Biology subjects

Pandey, G.

Publications and source records attributed to Pandey, G..

31 records · Page 2Linked to original sources

bioRxiv2023

KinAce: a web portal for exploring kinase-substrate interactions

Interactions between protein kinases and their substrates are critical for the modulation of complex signaling pathways. Currently, there is a large amount of information available about kinases and their substrates in disparate public databases. However, these data are difficult to interpret in the context of cellular systems, which can be facilitated by examining interactions among multiple proteins at once, such as the network of interactions that constitute a signaling pathway. We present KinAce, a user-friendly web portal that integrates and shares information about kinase-substrate interactions from multiple databases of post-translational modifications. KinAce enables the visual exploration of these interactions in systems contexts, such as pathways, domain families, and custom protein set inputs, in an interactive fashion. We expect KinAce to be useful as a knowledge discovery tool for kinase-substrate interactions, and the aggregated KinAce dataset to be useful for protein kinase studies and systems-level analyses. The portal is available at https://kinace.kinametrix.com/.

systems biology↗

bioRxiv2023

Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and [a]C-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2s ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameters ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins. Significance StatementGreater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithms multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

bioinformatics↗

bioRxiv2023

Functional connectivity in northern swamp deer (Rucervus duvaucelii duvaucelii) population across a fragmented, human-dominated landscape along Gangetic Plains of north India: Implications for conservation in non-protected areas

The Indian subcontinent has witnessed disproportionate declines in large mammalian herbivore communities. The northern swamp deer (Rucervus duvaucelii duvaucelii) exemplifies the conservation challenges of typical non-protected area species, where apart from distribution status other ecological information is limited for the upper Gangetic basin population. We combined elements of radio-telemetry and conservation genetics to evaluate dispersal patterns, population connectivity and assess genetic variation and inbreeding status of this population living across a highly human-dominated area. We genetically identified 266 unique swamp deer and further analyses revealed presence of two spatially-admixed genetic lineages with moderate heterozygosity (Ho=0.51, SD= 0.10) and low inbreeding (FIS=0.133) status. Multi- disciplinary evidence suggests that the small, isolated grassland patches between Jhilmil Jheel Conservation Reserve (JJCR) and Hastinapur Wildlife Sanctuary (HWLS) are highly preferred by swamp deer during migrations and are genetically connected. The southern part of the area in HWLS showed early signatures of genetic discontinuity that require immediate conservation attention. We hypothesized that the human settlement history of this landscape, river dynamics and species ability to negotiate various pressures and disperse has helped to maintain such connectivity. While these signatures are encouraging for this small, isolated cervid population, careful management interventions are required to ensure the integrity and functionality of this landscape. We recommend a scientifically robust population estimation approach across this landscape and a multi-stakeholder-driven strategies to augment population and habitat recovery, plantation and riverscape management to ensure long-term survival of this species.

ecology↗

bioRxiv2023

Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (Memory group) were compared with a matched Control group who did not have memory problems. The Random Forests model identified specific features from each domain that contributed to the classification of Memory vs. Control group (AUC=88.29%). Specifically, individuals from the Memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except some connections involving anterior cingulate cortex which were predominantly hypoconnected. Other significant contributing features were (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past 5 years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past 12 months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity collected [~]18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict alcohol-related memory problems that arise in later life.

neuroscience↗

bioRxiv2022

Rounding up the annual ryegrass genome: high-quality reference genome of Lolium rigidum

The genome of the major agricultural weed species, annual ryegrass (Lolium rigidum) was assembled, annotated and analysed. Annual ryegrass is a major weed in wheat cropping, and has the remarkable capacity to evolve resistance to herbicides with various modes of action. The chromosome-level assembly was achieved using short- and long-read sequencing in combination with Hi-C mapping. The assembly size is 2.44Gb with N50=361.79Mb across 1,764 scaffolds where the seven longest sequences correspond to the seven chromosomes. Genome completeness assessed through BUSCO returned a 99.8% score for complete (unique and duplicated) and fragmented genes using the Viridiplantae set. We found evidence for the expansion of herbicide resistance-related gene families including detoxification genes. The reference genome assembly of L. rigidum is pivotal for the management of this highly problematic weed species which leverages genomic tools to devise new control options.

genomics↗

bioRxiv2021

Predicting Individual Cell Division Events from Single-Cell ERK and Akt Dynamics

Predictive determinants of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. What drives proliferation decisions of single cells at any given time? We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window ([~]8.5-40 hours). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Machine learning algorithms show that simple metrics of central tendency in this time window are most predictive for subsequent cell division; median ERK and Akt activities classify individual division events with an AUC=0.76. Surprisingly, ERK dynamics alone predict division in individual cells with an AUC=0.74, suggesting Akt activity dynamics contribute little to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics are substantially more important than Akt activity dynamics for driving cell division in this non-transformed context. Single cell imaging along with machine learning algorithms provide a better basis to understand cell cycle progression on the single cell level.

systems biology↗

bioRxiv2021

An interpretable connectivity-based decoding model for classification of chronic marijuana use

