Biology subjects

Soni, V.

Publications and source records attributed to Soni, V..

29 records · Page 2Linked to original sources

bioRxiv2024

A whole-genome scan for evidence of recent positive and balancing selection in aye-ayes (Daubentonia madagascariensis) utilizing a well-fit evolutionary baseline model

The aye-aye (Daubentonia madagascariensis) is one of the 25 most endangered primate species in the world, maintaining amongst the lowest genetic diversity of any primate measured to date. Characterizing patterns of genetic variation within aye-aye populations, and the relative influences of neutral and selective processes in shaping that variation, is thus important for future conservation efforts. In this study, we performed the first whole-genome scans for recent positive and balancing selection in the species, utilizing high-coverage population genomic data from newly sequenced individuals. We generated null thresholds for our genomic scans by creating an evolutionarily appropriate baseline model that incorporates the demographic history of this aye-aye population, and identified a small number of candidate genes. Most notably, a suite of genes involved in olfaction -- a key trait in these nocturnal primates -- were identified as experiencing long-term balancing selection. We also conducted analyses to quantify the expected statistical power to detect positive and balancing selection in this population using site frequency spectrum-based inference methods, once accounting for the potentially confounding contributions of population history, recombination and mutation rate variation, and purifying and background selection. This work, presenting the first high-quality, genome-wide polymorphism data across the functional regions of the aye-aye genome, thus provides important insights into the landscape of episodic selective forces in this highly endangered species.

evolutionary biology↗

bioRxiv2024

Inferring demographic and selective histories from population genomic data using a two-step approach in species with coding-sparse genomes: an application to human data

The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

genetics↗

bioRxiv2024

Development and Application of Decontamination Methods for the Re-Use of Laboratory Grade Plastic Pipette Tips

During the SARS-CoV-2 pandemic, a need for methods to decontaminate and reuse personal protective equipment (PPE) and medical plastics became a priority. In this investigation we aimed to develop a contamination evaluation protocol for laboratory pipette tips, after decontamination. Decontamination methods tested in this study included cleaning with a common laboratory detergent (2.5% Alconox(R) solution followed with steam decontamination), exposure of ozone vapor at 250 and 14400 PPM * minute, and exposure to cold atmospheric plasma (CAP). All tips (control and experimental groups) were introduced to the methods described, while tips exposed to DNA extracts of Aeromonas hydrophila (ATCC-23211) were assessed for experimental groups. Decontamination was determined by turnover ratio and log reduction in detectable genomic material on the contaminated products using real-time quantitative PCR (qPCR) assay. Our results showed, cleaning tips with lab detergents along with steam decontamination removed genetic material, resulting in the highest log reduction, compared with ozone or CAP treatments. Detergent/washing methods showed the highest turnover ratio (95.9 %) and log reduction (5.943). However, the excessive residue (post-cleaning) on the plastic, within inner filters, and tip boxes suggested that washing with lab detergents was not favorable for reuse. Ozone vapor at 14400 PPM * minutes showed the second highest turnover ratio (98.4 %) and log reduction (4.511). CAP exposure with tips inverted (the tip end exposed closer to the plasma flame) for 1 minute showed a turnover ratio of (68.3 %) and log reduction (4.002). Relatively, lower turnover ratio and log reduction of CAP could be attributed to development/optimization of treatment conditions, including increases in exposure time and relative to tip positioning.

molecular biology↗

bioRxiv2024

The Bacillus subtilis yqgC-sodA operon protects magnesium-dependent enzymes by supporting manganese efflux

Microbes encounter a myriad of stresses during their life cycle. Dysregulation of metal ion homeostasis is increasingly recognized as a key factor in host-microbe interactions. Bacterial metal ion homeostasis is tightly regulated by dedicated metalloregulators that control uptake, sequestration, trafficking, and efflux. Here, we demonstrate that deletion of the Bacillus subtilis yqgC-sodA (YS) complex operon, but not deletion of the individual genes, causes hypersensitivity to manganese (Mn). YqgC is an integral membrane protein of unknown function and SodA is a Mn-dependent superoxide dismutase (MnSOD). The YS strain has reduced expression of two Mn efflux proteins, MneP and MneS, consistent with the observed Mn sensitivity. The YS strain accumulated high levels of Mn, had increased reactive radical species (RRS), and had broad metabolic alterations that can be partially explained by the inhibition of Mg-dependent enzymes. Although the YS operon deletion strain and an efflux-deficient mneP mneS double mutant both accumulate Mn and have similar metabolic perturbations they also display phenotypic differences. Several mutations that suppressed Mn intoxication of the mneP mneS efflux mutant did not benefit the YS mutant. Further, Mn intoxication in the YS mutant, but not the mneP mneS strain, was alleviated by expression of Mg-dependent, chorismate-utilizing enzymes of the menaquinone, siderophore, and tryptophan (MST) family. Therefore, despite their phenotypic similarities, the Mn sensitivity in the mneP mneS and the yqgC-sodA deletion mutants results from distinct enzymatic vulnerabilities. ImportanceBacteria require multiple trace metal ions for survival. Metal homeostasis relies on the tightly regulated expression of metal uptake, storage, and efflux proteins. Metal intoxication occurs when metal homeostasis is perturbed and often results from enzyme mis-metalation. In Bacillus subtilis, MnSOD is the most abundant Mn-containing protein and is important for oxidative stress resistance. Here, we report novel roles for MnSOD and a co-regulated membrane protein, YqgC, in Mn homeostasis. Loss of both MnSOD and YqgC (but not the individual mutations) prevents the efficient expression of Mn efflux proteins and leads to a large-scale perturbation of the metabolome due to inhibition of Mg-dependent enzymes, including key chorismate-utilizing MST (menaquinone, siderophore, and tryptophan) family enzymes.

