Biology subjects

Mehta, K.

Publications and source records attributed to Mehta, K..

34 records · Page 2Linked to original sources

bioRxiv2023

Association of secondary metabolite gene clusters with host specialization in the cereal blast fungus Pyricularia oryzae

Fungal plant pathogens constantly evolve and deploy novel peptide and metabolite effectors to break down plant resistance and adapt to new host plants. The blast fungal pathogen Pyricularia oryzae is a single species subdivided into multiple host-specific lineages that have evolved through gain and/or loss of virulence and/or effector related genes through chromosomal rearrangement. Here, we mined 68 genomes of P. oryzae, belonging to six host-specific lineages, to identify secondary metabolite (SM) biosynthetic gene clusters (BGCs) likely associated with potential metabolite effectors involved in host specialization. A similarity network analysis grouped a total of 4501 BGCs into 283 gene cluster families (GCFs), based on the content and architecture of the BGCs. While most of the GCFs were present in all the P. oryzae lineages, two (BGC-O1 and BGC-O2) were found specifically in the Oryza lineage and one (BGC-TLE) was found in the lineage specific to Triticum, Lolium and Eleusine hosts. Further analysis of the phylogenetic relationships between core biosynthetic genes confirmed that BGC-O1, which comprises a reducing polyketide synthase gene (MGG_08236) and four putative tailoring genes, was present only in the Oryza lineage. Importantly, most genes, including MGG_08236, from the BGC-O1 were expressed specifically during pathogenesis. We propose that the Oryza lineage-specific BGC-O1 produces a metabolite effector likely involved in specialization of P. oryzae to the rice host. In addition, we identified five SM genes under positive or balancing selection only in the Oryza lineage, suggesting a role in the interaction with rice specifically. Our findings highlight the importance of further mining novel metabolite effectors in specialization and virulence of the blast fungus to different cereal hosts.

genomics↗

bioRxiv2023

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

neuroscience↗

bioRxiv2023

Generalizable links between symptoms of borderline personality disorder and functional connectivity

BackgroundSymptoms of borderline personality disorder (BPD) often manifest in adolescence, yet the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here we aimed to investigate how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents. MethodsWe used high-quality functional Magnetic Resonance Imaging (fMRI) data from young adults from the Human Connectome Project: Young Adults (HCP-YA; N = 870, ages 22-37 years, 457 female) and youth from the Human Connectome Project: Development (HCP-D; N = 223, age range 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five Factor Inventory (NEO-FFI). A ridge regression model with 10-fold cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity, while controlling for in-scanner motion, age, and sex. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with agerelated changes in connectivity. ResultsMultivariate functional connectivity patterns significantly predicted out-of-sample BPD proxy scores in unseen data in both young adults (HCP-YA; pperm = 0.001) and older adolescents (HCP-D; pperm = 0.001). Predictive capacity of regions was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD proxy scores aligned with those associated with development in youth. ConclusionIndividual differences in functional connectivity in developmentally-sensitive regions are associated with the symptoms of BPD.

neuroscience↗

bioRxiv2023

Functional Connectivity Development along the Sensorimotor-Association Axis Enhances the Cortical Hierarchy

Human cortical maturation has been posited to be organized along the sensorimotor-association (S-A) axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the S-A axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3,355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1,207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1,126). In each dataset, the development of functional connectivity systematically varied along the S-A axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These robust and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

neuroscience↗

bioRxiv2023

Dual functioning by the PhoR sensor is a key determinant to Mycobacterium tuberculosis virulence

PhoP-PhoR empowers M. tuberculosis to adapt to diverse environmental conditions, and remains essential for virulence. Although PhoP and PhoR have been structurally characterized, the signal(s) that this TCS responds to remains unknown. In this study, we show that PhoR is a sensor of acidic pH/high salt conditions, which activate PhoP via phosphorylation. Transcriptomic studies uncover that acidic pH-inducible expression of PhoP regulon is significantly inhibited in a PhoR-deleted M. tuberculosis. Using genome-wide screening we further identify a non-canonical mechanism of PhoP phosphorylation by the sensor kinase PrrB. To investigate how phosphorylation of PhoP is regulated, we discovered that PhoR functions as a phosphatase. Our results identify the motif/residues responsible for contrasting kinase/phosphatase dual functioning of PhoP, and collectively determine the homeostatic regulation of intra-mycobacterial P~PhoP which controls the final output of PhoP regulon. Together, these data uncover that PhoR plays a central role in mycobacterial adaptation to low pH conditions within the host macrophage phagosome. Consistent with these results a PhoR-deleted M. tuberculosis remains significantly attenuated in macrophages and animal models.

