Biology subjects

Arora, J.

Publications and source records attributed to Arora, J..

16 recordsLinked to original sources

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

neuroscience↗

bioRxiv2026

Image-based Disease-wide Association Study via Self-supervised Learning links Abdominal MRI Features to 158 Diseases

Medical images contain rich phenotypic information that is often not fully captured by manual clinical assessment. Here, we present a systematic framework for extracting such information and conducting image-based disease-wide association studies (iDWAS) using self-supervised learning (SSL). We applied this framework to abdominal MRI data from the UK Biobank and evaluated associations between MRI-derived features and 562 diseases. Features were learned using a VICReg-based SSL model and tested for disease associations using logistic regression models. We identified 158 diseases that were significantly associated with the MRI-derived features, including the ones which are not directly linked to abdominal anatomy. Focusing on Metabolic dysfunction-associated steatohepatitis (MASH), we showed that the MRI-derived features captured disease-relevant information and separated MASH cases from controls better than established biomarkers such as PDFF and Iron-cT1. These findings highlight the ability of SSL to uncover clinically meaningful signals from routine imaging data. The proposed framework is broadly applicable to other imaging datasets and modalities, enabling more systematic approaches to incidental and early disease detection. The trained model and analysis pipeline are publicly available at https://github.com/srm2022/iDWAS. BackgroundMedical images, often analyzed manually by clinicians, contain valuable information that may not be fully captured by the human eye. Machine Learning (ML) has demonstrated significant potential in extracting this information, allowing us to enhance our understanding of underlying pathologies. We present a systematic framework for extracting such information and performing image-based disease-wide association studies (iDWAS) using self-supervised learning. We then applied the framework to abdominal Magnetic Resonance Imaging (MRI) from UK Biobank (UKB) and investigated the associations between MRI-derived features and 562 diseases. Finally, we delved deeper into the observed associations for Metabolic dysfunction-associated steatohepatitis (MASH), a prevalent yet underdiagnosed condition with a high unmet need for non-invasive diagnostic tools. MethodsWe extracted features from MRI images using VICReg-based self-supervised learning and modeled the associations of the extracted features with 562 diseases using logistic regression. Using the likelihood ratio (LR) test, we then assessed how much additional variance can be explained by the MRI-derived features relative to the variance explained by a null model including only common confounders such as age, gender, and BMI. ResultsOf these 562 diseases, we identified 158 diseases to be significantly associated with the MRI-derived features. While many of them (e.g. MASH) are known to primarily emerge in the abdomen, others (e.g. neuropsychiatric diseases) are not. Taking MASH as a use-case and adjusting for MASH-specific confounding, we then showed that our MRI-derived features captured additional MASH-relevant information compared to standard MRI-derived biomarkers of liver health such as proton density fat fraction (PDFF) and iron-corrected T1 (Iron-cT1). The trained SSL model and analysis pipeline are available at https://github.com/srm2022/iDWAS. ConclusionsOur results illustrate (1) how MRIs from one organ of the body can inform about the diseases primarily rooted in other organs of the body, reflecting patients overall health status, and (2) how SSL can extract more information relevant for specific diseases compared to current state-of-the-art biomarkers. Our framework can be applied to other MRI datasets as well as other imaging modalities with minimal adjustment, providing opportunities for the development of more systematic approaches to incidental diagnosing, i.e. (early) detection of diseases falling outside the scope of the original imaging procedure.

bioinformatics↗

bioRxiv2025

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediate phenotype supporting cognitive control in individuals with a spectrum of clinical symptoms. We leveraged machine learning and external validation to explore this question in three independent, transdiagnostic datasets (N>600), including participants with anxiety disorders, schizophrenia, mood disorders, substance use disorders, post-traumatic stress disorder, obsessive-compulsive disorder, and neurodevelopmental disorders. To capture cognitive controls multifaceted nature, we assessed two of its components--inhibition and shift-- using both task-based and questionnaire data. Flexible brain state engagement predicted all cognitive control metrics in previously unseen individuals transdiagnostically, regardless of which dataset was used for model training. Connectome-based predictive modeling also revealed that shared brain networks underpinned flexible brain state engagement in a transdiagnostic manner. Leveraging brain network dynamics, we further observed that moments of more flexible brain state engagement aligned with moments of network connectivity related to better cognitive control within the same individual. This temporal alignment was replicated in all three datasets with heterogeneous samples. Altogether, this study suggests flexible engagement of brain states may support both inter- and intra-individual differences in cognitive control across individuals with diverse mental health profiles. Funding sourcesThis work was supported by F31AA032179 and R01MH121095. Data were provided in part by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410) and the Transdiagnostic Connectome Project (TCP) dataset. Datasets were accessed through OpenNeuro (https://openneuro.org/datasets/ds005237 and https://openneuro.org/datasets/ds000030/versions/00016).

