Biology subjects

Arora, M.

Publications and source records attributed to Arora, M..

10 recordsLinked to original sources

bioRxiv2026

Metabolic engineering of Salmonella enterica for coupled kynurenine sensing and depletion enhances antitumor efficacy

Immunosuppressive tumor metabolic microenvironment remains a major barrier to effective cancer therapy. Tumor-targeting Salmonella enterica strains were engineered to deplete the intratumoral immunosuppressive kynurenine, triggering metabolic rewiring and fostering an active antitumor immune microenvironment. Equipping S. enterica VNP20009 with bacterial kynureninase enzyme (KynU) and a kynurenine transporter enabled efficient intratumoral kynurenine degradation. We then developed a next-generation strain, AD51, combining quorum-sensing controlled KynU expression with kynurenine-dependent growth and tumor targeting. AD51 induced robust immune activation and exhibited better anti-tumor efficacy. The addition of intratumoral exogenous IFN{gamma} to AD51 synergistically enhanced immune activation and antitumor efficacy in murine ovarian cancer and melanoma models, reducing tumor burden by 75% and 84%, respectively, compared with untreated controls (p<0.0001 for both). Our data establishes a framework for engineering bacteria to target and degrade tumor immunosuppressive metabolites while coupling bacterial growth to metabolite availability, paving the way for more precise and programmable microbial therapies.

cancer biology↗

bioRxiv2025

A Novel Approach-Avoidance Task to Study Decision Making Under Outcome Uncertainty

To behave adaptively, people need to integrate information about probabilistic outcomes and balance drives to approach positive outcomes and avoid negative outcomes. However, questions remain about how uncertainty in positive and negative outcomes influence approach-avoid decision-making dynamics. To fill this gap, we developed a novel Probabilistic Approach Avoidance Task (PAAT) and characterized behavior in this task using sequential sampling models In this task, participants (Study 1: blinded mixed clinical sample N=34; Study 2: online nonpsychiatric sample N = 58) made a series of choices between pairs of options, each consisting of variable probabilities of reaching a positive outcome (monetary reward) and of reaching a negative outcome (aversive image). Participants tended to choose options that maximized the likelihood of reward and minimized the likelihood of aversive outcomes. Moreover, the weights they placed on each of these differed for choices where these likelihoods were in opposition (i.e., the riskier option was also more rewarding; incongruent trials) relative to when these were aligned (congruent trials). Computational modeling revealed that the relative influence of rewarding and aversive outcomes on choice was captured by differences in the rate of decision-relevant information accumulation. These modeling results were validated with a series of model comparisons and posterior predictive checks, demonstrating that our sequential sampling models reliably captured our behavioral data. Together, these findings improve our understanding of the influence of motivational conflict, outcome type, and levels of uncertainty on approach-avoid decision-making.

neuroscience↗

bioRxiv2025

Lamin A/C Deficiency Drives Genomic Instability and Poor Survival in Small-Cell Lung Cancer through Increased R-loop Accumulation

Lamin A/C (LMNA), a key component of the nuclear envelope, is essential for maintaining nuclear integrity and genome organization [1]. While LMNA dysregulation has been implicated in genomic instability across cancer and aging, the underlying mechanisms remain poorly understood [2]. Here, we investigate LMNAs role in small-cell lung cancer (SCLC), a highly aggressive malignancy characterized by extreme genomic instability [3, 4]. We demonstrate that LMNA depletion promotes R-loop accumulation, transcription-replication conflicts, replication stress, DNA breaks, and micronuclei formation. Mechanistically, LMNA loss disrupts nuclear pore complex distribution, reducing phenylalanine-glycine (FG)-nucleoporin incorporation and impairing RNA export efficiency. Furthermore, we show that LMNA expression is epigenetically repressed by EZH2 during SCLC differentiation from neuroendocrine (NE) to non-NE states. Clinically, low LMNA levels correlate with significantly worse survival in SCLC patients. These findings uncover a novel role for LMNA in safeguarding genome integrity and shaping tumor heterogeneity, with broad implications for cancer and aging. Significance StatementLamin A/C, a key structural component of the nuclear envelope, is frequently lost or mutated in cancer, laminopathies, and aging-related disorders. Lamin A/C loss is associated with genomic instability, but the underlying mechanisms remain incompletely understood. We demonstrate that LMNA loss drives genomic instability by promoting R-loop accumulation through disrupted nuclear pore dynamics and impaired RNA export. These findings reveal a previously unrecognized link between LMNA loss, nuclear envelope dysfunction, and genome instability. Targeting this pathway could help mitigate genomic instability in aging and laminopathies, while leveraging R-loop accumulation may enhance the efficacy of DNA-damaging therapies in cancer.

