-E1S at 37 °C. Time-dependent uptake showed linearity up to 120 s (data not shown). Michaelis-Menten analysis (figure 5b) revealed a Km of 4.5 ± 0.4 µM and a Vmax of 332 ± 12 pmol/mg*min-1, after incubating the vesicles for 60 s at 37 °C. In control (eYFP) vesicles, a maximum uptake rate of 6 pmol/mg*min-1 was found.

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Figure 3. Representative immunohistochemical images of Bcrp distribution in wild type (left panels) and Bcrp-/- (negative control; right panels) mouse adrenal gland, pituitary gland and epididymal fat. Because of an absence of gender difference in localization, only organs from female mice are shown. Perfusion fixed, paraffin embedded organ-sections were incubated with a primary antibody against mouse Bcrp (BXP-9). DAB chromogene staining (brown) visualizes Bcrp localization. The sections were counterstained with Mayer’s hematoxylin. Bars 100 µm for adrenal gland and 50 µm for pituitary gland and epididymal fat. (a) zona glomerulosa, (b) zona fasciculata, (c) zona reticularis of the adrenal gland are indicated. Inserts represent details of Bcrp positivity and corresponding negative controls. Bcrp was located in the sinusoidal capillaries in adrenal gland cortex, in the capillaries of the pituitary gland and in adipocytes.

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Figure 4. Representative immunohistochemical images of Bcrp distribution in wild type (left panels) and Bcrp-/- (negative control; right panels) mouse pancreas (female), testis and ovary. Perfusion fixed, paraffin embedded organ-sections were incubated with a primary antibody against mouse Bcrp (BXP-9). DAB chromogene staining (brown) visualizes Bcrp localization. The sections were counterstained with Mayer’s hematoxylin. Bars 100 µm. Inserts represent details of Bcrp positivity and corresponding negative controls. Blood vessels in the testis and ovary showed distinct Bcrp positivity, whereas in the pancreas, they showed very subtle Bcrp staining.

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Figure 5. (a) Western blot analysis of HEK293-eYFP- and -BCRP membrane vesicles. BCRP was detected using mouse-anti-BCRP antibody (BXP-21). A Michaelis-Menten plot (b) revealed net BCRP-mediated uptake of E1S. Vesicles were incubated with increasing concentrations of [3H]-E1S for 60 s at 37 °C in the presence of AMP or ATP. Net ATP-dependent uptake was calculated by subtraction of AMP values. Control (eYFP) vesicles showed maximum uptake of 6 pmol/mg*min-1. Graph represents means ± SEM of triplicate measurements in a representative experiment.

Inhibition of transport activity of BCRP and MRP3 by steroidsThe effects of several androgens, estrogens and progestagens on BCRP-mediated E1S uptake was assessed by incubating the membrane vesicles with [3H]-E1S in the presence of 50 µM of the steroid for 60 s at 37 °C. A substrate concentration of 250 nM E1S was used (figure 5B, which was well below the Km of E1S uptake to ascertain initial uptake kinetics. Estradiol, testosterone, progesterone and androstenedione inhibited the efflux pump most potently (Table 1) and were, therefore, selected to be studied in more detail. Concentration-dependent inhibition of BCRP-mediated uptake of E1S was found for all four steroids (figure 6a-d). Progesterone was able to inhibit BCRP activity for >90% within the concentration-range tested. Estradiol, testosterone and androstenedione inhibited the efflux pump for 80%, 86% and 70%, respectively, at the highest possible concentrations tested. We were not able to assess whether the steroids were able to fully inhibit the efflux pump using our vesicular transport system because this was limited by the maximum solubility of the steroids in ethanol.

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Table 1. Effect of steroids (50 µM) on BCRP-mediated vesicular E1S uptake Steroid Vesicular E1S uptake (%)Cholesterol 71.9 ± 5.2Pregnenolone ND17α-hydroxypregnonolone 77.2 ± 17.1Dehydroepiandrosterone 58.7 ± 7.8Androstenediol 70.2 ± 18.7Progesterone 28.7 ± 14.4a

17α-hydroxyprogesterone 108.0 ± 18.7Androstenedione 42.4 ± 9.4a

Testosterone 20.1 ± 12.2a

Dihydrotestosterone 48.4 ± 24.6Estrone NDEstradiol 40.4 ± 8.4a

Estriol 53.1 ± 22.5Values are shown as mean ± SEM of one experiment performed in triplicate. Vesicular E1S uptake is expressed as a percentage of maximum uptake after incubation with 250 nM [3H]-E1S with or without 50 µM of the steroid for 60 s at 37 °C. ND, Not determined.a The most potent inhibitors of BCRP-mediated E1S uptake were selected to be studied in more detail

Figure 6. Concentration-dependent inhibition of net BCRP-mediated E1S uptake by estradiol (a), testosterone (b), progesterone (c), and androstenedione (d). Vesicles were incubated with 250 nM [3H]-E1S and increasing concentrations of the steroid, for 60 s at 37 °C in the presence of AMP or ATP. AMP values were subtracted from ATP values. Net BCRP-mediated E1S uptake was calculated by subtraction of corresponding eYFP values and expressed as a percentage of maximum uptake. Curve fitting was performed by non-linear regression analysis. Graphs represent means ± SEM of three independent experiments.

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Because of their lipophilicity, steroids move readily through the plasma membrane and potentially might interfere with other membrane transporters. To exclude the possibility that the inhibitory effects on BCRP activity resulted from such a nonspecific phenomenon, the steroids were tested for their inhibitory effects on the activity of another ABC transporter, viz. MRP3. Unlike substrates for other ABC transporters, such as MRP1, MRP2 and P-glycoprotein, MRP3 substrates do not overlap with the ones known to be transported by BCRP. Because of this, we did not expect steroids to interact with MRP3. Increased synthesis of human MRP3 in HEK293 membranes was confirmed using Western blot analysis (figure 7a). Concentrations of the steroids which were able to inhibit BCRP activity for more than 50% were used to determine their effects on MRP3 activity, using [3H]-E217βG as a substrate. Under these conditions, none of the steroids inhibited MRP3-mediated [3H]-E217βG uptake (figure 7b). Instead, testosterone and progesterone even stimulated MRP3 activity. Moreover, the positive control, unlabeled E217βG (100 µM), did inhibit [3H]-E217βG uptake significantly, to 19.4 ± 0.6 % of maximum uptake (P < 0.001).

Figure 7. (a) Western blot analysis of HEK293-eYFP- and -MRP3 membrane vesicles. MRP3 was detected using mouse-anti-MRP3 antibody (M3II-21). (b) Effects of estradiol, testosterone, progesterone and androstenedione on MRP3 activity. Membrane vesicles were incubated with 80 nM [3H]-E217βG (substrate) and indicated concentrations of the steroids, for three min at 37 °C. The effect of the steroids on net MRP3-mediated E217βG uptake is expressed as a percentage of uptake in negative control samples. Bars represent means ± SEM of three independent experiments. Differences in MRP3-mediated E217βG uptake were assessed by means of a one-way ANOVA test followed by Dunnett’s post hoc test.

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Dixon plot analysisTo determine the mode of interaction, we measured concentration-dependent effects of the steroids on BCRP activity at three different E1S concentrations. Figure 8 depicts Dixon plots of the data, which were analyzed by linear regression. The three lines representing steroid inhibition at different E1S concentrations of the four steroids intersected all at the x-axis, indicating a non-competitive inhibitory effect. The apparent inhibitory constant (Ki) values were 5.0 ± 0.2 µM for estradiol, 36 ± 14 µM for testosterone, 14.7 ± 1.3 µM for progesterone, and 217 ± 13 µM for androstenedione.

Figure 8. Dixon plots of inhibitory effects of estradiol (a), testosterone (b), progesterone (c), and androstenedione (d) on BCRP-mediated E1S uptake. Membrane vesicles were incubated with indicated concentrations of [3H]-E1S and increasing concentrations of the steroid. The reciprocal of transport velocity (1/V) was calculated and the mode of inhibition was determined using Dixon’s method combined with linear regression analysis to estimate the inhibitory constant (Ki) values; 5.0 ± 0.2 µM (a), 36 ± 14 µM (b), 14.7 ± 1.3 µM (c), 217 ± 13 µM (d). A representative of one experiment, out of three independent measurements, is depicted.

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Discussion

This study reports a clear overview of Bcrp presence in several murine organs as well as its cellular localization in these tissues. In addition to adrenal gland, pancreas and testis, we show for the first time localization of Bcrp in ovary, pituitary gland and adipose tissues. Moreover, we report an efficient inhibitory effect of steroid hormones on BCRP function. Estradiol, testosterone, progesterone and androstenedione attenuated BCRP-mediated E1S transport in a non-competitive manner, which implies an allosteric inhibition of BCRP function by these steroids. Presence of BCRP in various tissues with a barrier function, including placenta, prostate, small intestine, brain, colon, liver, mammary gland and kidney has been shown previously.4,6-9 Investigations on the presence of the efflux pump in endocrine organs received much less attention and localization of the protein has hardly been described. We assessed Bcrp on both mRNA level as well as protein presence in endocrine organs. In addition, endocrine organs from Bcrp-/- mice were used for comparison, allowing discrimination between background and Bcrp-specific staining. In accordance with the findings of Langmann et al.27, who quantified Bcrp mRNA in various human tissues, we found Bcrp mRNA in mouse kidney, testis and adrenal gland. While our results show highest mRNA levels in the kidney, Langmann et al. described highest mRNA levels in the testis. This could be attributed to interspecies differences. We described previously that Bcrp mRNA levels were much higher in mouse kidneys compared to rat and human kidneys.8 Additionally, our results revealed that for all organs tested, mRNA concentrations of Bcrp in male mice were significantly higher than in females, which is in agreement with previously published findings. Despite the clear gender differences in Bcrp mRNA levels, there were no differences in localization of the protein between male and female organs (data not shown). Tanaka et al.23 described male-predominant Bcrp gene expression in rat kidney and mouse liver. Moreover, Merino et al.30 found differences in the disposition of the BCRP substrate, nitrofurantoin, between male and female mice, with higher plasma levels in females. They also reported higher hepatic protein levels of human BCRP in men compared to women, determined by Western blot analysis using crude membrane fractions from human liver. The precise role of the efflux pump in males vs. females was beyond the scope of the present study, and further research should elucidate the consequences of this gender difference.In accordance with previous findings8, immunohistochemical analysis revealed that Bcrp was prominently located in the brush borders of proximal tubule epithelium of the murine kidney. Furthermore, Bcrp was found to be present in adrenal gland, pituitary gland, testis

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and ovary, where it was located predominantly in endothelial cells lining blood vessel walls. In agreement, Maliepaard et al.9 reported Bcrp localization in venous and capillary endothelial cells of almost all tissues. In addition to the arterioles in the adrenal gland cortex, Bcrp was found to be present in the cells lining the zona glomerulosa and zona reticularis, which secrete mineralocorticosteroids and androgens, respectively. The latter is in agreement with the findings of Fetsch et al.7. For the pituitary gland, we are the first to reveal Bcrp localization in the capillaries. Bcrp presence in blood vessels was also found in the pancreas, though, it was low as compared to the other organs studied. Presence of Bcrp in the pancreas was described previously, but mainly in pancreatic progenitor cells.31,32. In contrast to Fetsch et al., who reported Bcrp localization in the islet and acinar cells, we did not find Bcrp in the endocrine and exocrine cells of the pancreas.In ovary, Bcrp was clearly present in capillaries and some larger blood vessels and in testis the capillaries showed positive staining as well. The fact that not all vessels in ovary tissue were Bcrp positive, might be characteristic for the dynamic environment of the estrous cycle, where there is a constant regeneration and degradation of capillaries. The degrading capillaries lose their endothelial function and thereby endothelial protein is decreased. Tanaka et al.23 reported Bcrp in ovary and testis of mice. In accordance with our results, they demonstrated that Bcrp protein levels were higher in testis than in ovary. Enokizono et al.33 did not find evidence for Bcrp activity when evaluating genistein accumulation in the ovaries of wild-type and Bcrp-/- mice, but they did find Bcrp activity in murine testis. It was reported that the interstitial cells of Leydig7 and spermatogonial cells34 of the testis localized Bcrp. Conversely, our findings could not endorse these reports. Though, presence of Bcrp in the lumina of testicular blood vessels was evident.6

In both epididymal and abdominal fat pads, we found Bcrp to be present in mature adipocytes, which has as yet not been described. Adipocytes are metabolically active cells that play a key role in the control of the body’s energy balance by the secretion of resistin, adiponectin, and leptin. Moreover, adipocytes are involved in steroid hormone regulation since they highly express aromatase (CYP19A1), which is responsible for a key step in the biosynthesis of estrogens.35 Therefore, it will be interesting to unravel further the role of Bcrp in adipocytes. Another important endocrine organ, which we did not evaluated, is the mammary gland. Steroids highly influence the growth and function of the mammary gland. It is, therefore, interesting to study the presence and function Bcrp this organ. In 2005, Jonker et al.5 published the localization of BCRP/Bcrp in breast tissue of mice, cows and humans. They clearly showed that the transporter was localized in mouse mammary gland epithelium

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during late pregnancy and in lactating mice but presence of the protein was negligible in the mammary gland of nonpregnant and nonlactating mice. It would be interesting to study the mechanism by which Bcrp synthesis and function is influenced during pregnancy and lactation.In all the organs we analysed, Bcrp was principally found in the endothelial layer of sinusoids, capillaries and veins. The fact that Bcrp was located in the canalicular membrane of the liver indicates that the efflux pump may be involved in excretion processes in the liver, similar to other ABC transporters, such as P-gp and MRP2.9 In the brain and in the testis, the endothelial cells form tight junctions, creating a blood-tissue barrier, preventing substances to freely enter the tissue. Here, efflux transporters, such as Bcrp, are thought to protect the brain and developing sperm cells against toxic agents. In other organs, normal endothelium consists of loosely connected endothelial cells and is known to be quite permeable for several substances. Nevertheless, the fact that Bcrp is so clearly present in these endothelial cells and it is able to transport substances against a concentration gradient indicates a contribution of the efflux pump to transport compounds across the endothelium. Although, the endocrine organs, obviously, have a significant secretory function, we did not find Bcrp localized in endocrine and exocrine cells, except for adipocytes and the adrenal gland. Hence, the efflux pump may contribute to the transport of steroids, or their derivates, into the blood stream.Using Dixon’s method, we proved indirectly that estradiol, testosterone, progesterone and androstenedione are efficient inhibitors, but likely not transported by BCRP via the same binding site as E1S. The question whether steroids are substrates for BCRP has been discussed before and contradictory findings have been reported.18,25,26 Most studies support the conclusion that steroids interact but are not substrates for BCRP, which is in agreement with the present findings. Nevertheless, we cannot completely rule out the possibility of steroids being transported by BCRP. The idea that there is no need for steroids to be transported actively over the plasma membrane because of their high lipophilicity is outdated, since it has been described that steroids are substrates for other ABC transporters.36-39 Besides, steroids are derived from cholesterol, which itself is transported by other members of the ABCG subfamily.40,41 We have undertaken studies to measure steroid uptake in HEK293-BCRP membrane vesicles, but unreliable results were obtained due to the large passive permeability of these lipophilic compounds.To exclude the possibility that the inhibitory effects of the steroids on BCRP activity were nonspecific, their effects on the activity of MRP3 was tested. None of the steroids inhibited MRP3-mediated E217βG uptake. Interestingly, however, testosterone and progesterone stimulated this uptake. Stimulation of MRP3 has been described before, although, the

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mechanism explaining these complex stimulatory effects has not yet been elucidated.28,42,43. The fact that MRP3 function was not inhibited by the steroids indicates that the inhibition of BCRP activity by the steroids was a transporter-specific effect.Estradiol, testosterone, progesterone and androstenedione inhibited BCRP activity for more than 70%. However, the concentrations needed to cause this inhibitory effect were rather high compared to plasma levels. These concentrations are indicative of the affinity of the steroids for BCRP compared to the affinity of the substrate (E1S) for BCRP, which is known to be very high. When the affinity of the steroids is much lower, then the concentrations needed to cause an effect are obviously much higher. Furthermore, the steroids themselves are very lipophilic and are therefore prone to stick to plastic tubes and/or wells, which might result in an over-estimation of the actual concentration. This problem was acknowledged earlier by Tanneberger et al.44 To predict the clinical relevance of the interaction between steroids and BCRP, knowledge on intracellular steroid concentrations is important. Plasma steroid levels are not relevant for predicting their effects on a membrane transporter that transports molecules from the inside to the outside of the cell. Intracellular levels are, so far, unknown and difficult to determine. One can imagine that intracellular concentrations in organs responsible for the production of hormones may be much higher than plasma levels and, thereby, could potentially influence BCRP function in vivo.Not only BCRP function is altered by steroids, as shown in our results, also membranal localization of BCRP is highly influenced by sex steroids. Imai et al.22 found that estrogens post-transcriptionally downregulated BCRP in estrogen-responsive cancer cells. They found estradiol-mediated reduction in BCRP protein in MCF-7 cells, however, not on mRNA level. This downregulation was counteracted by gene silencing of estrogen receptor-α (ERα), indicating that ERα is necessary for the suppression of BCRP protein. In agreement, Hartz et al.21 described that estradiol signals through ERβ and ERα to initiate Bcrp internalization and acts via ERβ to stimulate proteosomal degradation of Bcrp in murine brain capillaries. In contrast, Ee et al.20 found that estradiol enhanced Bcrp mRNA levels in cells stably expressing ERα, at similar estradiol concentrations. Ovariectomy and castration of mice and rats, solely or in combination with sex steroid treatment, revealed regulation of Bcrp mRNA by estradiol and testosterone.23 In human placental BeWo cells, progesterone and estradiol significantly increased and decreased BCRP mRNA levels, respectively.45 Estradiol by itself likely downregulated BCRP through an estrogen receptor (ER), while progesterone alone upregulated BCRP via a mechanism other than progesterone receptor (PR). In conclusion, we found Bcrp to be present in numerous murine endocrine organs, including ovary, pituitary gland and adipose tissues. Furthermore, the efficient inhibition of BCRP-

49


2

mediated transport by estradiol, testosterone, progesterone and androstenedione implies a clear interaction between the steroids and the efflux pump. Together, our results support the speculation that BCRP has a role in steroid hormone regulation. Further research will address this hypothesis.