neuroscience↗

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2025

Generating functional plasmid origins with OriGen

While generative artificial intelligence (AI) has shown promise for biological design, no computational system has yet created and validated sequences capable of autonomous replication. Focusing on plasmids, one of the smallest types of replicating elements, we develop OriGen, a sequence model that generates new plasmid origins of replication while maintaining key elements essential for their replication. We experimentally validate OriGens ability to create functional origins that diverge from existing wild types, demonstrating the models capacity to capture the complex and often cryptic mechanisms of biological replication.

synthetic biology↗

bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

neuroscience↗

bioRxiv2024

Identification of Proteins Influencing CRISPR-Associated Transposases for Enhanced Genome Editing

CRISPR-Associated Transposases (CASTs) hold tremendous potential for microbial genome editing due to their ability to integrate large DNA cargos in a programmable and site-specific manner. However, the widespread application of CASTs has been hindered by their low efficiency in diverse, non-model bacteria. In an effort to address this shortcoming, we conducted the first genome-wide screen for host factors impacting Vibrio cholerae CAST (VchCAST) activity and used the findings to increase VchCAST editing efficiency. A genome-wide loss-of-function mutant library in E. coli was screened to identify 15 genes that impact type VchCAST transposition. Of these, seven factors were validated to improve VchCAST activity and two were found to be inhibitory. Informed by homologous recombination involved effectors, RecD and RecA, we tested the {lambda}-Red recombineering system in our VchCAST editing vectors, which increased its insertion meditated-editing efficiency by 25.7-fold in E. coli while maintaining high target specificity and similar insertion arrangements. Furthermore, {lambda}-Red-enhanced VchCAST achieved increased editing efficiency in the industrially important bacteria Pseudomonas putida and the emerging pathogen Klebsiella michiganensis. This study improves understanding of factors impacting VchCAST activity and enhances its efficiency as a bacterial genome editor. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/612086v2_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@12609e5org.highwire.dtl.DTLVardef@53a128org.highwire.dtl.DTLVardef@794e63org.highwire.dtl.DTLVardef@9835f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2023

High-dimensional phenotyping to define the genetic basis of cellular morphology

The morphology of cells is dynamic and mediated by genetic and environmental factors. Characterizing how genetic variation impacts cell morphology can provide an important link between disease association and cellular function. Here, we combined genomic and high-content imaging approaches on iPSCs from 297 unique donors to investigate the relationship between genetic variants and cellular morphology to map what we term cell morphological quantitative trait loci (cmQTLs). We identified novel associations between rare protein altering variants in WASF2, TSPAN15, and PRLR with several morphological traits related to cell shape, nucleic granularity, and mitochondrial distribution. Knockdown of these genes by CRISPRi confirmed their role in cell morphology. Analysis of common variants yielded one significant association and nominated over 300 variants with suggestive evidence (P<10-6) of association with one or more morphology traits. Our results showed that, similar to other molecular phenotypes, morphological profiling can yield insight about the function of genes and variants.

genomics↗

bioRxiv2022

Vitamin D and the ability to produce 1,25(OH)2D are critical for protection from viral infection of the lungs.

Vitamin D supplementation has been linked to improved outcomes from respiratory virus infection, and the COVID19 pandemic has renewed interest in understanding the potential role of vitamin D in protecting the lung from viral infections. Therefore, we evaluated the role of Vitamin D using animal models of pandemic H1N1 influenza and SARS-CoV-2 infection. In mice, dietary induced vitamin D deficiency resulted in lung inflammation that was present prior to infection. Vitamin D sufficient (D+) and deficient (D-) wildtype (WT) and D+ and D-Cyp27B1 (Cyp) knockout (KO, cannot produce 1,25(OH)2D) mice were infected with pandemic H1N1. D- WT, D+ Cyp KO, and D- Cyp KO mice all exhibited significantly reduced survival compared to D+ WT mice. Importantly, survival was not the result of reduced viral replication as influenza M gene expression in the lungs was similar for all animals. Based on these findings, additional experiments were performed using the mouse and hamster models of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. In these studies, high dose vitamin D supplementation reduced lung inflammation in mice but not hamsters. A trend to faster weight recovery was observed in 1,25(OH)2D treated mice that survived SARS-CoV-2 infection. There was no effect of vitamin D on SARS-CoV-2 N gene expression in the lung of either mice or hamsters. Therefore, vitamin D deficiency enhanced disease severity, while vitamin D sufficient/supplementation reduced inflammation following infections with H1N1 influenza and SARS-CoV-2.

