Transcriptomic analysis of the non obstructive azoospermia noa to address gene expression regulation in human testis

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

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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

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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

a

Institute 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

There is a need to understand the molecular basis of testes under Non-Obstructive

Azoospermia (NOA), a state of failed spermatogenesis There has been a lack of attention to

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

of NOA-testes, and explore molecular mechanisms – especially those related to gene

expres-sion regulation We sequenced mRNAs from testicular samples of donors with complete

spermatogenesis (control samples) and a failure of spermatogenesis (NOA samples) We

identified differentially expressed genes and their iso-mRNAs via standard NGS data

analy-ses We then listed these iso-mRNAs hierarchically based on the extent of consistency of

dif-ferential quantities across samples and groups, and validated the lists via RT-qPCRs (for 80

iso-mRNAs) 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-ranking down-regulated genes and iso-mRNAs, i.e., those down-regulated more

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

ARTICLE HISTORY

Received 9 August 2022 Revised 27 January 2023 Accepted 30 January 2023

KEYWORDS

Spermatogenesis; non-obstructive Azoospermia; transcription regulation; transcription factors;

regulatory network; gene expression regulation; RNA-seq; molecular interactions; transcriptomics; male infertility

Introduction

The proportion of men with infertility may have

increased in recent years (Agarwal et al 2015; Kumar

and Singh 2015) A literature review indicated that

approximately 3 to 9 men in every 1000 may have the Non-Obstructive Azoospermia (NOA) disorder NOA

is difficult to deal with clinically (Esteves 2015; Chiba

et al 2016) Understanding the genetic and molecular

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

https://doi.org/10.1080/19396368.2023.2176268

basis of the NOA condition can help to explore

potential novel solutions to address the issues

associ-ated with this disease (Omolaoye et al 2022)

Identifying alternatively spliced transcript isoforms

(iso-mRNAs) with such associations is particularly

essential (Modrek and Lee2002; Stastna and Van Eyk

2012) for beginning a system-level understanding of

the disease condition within the testis tissue

The non-obstructive azoospermia condition

repre-sents a failed state of spermatogenesis, with the

reduc-tion or absence of multiple types of male germ cells

in the testes, particularly the late spermatid and

sper-matozoa that are either absent or present in highly

reduced numbers (Ramasamy et al 2009; Esteves

2015; Chiba et al 2016) The significance of

transcrip-tomics in understanding the molecular basis of

sperm-atogenesis and male infertility conditions is also

well-known (Omolaoye et al 2022) Thus, a

compre-hensive list of genes and transcripts expressed

differ-entially in the testes of NOA patients may also

include several molecules playing a key role in normal

human spermatogenesis We think that the genes

con-sistently down-regulated in NOA could particularly

help identify novel molecules potentially associated

with spermatogenesis in men Research efforts towards

developing new and improved contraceptives for men

(Robertson et al 2020; Dominiak et al 2021) could

also be assisted by such a list of genes and their

corre-sponding iso-mRNAs We propose that a hierarchical

listing of testicular iso-mRNAs, based on their

strength of expression-based association with the

NOA condition, can enable not only prioritizing

research on specific key molecules in multiple applied

contexts such as novel contraceptive development but

also basic research related to the molecular

mecha-nisms associated with various stages of normal

spermatogenesis

The mechanism of regulation of gene expression is

particularly an important functional aspect (Hoopes

2008) that has been less studied in the context of

NOA Logically, exploring the testicular transcriptome

under the NOA condition would also help identify

key transcription factors important in testicular gene

expression regulation under normal conditions Many

studies (e.g., Mohsin et al 2022) have used

transcrip-tomic profiling followed by molecular network

ana-lysis to predict the interaction of transcription factors

with other molecules

Several studies have been conducted on gene

expression in normal testis of mice and humans (e.g.,

Sassone-Corsi 1997; Reddi et al 2002; Acharya et al

2006) Many scientists used the microarray techniques

for mass-scale differential expression profiling of NOA-testes (e.g., Okada et al 2008; Cappallo-Obermann et al 2010; Malcher et al 2013; Baksi et al

2018) However, microarray-based gene expression profiling studies, particularly the earlier ones, have several limitations – including coverage of a limited number of genes among the probes used in the array (Draghici et al 2006; Jaksik et al.2015)

