Basharat Bhat

Protein–protein interactions (PPIs) are intricate and vital components of cellular processes, coordinating the different biological processes. The accurate prediction of PPIs is significant for unraveling the functionality of known cancer proteins, comprehending the underlying oncogenesis and determining the possible therapeutics. Although cancer- specific PPI datasets are crucial, but are currently unavailable. The research addresses the gap by curating a novel structural Cancer PPI (CPPI) dataset encompassing significant cancer proteins to represent an extensive picture of Cancer Protein interactions. The paper introduces an innovative approach integrating graph-based methods, viz: GraphSAGE and GIN with attention, to identify and interpret intricate interactions for accurate prediction of CPPIs. Moreover, incorporating attention mechanisms allows the model to prioritize pertinent information while transmitting messages, improving interpretability and predictive capabilities. The performance of the proposed approach is systematically evaluated, compared, and cross-validated with the existing baseline models. The results demonstrate the enhanced remarkable comparative outcomes and highlight the potential of attention-augmented graph-based learning for providing vital insights into the complex world of CPPIs by capturing the structural features and the sites that regulate CPPIs.

DNA methylation (DNAm) epigenome-wide association studies (EWAS) have been performed on diverse ethnicities to discover novel biomarkers associated with various diseases, such as cancers, autoimmune diseases, and neurological disorders. However, genetic polymorphisms can influence DNAm levels resulting in methylation quantitative trait loci (meQTL). These can be either direct effects, by altering the sequence of the methylation (CpG) site itself, or, in the case of array-based measures, indirectly altering the detection probe-binding site interaction. Given that genetic variant frequencies associated with meQTL can differ between population groups, these have the potential to confound EWAS observations, particularly in multi-ethnic populations. In this study, we analysed publicly available DNA methylation profiles (450K array), consisting of 1342 individuals from 6 distinct ancestral groups. We investigate two distinct tools (GapHunter and MethylToSNP) specifically designed to identify CpG sites that may be influenced by genetic variation. Results from this aggregated trans-ancestral epigenome-wide dataset suggest that both tools fail to consistently identify not only rarer (MAF < 0.05) genetic variant effects but also more than half of sites predicted to be associated with variants with much higher allele frequencies (MAF >0.2). In addition, there is a relatively low concordance in the detection of polymorphic CpGs between GapHunter and MethylToSNP. Screening of CpG site associations from EWAS using either of these tools is unlikely to be a robust or comprehensive means of identifying all genetic variant confounding effects.

Deep RNA sequencing experiment was employed to detect putative single nucleotide polymorphisms (SNP) in mammary epithelial cells between two diverse cattle breeds (Jersey and Kashmiri) to understand the variations in the coding regions that reflect differences in milk production traits. The low milk-producing Kashmiri cattle are being replaced by crossbreeding practices with Jersey cattle with the aim of improving milk production. However, crossbred animals are prone to infections and various other diseases resulting in unsustainable milk production. In this study, we tend to identify high-impact SNPs from Jersey and Kashmiri cows (utilizing RNA-Seq data) to delineate key pathways mediating milk production traits in both breeds. A total of 607 (442 SNPs and 169 INDELs) and 684 (464 SNPs and 220 INDELs) high-impact variants were found specific to Jersey and Kashmir cattle, respectively. Based on our results, we conclude that in Jersey cattle, genes with high-impact SNPs were enriched in nucleotide excision repair pathway, ABC transporter, and metabolic pathways like glycerolipid metabolism, pyrimidine metabolism, and amino acid synthesis (glycine, serine, and threonine). Whereas, in Kashmiri cattle, the most enriched pathways include endocytosis pathway, innate immunity pathway, antigen processing pathway, insulin resistance pathway, and signaling pathways like TGF beta and AMPK which could be a possible defense mechanism against mammary gland infections. A varied set of SNPs in both breeds, suggests a clear differentiation at the genomic level; further analysis of high-impact SNPs are required to delineate their effect on these pathways.

Pashmina goats produce the world's finest and the most costly animal fiber (Pashmina) with an average fineness of 11–13 microns and have more evolved mechanisms than any known goat breed around the globe. Despite the repute of Pashmina goat for producing the finest and most sought-after animal fiber, meager information is available in the public domain about Pashmina genomics and transcriptomics. Here we present a 2.94 GB genome sequence from a male Changthangi white Pashmina goat. We generated 294.8 GB (>100X coverage) of the whole-genome sequence using the Illumina HiSeq 2500 sequencer. All cleaned reads were mapped to the goat reference genome (2,922,813,246 bp) which covers 97.84% of the genome. The Unaligned reads were used for de novo assembly resulting in a total of 882 MB non-reference contigs. De novo assembly analysis presented in this study provides important insight into the adaptation of Pashmina goats to cold stress and helps enhance our understanding of this complex phenomenon. A comparison of the Pashmina goat genome with a wild goat genome revealed a total of 2,823 high impact single nucleotide variations and small insertions and deletions, which may be associated with the evolution of Pashmina goats. The Pashmina goat genome sequence provided in this study may improve our understanding of complex traits found in Pashmina goats, such as annual fiber cycling, defense mechanism against hypoxic, survival secret in extremely cold conditions, and adaptation to a sparse diet. In addition, the genes identified from de novo assembly could be utilized in differentiating Pashmina fiber from other fibers to avoid falsification at marketing practices.

