Male infertility biomarkers and genomic aberrations in azoospermia

17 02 2014

To the Editor:

Estimates indicate that 15-30% (or more) of male infertility is due to whole-organism genetic abnormalities with large numbers of genes already discovered to play important roles (1, 2). Numerous methods have yielded new genetic discoveries with the karyotype, fluorescent in situ hybridization, comparative genomic hybridization and microarrays all contributing (1). All identified genetic aberrations are further complicated by epigenetic modifications (i.e., methylation and protamination), as well as individual differences and environmental influences that make diagnosis and treatment frustrating (1). Unfortunately, in many men, the result of multiple investigations often yields inconclusive, or slightly abnormal results, with a subsequent diagnosis of idiopathic infertility.

A biomarker has been defined as a distinctive biological indicator of a process, event, or condition that can be objectively measured, evaluated, and compared (1). In this context, a recent study by Malcher et al. (3) examined tissues from 27 testicular biopsy specimens from 18 men in an attempt to determine genetic biomarkers for male infertility. Using a GeneChip Human Gene 1.0 ST array (Affymetrix), testicular tissues were classified into categories based on their histopathology and compared to controls with real-time polymerase chain reaction (PCR) used to confirm expression. In infertile men, differential expression levels of several genes (i.e., AKAP4, ODF1, PRM1, PRM2, LRWD1) were identified (3). The authors compared the expression levels of several specifically selected genes that demonstrated a minimum change (four-fold) in relation to the infertile subgroup (UBQLN3, FAM71F1, CAPN11, SPATA3, GGN, and SPACA4) (3). These genes, along with others, were then proposed to be candidates for biomarkers of spermatogenic failure in men with azoospermia.

The authors have correctly classified their results as an identification of novel male infertility biomarkers; however, this may be too simplistic a view. Indeed, the primary criticism of the manuscript rests with the tissues that the authors have elected to study. They compare testicular samples from azoospermic patients without germ cells (Sertoli Cell Only disease) to those patients with normal spermatogenesis (controls). Given that spermatocytes and round spermatids have very high rates of RNA synthesis (4), their presence in the control samples impacts the relative amount of gene RNA captured. Thus, the authors are not truly examining the genetic regulation of infertility, but are instead classifying cellular heterogeneity and documenting the genetic composition of Sertoli cells and germ cell RNA. Accordingly, a number of the genes already known to be expressed in spermatogenesis could serve this same purpose (1).

While the caveat that the genes identified are biomarkers is written within the Malcher manuscript, they could not be obtained from a blood sample. Instead, patients would need testicular biopsies and direct tissue homogenate analysis – eliminating a major reason why a biomarker would be beneficial. A more effective approach to identifying biomarkers would be to focus on the conserved pathways involved in NOA through analysis of fibroblasts. This would better classify the actual genetic defects involved in male infertility.

In summary, the genes presented in this study (discovered via testicular biopsy and perhaps the result of a few rare sperm) are coupled with a complicated and expensive microarray technique to paint an unclear picture as to how they improve current management techniques. Indeed, the ability to identify foci encompassing rare sperm that are then micro-dissected and physically identified in the biopsy specimen remains the gold standard. However, there is no doubt that specific biomarkers could be used with great utility to improve patient diagnosis. While the work of Malcher et al. (3) contributes to the quest, the genetic pathways underlying male infertility remain to be elucidated.

Jason R. Kovac, M.Sc., M.D., Ph.D. and Dolores J. Lamb, Ph.D.
Scott Department of Urology, The Center for Reproductive Medicine and the Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas

References

1. Kovac JR, Pastuszak AW, Lamb DJ. The use of genomics, proteomics, and metabolomics in identifying biomarkers of male infertility. Fertil Steril 2013;99:998-1007.

2. Matzuk MM, Lamb DJ. The biology of infertility: research advances and clinical challenges. Nature medicine 2008;14:1197-213.

3. Malcher A, Rozwadowska N, Stokowy T, Kolanowski T, Jedrzejczak P, Zietkowiak W, et al. Potential biomarkers of nonobstructive azoospermia identified in microarray gene expression analysis. Fertil Steril 2013;100:1686-94 e1-7.

4. Geremia R, Boitani C, Conti M, Monesi V. RNA synthesis in spermatocytes and spermatids and preservation of meiotic RNA during spermiogenesis in the mouse. Cell differentiation 1977;5:343-55.

Published online in Fertility and Sterility doi: 10.1016/j.fertnstert.2014.02.029

The authors respond:

Recently, we had a chance to acknowledge a letter to Fertility and Sterility concerning our recent publication by Malcher et al. (1) dedicated to detection of novel biomarkers in azoospermia. We are deeply thankful to both authors of the letter, and we are thankful to the editor for the opportunity to reply.