immunology↗

bioRxiv2021

The functional evolution of termite gut microbiota

Termites primarily feed on lignocellulose or soil in association with specific gut microbes. The functioning of the termite gut microbiota is partly understood in a handful of wood-feeding pest species, but remains largely unknown in other taxa. We intend to feel this gap and provide a global understanding of the functional evolution of termite gut microbiota. We sequenced the gut metagenomes of 145 samples representative of the termite diversity. We show that the prokaryotic fraction of the gut microbiota of all termites possesses similar genes for carbohydrate and nitrogen metabolisms, in proportions varying with termite phylogenetic position and diet. The presence of a conserved set of gut prokaryotic genes implies that key nutritional functions were present in the ancestor of modern termites. Furthermore, the abundance of these genes largely correlated with the host phylogeny. Finally, we found that the adaptation to a diet of soil by some termite lineages was accompanied by a change in the stoichiometry of genes involved in important nutritional functions rather than by the acquisition of new genes and pathways. Our results reveal that the composition and function of termite gut prokaryotic communities have been remarkably conserved since termites first appeared [~]150 million years ago. Therefore, the "world smallest bioreactor" has been operating as a multipartite symbiosis composed of termites, archaea, bacteria, and cellulolytic flagellates since its inception.

evolutionary biology↗

bioRxiv2021

Neoisoptera repetitively colonised Madagascar after the Middle Miocene climatic optimum

Madagascar is home to many endemic plant and animal species owing to its ancient isolation from other landmasses. This unique fauna includes several lineages of termites, a group of insects known for their key role in organic matter decomposition in many terrestrial ecosystems. How and when termites colonised Madagascar remains unknown. In this study, we used 601 mitochondrial genomes, 93 of which were generated from Madagascan samples, to infer the global historical biogeography of Neoisoptera, a lineage containing upwards of 80% of described termite species. Our results indicate that Neoisoptera colonised Madagascar between seven to ten times independently during the Miocene, between 8.4-16.6 Ma (95% HPD: 6.1-19.9 Ma). This timing matches that of the colonization of Australia by Neoisoptera. Furthermore, the taxonomic composition of the Neoisopteran fauna of Madagascar and Australia are strikingly similar, with Madagascar harbouring an additional two lineages absent from Australia. Therefore, akin to Australia, Neoisoptera colonised Madagascar during the global expansion of grasslands, possibly helped by the ecological opportunities arising from the spread of this new biome.

ecology↗

bioRxiv2021

Historical biogeography of early diverging termite lineages (Isoptera: Teletisoptera)

Termites are social cockroaches distributed throughout warm temperate and tropical ecosystems. The ancestor of modern termites (crown-Isoptera) occurred during the earliest Cretaceous, approximately 140 million years ago, suggesting that both vicariance through continental drift and overseas dispersal may have shaped the distribution of early diverging termite lineages. We reconstruct the historical biogeography of three early diverging termite families - Stolotermitidae, Hodotermitidae, and Archotermopsidae - using the nuclear rRNA genes and mitochondrial genomes of 27 samples. Our analyses confirmed the monophyly of Stolotermitidae + Hodotermitidae + Archotermopsidae (clade Teletisoptera), with Stolotermitidae diverging from a monophyletic Hodotermitidae + Archotermopsidae approximately 100.3 Ma (94.3-110.4 Ma, 95% HPD), and with Archotermopsidae paraphyletic to a monophyletic Hodotermitidae. The Oriental Archotermopsis and the Nearctic Zootermopsis diverged 50.8 Ma (40.7-61.4 Ma, 95% HPD) before land connections between the Palearctic region and North America ceased to exist. The African Hodotermes + Microhodotermes diverged from Anacanthotermes, a genus found in Africa and Asia, 32.1 Ma (24.8-39.9 Ma, 95% HPD), and the most recent common ancestor of Anacanthotermes lived 10.7 Ma (7.3-14.3 Ma, 95% HPD), suggesting that Anacanthotermes dispersed to Asia using the land bridge connecting Africa and Eurasia [~]18-20 Ma. In contrast, the common ancestors of modern Porotermes and Stolotermes lived 20.2 Ma (15.7-25.1 Ma, 95% HPD) and 26.6 Ma (18.3-35.6 Ma, 95% HPD), respectively, indicating that the presence of these genera in South America, Africa, and Australia involved over-water dispersals. Our results suggest that early diverging termite lineages acquired their current distribution through a combination of over-water dispersals and dispersal via land bridges. We clarify the classification by resolving the paraphyly of Archotermopsidae, restricting the family to Archotermopsis and Zootermopsis, and elevating Hodotermopsinae (Hodotermopsis) as Hodotermopsidae (status novum).

evolutionary biology↗

bioRxiv2019

The microbiome wants what it wants: microbial evolution overtakes experimental host-mediated indirect selection

