Biology subjects

Gupta, S.

Publications and source records attributed to Gupta, S..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

OmniCorr: An R-package for visualizing putative host-microbiota interactions using multi-omics data

Holo-omics leverages omics datasets to explore the interactions between hosts and their associated microbiomes. Although the generation of omics data from matching host and microbiome samples is steadily increasing, there remains a scarcity of computational tools capable of integrating and visualizing this data to facilitate the interpretation and prediction of host-microbiota interactions. We present OmniCorr, an R package designed to: (1) manage the complexity of omics data by clustering similar observations into modules, (2) visualize correlations of these modules across different omics layers, host-microbiota interfaces, and meta-data, and (3) identify statistically significant associations indicative of putative host-microbiota interactions. OmniCorrs utility is demonstrated using datasets from two systems: (i) Atlantic salmon, integrating host transcriptomics with metagenomics and metatranscriptomics to explore dietary impacts, and (ii) cattle, combining host proteomics with metaproteomics and metabolomics to investigate methane emission variability. Availability and ImplementationOmniCorr is freely available at: https://github.com/shashank-KU/OmniCorr.

bioinformatics↗

bioRxiv2025

The ZNF512B-NuRD complex co-ordinates a neural-specific gene expression program

The nucleosome remodelling and deacetylase complex (NuRD) plays a key role in chromatin regulation and a wide range of biological processes including development, haemopoiesis, immunity and neurogenesis. Its interaction with tissue-enriched and sequence-specific transcription factors (TFs) leads to distinct functional outputs in the given tissue by targeting a specific set of genes. However, how NuRD dynamically and specifically regulates gene expression in a tissue-specific manner is poorly understood. Here, we refine an N-terminal specific NuRD interaction motif which enables direct engagement with many transcriptional regulatory proteins. Using a series of structural modelling and biochemical techniques, we show that ZNF512B, a poorly characterised neuronal-expressed zinc finger protein, directly binds to the RBBP4 subunit of the NuRD complex. Subsequent knockdown of ZNF512B results in the downregulation of several neural-related molecular pathways suggesting that ZNF512B may play a regulatory role during neurogenesis. We also show that in NTERA-2 neural cells, the expression of ZNF512B is necessary for cell growth and survival, and is markedly enhanced during neural progenitor cell (NPC) differentiation. In summary, our data suggest that ZNF512B might regulate neural-specific transcriptional programs via engagement with the NuRD complex.

molecular biology↗

bioRxiv2025

Dynamic but constrained: Repeated acquisitions of nutritional symbionts in bed bugs (Heteroptera: Cimicidae) from a narrow taxonomic pool.

BackgroundBed bugs (Heteroptera: Cimicidae), like other strictly blood-feeding insects, harbor obligate bacterial symbionts that supply essential nutrients (especially B vitamins) lacking in their blood diet. The primary obligate symbionts are transovarially transmitted Wolbachia, notable for having horizontally acquired biotin operon. In addition to these symbionts with the confirmed nutritional role, bacteria from the genera Symbiopectobacterium and Tisiphia have also been found to be transovarially transmitted in bed bugs. Their significance to the host fitness remains unclear; they are considered facultative, and thus likely non-essential. However, this understanding of bed bugs symbiosis is based exclusively on studies of the human-associated common bed bug (Cimex lectularius) and few related Cimicinae species. Virtually nothing is known about the diversity, origins, or metabolic roles of symbionts across the more than hundred described bed bug species. ResultsUsing amplicon and metagenomic analyzes, we identified five different bacterial genera as potential bed bug symbionts: Wolbachia, Symbiopectobacterium, Sodalis, Serratia, and Tisiphia. The distribution of these bacteria among 13 bed bug species indicated at least 16 independent origins of symbiosis (some samples harboring multiple symbiont strains). A comparison of host and symbiont phylogenies suggested that some of these origins were followed by cospeciation. Not all bed bugs, however, harbored Wolbachia. In the subfamily Cacodminae, Symbiopectobacterium (previously known as facultative symbiont) was detected as the sole symbiont, suggesting its role as an essential, obligate symbiont. Analysis of 23 obtained genome drafts revealed considerable differences in their size and gene content, indicating that these bacteria have reached different stages in the evolution towards obligate symbiosis. As is common in symbiotic bacteria, the analyzed genomes have lost many biosynthetic capacities. A comparison of B-vitamin synthesis pathways showed that only two, riboflavin and lipoic acid, were preserved across all symbionts. ConclusionsAn overview of symbiosis across a broad phylogenetic span of bed bugs reveals that this insect family has undergone remarkably dynamic evolution, characterized by multiple independent symbiont acquisitions, episodes of cospeciations, and frequent co-occurrence of multiple symbionts within individual hosts. Interestingly, although these symbionts were acquired through multiple independent events, the majority belong to just three bacterial genera (Wolbachia, Symbiopectobacterium, and Sodalis), suggesting an unknown mechanisms underlying bed bug-symbiont specificity. Two aspects of this study warrant further investigation. First, the finding of Symbiopectobacterium as the sole obligate symbiont in Cacodminae suggests that expanding the taxonomic sampling may reveal an even more complex structure of symbiosis within bed bugs. Second, the inconsistencies and uncertainties in evaluating the functionality of the biotin synthesis indicate that further research will be necessary to better understand the evolution of this B vitamin pathway in symbiotic bacteria.

