response to comment on absence of sperm rna elements ...occurs during spermatogenesis. in fig. 1,...
TRANSCRIPT
SC I ENCE TRANS LAT IONAL MED I C I N E | T ECHN I CA L COMMENT
I N FERT I L I TY
1Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI48201, USA. 2Center for Molecular Medicine and Genetics, Wayne State University,Detroit, MI 48201, USA. 3CReATe Fertility Centre, Toronto, Ontario M5G 1N8, Canada.4Department of Obstetrics and Gynaecology, University of Toronto, Toronto, OntarioM5G 1E2, Canada. 5Department of Gyneacology, Women’s College Hospital, Toronto,Ontario M5S 1B2, Canada. 6Vincent Memorial Obstetrics and Gynecology Service,Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.7Departments of Environmental Health and Epidemiology, Harvard T.H. Chan Schoolof Public Health, Boston, MA 02115, USA. 8Department of Obstetrics and Gynecology,Augusta University, Augusta, GA 30912, USA.*Present address: Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS),University of Barcelona, Barcelona 08036, Spain.†Corresponding author. Email: [email protected]
Jodar et al., Sci. Transl. Med. 8, 353tr1 (2016) 24 August 2016
2016 © The Authors,
some rights reserved;
exclusive licensee
American Association
for the Advancement
of Science.
Response to Comment on “Absence of sperm RNAelements correlates with idiopathic male infertility”Meritxell Jodar,1,2* Edward Sendler,1,2 Sergey I. Moskovtsev,3,4 Clifford L. Librach,3,4,5
Robert Goodrich,1,2 Sonja Swanson,3 Russ Hauser,6,7
Michael P. Diamond,1,8 Stephen A. Krawetz1,2†
RNAs from other cell types have minimal impact on male fecundity–associated sperm RNA elements.
by guest on February 16, 2020
http://stm.sciencem
ag.org/D
ownloaded from
The objective of the study by Jodar et al. (1) was to examine the generalsperm transcript profile from different patients with idiopathic infer-tilitypresentingwithnormal semenparameters.Reference values of spermparameters established by the World Health Organization in 2010 are atleast 39 million sperm per ejaculate with 32% motility and 4% normalmorphology (2). Accordingly, normal semen samples could presentwith 96%morphologically abnormal and 68% immotile sperm, indicat-ing the heterogeneity of sperm population in each individual.
The technical comment from Cappallo-Obermann and Spiess (3)suggests that the samples used in Jodar et al. (1) also contained differentsomatic cell types. This assertionmay reflect their past experience with-in their patient population, where 30% of the samples have a round cellconcentration of >5 × 106 /ml. In contrast, the clinic from which mostsamples were obtained reported that only 4% of patients (49 individualsfrom 1230 unselected nonazoospermic patients) presented a round cellconcentration of >1 × 106/ml (4). It is important to emphasize that allsamples included in the study of Jodar et al. (1) had a round cell count of<1 × 106/ml before PureSperm gradient purification, with the exceptionof a single sample. This is substantially lower than the reference valuesrecommended by theWorldHealthOrganization (2). In addition, noneof the samples included in the study had a notable number of epithelialcells or other identifiable cells, as evaluated by optical microscopy afterPureSperm (table S1).
Approaches to purify spermatozoa from semen such as swim-up (5)and density gradient centrifugation above 50% markedly decrease therecovery of spermatozoa. This is consistent with the physiological selec-tion of the sperm. Moreover, as suggested by the decreased recovery ofmitochondrial RNAs, somatic cell lysis buffer treatment likely compro-mises themidpiece of spermatozoa (6). Because the samples used in thestudy contained very low numbers of somatic cells, we chose the 50%PureSpermmethodology for spermpurification. Thisminimized spermselection while maintaining the integrity of spermatozoa. In those caseswhere round cells were detected by optical microscopy after the initialgradient, the 50% PureSperm gradient was repeated. Their efficient re-moval was verified by CD45/PTPRC reverse transcription polymerasechain reaction (RT-PCR) (table S1 and fig. S1).
