Biology subjects

Thakkar, N.

Publications and source records attributed to Thakkar, N..

12 recordsLinked to original sources

bioRxiv2025

Withania somnifera root extract reduces susceptibility of the model worm Caenorhabditis elegans to different types of stressors

BackgroundWithania somnifera, commonly known as Ashwagandha, is one of the most widely prescribed plants in the Indian system of traditional medicine. Its roots are believed to impart comprehensive health benefits supporting lifespan and healthspan through its anti-stress, immunomodulatory, and neuroprotective activity. Scientific validation of its claimed biological effects is warranted. MethodsThe nematode worm Caenorhabditis elegans was challenged with heat or certain neurotoxic/mitotoxic chemical stressors, and worm activity (healthspan) was compared between W. somnifera root extract (WSRE)-exposed and control worm populations, through an automated worm tracker. ResultsPrevious exposure of worms to WSRE conferred prophylactic benefit on them in face of subsequent challenge with rotenone, MnCl2, or levamisole. Worms pre-exposed to either of these toxins were able to recover faster in presence of WSRE. This extract was also able to reduce the susceptibility of wild type worms to heat-induced paralysis, and that of transgenic worms to beta-amyloid mediated paralysis. ConclusionsAdaptogenic potential of W. somnifera roots was demonstrated in worm model. WSRE can be said to possess a wide anti-stress spectrum as it protected the worms against different stressors inducing toxicity through different mechanisms including interference with acetylcholine and dopamine neurons and function. Our results support the candidature of W. somnifera roots as a potent nutraceutical.

pharmacology and toxicology↗

bioRxiv2025

Berberine, the major bioactive compound from Berberis aristata, attenuates virulence of multidrug resistant Chromobacterium violaceum at non-lethal concentrations by targeting bacterial efflux and denitrification machinery

Berberis aristata root extract, and berberine were assessed for their anti-pathogenic activity against multidrug resistant Chromobacterium violaceum. Berberine was found to be more potent than the parent extract with respect to attenuation of the pathogens virulence against the model host Caenorhabditis elegans. It also performed better than six different antibiotics, when compared at same concentration. Berberine was able to modulate multiple traits of the target pathogen, such as, biofilm formation, haemolytic activity, efflux/transport activity, and exopolysaccharide production. Repeated exposure of C. violaceum to berberine did not induce resistance. Whole transcriptome analysis of the berberine-treated C. violaceum revealed differential expression of genes associated with stress response, efflux, denitrification, and metalloproteases. Downregulation of two genes of the denitrification pathway, nirK and norB, was confirmed through RT-PCR too.

microbiology↗

bioRxiv2024

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.

pathology↗

bioRxiv2024

Withania somnifera root extract (LongeFera-TM) confers beneficial effects on health and lifespan of the model worm Caenorhabditis elegans

Withania somnifera is among the most widely prescribed medicinal plants in traditional Indian medicine. Hydroalcoholic extract of the roots of this plant was investigated for its effects on the overall health and lifespan of the model worm Caenorhabditis elegans. Extract-exposed worms registered longer lifespan, higher fertility, better motility and metabolic activity. Whole transcriptome analysis of the extract-treated worms revealed the differential expression of the genes associated with lifespan extension, eggshell assembly and integrity, progeny formation, yolk lipoproteins, collagen synthesis, cuticle molting, etc. This extract seems to exert its beneficial effect on C. elegans partly by triggering remodeling of the developmentally programmed apical extracellular matrix (aECM). Differential expression of certain important genes (cpg-2, cpg-3, sqt- 1, dpy-4, dpy-13, and col-17) was confirmed through PCR assay too. Some of the differently expressed genes (gfat-2, unc-68, dpy-4, dpy-13, col-109, col-169, and rmd-1) in worms experiencing pro-health effect of the extract were found through cooccurrence analysis to have their homologous counterpart in humans. Our results validate the potential of W. somnifera extract as a phytopharmaceutical.

molecular biology↗

bioRxiv2024

Silversol (a colloidal nanosilver formulation) inhibits growth of antibiotic-resistant Staphylococcus aureus by disrupting its physiology in multiple ways

Antibacterial effect of a colloidal nanosilver formulation Silversol(R) was investigated against an antibiotic-resistant strain of Staphylococcus aureus. Lower concentrations of the test formulation exerted bacteriostatic and, its higher concentrations exerted bactericidal effect against this pathogen. Silversol(R) at sub-lethal concentration was found to disturb multiple physiological traits of S. aureus such as growth, antibiotic susceptibility, membrane permeability, efflux, protein synthesis and export, biofilm and exopolysaccharide production, etc. Transcriptome data revealed the genes coding for transcriptional regulators, efflux machinery, transferases, {beta}-lactam resistance, oxidoreductases, metal homeostasis, virulence factors, and arginine biosynthesis to get expressed differently under influence of the test formulation. Genes (argG and argH) involved in arginine biosynthesis emerged among the major targets of Silversol(R) in S. aureus.

