Biology subjects

Raja, K.

Publications and source records attributed to Raja, K..

8 recordsLinked to original sources

bioRxiv2026

HIPK2 regulates homology-directed DNA repair and PARP inhibitor sensitivity

Repair of DNA double-strand breaks (DSBs) by homologous recombination (HR) counteracts genome instability and carcinogenesis. Cancer cells frequently show defects in HR which can be therapeutically exploited by hypersensitivity to poly(ADP-ribose) polymerase inhibitor (PARPi) treatment. Here we identify an unforeseen function of HIPK2 in HR repair and PARPi sensitivity. HIPK2 accumulates at DSBs and associates with DSB repair factors at DNA damage foci. DSB recruitment of HIPK2 requires checkpoint kinase ATM activity. DNA repair pathway analysis revealed that HIPK2 depletion specifically impairs HR. Mechanistically, we found that HIPK2 binds BRCA1 and phosphorylates BRCA1 at Ser1191, a site that regulates damage-induced BRCA1 protein stability. Consistently, HIPK2 depletion or pharmacological inhibition of HIPK2 results in reduced BRCA1 protein levels, and sensitizes BRCA1-proficient cancer cells to IR damage and PARPi treatment. In sum, our results identify a role for HIPK2 in HR through regulating BRCA1 protein levels, and propose HIPK2 inhibition as a novel strategy to sensitize BRCA1-proficient cancer cells to PARPi treatment.

cell biology↗

bioRxiv2026

Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive, rapid deterioration of motor neurons (MNs). Rare mutations in a handful of genes are sufficient to cause ALS; however, 90% of ALS cases are not linked to these genes and their underlying cause remains unknown. Abnormal subcellular distribution, structure or aggregation of the TDP-43 protein are nearly universal hallmarks of the disease, suggesting a shared molecular mechanism across both genetic and sporadic ALS (sALS). However, the heterogeneity of the ALS clinical syndrome suggests that the underlying mechanisms culminating in ALS and TDP-43 pathology may partly differ among individuals and may need to be understood to develop successful therapies that target subgroups of patients. Here, we harnessed the power of machine learning (ML) to begin to decode, in a systematic and unbiased fashion, the cellular signatures of ALS. We used high-content imaging of live, human iPSC-derived motor neurons (iMNs) from ALS patients or gene-edited and gene-corrected TDP-43 mutant lines to train shallow connected ML algorithms (SMLs) and deep convolutional neural networks (DNNs). Our models identified and distinguished mutant and control iMNs with moderately high accuracy. We then used explainability methods to uncover the discriminating cellular signals and found that the strongest ones mapped to the nuclear area, suggesting underlying alterations within the nucleus. We validated this finding by revealing that TDP-43 mutant iMNs display alterations in nucleocytoplasmic shuttling and cellular integrity. Further, a time-interaction ML model uncovered dynamic morphological transitions preceding degeneration, offering a window into early pathogenic events as well as neurodevelopmental changes. Extending our ML pipeline to iMNs with mutations in the ALS gene C9orf72 or derived from sALS revealed both overlapping and distinguishable signatures, suggesting shared yet distinct mechanistic pathways. Together, these findings establish ML-driven phenotypic profiling as a powerful approach to stratify people with ALS, help disentangle the molecular heterogeneity of ALS and produce a more holistic phenotypic definition in cell-based models, and ultimately find causes and treatments. This strategy offers a scalable and innovative paradigm for uncovering early disease mechanisms not only in ALS but potentially across a spectrum of neurodegenerative and sporadic disorders.

neuroscience↗

bioRxiv2025

Attenuated adenosine mediated immune-dampening increases natural killer cell activity in early age-related macular degeneration