Pashmina goat (Capra hircus) is an economically important livestock species, which habitats the cold arid desert of the Ladakh region (India), and produces a princely animal fiber called Pashmina. The Pashmina goat has a double coat fleece as an adaptation to the very harsh cold winters the outer long coarse hair (guard hair) produced from primary hair follicles and the inner fine Pashmina fiber produced from secondary hair follicles. Pashmina fiber undergoes a circannual and synchronized growth cycle. In the present study, we analyzed transcriptome profiles from 10 different Pashmina goats during anagen and telogen to delineate genes and signaling pathways regulating active (anagen) and regressive (telogen) phases of the follicle growth. During anagen, 150 genes were expressed at significantly higher levels with log (FC) > 2 and p_adj < 0.05. The RNA seq results were subjected to qRT-PCR validation. Among the nine genes selected, the expression of HAS1, TRIB2, P2RX1. PRG4, CNR2, and MMP25 were significantly higher (p < 0.05) in the anagen phase, whereas MC4R, GIPC2, and CDO1 were significantly expressed (p < 0.05) in the telogen phase which supports and validates the gene expression pattern from the RNA-sequencing. Differentially expressed genes revealed that Pashmina fiber initiation is largely controlled by signaling pathways like Wnt, NF-Kappa, JAK-STAT, Hippo, MAPK, Calcium, and PI3K-Akt. Expression of genes from the Integrin family, Cell adhesion molecules, and ECM-receptors were observed to be at much higher levels during anagen. We identified key genes (IL36RN, IGF2, ITGAV, ITGA5, ITCCR7, CXCL5, C3, CCL19, and CXCR3) and a collagen cluster which might be tightly correlated with anagen-induction. The regulatory network suggests the potential role of RUNX3, NR2F1/2, and GATA family transcription factors in anagen-initiation and maintaining fiber quality in Pashmina goats.

Changes in whole blood DNA methylation levels at several CpG sites have been associated with circulating blood lipids, specifically high-density lipoprotein and triglycerides. This study performs a discovery and validation epigenome-wide association study (EWAS) for circulating lipoprotein(a) [Lp(a)], an independent risk factor for cardiovascular diseases. Whole-blood DNA methylation profiles were assessed in a cohort of 1020 elderly individuals using the Illumina EPIC array and independent validation in 359 elderly males using the Illumina 450 k array. Plasma Lp(a) was measured using an apolipoprotein(a)-size-independent ELISA. Epigenome-wide rank regression analysis identified and validated a single CpG site, cg17028067 located in intron 1 of the LPA gene, that was significantly associated with plasma Lp(a) levels after correction for multiple testing. Genotyping of the site identified a relatively uncommon SNP (rs76735376, MAF <0.02) at the CpG site that largely explained the observed methylation effect. Rs76735376 is an expression quantitative trait loci for the LPA gene and could affect expression by altering enhancer activity. This EWAS for plasma Lp(a) identified a single CpG site within LPA. This association is due to an uncommon, but highly effective genetic variant, which was not in significant linkage disequilibrium with other variants known to influence Lp(a) levels or apo(a) isoform size. This study highlights the utility of CpG site methylation to identify potentially important genetic associations that would not be readily apparent in a comparable size genetic association study.

Tropical Calcific Pancreatitis (TCP) is a neglected juvenile form of chronic non-alcoholic pancreatitis. Cathepsin B (CTSB), a lysosomal protease involved in the cellular degradation process, has recently been studied as a potential candidate gene in the pathogenesis of TCP. According to the Cathepsin B hypothesis, mutated CTSB can lead to premature intracellular activation of trypsinogen, a key regulatory mechanism in pancreatitis. So far, CTSB mutations have been studied in pancreatitis and neurodegenerative disorders, but little is known about the structural and functional effect of variants in CTSB. In this study, we investigated the effect of single nucleotide variants (SNVs) specifically associated with TCP, using molecular dynamics and simulation algorithms. There were two non-synonymous variants (L26V and S53G) of CTSB, located in the propeptide region. We tried to predict the effect of these variants on structure and function using multiple algorithms: SIFT, Polyphen2, PANTHER, SDM sever, i-Mutant2.0 suite, mCSM algorithm, and Vadar. Further, using databases like miRdbSNP, PolymiRTS, and miRNASNP, two SNPs in the 3′UTR region were predicted to affect the miRNA binding sites. Structural mutated models of nsSNP mutants (L26V and S53G) were prepared by MODELLER v9.15 and evaluated using TM-Align, Verify 3D, ProSA and Ramachandran plot. The 3D mutated structures were simulated using GROMACS 5.0 to predict the impact of these SNPs on protein stability. The results from in silico analysis and molecular dynamics simulations suggested that these variants in the propeptide region of Cathepsin B could lead to structural and functional changes in the protein and thus could be pathogenic. Hence, the structural and functional analysis results have given interim conclusions that these variants can have a deleterious effect in TCP pathogenesis, either uniquely or in combination with other mutations. Thus, it could be extrapolated that Cathepsin B gene can be screened in samples from all TCP patients in future, to decipher the distribution of variants in patients.

The genetics of coat color variation remains a classic area. Earlier studies have focused on a limited number of genes involved in color determination; however, the complete set of trait determinants are still not well known. In this study, we used high-throughput sequencing technology to identify and characterize intricate interactions between genes that cause complex coat color variation in Changthangi Pashmina goats, producer of finest and costly commercial animal fiber. We systematically identified differentially expressed mRNAs and lncRNAs from black, brown and white Pashmina goat skin samples by using RNA-sequencing technique. A pairwise comparison of black, white and brown skin samples yielded 2479 significantly dysregulated genes (2422 mRNA and 57 lncRNAs). Differentially expressed genes were enriched in melanin biosynthesis, melanocyte differentiation, developmental pigmentation, melanosome transport activities GO terms. Our analysis suggested the potential role of lncRNAs on color coding mRNAs in cis and trans configuration. We have also developed online data repository as a component of the study to provide a central location for data access, visualization and interpretation accessible through http://pcd.skuastk.org/.