Acknowledgements

We want to thank dr. Henry B. P. M. Dijkman (Department of Pathology), dr. Hans Westphal (Department of Obstetrics and Gynaecology) and dr. Catharina E.E.M van der Zee (Department of Cell Biology) for their excellent help with the evaluation of the immunohistochemical stainings. Besides, we thank Ms. Jorien W.H. Sanderink for her help regarding the transport inhibition assays.

50

Chapter 2

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17. Lee YJ, Kusuhara H, Jonker JW, Schinkel AH, Sugiyama Y (2005) Investigation of efflux transport of dehydroepiandrosterone sulfate and mitoxantrone at the mouse blood-brain barrier: a minor role of breast cancer resistance protein. J. Pharmacol. Exp. Ther. 312(1):44-52

18. Imai Y, Asada S, Tsukahara S, Ishikawa E, Tsuruo T, Sugimoto Y (2003) Breast cancer resistance protein exports sulfated estrogens but not free estrogens. Mol. Pharmacol. 64(3):610-618

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22. Imai Y, Ishikawa E, Asada S, Sugimoto Y (2005) Estrogen-mediated post transcriptional down-regulation of breast cancer resistance protein/ABCG2. Cancer Res. 65(2):596-604

23. Tanaka Y, Slitt AL, Leazer TM, Maher JM, Klaassen CD (2005) Tissue distribution and hormonal regulation of the breast cancer resistance protein (Bcrp/Abcg2) in rats and mice. Biochem. Biophys. Res. Commun. 326(1):181-187

24. Wang H, Lee EW, Zhou L, Leung PC, Ross DD, Unadkat JD, Mao Q (2008) Progesterone receptor (PR) isoforms PRA and PRB differentially regulate expression of the breast cancer resistance protein in human placental choriocarcinoma BeWo cells. Mol. Pharmacol. 73(3):845-854

25. Huss WJ, Gray DR, Greenberg NM, Mohler JL, Smith GJ (2005) Breast cancer resistance protein-mediated efflux of androgen in putative benign and malignant prostate stem cells. Cancer Res. 65(15):6640-6650

26. Janvilisri T, Venter H, Shahi S, Reuter G, Balakrishnan L, van Veen HW (2003) Sterol transport by the human breast cancer resistance protein (ABCG2) expressed in Lactococcus lactis. J. Biol. Chem. 278(23):20645-20651

27. Langmann T, Mauerer R, Zahn A, Moehle C, Probst M, Stremmel W, Schmitz G (2003) Real-time reverse transcription-PCR expression profiling of the complete human ATP-binding cassette transporter superfamily in various tissues. Clin. Chem. 49(2):230-238

28. Wittgen HG, van den Heuvel JJ, van den Broek PH, Dinter-Heidorn H, Koenderink JB, Russel FG (2011) Cannabinoid type 1 receptor antagonists modulate transport activity of multidrug resistance-associated proteins MRP1, MRP2, MRP3, and MRP4. Drug Metab Dispos. 39(7):1294-1302

29. El-Sheikh AA, van den Heuvel JJ, Koenderink JB, Russel FG (2007) Interaction of nonsteroidal anti-inflammatory drugs with multidrug resistance protein (MRP) 2/ABCC2- and MRP4/ABCC4-mediated methotrexate transport. J. Pharmacol. Exp. Ther. 320(1):229-235

30. Merino G, van Herwaarden AE, Wagenaar E, Jonker JW, Schinkel AH (2005) Sex-dependent expression and activity of the ATP-binding cassette transporter breast cancer resistance protein (BCRP/ABCG2) in liver. Mol. Pharmacol. 67(5):1765-1771

31. Mato E, Lucas M, Petriz J, Gomis R, Novials A (2009) Identification of a pancreatic stellate cell population with properties of progenitor cells: new role for stellate cells in the pancreas. Biochem. J. 421(2):181-191

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33. Enokizono J, Kusuhara H, Sugiyama Y (2007) Effect of breast cancer resistance protein (Bcrp/Abcg2) on the disposition of phytoestrogens. Mol. Pharmacol. 72(4):967-975

34. Lassalle B, Bastos H, Louis JP, Riou L, Testart J, Dutrillaux B, Fouchet P, Allemand I (2004) ‘Side Population’ cells in adult mouse testis express Bcrp1 gene and are enriched in spermatogonia and germinal stem cells. Development 131(2):479-487

35. Raven G, de Jong FH, Kaufman JM, de RW (2006) In men, peripheral estradiol levels directly reflect the action of estrogens at the hypothalamo-pituitary level to inhibit gonadotropin secretion. J. Clin. Endocrinol. Metab 91(9):3324-3328

36. Dikkers A and Tietge UJ (2010) Biliary cholesterol secretion: more than a simple ABC. World J. Gastroenterol. 16(47):5936-5945

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41. Schmitz G, Langmann T, Heimerl S (2001) Role of ABCG1 and other ABCG family members in lipid metabolism. J. Lipid Res. 42(10):1513-1520

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Frutiger


Chapter 2

Frequency of joint involvement in juvenile idiopathic

arthritis during a 5-year follow-up of newly diagnosed

patients: implications for MR imaging as outcome measure

R. Hemke

C.M. Nusman

D.F.M. van der Heijde

A. Doria

T.W. Kuijpers

M. Maas

M.A.J. van Rossum

Submitted

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2 | Chapter 2

Abstract

Objectives

To assess the sequence and type of active joints in a cohort of newly diagnosed juvenile idiopathic

arthritis (JIA) patients with full access to current treatment at first visit and during a follow-up

period of 5-years, in order to identify an index joint/group of joints for MRI in JIA.

Methods

Patient charts of all consecutive newly diagnosed JIA patients with a follow-up duration of at least

5 years were analyzed. Patients were derived from two tertiary pediatric rheumatology centers.

Patient characteristics and data concerning the presence of joints with arthritis and the use of

medication were recorded.

Results

Findings from 95 JIA patients (40 [42%] oligoarticular and 45 [43%] polyarticular) were analyzed.

At first visit, distribution of active joints among patients was as follows: knee (n = 70, 74%),

ankle (n = 55, 58%), elbow (n = 23, 24%), wrist (n = 23, 24%), metacarpo-phalangeal [MCP]

(n = 20,21%), proximal-interphalangeal [PIP] (n = 13, 14%), hip (n = 6, 6%), shoulder (n = 5,

5%), and distal-interphalangeal [DIP] (n = 4, 4%) joints. After a follow-up period of 5 years, the

cumulative percentage of patients with specific joint involvement changed into: knee (n = 88,

93%), ankle (n = 79, 83%), elbow (n = 43, 45%), wrist (n = 38, 40%), MCP (n = 36, 38%), PIP

(n = 29, 31%), shoulder (n = 20, 21%), hip (n = 17, 19%), and DIP (n = 9, 10%) joints.

Conclusions

Despite changes in treatment strategies over the years, the knee remains the most commonly

involved joint at onset and during follow-up in JIA, followed by the ankle, elbow and wrist. For

the evaluation of outcome with MRI the knee appears the most appropriate joint in JIA.


Frequency of joint involvement in juvenile idiopathic arthritis during a 5-year follow-up of newly diagnosed patients | 3

2

Introduction

The umbrella term juvenile idiopathic arthritis (JIA) covers a group of heterogeneous conditions

that encompass all forms of arthritis of unknown etiology and pathophysiology which begin

before the age of 16 years and persist for more than six weeks (1). Studies in developed countries

reported a prevalence that varies between 16 and 150 per 100.000 (2, 3). The JIA entity is

characterized by prolonged synovial inflammation that can lead to destruction of joints, pain and

loss of function (2).

Early therapeutic interventions improve long-term outcome. Therefore objective and accurate

measures in the assessment of disease activity are needed for the evaluation of individual response

to therapy and general efficacy of treatment in JIA (4, 5). To date, magnetic resonance imaging

(MRI) is considered to be the most suitable imaging modality in this respect, as advances in MRI

techniques have substantially improved evaluation of joint pathologies in JIA. Although there is

fair strength of evidence that MRI is an accurate diagnostic method for evaluating synovium and

cartilage and for assessing clinical responsiveness to treatment in peripheral joints in JIA (6, 7),

currently there is no evidence supporting imaging of a specific joint within the scope of clinical

trials. The performance of an MRI examination for assessment of more than one or two joints/

group of joints has limitations because of time constraints and, thereby, reduces feasibility of MRI

in children with JIA. Therefore, it is important to identify an index joint, and in that way increase

the value of MRI as an outcome measure for research in JIA.

An index joint can be defined as the joint that is representative of the overall burden of the

disease. One important aspect is that it should be a clinically frequently involved joint, with

respect to presence of clinical arthritis, and thereby enclose most of the JIA patients. Such a

joint has already been identified in adult rheumatoid arthritis patients concerning MRI research

outcomes (e.g. the wrist) (8), but this information is lacking for JIA patients. Although there is

literature on the diverse pattern of disease course and disease outcome in JIA (9), there are - to

the best of our knowledge - no data available regarding the long-term distribution and course

of the disease in specific joints in children with JIA. Therefore, the aim of our study was to assess

the sequence and type of active joints in a cohort of newly diagnosed JIA patients with full access

to current treatment at first visit and during a follow-up period of 5-years, in order to identify an

index joint/group of joints for MR imaging in JIA.

R1R2R3R4R5R6R7R8R9


4 | Chapter 2

Materials and methods

Patients

This study was performed in accordance with the declaration of Helsinki and the local medical

ethical regulations. The requirement for informed consent was waived by the institutional review

board. Data of all consecutive patients visiting the outpatient clinics of two tertiary pediatric

rheumatology centers between January 2002 and January 2007 were identified in the electronic

hospital administration system and retrospectively analyzed. Patients fulfilling the following criteria

were included: a confirmed diagnosis of JIA according the International League of Associations

for Rheumatology (ILAR) criteria for JIA (1); age between 0 and 16 years at the first visit; and a

regular follow-up (defined as 2 or more outpatient clinic visits per year) for at least 5 years. All

patient charts were reviewed by one person.

For eligible patients, data regarding the presence of joint involvement throughout the follow-up

and use of medication were recorded. Collected data included age at the first visit, gender, date

of disease onset, date of diagnosis, subtype of JIA, presence of joints with clinical arthritis as

judged by the pediatric rheumatologists (temporomandibular joint (TMJ), shoulder, elbow, wrist,

metacarpophalangeal joint (MCP), proximal interphalangeal joint (PIP), distal interphalangeal joint

(DIP), sacroiliac joint (SI), hip, knee, ankle, cervical spine, thoracic spine, lumbar spine), used

medication after the first visit (no medication, NSAID, prednisone, methotrexate, sulfasalazine,

tumor necrosis factor-α (TNF-α) blockers, other DMARD).

At both sites, the standard clinical assessment includes a 67-joint count for assessing clinical

actively inflamed joints. Actively inflamed joints were defined as joints with swelling as well as

joints with tenderness or pain on motion and limited range of motion (1, 10). The number and type

of actively inflamed joints were documented in the patient chart by the pediatric rheumatologist.

Depending on the pediatric rheumatologist, joint findings were marked on a homunculus or

systematically described in words.

Changes during the follow-up period, with respect to joints with arthritis and use of anti-rheumatic

medication, were collected. Joints with questionable or equivocal joint findings were considered

to be not actively inflamed; the definition of clinically active joints was strictly maintained. Patient

charts with incomplete or missing data concerning physical examination findings were excluded.



2

Statistics

Descriptive statistics were reported in terms of medians and inter-quartile ranges (IQR) for the

continuous variables and in terms of absolute frequencies and percentages for the categorical

variables. The Fisher’s exact test and Mann-Whitney U test were used to analyze differences

between groups. A P value of less than 0.05 indicated a statistically significant difference. All data

were analyzed by using SPSS version 18.0 (SPSS, Chicago, ILL, USA).

Results

Patients

As shown in Figure 1, the electronic hospital administration system identified 316 patients visiting

the outpatient clinics of the two tertiary pediatric rheumatology centers between 2002 and 2006.

Patients not fulfilling the ILAR criteria for JIA, patients without a regular follow-up of at least 5

years, and patients with missing data were excluded (Figure 1). Therefore, data from 95 (66.3%

female) newly diagnosed JIA patients with a mean age at first visit of 9.0 years (SD 4.8) could be

included for the purpose of this study. Patient characteristics are shown in Table 1. Depending

on the disease severity as judged by the pediatric rheumatologist, the number of visits varied

between two and eight times a year.

Table 1. Patient characteristics of 95 newly diagnosed JIA patients at baseline

All patientsn = 95

No. (%) of female patients 63 (66.3)Age at study visit, mean years (SD) 9.0 (4.8)Disease duration at first visit, median months (IQR) 6 (3 – 16)

JIA category, no. (%) Systemic arthritis 1 (1.1) Persistent oligoarthritis 27 (28.4) Extended oligoarthritis 13 (13.7) Polyarthritis RF-negative 39 (41.1) Polyarthritis RF-positive 2 (2.1) Enthesitis related arthritis (ERA) 8 (8.4) Psoriatic arthritis* 3 (3.2) Undifferentiated JIA 2 (2.1)

* Two patients had poly-articular onset

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6 | Chapter 2

95 JIA patients included in study

98 JIA patients

3 JIA patients with incomplete dataconcerning physical examination �ndings

316 Patients vistining the outpatient clinicsbetween Jan 2002 and Jan 2007

163 Newly diagnosed JIA patients

65 JIA patients without regular follow-up(<2 visits per year) of at least 5 years

52 Patients not ful�lling theILAR criteria for JIA

215 Patients suspect for JIA

Figure 1. Study flow chart.

Joint involvement

At first visit the percentage of patients with arthritis of the following joints were noted: knee

(n = 70, 74%), ankle (n = 55, 58%), elbow (n = 23, 24%), wrist (n = 23, 24.2%), MCP (n = 20,

21%), PIP (n = 13, 14%), hip (n = 6, 6%), shoulder (n = 5, 5%), DIP (n = 4, 4%), cervical spine

(n = 2, 2%), TMJ (n = 2, 2%), SI (n = 1, 1%), lumbar spine (n = 1, 1%), and thoracic spine (n =

0, 0%). Joint involvement during a follow-up period of 5 years of the most informative joints is

depicted in Figure 2. The cumulative frequency of joint involvement after a follow-up period of 5



2

years was: knee (n = 88, 93%), ankle (n = 79, 83%), elbow (n = 43, 45%), wrist (n = 38, 40%),

MCP (n = 36, 38%), PIP (n = 29, 31%), shoulder (n = 20, 21%), hip (n = 17, 19%), DIP (n = 9,

10%), cervical spine (n = 8, 8%), TMJ (n = 6, 6%), SI (n = 4, 4%), lumbar spine (n = 3, 3%), and

thoracic spine (n = 0, 0%), as shown in Figure 3.

Persistent-oligoarticular versus polyarticular onset JIA patients

Polyarticular onset was observed in 43 (45%) JIA patients; RF-negative [n = 39], RF-positive

polyarthritis [n = 2], and psoriatic arthritis [n = 2]. No polyarticular onset was observed in the

systemic arthritis, undifferentiated, or enthesitis related arthritis JIA subgroups. Therefore, we

included 27 (28%) persistent-oligoarticular and 43 (45%) polyarticular onset JIA patients.

No statistical significant differences were detected between the persistent-oligoarticular and

polyarticular onset JIA patients with respect to age, gender, and disease duration. At the first

visit frequency of joint involvement between persistent-oligo and polyarticular onset JIA patients

differed significantly with respect to the elbow (n = 2 [7%] vs. n = 18 [42%]; P = 0.002), MCP

(n = 0 [0%] vs. n = 16 [37%]; P < 0.001), PIP (n = 0 [0%] vs. n = 9 [21%]; P = 0.010), and ankle

(n = 9 [33%] vs. n = 31 [72%], respectively; P = 0.003). No such differences were found regarding

the other joints, including the wrist (n = 5 [19%] vs. n = 14 [33%]; P = 0.272), and knee (n =

20 [74%] vs. n = 33 [77%], respectively; P = 1.0). Changes in frequency of joint involvement in

persistent-oligoarticular and polyarticular onset JIA patients during a 5-year follow-up period are

depicted in Figure 4.