evolutionary biology↗

bioRxiv2025

Clusterin reverses epitheliopathy, reduces inflammation, and restores goblet cells and corneal nerves in a mouse model of autoimmune dry eye

Chronic ocular surface disease (OSD) is characterized by corneal epitheliopathy, reduced barrier function and loss of nerves, accompanied by persistent inflammation. Current treatments offer limited relief and there is no approved therapy that promotes neurosensory regeneration in OSD. Here, we tested the therapeutic efficacy of clusterin (CLU), a molecular chaperone and MMP9 inhibitor found in tears, in Thbs1-deficient mice, a preclinical model of autoimmune dry eye associated with Sjogrens disease (SjD). These mice were treated topically at the ocular surface, bilaterally, for 3 weeks with recombinant human CLU (rhCLU) or human plasma-derived CLU (pCLU) eyedrops and compared to standard-of-care 0.1% dexamethasone eyedrops. Treatment with CLU significantly improved corneal barrier integrity, increased corneal nerve density, enhanced the proportion of corneal nerves with immunoreactivity for CGRP and promoted conjunctival goblet cell regeneration. Furthermore, CLU reduced immunoreactivity for ADAM17 in the corneal epithelium and reduced Tnfa expression in the conjunctiva, supporting its anti-inflammatory effect. Notably, all these effects were comparable to, or even exceeded, those resulting from treatment with dexamethasone. Based on its efficacy, we introduce CLU as a multifunctional and promising biotherapeutic for a widespread range of ocular inflammatory conditions involving corneal epitheliopathy and nerve loss, including dry eye associated with SjD.

immunology↗

bioRxiv2025

cGAS-STING dependent type I IFN protects against Leptospira interrogans renal colonization in mice.

Leptospira interrogans is the major causative agent of leptospirosis. Humans, canines and livestock animals are susceptible to Leptospira species and can develop fulminant disease. Rodents serve as reservoir hosts in which the bacteria colonize the renal tubules and are excreted in the urine. The host immune response to Leptospira spp. remains poorly defined. We show that L. interrogans induces a robust type I interferon (IFN) response in human and murine macrophages that is dependent on the cytosolic dsDNA sensor Cyclic GMP-AMP Synthase (cGAS) and the Stimulator of IFN Genes (STING) signaling pathway. Further, we show that mice deficient in the IFN/{beta} receptor subunit 1 (IFNAR1) or STING had higher bacterial burdens and increased renal colonization following infection in vivo suggesting that cGAS-STING-driven type I IFN is required for the host defense against L. interrogans. These findings demonstrate the significance of cGAS-STING-dependent type I IFN signaling in mammalian innate immune responses to L. interrogans. Author SummaryLeptospirosis is a globally distributed zoonotic disease caused by spirochetes belonging to the genus Leptospira. While humans, livestock, and dogs can develop severe or even fatal illness upon infection, rodents typically serve as asymptomatic reservoir hosts. A defining feature of the leptospiral life cycle is the ability of the pathogen to colonize the kidney in these reservoir host species, leading to prolonged urinary shedding and environmental dissemination. Despite the significant global burden of leptospirosis, the innate immune pathways that detect this pathogen and prevent renal colonization remain poorly understood. In this study we demonstrate that L. interrogans induces a robust type I IFN cytokine response from macrophages. The induction of this type I IFN response is dependent on sensing cytosolic DNA by the cGAS-STING pathway. Using in vivo mouse models of L. interrogans infection we further show that activation of this pathway is required to control bacterial burdens and reduce long-term kidney colonization. This study is the first to demonstrate a critical role for cGAS-STING and type I IFN in controlling L. interrogans infection.