Nevertheless, the technical comment (3) suggested that a high pro-portion of somatic transcripts reside in spermatozoal RNA sequencing(RNA-seq) data sets and now microarray data when sperm were pur-ified using a 50% density gradient, which yields a sperm and/or sperm-enriched fraction (1, 7). In sperm, several RNA singularities should beconsidered before analyzing total RNA-seq data. Onemust consider thebackground level present in any total sperm RNA-seq data. The abso-lute abundance of sperm transcripts, including ribosomal and mito-chondrial RNA, varies widely between samples. This likely reflects theend result of differential fragmentation and targeted RNA removal thatoccurs during spermatogenesis. In Fig. 1, note the expanse of the deepblue color that essentially corresponds to 0 values for most of the278,604 possible RNA coding, noncoding, and sperm-specific RNAelements in the 72 samples used in the study. As shown in Fig. 1A, onlya small group of the most abundant RNA elements (>90th percentilerank) exhibits uniform consistency among samples in >60% of thesamples studied. In contrast, as emphasized in Fig. 1B, the majorityof lower-ranked RNA elements (<90th percentile rank) consistently ap-pear at the equivalent rank in less than 20% of samples studied. Thisreflects the degree of dispersion among individual sample rankingsfor elements contributing to the average element rank at that given level.Accordingly, rigorous criteria were developed to select only thoseconsistently high-ranked sperm elements that are likely of critical andfunctional importance (1).
As noted above, the first goal was the identification of sperm RNAelements (SREs) required for natural conception. These RNA elementswere expected to be both consistently observed and at high levels insperm of the seven fertile controls, those couples that achieved a livebirth from natural conception during their first attempt at timed inter-course (TIC). Only a relatively small specific subset of elements withconcordant high abundance among the fertile controls met these crite-ria, and these should be considered as candidate spermbenchmarks [seeFig. 1 in the study of Jodar et al. (1)]. It is also important to note that thepossibility of transcripts of nondescript origin to be passengers in spermwas considered, because we have now begun to appreciate the commu-nication pathways between sperm and their environment (8–10). It isfor this reason that we chose the stringent criteria (average >99thpercentile rank among the seven controls) to further consider 1223sperm elements. As summarized in Table 1, of the 28 possible differen-tiated germ cell marker sperm elements derived from PRM1, PRM2,TNP1, LELP1, SMCP, and OAZ3 that were assessed in the technicalcomment (3), a total of 13 have an average percentile rank above the99th percentile rank threshold. However, as described in our originalmethods, a stringent approach was taken to obtain a precisely definedset of elements that were consistently observed as highly abundant insperm. These SREs were within the group that was greater than the99th percentile rank among the seven fertile controls and did not
1 of 5
SC I ENCE TRANS LAT IONAL MED I C I N E | T ECHN I CA L COMMENT
by guest on February 16, 2020
http://stm.sciencem
ag.org/D
ownloaded from
present an outlier [percentile rank <Q1–1.5 × IQR (interquartilerange)]. As shown in Table 1, only 6 of the 13 sperm elements that com-prise these specific differentiated germ cell markers meet the stringentcriteria to be included in the list of 648 retained sperm elements re-quired for natural conception. Although it is likely that on the basisof our strict criteria we do not include some elements that are also im-portant in sperm function, we specifically wanted to lessen the chance
Jodar et al., Sci. Transl. Med. 8, 353tr1 (2016) 24 August 2016
of type I error. The use of these accurately selected 648 SREs enabledthe identification of a group of patients with a low success rate of con-ceiving using the less invasive techniques such as TIC and intrauterineinsemination.
