Biology subjects

Kalra, A.

Publications and source records attributed to Kalra, A..

8 recordsLinked to original sources

bioRxiv2026

Beyond the Axenic Culture Filtrate Secretome: BONCAT and conventional proteomics together reveal the Infection-associated secreted protein repertoire of Acinetobacter baumannii in the human lung Epithelial cell line A549

Acinetobacter baumannii (Ab) is an important opportunistic pathogen responsible for pneumonia, bloodstream infections, and sepsis. Although several virulence determinants have been characterized, the bacterial secreted protein repertoire associated with host cell infection remains incompletely understood. Conventional proteomic analyses of axenic cultures provide valuable information but cannot distinguish proteins synthesized before infection from those produced during host pathogen interactions. This study combined bioorthogonal noncanonical amino acid tagging (BONCAT) with conventional proteomics to characterize the infection associated Ab secreted protein repertoire recovered from human lung epithelial A549 cells. To achieve pathogen selective metabolic labelling, an Ab clinical isolate that had caused neonatal sepsis (Ab7) was engineered to express a methionyl tRNA synthetase variant, MetRSNLL (L10N, Y258L, H299L), capable of incorporating azidonorleucine (ANL) into newly synthesised bacterial proteins. Labelling conditions were optimized using methionine biosynthesis inhibitory amino acids, which reduced endogenous methionine availability without appreciably affecting bacterial growth. Efficient ANL incorporation was confirmed by flow cytometry, confocal microscopy, biotin streptavidin assays, and in gel fluorescence. Metabolic labelling also remained compatible with bacterial intracellular survival. Sequential labelling with azidohomoalanine (AHA) before infection and ANL during A549 infection enabled the temporal assignment of bacterial protein synthesis. Subsequent SDS PAGE fractionation and LC MS/MS identified 152 infection-associated proteins, including 23 containing experimentally detected amino acid analogue modifications. Among these, 13 were AHA only, nine were ANL only, and one contained both analogues. Notably, 117 proteins were not detected in either axenic reference proteome. The infection associated fraction exhibited enrichment of predicted signal peptide containing and outer membrane proteins, while functional annotation identified enrichment of bacterial secretion system components among infection-only proteins. These findings demonstrate the complementary strengths of BONCAT and conventional proteomics: BONCAT provides evidence of bacterial protein synthesis during infection, whereas conventional proteomics expands detection beyond proteins containing identifiable analogue modified peptides. Although extracellular recovery does not establish active secretion, the combined approach reveals a host associated bacterial protein repertoire extending beyond axenic observations. This framework supports future investigations into bacterial adaptation, host pathogen interactions, and the prioritization of candidate diagnostic biomarkers, subject to functional characterization and clinical validation. The study establishes a methodological foundation for investigating infection-associated protein synthesis and release in complex host pathogen environments.

microbiology↗

bioRxiv2026

Deciphering the Gut-Brain Dialogue: A Survey-Based and In-Silico Comparative Analysis of Gut Microbial Dysbiosis in Common Neurological Disorders

The gut microbiome maintains a complex, bidirectional communication network with the central nervous system, commonly referred to as the gut-brain axis and its disruption has been implicated in several neurological disorders. This study combines a survey-based assessment of public awareness with an in-silico comparative analysis of gut microbial dysbiosis across four prevalent neurological disorders as observed in the current study: depression, anxiety, schizophrenia and autism spectrum disorder (ASD). A structured, anonymous online survey (n = 230) captured perceptions of the gut-brain connection along with dietary, lifestyle and gastrointestinal correlates of stress in a predominantly young, health-sciences-affiliated Indian cohort. In parallel, disorder-specific lists of elevated and reduced faecal microbial taxa were retrieved from the Disbiome database, compared using a multiple list comparator and taxonomically classified using the NCBI Taxonomy tool to construct phylogenetic trees in iTOL. Approximately three-quarters of respondents were aware of a potential gut-mental health link, yet about half reported no specific dietary practice and roughly 60% experienced stress-related digestive symptoms while rarely seeking medical consultation for them. Comparative analysis showed that depression, anxiety and schizophrenia shared a substantially overlapping dysbiosis signature, with common elevation of Actinomyces, Bacteroidaceae, Blautia, Eggerthella, Oscillibacter, Parasutterella and Veillonella and common reduction of Coprococcus, Lachnospiraceae, Ruminococcaceae, Clostridium, Faecalibacterium and Sutterella. In contrast, ASD displayed a distinct microbial signature with limited overlap with the other three disorders. Phylogenetic clustering confirmed that the shared taxa belonged predominantly to the phyla Bacillota (formerly Firmicutes), Bacteroidota (formerly Bacteroidetes), Actinomycetota (formerly Actinobacteria) and Pseudomonadota (formerly Proteobacteria). Notably, this phylum-level pattern parallels recent comparative analyses of microbial dysbiosis in neurodegenerative diseases, suggesting that broad phylogenetic shifts may be a relatively general correlate of chronic neurological disease, while disorder specificity emerges at the level of individual taxa. These findings support a shared microbial pathway linking depression, anxiety and schizophrenia that is distinct from the dysbiosis pattern observed in ASD and they underscore the value of microbiome-informed, disorder-specific therapeutic strategies.