immunology↗

bioRxiv2025

Deep-Learning on condition specific expression profiles reveal critical cytosines in gene regulation

Compared to other nucleotides, the cytosines stand as the most expressive one for gene regulation in plants due to its status as methylation-based epigenetic switch. Methylation of some of these cytosines may have higher impact on downstream genes, making them critical ones. To this date not much has been done to decipher the criticality of such cytosines. This is first such pioneering study in decoding the critical Cs where a large volume of bisulfite and RNA-seq data, including 232 WGBS and 260 corresponding RNA-seq datasets from A. thaliana and rice, have been utilized. Using a deep learning system, strong relationship was established between methylation states of cytosines in contextual manner with respect to the downstream genes expression levels. Using the same system, all the 2kb upstream regions for Arabidopsis and rice genes have been annotated for critical cytosines. Experimental validation demonstrated specific methylation changes at critical cytosines under heat stress, affecting gene expression. The universal method developed may be applied to annotate other plant genomes for critical cytosine identification. GC% similarity, rather than homology, explains gene regulatory behavior under methylation control. The resource, CritiCal-C portal, is publicly accessible, paving the way for revolutionary strategies in gene regulation and expression with minimal interventions.

bioinformatics↗

bioRxiv2025

Glycolysis-dependent Sulfur Metabolism Orchestrates Morphological Plasticity and Virulence in Fungi

Fungi exhibit remarkable morphological plasticity, which allows them to undergo reversible transitions between distinct cellular states in response to changes in their environment. This phenomenon, termed fungal morphogenesis, is critical for fungi to survive and colonize diverse ecological niches and establish infections in a variety of hosts. Despite significant advancements in the field with respect to understanding the gene regulatory networks that control these transitions, the metabolic determinants of fungal morphogenesis remain poorly characterized. In this study, we uncover a previously uncharacterized, conserved dependency between central carbon metabolism and de novo biosynthesis of sulfur-containing amino acids that is critical for fungal morphogenesis, in two key fungal species. Using a multidisciplinary approach, we demonstrate that glycolytic flux is crucial to drive fungal morphogenesis in a cAMP-independent manner and perturbation of this pathway leads to a significant downregulation in the expression of genes involved in de novo biosynthesis of sulfur-containing amino acids. Remarkably, exogenous supplementation of sulfur-containing amino acids robustly rescues the morphogenesis defect induced by the perturbation of glycolysis in both Saccharomyces cerevisiae and Candida albicans, underscoring the pivotal role of de novo biosynthesis of sulfur-containing amino acid as a downstream effector of morphogenesis. Furthermore, a C. albicans mutant lacking the glycolytic enzyme, phosphofructokinase-1 (Pfk1) exhibited significantly reduced survival within murine macrophages and attenuated virulence in a murine model of systemic candidiasis. Overall, our work elucidates a previously uncharacterized coupling between glycolysis and sulfur metabolism that is critical for driving fungal morphogenesis, contributing to our understanding of this conserved phenomenon.

