In addition, we performed extensive bioinformatic analysis of the splicing fea-tures, domains, interactions, and functions of differentially expressed genes and iso-mRNAs.Many top-rankin
Systems Biology in Reproductive Medicine ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/iaan20 Transcriptomic analysis of the Non-Obstructive Azoospermia (NOA) to address gene expression regulation in human testis Govindkumar Balagannavar, Kavyashree Basavaraju, Akhilesh Kumar Bajpai, Sravanthi Davuluri, Shruthi Kannan, Vasan S Srini, Darshan S Chandrashekar, Neelima Chitturi & Kshitish K Acharya To cite this article: Govindkumar Balagannavar, Kavyashree Basavaraju, Akhilesh Kumar Bajpai, Sravanthi Davuluri, Shruthi Kannan, Vasan S Srini, Darshan S Chandrashekar, Neelima Chitturi & Kshitish K Acharya (2023) Transcriptomic analysis of the Non-Obstructive Azoospermia (NOA) to address gene expression regulation in human testis, Systems Biology in Reproductive Medicine, 69:3, 196-214, DOI: 10.1080/19396368.2023.2176268 To link to this article: https://doi.org/10.1080/19396368.2023.2176268 View supplementary material Published online: 08 Mar 2023 Submit your article to this journal Article views: 229 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=iaan20 SYSTEMS BIOLOGY IN REPRODUCTIVE MEDICINE 2023, VOL 69, NO 3, 196–214 https://doi.org/10.1080/19396368.2023.2176268 RESEARCH ARTICLE Transcriptomic analysis of the Non-Obstructive Azoospermia (NOA) to address gene expression regulation in human testis Govindkumar Balagannavara,b, Kavyashree Basavarajua,c, Akhilesh Kumar Bajpaic, Sravanthi Davuluric, Shruthi Kannana, Vasan S Srinid, Darshan S Chandrashekara, Neelima Chitturic, and Kshitish K Acharyaa,c aInstitute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, Karnataka, India; bResearch Scholar, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India; cBdataA: Biological data Analyzers’ Association (virtual organization http:// startbioinfo.com/BdataA/), India; dManipal Fertility, Manipal Hospital, Bengaluru, Karnataka, India ABSTRACT ARTICLE HISTORY Received August 2022 There is a need to understand the molecular basis of testes under Non-Obstructive Revised 27 January 2023 Azoospermia (NOA), a state of failed spermatogenesis There has been a lack of attention to Accepted 30 January 2023 the transcriptome at the level of alternatively spliced mRNAs (iso-mRNAs) and the mechan- ism of gene expression regulation Hence, we aimed to establish a reliable iso-mRNA profile KEYWORDS of NOA-testes, and explore molecular mechanisms – especially those related to gene expres- Spermatogenesis; non- sion regulation We sequenced mRNAs from testicular samples of donors with complete obstructive Azoospermia; spermatogenesis (control samples) and a failure of spermatogenesis (NOA samples) We transcription regulation; identified differentially expressed genes and their iso-mRNAs via standard NGS data analy- transcription factors; ses We then listed these iso-mRNAs hierarchically based on the extent of consistency of dif- regulatory network; gene ferential quantities across samples and groups, and validated the lists via RT-qPCRs (for 80 expression regulation; RNA- iso-mRNAs) In addition, we performed extensive bioinformatic analysis of the splicing fea- seq; molecular interactions; tures, domains, interactions, and functions of differentially expressed genes and iso-mRNAs transcriptomics; male Many top-ranking down-regulated genes and iso-mRNAs, i.e., those down-regulated more infertility consistently across the NOA samples, are associated with mitosis, replication, meiosis, cilium, RNA regulation, and post-translational modifications such as ubiquitination and phosphoryl- ation Most down-regulated iso-mRNAs correspond to full-length proteins that include all expected domains The predominance of alternative promoters and termination sites in these iso-mRNAs indicate their gene expression regulation via promoters and UTRs We compiled a new, comprehensive list of human transcription factors (TFs) and used it to iden- tify TF-’TF gene’ interactions with potential significance in down-regulating genes under the NOA condition The results indicate that RAD51 suppression by HSF4 prevents SP1-activa- tion, and SP1, in turn, could regulate multiple TF genes This potential regulatory axis and other TF interactions identified in this study could explain the down-regulation of multiple genes in NOA-testes Such molecular interactions may also have key regulatory roles during normal human spermatogenesis Abbreviations: NOA: Non-Obstructive Azoospermia; OA: Obstructive Azoospermia; VA: Varicocele; iso-mRNAs: alternatively spliced transcript isoforms; NGS: Next-Generation Sequencing; scRNAseq: single-cell RNA sequencing; TFgene: transcription-factor-coding gene; Tu250: top up-regulated 250; Td250: top down-regulated 250; Tu3000genes: Top 3000 up-regulated; Td3000genes: Top 3000 down-regulated; GO: Gene Ontology; Bpv: Benjamini P-value; AP: Alternative promoter; AT: Alternative terminator; AP/AT: Alternative promoter or terminator; A5’-SS: Alternative 5’ Splice Sites; A3’-SS: Alternative 3’ Splice Sites; RI: Intron Retention; SE: Skipping Exon; sub_RI: Skipping Exon; SE/sub_RI: Skipping Exon/Sub-Intron Retention; SPC: Single protein coding; TFs: Transcription Factors; TFgenes: Trancription-fac- tor-coding-genes; cTFs: co-Transcription Factors; RT-qPCR: Reverse transcription-quantitative polymerase chain reaction Introduction approximately to men in every 1000 may have the Non-Obstructive Azoospermia (NOA) disorder NOA The proportion of men with infertility may have is difficult to deal with clinically (Esteves 2015; Chiba increased in recent years (Agarwal et al 2015; Kumar et al 2016) Understanding the genetic and molecular and Singh 2015) A literature review indicated that CONTACT Kshitish Acharya kshitish@ibab.ac.in Institute of Bioinformatics and Applied Biotechnology (IBAB), Bengaluru, Karnataka 560 100, India Supplemental data for this article can be accessed online at https://doi.org/10.1080/19396368.2023.2176268 ß 2023 Informa UK Limited, trading as Taylor & Francis Group SYSTEMS BIOLOGY IN REPRODUCTIVE MEDICINE 197 basis of the NOA condition can help to explore for mass-scale differential expression profiling of potential novel solutions to address the issues associ- NOA-testes (e.g., Okada et al 2008; Cappallo- ated with this disease (Omolaoye et al 2022) Obermann et al 2010; Malcher et al 2013; Baksi et al Identifying alternatively spliced transcript isoforms 2018) However, microarray-based gene expression (iso-mRNAs) with such associations is particularly profiling studies, particularly the earlier ones, have essential (Modrek and Lee 2002; Stastna and Van Eyk several limitations – including coverage of a limited 2012) for beginning a system-level understanding of number of genes among the probes used in the array the disease condition within the testis tissue (Draghici et al 2006; Jaksik et al 2015) The non-obstructive azoospermia condition repre- In recent years, the Next-Generation Sequencing sents a failed state of spermatogenesis, with the reduc- (NGS) methods have been extensively used for tran- tion or absence of multiple types of male germ cells scriptomics (Lea et al 2011; Hrdlickova et al 2017) as in the testes, particularly the late spermatid and sper- they have the potential to screen a maximum number matozoa that are either absent or present in highly of genes as well as identify key iso-mRNAs (Ozsolak reduced numbers (Ramasamy et al 2009; Esteves and Milos 2011) It has been well-established that 2015; Chiba et al 2016) The significance of transcrip- alternative splicing forms an important component in tomics in understanding the molecular basis of sperm- spermatogenesis and testicular disorders (Song et al atogenesis and male infertility conditions is also 2020) However, exploring the transcriptome at the well-known (Omolaoye et al 2022) Thus, a compre- level of specific mRNA isoforms requires high-depth hensive list of genes and transcripts expressed differ- NGS of a reasonable number of samples (https://gen- entially in the testes of NOA patients may also ome.ucsc.edu/ENCODE/protocols/dataStandards/ include several molecules playing a key role in normal ENCODE_RNAseq_Standards_V1.0.pdf; human spermatogenesis We think that the genes con- Giannopoulou et al 2015), which has not been done sistently down-regulated in NOA could particularly till date using either bulk (non-single-cell) tissue RNA help identify novel molecules potentially associated or scRNA from the testis under the NOA condition with spermatogenesis in men Research efforts towards developing new and improved contraceptives for men In recent years, single-cell RNA sequencing (Robertson et al 2020; Dominiak et al 2021) could (scRNAseq) has gained popularity Normal human also be assisted by such a list of genes and their corre- testis samples have been subjected to scRNAseqs sponding iso-mRNAs We propose that a hierarchical (Zhu et al 2016; Jan et al 2017; Guo et al 2018; listing of testicular iso-mRNAs, based on their Hermann et al 2018; Wang