Biology subjects

Ghosh, D.

Publications and source records attributed to Ghosh, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Temporal dynamics of RNA phage infection reveal bacterial recovery despite ongoing viral replication

Bacterial defenses against DNA phages are well documented, yet responses to RNA phages remain largely unexplored. Here, we characterize the population dynamics and host responses of Escherichia coli infected by the lytic RNA phage MS2 across multiple timescales, using bulk and single-cell transcriptomics, fluorescence microscopy, whole-genome sequencing, and genetic perturbations. Despite rapid viral takeover and extensive cell death, the bacterial population subsequently rebounds, revealing an unusual state in which host proliferation persists under intense phage pressure. Early in infection, heterogeneous subpopulations are evident, including one with low or undetectable viral transcript levels, low expression of the F-plasmid genes encoding the F-pilus (the MS2 receptor), and a distinct metabolic state. Strikingly, the rebounding population carries MS2 RNA in the great majority of cells, with evidence for active viral replication and continued cell division despite high viral loads. This rebound coincides with strong induction of biofilm-associated genes, including the pga operon, which synthesizes the biofilm-matrix exopolysaccharide PNAG. Deletion of pgaA causes earlier population decline and reduced population survival, indicating that pga-dependent matrix production delays viral lysis. Over longer timescales, phage-exposed populations show heterogeneous F-plasmid prevalence and plasmid-wide expression, consistent with dynamic cycling between plasmid-bearing and plasmid-free states: MS2 selects against F-plasmid-bearing cells, whereas conjugation may restore plasmid carriage. We propose that the biofilm-associated matrix delays viral lysis relative to continued cell division, long enough for rare F-plasmid-free daughter cells to arise and expand. Our results suggest that bacterial survival is determined by a dynamic race between viral lysis and host-cell division.

microbiology↗

bioRxiv2026

A Multilayer Human Skin Diseasome Links Molecular Architecture, Clinical Co-occurrence, and Drug-Reversal Signatures

Abstract Background: Immune-mediated skin diseases frequently co-occur, but the molecular architecture connecting clinically distinct disorders, and the extent to which molecular proximity predicts shared therapeutic opportunity, remain incompletely defined. We constructed a multilayer skin diseasome integrating transcriptomic overlap, pathway enrichment, protein-interactome topology, curated disease associations, real-world clinical co-occurrence, and drug-perturbation signatures across psoriasis (PS), atopic dermatitis (AD), hidradenitis suppurativa (HS), vitiligo (VL), and chronic spontaneous urticaria (CSU), using sodium lauryl sulfate-induced irritant contact dermatitis (SLS-ICD) as a mechanistically distinct barrier-injury comparator. Methods: Differentially expressed genes (DEGs) were derived from publicly available NCBI Gene Expression Omnibus datasets and compared using exclusive pairwise and higher-order intersections. Gene Ontology biological-process enrichment characterized shared and condition-specific programs. Interactome-based network separation (S_AB) quantified the relative topological proximity of disease modules. DisGeNET enrichment generated candidate disease +associations, a selected subset of which was examined descriptively for patient co-occurrence in TriNetX. Candidate compounds were ranked by reversal of disease-associated expression signatures against LINCS L1000 perturbation profiles. Results: The largest exclusive DEG intersection occurred between PS and HS (2,060 genes), followed by AD-PS-HS (490), VL-PS (464), AD-PS (402), and VL-PS-HS (379). Fourteen genes were shared across PS, AD, HS, VL, and CSU but not SLS-ICD. Neutrophil-associated processes recurred across the immune-mediated conditions, whereas SLS-ICD was dominated by keratinization and epidermal-differentiation programs. PS and HS had the lowest observed network-separation value (S_AB = -0.594); SLS-ICD had positive S_AB values in all five comparisons, although this separation did not persist once module sizes were harmonized. DisGeNET linked the index conditions to diverse immune, cardiovascular, neurologic, and neoplastic categories; 42 of these candidate diseases were observed to co-occur with an index condition in the queried TriNetX cohorts. Drug-signature overlap was greatest between PS and CSU, despite their marginal interactome separation, indicating that interactome proximity and predicted pharmacologic reversal capture different dimensions of disease relatedness. mTOR/PI3K-pathway inhibitors were prominent among the psoriasis reversal candidates, while methotrexate and mycophenolate mofetil emerged from both the CSU signature and the curated gene set associated with HIV infection. Conclusion: Multilayer integration identified a connected molecular core among five immune-mediated skin diseases and a distinct SLS-ICD injury module. The discordance between interactome proximity and drug-signature overlap suggests that no single layer adequately defines disease relatedness. These findings provide a hypothesis-generating framework for prioritizing disease links and repurposing candidates, but controlled epidemiologic analyses and experimental validation are required before clinical or therapeutic inference.

