Biology subjects

Oza, V.

Publications and source records attributed to Oza, V..

4 recordsLinked to original sources

bioRxiv2025

Mapping of genotype-by-environment interaction loci for Metabolic Syndrome-like traits using the multi-parent Drosophila Synthetic Population Resource determines that main genetic effects are distinct from environment dependent plastic loci

Metabolic Syndrome (MetS) risk, driven by genotype-environment interactions like diet, is rising globally. Due to its genetic and environmental complexity, the genetic architecture and interconnected traits underlying MetS is poorly understood. In Drosophila, genotype-by-diet interactions significantly influence MetS-like traits. This study used the Drosophila Synthetic Population Resource to dissect the genetic architecture of both genotypic and genotype-by-diet interaction effects underlying trait variation. The study hypotheses were: 1) Loci responsible for metabolic phenotypic variation should be shared across traits. 2) Genetic loci responsible for plasticity and epistatic interactions for metabolic traits should also be the loci responsible for the main effects. 3) Genes responsible for variation in metabolic traits should share common functions. Using a round-robin crossing scheme and novel analyses, we mapped additive, dominance, and epistatic loci--some diet-specific, others diet-independent. Main-effect and plastic loci were largely distinct, as were epistatic loci from main-effect loci, highlighting that main genetic effects alone will not explain how genetic variants interact with the environment or the genome to influence disease risk. gene-by-diet or gene-by-gene interactions influencing MetS risk. Further, tremendous cryptic genetic variation for metabolic traits is lurking in natural populations. We explored the function of candidate genes from our study empirically and with bioinformatics. While some of the candidate genes might have been expected, most would not have been identified a priori, thus with this study we have identified many new candidate mechanisms contributing to the genetic and genotype-by-diet interaction effects on MetS variance.

genetics↗

bioRxiv2025

Gastrointestinal colonization as a source of Staphylococcus aureus in atopic dermatitis

Atopic dermatitis (AD) is a prevalent inflammatory skin disease with complex pathogenesis. Both skin and gut microbiota influence AD, with Staphylococcus aureus, in particular, exacerbating the disease. However, the relationship between S. aureus colonization in the gut and skin, and whether it affects AD, remains unclear. Using a combination of culture-based methods, microbiome analysis, and genome sequencing of S. aureus from multiple body sites of children with and without AD, we found that the gut represents a major reservoir of genetically diverse S. aureus that is transmitted to the skin, including mutants associated with worse disease. We validated this association between S. aureus gastrointestinal colonization and AD in an independent human cohort and demonstrated its direct effect on disease in an infantile AD mouse model, wherein S. aureus gastrointestinal colonization worsened skin inflammation. Overall, this study identifies a previously unrecognized S. aureus reservoir, with implications for microbiota-targeting therapies in AD.

microbiology↗

bioRxiv2020

Super-human cell death detection with biomarker-optimized neural networks

Cell death is an essential process in biology that must be accounted for in live microscopy experiments. Nevertheless, cell death is difficult to detect without perturbing experiments with stains, dyes or biosensors that can bias experimental outcomes, lead to inconsistent results, and reduce the number of processes that can be simultaneously labelled. These additional steps also make live microscopy difficult to scale for high-throughput screening because of the cost, labor, and analysis they entail. We address this fundamental limitation of live microscopy with biomarker-optimized convolutional neural networks (BO-CNN): computer vision models trained with a ground truth biosensor that detect live cells with superhuman, 96% accuracy more than 100 times faster than previous methods. Our models learn to identify important morphological characteristics associated with cell vitality without human input or additional perturbations, and to generalize to other imaging modalities and cell types for which they have no specialized training. We demonstrate that we can interpret decisions from BO-CNN models to gain biological insight into the patterns they use to achieve superhuman accuracy. The BO-CNN approach is broadly useful for live microscopy, and affords a powerful new paradigm for advancing the state of high-throughput imaging in a variety of contexts.

cell biology↗

bioRxiv2019

Genetically encoded cell-death indicators (GEDI) to detect an early irreversible commitment to neurodegeneration

Cell death is a critical process that occurs normally in health and disease. However, its study is limited due to available technologies that only detect very late stages in the process or specific death mechanisms. Here, we report the development of a new fluorescent biosensor called genetically encoded death indicator (GEDI). GEDI specifically detects an intracellular Ca2+ level that cells achieve early in the cell death process and marks a stage at which cells are irreversibly committed to die. The time-resolved nature of GEDI delineates a binary demarcation of cell life and death in real time, reformulating the definition of cell death. We demonstrate that GEDI acutely and accurately reports death of rodent and human neurons in vitro, and show GEDI enables a novel automated imaging platform for single cell detection of neuronal death in vivo in zebrafish larvae. With a quantitative pseudo-ratiometric signal, GEDI facilitates high-throughput analysis of cell death in time lapse imaging analysis, providing the necessary resolution and scale to identify early factors leading to cell death in studies of neurodegeneration.

neuroscience↗