cancer biology↗

bioRxiv2025

4D mitochondrial network assumes distinct and predictive phenotypes through human lung and intestinal epithelial development

Mitochondria form a dynamic three-dimensional network within the cell that exhibits a wide range of morphologies and behaviors. Depending on cell state, cell type, and cell fate, a cells mitochondrial phenotype might range from relatively isolated mitochondrial segments to complex branching networks, and from stationary mitochondria to highly motile structures. While isolated mitochondrial phenotypes have been described for a subset of cell states, types, and fates, an integrated map of how mitochondrial phenotypes change over the full course of tissue development has so far been lacking. Here, we identify the mitochondrial phenotypes that appear throughout the course of lung and intestinal epithelial development from stem cells to differentiated tissue. Using human stem cell-derived intestinal and branching lung organoids that mimic developing human organs as model systems, we extract and analyze key mitochondrial biophysical phenotypes in human development. To achieve this, we employ lattice light-sheet microscopy (LLSM), which enables high-resolution, 4D (x, y, z, time) imaging of mitochondria in organoid tissues with minimal damage to the sample. We image at key developmental time points from stem cell differentiation into mature organoid tissue. For data processing, we utilize the MitoGraph and MitoTNT software packages along with our developed custom computational tools. These tools allow for automated 4D organoid to single cell image processing and quantitative 4D single cell mitochondrial temporal network tracking. This work represents the first 4D high spatiotemporal-resolution quantification of live human organoid tissues at the single-cell level through development. We identified distinct mitochondrial phenotypes unique to each organoid type and found correlations between mitochondrial phenotypes, cellular age, and cell type. Furthermore, we demonstrate that mitochondrial network characteristics can predict both organoid type and cell age. Our findings reveal fundamental aspects of mitochondrial biology that were previously unobservable, offering new insights into cell-type-specific mitochondrial dynamics and enabling new findings in relevant human model systems. We believe that our findings and methods will be essential for advancing 4D cell biology, providing a powerful framework for characterizing organelles in organoid tissues.

cell biology↗

bioRxiv2023

Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypes

Imaging-based high-content screening aims to identify substances that modulate cellular phenotypes. Traditional approaches screen compounds for their ability to shift disease phenotypes toward healthy phenotypes, but these end point-based screens lack an atlas-like mapping between phenotype and cell state that covers the full spectrum of possible phenotypic responses. In this study, we present MitoSpace: a novel mitochondrial phenotypic atlas that leverages self-supervised deep learning to create a semantically meaningful latent space from images without relying on any data labels for training. Our approach employs a dataset of [~]100,000 microscopy images of Cal27 and HeLa cells treated with 25 drugs affecting mitochondria, but can be generalized to any cell type, cell organelle, or drug library with no changes to the methodology. We demonstrate how MitoSpace enhances our understanding of the range of mitochondrial phenotypes induced by pharmacological interventions. We find that i) self-supervised learning can automatically uncover the semantic landscape of drug induced latent mitochondrial phenotypes and can map individual cells to the correct functional area of the drug they are treated with, ii) the traditional classification of mitochondrial morphology along a fragmented to fused axis is more complex than previously thought, with additional axes being identified, and iii) latent spaces trained in a self-supervised manner are superior to those trained with supervised models, and generalize to other cell types and drug conditions without explicit training on those cell types or drug conditions. Future applications of MitoSpace include creating mitochondrial biomarkers for drug discovery and determining the effects of unknown drugs and diseases for diagnostic purposes.

pharmacology and toxicology↗

bioRxiv2023

Oxygen isotopes in orangutan teeth reveal recent and ancient climate variation

Studies of climate variation commonly rely on chemical and isotopic changes recorded in sequentially-produced growth layers, such as in corals, shells and tree rings, as well as in accretionary deposits--ice and sediment cores, and speleothems. Oxygen isotopic compositions ({delta}18O) of tooth enamel are a direct method of reconstructing environmental variation experienced by an individual animal. Here we utilize long-forming orangutan dentitions (Pongo spp.) to probe recent and ancient rainfall trends on a weekly basis over [~] 3-11 years per individual. We first demonstrate the lack of any consistent isotopic enrichment effect during exclusive nursing, supporting the use of primate first molar teeth as environmental proxies. Comparisons of {delta}18O values (n = 2016) in twelve molars from six modern Bornean and Sumatran orangutans reveal a high degree of overlap, with more consistent annual and bimodal rainfall patterns in the Sumatran individuals. Comparisons with fossil orangutan {delta}18O values (n = 955 measurements from six molars) reveal similarities between modern and late Pleistocene fossil Sumatran individuals, but differences between modern and late Pleistocene/early Holocene Bornean orangutans. These suggest drier and more open environments with reduced monsoon intensity during this earlier period in northern Borneo, consistent with other Niah Caves studies and long-term speleothem {delta}18O records in the broader region. This approach can be extended to test hypotheses about the paleoenvironments that early humans encountered in southeast Asia.