Marked differences with respect to the cumulative frequency of joint involvement in persistent-

oligoarticular and polyarticular onset JIA patients with clinical arthritis after a follow-up period

of 5 years were noted in the shoulder (n = 3 [11%] vs. n = 16 [37%]; P = 0.026), elbow (n = 7

[26%] vs. n = 27 [63%]; P = 0.003), wrist (n = 6 [22%] vs. 22 [51%]; P = 0.024), MCP (n = 1

[4%] vs. n = 24 [56%]; P < 0.001), PIP (n = 1 [4%] vs. n = 20 [47%]; P < 0.001), hip (n = 2 [7%]

vs. n = 14 [33%]; P = 0.019), and ankle (n = 16 [59%] vs. n = 42 [98%], respectively; P < 0.001).

Joint involvement of the knee (n = 24 [89%] vs. n = 42 [98%], respectively; P = 0.291) were

comparable between the persistent oligoarticular and polyarticular-onset JIA patients.

Medication

NSAIDs were the most commonly used drugs after the first visit used by 84 (88%) patients. The

cumulative frequency of JIA patients using NSAIDs was 94 (99%). Methotrexate was started by

14 (15%) patients after the first visit, though after a follow-up period of 5 years the cumulative

frequency of patients using MTX was 74 (78%). No TNF-α blockers were prescribed during the

R1R2R3R4R5R6R7R8R9


8 | Chapter 2

first half-year after diagnosis for any of the evaluated patients, and the cumulative frequency of

patients using TNF-α blockers was 25 (26%) at the end of the 5-year follow-up period.

Discussion

This paper reports the frequency of joint involvement in different JIA subtypes in patients with full

access to current treatment during a total follow-up duration of 5 years in order to get insight

towards the sequence of joint involvement over time, with the aim of identifying index joints in

JIA for MR imaging. Our study shows differences with respect to the presence of arthritis between

different joints and between patients with different subtypes of JIA (persistent-oligoarticular vs.

polyarticular onset). In our series the knee was the most commonly involved joint, as clinically

evaluated at the first visit (up to 77% of cases) and during a follow-up period of 5 years (up to

98% of cases), followed by the ankle, elbow and wrist.

In our study the most significant changes with respect to the frequency of joint involvement in

patients with arthritis were noted during the first year of follow-up after the first clinic visit. An

increase in the absolute frequency of joint involvement was observed during the first six months

concerning all joints, consequently these frequencies decreased. This is in line with the results

of Albers et al., who found that the time of disease activity significantly decreases during the

first two years after the diagnosis of JIA (11). Although the frequency of joints with arthritis

decreased over time (during a follow-up period of 5 years), in our study population a major

percentage of the JIA patients still showed arthritis at the 5-year final follow-up time point. This

is consistent with the results of Oen et al., who found that clinically active JIA often continues to

be present for several years after the initial diagnosis and most often extends into adulthood (12,

13). Although JIA patients with predominant wrist involvement have been reported to show poor

therapy response and a destructive course of the disease (5, 14), in our study only a relatively

small subgroup of the JIA population presented with wrist involvement at the first visit (n = 23,

24%). Even after a follow-up period of 5 years, the cumulative frequency of polyarticular onset

JIA patients with wrist involvement remained relatively low (n = 22, 51%), as compared to the

cumulative frequency of JIA patients who presented with knee involvement, which increased up

to 98% in the polyarticular onset JIA subgroup.

The number of patients with clinical signs of temporomandibular joint arthritis was low in our

study. Other studies have shown that TMJ involvement commonly occurs in JIA patients, but their



2

symptoms are typically subtle and can therefore be easily missed (15, 16). MRI of the TMJ has

increased the awareness of TMJ arthritis, and MRI follow-up of the TMJ may reflect changes in

disease activity as a result of treatment (17).

To date, the evaluation of disease activity both in daily practice as well as in clinical trials is

based on physical examination (18, 19), which has, even by experienced observers, only limited

reliability (20). Advances in therapies have increased the number of patients reaching clinical

inactive disease, which cannot always be reliably demonstrated by physical examination alone

(19, 21, 22). Within the past decade, the use of MRI and advances in MRI techniques have

substantially improved the evaluation of joint pathologies in JIA. MRI is considered to be the most

sensitive imaging tool for the detection of (subclinical) synovitis, as well as for the detection of

cartilage lesions and bone erosions (6, 23).

Limitations of our study should be considered. The retrospective cohort design might have resulted

in missing data. Although some information of this study requires confirmation in larger studies,

such as the relatively low frequency of involvement of wrists and TMJs in this study, our results

are representative for the frequency of joint involvement in JIA patients visiting referral pediatric

rheumatology centers. Furthermore, our results should be interpreted cautiously with respect to

some of the JIA subtypes given their small sample size. This might hamper the generalizability of

our study findings for the complete JIA population.

Despite the continuous implementation of modern imaging techniques there is a lack of consensus

on clinical attributes that should be considered in the assessment of JIA patients. Knowledge on

such clinical attributes for instance expected frequency and sequence of joint involvement in JIA

in association with standardization of imaging protocols will enable the development of MRI

scores for assessment of JIA prioritizing target joints. Such scoring systems are urgently needed

both in clinical practice and in the conduct of clinical trials. Without knowledge of these clinical

attributes it becomes challenging if ever possible to develop and validate more objective and

accurate measures for evaluation of individual response to therapy and efficacy of treatment.

In this study we concluded that upon clinical assessment the knee is the most commonly affected

joint in JIA both at the first visit and during a follow-up period of 5 years, followed by the ankle,

elbow and wrist. We also demonstrated that the involvement of the shoulder, elbow, MCP, PIP,

hip and ankle joints differs between persistent-oligoarticular and polyarticular onset JIA patients.

This study helps to determine the index joints / groups of index joints for MR imaging in JIA which

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10 | Chapter 2

can be useful in the development of MRI assessment scores targeted to specific joints within the

scope of future clinical trials. According to our results the knee is the clinically most frequently

involved joint in JIA and can, therefore, be considered as the most appropriate joint to be used as

outcome for MR imaging research.



2

References

1. Petty RE, Southwood TR, Manners P, et al. International League of Associations for Rheumatology classification of juvenile idiopathic arthritis: second revision, Edmonton, 2001. JRheumatol. 2004;31(2):390-2.

2. Ravelli A, Martini A. Juvenile idiopathic arthritis. Lancet. 2007;369(9563):767-78.

3. Modesto C, Anton J, Rodriguez B, et al. Incidence and prevalence of juvenile idiopathic arthritis in Catalonia (Spain). Scand J Rheumatol. 2010;39(6):472-9.

4. Albers HM, Wessels JA, van der Straaten RJ, et al. Time to treatment as an important factor for the response to methotrexate in juvenile idiopathic arthritis. Arthritis Rheum. 2009;61(1):46-51.

5. Vilca I, Munitis PG, Pistorio A, et al. Predictors of poor response to methotrexate in polyarticular-course juvenile idiopathic arthritis: analysis of the PRINTO methotrexate trial. AnnRheumDis. 2010;69(8):1479-83.

6. Miller E, Uleryk E, Doria AS. Evidence-based outcomes of studies addressing diagnostic accuracy of MRI of juvenile idiopathic arthritis. AJR AmJRoentgenol. 2009;192(5):1209-18.

7. Hemke R, van Rossum MA, van Veenendaal M, et al. Reliability and responsiveness of the Juvenile Arthritis MRI Scoring (JAMRIS) system for the knee. EurRadiol. 2013;23(4):1075-83.

8. McQueen F, Lassere M, Edmonds J, et al. OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging Studies. Summary of OMERACT 6 MR Imaging Module. JRheumatol. 2003;30(6):1387-92.

9. Bertilsson L, Andersson-Gare B, Fasth A, Forsblad-d’Elia H. A 5-year prospective population-based study of juvenile chronic arthritis: onset, disease process, and outcome. Scand J Rheumatol. 2012;41(5):379-82.

10. Wallace CA, Ruperto N, Giannini E. Preliminary criteria for clinical remission for select categories of juvenile idiopathic arthritis. JRheumatol. 2004;31(11):2290-4.

11. Albers HM, Brinkman DM, Kamphuis SS, et al. Clinical course and prognostic value of disease activity in the first two years in different subtypes of juvenile idiopathic arthritis. Arthritis Care Res(Hoboken). 2010;62(2):204-12.

12. Oen K, Malleson PN, Cabral DA, Rosenberg AM, Petty RE, Cheang M. Disease course and outcome of juvenile rheumatoid arthritis in a multicenter cohort. JRheumatol. 2002;29(9):1989-99.

13. Wallace CA, Huang B, Bandeira M, Ravelli A, Giannini EH. Patterns of clinical remission in select categories of juvenile idiopathic arthritis. Arthritis Rheum. 2005;52(11):3554-62.

14. Guillaume S, Prieur AM, Coste J, Job-Deslandre C. Long-term outcome and prognosis in oligoarticular-onset juvenile idiopathic arthritis. Arthritis Rheum. 2000;43(8):1858-65.

15. Twilt M, Mobers SM, Arends LR, ten CR, van Suijlekom-Smit L. Temporomandibular involvement in juvenile idiopathic arthritis. JRheumatol. 2004;31(7):1418-22.

16. Leksell E, Ernberg M, Magnusson B, Hedenberg-Magnusson B. Orofacial pain and dysfunction in children with juvenile idiopathic arthritis: a case-control study. Scand J Rheumatol. 2012;41(5):375-8.

17. Küseler A, Pedersen TK, Gelineck J, Herlin T. A 2 year followup study of enhanced magnetic resonance imaging and clinical examination of the temporomandibular joint in children with juvenile idiopathic arthritis. JRheumatol. 2005;32(1):162-9.

18. Giannini EH, Ruperto N, Ravelli A, Lovell DJ, Felson DT, Martini A. Preliminary definition of improvement in juvenile arthritis. Arthritis Rheum. 1997;40(7):1202-9.

19. Wallace CA, Giannini EH, Huang B, Itert L, Ruperto N. American College of Rheumatology provisional criteria for defining clinical inactive disease in select categories of juvenile idiopathic arthritis. Arthritis Care Res(Hoboken). 2011;63(7):929-36.

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12 | Chapter 2

20. Guzman J, Burgos-Vargas R, Duarte-Salazar C, Gomez-Mora P. Reliability of the articular examination in children with juvenile rheumatoid arthritis: interobserver agreement and sources of disagreement. JRheumatol. 1995;22(12):2331-6.

21. Rebollo-Polo M, Koujok K, Weisser C, Jurencak R, Bruns A, Roth J. Ultrasound findings on patients with juvenile idiopathic arthritis in clinical remission. Arthritis Care Res(Hoboken). 2011;63(7):1013-9.

22. Nistala K, Babar J, Johnson K, et al. Clinical assessment and core outcome variables are poor predictors of hip arthritis diagnosed by MRI in juvenile idiopathic arthritis. Rheumatology(Oxford). 2007;46(4):699-702.

23. Doria AS, Babyn PS, Feldman B. A critical appraisal of radiographic scoring systems for assessment of juvenile idiopathic arthritis. PediatrRadiol. 2006;36(8):759-72.

Garamond


Chapter5

Ecological effects of selective decontamination on resistant

gram-negative bacterial colonization

Evelien A.N. Oostdijk, Anne Marie G.A. de Smet, Hetty E. Blok,

Emily S. Thieme Groen, Gerard J. van Asselt GJ, Robin F. Benus,

Alexandra T. Bernards, Ine H. Frénay, Arjan R. Jansz, Bartelt M. de Jongh,

Jan A. Kaan, Maurine A. Leverstein-van Hall, Ellen M. Mascini, Wouter Pauw,

Patrick D. Sturm, Steven F. Thijsen SF, Jan A. Kluytmans and Marc J.M. Bonten

Published in the American Journal of Respiratory and Critical Care Medicine 2010, 181:452-457

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2 | Chapter 5

ABSTRACT

Rationale: Selective Digestive tract Decontamination (SDD) and Selective Oropharyngeal

Decontamination (SOD) eradicate Gram-negative bacteria (GNB) from the intestinal en

respiratory tract in intensive-care-unit (ICU) patients, but its effect on antibiotic resistance

remains controversial.

Objectives: We quantified the effects of SDD and SOD on bacterial ecology in 13 ICUs that

participated in a study, in which SDD, SOD or standard care was used during consecutive periods

of 6 months (NEJM 2009;360:20).

Methods: Point prevalence surveys of rectal and respiratory samples were performed once monthly

in all patients in ICU (receiving or not receiving SOD/SDD). Effects of SDD on rectal and of

SDD/SOD on respiratory tract carriage with GNB were determined by comparing results from

consecutive point prevalence surveys during intervention (6 months for SDD and 12 months for

SDD/SOD) to consecutive point prevalence data in the pre- and post-intervention periods.

Measurements and Main Results: During SDD average proportions of patients with intestinal

colonization with GNB resistant to either ceftazidime, tobramycin or ciprofloxacin were 5%,

7% and 7%, and increased to 15%, 13% and 13% post-intervention (p<0.05). During SDD/

SOD resistance levels in the respiratory tract were ≤6% for all three antibiotics, but increased

gradually (for ceftazidime; p<0.05 for trend) during intervention and to levels ≥10% for all three

antibiotics post-intervention (p<0.05).

Conclusion: SOD and SDD have marked effects on the bacterial ecology in an ICU with

rising ceftazidime resistance prevalence rates in the respiratory tract during intervention and a

considerable rebound effect of ceftazidime resistance in the intestinal tract after discontinuation

of SDD.


Ecological effects of selective decontamination on resistant gram-negative bacterial colonization | 3

InTROduCTIOn

Selective decontamination of the digestive tract (SDD) and Selective Oropharyn-geal

Decontamination (SOD) are powerful infection prophylaxis regimens for patients in intensive

care units (ICU). First introduced in 19841, both interventions have been associated with reduced

incidences of Ventilator Associated Pneumonia (VAP)2-4 and improved patient survival.5;6

Both SDD and SOD consist of non-absorbable antimicrobial agents with activity against yeasts,

Staphylococcus aureus and aerobic Gram-negative bacteria, including Enterobacteriaceae and

Pseudomonas aeruginosa. SOD is applied in the oropharynx only, while in SDD antibiotics are, in

addition to oropharyngeal application, also administered to the gastrointestinal tract combined

with systemic prophylaxis with a third generation cephalosporin during the first four days. This

systemic prophylaxis aims to treat infections caused by commensal respiratory tract flora, such as

Streptococcus pneumonia and Haemophilus influenza already incubating at the time of ICU admission.7

The aim of the intestinal component is to selectively eradicate potentially pathogenic micro-

organisms, while sparing the anaerobic flora. The latter presumably protects against colonization

and overgrowth with potential pathogens, the so-called concept of “colonization resistance”.8

The effects of SDD on antibiotic resistance have been the subject of intense controversy. In theory,

SDD may select micro-organisms already intrinsically resistant to the regimen, such as Gram-

positive bacteria, or those with acquired resistance for the antibiotics used.7;9;10 Up till now, SDD

and SOD have not been associated with increased resistance in settings with low endemicity

of antibiotic resistance. Actually, in such settings SDD and SOD were associated with lower

incidences of carriage and infections with antibiotic resistant Gram-negative bacteria 5;6;11-13

However, emergence of plasmid mediated extended spectrum ß-lactamase (ESBL)14 and other

pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA) 15;16, have been reported as

well.

The question to what extent and in what time SDD and SOD affect the bacterial ecology in an

ICU ward remains unanswered. Most trials performed so far focussed on antibiotic resistance

rates in individual patients. Yet, since SDD, or SOD, is usually administered to patients with an

expected ICU-stay of at least two days only, there will always remain ICU patients not receiving

SDD or SOD. In a Dutch multi-center SDD-SOD study such patients accounted, on average, for

about 70% of all admitted patients, representing about 20% of all patient days in ICU.5 In that

study, surveillance cultures (rectal and oropharyngeal swabs) were obtained once monthly in all

patients (including short-stay patients) present in the ICU, during SDD, SOD and standard care,

to determine point-prevalence rates of antibiotic resistant Gram-negative bacteria.5 In the current

study, we determined the ecological effects of SDD and SOD.

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4 | Chapter 5

MeThOdS

Patients and design

Microbiological data were used from monthly point prevalence surveys performed as part of

an open clustered group-randomized cross over study in 13 Intensive Care Units (ICU) in the

Netherlands between May 2004 and July 2006 comparing SDD and SOD to standard care.5

Two six-month intervention periods (SDD and SOD) and one standard care period of six months

were conducted in each ICU, with the study order of regimens randomly assigned. Between each

period a one month wash-in/wash out period was carried out, during which the new treatment

(either SDD or SOD) was implemented, but patient data were not used for analysis.

