Biology subjects

Upadhyay, V.

Publications and source records attributed to Upadhyay, V..

24 records · Page 2Linked to original sources

bioRxiv2021

SARS-CoV-2 comes up with a perfect recipe for disaster: Each mutation plays a specific role in the evolution of variants of concern

Multiple mutations have been seen to undergo convergent evolution in SARS-CoV-2 variants of concern. One such evolution occurs in Beta, Gamma, and Omicron variants at three amino acid positions K417, E484, and N501 in the receptor binding domain of the spike protein. We examined the physical mechanisms underlying the convergent evolution of three mutations K417T/E484K/N501Y by delineating the individual and collective effects of mutations on binding to angiotensin converting enzyme 2 receptor, immune escape from neutralizing antibodies, protein stability and expression. Our results show that each mutation serves a distinct function that improves virus fitness supporting its positive selection, even though individual mutations have deleterious effects that make them prone to negative selection. Compared to the wild-type, K417T escapes Class 1 antibodies, has increased stability and expression; however, it has decreased receptor binding. E484K escapes Class 2 antibodies; however, it has decreased receptor binding, stability and expression. N501Y increases receptor binding; however, has decreased stability and expression. When these mutations come together, the deleterious effects are mitigated due to the presence of compensatory effects. Triple mutant K417T/E484K/N501Y has increased receptor binding, escapes both Class 1 and Class 2 antibodies, and has similar stability and expression as that of the wild-type. These results show the implications of presence of multiple mutations on virus evolution that enhance viral fitness on different fronts by balancing both positive and negative selection and improves the chances of selection of mutations together.

biophysics↗

bioRxiv2021

Interplay between protein stability, binding to ACE2 and escape from neutralizing antibodies determines the natural selection of SARS-CoV-2 receptor binding domain variants

Emergence of new SARS-CoV-2 variants has raised concerns at the effectiveness of vaccines and antibody therapeutics developed against the unmutated wild-type virus. We examined the effect of 12 most commonly occurring mutations in the receptor binding domain on its expression, stability, activity, and antibody escape potential-some of the factors that may influence the natural selection of mutants. Recombinant proteins were expressed in human cells. Stability was measured using thermal denaturation melts. Activity and antibody escape potential were measured using isothermal titration calorimetry in terms of binding to ACE2 and to a neutralizing human antibody CC12.1, respectively. Our results show that variants differ in their expression levels with the two least stable variants showing lesser expression. Out of the 8 well-expressed mutants, only 2 (N501Y and K417T/E484K/N501Y) showed stronger affinity to ACE2, 4 (Y453F, S477N, T478I and S494P) have similar affinity, whereas the other 2 (K417N and E484K) have weaker affinity when compared to the wild-type. In terms of CC12.1 binding, when compared to the wild-type, 4 variants (K417N, Y453F, N501Y and K417T/E484K/N501Y) have weaker affinity, 2 (S477N and S494P) have similar affinity, and 2 (T478I and E484K) have stronger affinity. Taken together, these results indicate that multiple factors contribute towards the natural selection of variants, and all these factors need be considered to understand the evolution of the virus. In addition, since not all variants can escape a given neutralizing antibody, antibodies to treat new variants can be chosen based on the specific mutations in that variant.

biophysics↗

bioRxiv2021

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs free energy change ({Delta}rG'o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has a higher prediction accuracy as compared to existing GC methods and can capture free energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictors ability to predict the Gibbs free energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic. This enables performing a thermodynamic analysis for synthetic pathways, thus safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs free energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor). Author summaryThe genome-scale metabolic networks consist of a large number of biochemical reactions interconnected in a complex system. The standard Gibbs free energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs free energy change with limited experimental measurements. However, current methods using functional groups have major issues that limit its ability to cover all the metabolites and reactions as well as the inability to consider stereochemistry leads to erroneous estimation of free energy that undergoes only stereochemical change such as isomerases. Here, we introduce a molecular fingerprint-based thermodynamic tool dGPredictor that enables stereochemistry in metabolites and thus improves the reaction coverage with higher prediction accuracy compared to current GC methods. It also allows the ability to predict free energy change for novel reactions which can aid the de novo metabolic pathway design tool to ensure the reaction feasibility. We apply and test our method on reactions in the KEGG database and isobutanol synthesis pathway. In addition, we provide an open-source user-friendly web interface to facilitate easy access for standard Gibbs free energy change of reactions at different physiological states.

