Biology subjects

Mishra, S. R.

Publications and source records attributed to Mishra, S. R..

4 recordsLinked to original sources

bioRxiv2025

No molecular evidence for Muller's ratchet in mitochondrial genomes

AO_SCPLOWBSTRACTC_SCPLOWMullers ratchet predicts that non-recombining genomes can accumulate deleterious mutations, though molecular evidence for it is rare. Previous studies have tried to detect ratchet-like behavior in mitochondria (mtDNA), contrasting the rate of nucleotide substitutions in mitochondrial transfer RNA (tRNA) genes with those in the nuclear genome. However, these studies relied on small datasets that could not control for the higher mutation rate in animal mtDNA. In this study, we re-evaluate evidence for Mullers ratchet with well-annotated mitochondrial and nuclear genomes from three species pairs. Across all three comparisons, mitochondrial tRNA (and protein-coding) genes are evolving under strong constraint, with dt/dS equal to 0.12 in primates, 0.07 in birds, and 0.04 in fruit flies. Compared with nuclear dt/dS, only the fruit fly comparison showed slightly lower constraint in mtDNA. Our results therefore provide evidence for strong, efficacious selection in animal mtDNA, with no ratchet-like accumulation of slightly deleterious mutations.

evolutionary biology↗

bioRxiv2025

Dual role of Human antigen R in Dengue virus infection: suppression of replication and promotion of cap-independent translation

Host RNA-binding proteins (RBPs) play a pivotal role in regulating dengue virus (DENV) translation and replication through interactions with untranslated regions (UTRs) of viral RNA. We investigated host proteins associated with detergent-resistant membranes (DRMs) of the DENV replication complex and identified Human antigen R (HuR) as a key RBP enriched in the DRM. HuR was found to negatively regulate DENV replication by binding the DENV-3'UTR and impeding the association of polypyrimidine tract-binding protein (PTB), a known RNA stabilizer. Additionally, infection-induced modulation of HuR stabilized host mRNAs involved in innate immunity. Interestingly, in vivo validation in AG129 mice model highlights an inverse correlation between HuR expression and viral load and implicates HuR in cytokine dysregulation. Notably, HuR promoted cap-independent translation of viral RNA during later stages of infection, when cap-dependent translation is suppressed. These findings reveal a dual role for HuR: restricting viral RNA replication while enhancing translation, highlighting its critical, phase-specific function in the DENV life cycle. Author summaryUnderstanding how Dengue virus interacts with human cells is key to finding better treatments. Our study looked at a human protein called HuR, which normally control the stability and the use of RNA inside our cells. We discovered that HuR has two important but opposite roles during dengue infection. Early on, it slows down the viruss ability to make copies of its genetic material, helping to reduce infection. But later, when the virus struggles to use the normal method of making proteins, HuR steps in to help the virus make its proteins in a different way. This double-edged role of HuR shows how the virus cleverly uses host cell machinery at different stages of infection. Overall, this study uncovers the novel role of a specific RNA-binding protein of the host in orchestrating dengue virus infection and pathogenesis, highlighting its potential as a target for antiviral therapies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/666571v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1a5a0fdorg.highwire.dtl.DTLVardef@ec7734org.highwire.dtl.DTLVardef@860cf0org.highwire.dtl.DTLVardef@bb65f5_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Estimating recombination using only the allele frequency spectrum

Standard methods for estimating the population recombination parameter,{rho} , are dependent on sampling individual genotypes and calculating various types of disequilibria. However, recent machine learning (ML) approaches to estimating recombination have used pooled sequencing data, which does not sample individual genotypes and cannot be used to calculate disequilibria beyond the length of a single sequence read. Motivated by these results, this study examines the "black box" of such ML methods to understand what signals are being used to infer recombination rates. We find that it is indeed possible to estimate recombination solely using the allele frequency spectrum, and we provide a genealogical interpretation of these results. We further show that even a simplified representation of the allele frequency spectrum can be used to estimate recombination. We demonstrate the accuracy of such inferences using both simulations and data from humans. These results offer a new way to understand the effects of recombination on patterns of sequence data, as well as providing an example of how the internal workings of ML methods can give insight into biological processes. Article SummaryMachine learning methods are becoming more common, offering powerful approaches to study the natural world. We investigated a popular machine learning method to see how it worked, discovering that it was exploiting data (the allele frequency spectrum) to estimate genetic recombination rates that had not been considered before. Our study demonstrates that this approach is indeed quite powerful, opening up new avenues of research. The work also demonstrates that looking inside machine learning models can sometimes teach us novel things about nature.

evolutionary biology↗

bioRxiv2020

Corona virus fear among health workers during the early phase of pandemic response in Nepal: a web-based cross-sectional study

BackgroundHealth workers involved in COVID-19 response might be at risk of developing fear and psychological distress. This study aimed to identify factors associated with COVID-19 fear among health workers in Nepal during the early phase of pandemic. MethodsA web-based cross-sectional survey was conducted in the month of April-May 2020 among 475 health workers directly involved in COVID-19 management. The Fear Scale of COVID 19 (FCV-19S) was used to measure the status of fear. Scatter plots were used to observe the relationship between fear and other psychological outcomes: anxiety, depression and insomnia. Multivariable logistic regression was done to identify factors associated with COVID fear. ResultsCOVID-19 fear score was moderately correlated with anxiety and depression, and weakly correlated with insomnia (p<0.001). Nurses (AOR=2.29; 95% CI: 1.23-4.26), health workers experiencing stigma (AOR=1.83; 95% CI: 1.12-2.73), those working in affected district(AOR=1.76; 95% CI: 1.12-2.77) and presence of family member with chronic diseases (AOR=1.50; 95% CI: 1.01-2.25) was associated with higher odds of developing COVID-19 fear as compared to other health workers, health workers not experiencing stigma, working in non-affected district and not having family member with chronic diseases respectively. ConclusionNurses, health workers facing stigma, those working in affect district and having family member with chronic diseases were more at risk of developing COVID-19 fear. It is thus recommended to improve work environment to reduce fear among health workers, employ stigma reduction interventions, and ensure personal and family support for those having family member with chronic diseases.

animal behavior and cognition↗