microbiology↗

bioRxiv2024

Deciphering the molecular mechanisms underlying anti-pathogenic potential of a polyherbal formulation Enteropan against multi-drug resistant Pseudomonas aeruginosa

Anti-pathogenic potential of a polyherbal formulation Enteropan(R) was investigated against a multidrug resistant strain of the bacterium Pseudomonas aeruginosa. Enteropan-pre-exposed P. aeruginosa displayed reduced ([~]70%{downarrow}) virulence towards the model host Caenorhabditis elegans. Enteropan affected various traits like biofilm formation, protein synthesis and secretion, quorum-modulated pigment production, antibiotic susceptibility, nitrogen metabolism etc. in this pathogen. P. aeruginosa could not develop complete resistance to the virulence-attenuating activity of Enteropan even after repeated exposure to this polyherbal formulation. Whole transcriptome analysis showed 17% of P. aeruginosa genome to get differentially expressed under influence of Enteropan. Major mechanisms through which Enteropan exerted its anti-virulence activity were found to be generation of nitrosative stress, oxidative stress, envelop stress, quorum modulation, disturbance of protein homeostasis and metal homeostasis. Network analysis of the differently expressed genes resulted in identification of 10 proteins with high network centrality as potential targets from among the down-regulated genes. Differential expression of genes coding for five (rpoA, tig, rpsB, rpsL, and rpsJ) of these targets was validated through RT-PCR too, and they can further be pursued as potential targets by various drug discovery programmes.

microbiology↗

bioRxiv2023

Protein generation with evolutionary diffusion: sequence is all you need

Deep generative models are increasingly powerful tools for the in silico design of novel proteins. Recently, a family of generative models called diffusion models has demonstrated the ability to generate biologically plausible proteins that are dissimilar to any actual proteins seen in nature, enabling unprecedented capability and control in de novo protein design. However, current state-of-the-art diffusion models generate protein structures, which limits the scope of their training data and restricts generations to a small and biased subset of protein design space. Here, we introduce a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural sequence and functional space. We show experimentally that EvoDiff generations express, fold, and exhibit expected secondary structure elements. Critically, EvoDiff can generate proteins inaccessible to structure-based models, such as those with disordered regions, while maintaining the ability to design scaffolds for functional structural motifs. We validate the universality of our sequence-based formulation by experimentally characterizing intrinsically-disordered mitochondrial targeting signals, metal-binding proteins, and protein binders designed using EvoDiff. We envision that EvoDiff will expand capabilities in protein engineering beyond the structure-function paradigm toward programmable, sequence-first design.

bioengineering↗

bioRxiv2023

Identifying molecular targets of a colloidal nanosilver formulation (Silversol) in multidrug resistant Pseudomonas aeruginosa

P. aeruginosa is a notorious pathogen. A multi-drug resistant strain of this bacterium was challenged with a colloidal nano-silver formulation- Silversol(R). Its minimum inhibitory concentration against P. aeruginosa was found to be 1.5 ppm, and at sub-MIC of 1 ppm, it was able to alter quorum-sensing regulated pigmentation, exopolysaccharide synthesis and biofilm formation, antibiotic susceptibility, protein synthesis and export, nitrogen metabolism, and siderophore production in this pathogen. Transcriptome analysis of the silver-exposed P. aeruginosa indicated generation of nitrosative stress and disturbance of iron homeostasis to be the major mechanisms associated with anti-Pseudomonas activity of Silversol(R). Network analysis of the differentially expressed genes in silver-treated bacterium identified ten genes as the potential molecular targets: norB, norD, nirS, nirF, nirM, nirQ, nosZ, nosY, narK1, and norE (all associated with nitrogen metabolism or denitrification). Three of them (norB, narK1, and norE) were also validated through RT-PCR.

microbiology↗

bioRxiv2023

Network analysis for identifying potential anti-virulence targets through whole transcriptome analysis of Pseudomonas aeruginosa and Staphylococcus aureus exposed to certain anti-pathogenic polyherbal formulations