As expected, none of the 72 samples included in the study presentany of the somatic cell markers (IL8, CD45/PTPRC, andCDH1) or theundifferentiated germ cell marker (KIT) elements at or above the 99thpercentile rank (table S2). The background level (<90th percentilerank) of most of these elements and the inability to assemble full-length transcripts from these transcripts that in contrast to spermdo not present a biologically fragmented RNA population confirmthe absence of considerable somatic cell contamination as observedby optical microscopy. These results are summarized in table S1 andsupported by PTPRC quantitative RT-PCR (qRT-PCR) and sequenc-ing of PTPRC as shown in fig. S1, which shows disjointed andmarginal coverage, if any, throughout a few exons. Together, thisshows that the relative amount, if any, of this or other transcripts iswell below the threshold of reliable detection.
A few samples do present some elements above the 99th percentilerank, which are associated with or used as prostate and seminal vesiclemarkers. These transcripts are also present in human sperm RNAsprepared by swim-up (5). As above, it is important to note that thesetranscripts (Table 2)were not consistently observed (<Q1–1.5 × IQR) inall fertile controls, nor did they reach the threshold of an averagepercentile ranking >99. Accordingly, these elements did not meet thestrict criteria to be included in the list of 648 retained sperm elements.To estimate their likely influence or dilutive effect, we performed a com-parison of Human BodyMap 2.0 (www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-513/) testes versus prostate transcript expres-sion (SupplementaryMaterials andMethods). However, it is importantto note that expression measured in the various cell types is quite dif-ferent across different tissue expression databases, such as HumanBodyMap 2.0 versus GTEx (www.gtexportal.org) (11). This observationmakes it all the more difficult to suggest that the observed presence ofany specific markers is an absolute indication of presence of a specificcell type. With these caveats in mind, examining the distribution ofprostate versus testes marker percentile ranks (SupplementaryMaterials andMethods) indicates that testes markers show significantlyhigher overall abundance rank in sperm as compared to the prostatemarker transcripts (P = 0.0066; table S3). These comparisons suggestthat even if prostate cells contribute in some part to the RNA queried,their effect on detection of sperm elements would be marginal. It is ofnote that the four seminal vesicle and prostate markers SEMG1,SEMG2, TGM4, and MSMB described in the technical comment (3)are among the more abundant transcripts in extracellular vesicles con-tained in semen (SupplementaryMaterials andMethods). Their relativerank based on exosomal RNA-seq data of the 23,181 transcripts as-sessed is as follows: MSMB, 6; SEMG1, 22; TGM4, 157; and SEMG2,200. Seminal fluid contains an extremely large number of extracellularvesicles (1011 to 1013 particles per ejaculate) containing a large repertoireof RNAs and proteins (12). Most of these originate from the prostate,although other accessory sex glands also release extracellular vesicles,contributing to the population found among the epididymal and semi-nal vesicles (13). Our recent integrative analysis of human sperm andseminal fluid transcriptomic and proteomic data suggests that theseminal fluid and spermatozoa may communicate through extra-cellular vesicles (8). The enrichment of RNAs from seminal fluid ex-tracellular vesicles in the peripheral membrane on mouse spermatozoasuggests the presence of extracellular vesicles on the spermatozoal surface,
Fig. 1. Distribution of all possible RNA elements’ percentile rankings. The278,604 possible RNA-coding, noncoding, and sperm-specific RNA elements weremeasured in 72 sperm samples, and their abundance was ranked by percentile. Ele-ments were sorted by average rank as measured in all samples, and the relative per-centage of individual samples with specific rank at each average percentile tier wascalculated. (A) On the basis of the sort by average rank, the surface plot shows thedistributionof individual sample ranks as compared to the average. (B) Higher-resolutionheat map with nonlinear scaling and a lower-threshold color scale bar* with higher-ranked sample fractions >0.20 combined. This resolves the relatively wide distributionamong individual samples for lower-ranked elements (indicated in green). Both panelsshowminimal intersample variation for the top-tier rank elements (indicated in yellow)compared to the broad dispersion of lower-ranked elements.