Microbes ubiquitously inhabit animals and plants, often affecting their hosts phenotype. As a result, even in a constant genetic background, the hosts phenotype may evolve through indirect selection on the microbiome. Microbiome engineering offers a promising novel approach for attaining desired host traits but has been attempted only a few times. Building on the known role of the microbiome on development in fruit flies, we attempted to evolve earlier eclosing flies by selecting on microbes in the growth media. We carried out parallel evolution experiments in no- and high-sugar diets by transferring media associated with fast-developing fly lines over the course of four rounds of selection. In each round, we used sterile eggs from the same inbred population, and assayed fly mean eclosion times. Ultimately, flies eclosed seven to twelve hours earlier, depending on the diet, but selection had no effect. 16S sequencing showed that the microbiome did evolve, particularly in the no sugar diet, with an increase in alpha diversity over time. Thus, while microbiome evolution did affect host eclosion times, these effects were incidental. Instead, any experimentally enforced selection effects were swamped by independent microbial evolution. These results imply that selection on host phenotypes must be strong enough to overcome other selection pressures simultaneously operating on the microbiome. The independent evolutionary trajectories of the host and the microbiome may limit the extent to which indirect selection on the microbiome can ultimately affect host phenotype. Random-selection lines accounting for independent microbial evolution are essential for experimental microbiome engineering studies.

microbiology↗

bioRxiv2019

Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

Understanding how genetic regulatory variation affects gene expression in different T cell states is essential to deciphering autoimmunity. We conducted a high-resolution RNA-seq time course analysis of stimulated memory CD4+ T cells from 24 healthy individuals. We identified 186 genes with dynamic allele-specific expression, where the balance of alleles changes over time. These genes were four fold enriched in autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes, particularly for a major autoimmune risk gene, HLA-DQB1. Each HLA-DQB1 allele had one of three distinct transcriptional regulatory programs. Using CRISPR/Cas9 genomic editing we demonstrated that a single nucleotide variant at the promoter is causal for T cell-specific control of HLA-DQB1 expression. Our study in CD4+ T cells shows that genetic variation in cis regulatory elements may affect gene expression in a lymphocyte activation status-dependent manner contributing to the inter-individual complexity of immune responses.

genomics↗

bioRxiv2018

Parallel genomic evolution of parasite tolerance in wild honey bee populations

Sudden biotic pressures, such as those from novel diseases and pathogens, require populations to respond rapidly or face potential extinction. How this response process takes place remains poorly understood, particularly in natural environments. In this study we take advantage of unique decade-long data sets of two wild honey bee (Apis mellifera) populations in the United States to reconstruct the evolution of tolerance to a novel parasite, the ectoparasitic mite Varroa destructor. Upon the arrival of Varroa, the two geographically isolated populations simultaneously suffered massive Varroa-induced mortality, but stabilized within two years. Here we sequenced and phased genomes of 465 bees sampled from both populations annually over the decade that spanned Varroas arrival. Remarkably, we found that genetic changes were strongly correlated between the populations, suggesting parallel selective responses to Varroa parasitization. The arrival of Varroa was also correlated with an influx of genes of African origin into both populations, though surprisingly it did not substantially contribute to the overall similarity of the evolutionary response between them. Genes involved in metabolic, protein processing and developmental pathways were under particularly strong selection. It is possible that interactions among highly connected gene groups in these pathways may help channelize selective responses to novel parasites, causing completely unrelated populations to exhibit parallel evolutionary trajectories when faced with the same biotic pressure. Our analyses illustrate that ecologically relevant traits emerge from highly polygenic selection involving thousands of genes contributing to complex patterns of evolutionary change.

evolutionary biology↗

bioRxiv2018

HIV Peptidome-Wide Association Study Reveals Patient-Specific Epitope Repertoires Associated with HIV Control

Genetic variation in the peptide-binding groove of the highly polymorphic human leukocyte antigen (HLA) class I molecules has repeatedly been associated with HIV-1 control and progression to AIDS, accounting for up to 12% of the variation in HIV-1 set point viral load (spVL). This suggests a key role in disease control for HLA presentation of HIV-1 epitopes to cytotoxic T cells. However, a comprehensive understanding of the relevant HLA-bound HIV epitopes is still elusive. Here we developed a peptidome-wide association study (PepWAS) approach that integrates HLA genotypes and spVL data from 6,311 HIV-infected patients to interrogate the entire HIV-1 proteome (3,252 unique peptides) for disease-relevant peptides. This PepWAS approach revealed a core set of epitopes associated with spVL, including previously characterized epitopes but also several novel disease-relevant peptides. More importantly, each patient presents only 16 ({+/-}7) and 6 ({+/-}6) of these core epitopes through their individual HLA-B and HLA-A variants, respectively. Differences in these patient-specific epitope repertoires account for almost all the variation in spVL previously associated with HLA genetic variation. PepWAS thus enables a comprehensive functional interpretation of the robust but little understood association between HLA and HIV-1 control, prioritizing a short and targetable list of disease-associated epitopes for personalized immunotherapy.

genomics↗