In recent years, the Next-Generation Sequencing (NGS) methods have been extensively used for tran-scriptomics (Lea et al 2011; Hrdlickova et al 2017) as they have the potential to screen a maximum number

of genes as well as identify key iso-mRNAs (Ozsolak and Milos 2011) It has been well-established that alternative splicing forms an important component in spermatogenesis and testicular disorders (Song et al

2020) However, exploring the transcriptome at the level of specific mRNA isoforms requires high-depth NGS of a reasonable number of samples ( https://gen-ome.ucsc.edu/ENCODE/protocols/dataStandards/

Giannopoulou et al 2015), which has not been done till date using either bulk (non-single-cell) tissue RNA

or scRNA from the testis under the NOA condition

In recent years, single-cell RNA sequencing (scRNAseq) has gained popularity Normal human testis samples have been subjected to scRNAseqs (Zhu et al 2016; Jan et al 2017; Guo et al 2018; Hermann et al 2018; Wang et al 2018; Sohni et al

2019; Shami et al.2020) Such scRNA-seq datasets have also been meta-analyzed (Soraggi et al 2021) NOA testes have also been subjected to scRNAseq (Wang

et al 2018; Zhao et al 2020) However, it should be noted that while scRNAseq makes several new prom-ises, the associated methods also have disadvantages (Lowe et al 2017; Chambers et al 2019; Kim et al

2019; L€ahnemann et al 2020) The technical variability

of gene expression profiles derived using this approach

is higher than those from bulk tissue RNA-seq (Hicks

et al 2018; Kim et al 2019) In addition, we note that the cell identification methods introduce an additional level of variation during the interpretation of scRNAseq results We realize that reliable profiling of mRNAs using bulk testis samples will help to corrobor-ate the scRNA data, and such transcriptomic profiling can help analyze molecular interactions in the context

of many tissue-level molecular functions

Hence, we decided to use bulk-tissue RNA-sequencing to obtain potential insights into the func-tions of iso-mRNAs expressed differentially in the testis of NOA patients, particularly regarding gene expression regulation We postulate that hierarchical

listing of the iso-mRNAs based on the extent of their

consistency of differential expression and analyzing

molecular interactions for the top-ranking iso-mRNAs

can help predict key functions and transcription

factors

Specifically, we seek answers to the following

ques-tions: (a) Which genes and iso-mRNAs exhibit

expres-sion-based association with the NOA? Moreover,

which ones are more strikingly and consistently

up-or down-regulated under the NOA condition across

the samples? (b) What testicular functions are likely

to be affected in NOA, particularly considering the

specific spliced variants of mRNAs (iso-mRNAs)? (c)

Which of the differentially transcribed

transcription-factor-coding genes (TFgenes) and the corresponding

iso-mRNAs, could be significant in targeting other

genes, particularly the other TFgenes, which are

differ-entially transcribed in NOA-testes?

In addition, we also performed extensive data

analysis to address the following questions about

the testicular TFs and iso-mRNAs in NOA: (d)

Among the iso-mRNAs of each corresponding gene

expressed in the NOA-testes, are the ‘principal

tran-script isoforms’ produced differentially, or the

minor ones? (e) Are any of the key domain-coding

regions removed among the iso-mRNAs that are

over- or under-represented in NOA-testes? (f) In

which cell types are the key TFgenes expressed

under the normal condition?

Results

As summarized in Figure 1, sequencing of testicular RNA samples and the resulting analysis in combin-ation with existing RNA-seq data helped us generate a comprehensive list of genes and transcripts arranged hierarchically based on their expression-based associ-ation with the Non-Obstructive Azoospermia (NOA)

Clustering analysis of samples

The expression levels of all genes in the testes of donors with (test samples) and without (control sam-ples) Non-Obstructive Azoospermia (NOA) were compared The results showed that NOA samples grouped separately from the control samples (with normal spermatogenesis) – irrespective of possible sub-types within each set (Figure 2) This observation supported the decision to group normal testis samples with those from other conditions where spermatogen-esis occurs normally, viz., Varicocele (VA), and Obstructive Azoospermia (OA) as ‘control samples’ The results of the comparison of cell-type-specific gene expression patterns across samples also con-firmed the grouping of control samples For example, there was a significant reduction in the mean number

of germ-cell-specific genes expressed across NOA-tes-tis samples compared to normal tesNOA-tes-tis samples (Mann-Whitney U test; p < 0.0004) (also see

