Biology subjects

Dhar, R.

Publications and source records attributed to Dhar, R..

10 recordsLinked to original sources

bioRxiv2025

Pan-cancer analysis reveals mtDNA copy number as a key determinant of mutational load and disease progression in cancer

Mitochondrial DNA (mtDNA) copy number determines the functional state of mitochondria and thus, influences cellular energy production, growth, metabolism, and stress response. Variation in mtDNA copy number has been observed across many cancer types and has been linked to changes in gene expression programs. However, whether mtDNA copy number has any influence on mutation accumulation in cancer is unknown. Further, the actual impact of mtDNA copy number variation on cancer progression remains unclear with conflicting reports across a few cancer types. Here, through a pan-cancer analysis of whole genome data, we show that mtDNA copy number increases with an increase in mutational load in cancer. The increase in mtDNA copy number bears a signature of compensation for detrimental effects on mitochondrial function caused by increased mutational load. We also show that the samples with low mtDNA generally have increased expression of cancer promoting genes whereas high mitochondrial activity is linked to higher activity of tumor suppressor genes. We further demonstrate that low mtDNA copy number increases the likelihood of chemotherapy resistance. Taken together, these results reveal a central role of mtDNA copy number variation in determining mutational load, disease progression, and therapy response across a variety of cancer types. These findings can help design new strategies for disease management and therapy development in cancer.

genomics↗

bioRxiv2025

Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression

Transcription factors play a central role in cancer growth, progression, and metastasis, and contribute to intratumor phenotypic plasticity which enable drug tolerance and cancer relapse. Changes in the regulatory activities of transcription factors in cancer may not always be detected by investigating mutational signatures or differential expression of the transcription factor, as done in traditional analysis. In addition, past studies have focused on the activities of transcription factors in tumors as a whole and thus, have not fully captured the heterogeneity in gene regulation among different cell types within the tumor microenvironment. In this work, through an analysis of the transitions in regulatory network architecture and gene regulation dynamics, we identify the central transcription factors associated with lung adenocarcinoma progression. The gene NR2F1, associated with neurodevelopment and cancer dormancy, emerge as a key transcription factor in the progression of lung adenocarcinoma. We further identify transcription factors that are only active in cancer samples and uncover how changes in gene regulation dynamics influence intratumor heterogeneity. Taken together, our work elucidates the transitions in gene regulatory network during cancer progression, identifies central transcription factors in this process, and reveals the complex regulatory changes cooccurring in different cell types within the tumor microenvironment.

systems biology↗

bioRxiv2024

Exaptation and de novo mutations transcend cryptic variations as drivers of adaptation in yeast

Many organisms live in predictable environments with periodic variation in growth condition which can allow populations to accumulate cryptic genetic variations. Cryptic variations can facilitate adaptation to new environments, as observed in evolution experiments with a ribozyme and a protein. Whether the same holds for cell populations remains unclear. Alternatively, living in a near-constant condition can lead to loss of nonessential cellular functions, which could be maladaptive in new environments. Through laboratory evolution experiments in yeast, we show that populations grown in a predictable nutrient-rich environment for 1000 generations start to lose their ability to respond and adapt to new stressful environments. Growth of yeast populations in the nutrient-rich environment was associated with modest fitness increase in this environment, metabolic remodeling, and increased lipid accumulation. In novel stressful environments, however, these populations generally had reduced fitness, except in salt-stress where lipid accumulation seemed to provide osmotic protection. We further found that adaptation to stressors was primarily driven by de novo mutations, with very little contribution from the mutations accumulated prior to the exposure to stressors. Thus, our work suggests that in the absence of occurrence of new environments, natural populations might not accumulate cryptic variations that could be beneficial for adaptation to these environments. In addition, presence of selection in predictable condition in natural populations may purge away some of the cryptic variations. Taken together, these findings raise questions about persistence of cryptic variations in natural populations and their importance in evolutionary adaptation.

evolutionary biology↗

bioRxiv2023

Ribosome demand links transcriptional bursts to protein expression noise

Stochastic variation in protein expression generates phenotypic heterogeneity in a cell population and has an important role in antibiotic persistence, mutation penetrance, tumor growth and therapy resistance. Studies investigating molecular origins of noise have predominantly focused on the transcription process. However, the noise generated in the transcription process is further modulated by translation. This influences the expression noise at the protein level which eventually determines the extent of phenotypic heterogeneity in a cell population. Studies across different organisms have revealed a positive association between translational efficiency and protein noise. However, the molecular basis of this association has remained unknown. In this work, through stochastic modeling of translation in single mRNA molecules and empirical measurements of protein noise, we show that ribosome demand associated with high translational efficiency in a gene drives the correlation between translational efficiency and protein noise. We also show that this correlation is present only in genes with bursty transcription. Thus, our work reveals the molecular basis of how coding sequence of genes, along with their promoters, can regulate noise. These findings have important implications for investigating protein noise and phenotypic heterogeneity across biological systems.