We fully agree with the point raised in the letter emphasizing the scope of human infertility, including male infertility factor and percentage of azoospermia cases encompassing 1% of the male population and constituting 10% of male infertility syndromes (2). These figures make the issue unusually important both because of the magnitude of disease, as well as broken demographic chains in many countries. The reverse side of the coin, however, underlines two other facts: i) difficult and low-scale improvement in treatment of azoospermia, ii) lack of novel tools helping the monitoring and prognosis of azoospermia management. Therefore, we must disagree because of a lack of progress in systemic biology tools attempting to offer new methods of prognosis and treatment monitoring. On the contrary, it is difficult not to notice the emerging studies indicating a “light in the tunnel” — using expression microarrays both in laboratory animals, as well as in humans. This can be done simply by a new range of potential spermatogenetic biomarkers (i.e., UBQLN3, FAM71F1, CAPN11, SPATA3, GGN, and SPACA4), proposed in our study (criticized by the authors of the letter). It should be of considerable interest to note that 2 out of these 6 discussed genes were previously described in well-known mouse models (CAPN11 and GGN) (3,4), linking these genes either with infertility (GGN) or mammalian spermatogenesis (CAPN11). In our other series of experiments, conducted in collaboration with University of Pittsburgh (5), we have also found differences in the gene expression of PTCHD3 (confirmed in our microarrays from azoospermia oligobiopsy), which then appeared independently after extensive bioinformatic analysis from the whole exome sequencing of the male translocation carrier [46,XY,der(1)(1q44::1p22.3→1q44::7p15.2 or 7p15.3→7pter),der(7)(1pter→1p22.3::7p15.3 or 7p15.2→7qter)] as a novel gene candidate for a biomarker critical for spermatogenesis. These scientific facts strongly underline the power of such systemic analysis (expression microarrays) in azoospermic individuals.

As we may further agree with assumption that in some cases gene expression may go along attenuation of transcription in early spermatogenic blocks (due to the diluted number of gametogenic cells), first, we shall emphasize that we have discussed this issue in our recently published study (1). Second, we shall argue that this phenomenon does not exclude the possible potential power of such detected biomarkers, since the dilution of germ cells as such is not directly proportional to the observed gene expression through the different spermatogenetic arrests as it is indicated in Figure 1, both in respect to our studies and independent microarray data obtained from ArrayExpress database. Although SCOS, premeiotic, and meiotic dilution of the gametogenic cell number occurs, this effect is not directly reflected in terms of the observed gene expression. We can further observe in this figure that the candidates for biomarkers have been significantly down-regulated (P<0.05), with minimum 2-fold change in each azoospermic type. This was equally true for the group with postmeiotic arrest, where spermatocytes and spermatids occur. It is therefore difficult to agree with an argument made in the letter that we did not take into account the cellular heterogeneity occurring in different azoospermic blocks, simply comparing Sertoli-cell-only-syndrome with microarrays made for normal spermatogenesis.

Again, we are deeply grateful for the possibility to exchange our views with such prominent group of researchers on potential use of expression microarrays in human infertility.

Agnieszka Malcher and Maciej K Kurpisz, M.D., Ph.D.
Department of Reproductive Biology and Stem Cells, Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland

References

1. Malcher A, Rozwadowska N, Stokowy T, Kolanowski T, Jedrzejczak P, Zietkowiak W, et al. Potential biomarkers of nonobstructive azoospermia identified in microarray gene expression analysis. Fertil Steril 2013;100:1686–94.e7.

2. Ferlin A, Raicu F, Gatta V, Zuccarello D, Palka G, Foresta C. Male infertility: role of genetic background. Reprod Biomed Online 2007;14:734-45.

3. Ben-Aharon I, Brown PR, Shalgi R, Eddy EM. Calpain 11 is unique to mouse spermatogenic cells. Mol Reprod Dev 2006;73:767–73.

4. Jamsai D, Bianco DM, Smith SJ, Merriner DJ, Ly-Huynh JD, Herlihy A, et al. Characterization of gametogenetin 1 (GGN1) and its potential role in male fertility through the interaction with the ion channel regulator, cysteine-rich secretory protein 2 (CRISP2) in the sperm tail. Reproduction 2008;135:751–9.

5. Yatsenko AN, Georgiadis AP, McGuire MM, Zorilla M, Bunce KD, Peters D, et al. Multiple mutations discovered in a familial case of azoospermia using whole exome sequencing (WES). Hum Reprod 2013;28(supp11):i298-i299;ESHRE 2013, London, UK.

Published online in Fertility and Sterility doi: 10.1016/j.fertnstert.2014.02.031

Click on figure for larger view. Figure 1. The graphs represent the genes whose expression level was down-regulated in each azoospermic type group, compared with controls. A) By microarrays, B) Independent microarray data obtained from ArrayExpress database (E-TABM-234). The data were RMA normalized and log2 transformed. Each gene is described by mean expression level, fold change value, and the p-value (the green color indicates statistical significance).

Click on figure for larger view. Figure 1. The graphs represent the genes whose expression level was down-regulated in each azoospermic type group, compared with controls. A) By microarrays, B) Independent microarray data obtained from ArrayExpress database (E-TABM-234). The data were RMA normalized and log2 transformed. Each gene is described by mean expression level, fold change value, and the p-value (the green color indicates statistical significance).

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