bioinformatics↗

bioRxiv2020

Differences in the gut microbiomes of distinct ethnicities within the same geographic area are linked to host metabolic health

BackgroundThe human gut microbiota exhibits marked variation around the world, which has been attributed to dietary intake and other environmental factors. However, the degree to which ethnicity-associated differences in gut microbial community structure and function are maintained following immigration or in the context of metabolic disease is poorly understood. ResultsWe conducted a multi-omic study of 46 lean and obese East Asian and White participants living in the San Francisco Bay Area. 16S rRNA gene sequencing revealed significant differences between ethnic groups in bacterial richness and community structure. White individuals were enriched for the mucin-degrading Akkermansia muciniphila. East Asian participants had increased levels of multiple bacterial phyla, fermentative pathways detected by metagenomics, and the short-chain fatty acid end products acetate, propionate, and isobutyrate. Differences in the gut microbiota between the East Asian and White groups could not be explained by reported dietary intake, were more pronounced in lean individuals, and were associated with current geographical location. Microbiome transplantations into germ-free mice confirmed that the differences in the gut microbiota of the East Asian and White individuals we analyzed are independent of diet and that they differentially impact host body weight and adiposity in genetically identical mouse recipients. ConclusionsThe reported findings emphasize the utility of studying diverse ethnic groups within a defined geographical location and provide a starting point for dissecting the mechanisms contributing to the complex interactions between the gut microbiome and ethnicity-associated lifestyle, demographic, metabolic, and genetic factors.

microbiology↗

bioRxiv2020

GroEL/ES Buffers Entropic Traps In Folding Pathway During Evolution Of A Model Substrate

The folding landscape of proteins can change during evolution with the accumulation of mutations that may introduce entropic or enthalpic barriers in the protein folding pathway, making it a possible substrate of molecular chaperones in vivo. Can the nature of such physical barriers of folding dictate the feasibility of chaperone-assistance? To address this, we have simulated the evolutionary step to chaperone-dependence keeping GroEL/ES as the target chaperone and GFP as a model protein in an unbiased screen. We find that the mutation conferring GroEL/ES dependence in vivo and in vitro encode an entropic trap in the folding pathway rescued by the chaperonin. Additionally, GroEL/ES can edit the formation of non-native contacts similar to DnaK/J/E machinery. However, this capability is not utilized by the substrates in vivo. As a consequence, GroEL/ES caters to buffer mutations that predominantly cause entropic traps, despite possessing the capacity to edit both enthalpic and entropic traps in the folding pathway of the substrate protein.

molecular biology↗

bioRxiv2019

Diet induces reproducible alterations in the mouse and human gut microbiome

The degree to which diet reproducibly alters the human and mouse gut microbiota remains unclear. Here, we focus on the consumption of a high-fat diet (HFD), one of the most frequently studied dietary interventions in mice. We employed a subject-level meta-analysis framework for unbiased collection and analysis of publicly available 16S rRNA gene and metagenomic sequencing data from studies examining HFD in rodent models. In total, we re-analyzed 27 studies, 1101 samples, and 106 million reads mapping to 16S rRNA gene sequences. We report reproducible changes in gut microbial community structure both within and between studies, including a significant increase in the Firmicutes phylum and decrease in the Bacteroidetes phylum; however, reduced alpha diversity is not a consistent feature of HFD. Finer taxonomic analysis revealed that the strongest signal of HFD on microbiota species composition is Lactococcus spp., which we demonstrate is a common dietary contaminant through the molecular testing of dietary ingredients, culturing, microscopy, and germ-free mouse experiments. After in silico removal of Lactococcus spp., we employed machine learning to define a unique operational taxonomic unit (OTU)-based signature capable of predicting the dietary intake of mice and demonstrate that phylogenetic and gene-family transformations of this model are capable of accurately predicting human samples in controlled feeding settings (area under the receiver operator curve = 0.75 and 0.88 respectively). Together, these results demonstrate the utility of microbiome meta-analyses in identifying robust bacterial signals for mechanistic studies and creates a framework for the routine meta-analysis of microbiome studies in preclinical models.

microbiology↗