2 of 5
SC I ENCE TRANS LAT IONAL MED I C I N E | T ECHN I CA L COMMENT
by guest on February 16, 2020
http://stm.sciencem
ag.org/D
ownloaded from
contributing to the growing evidence of spermand seminal fluid commu-nication through extracellular vesicles (9, 13, 14). It has recently beenshown that communication through extracellular vesicles could be crucialfor epididymal RNAs to be delivered to sperm to promote transgenera-tional epigenetic inheritance of metabolic phenotypes (10). Together,these results suggest that even the moderate to low levels of RNA
Jodar et al., Sci. Transl. Med. 8, 353tr1 (2016) 24 August 2016
elements corresponding to seminal vesicle and prostate markers likelyarise from the interaction of extracellular vesicles released by the acces-sory sex gland and sperm but not from a contamination with cells fromthe prostate and/or seminal vesicles (Fig. 2).
To fully evaluate the possible admixture effects in sperm RNA-seqsamples, we took advantage of the comprehensive GTEx tissue RNA-seq
Table 1. SRE criteria for natural conception applied to differential germ cell markers.Differentiated germ cell markers provided in the technical comment of Cappallo-Obermann and Spiess (3) were examined. The first SRE criterion for natural conception required that a sperm element exceed the 99th percentile rank on average acrossseven fertile control samples. From the28 spermelements examined, only 13met the first criterion (markwith anasterisk, percentile rank average). Someelements, such asODF1and OAZ3, that did not satisfy the first criterion are moderately abundant (95th to 98th percentile rank). Other elements, such as PFN4 and ACRV1, are absent in the majorityof control samples (zeroth percentile rank). These spermatid-specific RNAs are not present inmature spermatozoa and accordingly were not considered. If the first criterionwassatisfied, the second outlier criterion was applied. This discarded those elements that did not satisfy the IQR rule. Of the 13 elements exceeding the 99th percentile rangeon average across the control samples, only 6 satisfied the IQR rule (denoted as dagger sign) and were retained as SREs from four genes: TNP1, LELP1, SMCP, and OAZ3.
Transcriptname
S1
S2 S3 S4 S5 S6 S7 Percentile rankaverage
Outlier(<Q1–1.5 × IQR)
Outliers incontrolsamples
PRM1*
0.999996 0.999964 0.999980 0.999978 0.999943 0.999867 0.999993 0.999952 0.999891 1PRM1*
0.999989 0.999928 0.99996 0.999964 0.999925 0.999749 0.999982 0.999917 0.999867 1PRM2*
0.999978 0.999986 0.999971 0.999996 0.999928 0.999964 0.999975 0.999967 0.999948 1PRM2*
0.999939 0.999903 0.99994 0.999968 0.999767 0.999921 0.999957 0.999910 0.999867 1ODF1
0.989871 0.973425 0.970851 0.974397 0.887396 0.961544 0.986745 0.9561866ODF1
0.992516 0.988008 0.989049 0.993819 0.980101 0.986249 0.989914 0.9878264TNP1*†
0.999964 0.999264 0.999935 0.999935 0.999160 0.999444 0.999953 0.999685 0.998708 0TNP1*†
0.999993 0.999896 0.999982 0.999986 0.999770 0.999896 0.999978 0.999922 0.999767 0LELP1*
0.999616 0.998894 0.999300 0.999688 0.997337 0.998726 0.999174 0.998845 0.997865 1LELP1*†
