Biology subjects

Parikh, R.

Publications and source records attributed to Parikh, R..

9 recordsLinked to original sources

bioRxiv2026

Divergent venoms among two closely related co-distributed centipede species, Scolopendra morsitans and S. hardwickei in tropical Asia

Venom is an important functional trait that helps predatory animals capture prey. Centipede predatory venoms are complex cocktails of multiple proteins, such as neurotoxins (scoloptoxins), cytotoxins, {beta}-pore-forming toxins, and enzymes. We examined venom phenotypes in two closely related and co-occurring centipede species, Scolopendra morsitans (n=28) and S. hardwickei (n=11), in peninsular India to determine whether their venoms are similar or dissimilar. An integrated proteo-transcriptomic approach was used to characterise the venom phenotypes of the two species across multiple individuals in peninsular India. We used species occurrence records and species distribution models to assess the distributional overlap among these species within the peninsular Indian region. The species showed significant overlap in their current and projected geographical ranges, corresponding with their co-occurrence. We characterised the venom profiles of both species and found that the venoms were cocktails of enzymes, {beta}-pore-forming toxins, and neurotoxins comprising 110 and 84 proteins in S. morsitans and S. hardwickei, respectively. However, the venom composition of both species differed significantly in toxin abundance and species-specific protein repertoires. This indicates trait divergence in venom phenotypes, suggesting that distinct venom compositions may facilitate coexistence among ecologically similar predatory centipedes. The observed variation in venom phenotypes among co-distributed species opens up important avenues for future research into their ecological roles and functional significance. In this study, we provided a detailed account of venom composition across multiple individuals from the species geographic range and highlighted the importance of investigating the role of venom as a trait that could influence species interactions and shape communities in these diverse tropical forests.

ecology↗

bioRxiv2025

Decoy Antibodies Block Extracellular HSP70, Prevent Self Signaling and Inhibit Melanoma Cell Survival

Melanoma cells actively secrete melanosomes-large, extracellular vesicles (EVs) enriched with oncogenic factors that reprogram the tumor microenvironment, enhance self-signaling, and promote tumor growth. Despite their abundance and immunogenic potential, humoral responses to melanoma-derived melanosomes remain unexplored. Here, we identify a novel immune surveillance mechanism in which melanosome-elicited decoy antibodies target melanoma-derived melanosomes by binding to the extracellular form of heat shock protein 70 (HSP70), a chaperone broadly implicated in cancer cell survival and stress adaptation. Anti-HSP70 decoy antibodies potently and effector-independently inhibit growth and survival of both murine and human melanoma cells and suppress key transcriptional programs involved in proliferation, cytoskeletal dynamics, and metabolism. In a preclinical B16 melanoma model, prophylactic administration of decoy monoclonal antibodies Mel322-34 and Mel321-35 conferred significant survival benefits of 27% and 48%, respectively. Strikingly, anti-HSP70 antibodies were enriched in the sera of melanoma patients achieving complete responses to immune checkpoint blockade, in contrast to non-responders with progressive disease. Collectively, our findings uncover a novel EV-antibody axis as a promising avenue to block cancer-promoting signaling pathways. Decoy autoantibodies targeting the extracellular form of HSP70 advance the understanding of tumor-intrinsic vulnerability and promote biomarker-driven immunotherapy in melanoma.

immunology↗

bioRxiv2023

Optical nanosensor passivation enables highly sensitive detection of the inflammatory cytokine IL-6