microbiology↗

bioRxiv2023

Multiple mechanisms of self-association of chemokine receptors CXCR4 and CCR5 demonstrated by deep mutagenesis

Chemokine receptors are members of the rhodopsin-like class A GPCRs whose signaling through G proteins drives the directional movement of cells in response to a chemokine gradient. Chemokine receptors CXCR4 and CCR5 have been extensively studied due to their roles in white blood cell development and inflammation and their status as coreceptors for HIV-1 infection, among other functions. Both receptors form dimers or oligomers but the function/s of self-associations are unclear. While CXCR4 has been crystallized in a dimeric arrangement, available atomic resolution structures of CCR5 are monomeric. To investigate the dimerization interfaces of these chemokine receptors, we used a bimolecular fluorescence complementation (BiFC)-based screen and deep mutational scanning to find mutations that modify receptor self-association. Many disruptive mutations promoted self-associations nonspecifically, suggesting they aggregated in the membrane. A mutationally intolerant region was found on CXCR4 that matched the crystallographic dimer interface, supporting this dimeric arrangement in living cells. A mutationally intolerant region was also observed on the surface of CCR5 by transmembrane helices 3 and 4. Mutations from the deep mutational scan that reduce BiFC were validated and were localized in the transmembrane domains as well as the C-terminal cytoplasmic tails where they reduced lipid microdomain localization. The reduced self-association mutants of CXCR4 had increased binding to the ligand CXCL12 but diminished calcium signaling. There was no change in syncytia formation with cells expressing HIV-1 Env. The data highlight that multiple mechanisms are involved in self-association of chemokine receptor chains.

biochemistry↗

bioRxiv2023

Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in Youth

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

neuroscience↗

bioRxiv2022

ModelArray: a memory-efficient R package for statistical analysis of fixel data

Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel data exist, currently available tools are memory intensive, difficult to scale to large datasets, and support only a limited number of statistical models. Here we introduce ModelArray, a memory-efficient R package for mass-univariate statistical analysis of fixel data. With only several lines of code, even large fixel datasets can be analyzed using a standard personal computer. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n=938). ModelArray required far less memory than existing tools and revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides an efficient and flexible platform for statistical analysis of fixel data. HIGHLIGHTSO_LIModelArray is an R package for mass-univariate statistical analysis of fixel data C_LIO_LIModelArray is memory-efficient even for large-scale datasets C_LIO_LIModelArray supports linear and nonlinear modeling and is extensible to more models C_LIO_LIModelArray facilitates easy statistical analysis of large-scale fixel data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/499631v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1946469org.highwire.dtl.DTLVardef@14c3076org.highwire.dtl.DTLVardef@10197beorg.highwire.dtl.DTLVardef@19170cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Curation of BIDS (CuBIDS): a workflow and software package for streamlining reproducible curation of large BIDS datasets

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets. HIGHLIGHTSO_LICuBIDS is a workflow and software package for curating BIDS data. C_LIO_LICuBIDS summarizes the heterogeneity in a BIDS dataset. C_LIO_LICuBIDS prepares BIDS data for successful preprocessing pipeline runs. C_LIO_LICuBIDS helps users perform metadata-based quality control. C_LI

neuroscience↗

bioRxiv2022

Circuit analysis of the Drosophila brain using connectivity-based neuronal classification reveals organization of key information processing pathways