The study interventions, SDD and SOD, were described previously.5 In short, both consisted of the

non-absorbable anti-microbial agents tobramycin, polymyxin E and amphotericin B. During the

use of SDD a 2% mixture of these antibiotics was applied on the buccal mucosa and a suspension

(respective doses 80 mg, 100 mg and 500 mg) was administered in the gastrointestinal tract

four times a day via a nasogastric tube. Furthermore, for the first four days after ICU admission

1000 mg cefotaxime was administered intravenously four times a day. SOD consisted only of the

oropharyngeal application of the 2% mixture of these antibiotics.

An expanded microbiological methods description can be found in the online supplement.

data analysis

Since the order of study regimens was randomized in each ICU, there were six possible study

orders (table 1A). For the analysis of the effects of SDD on rectal colonization, the results of the

six point prevalence studies during the SDD period were compared to the consecutive point

prevalence results in the periods before and after the SDD period (table 1B). In this analysis, SOD

and standard care were combined, since SOD was found to have no effect on rectal colonization.5

This is in accordance with previous findings.2 Data from monthly point prevalence cultures from

different centres were pooled according to study period and specific time point. Similarly, for the

analysis of respiratory tract colonization SDD and SOD were combined, as both intended similar

effects on respiratory tract colonization (Table 1C).

In this way the results for rectal colonization from the six consecutive point-prevalence surveys

during SDD in 13 units were compared to the results from eight periods (four SOD and four

standard care) in the six months before SDD and four periods (two SOD and two standard care)

in the period between twelve and six months before SDD. Similarly, SDD was compared to equal

periods following SDD as shown in table 1B. For respiratory tract colonization 12 consecutive

point prevalence results (for SDD and SOD, interrupted by one month wash-in/wash-out) were

compared to six and seven periods of standard care preceding and following the SDD/SOD

periods, respectively. In this analysis, four SOD periods were not used, as standard care followed

SOD but preceded SDD (Table 1C). Data obtained during wash in-wash out periods were not

used.



Statistical analysis

Proportions of patients colonized with Gram-negative bacteria for every time point were calculated

by determining the numerator and denominator of the prevalence. 95% Confidence intervals for

the proportions were derived from the standard error of proportion. Differences in proportions

between subsequent periods were analyzed with Poisson regression analysis. Non-segmented

Poisson regression was performed to quantify changes in time trend within the periods.17 Poisson

regression is preferred over more common statistical methods, such as linear regression, because

counts are not normally distributed. We adjusted for differences between centers as unit-level

observations, in contrast to individual-level observations, could lead to within unit correlation18.

An alpha value of P < 0.05 was set to define statistical significance. Data were analyzed using

SPSS version 12.0 (SPSS, Chicago, IL. USA) and R version 2.9.0.

ReSulTS

Microbiology

During the monthly point prevalence surveillance studies a total of 2963 rectal and 2304

respiratory tract samples were obtained and processed for specific antimicrobial susceptibility

testing. The mean (SD) number of patients sampled at each time point was 99 (38) (median, 89;

range, 55 – 165) for rectal samples and 96 (24) (median, 91; range, 64 – 134) for respiratory tract

samples. Adherence to obtaining cultures was estimated to be 87% for rectal swabs (ranging from

67% to 98% per center) and 82% for respiratory samples (ranging from 69% to 95% per center).

For rectal swabs, growth of any pathogen on the selective media was highest during standard

care (44.5 +1.5%; range 42% - 51%) and 6.1% lower during SOD (38.3 +1.6%; range 37% -

50%) and 19.6% lower during SDD (24.9 +1.4%; range 22% - 33%). In the respiratory tract,

growth was lower during both SDD (17.7 +1.3%; range 15% - 27%) and SOD (22.1 +1.5%;

range 13% - 26%), as compared to standard care (42.5 +1.7%; range 32% - 48%). Because of the

small differences between SOD and standard care for rectal swabs and between SDD and SOD for

respiratory samples, these groups were analyzed simultaneously (for separate analysis see Table E1

in the online data supplement).

Rectal colonization

Average prevalence rates of colonization with ceftazidime-resistant bacteria were 6% (95% CI,

4.7% - 7.5%) in the pre-SDD period, 5% (95% CI, 3.9%-6.7%) during the SDD, and 15%

(95% CI, 12.4%-17.0%) in the period after SDD (table 2). Before SDD there was a decline in

the prevalence rates of ceftazidime-resistance (Beta-coefficient = -0.07 ; p = 0.038), with stable

resistance levels during the entire period of SDD (figure 1), followed by an increase from 2.3%

in the last month of SDD to 11.1% in the first month after SDD (p<0.05). Resistance levels

remained stable in the subsequent twelve months.

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6 | Chapter 5

Average rates of ciprofloxacin resistance were 12% (95% CI, 9.7%-13.5%) in the pre-SDD

periods, which decreased to 7% (95% CI, 5.1%-8.2%) during SDD, and then increased to 13%

(95% CI, 10.8%-15.2%) after discontinuation of SDD. Differences between the subsequent

periods were statistically significant. Similar trends were observed for tobramycin resistance; the

average resistance prevalence was 9% (95% CI, 7.7%-11.2%) before, 7% (95% CI, 5.5%-8.7%)

during and 13% (95% CI, 10.4%-14.7%) after SDD.

In all 263, 244 and 228 Gram-negative micro-organisms were isolated from rectal samples

with resistance for ceftazidime, ciprofloxacin and tobramycin, respectively (table 3). Among

those being resistant to ceftazidime 39.9% (n=105) were Enterobacter cloacae. Strains resistant to

ciprofloxacin and tobramycin most frequently were Escherichia coli: 122 (50.0%) for ciprofloxacin

and 110 (48.2%) for tobramycin.

Respiratory tract colonization

The prevalence rates of antibiotic resistant micro-organisms in respiratory tract samples decreased

significantly after introduction of SDD or SOD (Figure 1). Before intervention, average prevalence

rates of resistance were 10%, 10% and 14% for ceftazidime, tobramycin and ciprofloxacin,

respectively, which dropped to levels below 7% for all antibiotics (p<0.05) after introduction of

either SDD or SOD. This immediate drop was followed by a gradual increase during SDD/SOD

(for ceftazidime resistance; p<0.05 for time trend). There was a slight but statistically significant

increase in prevalence of resistant bacteria for all three antibiotics after the intervention period.

As mentioned, in streamlining the three studies periods we excluded four SOD periods. We,

therefore, also repeated our analysis with the inclusion of these four periods, while now excluding

the four SDD periods. The interpretation did not change, although the gradual increase in

ceftazidime during SDD/SOD fell short of statistical significance.

In all 131, 113 and 108 Gram-negative micro-organisms were isolated from respiratory samples

with resistance for ceftazidime, ciprofloxacin and tobramycin, respectively (table 3). Among those

being resistant to ceftazidime 38.2% consisted of E. cloacae (n=50) and 33.6% of P. aeruginosa

(n=44). Strains resistant to ciprofloxacin and tobramycin most frequently were P. aeruginosa: more

specific 49.6% (n=56) for ciprofloxacin and 43.5% (n=47) for tobramycin.

Proportions of ciprofloxacin resistant Gram-negative bacteria (both from rectal swabs and

respiratory samples) with co-resistance for both ciprofloxacin and ceftazidime were 30.2%,

22.2% and 35.0% during standard care, SOD and SDD, respectively (p = 0.08). Co-resistance for

both ciprofloxacin and tobramycin was documented in 37.4%, 44.4%, and 35.0% of the isolates

during standard care, SOD and SDD, respectively (p = 0.26).

All samples were also analyzed for vancomycin-resistant enterococci (VRE) and MRSA. MRSA

was not detected in any sample and VRE was isolated from eight rectal swabs, none during SDD.



Figure 1: Percentage of patients colonized with Gram- negative rods before, during and after the use of SDD during monthly point prevalence surveillance studies of (1) rectal samples and (2) respiratory samples. (●) pre intervention period; (□) intervention period; (▲) post intervention period for three types of antibiotic agents (A) ceftazidime; (B) tobramycin; (C) ciprofloxacin.

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8 | Chapter 5

Rectal Respiratory tractCFT CIP TOB CFT CIP TOB

Total 263 244 228 131 113 108

E. coli 69 (26,2%) 122 (50,0%) 110 (48,2%) 13 (9,9%) 15 (13,3%) 16 (14,8%)

Klebsiella. pneumoniae

38 (14,4%) 51 (20,9%) 50 (21,9%) 24 (18,3%) 26 (23,0%) 24 (22,2%)

P. aeruginosa 51 (19,4%) 38 (15,6%) 28 (12,3%) 44 (33,6%) 56 (49,6%) 47 (43,5%)E. cloacae 105 (39,9%) 33 (13,5%) 40 (17,5%) 50 (38,2%) 16 (14,2%) 21 (19,4%)

Table 3: Number and percentage of Gram-negative isolates and species with conferred resistance to ceftazidime, tobramycin or tobramycin obtained from rectal en respiratory tract samples during all monthly point prevalence surveys. Ceftazidime (CFT), ciprofloxacin (CIP), tobramycin (TOB).

dISCuSSIOn

This longitudinal study demonstrates that SOD and SDD both have marked effects on the

bacterial ecology in an ICU. The ecological effects were most obvious in the respiratory tract,

with large reductions in resistance prevalence rates of Gram-negative bacteria after the start of

SDD or SOD, a trend towards increasing rates during the interventions, followed by a rapid

return to pre-intervention resistance levels after the interventions. In the intestinal tract, the

reduction in resistance prevalence was less pronounced during SDD as compared to respiratory

tract colonization, but with a considerable rebound effect of ceftazidime resistance after SDD, with

significantly increased prevalence rates as compared to prevalence rates during the intervention

and even before intervention. Ceftazidime resistant isolates in rectal samples mainly included E.

cloacae whereas tobramycin and ciprofloxacin resistant isolates predominantly included E. coli.

To the best of our knowledge this is the first study to determine the ecological effects of SDD

and SOD on a ward-level in a longitudinal and multi-center study design. Furthermore, based

upon the determined adherence to study protocol, the microbiological screening results can be

considered to represent “whole-ward ecology” as completeness of sampling was estimated to be

82% for the respiratory and 87% of the rectal cultures. Previous studies focussed on the effects of

SDD and SOD in individual patients. 2;6;15;19;20

To guarantee uniform data collection in 13 microbiology laboratories, a pragmatic and relatively

simple study design was needed. We have, therefore, used three antibiotic-containing media as

a first selection to detect antibiotic-resistant aerobic Gram-negative micro-organisms, followed

by susceptibility testing using automated susceptibility testing systems. The selection of marker

antibiotics was based on resistance profiles of Gram-negative bacteria in Dutch ICUs and the

antibiotics used in SDD and SOD.

Intestinal carriage with antibiotic resistant bacteria markedly increased after discontinuation of

SDD. This association was most obvious for ceftazidime resistance. During SDD intravenous



cephalosporin use increased with 87% 5. We hypothesize that the increase in cephalosporin use

selected for cephalosporin resistant isolates, which were suppressed by enterally administered

antibiotics without complete eradication, and their growth emerged after discontinuation

of these topical antibiotics then facilitates emergence of such strains. Indeed, SDD has been

associated with emergence of intestinal carriage with multidrug-resistant Gram-negative bacteria

before14 and prior use of third-generation cephalosporins has been stated as a major risk factor for

subsequent cephalosporin resistance.21 The increase of ciprofloxacin resistance, however, cannot

be explained by this scenario, as ciprofloxacin was not part of SDD and its systemic use was

31% lower during SDD.5 Yet, antibiotic resistance is frequently present for multiple classes

of antibiotics.22 However in the present study the proportions of ciprofloxacin resistant Gram-

negative pathogens not susceptible to both ciprofloxacin and either ceftazidime or tobramycin

were comparable in all three periods. Unfortunately, it was not feasible to investigate the genetic

mechanisms of resistance in this study. The overall use of systemic antibiotics has been reported

previously 5 but are also provided in the supplementary data.

There was a tendency towards a reduction in rectal colonization during the pre-intervention

period, which might reflect a so-called “Hawthorne-effect”. This implies that nurses and

physician’s behavior might have been affected by the fact that a trial was executed.23;24 As part of

the study, specific attention was paid to hygiene measurements, which might have reduced the

occurrence of cross transmission.

During SDD and SOD, proportions of patients carrying resistant Gram-negative bacteria in the

respiratory tract gradually increased, again most prominently for ceftazidime resistance. Opposite

to the effects of SDD on intestinal resistance rates, the trend line increased until the last point

prevalence survey, suggesting that its maximum level had not been reached after twelve months.

There are several limitations of this study that must be addressed. The adherence to study

protocol (i.e., the completeness of cultures obtained) and inclusion rates were based on estimates

determined during regular controls in the units by our research nurses. Therefore, exact proportions

of patients receiving either SDD or SOD at each specific time point and the exact proportion of

cultures obtained were not available. In addition, cultures were processed anonymously, and it was

therefore not possible to link carriage to received medications, and isolates were not genotyped

precluding determination of the relevance of clonal spread. And as there were no “control” ICUs

with similar point prevalence measurements but without interventions, we could not determine

time effects of resistance levels in the absence of interventions.

Furthermore, in our statistical analysis we assumed that prevalence points were independent from

each other. The duration of ICU-stay at the time of sampling is unknown due to the anonimyzed

nature of sample taking. However, the average duration of ICU-stay for patients included in

the trial (with an expected ICU-stay >48 hours) was nine days during all periods (inter-quartile

range: standard care 3-17; SOD 4-15; SDD 4-15) and the average proportion of patients with

a length of ICU-stay of more than 30 days was 9% during all periods (standard care 8.7%;

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10 | Chapter 5

SOD 8.9%; SDD 8.6%)5, which implies that almost all patients were included only once in a

point prevalence study. Yet, the clear differences in prevalence rates between and uniformity

within study periods, suggests at least some data-dependency.25 We have calculated average

prevalence for each time point and for whole periods (i.e., multiple time points) as numerator and

denominator of the prevalence and performed Poisson regression analysis to control for within

unit correlation. Although we would have preferred to perform a time series analysis to adjust

for any (auto)correlation over time, the number of point prevalence surveys per time period was

insufficient to achieve an acceptable level of variability 26 as well as the number of time points to

estimate complex correlation structures.18

In the current cluster-randomized cross-over study SDD and SOD were associated with an 13%

and 11% mortality reduction at day 28, which provides strong evidence for a beneficial effect of

both regimens in ICU settings with low endemic levels of antibiotic resistance. This ecological

analysis provides detailed insights in the ecological changes induced by both regimens. SDD

and SOD are both associated with a gradual increase in antibiotic resistance in the respiratory

tract, which is magnified after discontinuation of both regimens, most prominent for ceftazidime

resistance. Therefore, emergence of antibiotic resistance remains a major concern associated

with these infection control measures. Future studies should compare the long-term effects of

both regimens on antibiotic regimens, systematic as well as the effects of less cephalosporin use

during SDD, in order to determine the most “cost-beneficial” infection control measure from an

ecological perspective for ICU patients.

Acknowledgements:

We thank Heidi S.M. Ammerlaan and Marieke J.A. de Regt for their assistance with the statistical

analyzes.



ReFeRenCe lIST

1. Stoutenbeek, C. P., H. K. van Saene, D. R. Miranda, and D. F. Zandstra. 1984. The effect of selective decontamination of the digestive tract on colonisation and infection rate in multiple trauma patients. Intensive Care Med. 10:185-192.

2. Bergmans, D. C., M. J. Bonten, C. A. Gaillard, J. C. Paling, van der Geest S., F. H. van Tiel, A. J. Beysens, P. W. de Leeuw, and E. E. Stobberingh. 2001. Prevention of ventilator-associated pneumonia by oral decontamination: a prospective, randomized, double-blind, placebo-controlled study. Am.J.Respir.Crit Care Med. 164:382-388.

3. D’Amico, R., S. Pifferi, C. Leonetti, V. Torri, A. Tinazzi, and A. Liberati. 1998. Effectiveness of antibiotic prophylaxis in critically ill adult patients: systematic review of randomised controlled trials. BMJ 316:1275-1285.

4. Collard, H. R., S. Saint, and M. A. Matthay. 2003. Prevention of ventilator-associated pneumonia: an evidence-based systematic review. Ann.Intern.Med. 138:494-501.

5. de Smet, A. M., J. A. Kluytmans, B. S. Cooper, E. M. Mascini, R. F. Benus, T. S. van der Werf, J. G. van der Hoeven, P. Pickkers, D. Bogaers-Hofman, N. J. van der Meer, A. T. Bernards, E. J. Kuijper, J. C. Joore, M. A. Leverstein-van Hall, A. J. Bindels, A. R. Jansz, R. M. Wesselink, B. M. de Jongh, P. J. Dennesen, G. J. van Asselt, L. F. te Velde, I. H. Frenay, K. Kaasjager, F. H. Bosch, I. M. van, S. F. Thijsen, G. H. Kluge, W. Pauw, J. W. de Vries, J. A. Kaan, J. P. Arends, L. P. Aarts, P. D. Sturm, H. I. Harinck, A. Voss, E. V. Uijtendaal, H. E. Blok, E. S. Thieme Groen, M. E. Pouw, C. J. Kalkman, and M. J. Bonten. 2009. Decontamination of the digestive tract and oropharynx in ICU patients. N.Engl.J.Med. 360:20-31.

