Application of multiplexed ion mobility spectrometry-MS towards the identification of host response protein signatures of treatment of pulmonary tuberculosisKomal Kedia1, Jason Wendler1, Erin S. Baker1, Leah G. Jarsberg2, Aaron T. Wright1, Paul D. Piehowski1, Marina A. Gritsenko1, David M. Lewinsohn4, Marc H. Weiner6, Richard D. Smith1, Payam Nahid2, and Jon M. Jacobs1 1 Biological Science Division, Pacific Northwest National Laboratory (PNNL), Richland WA, 3Computational and Statistical Analysis Division,Pacific Northwest National Laboratory, Richland, WA 2School of Medicine, University of California San Francisco, San Francisco, CA, 4Pulmonary and Critical Care Medicine, Oregon Health & Science University, Portland, OR 5University of Texas Health Science Center at San Antonio and the South Texas VAMC, San Antonio, TX

Introduction

Overview Methods Results

AcknowledgementsThis work was funded by NIH NIAID 1R01 Al104589-01. Samples were analyzed using capabilities developed under the support of NIH National Institute of General Medical Sciences (8 P41 GM103493-10). This project was performed in the Environmental Molecular Sciences Laboratory, a DOE OBER national scientific user facility on the PNNL campus. PNNL is a multi-program national laboratory operated by Battelle for the DOE under contract DE-AC05-76RL01830.

References1. Gulland, A. (2015). Tuberculosis killed 1.5 million people in

2014. Bmj, 351, h5798. doi:10.1136/bmj.h5798

2. Achkar, J. M., Lawn, S. D., Moosa, M.-Y. S., Wright, C. A., & Kasprowicz, V. O. (2011). Adjunctive Tests for Diagnosis of Tuberculosis: Serology, ELISPOT for Site-Specific Lymphocytes, Urinary Lipoarabinomannan, String Test, and Fine Needle Aspiration. Journal of Infectious Diseases, 204(suppl 4), S1130-S1141. doi:10.1093/infdis/jir450

3. Horne, D. J., Royce, S. E., Gooze, L., Narita, M., Hopewell, P. C., Nahid, P., & Steingart, K. R. (2010). Sputum monitoring during tuberculosis treatment for predicting outcome: systematic review and meta-analysis. Lancet Infect Dis, 10(6), 387-394. doi:10.1016/s1473-3099(10)70071-2

4. Baker, E. S., Burnum-Johnson, K. E., Jacobs, J. M., Diamond, D. L., Brown, R. N., Ibrahim, Y. M., Smith, R. D. (2014). Advancing the high throughput identification of liver fibrosis protein signatures using multiplexed ion mobility spectrometry. Mol Cell Proteomics, 13(4), 1119-1127. doi:10.1074/mcp.M113.034595

Conclusions

CONTACT: Komal Kedia, Ph.D.Biological Sciences DivisionPacific Northwest National LaboratoryE-mail: komal.kedia@pnnl.gov

• Identify host serum protein signatures reflective of 8 weeks of TB treatment

• Identify significantly different proteins between culture negative (responders) and culture positive (slow responders) patients stratified based on their culture status at week 6 and week 8

• Serum samples were collected from 289 subjects enrolled in the TB Trials Consortium Study 29

• Data interpretation including protein identification and quantitation was performed using the Accurate Mass and Time (AMT) tag approach

• 244 proteins were differentially abundant between baseline and 8 weeks post-treatment

• 41 proteins for week 8 and 54 proteins for week 6 culture status were significantly different between culture negative and culture positive patients in response to treatment

• 872 unique proteins were identified using multidimensional capabilities of LC-IM-MS

• Of those 244 proteins serve as potential candidate markers of treatment response

• 17 proteins were consistently discriminatory for both week 6 and 8 culture status stratifications overlapping with 244 proteins of treatment effect

• Baseline concentrations of these proteins can provide predictive information on future culture conversion

• Tuberculosis (TB) is one of the leading causes of deaths from infectious diseases

• According to WHO, 1/3 of world’s population has TB infection

• In 2016, 10.4 million new TB cases with 1.4 million deaths were reported worldwide

• Microscopy and sputum culture are the current gold standards for diagnosing and monitoring disease

• However, these markers have low to moderate sensitivity and specificity, prompting efforts to identify new quantitative, non-sputum based TB biomarkers

• A blood-based protein signature could hold the promise to improve the efficiency and predictive accuracy of monitoring treatment response.

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• 289 participants enrolled from clinical trials sites in North America, South Africa, Uganda, Spain, Brazil, Peru, and Vietnam

• Participants had sputum smears positive for acid fast bacilli at baseline, and were culture positive for drug-susceptible pulmonary TB

• Serum was collected, processed and stored at baseline (time of enrollment), and after 8 weeks (40 doses)

• Serum samples were depleted of 14 highly abundant proteins using a ProteomeLabTM 12.7 × 79.0-mm IgY14 LC10 affinity LC column

• Analysis was performed on an in-house built instrument: 1-m ion mobility drift tube with an Agilent 6224 TOF MS coupled with LC

• A 60-min gradient was used with the IM-MS platform and data was collected from 100-3200 m/z

• Identification and quantification of the detected peptide peaks was performed utilizing the AMT tag approach

Patient Pairs log2(8 week/baseline)

244

Prot

eins

289 Patient Pairs log2(8 week/baseline)

A) Heatmap of 244 significant proteins of treatment effect B) Protein based pathway mapping of significant proteins

A.

C.

Discriminatory proteins from week 0 to week 8 for Culture + and culture – patients C) Week 6 D) Week 8 E) Venn diagram representing overlap of significant proteins

E.

B.

D.