Figure 1 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

Supplemental Table 1) This trend was also seen in

Sertoli cells, in some cases, even though the difference

in the percentage was less (see Supplemental Table

1A) A detailed comparison of individual NOA and

control samples for the expression of such earlier

reported cell-type-specific genes confirmed a

signifi-cant reduction in their number in many NOA samples

reduction of about 10, 29, 43, and 54% of genes

spe-cific for Sertoli cells, spermatogonia, spermatocytes,

and spermatids, respectively, were not detected in

NOA samples However, there were significant

varia-tions among the current NOA samples in the relative

occurrence of these cell-type-specific genes A much

higher standard deviation was observed among the

average cell-type-specific genes/markers across

data-sets among the NOA samples (6.47) compared to the

normal ones (2.34) The mean percentage variations

among NOA samples across the four comparisons

were 6.6, 26.3, 21.4, and 18, for Sertoli cells,

spermato-gonia, spermatocytes, and spermatids, respectively (see

Supplemental Table 1B) Across samples, there was no

consistent trend of the abundance of any cell-type

specific gene set that could help the sample-clustering

into further sub-types Hence, the current results of

analysis of cell type marker genes across samples

can-not help to identify the classical sub-types of NOA,

such as the ‘Sertoli cell only’ or ‘post-meiotic arrest’

Correlations between typical histological or flow

cytometry results and the cell-type specific gene expressions need to be explored via a separate study Nevertheless, in the absence of traditional character-ization of NOA subtypes in the current study, the cell-type marker data will give a hint of the possible germ-cell distribution across samples

Differential expression of genes and transcripts

There were substantially more down-regulated tran-scripts compared to up-regulated ones This trend in differential expression is probably because of the absence of, or a very low number of, germ cells in NOA-testis, particularly the post-meiotic cell types The differential transcription was also found to be more prominent, and generally more reliable, among the down-regulated transcripts than among the up-regulated ones For example, the highest False Discovery Rate (FDR) among the ‘top up-regulated 250’ (Tu250) genes was 0.0068, while it was 7.69E-09 among the ‘top down-regulated 250’ (Td250) genes (Supplemental Table 2)

Validation of differential expression of transcripts

No contradictions were observed among eighty tran-scripts selected from the top 200 up- or down-regu-lated ones (Supplemental Figure 1) This observation

Figure 2 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)

indicated the reliability of the RNA-seq data and the

differential expression profiles generated

Data mining and analysis of the top 500

differentially transcribed genes

The results provide a relative rank for the strength of

association, based on differential expression in NOA

condition, for many genes already established to be

associated with NOA The Tu250 and Td250 genes

were analyzed in detail In addition, the results also

specify the iso-mRNAs of these genes

A literature review and GO analysis indicated that

the up-regulated genes are not associated with

sperm-atogenesis This trend was expected as most of the

genes up-regulated in the testes of NOA patients

would be due to a relatively higher number of somatic

cells compared to germ cells, which are reduced

com-pared to normal testes

ENST00000576586), LDHC (ENST00000280704), SPAG9

(ENST00000357122), PRM1 (ENST00000312511), TNP2

(ENST00000312693), TNP1 (ENST00000236979) and

PRM2 (ENST00000241808) are such important genes

down-regulated in NOA that seem to be well studied in

the context of NOA as well as spermatogenesis These

genes have more than 200 research articles corresponding

to each in general and more than ten articles each in the

context of spermatogenesis or NOA In addition, there

were also many well-studied genes corresponding to the

Td250 transcripts, which do 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

represented by the corresponding down-regulated transcripts (from Biomart) (see Supplemental Table

3) Thus, the overall trend in the types of functions associated with up- and down-regulated genes was reproduced at the transcript level It should also be noted that an analysis done with the APPRIS database also showed that most of the down-regulated tran-scripts were the main transcript isoforms (see

Supplemental Table 4)

Data mining and analysis of the top 6000 differentially-regulated genes

The Top 3000 down-regulated (Td3000genes), as well

as the top 3000 up-regulated (Tu3000genes) genes, were also analyzed in detail (Supplemental Table 2), mainly to check the consistency of the trends observed among the Tu250 and Td250 genes Many

of the significant molecular functions, biological proc-esses, cellular components, and pathways enriched among the top 250 up- or down-regulated (Tu250 and Td250) genes were also enriched among the cor-responding larger set (3000 genes each; see

indicated the reliability and utility of the hierarchical listing of the differentially transcribed genes and a good representation of the differentially expressed genes by the top 250 Even though a few GO terms and pathways found among the top 3,000 up- or down-regulated genes were not among the top 250 of the corresponding set, some discrepancies across the sets were expected due to the varying number of genes