microbiology↗

bioRxiv2025

Regulation of translational fidelity by repression of C/D box SNoRNPs during UPR

Endoplasmic reticulum (ER) is the major cellular compartment where the folding and maturation of secretory and membrane proteins take place. When protein folding demands exceed the protein handling capacity of ER, the unfolded protein response (UPR) pathway is activated. UPR reduces the client protein load in the ER by inhibiting protein translation and increases protein handling capacity by upregulating genes encoding ER-resident molecular chaperones. The main pathway for translational repression in response to ER stress has been the phosphorylation of eIF2 by PERK, which also suppresses rRNA synthesis. rRNA methylation and pseudouridylation can fine-tune protein synthesis. Small nucleolar ribonucleoproteins (SnoRNPs) are mainly separated into two subtypes: C/D box and H/ACA box. C/D box SNoRNPs guides rRNA 2-O-methylation, while H/ACA SNoRNPs catalyze the pseudouridylation modifications of rRNAs. We hypothesize that the UPR may regulate translational fidelity by regulating the expression of core proteins of C/D and H/ACA box SnoRNPs. In this study, we show that the expressions of NHP2L1, NOP56 and FBL were significantly downregulated by UPR. In addition, FBL knockdown reduced cell proliferation and colony formation. Furthermore, we show the effects of UPR and FBL knockdown on rRNA methylation and translation fidelity, including alterations in nonsense suppression, frameshifts, ribosome pausing and translation initiation by IRES. In conclusion, this study reveals the regulation of translational fidelity during UPR by change in rRNA methylation due to reduced function of C/D box SnoRNPs.

cell biology↗

bioRxiv2025

The Consortium for Genomic Diversity, Ancestry, and Health in Colombia (CODIGO): building local capacity in genomics, bioinformatics, and precision medicine

The Consortium for Genomic Diversity, Ancestry, and Health in Colombia (CODIGO) aims to build a community of Colombian researchers in support of local capacity in genomics, bioinformatics, and precision health. Here, we present the first CODIGO data release and the consortium web platform, including annotations for more than 95 million genetic variants from 1,441 samples representing 14 populations from across the country. CODIGO samples show a wide range of African (16.7%), European (50.6%), and Indigenous American (32.8%) genetic ancestry components, with five distinct ancestry clusters. Thousands of ancestry-enriched variants, with divergent allele frequencies across clusters, show pharmacogenomic and clinical genetic associations. Examples include African ancestry-enriched variants associated with fast metabolism of the immunosuppressive drug tacrolimus and malaria resistance and European ancestry-enriched variants associated with nicotine dependence and hereditary hemochromatosis. CODIGO reveals the nexus between ancestry and health in Colombia and underscores the utility of collaborative genome sequence analysis efforts.

genomics↗

bioRxiv2025

Essential Role of Protein Kinase R in the Pathogenesis of Pulmonary Veno-occlusive Disease

Pulmonary veno-occlusive disease (PVOD) is a rare and severe subtype of pulmonary arterial hypertension, marked by progressive remodeling of small pulmonary arteries and veins with no therapies. Using a mitomycin C (MMC)-induced rat model, we previously demonstrated that protein kinase R (PKR)-mediated integrated stress response (ISR) drives endothelial dysfunction and vascular remodeling. To determine if PKR is the sole mediator of ISR and the pathogenesis, we treated control (Ctrl) and PKR knockout (KO) mice with the same dose of MMC. Consistent with rat data, Ctrl mice displayed ISR activation, vascular remodeling, and pulmonary hypertension after MMC treatment, while KO mice showed none of these phenotypes. Proteomic analysis revealed that MMC-mediated ISR activation attenuates protein synthesis in Ctrl but not in KO mice. These findings underscore the significance of PKR-dependent ISR activation and subsequent perturbation of proteostasis as central mechanisms driving PVOD pathogenesis and identifying PKR as a promising therapeutic target.

cell biology↗

bioRxiv2025

Systemic Multi-Omics Analysis Reveals Interferon Response Heterogeneity and Links Lipid Metabolism to Immune Alterations in Severe COVID-19