Interleukin-6 (IL-6) is known to a play critical role in the progression of inflammatory diseases such as cardiovascular disease, cancer, sepsis, viral infection, neurological disease, and autoimmune diseases. Emerging diagnostic and prognostic tools, such as optical nanosensors, experience challenges in successful clinical application in part due to protein corona formation dampening their selectivity and sensitivity. To address this problem, we explored the rational screening of several classes of biomolecules to be employed as agents in non-covalent surface passivation as a strategy to screen interference from non-specific proteins. Findings from this screening were applied to the detection of IL-6 by a fluorescent antibody-conjugated single-walled carbon nanotube (SWCNT)-based nanosensor. The IL-6 nanosensor exhibited highly sensitive and specific detection after passivation with a polymer, poly-L-lysine, as demonstrated by IL-6 detection in human serum within a clinically relevant range of 25 pg/mL to 25,000 pg/mL, exhibiting a limit of detection over three orders of magnitude lower than prior antibody-conjugated SWCNT sensors. This work holds the potential for rapid and highly sensitive detection of IL-6 in clinical settings with future application to other cytokines or disease-specific biomarkers.

bioengineering↗

bioRxiv2021

tRForest: a novel random forest-based algorithm for tRNA-derived fragment target prediction

tRNA fragments (tRFs) are small RNAs comparable to the size and function of miRNAs. tRFs are generally Dicer independent, are found associated with Ago, and can repress expression of genes post-transcriptionally. Given that this expands the repertoire of small RNAs capable of post-transcriptional gene expression, it is important to predict tRF targets with confidence. Some attempts have been made to predict tRF targets, but are limited in the scope of tRF classes used in prediction or limited in feature selection. We hypothesized that established miRNA target prediction features applied to tRFs through a random forest machine learning algorithm will immensely improve tRF target prediction. Using this approach, we show significant improvements in tRF target prediction for all classes of tRFs and validate our predictions in two independent cell lines. Finally, Gene Ontology analysis suggests that among the tRFs conserved between mice and humans, the predicted targets are enriched significantly in neuronal function, and we show this specifically for tRF-3009a. These improvements to tRF target prediction further our understanding of tRF function broadly across species and provide avenues for testing novel roles for tRFs in biology. We have created a publicly available website for the targets of tRFs predicted by tRForest.

bioinformatics↗

bioRxiv2020

Microengineered 3D pulmonary interstitial mimetics highlight a critical role for matrix degradation in idiopathic pulmonary fibrosis

Fibrosis is often untreatable and is characterized by aberrant tissue scarring from activated myofibroblasts. Although the extracellular matrix becomes increasingly stiff and fibrous during disease progression, how these physical cues impact myofibroblast differentiation in 3D is poorly understood. Here we describe a multicomponent hydrogel that recapitulates the 3D fibrous structure hallmark to the interstitial tissue regions where idiopathic pulmonary fibrosis (IPF) initiates. In contrast to findings on 2D hydrogels, myofibroblast differentiation in 3D was inversely correlated with hydrogel stiffness, but positively correlated with matrix fiber density. Employing a multi-step bioinformatics analysis of IPF patient transcriptomes and in vitro pharmacologic screening, we identify matrix-metalloprotease activity to be essential for 3D but not 2D myofibroblast differentiation. Given our observation that compliant degradable 3D matrices amply support fibrogenesis, these studies demonstrate a departure from the established relationship between stiffness and myofibroblast differentiation in 2D, and provide a new 3D model for studying fibrosis.

bioengineering↗

bioRxiv2018

Large-scale neuron cell classification of single-channel and multi-channel extracellular recordings in the anterior lateral motor cortex

Identification of neuron cell type helps us connect neural circuitry and behavior; greater specificity in cell type and subtype classification provides a clearer picture of specific relationships between the brain and behavior. With the advent of high-density probes, large-scale neuron classification is needed, as typical extracellular recordings are identity-blind to the neurons they record. Current methods for identification of neurons include optogenetic tagging and intracellular recordings, but are limited in that they are expensive, time-consuming, and have a limited scope. Therefore, a more automated, real-time method is needed for large-scale neuron identification. Data from two recordings was incorporated into this research; the single-channel recording included data from three neuron types in the motor cortex: FS, IT, and PT neurons. The multi-channel recording contained data from two neuron subtypes also in the motor cortex: PT_L and PT_U neurons. This allowed for an examination of both general neuron classification and more specific subtype classification, which was done via artificial neural networks (ANNs) and machine learning (ML) algorithms. For the single-channel neuron classification, the ANNs achieved 91% accuracy, while the ML algorithms achieved 98% accuracy, using the raw electrical waveform. The multi-channel classification, which was significantly more difficult due to the similarity between the neuron types, yielded an ineffective ANN, reaching 68% accuracy, while the ML algorithms reached 81% using 8 calculated features from the waveform. Thus, to distinguish between different neuron cell types and subtypes in the motor cortex, both ANNs and specific ML algorithms can facilitate rapid and accurate near real-time large-scale classification.