paleontology↗

bioRxiv2022

Transcriptomic and functional mapping of autism associated environmental factors in developing human neurons

Research continues to identify genetic variation, environmental exposures, and their mixtures underlying different diseases and conditions. There is a need for screening methods to understand the molecular outcomes of such factors. Here, we investigate a highly efficient and multiplexable, fractional factorial experimental design (FFED) to study six environmental factors and four human induced pluripotent stem cell line derived differentiating human neural progenitors. We showcase the FFED coupled with RNA-sequencing to identify the effects of low-grade exposures to these environmental factors and analyse the results in the context of autism spectrum disorder (ASD). We performed this after five-day exposures on differentiating human neural progenitors accompanied by a layered analytical approach and detected several convergent and divergent, gene and pathway level responses. We revealed significant upregulation of pathways related to synaptic function and lipid metabolism following lead and fluoxetine exposure, respectively. The lipid changes were validated using mass spectrometry- based metabolomics after fluoxetine exposure. Our study demonstrates that the FFED can be used for multiplexed transcriptomic analyses to detect relevant pathway-level changes in human neural development caused by low-grade environmental risk factors. Future studies will require multiple cell lines with different genetic backgrounds for characterising the effects of environmental exposures in ASD.

neuroscience↗

bioRxiv2022

Characterization of Morreton Virus (MORV) as a Novel Oncolytic Virotherapy Platform for Liver Cancers

Morreton virus (MORV) is a novel oncolytic Vesiculovirus, genetically distinct from vesicular stomatitis virus (VSV). we report that MORV induced potent cytopathic effects in a panel of cholangiocarcinoma (CCA) and hepatocellular carcinoma (HCC) cell lines. In addition, high intranasal doses of MORV were not associated with significant adverse effects and were well tolerated in mice bearing liver tumor xenografts and syngeneic liver cancers. Furthermore, single intratumoral treatments with MORV (1 x 107 TCID50) triggered a robust antitumor immune response leading to substantial tumor regression and disease control in a syngeneic CCA model, using 10-fold lower dose compared to VSV (1 x 108 TCID50). In addition, MORV and VSV both induced prominent tumor growth delay in immunodeficient mice bearing Hep3B hepatocellular carcinoma (HCC) but not in mice bearing HuCCT-1 CCA xenografts. Our findings indicate that wild-type MORV is safe and can induce potent tumor regression in HCC and CCA animal models without adverse events via immune-mediated and immune-independent mechanisms. Further development and clinical translation of MORV as virotherapy for liver cancers are warranted.

immunology↗

bioRxiv2022

IDSL.CCDB: a database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among reported chemicals measured in human specimens. With an increase in the size of these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. While co-regulatory genes databases have been developed, a similar database for metabolites and chemicals have not been developed yet. We have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics - Chemical Correlation Database (IDSL.CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views that are clear, readable and meaningful. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 36 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. IDSL.CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The IDSL.CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

bioinformatics↗

bioRxiv2020

Synergistic Combination of Cytotoxic Chemotherapy and Cyclin Dependent Kinase 4/6 Inhibitors in Biliary Tract Cancers

Biliary tract cancers (BTCs) are uncommon but highly lethal gastrointestinal malignancies. Gemcitabine/cisplatin is a standard-of-care (SOC) systemic therapy, but has a modest impact on survival and harbor toxicities including myelosuppression, nephropathy, neuropathy and ototoxicity. While BTCs are characterized by aberrations activating the cyclinD1-CDK4/6-CDKN2A-RB pathway, clinical use of CDK4/6 inhibitors as monotherapy is limited by lack of validated biomarkers, diffident pre-clinical efficacy and development of acquired drug resistance. Emerging studies have explored therapeutic strategies to enhance the anti-tumor efficacy of CDK4/6 inhibitors by combination with chemotherapy-regimens but their mechanism of action remains elusive. Here, we report in vitro and in vivo synergy in BTC models, showing enhanced efficacy, reduced toxicity and better survival with a combination comprising gemcitabine/cisplatin and CDK4/6 inhibitors. Furthermore, we demonstrated that abemaciclib monotherapy had only modest efficacy due to autophagy induced resistance. Notably, triplettherapy was able to potentiate efficacy through elimination of the autophagic flux. Correspondingly, abemaciclib potentiated RRM1 reduction, resulting in sensitization to gemcitabine. Conclusions: As such, these data provide robust pre-clinical mechanistic evidence of synergy between gemcitabine/cisplatin and CDK4/6 inhibitors, and delineate a path forward for translation of these findings to preliminary clinical studies in advanced BTC patients.

cancer biology↗