0.999519 0.998772 0.999243 0.999698 0.998367 0.999142 0.998866 0.999063 0.998301 0SMCP*†
0.999677 0.999864 0.999864 0.999939 0.999698 0.999810 0.999950 0.999852 0.999617 0SMCP*†
0.999436 0.999519 0.999594 0.999889 0.999490 0.999691 0.999838 0.999700 0.999228 0OAZ3
0.979372 0.977154 0.982592 0.992114 0.835035 0.966426 0.982079 0.951649OAZ3*
0.997473 0.997197 0.998690 0.999210 0.994742 0.997258 0.998851 0.997750 0.994869 1OAZ3*†
0.997301 0.994200 0.998270 0.998686 0.994189 0.997470 0.997656 0.997254 0.996270 0OAZ3*
0.993543 0.983572 0.992689 0.997347 0.974792 0.992983 0.993234 0.990209 0.991872 2OAZ3
0.833190 0.967233 0.971013 0.987509 0.915253 0.986296 0.982376 0.968489OAZ3
0.984850 0.979053 0.978791 0.996655 0.945722 0.989932 0.991838 0.980588PFN4
0.000000 0.000000 0.000000 0.748935 0.000000 0.000000 0.000000 0.149787PFN4
0.000000 0.000000 0.000000 0.783611 0.000000 0.000000 0.000000 0.156722PFN4
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000PFN4
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000PFN4
0.000000 0.000000 0.000000 0.849331 0.000000 0.000000 0.000000 0.169866ACRV1
0.000000 0.805897 0.000000 0.735138 0.000000 0.000000 0.000000 0.147028ACRV1
0.000000 0.889941 0.942438 0.917367 0.577861 0.000000 0.000000 0.487533ACRV1
0.770557 0.751257 0.880742 0.882149 0.609418 0.000000 0.000000 0.474462ACRV1
0.000000 0.000000 0.000000 0.869902 0.000000 0.775474 0.000000 0.329075ACRV1
0.000000 0.000000 0.000000 0.818891 0.000000 0.702705 0.000000 0.3043193 of 5
SC I ENCE TRANS LAT IONAL MED I C I N E | T ECHN I CA L COMMENT
by guest on February 16, 2020
http://stm.sciencem
ag.org/D
ownloaded from
database to identify transcripts that are specific to 11 tissues and celltypes (testes, prostate, whole blood, skin, adipose, Epstein-Barrvirus–transformed lymphocytes, fibroblast, pituitary, ovary, salivarygland, and adrenal). Table S4 provides a summary of marker transcriptsidentified, expressed as a function of the mean GTEx RPKM (reads perkilobase permillionmapped reads) value.Marker transcripts were iden-tified by virtue of being highly abundant in their host tissue and signif-
Jodar et al., Sci. Transl. Med. 8, 353tr1 (2016) 24 August 2016
icantly lower in all other tissues assayed. This ranged from STAR, whichwas at least 10-fold lower (P = 3.6 × 10−21), to a median relative dif-ference of 178-fold for other transcripts and to a maximum differenceof 11,969-fold for PRL. This also excluded transcripts like IL8 that ex-hibit broad and overlapping expression patterns. Possible evidence for anadmixture was then considered by assessing the RPKM observed insperm for this set of marker transcripts. Table S5 shows the relative
Table 2. SRE criteria for natural conception applied to seminal vesicle and prostate markers. Although some samples presented seminal vesicle andprostate markers as candidate sperm elements above the 99th percentile rank as indicated in bold text, none of the elements met the first criterion, above the99th percentile rank on average across seven control samples (average percentile rank), so they were marked as variable and excluded from analysis.