6. de Jonge E., M. J. Schultz, L. Spanjaard, P. M. Bossuyt, M. B. Vroom, J. Dankert, and J. Kesecioglu. 2003. Effects of selective decontamination of digestive tract on mortality and acquisition of resistant bacteria in intensive care: a randomised controlled trial. Lancet 362:1011-1016.

7. Bonten, M. J., B. J. Kullberg, R. van Dalen, A. R. Girbes, I. M. Hoepelman, W. Hustinx, J. W. van der Meer, P. Speelman, E. E. Stobberingh, H. A. Verbrugh, J. Verhoef, and J. H. Zwaveling. 2000. Selective digestive decontamination in patients in intensive care. The Dutch Working Group on Antibiotic Policy. J.Antimicrob.Chemother. 46:351-362.

8. Donskey, C. J. 2004. The role of the intestinal tract as a reservoir and source for transmission of nosocomial pathogens. Clin.Infect.Dis. 39:219-226.

9. Kollef, M. H. 2003. Selective digestive decontamination should not be routinely employed. Chest 123:464S-468S.

10. Ebner, W., A. Kropec-Hubner, and F. D. Daschner. 2000. Bacterial resistance and overgrowth due to selective decontamination of the digestive tract. Eur.J.Clin.Microbiol.Infect.Dis. 19:243-247.

11. de Jonge E. 2005. Effects of selective decontamination of digestive tract on mortality and antibiotic resistance in the intensive-care unit. Curr.Opin.Crit Care 11:144-149.

12. Krueger, W. A., F. P. Lenhart, G. Neeser, G. Ruckdeschel, H. Schreckhase, H. J. Eissner, H. Forst, J. Eckart, K. Peter, and K. E. Unertl. 2002. Influence of combined intravenous and topical antibiotic prophylaxis on the incidence of infections, organ dysfunctions, and mortality in critically ill surgical patients: a prospective, stratified, randomized, double-blind, placebo-controlled clinical trial. Am.J.Respir.Crit Care Med. 166:1029-1037.

13. Silvestri, L., H. K. van Saene, A. Casarin, G. Berlot, and A. Gullo. 2008. Impact of selective decontamination of the digestive tract on carriage and infection due to Gram-negative and Gram-positive bacteria: a systematic review of randomised controlled trials. Anaesth.Intensive Care 36:324-338.

14. Al Naiemi, N., E. R. Heddema, A. Bart, E. de Jonge, C. M. Vandenbroucke-Grauls, P. H. Savelkoul, and B. Duim. 2006. Emergence of multidrug-resistant Gram-negative bacteria during selective decontamination of the digestive tract on an intensive care unit. J.Antimicrob.Chemother. 58:853-856.

15. Lingnau, W., J. Berger, F. Javorsky, M. Fille, F. Allerberger, and H. Benzer. 1998. Changing bacterial ecology during a five-year period of selective intestinal decontamination. J.Hosp.Infect. 39:195-206.

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16. Verwaest, C., J. Verhaegen, P. Ferdinande, M. Schetz, G. Van den Berghe, L. Verbist, and P. Lauwers. 1997. Randomized, controlled trial of selective digestive decontamination in 600 mechanically ventilated patients in a multidisciplinary intensive care unit. Crit Care Med. 25:63-71.

17. Kianifard, F. and P. P. Gallo. 1995. Poisson regression analysis in clinical research. J.Biopharm.Stat. 5:115-129.

18. Shardell, M., A. D. Harris, S. S. El-Kamary, J. P. Furuno, R. R. Miller, and E. N. Perencevich. 2007. Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies. Clin.Infect.Dis. 45:901-907.

19. Hammond, J. M. and P. D. Potgieter. 1995. Long-term effects of selective decontamination on antimicrobial resistance. Crit Care Med. 23:637-645.

20. Leone, M., J. Albanese, F. Antonini, A. Nguyen-Michel, and C. Martin. 2003. Long-term (6-year) effect of selective digestive decontamination on antimicrobial resistance in intensive care, multiple-trauma patients. Crit Care Med. 31:2090-2095.

21. Livermore, D. M. and N. Woodford. 2006. The beta-lactamase threat in Enterobacteriaceae, Pseudomonas and Acinetobacter. Trends Microbiol. 14:413-420.

22. Denton, M. 2007. Enterobacteriaceae. Int.J.Antimicrob.Agents 29 Suppl 3:S9-S22. 23. Braunholtz, D. A., S. J. Edwards, and R. J. Lilford. 2001. Are randomized clinical trials good for us

(in the short term)? Evidence for a “trial effect”. J.Clin.Epidemiol. 54:217-224. 24. McCarney, R., J. Warner, S. Iliffe, R. van Haselen, M. Griffin, and P. Fisher. 2007. The Hawthorne

Effect: a randomised, controlled trial. BMC.Med.Res.Methodol. 7:30. 25. Harris, A. D., E. Lautenbach, and E. Perencevich. 2005. A systematic review of quasi-experimental

study designs in the fields of infection control and antibiotic resistance. Clin.Infect.Dis. 41:77-82. 26. Wagner, A. K., S. B. Soumerai, F. Zhang, and D. Ross-Degnan. 2002. Segmented regression analysis

of interrupted time series studies in medication use research. J.Clin.Pharm.Ther. 27:299-309.

Palatino

Chapter 4Influence of unintentional IgG coating to

solid phase on in vitro activation of human

neutrophils by intravenous immunoglobulin G

(IVIg) products

Theresa Guhr*, Iwan Kustiawan*, Ninotska Derksen, Rob Aalberse and Theo Rispens

Sanquin Research, Amsterdam, The Netherlands, and Landsteiner Laboratory,

Academic Medical Centre, University of Amsterdam, The Netherlands

MANUSCRIPT IN PREPARATION* TG and IK have evenly contributed to this paper.

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Abstract

The aim of our study was to confirm that IgG dimers in IVIg can activate human neutrophils. The amount of elastase released during in vitro degranulation of isolated human neutrophils was used as read-out. We used two IVIg products with different amounts of IgG dimers as well as their dimeric and monomeric IgG fractions as stimuli. Both IVIg products induced a similar dose-dependent elastase release of isolated human neutrophils. Isolated IgG dimers were 4 times more potent in activating neutrophils. Unexpectedly, however, isolated IgG monomers were also found to activate neutrophils. We found that activation by IVIg is at least in part the result of stimulation by IgG bound/coated to the solid phase. In preliminary experiments we showed that poloxamer 407 is very effective in preventing the binding of IgG to the solid phase. In its presence, IVIg was not able to activate human neutrophils anymore. Further experiments are needed to substantiate that soluble IgG dimers as found in IVIg can induce activation of human neutrophils.

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Introduction

Intravenous immunoglobulin G (IVIg) is often administered as treatment for a variety of immune deficiencies, autoimmune diseases and acute infections. During IVIg administration often only mild side effects are detectable 1,2. However, in some cases IVIg is associated with more severe side effects 1,2.

During an acute phase of inflammation, particularly during bacterial infection, neutrophil granulocytes, also referred to as neutrophils, migrate toward the site of inflammation and are activated 3-5. Activation of resting human neutrophil requires triggering of their receptors. Neutrophils co-express two IgG-specific receptors, FcγRIIA (CD32A) containing a tyrosine-based activation motif (ITAM) and a glycosyl-phosphatidyl inositol (GPI)-anchored FcγRIIIB (CD16B). Both receptors have a low affinity for monomeric IgG, but recognize IgG oligomers (polymers) efficiently 6,7. Cross-linking of these low affinity receptors by IgG polymers leads to triggering of the receptors resulting in receptor internalisation, respiratory burst, and degranulation. Degranulation of neutrophils results in a release of serine proteases, such as elastase and cathepsin G 8-11. All these events contribute to the destruction and removal of pathogens and infected or damaged cells.

However, unwanted activation of neutrophils may result in an inflammatory response leading to damage of surrounding tissues 11,12. Such unwanted neutrophil activation may be responsible for the side effects occurring upon the administration of a high dose of IVIg 13,14. A study of Teeling et al. demonstrated that IVIg can activate human neutrophils in vitro 15. The binding of IgG polymers in particular caused this degranulation via the binding to FcγRIIA. Dimeric IgG caused less degranulation, whereas monomeric IgG caused the least degranulation of human neutrophils. However, the IVIg product used in this study contained high amounts of IgG polymers induced by storage at elevated temperature that could not be fully separated from dimeric IgG. The presence of these IgG polymers in the dimeric IgG containing fractions may have caused the observed neutrophil activation. Nevertheless, a recent study of Higurashi et al. showed a similar effect of isolated IVIg-derived IgG dimers on isolated human neutrophils suggesting a role of IgG dimers in the occurrence of side effects during or after IVIg administration 16. Nemes et al. demonstrated that the stimulation of isolated human neutrophils with dimeric or monomeric IgG derived from several IVIg products caused the release of O2

-

17. On the other hand, Van Mirre et al. found that monomeric IgG prevented neutrophil activation as triggered by polymeric IgG derived after heat aggregation 18. The aim of our study was to reassess the ability of IgG dimers in IVIg products to induce human neutrophil activation in vitro.

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Material and methods

Immunoglobulin productsTwo human immunoglobulin G products for intravenous use (IVIg) were used in this study: Nanogam® (Product A), a 5% IgG solution, Sanquin, Amsterdam, The Netherlands and Multigam® (Product B), a 5% IgG solution, CAF-DCF, Brussels, Belgium.

Size exclusion chromatography (SEC)To obtain dimeric and monomeric IgG, IVIg (300 mg) was fractionated SEC using a Hiload 16/600 Superdex200 column (60 cm, 124 ml GE Healthcare, Uppsala, Sweden) with a flow rate of 0.8 ml/min and phosphate buffered saline (PBS) as a running buffer. Fractions containing monomeric or dimeric IgG were collected and 0.05% NaN3 was added. Fractions with dimeric IgG were pooled and concentrated using a Centrifugal filter device (cut off 10 kD, Amicon Ultra, Millipore, Billerica, MA, USA) and stored at 4 °C until further use. The concentration of the monomeric and dimeric fractions was approximately 6 mg/ml with the exception of the dimeric fraction isolated from Product A which had a concentration of 3 mg/ml.

The IVIg products and their collected fractions used in the experiments were analyzed for actual monomer, dimer and polymer contents on a calibrated Superdex200 10/300 gel filtration column connected to a HPLC system (30 cm, 24 ml, GE Healthcare, Uppsala, Sweden) with a flow rate of 0.5 ml/min and PBS as running buffer. A computer program (Unicorn version 5.11) was used to calculate the peak areas of the peaks in the chromatograms. As an estimate of the accuracy of this method, a prediction interval was determined. For a mean of 2.9% of IgG dimers the prediction interval was 2.6 - 3.2% (n = 15).

Isolation of neutrophilsVenous blood was obtained from healthy volunteers by venapunture and collected in citrate-containing tubes. Citrated blood was diluted in a ratio of 1:1 in PBS containing 0.5% human serum albumin (HSA) and 0.4% trisodium citrate, layered on Ficoll (1.077 g/ml) and centrifuged at 1500 rpm for 25 minutes at room temperature (RT) without brake. The pellet containing erythrocytes and neutrophils was washed twice in ice-cold isotonic NH4Cl solution (155 mmol/l NH4Cl, 10 mmol/l KHCO3, 0.1 mmol/l EDTA, pH 7.4). After centrifugation, neutrophils were washed in PBS at RT, resuspended in endotoxin-free Iscove’s Modified Dulbeccos Medium with 25 mM HEPES and L-glutamine (IMDM, Biowhittaker, Lonza, Walkersville, USA) supplemented with 5% endotoxin-free fetal calf serum (FCS, Biowhittaker, Walkersville, USA), 100 IU/ml (w/v) penicillin and 100 µg/ml (w/v) streptomycin, further referred to as culture medium. After counting the

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isolated neutrophils using a Casy counter (Innovatis, Reutlingen, Germany), the cell suspension was diluted in culture medium to a final concentration of 2*105 cells/ml. To minimize spontaneous degranulation, we equilibrated the cell suspension for 1 hour at RT before further use.

Degranulation of neutrophilsIsolated neutrophils were incubated with several stimuli, i.e. a titration of respectively IVIg, dimeric or monomeric IgG dialyzed against culture medium. 1 µM N-formyl-L-methionyl-L-leucyl-L-phenylalanine (fMLP; Sigma-Aldrich, St.Louis, MO, USA) and 5 µg/ml cytochalasin B (cytoB; Sigma-Aldrich, St.Louis, MO, USA) were used as a positive control for degranulation, whereas culture medium alone was used as a negative control. The neutrophils (in a final volume of 0.2 ml) were incubated in 1.5 ml Eppendorf tubes using a thermo mixer (Eppendorf, Hamburg, Germany) operated at 700 rpm for 1 hour at 37 °C. To harvest the supernatants, the tubes were centrifuged at 1500 rpm for 1 minute at RT and the cell free supernatants were transferred into microtiter plates and stored at -20 °C until further analysis.

To prevent attachment of IgG to the tube wall, titration of IVIg and subsequent neutrophil incubation was made using IMDM supplemented with 0.05% poloxamer 407 (a triblock copolymer consisting of a central hydrophobic block of polypropylene glycol flanked by two hydrophilic blocks of polyethylene glycol, kindly provided by BASF, The Netherlands). After washing, the cells were centrifuged at 1800 rpm for 3 minutes and supernatant containing elastase was frozen at -20 °C before the measurement.

IgG ELISA To determine whether the IgG used as stimulus for the neutrophil experiments binds to the surface, we measured IgG binding using peroxidase-labeled anti-IgG. We incubated 100 µl of 5 mg/ml IVIg (Product A) in IMDM for 4 hours at RT in plates or tubes with or without poloxamer 407. After 5 times washing with PBS/0.02% Tween20, peroxidase-labeled mouse anti-human IgG (clone MH16, Sanquin, The Netherlands) diluted in high performance ELISA buffer (HPE, Sanquin, The Netherlands) was added. After an 1 hour incubation at RT, the plate or tubes were 5 times washed with PBS/0.02% Tween20 followed by the addition of TMB peroxidase buffer (Interchim, Montluçon Cedex, France) diluted 1:2 in distilled water. Substrate conversion was stopped by adding 2 M H2SO4. The supernatant was transferred into a microtiter plate and the absorbance at 450/540 nm was measured using the Titertek Multiscan (Flow Laboratories, Mc Lean, VA). For comparison, the same procedure was performed in with two other types of tubes (luminex tubes and Lobind) and in Maxisorp and Polysorb mictortiter plates.

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Elastase ELISA Released elastase was measured using a sandwich ELISA. Briefly, a microtiter plate coated with 1.5 µg/ml polyclonal rabbit IgG directed against human elastase kindly provided by the department of Immunopathology (Sanquin, Amsterdam, The Netherlands) was incubated with supernatants diluted 1:50 in PBS/0.1% Tween20/0.2% gelatine (PTG) for 1 hour shaking at RT. After subsequent washing with PBS/0.02% Tween20 1 µg/ml biotinylated rabbit anti-human elastase diluted in PTG was added and the plates were incubated for 1 hour shaking at RT. Thereafter, the plates were washed with PBS/0.02% Tween20 and streptavidin-horseradish peroxidase (Amersham Life Science, Buckinghamshire, UK) diluted 1:1000 in PTG was added and incubated for 30 minutes at RT. Finally, after washing the plates were developed with TMB peroxidase buffer (Interchim, Montluçon Cedex, France) diluted 1:2 in distilled water. Substrate conversion was stopped by adding 2 M H2SO4 and the absorbance at 450/540 nm was measured using a Titertek Multiscan (Flow Laboratories, Mc Lean, VA). Human neutrophil elastase purified from sputum (Elastin Products Co., Pacific, MO) in PTG was used as a standard. According to the manufacturer, the purity of this neutrophil elastase is > 95% as assessed by SDS-PAGE.

Analysis of dataResults are depicted as means with error bars representing the standard error of the mean (SEM). To compare the neutrophil activation potential of IVIg products and the IVIg-derived fractions we calculated their relative potency by parallel line analysis 20. A value above 1 for a sample indicates that this sample is more potent than the reference.

Results

Activation of neutrophils Activation by fMLP/cytoB Neutrophils of healthy donors were isolated and stimulated by adding fMLP/cytoB (positive control for degranulation) or culture medium (negative control for degranulation). The degree of degranulation was determined by measuring the amount of elastase in the supernatant. Incubation with fMLP/cytoB caused a high elastase release (2186 ng/ml ± 372 ng/ml), whereas culture medium alone resulted in a low elastase release (378 ng/ml ± 94 ng/ml). To be able to compare the elastase release after stimulation with IVIg and corresponding fractions, we expressed data as a percentage of fMLP/cytoB release after correction for spontaneous release.