The immune response to SARS-CoV-2 infection is highly heterogeneous, and interferon (IFN)-stimulated genes (ISGs) play a central but context-dependent role in antiviral defense and immune dysregulation. To investigate how ISG heterogeneity relates to immune and metabolic states, we performed an integrated analysis of whole-blood transcriptomics, plasma proteomics, metabolomics, and immune activation markers in hospitalized COVID-19 patients and COVID-negative healthy controls and covalescent individuals. Patients segregated into low (LIS), moderate (MIS), and high (HIS) ISG expression endotypes, largely independent of clinical severity. While high ISG expression was associated with systemic inflammation and innate immune activation, severe disease within the HIS endotype was characterized by marked metabolic perturbations, including depletion of tricarboxylic acid cycle intermediates and multiple lipid classes involved in membrane integrity and immunometabolic signaling. Plasma-transfer assays demonstrated that plasma from severe HIS patients impaired neutrophil and monocyte activation ex vivo, indicating functional attenuation of innate immune responses despite elevated ISG expression. These metabolic alterations correlated with reduced immune activation, supporting the existence of an interferon-associated immune-metabolic axis that constrains immune functionality in severe disease. Although type I IFN neutralization was detected in a subset of patients with IFN antigen reactivity, these samples did not account for ISG heterogeneity or disease severity. Together, these findings show that high ISG expression defines a transcriptional endotype permissive for inflammation but insufficient for effective immune function, highlighting the importance of immune-metabolic context in shaping COVID-19 disease outcomes.

immunology↗

bioRxiv2025

Immune-Coagulation Dynamics in Severe COVID-19: Insights from Autoantibody Profiling and Transcriptomics

Severe COVID-19 is characterized by immune dysregulation and coagulation abnormalities, leading to complications such as thromboembolism and multi-organ failure. This study explores the relationship between autoantibodies targeting coagulation-related factors and gene expression in severe COVID-19. Whole-blood transcriptomics revealed upregulation of coagulation-related genes, including VWF and Factor V, in severe patients compared to mild cases and healthy controls. Autoantibody profiling against seven coagulation-related proteins (ADAMTS13, Factor V, Protein S, SERPINC1, Apo-H, PROC1, and Prothrombin) showed reactivities below established positivity thresholds, but mean-fluorescent intensities were elevated numerically in severe (Protein S) and convalescent (SERPINC1) patients. Correlation analysis revealed trends of negative associations between autoantibody reactivities and coagulation gene expression in severe cases, suggesting a potential role for autoantibodies in modulating immune-coagulation interactions warranting further orthogonal validation. Furthermore, age-dependent increases in subthreshold autoantibody reactivities were observed in severe cases, highlighting the potential impact of immunosenescence on disease severity. These findings do not exclude the possibility that subthreshold autoantibodies may contribute indirectly to immune-coagulation dynamics in severe COVID-19 through mechanisms beyond direct transcriptional regulation. This study highlights the complexity of immune-coagulation interactions and provides foundation for future research into their biological and clinical relevance, particularly for identifying biomarkers and therapeutic targets in thromboinflammatory diseases.

immunology↗

bioRxiv2025

Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning

Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs), which interpret cis-regulatory DNA sequences to determine the timing and location of gene expression. The diversification of TFs and their interactions with cis-regulatory elements (CREs) underpins plant adaptation to stress through the formation of gene regulatory networks (GRNs). However, deciphering condition-specific GRNs and identifying transcription factor binding motifs (TFBMs) for spatio-temporal gene expression remain major challenges in plant biology. To decipher the conditional networks governing TF-Target gene interactions, we developed CTF-BIND, a novel computational framework designed to reason about the spatio-temporal dynamics of TF activity. Leveraging over [~]23TB of multi-omics data (ChIP-seq, RNA-seq, and protein-protein interaction data), we constructed Bayesian causal networks capable of explaining TF activity across diverse conditions. These networks, validated against extensive experimental data, were then integrated into a Graph Transformer deep learning system. This system uses expression information of network components to quantitatively determine TF activity levels. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, eliminating the need for separate ChIP-seq experiments. CTF-BIND achieved a high average accuracy of [~]93% when tested against experimentally established data from various conditions. It is implemented as an interactive, open-access web server, it not only provides TF binding profiles but also facilitates downstream functional analysis. Furthermore, we developed CTF-BIND-DB, (https://hichicob.ihbt.res.in/ctfbind/) a database capturing dynamic shifts in regulatory pathways, providing information on TGs, network ontology, and binding motifs. CTF-BIND and CTF-BIND-DB represent a transformative approach for understanding and determining TF activity in plant stress responses, offering a powerful tool for crop improvement and bypassing the limitations of traditional methods and extensive experimental validation.

