Biology subjects

Thirumurugan, K.

Publications and source records attributed to Thirumurugan, K..

4 recordsLinked to original sources

bioRxiv2023

Myosin-5 varies its steps along the irregular F-actin track

Molecular motors employ chemical energy to generate unidirectional mechanical output against a track. By contrast to the majority of macroscopic machines, they need to navigate a chaotic cellular environment, potential disorder in the track and Brownian motion. Nevertheless, decades of nanometer-precise optical studies suggest that myosin-5a, one of the prototypical molecular motors, takes uniform steps spanning 13 subunits (36 nm) along its F-actin track. Here, we use high-resolution interferometric scattering (iSCAT) microscopy to reveal that myosin takes strides spanning 22 to 34 actin subunits, despite walking straight along the helical actin filament. We show that cumulative angular disorder in F-actin accounts for the observed proportion of each stride length, akin to crossing a river on variably-spaced stepping stones. Electron microscopy revealed the structure of the stepping molecule. Our results indicate that both motor and track are soft materials that can adapt to function in complex cellular conditions.

biophysics↗

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↗

bioRxiv2020

New insights into TNFα/PTP1B and PPARγ pathway through RNF213- a link between inflammation, obesity, insulin resistance and Moyamoya disease

Diabetic patients are always at a higher risk of ischemic diseases like coronary artery diseases. One such ischemic carotid artery disease is Moyamoya. Moyamoya disease (MMD) has been associated with diabetes Type-I and II and the causality was unclear. RNF213 is the major susceptible gene for MMD. To understand the association between diabetes mellitus and MMD we chose the major players from both the anomalies, insulin and RNF213. But before establishing a role of RNF213 in insulin regulating pathway we had to understand the involvement of RNF213 within different biological systems. For this we have adopted a preliminary computational approach to understand the prominent interactions of RNF213. Our first objective was to construct an interactome for RNF213. We have analyzed several curated databases and adapted a list of RNF213 interacting partners to develop its interactome. Then to understand the involvement of this interactome in biological functions we have analyzed major biological pathways, biological processes and prominent clusters related to this interactome through computational approach. Then to develop a pathway that might give clue for RNF213 involvement in insulin regulatory pathway we have validated the intercluster and intracluster predictions and identified a regulatory pathway for RNF213. RNF213 interactome was observed to be involved in adaptive immunity with 4 major clusters; one of the cluster involved TNFα. Immune system involves several pathways, and therefore at this point we have chosen an event-based strategy to obtain an explicit target. Immunity is mediated by many pro-inflammatory cytokines like TNFα. TNFα-mediated inflammation, obesity and insulin resistance are associated. Therefore we chose to explore the role of RNF213 in TNFα-mediated inflammation in macrophages and inflammation-mediated insulin-resistance in adipocytes. We have observed an enhancement of RNF213 gene expression by LPS mediated pro-inflammatory stimuli and suppression by PPARγ-mediated anti-inflammatory, insulin sensitizing stimuli in macrophages. A more significant response was observed in adipocytes as well. Administration of the pro-inflammatory cytokine TNFα was able to impede the reduction in RNF213 expression during adipogenesis and this effect was observed to be mediated by PTP1B. Inactivation of PTP1B abolished RNF213 expression which in turn enhanced the adipogenesis process through enhanced PPARγ. Constitutive expression of RNF213 suppressed the adipocyte differentiation by the inhibition of PPARγ. We could show the expression of RNF213 has been regulated by TNFα/PTP1B pathway and PPARγ. The constitutive expression of RNF213 during adipogenesis appears to be an adipostatic measure that obese patients acquire to inhibit further adipogenesis. This is verified in silico by analyzing the gene expression data obtained from Gene Expression Omnibus database, which showed a higher expression of RNF213 in adipose tissue samples of obese people. Overall this study gives new insights in the TNFα-mediated pathway in adipogenesis and suggests a role of RNF213 in adipogenesis via this pathway.View Full Text

bioinformatics↗

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↗