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Activation by IVIgNext, we studied the capacity of the two IVIg products (Product A and B) to activate neutrophils. Product A and B contained respectively ~ 3 and ~10% IgG dimers (Table 1). Both IVIg products induced a similar dose dependent activation of neutrophils (Figure 1A). However, Product B caused a higher elastase release after stimulation with 20 mg/ml IgG compared to Product A. To rule out the possibility that this difference was caused by the composition of the formulation buffer, for both IVIg products the buffer was exchanged by dialysis to culture medium and the capacities of IgG in culture medium to activate neutrophils were measured again (Figure 1B). The buffer used had no effect on elastase release and the response was similar for IgG in formulation buffer or in culture medium, with the exception of tests performed at 20 mg/ml, where both dialyzed IVIg products caused a 1.4 - 2.0 fold higher elastase release, presumably due to a small negative effect of the formulation buffer. At this concentration stimulation with Product B resulted again in a higher elastase release compared to Product A.

To examine the possibility that the stronger neutrophil activation at 20 mg/ml IgG is caused by a higher IgG dimer content in Product B, the actual IgG dimer content in the samples was determined using a Superdex 200 10/300 column as described in material and methods. IgG in culture medium resulted in a 1.6 fold increase of IgG dimer content in Product A, whereas the IgG dimer content in Product B did not change (Table 1). The IgG dimer content in Product B at 20 mg/ml was twice as high as in Product A. This might be the reason for the higher neutrophil activation after stimulation with 20 mg/ml IgG as seen in Figure 1B. However, also at lower concentrations the IgG dimer content in Product B was twofold higher compared to Product A. In contrast to the highest tested concentration (20 mg/ml), these differences in IgG dimer content did not result in a different neutrophil activation.

Table 1: Dimer IgG content (%) in IVIg and IVIg dialyzed against culture medium after 1 hour incubation at 37 °C as measured by HPLC; n.d. not determined.

Product % dimer IgG20 mg/ml 10 mg/ml 5 mg/ml 2.5 mg/ml

A formulation buffer 2.9 2.5 n.d. n.d.B 10.1 8.8 n.d. n.d.A culture medium 4.6 4.0 3.4 2.8B 10.0 9.6 7.3 5.8

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Figure 1: Release of elastase after stimulation with IVIg or IVIg in culture medium. A) Dose dependent elastase release by isolated neutrophils after stimulation with Product A (▲) and Product B (Δ). Data are expressed as mean ± SEM, n = 6 donors. B) Dose dependent elastase release by isolated neutrophils after stimulation with Product A in culture medium (▲) and Product B in culture medium (Δ). Data are expressed as mean ± SEM, n = 16 donors. Values were corrected for spontaneous release. C) Individual elastase release at 20 mg/ml for Product A and B formulation buffer or in culture medium (CM). * P < 0.05, # P < 0.01 using a paired Student’s t-test without post hoc correction.

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Activation by dimeric and monomeric IgGTo study the activation of neutrophils by IgG dimers and IgG monomers, we fractionated both IVIg products using a Hiload 16/600 Superdex200 column. After dialysis against culture medium, IVIg fractions containing monomeric or dimeric IgG were incubated with isolated neutrophils for 1 hour at 37 °C. IgG dimers were more potent compared to IgG monomers to activate human neutrophils (Figure 2). In addition, IgG monomers were also capable of activating neutrophils. When expressed per mg IgG, there was no difference between IgG dimers or IgG monomers derived from Product A and B.

To compare the extent of neutrophil activation by IVIg and isolated IgG dimers and IgG monomers, we calculated the relative potencies (Table 2). The dimeric IgG fractions were around three-fold more potent than the monomeric IgG fractions to activate neutrophils.

Figure 2: Release of elastase after stimulation with IgG dimers and monomers.Elastase release by isolated neutrophils after stimulation with Product A-derived dimeric IgG fraction (■), Product B-derived dimeric IgG fraction (□), Product A-derived monomeric IgG fraction (▲), Product B-derived monomeric IgG fraction (∆). Data are expressed as mean ± SEM after correction for spontaneous release, n = 5 donors.

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Table 2: Neutrophil activating potencies of isolated IgG dimers and IgG monomers and unfractionated IVIg of five healthy donors.

Donor

Dimeric vs. monomeric IgG

Dimeric IgG vs. IVIg

Monomeric IgG vs. IVIg

Product A Product B Product A Product B Product A Product B1 2.0 2.8 1.9 3.0 0.7 1.22 2.4 2.3 6.8 5.5 3.1 2.53 3.5 3.4 5.6 6.0 1.3 1.54 5.3 1.4 5.9 3.2 0.8 2.15 3.6 2.5 2.2 3.3 0.7 1.6Geometrical mean 3.2 2.4 3.9 4.0 1.1 1.795% CIa 2.0 - 5.0 1.5 - 3.6 1.9 - 8.3 2.7 - 6.0 0.5 - 2.4 1.2 - 2.4

a CI: confidence interval.

The dose-dependent elastase release of neutrophils after stimulation with the monomeric IgG fractions was unexpected, because cross-linking of the low affinity receptors is needed for activating neutrophils. This activation might be caused by IgG dimers, which reform within the monomeric IgG fractions after fractionation and before the use in the assay. Therefore, we determined the actual IgG dimer content in the monomeric IgG fractions using a Superdex 200 10/300 column as described in material and methods. Monomeric IgG fractions obtained from both IVIg products contained IgG dimers (Table 3). The amount of IgG dimers formed was dependent on the IgG concentration. The monomeric IgG fraction of Product B contained 1.3 fold more IgG dimers compared to the monomeric IgG fraction of Product A. However, this higher IgG dimer content did not result in a higher activation of neutrophils as seen in Figure 2. We also determined the IgG dimer content in both dimeric IgG fractions. At 2 mg/ml IgG, the dimeric IgG fractions obtained from Product A and B contained respectively 41.5 and 42.5% of IgG dimers. Thus, the dimeric IgG fractions contain about ten times the amount of IgG dimers present in the monomeric IgG fractions.

Table 3: IgG dimer content (%) in the monomeric and dimeric IgG fractions of Product A and B at different IgG concentrations after 1 hour incubation at 37 °C as measured by HPLC; n.d. not determined.

Fractions % IgG dimer4 mg/ml 2 mg/ml 1 mg/ml 0.5 mg/ml

Monomeric IgG Product A 4.1 3.4 2.7 n.d.Product B 5.4 4.4 3.7 n.d.

Dimeric IgG Product A n.d. 41.5 41.0 36.0Product B n.d. 42.5 40.4 37.0

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Activation of neutrophils by IVIg in the presence of poloxamer 407Because of the unexpected neutrophil activation by monomeric IgG we investigated the possibility that this neutrophil activation might be due to IgG binding to the solid phase despite the presence of 5% FCS. We measured the capacity of IgG at 5 mg/ml to bind to different solid surfaces, i.e. microtiter plates and tubes. As shown in Figure 3A, IgG binds to microtiter plates and tubes. This IgG binding to the microtiter plate was inhibited by poloxamer 407 (Figure 3B).

Figure 3: IgG binding to solid phase measured by IgG ELISA.A) IgG (5 mg/ml) binding to different microtiter plates and tubes used for the incubation of neutrophils with IgG stimuli; B) Concentration dependent inhibition of IgG binding to the microtiter plate by poloxamer 407. Data are expressed as mean ± SEM, n = 3. Data are from Kustiawan et al. (manuscript submitted).

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Next we determined whether IVIg in the presence of poloxamer 407 still can activate human neutrophils. For this purpose, neutrophils were incubated with serial dilutions of Product A in the presence of 0.05% poloxamer 407. Without poloxamer 407 a concentration dependent activation of neutrophils by IVIg was detectable (Figure 4) which was comparable with earlier experiments. This activation was completely abrogated in the presence of poloxamer 407. A similar effect of the addition of poloxamer 407 was found using a serial dilution of Product B (data not shown). To rule out the possibility that poloxamer 407 interferes with the elastase release by neutrophils, we also determined the effect of poloxamer 407 on non FcγR stimulation using fMLP/cytoB (the positive control) and on the negative control (culture medium). We observed no influence of the poloxamer 407 on either the positive or the negative control (data not shown).

Figure 4: Elastase release by human neutrophils after stimulation with IVIg and IVIg in the presence of 0.05% poloxamer 407 (after correction for spontaneous release).Elastase release by isolated neutrophils after stimulation with Product A (■), and Product A in the presence of poloxamer 407 (□). Data are expressed as mean ± SEM, n = 3. Data are extracted from Kustiawan et al. (manuscript submitted).

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Discussion

The initial aim of this study was to determine whether essentially polymer-free IVIg products (Product A and B) containing between 3 and 10% of IgG dimers are capable to activate human neutrophils in vitro. We found a comparable dose-dependent elastase release of neutrophils after stimulation with Product A and B. Further analysis revealed that neutrophil activation with the dimeric IgG fractions is around 4 fold more efficient than the unfractionated IVIg products. However, no differences were found between Product A and B nor their isolated IgG dimer fractions indicating that the difference in IgG dimer composition as found by us (chapter 2 of this thesis) is not correlated with the degree of neutrophil activation. In addition, we found that also IgG monomers were able to stimulate neutrophils. This activation of neutrophils by IgG monomers might be caused by the low amounts of IgG dimers present in the monomeric IgG fraction which reform after the separation of dimeric IgG. Analysis of the actual IgG dimer content revealed that monomeric IgG fraction of both IVIg products contained IgG dimers, respectively 2.7 – 4.1% for Product A and 3.7 – 5.4% for Product B. The amount of IgG dimers was comparable with the amount of IgG dimers in unfractionated IVIg material.

However, our subsequent experiments indicated that the observed activation of neutrophils by IgG monomers is not a capacity of the IgG monomers itself, but is the result of the IgG binding to the solid phase. This solid phase bound IgG may cause cross-linking of the low affinity receptors and subsequent activation of the neutrophils. It is uncertain whether the neutrophil activation by IVIg reported by Teeling et al. and Higurashi et al. is really caused by IgG dimers in polymer-free IVIg products 15,16. Additional experiments are needed to determine whether soluble IgG dimers present in IVIg are capable to activate human neutrophils.

AcknowledgementsWe thank Marina Koning for technical assistance, Prof. Lucien Aarden as well as Anky Koenderman for comments on the manuscript.

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6. Selvaraj P, Fifadara N, Nagarajan S, Cimino A, Wang G 2004. Functional regulation of human neutrophil Fc gamma receptors. Immunol Res 29:219-230.

7. Nagarajan S, Fifadara NH, Selvaraj P 2005. Signal-specific activation and regulation of human neutrophil Fc gamma receptors. J Immunol 174:5423-5432.

8. Zhang W, Voice J, Lachmann PJ 1995. A systematic study of neutrophil degranulation and respiratory burst in vitro by defined immune complexes. Clin Exp Immunol 101:507-514.

9. Strohmeier GR, Brunkhorst BA, Seetoo KF, Bernardo J, Weil GJ, Simons ER 1995. Neutrophil functional responses depend on immune complex valency. J Leukoc Biol 58:403-414.

10. Voice JK, Lachmann PJ 1997. Neutrophil Fc gamma and complement receptors involved in binding soluble IgG immune complexes and in specific granule release induced by soluble IgG immune complexes. Eur J Immunol 27:2514-2523.

11. Fossati G, Moots RJ, Bucknall RC, Edwards SW 2002. Differential role of neutrophil Fcgamma receptor IIIB (CD16) in phagocytosis, bacterial killing, and responses to immune complexes. Arthritis Rheum 46:1351-1361.

12. Nishimura M, Ishikawa Y, Satake M 2004. Activation of polymorphonuclear neutrophils by immune complex: possible involvement in development of transfusion-related acute lung injury. Transfus Med 14:359-367.

13. Gale R, Bertouch JV, Gordon TP, Bradley J, Roberts-Thomson PJ 1984. Neutrophil activation by immune complexes and the role of rheumatoid factor. Ann Rheum Dis 43:34-39.

14. Jarius S, Eichhorn P, Albert MH, Wagenpfeil S, Wick M, Belohradsky BH, Hohlfeld R, Jenne DE, Voltz R 2007. Intravenous immunoglobulins contain naturally occurring antibodies that mimic antineutrophil cytoplasmic antibodies and activate neutrophils in a TNFalpha-dependent and Fc-receptor-independent way. Blood 109:4376-4382.

15. Teeling JL, De Groot ER, Eerenberg AJ, Bleeker WK, Van Mierlo G, Aarden LA, Hack CE 1998. Human intravenous immunoglobulin (IVIG) preparations degranulate human neutrophils in vitro. Clin Exp Immunol 114:264-270.

16. Higurashi S, Machino Y, Suzuki E, Suzuki M, Kohroki J, Masuho Y 2012. Both the Fab and Fc domains of IgG are essential for ROS emission from TNF-alpha-primed neutrophils by IVIG. Biochem Biophys Res Commun 417:794-799.

17. Nemes E, Teichman F, Roos D, Marodi L 2000. Activation of human granulocytes by intravenous immunoglobulin preparations is mediated by FcgammaRII and FcgammaRIII receptors. Pediatr Res 47:357-361.

18. van Mirre E, Teeling JL, van der Meer JW, Bleeker WK, Hack CE 2004. Monomeric IgG in intravenous Ig preparations is a functional antagonist of FcgammaRII and FcgammaRIIIb. J Immunol 173:332-339.

19. Bleeker WK, Teeling JL, Verhoeven AJ, Rigter GM, Agterberg J, Tool AT, Koenderman AH, Kuijpers TW, Hack CE 2000. Vasoactive side effects of intravenous immunoglobulin preparations in a rat model and their treatment with recombinant platelet-activating factor acetylhydrolase. Blood 95:1856-1861.

20. Finney DJ 1979. Statistical method in Biological assay , 3rd edition Edition ed. Oxford University Print.

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Chapter 3

Distribution of motor unit potential velocities in short

static and prolonged dynamic contractions at low forces:

use of the within-subject’s skewness and standard

deviation variables

E.G. Klaver-Król, N.R. Henriquez, S.J. Oosterloo, P. Klaver, J.M. Bos, M.J. Zwarts.

Eur J Appl Physiol (2007) 101:647-658

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Abstract

Behaviour of motor unit potential (MUP) velocities in relation to (low) force and duration was investigated in the biceps brachii muscle using surface electromyography (sEMG).

Short static tests of 3.8 s (41 subjects) and prolonged dynamic tests (prolonged tests) of 4 min (30 subjects) were performed as position tasks, applying forces up to 20% of maximal voluntary contraction (MVC). Four variables were extracted from the sEMG signal using the inter-peak latency technique: the mean muscle fibre conduction velocity (CV); the proportion between slow and fast MUPs expressed as the within-subject skewness of MUP velocities; the within-subject standard deviation of MUP velocities [SD-peak velocity (PV)]; and the amount of MUPs per second (peak frequency = PF).

In short static tests and the initial phase of prolonged tests, larger forces induced an increase of the CV and PF, accompanied with the shift of MUP velocities towards higher values, whereas the SD-PV did not change. During the first 1.5 – 2 min of the prolonged lower force levels tests (unloaded, and loaded 5% and 10% MVC) the CV and SD-PV slightly decreased and the MUP velocities shifted towards lower values; then the three variables stabilized. The PF values did not change in these tests. However, during the prolonged higher force (20% MVC) test, the CV decreased and MUP velocities (skewness) shifted towards lower values without stabilization, while the SD-PV broadened and the PF decreased progressively.

It is argued that these combined results reflect changes in both neural regulatory strategies and muscle membrane state.

The Method: the dynamics and new variables

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Introduction

Diverse laboratory conditions have been used in surface electromyography (sEMG) studies in order to gain insights into the neural regulatory strategies and muscle membrane alterations. The influence of force load on sEMG can be investigated by using force tasks or position tasks. The majority of studies have been performed as force tasks, which means that the subject controls the effort by maintaining a target force while the limb position is fixed. During the position tasks, in contrast, an inertial load is applied while the subject controls a target limb position. Both force and position tasks can be performed in static or dynamic conditions. Examples of the static force tasks are the well-known isometric experiments, with higher and lower force levels. In the recent past, force tasks in dynamic conditions have been performed sporadically, such as the cycling experiments by Pozzo et al. (2004) and Farina et al. (2004). Since Hunter et al. (2002) found that, with the same load torque, position tasks resulted in a shorter endurance time than force tasks, suggesting different regulatory mechanism for the both type of tasks, the position task studies gained field. Most position task experiments have been performed in static conditions, evaluating the underlying physiological phenomena during force versus position tasks (Hunter et al. 2002, 2003; Hunter and Enoka 2003 and Rudroff et al. 2005, 2007). Potvin (1997) has described the changes in the sEMG during position tasks in dynamic conditions. Previous findings suggest that, as compared with force tasks, position tasks induce greater synaptic input into the motor neurons (Mottram et al. 2005a) and greater adaptation in the motor unit discharge (MacGillis et al. 2003).

Changes in muscle activity during (static and dynamic) position tasks have been assessed using two of the three traditional sEMG parameters, the power spectrum and the global sEMG amplitude. However, the third parameter, the mean muscle fibre conduction velocity (CV), has been lacking. Spectral estimates are generally accepted in fatigue experiments as equivalents of CV because they highly correlate with the CV’s changes (Bigland-Ritchie 1981; Eberstein and Beatie 1985; Arendt-Nielsen and Mills 1985). But this correlation holds true only for the constant forces and isometric conditions; thus, a replacement of CV by power spectrum assessments does not always seem feasible (Farina et al. 2002; Broman et al. 1985). The favour of CV above power spectrum is, furthermore, that it renders direct and absolute values of conduction velocity, and is less sensitive to the anatomical local relationships, such as depth of the motor unit (MU) in relation to the muscle and skin surface (Farina et al. 2002). Yet the limitation of a global CV measurement remains the lack of sensitivity to the changes at the level of an individual motor unit potential/ motor unit.