bioinformatics↗

bioRxiv2026

A systematic assessment of machine learning for structural variant filtering

BackgroundAccurate discrimination of true structural variants (SVs) from artifacts in long-read sequencing data remains a critical bottleneck. Numerous machine learning solutions have been proposed, ranging from classical models using engineered features to advanced deep learning and foundation model interpretability methods. However, a systematic comparison of their performance, efficiency, and practical utility is lacking. ResultsWe conducted a comprehensive benchmark of five machine learning paradigms for SV filtering using standardized Genome in a Bottle (GIAB) data for samples HG002 and HG005. We evaluated classical Random Forest classifiers on 15 genomic features, computer vision models (ResNet/VICReg), diffusion-based anomaly detection, sparse autoencoders (SAEs) on the Evo2-7B foundation model, and multimodal ensembles. A simple Random Forest on interpretable features achieved a peak F1-score of 95.7%, effectively matching all more complex models (ResNet50: 95.9%, Diffusion: 95.8%). This study represents the first application of diffusion-based anomaly detection and sparse autoencoders to structural variant analysis; while diffusion models learned highly discriminative, disentangled representations and SAEs uncovered biologically interpretable features (including atoms that were specific for ALU deletions, chromosome X variants and insertion events), they did not significantly surpass this classification ceiling. Ensemble methods offered no performance benefit but may have future potential given the orthogonality of vision-based and linear features. ConclusionsOur findings demonstrate that for the established task of germline SV filtering, simpler, interpretable models provide an optimal balance of accuracy, speed, and transparency. This benchmark establishes a pragmatic framework for method selection and argues that increased model complexity must be justified by clear, unmet biological needs rather than marginal predictive gains.

bioinformatics↗

bioRxiv2025

The impact of microplastics on the mice gut microbiome: a meta-analysis

BackgroundMicroplastics are nearly ubiquitous in the natural environment. There is increasing awareness of the ingestion of microplastics in a wide range of wild animals and humans, but their health and fitness impacts remain poorly understood. Mounting evidence suggests that microplastics could accumulate in the gut, affecting the microbiome and hindering its function. This may constitute a direct pathway through which exposure to microplastics could affect health and disease. However, prior studies on the effects of microplastic exposure on the microbiome show contrasting findings raising the question whether microplastics truly affect the microbiome. Here, we performed the first meta-analysis on the effects of dietary microplastic exposure on the gut microbiome of mice. ResultsOverall, we found support for a significant impact of microplastic exposure on the relative abundance of gut bacteria. More in-depth analysis revealed microplastic exposure significantly increased the relative abundance of Bacillota and Pseudomonadota. In the gut microbiome, Bacillota is associated with carbohydrate metabolism and Pseudomonadota is associated with facultative anaerobes, thus these changes may have functional consequences. ConclusionsOur findings highlight the importance of distinguishing among differing bacterial phyla when assessing the impacts of microplastics on the gut microbiome as the direction and size of the impact varies by phyla.

microbiology↗

bioRxiv2024

Breaking free from the clocks tyranny restores memory to brain damaged flies

The relationship between sleep and memory is an active topic of investigation. In this context, we demonstrate that enhancing sleep restores memory to flies with ablated Mushroom Bodies (MB), a key memory center; this is consistent across several memory assays. Mapping the underlying circuitry reveals circadian modulation of a subset of Dopaminergic neurons (DANs) that modulate aversive learning. Using imaging, we show that MB-ablation disrupts, and sleep restores the time of day these neurons are most responsive. Knocking down the receptor for the clock output signal, Pigment-dispersing factor (Pdfr), in this subset of DANs restores memory to MB-ablated flies. Crucially, MB-ablation does not result in memory impairments in the absence of a functioning clock. Our results reveal neuromodulations key role in cognitive restoration, where sleep aids memory in damaged brains, but a functioning clock unexpectedly hinders this process.

neuroscience↗

bioRxiv2023

Sleep Deprivation, Sleep Fragmentation and Social Jet Lag increase temperature preference in Drosophila