Transcriptname
S1
S2 S3 S4 S5 S6 S7 Percentilerankaverage
SEMG2
0.846894 0.905012 0.901262 0.892705 0.721581 0.903688 0.949857 0.856337SEMG2
0.000000 0.680153 0.878132 0.869044 0.926624 0.924122 0.919276 0.909706SEMG2
0.000000 0.899119 0.000000 0.945920 0.712170 0.960916 0.977179 0.752753SEMG2
0.875067 0.939276 0.914632 0.914596 0.769494 0.927966 0.964035 0.884026SEMG2
0.000000 0.698426 0.000000 0.769609 0.592656 0.723706 0.835455 0.605398SEMG1
0.967061 0.992620 0.893534 0.991127 0.739427 0.992789 0.995449 0.906363SEMG1
0.933088 0.980435 0.774509 0.968873 0.809741 0.979993 0.990693 0.901935SEMG1
0.951336 0.951612 0.000000 0.968213 0.781178 0.984781 0.991630 0.784537SEMG1
0.938271 0.987014 0.797100 0.982735 0.839403 0.988356 0.994562 0.918559SEMG1
0.913498 0.946028 0.000000 0.860516 0.731785 0.945690 0.974096 0.741366MSMB
0.000000 0.997663 0.000000 0.000000 0.998956 0.998769 0.994659 0.712873MSMB
0.843316 0.997681 0.928365 0.851686 0.999261 0.999472 0.994433 0.967421MSMB
0.843319 0.997139 0.954452 0.851690 0.999038 0.999372 0.988338 0.970186MSMB
0.936519 0.995894 0.937219 0.747539 0.998744 0.999483 0.992179 0.953342TGM4
0.000000 0.998317 0.924919 0.965467 0.999390 0.998291 0.960370 0.978017TGM4
0.000000 0.998270 0.917112 0.965744 0.999149 0.998381 0.941017 0.974133TGM4
0.755974 0.856758 0.752973 0.000000 0.867142 0.827763 0.695117 0.691129TGM4
0.000000 0.981644 0.832325 0.000000 0.987251 0.976673 0.875203 0.805054TGM4
0.000000 0.814820 0.000000 0.000000 0.901036 0.853980 0.834612 0.620663TGM4
0.000000 0.728820 0.000000 0.000000 0.745862 0.783213 0.000000 0.436879TGM4
0.000000 0.938020 0.924506 0.877267 0.985115 0.983001 0.931785 0.952827TGM4
0.792204 0.992882 0.844030 0.000000 0.993270 0.990675 0.906236 0.816879TGM4
0.000000 0.984548 0.921958 0.934879 0.991134 0.992872 0.934269 0.965588TGM4
0.823758 0.962223 0.955446 0.978145 0.970126 0.960690 0.925734 0.960137TGM4
0.920978 0.968795 0.897238 0.933372 0.985180 0.965388 0.875332 0.943868TGM4
0.867734 0.935403 0.806881 0.000000 0.952933 0.960187 0.774017 0.772448TGM4
0.873226 0.978417 0.000000 0.873958 0.979717 0.973407 0.659532 0.777105TGM4
0.845100 0.989397 0.891772 0.853940 0.990219 0.990309 0.723860 0.918661TGM4
0.831837 0.970209 0.789548 0.761882 0.983654 0.978213 0.873545 0.9069584 of 5
SC I ENCE TRANS LAT IONAL MED I C I N E | T ECHN I CA L COMMENT
by guest on February 16, 2020
http://stm.sciencem
ag.org/D
ownloaded from
amount of themost prominent tissuemarker for each of the seven fertilesperm controls. It is apparent that the tissue-sourced RNAs show littleor no presence in the sperm controls. Comparison of the PRM1/KLK3ratio, representing testes/prostate markers, extends over a broad rangewith aminimum of 10-fold enrichment of PRM1 above its comparator.
To assess whether the presence of these transcripts could affect theidentification of the required SREs, the relative percentile rank of eachSRE in each of the 65 group II sampleswas comparedwith that ofKLK3.Among the 648 SREs identified (1), 14 displayed a Spearman rank co-efficient of r > 0.8 when compared to KLK3. These included fourelements from EEF1A1, two elements from RPS41, two elements fromRPS24, and single elements from RPL29, RPL37A, EEF1G, RPL3, EEF2,and TPT1. Only two of these elements, both exons of RPS24, affect theend result of the live-birth outcome analysis. These elements were bothnoted to be absent in a single test sample and, if excluded from the SREgroup, alter the prediction of this single sample to expectation of livebirth, which is the true outcome. This suggests that the influence of apossible admixture on the SREs was minimal. The methodology toassess the effects of possible admixture that is described above mayprove to be a valuable tool for others.