The Td3000 genes are likely to represent the molecular level of spermatogenic failure more cor-rectly than the Tu3000 genes This interpretation is based on two observations: (a) There was a higher consistency in the down-regulation status, across sam-ples, among Td3000 genes compared to the Tu30000 genes The highest p-value was 0.025 (FDR: 0.064), and the lowest fold change and percentage consistency were 0.26 and 0%, respectively, for Tu3000 However, among the Tu3000, there were only 207 genes with a 100% consistency, which had
3.29E-03 (FDR: 0.011) P-value, and 0.49 fold change, respectively On the contrary, the highest P-value was 3.38E-04 (FDR: 1.66E-03), while the lowest fold change and percent-age consistency, were 2.02 and 80% for Td3000, respectively And, there were 2810 genes among Td3000, with a 100% consistency, which had 1.81E-05 (FDR: 1.27E-04) P-value, and 2.02 fold change, respectively (b) The Td3000 also represented

spermatogenic functions, unlike Tu3000 genes, as

described below

The BP terms enriched among the Td3000genes

include obvious biological processes related to testis

functions About 232 genes are associated with sperm

development or related processes Two such broad

prominent processes were ’spermatogenesis’ (count:

163 genes, Benjamini P-value or Bpv: 2.66E-42) and

’spermatid development’ (42 genes, BPv: 1.08E-13)

Another set of prominent BP terms included sperm

motility and related functions (88 genes, BPv:

1.13E-01) Other specific processes that seem to be

nega-tively affected in NOA are cilium assembly, cilium

morphogenesis, and related processes, cell division

and related processes such as meiosis and DNA

repair, and several aspects of RNA synthesis, process

and post-translational modifications (Figure 4, also

representing a significant overlap of post-translation

modifications with the mitotic phase of

spermatogenesis was an interesting observation In most cases, however, the Td3000genes involved in such specific processes are not yet reported to be asso-ciated with spermatogenesis or fertilization-related processes This observation is probably because of the non-discovery of the association of many genes with spermatogenesis and NOA

To obtain a better insight into the involvement of genes in key processes during spermatogenesis, we analyzed the overlapping biological processes and pathways known for the top NOA down-regulated genes from the function-based core cluster indicated

in (Figure 3B) In addition, we also considered the well-established protein-protein interactions among the proteins coded by the selected genes

Several down-regulated genes among the top rankers were found to be involved in multiple func-tions and pathways For example, ABHD2 (Figure 3)

is linked to acrosome reaction and sperm capacitation IQCF1 is also linked to these functions but has an

Figure 3 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 spermatospermato-genesis 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 signalsignal-ing 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

additional association with sperm motility TRIM36 is

associated with the cytoskeleton, ubiquitination, and

acrosome reaction It interacts with the

multi-protein-interacting UBC associated with the regulation of

mRNA stability and deactivation of the beta-catenin

trans-activating complex KLHL10 and PAFAH1B1

were found to be involved in protein-ubiquitination as

well as other aspects related to spermatid development

and fertilization

Similarly, DNAAF1 and CPAF206 may be involved

in flagella formation during the latter part of

sperm-atogenesis In addition, some of the proteins coded by

key down-regulated genes seem to interact well with

other down-regulated proteins For example,

RANGAP1, for which the role in spermatogenesis is

not known well except for an indication of a rolevia

SUMO1-ylation (Marchiani et al.2014), interacts with

three proteins (KIF2B, KIF2C, and PAFAH1B1)

known to be involved in the microtubule-based

move-ment, and all these four proteins are known to be

involved in pathways related to mitotic chromatid

separation These observations hint that RANGAP1,

along with KIF2B, KIF2C, and PAFAH1B1, may be

involved in the microtubule-based movement of

chro-matids during the early (mitotic or meiotic) stages of

spermatogenesis Like UBC, PSMF1 is also associated

with protein modifications, interacts with many other

proteins associated with the same function, and is

associated with mRNA stability regulation

Interestingly, viral activity is indicated to be a major function among the up-regulated genes, while the antiviral mechanism is represented among the down-regulated genes, with a few contradictions The