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To accommodate with the limitation of a global CV, researchers have recently been trying to disentangle the propagation velocities of individual motor unit potentials (MUPs) from sEMG. One of the methods is the inter-peak latency (IPL) method proposed by Lange et al. (2002). The principle comprises calculating conduction velocities of the MUPs from the latencies between paired MUPs of two differential sEMG signals obtained parallel to the muscle fibres, and the distance between the recording electrodes. The negative peaks of two paired MUPs are then the elements determining the IPL. As MU propagation velocity reflects the intrinsic physiological properties of a MU, such as fast-twitch or slow-twitch type (Buchthal et al. 1973; Andreassen and Arendt-Nielsen 1987), the IPL method renders many diverse MUP velocities. Lange at al. (2002) proposed using the standard deviation of MUP velocities as an additional measure that offers information about muscle fibre properties. Changes in these velocities during prolonged effort may indicate, for example, slowing/fatigue of the activated MUs and/or appearance of fast/newly recruited MUs. Such shifts in the activated MUs’ populations were shown by Houtman et al. (2003) by eliciting the MUP velocities with the IPL method and presenting their distribution in histograms. The IPL method has not been applied much. It yields insights into the diversity of MUP velocities and thereby the underlying changes in the MU activity. The method is simple and does not require expensive apparatus or software. When compared with techniques that assess the propagation patterns of MUPs by multi-channel/spatial resolution sEMG (Masuda and Sadoyama 1986; Rau et al. 1997), the IPL method is unable to distinguish and follow individual MUPs belonging to the specific MUs.

In the present study, the sEMG signal was described with four variables derived using the IPL method: (1) the mean muscle CV which was the average of the obtained MUP velocities; the two statistical distribution variables, which were: (2) the within-subject MUP velocities’ skewness [Sk-peak velocity (PV)] and (3) the within-subject MUP velocities’ standard deviation (SD-PV); and (4) the peak frequency (PF), a variable expressing the amount of MUP activity (number of MUPs/peaks) per second.

The aim of the present study was to investigate, with the four variables, the changes in MUPs’ velocities of the biceps brachii (BB) muscle during prolonged dynamic position tasks, in relation to (low) force and duration. It is chosen for the dynamic position tasks as a study design because their physiology promised the finding of a large variety of MUP velocities (great amount of activity due to the position task character, and diversity because of the recruitment/derecruitment changes within the dynamic cycle). Whole cycles of movement


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with their concentric and eccentric phases were analyzed in order to evaluate a total of the sEMG activity with its evolution over time. To highlight the initial changes with the effect of force on it, the changes during the first 14.4 seconds of the dynamic tasks were evaluated separately. Additively, short static position tasks were performed in order to show the (early) changes on force, without any influence of movements on the signal. Methods

SubjectsThe study involved short static and prolonged dynamic experiments. Forty-one healthy and physically active males (24.7 ± 6.7 years, from 16 to 48) (mean ± SD) volunteered for the first experiment and 30 randomly chosen subjects from that group (25.4 ± 7.6 years, from 18 to 42) participated in both experiments. Exclusion criteria were drug abuse and the practice of body building. Three from a total of 44 subjects were excluded because of the impossibility to obtain a required correlation coefficient between the sEMG signals used to estimate the variables’ values. The experimental protocol was conducted according to the Helsinki Declaration and approved by the local ethics committee. All participants gave their written informed consent.

Experimental set-up Maximal voluntary contraction (MVC) of the elbow flexors was measured at least five days before the experiment with a hand-held dynamometer (Lameris Instruments, Utrecht, The Netherlands). During the MVC measurements, the subjects were seated upright. The shoulder was slightly abducted and flexed at 45˚, the elbow was firmly sustained and flexed at 90˚, and the forearm was supinated. The dynamometer was applied to the wrist by the break method (van der Ploeg and Oosterhuis 1991). The peak hold was switched off and the force was kept for at least three seconds. The mean of three maximal values was taken as a MVC. The MVC was assessed with the elbow at 90˚, although the tests were performed at the elbow angle of 135˚ (Philippou et al. 2004).

During the experiment, the subjects were seated in a chair. The upper arm was slightly abducted and comfortably supported at 45˚ of shoulder flexion; the forearm was free. When the elbow was stretched, the line of the upper arm-forearm was at 45˚ in relation to horizontal. When the elbow was flexed to the angle of 135˚, the forearm was horizontal. The forearm was supinated during static and dynamic tests.

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Static testsSubjects were asked to hold the forearm horizontally (elbow angle was then 135˚). A visual bar helped to maintain the correct (horizontal) position of the forearm. In the loaded tests, a sack filled with lead and sand was placed in the palm. Three levels of force were applied in blocks that were three min apart: unloaded, loaded 10 and 20% MVC. A block consisted of 3 tests (three repetitions at the same level of force); every test lasted for 3.8 s and was within a block separated 30 s from one another.

Dynamic tests All participants of the dynamic tests underwent previously static tests, separated by 5 min. Subjects were asked to swing the forearm from the stretched (elbow angle 180˚) to horizontal (elbow angle 135˚) position, thus moving over an angle of 45˚. They did it within a rate of 40 beats per minute (one up-and-down movement in one beat), given by a metronome sound. The visual bar indicated the horizontal position to which the lower arm returned after being stretched. Four force levels were applied: unloaded, and loaded 5, 10 and 20% MVC. The tests lasted for 4 min and were separated by 5 min.

EMG recordingMeasurements were performed on the short head of the BB muscle of a dominant arm. A surface electrode array consisted of three gold-coated electrodes (Harwin, P25-3526), diameter 1.5 mm, insulated in synthetic material plate, with a 10 mm distance between the electrodes (Sadoyama et al. 1985). The skin was cleaned with 95 % ethanol. The electrode array was placed parallel to the muscle fibres (Sollie et al. 1985). The proximal electrode was positioned exactly on the distal one-quarter point of the upper arm, measured between the coracoid and the elbow crease. This place of the electrode was about halfway between the endplate zone and the tendon, securing a sufficient distance from the endplate (Sadoyama et al. 1985; Masuda and Sadoyama 1987). Bipolar derivation was made from the proximal to distal direction, producing two differential signals. The optimal electrode position was controlled by both the observation of the signal on the monitor and the estimation of a correlation coefficient (CC) between the two sEMG signals, which was accepted at r > 0.7 for unloaded, r > 0.85 for 5% MVC and r > 0.9 for higher loaded tests. (During the static and dynamic tests, the maximal CC for unloaded arm was usually lower than that for loaded arm, which was consistent with Hogrel et al (1998), who have estimated for an unloaded arm r > 0.7). The ground electrode was placed on the lateral upper arm, slightly proximal from the derivation electrode.


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The temperature sensor was medial on the upper arm. Two obtained signals were differentially amplified (gain 2000 to 10,000x) and band pass filtered at 2-250 Hz by EMG apparatus (Viking IV, US).

Data processingThe signals were simultaneously A/D converted (sampling 10 kHz, 12 bits acquisition). Data were stored on a personal computer. The signal was analyzed with LabVIEW (version 6.1) software that also facilitated a partial on-line analysis. The peak selection and the correlation coefficient assessments for both static and dynamic tests were performed on 0.2 s epochs. In the static tests, measurements were taken every second during 0.8 s (comprising 4 epochs of 0.2 s). A static test was of 3.8 s duration and was repeated three times for each force level. The statistical analyses were performed on the data of these three repeated tests taken together. In the dynamic tests, the data were assembled every 30 s during 14.4 s (comprising 72 epochs of 0.2 s). The test duration was 4 min.

Peak selection The basic principle was that of Lange et al. (2002). The software was custom designed and written in LabView. The orientation of the signals is up-negative. 1st step: finding the peak-to-peak amplitude of the largest MUP in an epoch of 0.2 s. 2nd step: finding a peak-decline structure. The peak-decline is defined as a structure with a decline of ≥ 20% compared with the largest MUP amplitude of a 0.2 s epoch, over ≤ 4 ms (≤ 40 sample points). 3d step: finding a peak. A peak is the highest (most negative) point previous to the decline, and must be ≥ 10 μV (the threshold of the noise level). 4th step: finding a pair peak. A pair peak is a peak in the second signal with the properties as previous, found in a time window between 1.49 and 4 ms after the peak of the first signal. This window is chosen assuming the physiological CV values of 2.5 – 6.67 m/s. 5th step: excluding double peak. If a peak from the first signal matches two different peaks from the second signal, then the first peak from the second signal is true. 6th step: If during the fatiguing (20% MVC) tests the CV severely diminishes, then the low limit of CV is put at 1.3 m/s, making a window of 1.49 - 7.68 ms. As an objective pragmatic criterion to this change, a lowering of the PF with ≤ 30 % was assumed, compared with the first PF value of the 20% MVC test.

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The variablesThe following calculations were performed: (1) the basic calculation of peak velocities (PVs) following the IPL method; (2) the mean CV, expressed as an average value of the PVs; (3) the within-subject skewness of the peak velocities (Sk-PV), expressed as a skewness of a PVs’ population of a subject; (4) the within-subject standard deviation of the peak velocities (SD-PV), expressed as a standard deviation of a PVs’ population of a subject; and (5) the peak frequency (PF), expressed as a number of peaks per second.StatisticsOne-way analysis of variance (ANOVA) with repeated measures on force was used to compare the dependent variables for static tests (three levels) and for the initial values of the dynamic tests (four levels). A two-way ANOVA with repeated measures on force (four levels) and time (nine levels) was used to compare variables during the dynamic tests. In the case of interactions between force and time, to describe changes over time, when appropriate, four separated ANOVAs were performed with smaller time windows of 0 – 60, 60 – 120, 120 – 180 and 180 – 240 s. To be assured that the dependent variables met parametric assumptions, plots of residues were produced with SPSS software, model control as suggested by Kutner et al. (2005, p. 1157). No relevant deviations of model were detected. Pearson correlation coefficients were calculated to evaluate associations between variables. A level of P < 0.05 was used to identify statistical significance.

Results

Subjects’ physical characteristics are presented in the Table 1. The force of the elbow flexors correlated positively with the upper arm circumference (r = 0.484, P < 0.01). No association was found between either force or upper arm circumference and the sEMG variables. The average skin temperature increased during the dynamic tests by 1.65˚C (P < 0.001); it did not change during the static tests.


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Table 1. Characteristics of the participants to the static tests

N Minimum Maximum Mean SDAge (years) 41 16 48 24.7 6.7Height (cm) 41 166.0 197.0 183.2 8.0Weight (kg) 41 60.0 95.0 74.3 9.2Force (N) right 38 148.5 346.5 246.1 41.9Force (N) left (for left-handed) 3 262.3 267.3 265.1 2.5Skin thickness (mm) 41 1.4 4.2 2.4 .7Circumference upper arm (cm) 41 24.0 31.0 27.5 2.0Length of biceps (cm)* 41 30.50 39.50 35.4 2.0Length lower arm/radius (cm) † 33 24.0 30.0 26.4 1.6Length lower arm/palm (cm) ‡ 33 30.0 36.0 33.1 1.6Initial skin temp. (˚C) 41 30.8 33.8 32.3 .8Temp. upper arm before tests (˚C) 30 30.8 34.2 32.3 .9Temp. upper arm after tests (˚C) 30 32.2 35.7 33.9 1.1Room temp. (˚C) 41 20.0 24.0 22.3 1.0

* From the coracoid to the elbow crease; † From the lateral epicondyle to the wrist crease; ‡ From the lateral epicondyle to the middle of the palm

Static tests

Mean muscle fibre conduction velocity (CV) Histograms in Fig. 1 show an example of a PVs’ population in one subject during the static tests at three levels of force: unloaded, 10% and 20% MVC. Figure 2a presents the averages of the CVs over 41 subjects, calculated from the subjects’ PVs. The CV of the unloaded test was the lowest (3.92 ± 0.27 m·s ˉ¹) and it increased with augmenting force levels (effect of force, P < 0.001). A positive correlation existed between the CVs of unloaded test with 10% MVC, and 10% with 20% MVC (all r > 0.505, P < 0.05).

Skewness of peak velocities (Sk-PV) Figure 2b presents the averages of the within-subject PVs’ skewness over 41 subjects (see also the histograms of PVs in Fig. 1). In all the tests a moderate positive Sk-PV was found, which indicates a relative excess of lower PVs at the distribution scale. With increasing force the Sk-PV significantly diminished, indicating that the proportion of higher PVs increased (effect of force, P < 0.05). The PVs’ population as a whole shifted towards higher values with increasing forces too, which can be seen on the histograms in Fig. 1.

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Figure 1. Distribution of peak velocities of one subject during short static tests at three force levels: unloaded, and loaded 10 and 20% MVC. Note the shift of the velocities as a whole to the higher values with increasing level of force Standard deviation of peak velocities (SD-PV)The averages of the within-subject’s standard deviations of PVs are shown in Fig. 2c. The SD-PV did not change when the force levels increased (effect of force, n.s.).

Peak frequency (PF)Figure 2d shows the averages of PF in the static tests. The PF increased with increasing forces (effect of force, P < 0.001).


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Figure 2. Behaviour of peak velocities (PVs) as effect of force, expressed with four variables. (a) Mean conduction velocity (CV); (b) skewness of within-subject PVs; (c) standard deviation of within-subject PVs (SD); and (d) number of peaks per second (peak frequency = PF). Averages and standard errors are given, obtained from 41 subjects in short static tests at three levels of force: unloaded, and loaded 10 and 20% of maximal voluntary contraction (MVC). With increasing forces, the CV and amount of activity (PF) increases, accompanied with augmenting proportion of fast peaks (the skewness value diminishes). However, the spread of peak velocities within an individual (SD) does not change

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Dynamic tests

Muscle fibre conduction velocity (CV) Histograms in Fig. 3 show the PVs estimated from one subject during dynamic tests at four levels of force: unloaded, and loaded with 5%, 10% and 20% MVC. Figure 4a shows the averages of CV calculated from the subjects’ PVs over 30 subjects. As in the static tests, the initial CV of the dynamic tests increased with level of force (effect of force, P < 0.001). Further, a positive correlation was found between the CV of the static tests and the initial CV of the respective dynamic tests (all r > 0.409, P < 0.05).

The lowest initial CV was that of the unloaded test with about 4 (3.6 to 4.5) m·s ̄ ¹ and the highest was that at 20% MVC with approximately 4.6 (4.0 to 5.15) m·s ̄ ¹, increasing from the unloaded to 20% MVC test with 14 ± 9.6%. During all the dynamic tests, the CV significantly declined (effects of time for the unloaded test, P < 0.02; for other tests, P < 0.001), whereby the decline was steeper with larger forces (interaction between force and time, P < 0.001). At the three lowest force levels (unloaded, and loaded 5% and 10% MVC), the CV had two phases: a decline phase lasting for about 120-150 s and a steady phase continuing to the end of a test.

During the 20% MVC test, however, the CV continued to decline, without a stable phase. Twelve of 30 subjects (40%) reported fatigue during the 20% MVC test and terminated the task prematurely between 90 and 210 s. The CV decreased for the fatigued subjects from 4.6 ± 0.3 (4.2 – 5.0) to 3.6 ± 0.3 (3.0 – 4.1) m·s ̄ ¹ and for the continuing subjects from 4.5 ± 0.3 (4.0 – 5.15) to 3.8 ± 0.4 (3.0 – 4.7) m·s ̄ ¹. Neither the absolute initial CV nor the end CV differed significantly between the groups (P = 0.497 and P = 0.124, respectively). However, the relative decline of the CV tended to be larger for the fatigued subjects than for the continuing subjects, for fatigued being about -20 (-7 to -35)% and for continuing -15 (+7 to -28)%; P = 0.074.

Skewness of peak velocities (Sk-PV)Histograms in Fig. 3 show the distribution of PVs of one subject and Fig. 4b presents the averages of Sk-PV over 30 subjects. In the initial phase, consistent with the static tests, Sk-PV was most positive (= skewed in favour of lower velocities) in the unloaded test, and the Sk-PV diminished with increasing forces (effect of force, P < 0.001). That means that lower PVs dominated in the unloaded test and the proportion of higher PVs increased when forces augmented. Thus, in the 20% MVC test, the initial PVs approached a normal distribution. In addition, with increasing forces the PVs as a whole group seem to shift


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towards higher values, as can be seen in the histograms Fig. 3a-d at time zero. During the tests, the Sk-PV increased again, except for the unloaded test, indicating a growing proportion of lower PVs over time and decreasing amount of higher PVs (histograms Fig. 3a-c). The larger the forces the steeper increase of the Sk-PV over time (for all tests together: effect of time, P < 0.001; interaction between force and time, P = 0.005; effect of time in unloaded test, P = n.s.; for the tests 5%, 10% and 20% MVC: interaction between force and time, P < 0.001). During the last 2 minutes of the 5% and 10% MVC tests, the Sk-PV stabilized. In the 20% MVC test, however, the Sk-PV still tended to increase up to the end of the test (over the time windows 120-180 and 180-240 s: effect of time for the 5%, 10% and 20% MVC tests, n.s.; interaction between force and time, P = 0.106; for the 5% and 10% MVC tests effect of time, n.s.; for 20% MVC test effect of time over 120-180s n.s., over 180-240s P = 0.052). At the end of the 20% MVC test, the whole population of PVs appeared to shift towards the lower values too, as can be seen at the last two histograms in the Fig. 3d.

