LETTER TO THE EDITOR REPLY OF THE AUTHORS: Recently, we had a chance to acknowledge a letter to Fertility and Sterility concerning our recent publication by Malcher et al. (1), which was 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 factor infertility syndromes (2). These figures make the issue unusually important both because of the magnitude of disease and because of broken demographic chains in many countries. The reverse side of the coin, however, underlines two other facts: [1] difficult and low-scale improvement in the treatment of azoospermia and [2] a lack of novel tools that help in the monitoring and prognosis of azoospermia management. Therefore, we must disagree because of a lack of progress in systemic biology tools that attempt 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) that were proposed in our study (and criticized by the authors of the letter). It should be of considerable interest to note that two of these six 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 the 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 the assumption that in some cases gene expression may go along attenuation of transcription in early spermatogenic blocks (owing to the diluted number of gametogenic cells), we shall first emphasize that we have discussed this issue in our recently published study (1). Second, we shall argue that this phenomenon does not exclude the 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 indicated in Figure 1, with respect to both our studies and to independent microarray data obtained from the ArrayExpress database. Although Sertoli cell–only syndrome and premeiotic and meiotic dilution of the gametogenic cell number occur, these effects are 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< .05), with a minimum two-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 the 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 a prominent group of researchers on the potential use of expression microarrays in human infertility.
FIGURE 1
The graphs represent the genes whose expression level was down-regulated in each azoospermic type group, compared with controls, (A) by microarrays and (B) by independent microarray data obtained from the 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 P value (the green color indicates statistical significance). Letter to the editor. Fertil Steril 2014.
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VOL. 101 NO. 5 / MAY 2014
Fertility and Sterility® Agnieszka Malcher, M.Sc. Maciej K. Kurpisz, M.D., Ph.D. Department of Reproductive Biology and Stem Cells, Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland
2. 3. 4.
February 12, 2014 http://dx.doi.org/10.1016/j.fertnstert.2014.02.031
REFERENCES 1.
Malcher A, Rozwadowska N, Stokowy T, Kolanowski T, Jedrzejczak P, Zietkowiak W, et al. Potential biomarkers of nonobstructive azoospermia
VOL. 101 NO. 5 / MAY 2014
5.
identified in microarray gene expression analysis. Fertil Steril 2013;100: 1686–1694.e7. 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. Ben-Aharon I, Brown PR, Shalgi R, Eddy EM. Calpain 11 is unique to mouse spermatogenic cells. Mol Reprod Dev 2006;73:767–73. 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, cysteinerich secretory protein 2 (CRISP2) in the sperm tail. Reproduction 2008; 135:751–9. 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): i298–9.
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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 the assumption that in some cases gene expression may go along attenuation of transcription in early spermatogenic blocks (owing to the diluted number of gametogenic cells), we shall first emphasize that we have discussed this issue in our recently published study (1). Second, we shall argue that this phenomenon does not exclude the 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 indicated in Figure 1, with respect to both our studies and to independent microarray data obtained from the ArrayExpress database. Although Sertoli cell–only syndrome and premeiotic and meiotic dilution of the gametogenic cell number occur, these effects are 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< .05), with a minimum two-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 the 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 a prominent group of researchers on the potential use of expression microarrays in human infertility.
FIGURE 1
The graphs represent the genes whose expression level was down-regulated in each azoospermic type group, compared with controls, (A) by microarrays and (B) by independent microarray data obtained from the 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 P value (the green color indicates statistical significance). Letter to the editor. Fertil Steril 2014.
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VOL. 101 NO. 5 / MAY 2014
Fertility and Sterility® Agnieszka Malcher, M.Sc. Maciej K. Kurpisz, M.D., Ph.D. Department of Reproductive Biology and Stem Cells, Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland
2. 3. 4.
February 12, 2014 http://dx.doi.org/10.1016/j.fertnstert.2014.02.031
REFERENCES 1.
Malcher A, Rozwadowska N, Stokowy T, Kolanowski T, Jedrzejczak P, Zietkowiak W, et al. Potential biomarkers of nonobstructive azoospermia
VOL. 101 NO. 5 / MAY 2014
5.
identified in microarray gene expression analysis. Fertil Steril 2013;100: 1686–1694.e7. 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. Ben-Aharon I, Brown PR, Shalgi R, Eddy EM. Calpain 11 is unique to mouse spermatogenic cells. Mol Reprod Dev 2006;73:767–73. 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, cysteinerich secretory protein 2 (CRISP2) in the sperm tail. Reproduction 2008; 135:751–9. 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): i298–9.
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