Biology subjects

Sethi, P.

Publications and source records attributed to Sethi, P..

9 recordsLinked to original sources

bioRxiv2026

The influence of drug dosing regimen on extinction and adaptation during experimental evolution in Candida albicans

The antifungal azole drug fluconazole (FLC) is frequently prescribed for treating Candida albicans infections. Azole drugs are typically fungistatic; they inhibit cell growth rather than kill susceptible cells within a population. Therefore, fungal populations can adapt to fluconazole through increased drug resistance or increased drug tolerance (the ability of a subpopulation of susceptible cells to grow slowly at inhibitory drug concentrations). To our knowledge, no prior experiments have examined how different factors influence extinction propensity, although previous in vitro studies show that both drug concentration and strain background can influence adaptation. To determine how the drug dosing regimen, namely fluconazole concentration and transfer duration (i.e., the amount of time between transfer into fresh medium), influenced extinction and adaptation in Candida albicans, we conducted a fully factorial series of short in vitro evolution experiments. Dosing regimen significantly influenced extinction: all replicates evolved at all drug concentrations survived the 48 h and 72 h transfer experiments, yet the extinction rate increased sharply from low to high drug concentration with 24 h transfers. Increases in drug tolerance were frequent regardless of the dosing regimen. In contrast, drug resistance and growth rate both tended to decrease following evolution at higher drug concentrations. Fluconazole-evolved replicates exhibited frequent increases in cross-tolerance and decreases in cross-resistance in a panel of other azole drugs. Genome size changes were common in some strain backgrounds evolved to higher fluconazole concentrations, regardless of transfer times. Combined, our results indicate that many experimental factors can influence the trajectory of adaptation, and show that extinction in a fungistatic drug occurs readily at high drug concentrations with rapid transfers.

microbiology↗

bioRxiv2026

Synthesis cost is a hidden driver of convergent amino acid composition in plastid ribosomal proteins

Protein evolution is a walk in the evolutionary space directed by mutation and selection. While functional and structural constraints serve as the main determinant of amino acid substitution in most proteins, synthesis cost and mutational bias can also alter the direction and rate of amino acid evolution, especially in systems experiencing relaxed selection. Here, we focused on the highly expressed plastid ribosomal proteins (PRP), which comprise 58 conserved proteins encoded by both plastid and nuclear genomes. Relaxed selection has been repeatedly identified in three distantly related plant lineages, providing a valuable comparative framework to investigate the significance of synthesis cost and mutation. We first demonstrated that the hemiparasitic tribe Cymbarieae (Orobanchaceae) represented a new case where concerted cyto-nuclear rate elevation occurs in their PRP. Further investigation revealed convergent shifts in amino acid composition in all four plant lineages attributable to arginine-to-lysine and methionine-to-isoleucine/valine/leucine substitutions. The replacement residues were biophysically similar but had lower molecular weight and shorter side chains, which significantly destabilized protein folding as demonstrated by protein structure modeling. We found that the composition shifts ran counter to the expectation of mutational bias but were consistent with the expectation of synthesis cost minimization, which is potentially adaptive for highly expressed PRP. Further, cost minimization significantly influenced all conservative substitutions between biophysically similar amino acids but was absent in non-conservative substitutions. We thus propose cost minimization as a secondary selective drive for protein evolution in PRP, unmasked in lineages and sites with relaxed selection on their function.

evolutionary biology↗

bioRxiv2026

Genetic background shapes AI-predicted variant effects

Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.

genomics↗

bioRxiv2026

Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions

Bacteria play fundamental roles in ecosystems, human health, and biotechnology. Although bacterial genome sequencing data have accumulated rapidly over the past decade, the metabolic and ecological functions carried out by most sequenced bacteria remain poorly understood, apart from a few well-studied taxa and traits. Establishing a general framework that comprehensively captures the relationship between bacterial genomes and the diverse biological functions they encode remains a major challenge, as it requires embedding individual genes within their broader genomic context and modeling their combined effects across complex biological pathways and networks. The difficulty is further compounded by the limited functional annotations available for most bacterial genomes. Here, we introduce BacPT, a proteome foundation model trained on tens of thousands of complete genomes spanning diverse bacterial taxa. BacPT captures both local and genome-wide information, enabling the generation of contextualized gene embeddings and functionally rich representations of the whole genome. We demonstrate the utility of BacPT across diverse prediction tasks spanning multiple biological scales. BacPT embeddings improve the prediction of enzyme activities, biosynthetic gene clusters, metabolic traits, and ecological interaction outcomes. Our results highlight that unsupervised deep learning applied at the scale of entire proteomes provides a powerful approach for characterizing gene interactions and mapping functional landscapes for bacteria.

genomics↗

bioRxiv2025

Learning gene interactions and functional landscapes from entire bacterial proteomes

Unraveling complex gene interactions and understanding their functions in the genomes of bacteria will provide critical advancements in fields including bacterial genome evolution, microbiome studies, as well as drug and natural product discovery. This is a challenging problem due to the structural and functional complexity of bacterial genomes, and issues including poor gene annotation in non-model species. Language models (LMs) provide a feasible framework for learning the complex interactions among genes from a large number of publicly available, unannotated bacterial genomes. However, applications of language models have mostly been limited to developing models trained on short genomic sequences. Here, we introduce the first whole bacteria proteome foundation model to our knowledge. Our model was trained on ESM embeddings of tens of thousands of full-size proteomes and can generate contextual embeddings for individual proteins as well as embeddings representing the entire genome. We show that our model captures gene-gene interactions and genomic integrity. We further demonstrate that the learned embeddings can be used to achieve state-of-the-art performances for downstream tasks such as identifying operons, and predicting genotype-phenotype maps.