Taken together, in the initial phase of activity, the proportion of fast peaks increased with increasing force. In the prolonged tests loaded up to 10% MVC, the proportion of fast peaks declined again over the first 2 minutes and then stabilized at about the level of the unloaded test. During the 20% MVC test, however, the proportion of fast peaks still tended to decline up to the end of the test, accompanied with a growing amount of slow peaks.

Standard deviation of peak velocities (SD-PV)The averages of SD-PVs of 30 subjects are presented in Fig. 4c. The initial SD-PV was for all force levels similar (P = 0.65), which resembled the static tests. The values in the dynamic tests were significantly higher compared with those of respective static tests (paired sample t-test for the unloaded, 10% and 20% MVC tests, respectively P = 0.014, P = 0.027 and P = 0.001). During the tests, the SD-PV changed significantly over time, depending on the force level (for all tests effect of time, P = 0.011, interaction between force and time, P < 0.001). The course of the SD-PV had two phases which were different for the three lower force levels (unloaded, 5% and 10% MVC) compared with 20% MVC. In the three lower force levels, the SD-PV first declined over about 90 s and then stabilized (interaction between force and time over 0–240 s, P = n.s.; effect of time over the time windows 0–60 s, P < 0.001; 60–120 s, P = 0.075; 120–180 s and 180–240 s for both P > 0.343). However, during the 20% MVC test, the decline, which lasted for approximately 60 s, was followed by an extreme increase (effect of time over 0 – 240 s, P < 0.001; effect of time over 0–60 s, P = 0.019; over 60–120 s, which was in opposite direction, P = 0.019; over

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120–180 s and 180–240 s, P < 0.05). This pattern of results can also be seen in the histograms Fig. 3d.

]

Figure 3. Changes in the distribution of peak velocities (PVs) over time at different levels of force. The PVs are obtained from one subject (the same as in Fig. 1 for static tests) during prolonged dynamic contractions at four levels of force: unloaded, and loaded 5, 10 and 20% of maximal voluntary contraction (MVC). Every histogram represents a number of PVs within a period of 14.4 s. Initially (at time zero), a global shift of peak velocities is visible towards higher values when forces augment. During the unloaded, and loaded 5 and 10% MVC tests, the amount of the slower peaks is moderately increasing and of the faster peaks is diminishing. During the 20% MVC test, the peak velocities shift considerably as a whole towards lower regions, and the amount of peaks visibly diminishes

Peak frequency (PF)Figure 4d shows averages of PF over 30 subjects. The initial PF rose with increasing force levels (effect of force, P <0.001). Then, during the tests at three lowest force levels (unloaded, 5% and 10% MVC) the PF remained stable. But during the 20% MVC test, the PF significantly diminished, at the beginning gradually and from about 120 s steeply (interaction between force and time for all the four tests, P < 0.001; interaction between force and time for the three lowest force levels, P = 0.234, effect of time for the three lowest force levels,


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P = 0.541, effect of time for 20% MVC, P < 0.001). There was much variability among subjects in the size of decline in 20% MVC test. For those who were able to complete the test, the PF continued to decline up to the end, with exception of one subject in whom the PF increased instead. At 240th s the PF of the continuing subjects was reduced by – 35 (-78 to +3)%.

Figure 4. Effects of force and time on the behaviour of peak velocities (PVs), expressed with four variables: (a) Mean conduction velocity (CV); (b) skewness of within-subject PVs; (c) standard deviation of within-subject PVs (SD); and (d) number of peaks per second (peak frequency = PF). Averages and standard errors are given, obtained from 30 subjects during prolonged dynamic tests at four levels of load: unloaded, and loaded 5, 10 and 20% of maximal voluntary contraction (MVC). Note the difference in the decline pattern of the CV and the PF: the CV declines over time for all levels of force, whereas the PF remains stable for the three lower force level tests (unloaded, and loaded 5 and 10% MVC). In the higher force level (20% MVC) test, the CV starts declining immediately, whereas the PF declines first gradually and later on steeply. Note the stable SD values from about 90 s for the three lower force tests, whereas the SD of the higher force (20% MVC) test clearly increases.

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Discussion

Changes in the distribution of MUP velocities as an effect of (low) force and duration were described with four variables: (1) the global variable of mean CV; (2) the within-subject skewness of a population of MUP velocities; (3) the within-subject standard deviation of MUP velocities and (4) the amount of MUP activity, expressed as the MUP frequency. First we will comment on the four variables. Next, we will discuss the main findings, based on these variables.

The four variablesThe CV variable renders a mean value of the motor unit potentials’ propagation velocities. The CV will increase or decrease, depending on the type of the activated (fast-twitch and slow-twitch) motor units. It will also change with the alterations in muscle membrane potential, which influences the depolarisation/repolarisation processes. For example, CV decreases in muscular fatigue (Stalberg 1966; Milner-Brown and Miller 1986), and increases with a smaller interstimulus interval, such as that due to the rising discharge rate (Gydikov and Christova 1984; Radicheva et al. 1986; Nishizono et al. 1989). Because of the lack of studies, it is not possible to compare our CV results with those of any other dynamic position task experiment. However, the estimates are consistent with those of the studies using a static force task, especially with those of Lange et al. (2002), also obtained with the IPL method.

Skewness is used as an sEMG variable in the present study for the first time. This statistical measure of deviation from a normal distribution, in this case expresses the proportion between slower and faster MUPs within an individual. The Sk-PV will increase with the growing proportion of the activated slow/tonic/fatigue resistant MUs and will decrease with the augmenting proportion of fast/phasic/fatigable MUs. All the Sk-PV estimates were moderately positively skewed, which indicates a relative excess of lower MUP velocities.

The within-subject standard deviation of MUP velocities, a variable introduced by Lange et al. (2002), shows the spread of MUP velocities. For a fresh and healthy muscle, it will render information about diversity of the participating MUs. In a fatigued muscle, when membrane propagation is slowing, the SD-PV will broaden as a result of the temporal dispersion of velocities. Further, the SD-PV can be expected to narrow when the same velocities repeat, such as in a higher discharge rate of a certain group of MUs. In the short static position tasks, the SD-PVs were larger than those previously estimated by Lange and colleagues (2002) in the static force tasks, with 0.55 - 0.62 m sˉ¹ and 0.3 -


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0.52 m sˉ¹, respectively. This difference can be due to the different type of a task, as data are available suggesting that different excitatory/inhibitory inputs to the motoneurons play a part in the position tasks and the force tasks (Rudroff et al. 2005).

In the present study, the SD-PVs of the dynamic tests were larger than those of the static tests. This difference can have different explanations. With every contraction of a dynamic cycle, the muscle fibres’ diameter increases, leading to higher fibre propagation velocities (in a part of a cycle) (Arendt-Nielsen et al.1992). This problem was partially restrained by using a small movement angle of 45˚. However, the most important role in the increase of SD-PVs’ during dynamic contractions might be played by the cyclic changes in the motor units’ discharge characteristics. Several studies deliver the supporting data. For example, during dynamic contractions the amount of activity differs between the concentric and eccentric phases, suggesting different regulatory strategies for the two phases (Potvin 1997). Previous studies have also shown that the rate of MU discharge is related to the movement’s velocity, and the (angle) velocity alters depending on the elbow angle (Gillis 1972; Milner-Brown et al. 1973a; Potvin 1997). Thus, the discharge rates will alter through a cycle. In addition, eccentric movements are shown to further the activation of high-threshold (fast propagating) motor units (Komi and Tesch 1979; Nardone et al. 1989).

Peak frequency (MUP frequency) expresses the number of MUPs in a time. To our knowledge, it is used as sEMG variable in the present study for the first time. The PF is comparable with the zero crossings’ number parameter (Lynn 1979; Masuda et al. 1982; Hägg 1981). It is argued that the diminishing zero crossings’ number during prolonged exercises indicates a decrease in MU activity, as a sign of fatigue (Inbar et al 1986; Hägg and Suurküla 1991). Lange et al. (2002) mentioned a number of MUPs obtained during a 1.5 s measurement in an isometric force task experiment, which rendered the frequencies of about 4–5 MUPs/s for 10% MVC test, and about 8 MUPs/s for 20% MVC test. These values are much lower than ours with 37, 47 and 50 MUPs/s (for respectively unloaded, and loaded 10% and 20% MVC tests) in static (= isometric) position tasks. The findings are consistent with the interpretation that during position tasks more MUs are being recruited compared with force tasks, and the discharge rate of MUs is higher (Mottram et al. 2005a).

The initial changes on increasing force levels (in the static and dynamic tests) The effects of force on the behaviour of MUPs in the short static tests and the initial phase of the prolonged dynamic tests were similar. With increasing

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forces the CV grew higher and MUP frequency increased. In the population of MUP velocities, not only the proportion of the fast MUPs increased (see the skewness in Fig. 2b), but also the MUP velocities as a whole shifted towards higher values (histograms in Figs.1, 3 a–d at time zero). Despite of the changes in the skewness, the standard deviation of MUP velocities remained unaltered.

The increases in the CV with increasing force are in accordance with the previous findings in force tasks (Lange et al. 2002, Naeije and Zorn 1983, Sadoyama and Masuda 1987, Zwarts and Arendt-Nielsen 1988). It is generally accepted that these increases are caused by activating high threshold/fast/phasic MUs when demands on the muscle are augmented (Gantchev et al. 1992, Henneman et al. 1965, Milner-Brown et al. 1973b). This explanation is supported by the increasing proportion of the fast MUPs found in the present study. However, the global shift of MUP velocities towards higher values may be induced by either replacing the slow MUs by fast ones, or by increasing the propagation velocity of the muscle membrane due to the rising discharge rate (Van der Hoeven and Lange 1994). Little is known 4). Little is known about changes in the within-subject standard deviation of MUP velocities. Only Lange and colleagues (2002) found (using a force task), contrary to our results, increases in the SD-PV when increasing forces between 10% and 50% MVC, and no increases between 50% and 100%. The experiments of Lange et al. and the present short static experiments were both isometric, and the duration of the contraction did not differ much (our 3.8 s versus their 1.5 s). In fact, the two studies only differed in the type of a task (position tasks applied by us versus force tasks by Lange et al.). This task difference may play a part in the discrepancy of the standard deviation, as the excitatory and inhibitory inputs for the two tasks are supposed to be different (Rudroff et al. 2005). Thus, for the two tasks different types of motor units (with their different velocities) may be activated.

In short, increases of mean CV with increasing forces in the initial phase of muscle activity may be a result of both recruitment of the fast/phasic motor units, and faster membrane propagation.

Changes in the prolonged dynamic tests

Tests loaded below 20% MVCThe main feature of the prolonged tests loaded 5% and 10% MVC were changes in the CV, skewness and standard deviation over the first 90-120 seconds, followed by stabilizing. Thus, the CV first declined (with steeper decline for


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higher forces) and then stabilized at approximately the level of the unloaded test (Fig. 4a). The MUP velocities, which at the beginning of tests were shifted towards the higher values with increasing forces, re-shifted over time back to the lower values (skewness variable in Fig. 4b and histograms in Fig. 3 a-c). Subsequently, the MUP velocities stabilized nearly at the level of the unloaded test too. The standard deviation narrowed first, and later on stabilized at a new level (Fig. 4c). The MUP frequency held steady at the primary level, determined by the used force (Fig 4d). We suggest that this pattern of results may reflect an emerging equilibrium between phasic and tonic MU activity.

No comparison is possible between the variables used here and those of any other prolonged dynamic position tasks study. The decline of CV during contractions at low forces was in contradiction with the studies in isometric force tasks, which reported increases during sustained contractions at forces of 10-25 % MVC ((Arendt-Nielsen et al. 1989, Krogh-Lund 1993, Krogh-Lund and Jorgensen 1991, Krogh-Lund and Jorgensen 1993, Zwarts and Arendt-Nielsen 1988) This discrepancy may be caused by either/ or both the different contraction types (isometric versus dynamic) or different tasks (force versus position tasks). Increases of the CV in prolonged isometric contractions at low force levels are supposed to be due to the recruitment of fast (anaerobic) MUs in response to the hindered blood flow (Crenshaw et al. 1997; Zwarts et al 1987). In the dynamic conditions, the blood supply is assumed to be undisturbed, so the aerobic MUs can be activated. The decline of the CV followed by stability, along with the changes in the skewness, suggest that, within the cyclically fluctuating activity, the amount of initially recruited fast/fatigable/anaerobic MUs may successively diminish and the proportion of slow/fatigue resistant/aerobic MUs may augment. The maintaining activity of slow MUs is in accordance with the hypothesis that tonic/fatigue resistant (aerobic) MUs remain active through the whole muscular action (Grimby and Hannerz 1968, Hägg and Suurkula 1991).

On the other hand, the position character of the present tasks may have contributed to the discrepancy between the decline of CV in the present experiments and increases in previous studies. Firstly, the discharge characteristics of the same motor unit differ between the force and position tasks, and secondly, motor units show greater discharge adaptation during the position tasks (Mottram et al. 2005a, 2005b; MacGilles et al. 2003). The evolution of the standard deviation variable, with its narrowing followed by stabilizing, fits in with the idea that the discharge rate may temporarily increase and consecutively adapt, resulting in a new balance.

The MUP frequency variable, expressing the amount of MU activity produced as a result of recruitment and discharge rate, did not change during

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these tests. This suggests that all the changes in recruitment and discharge rate do not, in principle, affect the total amount of MU activity.

All subjects were able to complete the tests, and the sEMG variables became stable over time as well. Thus, one can assume that the three tests at lowest force levels were non-fatiguing. Taken together, the results of these apparently non-fatiguing dynamic position tasks suggest that, following the initially increased activation of fast MUs, their proportion shifts after about 2 minutes in favour of slower MUs. The amount of activity seems to remain stable throughout the tests.

The test at 20% MVCThe changes encountered during the prolonged test at 20% MVC differed clearly from those at lower forces (Fig. 4). During the 20% MVC test, the CV dropped below the level of the unloaded test. At the same time, the proportion of low MUP velocities increased (skewness increased), and finally the velocities’ population made a global move from higher towards lower values, while their standard deviation broadened clearly. The MUP frequency, in contrast with that of non-fatiguing tests, progressively diminished (histogram in Figs. 3d, 4d).

The CV’s decreases were in accordance with those shown by Farina et al. (2004) during fatiguing dynamic force tasks of the vastus medialis muscle. The behaviour of the skewness and standard deviation suggests that a large majority of the MUPs became extremely slow at the end. This general slowness best matches the slowing of the muscle membrane propagation, which is generally accepted as sign of muscular fatigue (Milner-Brown at al. 1986; Miller et al. 1987). The diminishing MUP frequency can be due to the synchronisation of discharges (Datta and Stephens 1990; Semler and Nordstorm 1999) and diminishing motor unit activity (Hägg 1981; Hägg and Suurkulla 1991). The non-linear decline pattern of MUP frequency, first slow and later on steep, suggests successively exhausting available MUs’ reserves, resulting in lower firing frequencies and failing recruitment. Accordingly, many subjects reported fatigue and stopped exercising.

In short, during these apparently fatiguing dynamic position tasks, a global slowing of MUP velocities appears, suggesting a fatigued muscle membrane. The amount of MU activity seems to diminish progressively and finally the recruitment stops.

Some aspects of the method must be explained. The load was applied to the palm, whereas the MVC was assessed from the wrist. By applying load to the palm, we intended to mimic natural circumstances, such as holding something


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in the hand. Assessment of the MVC from the palm was not feasible, however, due to the relative weakness of the wrist’s flexors compared with the elbow flexors, which influenced the estimates. A reasonable alternative was to measure the MCV from the wrist. Distances measured over the forearm and the palm (Table 1) enabled calculation of the real exerted load torque, which was about 20-25 % larger than the used one. The MVC was assessed at the elbow angle of 90˚ despite performing the tests at the angle of 135˚. The reason was that, when applying the dynamometer at the wrist with high forces, the elbow angle being at 135˚, subjects tended to overstretch the wrist and experienced pain. This was not the case at the 90˚ angle. The MVC values at 135 and 90˚ were similar, which was consistent with the findings of Philippou et al. (2004), so it was chosen for assessments at 90˚.

In conclusion, we present a set of variables derived from the interpeak latency method, which yields information about changes in MUP velocities’ distribution and amount of MUP activity. Skewness, standard deviation and peak frequency variables appear to corroborate the results of a global muscle conduction velocity. Together they can contribute to quantifying the dynamics of motor unit activity and membrane fatigue. The interconnected results may be useful in ergonomics (for assessment of fatigue) and in sports (for eliciting specific capabilities, such as explosive or endurance capabilities).

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