Jodar et al., Sci. Transl. Med. 8, 353tr1 (2016) 24 August 2016
It is clear that sperm RNA-seq is far more complex than that of asomatic cell and can be likened to that of formalin-fixed paraffin-embedded samples because of the intrinsic characteristics of spermato-zoal RNA, the heterogeneity of semen, and the differences in obtaining,preserving, and processing a semen sample. As we have shown, thefocus of this study on the consistently abundant and stable spermato-zoal RNA elements effectively mitigates these potentially confoundingfactors, thereby promoting their potential use in the clinical setting.
SUPPLEMENTARY MATERIALSwww.sciencetranslationalmedicine.org/cgi/content/full/8/353/353tr1/DC1Materials and MethodsFig. S1. Determining the abundance of PTPRC by qRT-PCR and by RNA-seq.Table S1. Summary of optical microscopy and qRT-PCR of PTPRC (provided as an Excel file).Table S2. Percentile ranks of IL8, CD45/PTPRC, CDH1, and KIT (provided as an Excel file).Table S3. Comparison of testes and prostate transcript markers (provided as an Excel file).Table S4. Tissue-specific markers (provided as an Excel file).Table S5. Comparative expression of tissue markers in seven control sperm samples (providedas an Excel file).
REFERENCES1. M. Jodar, E. Sendler, S. I. Moskovtsev, C. L. Librach, R. Goodrich, S. Swanson, R. Hauser,
M. P. Diamond, S. A. Krawetz, Absence of sperm RNA elements correlates with idiopathicmale infertility. Sci. Transl. Med. 7, 295re6 (2015).
2. T. G. Cooper, E. Noonan, S. von Eckardstein, J. Auger, H. W. Baker, H. M. Behre, T. B. Haugen,T. Kruger, C. Wang, M. T. Mbizvo, K. M. Vogelsong, World Health Organization referencevalues for human semen characteristics. Hum. Reprod. Update 16, 231–245 (2010).
3. H. Cappallo-Obermann, A.-N. Spiess, Comment on “Absence of sperm RNA elementscorrelates with idiopathic male infertility.” Sci. Transl. Med. 8, 353tc1 (2016).
4. S. I. Moskovtsev, J. Willis, J. White, J. B. M. Mullen, Leukocytospermia: Relationship tosperm deoxyribonucleic acid integrity in patients evaluated for male factor infertility.Fertil. Steril. 88, 737–740 (2007).
5. C. Flegel, F. Vogel, A.Hofreuter, B. S. P. Schreiner, S.Osthold, S. Veitinger, C. Becker,N.H. Brockmeyer,M. Muschol, G. Wennemuth, J. Altmüller, H. Hatt, G. Gisselmann, Characterization of the olfactoryreceptors expressed in human spermatozoa. Front. Mol. Biosci. 2, 73 (2015).
6. S. Mao, R. J. Goodrich, R. Hauser, S. M. Schrader, Z. Chen, S. A. Krawetz, Evaluation of theeffectiveness of semen storage and sperm purification methods for spermatozoatranscript profiling. Syst. Biol. Reprod. Med. 59, 287–295 (2013).
7. S. E. Pacheco, E. A. Houseman, B. C. Christensen, C. J. Marsit, K. T. Kelsey, M. Sigman,K. Boekelheide, Integrative DNA methylation and gene expression analyses identify DNApackaging and epigenetic regulatory genes associated with low motility sperm. PLOSOne 6, e20280 (2011).
8. M. Jodar, E. Sendler, S. A. Krawetz, The protein and transcript profiles of human semen.Cell Tissue Res. 363, 85–96 (2016).
9. G. D. Johnson, P. Mackie, M. Jodar, S. Moskovtsev, S. A. Krawetz, Chromatin andextracellular vesicle associated sperm RNAs. Nucleic Acids Res. 43, 6847–6859 (2015).
10. U. Sharma, C. C. Conine, J.M. Shea, A. Boskovic, A. G. Derr, X. Y. Bing, C. Belleannee, A. Kucukural,R. W. Serra, F. Sun, L. Song, B. R. Carone, E. P. Ricci, X. Z. Li, L. Fauquier, M. J. Moore, R. Sullivan,C. C. Mello, M. Garber, O. J. Rando, Biogenesis and function of tRNA fragments duringsperm maturation and fertilization in mammals. Science 351, 391–396 (2016).