bioinformatics↗

bioRxiv2025

A Pan-Cancer, Pan-Treatment (PCPT) Model For Predicting Drug Responses From Patient-Derived Xenografts With Application To Cancer Types With Sparse Training Data

Predicting personalized treatment-specific responses in cancer patients requires not only robust experimental models such as PDX, which generate accurate training data on different treatment responses for the same model, but also computational models that can translate this knowledge into decisions for real patients in a timely manner. The translatability of PDX data into patient-specific outcomes is limited by the challenges in the data size requirements for training machine learning models. Previously, ML models have been developed for the two most abundant cancer types, viz. BRCA and CRC, but the same could not be scaled up to other cancer types with a smaller number of PDX samples. Here, we provide an ML framework to train a single pan-cancer, pan-treatment model for predicting treatment outcomes. We show that such models give promising results for all cancer types considered and reproduce the accuracy levels of individually trained cancer types. In the proposed model, all PDX genomic profiles from all cancer types are used as the training data, and instead of partitioning them into cancer types for each model, the cancer type and treatment name are appended as the input features of the training model. Using genomic-only and treatment-only embeddings and combining them with PCA-based dimensionality reduction, our models show promising results and provide a framework for further improvements and real-time use for best treatment selections for cancer patients. Data and Code AvailabilityAll raw and processed data, along with analysis scripts, are available at: https://github.com/Sciwhylab/pcpt

bioinformatics↗

bioRxiv2025

Explainable machine learning for health disparities: type 2 diabetes in the All of Us research program

Type 2 diabetes (T2D) is a disease with high morbidity and mortality and a disproportionate impact on minority groups. Machine learning (ML) is increasingly used to characterize T2D risk factors; however, it has not been used to study T2D health disparities. Our objective was to use explainable ML methods to discover and characterize T2D health disparity risk factors. We applied SHapley Additive exPlanations (SHAP), a new class of explainable ML methods that provide interpretability to ML classifiers, to this end. ML classifiers were used to model T2D risk within and between self-identified race and ethnicity (SIRE) groups, and SHAP values were calculated to quantify the effect of T2D risk factors. We then stratified SHAP values by SIRE to quantify the effect of T2D risk factors on prevalence differences between groups. We found that ML classifiers (random forest, lightGBM, and XGBoost) accurately modeled T2D risk and recaptured the observed prevalence differences between SIRE groups. SHAP analysis showed the top seven most important T2D risk factors for all SIRE groups were the same, with the order of importance for features differing between groups. SHAP values stratified by SIRE showed that income, waist circumference, and education best explain the higher prevalence of T2D in the Black or African American group, compared to the White group, whereas income, education and triglycerides best explain the higher prevalence of T2D in the Hispanic or Latino group. This study demonstrates that explainable ML can be used to elucidate health disparity risk factors and quantify their group-specific effects. Author SummaryWhile machine learning (ML) methods hold great promise for epidemiological studies, their practical utility is limited by interpretability. Increasingly complex ML models are great at predicting disease risk, but how they arrive at a given prediction is often obscured by model complexity. Explainable ML is an emerging discipline that seeks to render ML models more transparent by elucidating how and why input features contribute to output predictions. This study reports a novel application of explainable ML to epidemiology, focusing on type 2 diabetes (T2D) as a paradigm of health disparities. We found that ML classifiers were able to accurately model T2D disparities, for a large cohort of Black, Hispanic, and White Americans, and explainable ML revealed which risk factors contributed to the observed disparities and how. The results demonstrate that explainable ML can be a powerful tool for the discovery and characterization of health disparity risk factors.

bioinformatics↗

bioRxiv2025

Liver transplant rejection features memory cell priming, spatially differentiated networks and novel drug targets.