GO terms such as ’viral transcription’ (p-value

<3.2E-22, 63 genes) and ’viral nucleocapsid’ (p-value

<0.0067, 11 genes), and the ’viral mRNA translation’ (p-value <3.71E-31, 66 genes) pathway are enriched among the top-3,000 up-regulated genes (Tu3000) On the contrary, the pathway ’ISG15 antiviral mechanism’ (Reactome p-value <3.16E-06, 24 genes) is over-repre-sented among the top 3,000 down-regulated genes (Td3000) The ’MHC class II antigen presentation’ pathway (Reactome p-value <4.65E-04, 28 genes) is also represented well among the down-regulated genes However, glutathione metabolism, a pathway likely to be associated with innate immunity and anti-viral processes (Diotallevi et al 2017), was over-repre-sented among some up-regulated genes Glutathione representation was statistically significant during both KEGG and Reactome analysis of the top 250 as well

as 3000 up-regulated genes (7–18 genes with p-values from <0.004 to <0.000005) Related molecular func-tions were also similarly over-represented Similarly, the ’L13a-mediated translational silencing of Ceruloplasmin expression’ (Genes: 68, P-value: 1.59E-24) pathway with a potential role in immunity is also well represented among up-regulated genes On the other hand, NS1-mediated effects on host pathways,

Figure 4 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-fertilization-related processes (spermatogenesis, spermatid development, spermatid nucleus elongation, and cell differentiation) (outermost circle)

rev-mediated nuclear export of HIV RNA, nuclear

import of Rev protein, and vpr-mediated nuclear

import of PICs were among other significant (P-value

0.02 to 4.6E-05) pathways or GO terms enriched

among down-regulated genes

Analysis of alternatively spliced mRNA isoforms

(iso-mRNAs)

About 61 and 42% of Td250 and Td3000 transcripts,

respectively, were the principal transcript isoforms

Similarly, about 51 and 37% of the up-regulated

tran-scripts were the principal isoforms among the Tu250

and Tu3000, respectively Only 8 of the Tu250 and 9 of

the Td250genes, respectively, had a completely

oppos-ing expression among their iso-mRNAs The transcript

ENST00000618113 of the down-regulated SPAG9 gene

was one such example The cases of partial

contradic-tions (i.e., up-/down-regulation vs non-differentiated

transcripts from a gene) were more, with 121 and 43

cases among the Tu250 and Td250 genes, respectively

The regions with functional domains did not seem to

be altered across the transcript isoforms of most genes

However, the transcription initiation and termination

sites were altered in many cases This observation

indi-cates a potential change in the NOA-associated gene

expression regulation mechanisms, mainly at the

tran-scription initiation and post-trantran-scriptional phases

involving promoters and the 5’ and 3’ UTRs

The types of alternative splicing events and transcripts

were different across up- and down-regulated transcripts

(Supplemental Figure 2 and Figure 5) ‘Alternative

pro-moter or terminator’ (AP/AT) and ‘Alternative 50’ Splice

Sites’ (A50-SS) combinations were more common in

down-regulated transcripts (204, which is 16.32%) than in

up-regulated ones (106, 7.89%) or undifferentiated (27,

6.89%) transcripts The transcripts with a shared pair of

event types were also found to have an uneven

distribu-tion across up- and down-regulated genes (Supplemental

Exon/Sub-Intron Retention (SE/sub_RI) were found to be exclusive

to 50 (4%) down-regulated transcripts Overall, all

alterna-tive splicing events were much more common among the

differentially expressed genes than the undifferentiated

ones Single protein-coding genes were also

proportion-ately very high among differentially transcribed genes

Regulatory network analysis

We first prepared a comprehensive list of 3056 human

Transcription Factors (TFs) or co-Transcription

Factors (cTFs) by reviewing the literature (seeTable 1,

five articles identified via a thorough literature search (Carro et al 2010; Ravasi et al 2010; Yusuf et al

2012; Lambert et al 2018; Hu et al 2019) Next, we used this list to identify genes coding for TFs and cTFs that are significantly (logFC > 2) differentially expressed in the testes of NOA patients We noted