11. GTEx Consortium, The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45,580–585 (2013).
12. L. Vojtech, S. Woo, S. Hughes, C. Levy, L. Ballweber, R. P. Sauteraud, J. Strobl,K. Westerberg, R. Gottardo, M. Tewari, F. Hladik, Exosomes in human semen carry adistinctive repertoire of small non-coding RNAs with potential regulatory functions.Nucleic Acids Res. 42, 7290–7304 (2014).
13. D. Tannetta, R. Dragovic, Z. Alyahyaei, J. Southcombe, Extracellular vesicles andreproduction-promotion of successful pregnancy. Cell. Mol. Immunol. 11, 548–563 (2014).
14. G. Ronquist, Prostasomes are mediators of intercellular communication: From basicresearch to clinical implications. J. Intern. Med. 271, 400–413 (2012).
Submitted 10 February 2016Accepted 3 June 2016Published 24 August 201610.1126/scitranslmed.aaf4550
Citation: M. Jodar, E. Sendler, S. I. Moskovtsev, C. L. Librach, R. Goodrich, S. Swanson, R. Hauser,M. P. Diamond, S. A. Krawetz, Response to Comment on “Absence of sperm RNA elementscorrelates with idiopathic male infertility.” Sci. Transl. Med. 8, 353tr1 (2016).
Fig. 2. Germ cell–differentiated andprostate RNAmarkers. (A) GTEx expression ofPRAC1, a prostate cancer antigen, is higher in the prostate than in the testis, suggestingthat PRAC1 may be used as a prostate marker (upper panel). RNA-seq (lower panel)shows the presence of PRAC1 exclusively in the exosomal fraction. (B) GTEx expressionof LELP1 is higher in the testis than in the prostate, suggesting that LELP1 could be usedas a testis marker (upper panel). LELP1 that is present in sperm is exclusively from thetestes. The absence of LELP1 in GTEx and the small amount of this transcript in theexosome fraction (lower panel) are consistent with an RNA transport pathway.
5 of 5
male infertility''Response to Comment on ''Absence of sperm RNA elements correlates with idiopathic
Hauser, Michael P. Diamond and Stephen A. KrawetzMeritxell Jodar, Edward Sendler, Sergey I. Moskovtsev, Clifford L. Librach, Robert Goodrich, Sonja Swanson, Russ
DOI: 10.1126/scitranslmed.aaf4550, 353tr1353tr1.8Sci Transl Med
ARTICLE TOOLS http://stm.sciencemag.org/content/8/353/353tr1
MATERIALSSUPPLEMENTARY http://stm.sciencemag.org/content/suppl/2016/08/22/8.353.353tr1.DC1
CONTENTRELATED
http://stm.sciencemag.org/content/scitransmed/3/92/92ra65.fullhttp://stm.sciencemag.org/content/scitransmed/3/92/92ps31.fullhttp://stm.sciencemag.org/content/scitransmed/5/212/212ra160.fullhttp://stm.sciencemag.org/content/scitransmed/6/249/249ra108.fullhttp://stm.sciencemag.org/content/scitransmed/7/295/295re6.fullhttp://stm.sciencemag.org/content/scitransmed/8/353/353tc1.full
REFERENCES
http://stm.sciencemag.org/content/8/353/353tr1#BIBLThis article cites 14 articles, 3 of which you can access for free
PERMISSIONS http://www.sciencemag.org/help/reprints-and-permissions
Terms of ServiceUse of this article is subject to the
registered trademark of AAAS. is aScience Translational MedicineScience, 1200 New York Avenue NW, Washington, DC 20005. The title
(ISSN 1946-6242) is published by the American Association for the Advancement ofScience Translational Medicine
Copyright © 2016, American Association for the Advancement of Science
by guest on February 16, 2020
http://stm.sciencem
ag.org/D
ownloaded from