genetics↗

bioRxiv2024

Importance of higher-order epistasis in large protein sequence-function relationships

Protein sequence-function relationships are inherently complex, as amino acids at different positions can interact in highly unpredictable ways. A key question for protein evolution and engineering is how often epistasis extends beyond pairwise interactions to involve three or more positions. Although experimental data has accumulated rapidly in recent years, addressing this question remains challenging, as the number of possible interactions is typically enormous even for proteins of moderate size. Here, we introduce an interpretable machine learning framework for studying higher-order epistasis scalable to full-length proteins. Our model builds on the transformer architecture, with key modifications allowing us to assess the importance of higher-order interactions by fitting a series of models with increasing complexity. Applying our method to 10 large protein sequence-function datasets, we found that while additive effects explain the majority of the variance, within the epistatic component, the contribution of higher-order epistasis ranges from negligible to up to 60%. We also found higher-order epistasis is particularly important for generalizing locally sampled fitness data to distant regions of sequence space and for modeling an additional multi-peak fitness landscape. Our findings suggest that higher-order epistasis can play important roles in protein sequence-function relationships, and thus should be properly considered in protein engineering and evolutionary data analysis.

genetics↗

bioRxiv2023

Clustered Regularly Interspaced Short Palindromic Repeats-Cas system regulates Salmonella virulence

ObjectivesInvestigating the type 1-E CRISPR-Cas-mediated regulation of Salmonella pathogenesis. MethodsWe assessed the pathogenicity of the wild-type and CRISPR-Cas knockout strains using infection models. The mechanisms were elucidated using antimicrobial assays and expression analysis. ResultsCRISPR-Cas knockout strains were defective in invasion and proliferation in intestinal epithelial cells and macrophages. However, proliferation defects were not observed in the Gp91phox-/- macrophages, suggesting the systems role in antioxidant defence. The knockout strains show hampered colonization in in-vivo infection models, possibly due to increased sensitivity against innate immune barriers like antimicrobial peptides, complement proteins and oxidative stress. The expression studies of various virulence regulators: pmr genes, anti-oxidant genes, SPI-1 and SPI-2 encoded master regulators, and effectors showed repressed expression in the knockout strains. Some of these genes could be directly regulated by the CRISPR-spacers owing to partial complementarity between the sequences. ConclusionOverall, our study shows that the CRISPR-Cas system positively regulates Salmonella pathogenesis by regulating the expression of different virulence factors.

microbiology↗

bioRxiv2021

Impaired T-cell and antibody immunity after COVID-19 infection in chronically immunosuppressed transplant recipients

Assessment of T-cell immunity to the COVID-19 coronavirus requires reliable assays and is of great interest, given the uncertain longevity of the antibody response. Some recent reports have used immunodominant spike (S) antigenic peptides and anti-CD28 co-stimulation in varying combinations to assess T-cell immunity to SARS-CoV-2. These assays may cause T-cell hyperstimulation and could overestimate antiviral immunity in chronically immunosuppressed transplant recipients, who are predisposed to infections and vaccination failures. Here, we evaluate CD154-expressing T-cells induced by unselected S antigenic peptides in 204 subjects-103 COVID-19 patients and 101 healthy unexposed subjects. Subjects included 72 transplanted and 130 non-transplanted subjects. S-reactive CD154+T-cells co-express and can thus substitute for IFN{gamma} (n=3). Assay reproducibility in a variety of conditions was acceptable with coefficient of variation of 2-10.6%. S-reactive CD154+T-cell frequencies were a) higher in 42 healthy unexposed transplant recipients who were sampled pre-pandemic, compared with 59 healthy non-transplanted subjects (p=0.02), b) lower in Tr COVID-19 patients compared with healthy transplant patients (p<0.0001), c) lower in Tr patients with severe COVID-19 (p<0.0001), or COVID-19 requiring hospitalization (p<0.05), compared with healthy Tr recipients. S-reactive T-cells were not significantly different between the various COVID-19 disease categories in NT recipients. Among transplant recipients with COVID-19, cytomegalovirus co-infection occurred in 34%; further, CMV-specific T-cells (p<0.001) and incidence of anti-receptor-binding-domain IgG (p=0.011) were lower compared with non-transplanted COVID-19 patients. Healthy unexposed transplant recipients exhibit pre-existing T-cell immunity to SARS-CoV-2. COVID-19 infection leads to impaired T-cell and antibody responses to SARS-CoV-2 and increased risk of CMV co-infection in transplant recipients.

immunology↗

bioRxiv2020

Automated Morphometric Analysis Reveals Plasticity Induced by Chronic Antidepressant Treatment in Hippocampal Astrocytes

Nervous system development and plasticity involves changes in cellular morphology, making morphological analysis a valuable exercise in the study of nervous system development, function and disease. Morphological analysis is a time-consuming exercise requiring meticulous manual tracing of cellular contours and extensions. We have developed a software tool, called SMorph, to rapidly analyse the morphology of cells of the nervous system. SMorph performs completely automated Sholl analysis. It extracts 23 morphometric features based on cell images and Sholl analysis parameters, followed by Principal Component Analysis. SMorph is tested on neurons, astrocytes and microglia and reveals subtle changes in cell morphology. Using SMorph, we found that chronic 21-day treatment with antidepressant desipramine results in a significant structural remodeling in hippocampal astrocytes. Given the proposed involvement of astroglial structural changes and atrophy in major depression in humans, our results reveal a novel kind of structural plasticity induced by chronic antidepressant administration.

neuroscience↗