microbiology↗

bioRxiv2023

The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects

Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and{delta} a{delta}i). We demonstrate that, if not properly modelled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination prior to utilizing population genomic data to quantify the effects of genetic drift (i.e., as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modelled in downstream inference. Significance StatementDespite evidence from numerous species that mutation and recombination rates vary along the genome, both rates tend to be modelled as fixed when performing population genetic inference. The impact of failing to account for this rate heterogeneity on the estimation of demographic and selective parameters has yet to be well quantified; thus, we here study this effect by comparing inference under both fixed and variable rate scenarios. Our results demonstrate that unaccounted for rate heterogeneity can increase uncertainty and lead to mis-inference in certain scenarios. We highlight the importance of utilizing mutation and recombination rate maps where possible, and of modelling the uncertainty underlying these estimates.

genetics↗

bioRxiv2023

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong. Teaser TextOutlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.

genomics↗

bioRxiv2022

Mutations in rpoB that confer rifampicin resistance can alter levels of peptidoglycan precursors and affect β-lactam susceptibility

Bacteria can adapt to stressful conditions through mutations affecting the RNA polymerase core subunits that lead to beneficial changes in transcription. In response to selection with rifampicin (RIF), mutations arise in the RIF resistance determining region (RRDR) of rpoB that reduce antibiotic binding. These changes can also alter transcription and thereby have pleiotropic effects on bacterial fitness. Here, we studied the evolution of resistance in Bacillus subtilis to the synergistic combination of RIF and the {beta}-lactam cefuroxime (CEF). Two independent evolution experiments led to the recovery of a single rpoB allele (S487L) that was able to confer resistance to RIF and CEF through a single mutation. Two other common RRDR mutations made the cells 32x more sensitive to CEF (H482Y) or led to only modest CEF resistance (Q469R). The diverse effects of these three mutations on CEF resistance are correlated with differences in the expression of peptidoglycan (PG) synthesis genes and in the levels of two metabolites crucial in regulating PG synthesis, glucosamine-6-phosphate (GlcN-6-P) and UDP-N-acetylglucosamine (UDP-GlcNAc). We conclude that RRDR mutations can have widely varying effects on pathways important for cell wall biosynthesis, and this may restrict the spectrum of mutations that arise during combination therapy. ImportanceRifampicin (RIF) is one of the most valued drugs in the treatment of tuberculosis. TB treatment relies on a combination therapy, and for multidrug resistant strains may include {beta}-lactams. Mutations in rpoB present a common route for emergence of resistance to RIF. In this study, using B. subtilis as a model, we evaluate the emergence of resistance for the synergistic combination of RIF and the {beta}-lactam cefuroxime (CEF). One clinically-relevant rpoB mutation conferred resistance to both RIF and CEF, whereas two others increased CEF sensitivity. We were able to link these phenotypes to accumulation of specific PG precursors. Mainly, UDP-GlcNAc through its GlmR mediated influence on GlmS activity has a strong impact on CEF resistance. Since these mutations are clinically relevant, these effects on CEF sensitivity may help refine the use of {beta}-lactams in TB therapy.

microbiology↗

bioRxiv2021

Site level factors that affect the rate of adaptive evolution in humans and chimpanzees; the effect of contracting population size.