Acute T-cell-mediated transplant rejection, the earliest contributor to immunological graft failure responds variably to T-cell suppression. Early rejection can co-exist with donor-specific antibodies which also shorten graft survival, without affecting graft histology visibly. Here, during early liver transplant rejection, blood leukocytes manifest primed T-cells with heat shock protein (HSP) signaling and transcriptional programs for memory cell expansion. Corresponding biopsies demonstrate upregulated immune synapse and proteasomal genes in T-cell-infiltrated portal and central regions of the liver lobule. With donor-specific antibodies, the intervening intralobular region demonstrates a germinal-center-like allograft response with upregulated CD40, chemokine, T-follicular and complement signaling, which intensifies along the direction of sinusoidal blood flow from portal to central regions. Proteasomal and HSP90 inhibitors suppress donor-specific alloresponses of T- and B-cells in blood samples from patients with early rejection. The molecular injury spectrum of early rejection is complex, spatially differentiated and reveals novel early immunosuppressive strategies to extend graft survival.

immunology↗

bioRxiv2025

DMRU: Generative Deep-Learning to unravel condition specific cytosine methylation in plants

Methylation at cytosines in plants influence spatio-temporal gene expression by regulating chromatin structure and accessibility. Some algorithms have been developed to identify DNA methylation but none of them are capable to tell the condition specific DNA methylation, making them hardly of any use. Here, we report a first of its kind an explainable Deep Encoders-Decoders generative system, DMRU, which learns the relationship between transcritpome status and DNA methylation states at any given time. It was also found that GC similarity is more relevant to the specificity of DNA methylation patterns than homology, concurring with reports of direct involvement of GC content in providing regulatory switches for DNA accessibility. Leveraging on which DMRU could perform with same level of accuracy in cross-species universal manner. In a comprehensive testing and benchmarking study across a huge volume of experimental data covering 85 different conditions, and multiple plant species, it has consistently achieved >90% accuracy. With this all, DMRU brings a completely new chapter in methylated cytosine discovery, giving a strong alternative to costly bisulfite sequencing experiments. DMRU may prove critical turning point in plant regulatory research and its acceleration.

bioinformatics↗

bioRxiv2025

Aerobic exercise decreases the number and transcript expression of inflammatory M1 macrophages and CD8+ T cells in the epicardial adipose tissue of female pigs

BackgroundEpicardial adipose tissue (EAT) regulates coronary artery function via lipid metabolism and immune cell recruitment. Increased EAT is a risk factor for coronary artery disease (CAD), but aerobic exercise mitigates CAD. The effect of aerobic exercise on immune cells in EAT is unknown. We hypothesized that aerobic exercise creates an anti-inflammatory environment characterized by increased M2 macrophages and up-regulation of anti-inflammatory cytokine transcripts in EAT. MethodsFemale Yucatan pigs (n=7) were allocated to sedentary or exercised groups. To mimic CAD, a coronary artery was chronically occluded or remained non-occluded. EAT samples were processed for bulk and single nuclei transcriptomic sequencing. ResultsSub-clustering identified immune, endothelial, smooth muscle, adipocytes, adipocyte progenitor cells (APSCs), and neuronal cells, with adipocytes and APSCs being dominant. Non-occluded sedentary EAT had the largest percentage of M1 macrophages and CD8+ T cells. Irrespective of occlusion, sedentary EAT had the largest fraction of cells expressing genes in the tumor necrosis factor (TNF) superfamily. Irrespective of occlusion, exercise upregulated peroxisome proliferator-activated receptor (PPAR) gamma (G) expression and enriched PPAR signaling pathways in adipocytes, macrophages, and T cells. However, PPARG expression was lowest in CD8+ T cells from non-occluded exercised EAT. The greatest number of significant cell-cell communications between adipocytes and immune cells via growth factors and adhesion molecules occurred in occluded sedentary EAT. ConclusionAerobic exercise mitigates the proinflammatory nature of EAT in CAD via modulation of immune cell subpopulations, decreased TNF superfamily and increased PPARG gene expression, and decreased growth factor communication between adipocytes and immune cells.

cell biology↗