Non-exudative age-related macular degeneration (AMD) involves retinal pigment epithelium (RPE) dysfunction and has been linked to altered intraocular immunity. Our investigation focuses on immune cell subsets and inflammation-associated factors in the eyes with early and intermediate AMD. We observed elevated levels of activated natural killer (NK) cells and interferon-{gamma}, concurrent with reduced myeloid-derived suppressor cells (MDSCs) and adenosine in AMD eyes. Aqueous humor from AMD patients had diminished ability to dampen NK cell activation, an effect rescued by adenosine supplementation. The Cryba1 cKO mouse model recapitulated these immune alterations, and single-cell RNA-sequencing identified NK cell-related genes and NK cell-RPE interactions. Co-culture of activated NK cells with RPE cells induced barrier dysfunction and Gasdermin-E driven pyroptosis providing a functional link relevant to AMD. These findings suggest a double-hit model where elevated immune activation and loss of immune dampening mechanisms drive AMD progression. Resetting the intraocular immune balance may be a promising therapeutic strategy for managing early and intermediate AMD. O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/634301v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1516bfaorg.highwire.dtl.DTLVardef@87f505org.highwire.dtl.DTLVardef@1e725caorg.highwire.dtl.DTLVardef@622717_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Illustra tion of pro posed mechanism underlying NK cell-RPE interaction in early AMD pathogenesis. Dysregulated NK cell communicate s with stressed RPE in early AMD immunopathology. Aberrant AMD aqueous humor and retina shows increased NK cells and NK effector molecules like IFN{gamma} with reduced MDSC and adenosine in human subjects, don or eye and animal model. Activated NK cells interaction with RPE causes dysfunction and pyroptotic cell death via Gasdermin-E pathway in AMD.. Created with BioRender.com C_FIG

immunology↗

bioRxiv2024

National-scale acoustic monitoring of avian biodiversity and phenology

Billions of birds migrate annually, triggered by endogenous behaviors but also by ecoclimatic drivers which are shifting with climate change. These dynamics play out over huge spatiotemporal scales, making monitoring of phenology challenging with traditional biodiversity survey approaches. In this study, over a complete spring migration season (April through June), we collected 37,429 hours of audio from 28 networked sensors in forests across Norway using a nationwide passive acoustic monitoring (PAM) system. We applied an open-source detection algorithm to automatically classify bird vocalizations; through expert validation we found the algorithm classified 57 species (14 full migrants) with at least 80% precision. Using these automated detections, we developed regional arrival curves for three common migratory passerines: Willow Warbler, Common Chiffchaff, and Spotted Flycatcher. We then demonstrate that PAM detections can be used to train audio species distribution models that map how species vocalization probability changes across Norway during spring migration. Lastly, we discuss how PAM can complement existing manual surveys to support the design and implementation of effective policy and conservation measures.

ecology↗

bioRxiv2023

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

BackgroundThe PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. ResultsWe demonstrate SKiMs ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface (https://skim.morgridge.org) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. ConclusionsSKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

bioinformatics↗

bioRxiv2023

Modeling the Spatiotemporal Influence of Climate on American Avian Migration

Birds have adapted to climatic and ecological cycles to inform their Spring and Fall migration timings, but anthropogenic global warming has affected these long-establish cycles. Understanding these dynamics is critical for conservation during a changing climate. Here, we employ a modeling approach to explore how climate spatiotemporally affects bird occurrence on eBird surveys. Specifically, we train an ensemble of multivariate and multi-response random forest models on North and South American climate data, then predict eBird survey occurrence rates for 41 migrating passerine bird species in a Northeastern American ecoregion from 2008-2018. In October, when many passerines have begun their southward winter migration, we achieve more accurate predictions of bird occurrence using lagged climate features alone to predict occurrence. These results suggest that analyses of machine learning model metrics may be useful for identifying spatiotemporal climatic cues that affect migratory behavior. Lastly, we explore the application and limitations of random forests for prediction of future bird occurrence using 2021-2040 climate projections.

ecology↗

bioRxiv2021

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

The Nile rat (Avicanthis niloticus) is an important animal model for biomedical research, including the study of diurnal rhythms and type 2 diabetes. Here, we report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3,613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including those that affect genes associated with type 2 diabetes and metabolic dysfunctions. These include 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse. Our findings reflect the exceptional level of genomic detail present in this assembly, which will greatly expand the potential of the Nile rat as a model organism for genetic studies.

genomics↗

bioRxiv2020

SKiM - A generalized literature-based discovery system for uncovering novel biomedical knowledge from PubMed

Literature-based discovery (LBD) uncovers undiscovered public knowledge by linking terms A to C via a B intermediate. Existing LBD systems are limited to process certain A, B, and C terms, and many are not maintained. We present SKiM (Serial KinderMiner), a generalized LBD system for processing any combination of A, Bs, and Cs. We evaluate SKiM via the rediscovery of discoveries by Don Swanson, who pioneered LBD. Using only literature from the 19th century up to a year before Swansons discoveries, SKiM uncovers all five discoveries. We apply SKiM to repurposing drugs for 26 conditions of high prevalence. Manual analysis confirmed 65 discoveries useful for four diseases from Swansons discoveries from one to 31 years prior to their first validation by clinical trials. SKiM predicts many new potential drug candidates representing prime targets for wet lab validation. SKiM can be applied to any biomedical inquiry sufficiently mentioned in the literature.

bioinformatics↗