We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on potential network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common "cell class" if they connect to neurons of other classes according to the same probability distributions. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect pathways and patterns of directional connectivity that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone communication pathway for multisensory integration. Additional predicted pathways pertain to the facilitation of circadian rhythmic activity, spatial orientation, fight-or-flight response, and olfactory learning. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture. AUTHOR SUMMARYThe potential synaptic circuitry of a neural system constitutes the fundamental architectural underpinning of its in vivo dynamics, plasticity, and functions. The fruit fly neural circuit presented here captures the latent stochastic patterns of network connectivity and provides a fundamental parts list for reverse-engineering brain computation. Mapping the interactions among connectivity-based neuronal classes to development, morphology, physiology, and transcriptomics result in testable hypotheses on the relationship between whole-brain neural architecture and behavior.

neuroscience↗

bioRxiv2022

Quantitative Systems Pharmacology Modeling Framework of Autophagy in Tuberculosis: Application to Adjunctive Metformin Host-Directed Therapy

BackgroundQuantitative systems pharmacology (QSP) modeling of the host-immune response against Mtb can inform rational design of host-directed therapies (HDTs). We aimed to develop a QSP framework to evaluate the effects of metformin-associated autophagy-induction in combination with antibiotics. MethodsA QSP framework for autophagy was developed by extending a model for host-immune response to include AMPK-mTOR-autophagy signalling. This model was combined with pharmacokinetic-pharmacodynamic models for metformin and antibiotics against Mtb. We compared the model predictions to mice infection experiments, and derived predictions for pathogen and host-associated dynamics in humans treated with metformin in combination with antibiotics. ResultsThe model adequately captured the observed bacterial load dynamics in mice Mtb infection models treated with metformin. Simulations for adjunctive metformin therapy in newly diagnosed patients suggested a limited yet dose-dependent effect of metformin on reducing the intracellular bacterial load and selected pro-inflammatory cytokines. Our predictions suggest that metformin may provide beneficiary effects when overall bacterial load, or extracellular-to-intracellular bacterial ratio is low, either early after infection or late during antibiotic treatment. ConclusionsWe present the first QSP framework for HDTs against Mtb, linking cellular-level autophagy effects to disease progression. This framework may be extended to guide design of HDTs against Mtb.

systems biology↗

bioRxiv2021

The evolution of regulatory elements in the emerging promoter variant strains of HIV-1

In a multicentric, observational, investigator-blinded, and longitudinal clinical study of 764 ART-naive subjects, we identified nine different promoter-variant strains of HIV-1 subtype C (HIV-1C) emerging in the Indian population, with some of these variants being reported for the first time. Unlike several previous studies, our work here focuses on the evolving viral regulatory elements, not coding sequences. The emerging viral strains contain additional copies of the existing transcription factor binding sites (TFBS), including TCF-1/LEF-1, RBEIII, AP-1, and NF-{kappa}B, created by sequence duplication. The additional TFBS are genetically diverse and may blur the distinction between the modulatory region of the promoter and the viral enhancer. In a follow-up analysis, we found trends, but not significant associations between any specific variant promoter and prognostic markers, probably because the emerging viral strains might not have established mono infections yet. Illumina sequencing of four clinical samples containing a co-infection indicated the domination of one strain over the other and establishing a stable ratio with the second strain at the follow-up time-points. Since a single promoter regulates viral gene expression and constitutes the master regulatory circuit with Tat, the acquisition of additional and variant copies of the TFBS may significantly impact viral latency and latent reservoir characteristics. Further studies are urgently warranted to understand how the diverse TFBS profiles of the viral promoter may modulate the characteristics of the latent reservoir, especially following the initiation of antiretroviral therapy. Significance StatementA unique conglomeration of TFBS enables the HIV-1 promoter to accomplish two diametrically opposite functions - transcriptional activation and transcriptional silencing. The various phases of viral latency -establishment, maintenance, and reversal -collectively determine the replication fitness of individual viral strains. A profound variation in the TFBS composition of the viral promoter may significantly alter the viral latency properties and the latent reservoir characteristics. Although the duplication of certain TFBS remains a quality unique to HIV-1C, the high-level genetic recombination of HIV-1 may promote the transfer of such molecular properties to the other HIV-1 subtypes. The emergence of several promoter-variant viral strains may make the task of a functional cure more challenging in HIV-1C.