et al 2018; Sohni et al strength of expression-based association with the 2019; Shami et al 2020) Such scRNA-seq datasets have NOA condition, can enable not only prioritizing also been meta-analyzed (Soraggi et al 2021) NOA research on specific key molecules in multiple applied testes have also been subjected to scRNAseq (Wang contexts such as novel contraceptive development but et al 2018; Zhao et al 2020) However, it should be also basic research related to the molecular mecha- noted that while scRNAseq makes several new prom- nisms associated with various stages of normal ises, the associated methods also have disadvantages spermatogenesis (Lowe et al 2017; Chambers et al 2019; Kim et al 2019; L€ahnemann et al 2020) The technical variability The mechanism of regulation of gene expression is of gene expression profiles derived using this approach particularly an important functional aspect (Hoopes is higher than those from bulk tissue RNA-seq (Hicks 2008) that has been less studied in the context of et al 2018; Kim et al 2019) In addition, we note that NOA Logically, exploring the testicular transcriptome the cell identification methods introduce an additional under the NOA condition would also help identify level of variation during the interpretation of key transcription factors important in testicular gene scRNAseq results We realize that reliable profiling of expression regulation under normal conditions Many mRNAs using bulk testis samples will help to corrobor- studies (e.g., Mohsin et al 2022) have used transcrip- ate the scRNA data, and such transcriptomic profiling tomic profiling followed by molecular network ana- can help analyze molecular interactions in the context lysis to predict the interaction of transcription factors of many tissue-level molecular functions with other molecules Hence, we decided to use bulk-tissue RNA- Several studies have been conducted on gene sequencing to obtain potential insights into the func- expression in normal testis of mice and humans (e.g., tions of iso-mRNAs expressed differentially in the Sassone-Corsi 1997; Reddi et al 2002; Acharya et al testis of NOA patients, particularly regarding gene 2006) Many scientists used the microarray techniques expression regulation We postulate that hierarchical 198 G BALAGANNAVAR ET AL Figure A schematic representation of the workflow, along with summary findings The quality of the RNA extracted from testis samples and the quality of reads obtained from RNA-seq were found to be good (mean quality score of 33.95, ±2.75) The sequencing depth was also good across samples (a mean of 56 million reads) Abbreviation used: SRA – Sequence Read Archive (repository); NOA: nonobstructive azoospermia; DE: differential expression or differentially expressed; TF: transcription factors listing of the iso-mRNAs based on the extent of their Results consistency of differential expression and analyzing molecular interactions for the top-ranking iso-mRNAs As summarized in Figure 1, sequencing of testicular can help predict key functions and transcription RNA samples and the resulting analysis in combin- factors ation with existing RNA-seq data helped us generate a comprehensive list of genes and transcripts arranged Specifically, we seek answers to the following ques- hierarchically based on their expression-based associ- tions: (a) Which genes and iso-mRNAs exhibit expres- ation with the Non-Obstructive Azoospermia (NOA) sion-based association with the NOA? Moreover, which ones are more strikingly and consistently up- Clustering analysis of samples or down-regulated under the NOA condition across the samples? (b) What testicular functions are likely The expression levels of all genes in the testes of to be affected in NOA, particularly considering the donors with (test samples) and without (control sam- specific spliced variants of mRNAs (iso-mRNAs)? (c) ples) Non-Obstructive Azoospermia (NOA) were Which of the differentially transcribed transcription- compared The results showed that NOA samples factor-coding genes (TFgenes) and the corresponding grouped separately from the control samples (with iso-mRNAs, could be significant in targeting other normal spermatogenesis) – irrespective of possible genes, particularly the other TFgenes, which are differ- sub-types within each set (Figure 2) This observation entially transcribed in NOA-testes? supported the decision to group normal testis samples with those from other conditions where spermatogen- In addition, we also performed extensive data esis occurs normally, viz., Varicocele (VA), and analysis to address the following questions about Obstructive Azoospermia (OA) as ‘control samples’ the testicular TFs and iso-mRNAs in NOA: (d) Among the iso-mRNAs of each corresponding gene The results of the comparison of cell-type-specific expressed in the NOA-testes, are the ‘principal tran- gene expression patterns across samples also con- script isoforms’ produced differentially, or the firmed the grouping of control samples For example, minor ones? (e) Are any of the key domain-coding there was a significant reduction in the mean number regions removed among the iso-mRNAs that are of germ-cell-specific genes expressed across NOA-tes- over- or under-represented in NOA-testes? (f) In tis samples compared to normal testis samples which cell types are the key TFgenes expressed (Mann-Whitney U test; p < 0.0004) (also see under the normal condition? SYSTEMS BIOLOGY IN REPRODUCTIVE MEDICINE 199 Figure Gene expression-based comparison of tissue samples used in the study The relative quantities of all mRNAs across samples were used to quantify the similarities or differences between the samples The samples with similar overall trends in the expression pattern of genes were clustered together, which reflected a physiological (and hence, pathological condition) similarity of samples (A) PCA results Two normal samples taken from public repositories clustered peculiarly and were not considered for hierarchical clustering or other analysis (B) Hierarchical clustering of samples after removing the two outlying normal ones In both types of clustering, the NOA samples were clustered together but separately from the control samples (including OA and vari- cocele samples) Supplemental Table 1) This trend was also seen in cytometry results and the cell-type specific gene Sertoli cells, in some cases, even though the difference expressions need to be explored via a separate study in the percentage was less (see Supplemental Table Nevertheless, in the absence of traditional character- 1A) A detailed comparison of individual NOA and ization of NOA subtypes in the current study, the control samples for the expression of such earlier cell-type marker data will give a hint of the possible reported cell-type-specific genes confirmed a signifi- germ-cell distribution across samples cant reduction in their number in many NOA samples (see Supplemental Table 1B) We noticed an average Differential expression of genes and transcripts reduction of about 10, 29, 43, and 54% of genes spe- cific for Sertoli cells, spermatogonia, spermatocytes, There were substantially more down-regulated tran- and spermatids, respectively, were not detected in scripts compared to up-regulated ones This trend in NOA samples However, there were significant varia- differential expression is probably because of the tions among the current NOA samples in the relative absence of, or a very low number of, germ cells in occurrence of these cell-type-specific genes A much NOA-testis, particularly the post-meiotic cell types higher standard deviation was observed among the average cell-type-specific genes/markers across data- The differential transcription was also found to be sets among the NOA samples (6.47) compared to the more prominent, and generally more reliable, among normal ones (2.34) The mean percentage variations the down-regulated transcripts than among the up- among NOA samples across the four comparisons regulated ones For example, the highest False were 6.6, 26.3, 21.4, and 18, for Sertoli cells, spermato- Discovery Rate (FDR) among the ‘top up-regulated gonia, spermatocytes, and spermatids, respectively (see 250’ (Tu250) genes was 0.0068, while it was 7.69E-09 Supplemental Table 1B) Across samples, there was no among the ‘top down-regulated 250’ (Td250) genes consistent trend of the abundance of any cell-type (Supplemental Table 2) specific gene set that could help the sample-clustering into further sub-types Hence, the current results of Validation of differential expression of transcripts analysis of cell type marker genes across samples can- not help to identify the classical sub-types of NOA, No contradictions were observed among eighty tran- such as the ‘Sertoli cell only’ or ‘post-meiotic arrest’ scripts selected from the top 200 up- or down-regu- Correlations between typical histological or flow lated ones (Supplemental Figure 1) This observation 200 G BALAGANNAVAR ET AL indicated the reliability of the RNA-seq data and the represented by the corresponding down-regulated differential expression profiles generated transcripts (from Biomart) (see Supplemental Table 3) Thus, the overall trend in the types of functions Data mining and analysis of the top 500 associated with up- and down-regulated genes was differentially transcribed genes reproduced at the transcript level It should also be noted that an analysis done with the APPRIS database The results provide a relative rank for the strength of also showed that most of the down-regulated tran- scripts were the main transcript isoforms (see association, based on differential expression in NOA Supplemental Table 4) condition, for many genes already established to be Data mining and analysis of the top 6000 differentially-regulated genes associated with NOA The Tu250 and Td250 genes The Top 3000 down-regulated (Td3000genes), as well were analyzed in detail In addition, the results also as the top 3000 up-regulated (Tu3000genes) genes, were also analyzed in detail (Supplemental Table 2), specify the iso-mRNAs of these genes mainly to check the consistency of the trends observed among the Tu250 and Td250 genes Many A literature review and GO analysis indicated that of the significant molecular functions, biological proc- esses, cellular components, and pathways enriched the up-regulated genes are not associated with sperm- among the top 250 up- or down-regulated (Tu250 and Td250) genes were also enriched among the cor- atogenesis This trend was expected as most of the responding larger set (3000 genes each; see Supplemental Tables 5, 6, and 7) This observation genes up-regulated in the testes of NOA patients indicated the reliability and utility of the hierarchical listing of the differentially transcribed genes and a would be due to a relatively higher number of somatic good representation of the differentially expressed genes by the top 250 Even though a few GO terms cells compared to germ cells, which are reduced com- and pathways found among the top 3,000 up- or down-regulated genes were not among the top 250 of pared to normal testes the corresponding set, some discrepancies across the sets were expected due to the varying number of PAFAH1B1 (Ensemble transcript ID: genes ENST00000576586), LDHC (ENST00000280704), SPAG9 The Td3000 genes are likely to represent the molecular level of spermatogenic failure more cor- (ENST00000357122), PRM1 (ENST00000312511), TNP2 rectly than the Tu3000 genes This interpretation is based on two observations: (a) There was a higher (ENST00000312693), TNP1 (ENST00000236979) and consistency in the down-regulation status, across sam- ples, among Td3000 genes compared to the Tu30000 PRM2 (ENST00000241808) are such important genes genes The highest p-value was 0.025 (FDR: 0.064), and the lowest fold change and percentage consistency down-regulated in NOA that seem to be well studied in were 0.26 and 0%, respectively, for Tu3000 However, among the Tu3000, there were only 207 genes with a the context of NOA as well as spermatogenesis These 100% consistency, which had 3.29E-03 (FDR: 0.011) P-value, and !0.49 fold change, respectively On the genes have more than 200 research articles corresponding contrary, the highest P-value was 3.38E-04 (FDR: 1.66E-03), while the lowest fold change and percent- to each in general and more than ten articles each in the age consistency, were 2.02 and 80% for Td3000, respectively And, there were 2810 genes among context of spermatogenesis or NOA In addition, there Td3000, with a 100% consistency, which had 1.81E-05 (FDR: 1.27E-04) P-value, and 2.02 fold change, were also many well-studied genes corresponding to the respectively (b) The Td3000 also represented Td250 transcripts, which not seem to -have been studied earlier in the context of spermatogenesis or NOA GO based association with spermatogenesis was detected in 43 of the Td250 genes (Figure 3A) As per GO analysis, many of the other Td250 genes were known to be directly involved in sperm functions such as fertil- ization (see Figure 3B) or post-meiotic events such as acrosome formation and sperm capacitation Around 17 relevant biological processes (BPs) (GOTERM_BP_all) and 12 pathways were enriched with Td250 genes Among these genes, cell-division-related multiple bio- logical processes, molecular functions, and cellular com- ponents were enriched with very high significance (FDR < 0.00010) Similarly, many spermatogenesis- or sperm- related GO terms were also represented well by the Td250genes Among general functions, protein binding was common in both Td250 and Tu250 gene-sets, whereas kinase activity and ATP binding were enriched molecular functions