Despite the fact that sleep deprivation substantially affects the way animals regulate their body temperature, the specific mechanisms behind this phenomenon are not well understood. In both mammals and flies, neural circuits regulating sleep and thermoregulation overlap, suggesting an interdependence that may be relevant for sleep function. To investigate this relationship further, we exposed flies to 12 h of sleep deprivation, or 48 h of sleep fragmentation and evaluated temperature preference in a thermal gradient. Flies exposed to 12 h of sleep deprivation chose warmer temperatures after sleep deprivation. Importantly, sleep fragmentation, which prevents flies from entering deeper stages of sleep, but does not activate sleep homeostatic mechanisms nor induce impairments in short-term memory also resulted in flies choosing warmer temperatures. To identify the underlying neuronal circuits, we used RNAi to knock down the receptor for Pigment dispersing factor, a peptide that influences circadian rhythms, temperature preference and sleep. Expressing UAS-PdfrRNAi in subsets of clock neurons prevented sleep fragmentation from increasing temperature preference. Finally, we evaluated temperature preference after flies had undergone a social jet lag protocol which is known to disrupt clock neurons. In this protocol, flies experience a 3 h light phase delay on Friday followed by a 3 h light advance on Sunday evening. Flies exposed to social jet lag exhibited an increase in temperature preference which persisted for several days. Our findings identify specific clock neurons that are modulated by sleep disruption to increase temperature preference. Moreover, our data indicate that temperature preference may be a more sensitive indicator of sleep disruption than learning and memory.

neuroscience↗

bioRxiv2022

Novel tumorigenic FOXM1-PTAFR-PTAF axis revealed by multi-omic profiling in TP53/CDKN2A-double knockout human gastroesophageal junction organoid model

Inactivation of the tumor suppressor genes TP53 and CDKN2A occurs early during gastroesophageal junction (GEJ) tumorigenesis. However, due to a paucity of GEJ-specific disease models, cancer-promoting consequences of TP53 and CDKN2A inactivation at the GEJ have been incompletely characterized. Here we report the development of the first wild-type primary human GEJ organoid model, as well as a CRISPR-edited transformed GEJ organoid model. CRISPR/Cas9 engineering to inactivate TP53 and CDKN2A (TP53/CDKN2AKO) in GEJ organoids induced morphologic dysplasia as well as pro-neoplastic features in vitro and tumor formation in vivo. Notably, lipidomic profiling identified several Platelet-Activating Factors (PTAFs) among the most upregulated lipids in CRISPR-edited organoids; and importantly, PTAF/PTAFR abrogation by siRNA knockdown or a pharmacologic inhibitor (WEB2086) significantly blocked proliferation and other pro-neoplastic features of TP53/CDKN2AKO GEJ organoids in vitro and tumor formation in vivo. In addition, murine xenografts derived from Eso26, an established esophageal adenocarcinoma (EAC) cell line, were suppressed by WEB2086. Mechanistically, TP53/CDKN2A dual inactivation disrupted both the transcriptome and the DNA methylome, likely mediated by key transcription factors, particularly Forkhead Box M1 (FOXM1). Importantly, FOXM1 activated PTAFR transcription by binding to the PTAFR promoter, further amplifying the PTAF-PTAFR pathway. In summary, we established a robust model system for investigating early GEJ neoplastic events, identified crucial metabolic and epigenomic changes occurring during GEJ model tumorigenesis, and discovered a potential cancer-therapeutic strategy, while providing insights into pro-neoplastic mechanisms associated with TP53/CDKN2A inactivation in early GEJ neoplasia. One Sentence SummaryNovel tumorigenic FOXM1-PTAFR-PTAF axis revealed by multi-omic profiling in TP53/CDKN2A-double knockout human gastroesophageal junction organoid model. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/491356v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@18dcb8dorg.highwire.dtl.DTLVardef@2ec0d6org.highwire.dtl.DTLVardef@17622b8org.highwire.dtl.DTLVardef@e184bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

HLA-SPREAD: A comprehensive resource for HLA associated diseases, drug reactions and SNPs across populations

Extreme complexity in the Human Leukocyte Antigens (HLA) system and its nomenclature makes it difficult to interpret and integrate relevant information for HLA associations with diseases, Adverse Drug Reactions (ADR) and Transplantation. PubMed search displays [~]144,000 studies on HLA reported from multiple diseases in diverse locations. Currently, IPD-IMGT/HLA database houses data on 28,320 HLA alleles. We developed an automated pipeline with a unified graphical user interface HLA-SPREAD that provides a structured information on SNPs, Populations, REsources, ADRs and Diseases information. Information on HLA was extracted from [~]24 million PubMed abstracts extracted using Natural Language Processing (NLP). Python scripts were used to mine and curate information on diseases, filter false positives and categorize to 24 tree hierarchical groups and named Entity Recognition (NER) algorithms followed by semantic analysis to infer HLA association(s). This resource from 112 countries and 32 ethnic groups provides interesting insights on: markers associated with allelic/haplotypic association in autoimmune, cancer, viral and skin diseases, transplantation outcome and ADRs for hypersensitivity. Summary information on clinically relevant biomarkers related to HLA disease associations with mapped susceptible/risk alleles are readily retrievable from HLASPREAD. The resource is available at URL http://hla-spread.igib.res.in/. This resource is first of its kind that can help uncover novel patterns in HLA gene-disease associations.

bioinformatics↗