Transcriptome of two important pathogens, Pseudomonas aeruginosa and Staphylococcus aureus exposed to two different quorum-modulatory polyherbal formulations were subjected to network analysis to identify the most highly networked differentially expressed genes (hubs) as potential anti-virulence targets. Genes associated with denitrification and sulfur metabolism emerged as the most important targets in P. aeruginosa. Increased build-up of nitrite (NO2) in P. aeruginosa culture exposed to the polyherbal formulation Panchvalkal was confirmed through in vitro assay too. Generation of nitrosative stress and inducing sulfur starvation seems to be effective anti-pathogenic strategies against this notorious gram-negative pathogen. Important targets identified in S. aureus were the transcriptional regulator sarA, immunoglobulin-binding protein Sbi, serine protease SplA, the saeR/S response regulator system, and gamma-haemolysin components hlgB and hlgC. Further validation of the potential targets identified in these pathogens is warranted through appropriate in vitro and in vivo assays in model hosts. Such validated targets can prove vital to many antibacterial drug discovery programmes globally.

microbiology↗

bioRxiv2022

Predicting A/B compartments from histone modifications using deep learning

Genomes fold into organizational units in the 3D space that can influence critical biological functions. In particular, the organization of chromatin into A and B compartments segregates its active regions from inactive regions. Compartments, evident in Hi-C contact matrices, have been used to describe cell-type specific changes in the A/B organization. However, obtaining Hi-C data for all cell and tissue types of interest is prohibitively expensive, which has limited the widespread consideration of compartment status. We present a prediction tool called Compartment prediction using Recurrent Neural Network (CoRNN) that models the relationship between the compartmental organization of the genome and histone modification enrichment. Our model predicts A/B compartments, in a cross-cell type setting, with an average area under the ROC curve of 90.9%. Our cell type-specific compartment predictions show high overlap with known functional elements. We investigate our predictions by systematically removing combinations of histone marks and find that H3K27ac and H3K36me3 are the most predictive marks. We then perform a detailed analysis of loci where compartment status cannot be accurately predicted from these marks. These regions represent chromatin with ambiguous compartmental status, likely due to variations in status within the population of cells. These ambiguous loci also show highly variable compartmental status between biological replicates in the same GM12878 cell type. Finally, we demonstrate the generalizability of our model by predicting compartments in independent tissue samples. Our software and trained model are publicly available at https://github.com/rsinghlab/CoRNN.

bioinformatics↗

bioRxiv2020

Direct Imaging of Liquid Domains in Membranes by Cryo Electron Tomography

Images of micron-scale domains in lipid bilayers have provided the gold standard of model-free evidence to understand the domains shapes, sizes, and distributions. Corresponding techniques to directly and quantitatively assess smaller (nanoscale and submicron) liquid domains have been lacking, leading to an inability to answer key questions. For example, researchers commonly seek to correlate activities of membrane proteins with attributes of the domains in which they reside; doing so hinges on identification and characterization of membrane domains. Although some features of membrane domains can be probed by indirect methods, these methods are often constrained by the limitation that data must be analyzed in the context of models that require multiple assumptions or parameters. Here, we address this challenge by developing and testing two new methods of identifying submicron domains in biomimetic membranes. Both methods leverage cryo-electron tomograms of ternary membranes under native solution conditions. The first method is optimized for probe-free applications: domains are directly distinguished from the surrounding membrane by their thickness. This technique measures area fractions of domains with quantitative accuracy, in excellent agreement with known phase diagrams. The second method is optimized for applications in which a single label is deployed for imaging membranes by both high-resolution cryo-electron tomography and diffraction-limited optical microscopy. For this method, we test a panel of probes, find that a trimeric mCherry label performs best, and specify criteria for developing future high-performance, dual-use probes. These developments have led to the first direct and quantitative imaging of submicron membrane domains under native conditions. SIGNIFICANCE STATEMENTFluorescence micrographs that capture the sizes, shapes, and distributions of liquid domains in model membranes have provided high standards of evidence to prove (and disprove) theories of how micron-scale domains form and grow. Corresponding theories about smaller domains have remained untested, partly because experimental methods of identifying submicron domains in vesicles under native solvent conditions have not been available. Here we introduce two such methods. Both leverage cryo-electron tomography to observe membrane features far smaller than the diffraction limit of light. The first method is probe-free and identifies differences in thicknesses between liquid domains and their surrounding membranes. The second method identifies membrane regions labeled by an electron-dense, fluorescent protein, which enables direct comparison of fluorescence micrographs with cryo-electron tomograms.

biophysics↗

bioRxiv2019

Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity

Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with MHC-presented epitopes from putative antigens, and thus encode the determinants of recognition. We here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes an appropriate specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies. We conclude that within this fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities.

bioinformatics↗