neuroscience↗

bioRxiv2018

Developing a unique medical-grade honey which maximizes glucose oxidase activity

In the fight against cancer and infection, honey is a compelling solution by virtue of its unique chemical composition; however, current medical-grade honeys are expensive and limited in their scope. In this study, a novel medical-grade honey was developed by maximizing the activity of glucose oxidase (GOX), an enzyme in honey that synthesizes hydrogen peroxide; maximization was done by neutralizing the effects of catalase and methylglyoxal (MGO), compounds in honey that interfere with H2O2 accumulation. Expressed GOX activity was quantified via H2O2 accumulation in honey after catalase, MGO, or both were inhibited. Results indicate the honeys tested have significant quantities of H2O2 inhibitors, greatly affecting expressed GOX activity; neutralization resulted in at least a 100% increase in H2O2 accumulation in all honeys. Blueberry honey with catalase inhibition by EGCG saw a 938% increase in H2O2 accumulation, reaching nearly three times the H2O2 accumulation in current medical grade honey for a tenth of the cost. This research presents a novel and readily reproducible method for maximizing H2O2 accumulation in any honey through neutralization of GOX inhibitors. GOX activity enhancement, combined with honeys diverse antioxidants, enables the emergence of global low-cost medical-grade honeys with immense potential to revolutionize cancer and infection treatment.\n\nThis paper is based on work presented at the Regional Science and Engineering Fair on March 15, 2018 at Riverside High School (Loudoun County, Virginia), the Virginia State Science and Engineering Fair on April 14, 2018 at the Virginia Tech Carilion School of Medicine (Roanoke, Virginia).\n\nAuthor SummaryWith cancer and post-operative infections on the rise, a natural, low-cost alternative treatment is necessary. Honey is an ideal candidate due to its diverse antioxidants and glucose oxidase, an enzyme that produces hydrogen peroxide. The purpose of this research was to maximize glucose oxidase activity by neutralizing compounds that interfere with H2O2 accumulation. This research yielded medical-grade honeys that are economical and more effective than current ones, presenting a novel method that is reproducible on a large scale, scalable to different needs, and applicable to any honey to create low-cost medical-grade honeys that can revolutionize cancer and infection treatment globally.

biochemistry↗

bioRxiv2017

A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

BackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.\n\nMethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.\n\nFindings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies--even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.\n\nInterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

bioinformatics↗

bioRxiv2017

mRNA detection in budding yeast with single fluorophores

Quantitative measurement of mRNA levels in single cells is necessary to understand phenotypic variability within an otherwise isogenic population of cells. Single-molecule mRNA Fluorescence In Situ Hybridization (FISH) has been established as the standard method for this purpose, but current protocols require a long region of mRNA to be targeted by multiple DNA probes. Here, we introduce a new single-probe FISH protocol termed sFISH for budding yeast, Saccharomyces cerevisiae using a single DNA probe labeled with a single fluorophore. In sFISH, we markedly improved probe specificity and signal-to-background ratio by using methanol fixation and inclined laser illumination. We show that sFISH reports mRNA changes that correspond to protein levels and gene copy number. Using this new FISH protocol, we can detect more than 50% of the total target mRNA. We also demonstrate the versatility of sFISH using FRET detection and mRNA isoform profiling as examples. Our FISH protocol with single-fluorophore sensitivity significantly reduces cost and time compared to the conventional FISH protocols and opens up new opportunities to investigate small changes in RNA at the single cell level.

genetics↗