433 down-regulated genes and 59 up-regulated genes coding for TFs/cTFs (Supplemental Table 2) We then used the TRRUST database to shortlist such TFs and cTFs with known binding sites on the promoters of other differentially expressed TF/cTF-coding genes Cytoscape-based visualization of these interactions revealed 35 TFs and 20 cTFs with known interactions, including multiple interesting TF-TFgene hubs of prominent interactions (Figure 6) CytoHubba (Chin

et al 2014) was used to calculate the node score Based on the averages scores of Closeness, Betweenness, and Degree (Supplemental Table 11), SP1 formed the most conspicuous central node with

an average node score of 372 in the network, with 15 potential direct target genes that code for TFs (10) or cTFs (5) Of these 15 potential targets, all of which were down-regulated, seven were known transcription activators SP1 also seems to be part of a regulatory axis along with HSF4 and RAD51 HSF4 is a known inhibitor of RAD51, which activates SP1 in turn We hypothesize that the up-regulation of HSF4 and the resulting down-regulation of RAD51 under NOA-tes-tis is one of the causes of SP1 down-regulation, which

in turn results in the down-regulation of at least seven other transcription factors The current results help us

to postulate that multiple spermatogenic genes (see

Table 2) might be regulated via this regulatory axis, where up-regulation of HSF4 under the NOA condi-tion results in suppression of RAD51 transcripcondi-tion, that, in turn, results in a lack of activation of SP1, thus resulting in the down-regulation of this key TF CREBBP could also be an important regulator, via RAD51, of SP1 Down-regulation of CREBBP and ATF5, which normally activate CREB1, may also have suppressed CREB1 under the NOA condition, and eventually causing an up-regulation of SNAI2 Another suppressor of this TF, EZH2, is also down-regulated, even though the activating TF for the SNAI2 gene, MTA1, is up-regulated under NOA The cell-type expression analysis of the TFgenes showed that SP1 and CREBBP are spermatocyte and sperm-atid enriched in normal adult testis

In contrast, RAD51 is enriched in spermatogonia, spermatocytes, and spermatids (see Table 2) SNAI2 is enriched in spermatocytes and spermatids in normal

men TBP seems to be another TFgene with complex

innate regulation in normal spermatocytes but

down-regulated in NOA Multiple activators were seen in

the network for CDK1, enriched among

spermatogo-nia, spermatocytes, and spermatids, and BIRC5,

enriched in spermatogonia, spermatocytes, and

sper-matids MUC1 was unusual because it had multiple

suppressors, while STAT1 seemed to activate it

Interestingly, none of the Td3000 genes, including

TFgenes, seem to be targeted by CDK1, BIRC5,

MUC1, and TDP The observations on the

interac-tions among the TF-coding genes and their products

differentially expressed in the NOA-testis also indicate their significance during normal spermatogenesis Therefore, the possibility of the key TFs identified in the current study having a causative role in inhibiting the development of multiple pre- and post-meiotic cells in the NOA testis needs to be explored

Interestingly, none of the NOA-down-regulated SP1 transcripts was found to be testis-specific or predomin-ant (Mammalian Alternatively Spliced RNA Isoforms for Normal Tissues: MASRINT, http://resource2.ibab.ac

down-regulated splice variants for RAD51 and CREBBP were

Figure 5 The number of transcripts among the top 3000 each of NOA up (green color), down (red color), and undifferenti-ated (grey color) genes with a single alternative splicing event per transcript The spliced events for the Td3000 and Tu3000 were obtained using the SpliceDetector tool The overall size of each pie is proportional to the total number of iso-mRNAs (alterna-tively spliced mRNAs) Iso-mRNAs represented each type of splicing event were more common in differentially expressed mRNAs than the undifferentiated ones There were only minor differences in the representation among up- vs down-regulated iso-mRNAs

in the case of most of the splicing event-type AP: Alternative promoter; AT: Alternative terminator; AP/AT: Alternative promoter or terminator; A3’-SS: Alternative 3’ Splice Sites; A5’-SS: Alternative 5’ Splice Sites; RI: Intron Retention; SE: Skipping Exon; sub_RI: Skipping Exon; SE/sub_RI: Skipping Exon/Sub-Intron Retention; SPC: Single protein-coding

Table 1 Compilation of human transcription factors (TFs) and co-transcription factors (cTFs) that are known or predicted

PubMed article ID Year; Journal Data type Total TFs / cTFs reported Active human gene IDs

New union list of gene Ids corresponding to potential TFs/cTFs

20211142 2010; Cell Experimental (qPCR) &

predicted

A thorough literature search helped to identify the relevant research reports The union list generated forms the largest number of (known and predicted) human TFs to date.