among Td250 genes only About 93% of the GO terms found over-repre- sented for the Td250 genes, via DAVID, were also SYSTEMS BIOLOGY IN REPRODUCTIVE MEDICINE 201 Figure Results of integrated functional analysis of the top 250 down-regulated (Td250) genes The first two histogram bars within each node indicate the number of papers related to NOA or spermatogenesis (Annokey), in that order, for each gene The last bar in each node indicates the number of papers in general (GenCLiP) Genes with a known GO association with the spermato- genesis process are marked with blue font (A) Of Td250 genes, 43 were associated with spermatogenesis function with a fold enrichment score and FDR of 9.4 and 6.43E-26, respectively (B) Of Td250 genes, 20 are involved in 12 pathways: PW1-PPAR signal- ing pathway, PW2-Glucagon signaling pathway, PW3-Biosynthesis of amino acids, PW4-Glycolysis, PW5-Synthesis of PE, PW6- Separation of Sister Chromatids, PW7-Gluconeogenesis, PW8-Mitotic Prometaphase, PW9-Deactivation of the beta-catenin transacti- vating complex, PW10-Antigen processing: Ubiquitination & Proteasome degradation, PW11-Resolution of Sister Chromatid Cohesion, PW12-RHO GTPases Activate Formins The first two histogram bars within each node indicate the number of papers related to NOA or spermatogenesis (Annokey), in that order, for each gene In contrast, the last bar in each node indicates the number of papers in general (GenCLiP) Genes with a known association with the spermatogenesis process are marked with blue font spermatogenic functions, unlike Tu3000 genes, as spermatogenesis was an interesting observation In described below most cases, however, the Td3000genes involved in such specific processes are not yet reported to be asso- The BP terms enriched among the Td3000genes ciated with spermatogenesis or fertilization-related include obvious biological processes related to testis processes This observation is probably because of the functions About 232 genes are associated with sperm non-discovery of the association of many genes with development or related processes Two such broad spermatogenesis and NOA prominent processes were ’spermatogenesis’ (count: 163 genes, Benjamini P-value or Bpv: 2.66E-42) and To obtain a better insight into the involvement of ’spermatid development’ (42 genes, BPv: 1.08E-13) genes in key processes during spermatogenesis, we Another set of prominent BP terms included sperm analyzed the overlapping biological processes and motility and related functions (88 genes, BPv: 1.13E- pathways known for the top NOA down-regulated 01) Other specific processes that seem to be nega- genes from the function-based core cluster indicated tively affected in NOA are cilium assembly, cilium in (Figure 3B) In addition, we also considered the morphogenesis, and related processes, cell division well-established protein-protein interactions among and related processes such as meiosis and DNA the proteins coded by the selected genes repair, and several aspects of RNA synthesis, process and post-translational modifications (Figure 4, also Several down-regulated genes among the top see Supplemental Table 8) The suppression of genes rankers were found to be involved in multiple func- representing a significant overlap of post-translation tions and pathways For example, ABHD2 (Figure 3) modifications with the mitotic phase of is linked to acrosome reaction and sperm capacitation IQCF1 is also linked to these functions but has an 202 G BALAGANNAVAR ET AL Figure Distribution of specific functions among the Td3000genes after removing some of the processes well known to be associated with spermatogenesis (Supplemental Table 8) Numbers mentioned for each function inside the pie chart represent the percentage of genes not known to be associated with (a) sperm motility-related processes (innermost circle), (b) fertilization- related processes (middle circle), or (c) spermatogenesis-related processes (spermatogenesis, spermatid development, spermatid nucleus elongation, and cell differentiation) (outermost circle) additional association with sperm motility TRIM36 is Interestingly, viral activity is indicated to be a associated with the cytoskeleton, ubiquitination, and major function among the up-regulated genes, while acrosome reaction It interacts with the multi-protein- the antiviral mechanism is represented among the interacting UBC associated with the regulation of down-regulated genes, with a few contradictions The mRNA stability and deactivation of the beta-catenin GO terms such as ’viral transcription’ (p-value