Biology subjects

Bajpai, A. K.

Publications and source records attributed to Bajpai, A. K..

5 recordsLinked to original sources

bioRxiv2023

Systems genetics identifies the association between Enc1 and cognitive function in the hippocampus

Ectodermal-Neural Cortex 1 (ENC1) is expressed in multiple regions of the brain, including the hippocampus. However, knowledge about its function has been well explored only in the context of peroxidative stress and cancer. In this study, we investigated the association of hippocampal Enc1 with cognitive function in BXD mice. We performed Pearson correlation, phenotype-wide association analysis (PheWAS), expression-Based PheWAS, pathway enrichment, and protein interaction networks on Enc1 and BXD phenotypes/transcriptome of the hippocampus, and the results indicated that Enc1 is inextricably linked to cognitive performance. In addition, we found that most of the Enc1 co-expressed genes were highly expressed in GABAergic neuronal cells. Expression quantitative trait loci analysis indicated that Enc1 was cis-regulated in the hippocampus of mice as well as human. Genome-wide association analysis revealed ENC1 to be significantly associated with cognitive-related traits, including age-related cognitive changes etc. In conclusion, our findings demonstrated that Enc1 is involved in cognitive functions mainly in hippocampal GABAergic neuronal cells through neurogenesis, synaptic signaling, and CGMP-PKG signaling pathways, and interacts with the neurological function-related genes.

genetics↗

bioRxiv2021

Integrated transcriptomic meta-analysis provides novel insights into breast cancer molecular subtypes

Breast cancer is the most common cancer in women worldwide. There are four major breast cancer subtypes (luminal A, luminal B, HER2-enriched and triple-negative/TNBC). TNBC is the most aggressive form with the worst prognosis. However, the differences among the subtypes have not been completely established at the molecular level, thereby limiting therapeutic and diagnostic options for TNBC. We performed a meta-analysis of microarray and RNA-sequencing data to identify candidate genes with an expression-based association in each molecular subtype of breast cancer. The protein interaction network of the candidate genes was analyzed to discover functionally significant gene clusters and hub genes. Potential therapeutic candidates for TNBC were explored through gene-miRNA interactions. We identified 316, 347, 382, and 442 candidate genes in luminal A, luminal B, HER2 and TNBC subtypes, respectively. A total of 135 (26 up- and 109 down-regulated) candidate genes were shared by all four subtypes. Functional analysis of the candidate genes indicated up-regulation of cell cycle and p53 signaling pathways and down-regulation of multiple signaling pathways. COL10A1 was found to be highly up-regulated in all subtypes. It may be a good target for research towards multiple types of applications, including therapeutics. KIF4A, a commonly up-regulated X-chromosome gene was significantly associated with the survival of breast cancer patients. Protein interaction network and centrality analysis revealed that low-moderately differentially regulated genes play an important role in functional cascades across proteins in breast cancer subtypes and may be potential candidates for therapeutics. Targeting FN1 (fibronectin 1), the key up-regulated hub gene by miR-1271 5p may be an important molecular event to be targeted for potential therapeutic application in TNBC.

cancer biology↗

bioRxiv2019

Listing candidate diagnostic markers and exploring the molecular basis of human spermatogenesis and a type of male infertility (Non-Obstructive Azoospermia), with transcriptomics via the next generation sequencing methods

Studying the molecular basis of Non-Obstructive Azoospermia (NOA), a type of male infertility with failed spermatogenesis at various stages, can also help in exploring molecular basis of human spermatogenesis and possibly pave way to identify new targets for male contraceptive development. Hence, we initiated a functional genomics study by applying RNA-seq. Testicular biopsies collected from donors with Non-Obstructive Azoospermia (NOA), Obstructive Azoospermia (OA), Congenital Bilateral Absence of the Vas Deferens (CBAVD), and Varicocele (VA) conditions. Strong association of 100+ genes with human spermatogenesis and NOA has been detected via NGS-based transcriptomic analysis. In addition, 20 RNA molecules have been short-listed for potential diagnostic applications (non-obstructive azoospermia vs. obstructive azoospermia, varicocele or normal). A hierarchical list of several genes and alternatively spliced mRNAs, transcribed differentially in NOA, is reported - based on a strength of association. Such association with NOA, spermatogenesis or both is a new finding for many genes as revealed by a comparison with a newly prepared comprehensive list of genes having such association with human spermatogenesis/NOA. Many top-ranking genes involved in viral gene expression were up-regulated in testes from NOA-patients, while those associated with an antiviral mechanism were down-regulated. A tangential finding: while most well-established control mRNAs did not qualify, two new ones worked best in RT-qPCR experiments. Needle-aspiration of testicular biopsies, followed by the use of short-listed promising candidate biomarkers (i.e., 16 mRNA & 4 chimeric transcripts) and control mRNAs in RT-qPCR-based diagnostic assays, may help to avoid open surgeries in future.