systems biology↗

bioRxiv2023

Evolutionary engineering a larger porin using a loop-to-hairpin mechanism

In protein evolution, diversification is generally driven by genetic duplication. The hallmarks of this mechanism are visible in the repeating topology of various proteins. In outer membrane {beta}-barrels, duplication is visible with {beta}-hairpins as the repeating unit of the barrel. In contrast to the overall use of duplication in diversification, a computational study hypothesized evolutionary mechanisms other than hairpin duplications leading to increases in the number of strands in outer membrane {beta}-barrels. Specifically, the topology of some 16- and 18-stranded {beta}-barrels appear to have evolved through a loop to {beta}-hairpin transition. Here we test this novel evolutionary mechanism by creating a chimeric protein from an 18-stranded {beta}-barrel and an evolutionarily related 16-stranded {beta}-barrel. The chimeric combination of the two was created by replacing loop L3 of the 16-stranded barrel with the sequentially matched transmembrane {beta}-hairpin region of the 18-stranded barrel. We find the resulting chimeric protein is stable and has characteristics of increased strand number. This study provides the first experimental evidence supporting the evolution through a loop to {beta}-hairpin transition. HighlightsO_LIWe find evidence supporting a novel diversification mechanism in membrane {beta}-barrels C_LIO_LIThe mechanism is the conversion of an extracellular loop to transmembrane {beta}-hairpin C_LIO_LIA chimeric protein modeling this mechanism folds stably in the membrane C_LIO_LIThe chimera has more {beta}-structure and a larger pore, consistent with a loop-to-hairpin transition C_LI

biochemistry↗

bioRxiv2021

Membrane barrels are taller, fatter, inside-out soluble barrels

Up-and-down {beta}-barrel topology exists in both the membrane and soluble environment. However, {beta}-barrels are virtually the only topology that exist in the outer membrane. By comparing features of these structurally similar proteins, we can determine what features are particular to the environment rather than the fold. Here we compare structures of membrane {beta}-barrels to soluble {beta}-barrels and evaluate their relative size, shape, amino acid composition, hydrophobicity, and periodicity. We find that membrane {beta}-barrels are generally larger than soluble {beta}-barrels in with more strands per barrel and more amino acids per strand, making them wider and taller. We also find that membrane {beta}-barrels are inside-out soluble {beta}-barrels. The inward region of membrane {beta}-barrels have similar hydrophobicity to the outward region of soluble {beta}-barrels, and the outward region of membrane {beta}-barrels has similar hydrophobicity to the inward region of the soluble {beta}-barrels. Moreover, even though both types of {beta}-barrel have been assumed to have strands with amino acids that alternate in direction and hydrophobicity, we find that the membrane {beta}-barrels have more regular alternation than soluble {beta}-barrels. These features give insight into how membrane barrels maintain their fold and function in the membrane.

biophysics↗

bioRxiv2020

Transcription factor binding dynamics shape noise across biological processes

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as presence of the TATA box sequence and promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation has not yet been assessed. In addition, how well these features can predict noise also remains unclear. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) was a key predictor of noise. With an increase in the number of regulatory TFs, we observed a rise in the number of TFs that bound cooperatively. In addition, increased number of TFs meant more overlaps in TF binding sites that caused competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and applying stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

systems biology↗

bioRxiv2020

Genotypic and antigenic study of SARS-CoV-2 from an Indian isolate.

Coronaviruses (CoVs) are one of the largest groups of positive-sense RNA virus families within the Nidovirales order, which are further classified into four genera: alpha, beta, gamma, and delta. Coronaviruses have an extensive range of natural hosts and are known to be responsible for a broad spectrum of diseases in multiple species. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) that has unleashed a global threat to public health and the economy. Coronaviruses are extensively present in birds and mammals, with horseshoe bats (Rhinolophus affinis), being the reservoir for the ongoing SARS-CoV-2 that seems to have resulted from a zoonotic spillover to the human host, causing respiratory infections, lung injury and Acute Respiratory Distress Syndrome(ARDS). About six coronavirus serotypes are linked with the disease in humans, namely HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, SARS-CoV-2, and MERS-CoV. SARS-CoV-2 is the seventh CoV to infect humans. We analyzed the genome sequence of CoV-2 from isolates derived from China as well from India and encountered minute variations in their sequence. A cladogram analysis revealed the predominant strain circulating in India belongs to the A2a clad. We took one such strain (MT012098) and performed a rigorous in-silico genotypic and antigenic analysis to identify its relatedness to other strains. Further, we also performed a detailed prediction for B and T cell epitopes using BepiPred 2.0 server and NetCTL 1.2 server (DTU Bioinformatics), respectively. We hope this information may assist in an effective vaccine designing program against SARS-CoV-2.

biochemistry↗

bioRxiv2019

Changes in gene expression shift and switch genetic interactions

An important goal in disease genetics and evolutionary biology is to understand how mutations combine to alter phenotypes and fitness. Non-additive interactions between mutations occur extensively and change across conditions, cell types, and species, making genetic prediction a difficult challenge. To understand the reasons for this, we reduced the problem to a minimal system where we combined mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, a change in gene expression altered both the strength and type of genetic interactions. These seemingly complicated changes could, however, be predicted by a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. We show that similar changes will be observed for many genes. These results provide fundamental insights into genotype-phenotype maps and illustrate how changes in genetic interactions can be predicted using hierarchical mechanistic models.\n\nOne sentence SummaryDeep mutagenesis of the lambda repressor reveals that changes in gene expression will alter the strength and direction of genetic interactions between mutations in many genes.\n\nHighlightsO_LIDeep mutagenesis of the lambda repressor at two expression levels reveals extensive changes in mutational effects and genetic interactions\nC_LIO_LIGenetic interactions can switch from positive (suppressive) to negative (enhancing) as the expression of a gene changes\nC_LIO_LIA mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype accurately predicts changes in mutational effects and interactions\nC_LIO_LIChanges in expression will alter mutational effects and interactions for many genes\nC_LIO_LIFor some genes, perfect mechanistic models will never be able to predict how mutations of known effect combine without measurements of intermediate phenotypes\nC_LI

genetics↗

bioRxiv2018

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell variation in proliferation, drug resistance and mutation outcome

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we developed a high-throughput automated microscopy assay and used it to quantify the impact of deleting >1,500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondria state - but not content - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.

genomics↗