microbiology↗

bioRxiv2020

Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding

This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) where neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding (ASE) of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation-maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (n ~ 212 - 215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of dimensional embedding and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

bioinformatics↗

bioRxiv2019

Cortical tethering of mitochondria by the dynein anchor Mcp5 enables uniparental mitochondrial inheritance during fission yeast meiosis

During sexual reproduction in eukaryotes, processes such as active degradation and dilution of paternal mitochondria ensure maternal mitochondrial inheritance. In the isogamous organism fission yeast, we employed high-resolution fluorescence microscopy to visualize mitochondrial inheritance during meiosis by differentially labeling mitochondria of the two parental cells. Remarkably, mitochondria, and thereby, mitochondrial DNA from the parental cells did not mix upon zygote formation, but remained segregated at the poles by attaching to clusters of the dynein anchor Mcp5 via its coiled-coil domain. We observed that this tethering of parental mitochondria to the poles results in uniparental inheritance of mitochondria, wherein two of the four spores formed subsequently contained mitochondria from one parent and the other spores, mitochondria from the other parent. Further, the presence of dynein on an Mcp5 cluster precluded the attachment of mitochondria to the same cluster. Taken together, we reveal a distinct mechanism that achieves uniparental inheritance by segregation of parental mitochondria.

cell biology↗

bioRxiv2018

Regulation of the error-prone DNA polymerase polκ by oncogenic signaling and its contribution to drug resistance

Mutations in the proofreading domains of the replicative DNA polymerases pol{delta} and pol{varepsilon} are associated with elevated mutation rates in cancer, but the roles of other DNA polymerases in tumorigenesis remain poorly understood. One such polymerase is pol{kappa}, an enzyme that plays a key role in translesion synthesis. pol{kappa} contributes to cell survival in the face of DNA damage but can be highly mutagenic due to lack of a proofreading domain. Here we demonstrate that cancer cells under stress from oncogene inhibition upregulate pol{kappa} and shift its localization from the cytoplasm to the nucleus. This effect can be phenocopied by mTOR inhibition or glucose deprivation, analogous to stress-induced mutagenesis in E. coli whereby cell stress and nutrient deprivation can upregulate and activate DinB/pol IV (the bacterial orthologue of pol{kappa}). We find that cancer cells normally sequester pol{kappa} in the cytoplasm via exportin-1, likely to prevent excess mutagenesis from the error-prone nature of this polymerase. Subverting the normal nuclear-cytoplasmic shuttling by forced overexpression of nuclear pol{kappa} increases resistance of melanoma cells to the BRAFV600E inhibitor vemurafenib. This data suggests a mechanism by which cancer cells regulate the expression and localization of the error-prone polymerase pol{kappa}, abrogation of which can contribute to drug resistance.\n\nOne Sentence Summary: Cancer cells under stress from oncogene or mTOR inhibition dysregulate the error-prone DNA polymerase pol{kappa}, which contributes to drug resistance in melanoma cells.

cancer biology↗

bioRxiv2017

Microtubule dynamics regulates mitochondrial fission

Mitochondria are organized as tubular networks in the cell and undergo fission and fusion. While several of the molecular players involved in mediating mitochondrial dynamics have been identified, the precise cellular cues that initiate fission or fusion remain largely unknown. In fission yeast, mitochondria are organized along microtubule bundles. Here, we employed deletions of kinesin-like proteins to perturb microtubule dynamics, and determined that cells with long microtubules exhibited long, but fewer mitochondria, whereas cells with short microtubules exhibited short, but several mitochondria due to reduced mitochondrial fission in the former and elevated fission in the latter. Correspondingly, upon onset of closed mitosis in fission yeast, wherein interphase microtubules assemble to form the spindle within the nucleus, we measured increased mitochondrial fission. We determined that the consequent rise in the mitochondrial copy number was necessary to reduce partitioning errors while stochastically partitioning mitochondria between daughter cells. We discovered that the association of mitochondria with microtubules physically impeded the assembly of the fission protein Dnm1 around mitochondria, resulting in inhibition of mitochondrial fission. Taken together, we demonstrate a novel mechanism for regulation of mitochondrial fission that is dictated by the interaction between mitochondria and the microtubule cytoskeleton.

cell biology↗