BackgroundPsychiatric neuroimaging typically proceeds with one of two approaches: encoding models, which aim to model neural mechanisms, or decoding models, which aim to predict behavioral or clinical characteristics from brain imaging data. In this study, we seek to combine these aims by developing interpretable decoding models that offer both accurate prediction and novel neural insights. We demonstrate the effectiveness of this combined approach in a case study of chronic marijuana use. MethodsChronic marijuana (MJ) users (n=195) and non-using healthy controls (n=128) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic MJ users and non-users based on task-evoked, whole-brain functional connectivity. We then used graph theoretic analyses to identify predictive functional connectivities among brain regions that contributed most substantially to the classification of chronic marijuana use. ResultsWe obtained high (~80% out-of-sample) accuracy across four different classification models, demonstrating that task-evoked, whole-brain functional connectivity can successfully differentiate chronic marijuana users from non-users. Subsequent network analyses revealed key predictive regions (e.g., anterior cingulate cortex, dorsolateral prefrontal cortex, and precuneus) that are often implicated in neuroimaging studies of substance use disorders, as well as some key exceptions. We also identified a core set of networks of brain regions that contributed to successful classification, comprised of many of the same predictive regions. ConclusionsOur dual aims of accurate prediction and interpretability were successful, producing a predictive model that also provides interpretability at the neural level. This novel approach may complement other predictive-exploratory approaches for a more complete understanding of neural mechanisms in drug use and other neuropsychiatric disorders.

neuroscience↗

bioRxiv2021

Extracellular S100B alters spontaneous Ca2+ fluxes in dopaminergic neurons via L-type voltage gated calcium channels: Implications for Parkinson's disease

Parkinsons disease (PD) is associated with an abnormal increase in S100B within the midbrain and cerebrospinal fluid. In addition, overexpression of S100B in mice accelerates the loss of substantia nigra pars compacta (SNc) dopaminergic (DA) neurons, suggesting a role for this protein in PD pathogenesis. We found that in the mouse SNc, S100B labeled astrocytic processes completely envelop the somata of tyrosine hydroxylase (TH) positive DA neurons. Based on this finding, we rationalized that abnormal increases in extracellularly secreted S100B by astrocytic processes in the SNc could alter DA neuron activity, thereby causing dysregulated midbrain function. To test this hypothesis, we measured the effect of bath perfused S100B peptide on the frequency and amplitude of spontaneous calcium fluxes in identified TH+ and TH- midbrain neurons from 3-week old mouse primary midbrain cultures. Acute exposure to 50 pM S100B caused a 2-fold increase in calcium flux frequency only in TH+ DA neurons. The L-type voltage gated calcium channel (VGCC) inhibitor, diltiazem eliminated S100B-mediated increases in DA neuron calcium flux frequency, while the T-type specific VGCC blocker mibefradil failed to inhibit the stimulatory effect of S100B. Chronic exposure to S100B caused a 3-fold reduction in calcium flux frequencies of TH+ neurons and also reduced calcium flux amplitudes in TH- neurons by [~]4-fold. Together, our results suggest that exposure to S100B pathologically alters spontaneous calcium activity in midbrain neurons via an extracellular mechanism involving L-type VGCCs expressed in DA neurons. These findings are relevant to understanding mechanisms underlying DA neuron loss during PD. Table of Contents Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/432751v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@7d3fd3org.highwire.dtl.DTLVardef@e96b3corg.highwire.dtl.DTLVardef@7f6d21org.highwire.dtl.DTLVardef@1d5bde4_HPS_FORMAT_FIGEXP M_FIG C_FIG Main PointsO_LIExtracellular S100B increases Ca2+ fluxes in dopaminergic neurons C_LIO_LIL-type VGCCs in dopaminergic neurons are required for S100B-mediated increases in Ca2+ fluxes C_LIO_LIChronic S100B alters Ca2+ fluxes in dopaminergic and non-dopaminergic neurons C_LI

neuroscience↗

bioRxiv2020

Data integration through heterogeneous ensembles for protein function prediction

MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities, but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms, and uses effective heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data, and mortality due to COVID-19 from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen (BUN) and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. AvailabilityCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Contactgaurav.pandey@mssm.edu

bioinformatics↗

bioRxiv2019

A machine learning approach predicts essential genes and pharmacological targets in cancer

Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

cancer biology↗

bioRxiv2018

Transcriptional heterogeneity in cancer-associated regulatory T cells is predictive of survival.

Regulatory T cells (Tregs) are a population of T cells that exert a suppressive effect on a variety of immune cells and non-immune cells. The suppressive effects of Tregs are detrimental to anti-tumor immunity. Recent investigations into cancer-associated Tregs have identified common expression patterns for tumor-infiltration, however the functional heterogeneity in tumor-infiltrating (TI) Treg is largely unknown. We performed single-cell sequencing on immune cells derived from renal clear cell carcinoma (ccRCC) patients, isolating 160 peripheral-blood (PB) Tregs and 574 TI Tregs. We identified distinct transcriptional TI Treg cell fates, with a suppressive subset expressing CD177. We demonstrate CD177+ TI-Tregs have preferential suppressive effects in vivo and ex vivo. Gene signatures derived the CD177+ Treg subset had superior ability to predict survival in ccRCC and seven other cancer types. Further investigation into the development and regulation of TI-Treg heterogeneity will be vital to the application of tumor immunotherapies that possess minimal side effects.

cancer biology↗

bioRxiv2018

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

Respiratory viruses are highly infectious; however, the variation of individuals physiologic responses to viral exposure is poorly understood. Most studies examining molecular predictors of response focus on late stage predictors, typically near the time of peak symptoms. To determine whether pre- or early post-exposure factors could predict response, we conducted a community-based analysis to identify predictors of resilience or susceptibility to several respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV) using peripheral blood gene expression profiles collected from healthy subjects prior to viral exposure, as well as up to 24 hours following exposure. This analysis revealed that it is possible to construct models predictive of symptoms using profiles even prior to viral exposure. Analysis of predictive gene features revealed little overlap among models; however, in aggregate, these genes were enriched for common pathways. Heme Metabolism, the most significantly enriched pathway, was associated with higher risk of developing symptoms following viral exposure.

immunology↗

bioRxiv2017

A Novel Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

systems biology↗