Transcriptomic analysis of the Non-Obstructive Azoospermia (NOA) to address gene expression regulation in human testis.

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 mechanism 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 expression 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 analyses. We then listed these iso-mRNAs hierarchically based on the extent of consistency of differential 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 features, 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 phosphorylation. 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 identify 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-activation, 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.

Effective Feature Selection for Classification of Promoter Sequences.

Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.