developmental biology↗

bioRxiv2019

Therapeutically actionable PAK4 is amplified, overexpressed and involved in bladder cancer progression

Muscle-invasive bladder carcinomas (MIBCs) are aggressive genitourinary malignancies. Disease incidence and survival rates vary based on aggressiveness and treatment options. Metastatic urothelial carcinoma of the bladder is generally incurable by current chemotherapy and leads to early mortality. For a minority ([~]20%) of patients, T-cell checkpoint inhibitors provide durable benefits following prior platinum therapy. Recent studies have identified molecular subtypes of MIBCs with different sensitivities to frontline therapy, suggesting heterogeneity in these tumors and pointing to the importance of molecular characterization of MIBCs to provide effective treatment. We have performed multi-omic profiling of the kinome to identify therapeutic targets that are overexpressed in a subset of BLCAs. Our analyses revealed amplification and overexpression of P21 (RAC1) activated kinase 4 (PAK4) in a subset of BLCAs. For these tumors, multiplex kinase assay profiling identified corresponding PAK4 target substrates. By performing experiments using cultured bladder cancer cells, we confirmed the role of PAK4 in BLCA cell proliferation and invasion. Furthermore, our studies showed that a PAK4 inhibitor was effective in curtailing growth of BLCA cells. Transcriptomic analyses identified elevated expression of another kinase, Protein Tyrosine Kinase 6 (PTK6), upon treatment with a PAK4 inhibitor. Similarly, RNA interference of PAK4 led to elevated expression of PTK6. Treatment with a combination of kinase inhibitors (vandetanib and dasatinib) showed enhanced sensitivity compared to either drug alone. Thus, PAK4 may be therapeutically actionable for a subset of MIBC patients with amplified and/or overexpressed PAK4 in their tumors. Our results also indicate that combined inhibition of PAK4 and PTK6 may overcome resistance to PAK4. These observations warrant clinical investigations with selected BLCA patients.

cancer biology↗

bioRxiv2019

How helpful are the protein-protein interaction databases and which ones?

Protein-protein interactions (PPIs) are critical, and so are the databases and tools (resources) concerning PPIs. But in absence of systematic comparisons, biologists/bioinformaticians may be forced to make a subjective selection among such protein interaction databases and tools. In fact, a comprehensive list of such bioinformatics resources has not been reported so far. For the first time, we compiled 375 PPI resources, short-listed and performed preliminary comparison of 125 important ones (both lists available publicly at startbioinfo.com), and then systematically compared human PPIs from 16 carefully-selected databases. General features have been first compared in detail. The coverage of experimentally verified vs. all PPIs, as well as those significant in case of disease-associated and other types of genes among the chosen databases has been compared quantitatively. This has been done in two ways: outputs manually obtained using web-interfaces, and all interactions downloaded from the databases. For the first approach, PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes associated with different tissues (specific to kidney, testis, and uterus, and ubiquitous) or diseases (breast cancer, lung cancer, Alzheimers, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. PPI-coverage for well-studied genes was also compared with that of less-studied ones. For the second approach, the back-end-data from the databases was downloaded and compared. Based on the results, we recommend the use of STRING and UniHI for retrieving the majority of experimentally verified protein interactions, and hPRINT and STRING for obtaining maximum number of total (experimentally verified as well as predicted) PPIs. The analysis of experimentally verified PPIs found exclusively in each database revealed that STRING contributed about 71% of exclusive hits. Overall, hPRINT, STRING and IID together retrieved ~94% of total protein interactions available in the databases. The coverage of certain databases was skewed for some gene-types. The results also indicate that the database usage frequency may not correlate with their advantages, thereby justifying the need for more frequent studies of this nature.

bioinformatics↗