It has previously been shown in other species that the rate of adaptive evolution is higher at sites that are more exposed in a protein structure and lower between amino acid pairs that are more dissimilar. We have investigated whether these patterns are found in the divergence between humans and chimpanzees using an extension of the MacDonald-Kreitman test. We confirm previous findings and find that the rate of adaptive evolution, relative to the rate of mutation, is higher for more exposed amino acids, lower for amino acid pairs that are more dissimilar in terms of their polarity, volume and lower for amino acid pairs that are subject to stronger purifying selection, as measured by the ratio of the numbers of non-synonymous to synonymous polymorphisms (pN /pS). However, the slope of this latter relationship is significantly shallower than in Drosophila species. We suggest that this is due to the population contraction that has occurred since humans and chimpanzees diverged. We demonstrate theoretically that population size reduction can generate an artefactual positive correlation between the rate of adaptive evolution and any factor that is correlated to the mean strength of selection acting against deleterious mutations, even if there has been no adaptive evolution (the converse is also expected). Our measure of selective constraint, pN /pS, is negatively correlated to the mean strength of selection, and hence we would expect the correlation between the rate of adaptive evolution to also be negatively correlated to pN /pS, if there is no adaptive evolution. The fact that our rate of adaptive evolution is positively correlated to pN /pS suggests that the correlation does genuinely exist, but that is has been attenuated by population size contraction.

evolutionary biology↗

bioRxiv2021

Factors that affect the rates of adaptive and non-adaptive evolution at the gene level in humans and chimpanzees

The rate of amino acid substitution has been shown to be correlated to a number of factors including the rate of recombination, the age of the gene, the length of the protein, mean expression level and gene function. However, the extent to which these correlations are due to adaptive and non-adaptive evolution has not been studied in detail, at least not in hominids. We find that the rate of adaptive evolution is significantly positively correlated to the rate of recombination, protein length and gene expression level, and negatively correlated to gene age. The correlations remain significant when each factor is controlled for in turn, except when controlling for expression in an analysis of protein length; and they also remain significant, or marginally significant, when biased gene conversion is controlled for. However, the positive correlations could be an artefact of population size contraction. We also find that the rate of non-adaptive evolution is negatively correlated to each factor, and all these correlations survive controlling for each other and biased gene conversion. Finally, we examine the effect of gene function on rates of adaptive and non-adaptive evolution; we confirm that virus interacting proteins (VIPs) have higher rates of adaptive and lower rates of non-adaptive evolution, but we also demonstrate that there is significant variation in the rate of adaptive and non-adaptive evolution between GO categories when removing VIPs. We estimate that the VIP/non-VIP axis explains about 5-8x more of the variance in evolutionary rate than GO categories.

evolutionary biology↗

bioRxiv2021

Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy

Current chemotherapy against Mycobacterium tuberculosis (Mtb), an important human pathogen, requires a multidrug regimen lasting several months. While efforts have been made to optimize therapy by exploiting drug-drug synergies, testing new drug combinations in relevant host environments remains arduous. In particular, host environments profoundly affect the bacterial metabolic state and drug efficacy, limiting the accuracy of predictions based on in vitro assays alone. In this study, we utilize conditional Mtb knockdown mutants of essential genes as an experimentally-tractable surrogate for drug treatment, and probe the relationship between Mtb carbon metabolism and chemical-genetic interactions (CGI). We examined the anti-tubercular drugs isoniazid, rifampicin and moxifloxacin, and found that CGI are differentially responsive to the metabolic state, defining both environment-independent and -dependent interactions. Specifically, growth on the in vivo-relevant carbon source, cholesterol, reduced rifampicin efficacy by altering mycobacterial cell surface lipid composition. We report that a variety of perturbations in cell wall synthesis pathways restore rifampicin efficacy during growth on cholesterol, and that both environment-independent and cholesterol-dependent in vitro CGI could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of novel chemical-genetic-environmental interactions that can be used to optimize drug-drug interactions as well as provide a framework for understanding in vitro correlates of in vivo efficacy. SignificanceEfforts to improve tuberculosis therapy include optimizing multi-drug regimens to take advantage of drug-drug synergies. However, the complex host environment has a profound effect on bacterial metabolic state and drug activity, making predictions of optimal drug combinations difficult. In this study, we leverage a newly developed library of conditional knockdown Mycobacterium tuberculosis mutants in which genetic depletion of essential genes mimics the effect of drug therapy. This tractable system allowed us to assess the effect of growth condition on predicted drug-drug interactions. We found that these interactions can be differentially sensitive to the metabolic state and select in vitro-defined interactions can be leveraged to accelerate bacterial killing during infection. These findings suggest new strategies for optimizing tuberculosis therapy.

microbiology↗

bioRxiv2021

A new test suggests that balancing selection maintains hundreds of non-synonymous polymorphisms in the human genome

The role that balancing selection plays in the maintenance of genetic diversity remains unresolved. Here we introduce a new test, based on the McDonald-Kreitman test, in which the number of polymorphisms that are shared between populations is contrasted to those that are private at selected and neutral sites. We show that this simple test is robust to a variety of demographic changes, and that it can also give a direct estimate of the number of shared polymorphisms that are directly maintained by balancing selection. We apply our method to population genomic data from humans and conclude that more than a thousand non-synonymous polymorphisms are subject to balancing selection.

evolutionary biology↗