Three group comparison (feature set = 5000) and gene ontology analysis.

<p>Bar graph showing gene ontology analysis on the basis of a) molecular function b) cellular location, and c) biological processes, when three groups (fertile control, asthenozoospermic and normozoospermic infertile) were compared simultaneously.</p

Differential Genes Expression between Fertile and Infertile Spermatozoa Revealed by Transcriptome Analysis

<div><p>Background</p><p>It was believed earlier that spermatozoa have no traces of RNA because of loss of most of the cytoplasm. Recent studies have revealed the presence of about 3000 different kinds of mRNAs in ejaculated spermatozoa. However, the correlation of transcriptome profile with infertility remains obscure.</p><p>Methods</p><p>Total RNA from sperm (after exclusion of somatic cells) of 60 men consisting of individuals with known fertility (n=20), idiopathic infertility (normozoospermic patients, n=20), and asthenozoospermia (n=20) was isolated. After RNA quality check on Bioanalyzer, AffymetrixGeneChip Human Gene 1.0 ST Array was used for expression profiling, which consisted of >30,000 coding transcripts and >11,000 long intergenic non-coding transcripts.</p><p>Results</p><p>Comparison between all three groups revealed that two thousand and eighty one transcripts were differentially expressed. Analysis of these transcripts showed that some transcripts [ribosomal proteins (<i>RPS25</i>, <i>RPS11</i>, <i>RPS13</i>, <i>RPL30</i>, <i>RPL34</i>, <i>RPL27</i>, <i>RPS5</i>), <i>HINT1</i>, <i>HSP90AB1</i>, <i>SRSF9</i>, <i>EIF4G2</i>, <i>ILF2</i>] were up-regulated in the normozoospermic group, but down-regulated in the asthenozoospermic group in comparison to the control group. Some transcripts were specific to the normozoospermic group (up-regulated: <i>CAPNS1</i>, <i>FAM153C</i>, <i>ARF1</i>, <i>CFL1</i>, <i>RPL19</i>, <i>USP22</i>; down-regulated: <i>ZNF90</i>, <i>SMNDC1</i>, <i>c14orf126</i>, <i>HNRNPK</i>), while some were specific to the asthenozoospermic group (up-regulated: <i>RPL24</i>, <i>HNRNPM</i>, <i>RPL4</i>, <i>PRPF8</i>, <i>HTN3</i>, <i>RPL11</i>, <i>RPL28</i>, <i>RPS16</i>, <i>SLC25A3</i>, <i>C2orf24</i>, <i>RHOA</i>, <i>GDI2</i>, <i>NONO</i>, <i>PARK7</i>; down-regulated: <i>HNRNPC</i>, <i>SMARCAD1</i>, <i>RPS24</i>, <i>RPS24</i>, <i>RPS27A</i>, <i>KIFAP3</i>). A number of differentially expressed transcripts in spermatozoa were related to reproduction (n = 58) and development (n= 210). Some of these transcripts were related to heat shock proteins (<i>DNAJB4</i>, <i>DNAJB14</i>), testis specific genes (<i>TCP11</i>, <i>TESK1</i>, <i>TSPYL1</i>, <i>ADAD1</i>), and Y-chromosome genes (<i>DAZ1</i>, <i>TSPYL1</i>).</p><p>Conclusion</p><p>A complex RNA population in spermatozoa consisted of coding and non-coding RNAs. A number of transcripts that participate in a host of cellular processes, including reproduction and development were differentially expressed between fertile and infertile individuals. Differences between comparison groups suggest that sperm RNA has strong potential of acting as markers for fertility evaluation.</p></div

Three group comparison and gene set analysis for comparison among normal fertile control, asthenozoospermic infertile, and idiopathic normozoospermic infertile groups.

<p>Only pathways that strongly correlated (r≥0.5) with expression of meta-gene set have been shown.</p><p>Three group comparison and gene set analysis for comparison among normal fertile control, asthenozoospermic infertile, and idiopathic normozoospermic infertile groups.</p

Three group comparison (feature set = 5000) and protein class analysis.

<p>PANTHER analysis of most significantly (top 5000) differentially expressed transcripts to classify them into protein classes.</p

Three group comparison and co-expression network analysis.

<p>Fruchterman-Reingold plot showing connections between co-expressing genes. Sample classes are colour-coded and probes are coloured according to the class with the highest median expression value across the corresponding samples. Colour-codes: class 1 (red: asthenozoospermic), class 2 (green: fertile control), and class 3 (blue: normozoospermic infertile).</p

Three group comparison and gene set analysis.

<p>Bar graph showing correlation between expression of meta-gene set and the pathway (outcome) involved.</p

Androgen Receptor CAG Repeats Length Polymorphism and the Risk of Polycystic Ovarian Syndrome (PCOS)

<div><p>Objective</p><p>Polycystic ovarian syndrome (PCOS) refers to an inheritable androgen excess disorder characterized by multiple small follicles located at the ovarian periphery. Hyperandrogenism in PCOS, and inverse correlation between androgen receptor (AR) CAG numbers and AR function, led us to hypothesize that CAG length variations may affect PCOS risk.</p><p>Methods</p><p>CAG repeat region of 169 patients recruited following strictly defined Rotterdam (2003) inclusion criteria and that of 175 ethnically similar control samples, were analyzed. We also conducted a meta-analysis on the data taken from published studies, to generate a pooled estimate on 2194 cases and 2242 controls.</p><p>Results</p><p>CAG bi-allelic mean length was between 8.5 and 24.5 (mean = 17.43, SD = 2.43) repeats in the controls and between 11 and 24 (mean = 17.39, SD = 2.29) repeats in the cases, without any significant difference between the two groups. Further, comparison of bi-allelic mean and its frequency distribution in three categories (short, moderate and long alleles) did not show any significant difference between controls and various case subgroups. Frequency distribution of bi-allelic mean in two categories (extreme and moderate alleles) showed over-representation of extreme sized alleles in the cases with marginally significant value (50.3% vs. 61.5%, χ² = 4.41; P = 0.036), which turned insignificant upon applying Bonferroni correction for multiple comparisons. X-chromosome inactivation analysis showed no significant difference in the inactivation pattern of CAG alleles or in the comparison of weighed bi-allelic mean between cases and controls. Meta-analysis also showed no significant correlation between CAG length and PCOS risk, except a minor over-representation of short CAG alleles in the cases.</p><p>Conclusion</p><p>CAG bi-allelic mean length did not differ between controls and cases/case sub-groups nor did the allele distribution. Over-representation of short/extreme-sized alleles in the cases may be a chance finding without any true association with PCOS risk.</p></div

Frequency distribution (%) of CAG bi-allelic mean length in three CAG length categories (short, moderate, and long).

*<p>Comparison of categorical variable between groups was done by <b>χ²</b>test.</p

Demographic features of PCOS group.

<p>Demographic features of PCOS group.</p

Summary of meta-analysis results.

*<p>indicates Egger’s regresstion test two tailed p value.</p