systems biology↗

bioRxiv2026

Predictability failure in glucose-insulin system for ICU patients

Modern medicine implicitly assumes that physiological responses to intervention are predictably determined by administered treatments. However, physiological systems containing intrinsic delays between the detection of a stimulus and the biological response may violate this assumption. We investigate the human glucose-insulin system as described by the Ultradian model and mathematically demonstrate that clinically relevant forcing protocols-such as pulsatile insulin delivery and step-wise glucose infusion, both commonly used in intensive care units (ICUs)-can induce sustained temporal chaos that may hamper accurate prediction of the physiological response. If not accounted for, these chaotic dynamics could create difficulties in achieving optimal dosing and timing when administering glucose and insulin in clinical or home care settings. This phenomenon, termed delay-induced uncertainty (DIU), arises from the interaction between physiological delay, intrinsic shear near a limit cycle, and external forcing. Using the Ultradian glucose-insulin model, we compute top Lyapunov exponents to quantify predictability. Across a range of pulsatile and step-wise forcing regimes, including stochastic amplitudes drawn from Markov processes, we observe positive Lyapunov exponents, indicating sustained chaos. Our results suggest that delayed endocrine regulation may fundamentally limit the predictive value of the models used to develop glycemic management strategies, with implications for clinical protocols in the ICU.

systems biology↗

bioRxiv2026

Nonparametric kernel-based detection of spatially variable genes with adaptive shrinkage and scalable multi-sample inference

AO_SCPLOWBSTRACTC_SCPLOWIdentifying spatially variable genes (SVGs), genes whose expression varies coherently across tissue space, is a central analytic goal in spatially resolved transcriptomics. Current methods rank spatially variable genes using either significance probabilities from parametric models or effect sizes such as the proportion of spatial variance, but parametric approaches impose distributional assumptions, such as Gaussian processes or negative binomial models, that may be violated for sparse or zero-inflated data. Furthermore, most detection tools cannot jointly model multiple biological replicates, and no existing framework provides both nonparametric significance probabilities and stabilized effect-size estimates with formal uncertainty quantification. Here, we introduce CytoKspace, a nonparametric framework that combines a sparse exponential kernel constructed from nearest-neighbor graphs with a quadratic-form test statistic and adaptive permutation testing. CytoKspace employs a multi-stage adaptive permutation schedule that yields substantial computational savings over fixed-permutation baselines, an adaptive shrinkage layer built on empirical Bayes estimation that stabilizes raw spatial effect sizes and provides posterior estimates with local false sign rates, and a scalable multi-sample extension via Fisher combination of significance probabilities and inverse-variance-weighted meta-analysis that accommodates studies with multiple biological replicates. In extensive simulations across a broad range of sample sizes, gene counts, spatially variable gene fractions, and effect sizes, as well as in applications to two real datasets from the Visium and seqFISH platforms, CytoKspace demonstrates competitive sensitivity, well-calibrated false positive rates, and practical computational requirements compared to existing methods. A software implementation of our method is freely available at https://github.com/Ghoshlab/CytoKspace.

bioinformatics↗

bioRxiv2026

LncRNA-mediated organization of oncogenic chromatin state is a targetable vulnerability in leukemia

Long non-coding RNAs (lncRNAs) are increasingly recognized as critical regulators of gene expression underlying various cellular functions, however, the functional and mechanistic contributions of most lncRNAs to tumorigenesis remain poorly defined, and targeting of lncRNAs is challenging with conventional therapeutic approaches. Here, we uncover human PAN3-AS1 and its murine ortholog Lnc35682, previously uncharacterized lncRNAs embedded within a conserved syntenic genomic locus, as highly expressed in acute myeloid leukemia (AML). Using genetic mouse models, human cell lines and primary patient samples, we show that PAN3-AS1 is essential for leukemia maintenance but dispensable for normal hematopoiesis. Mechanistically, we find that elevated PAN3-AS1 influences chromatin accessibility, thus promoting leukemia gene expression programs. This is mediated, at least in part, by PAN3-AS1s association with the nuclear lamina through a defined functional region that is required for its leukemogenic function. We further characterize a feed-forward regulatory circuit between PAN3-AS1 and its neighboring gene FLT3 that directly links the aberrant lncRNA functions to the FLT3-mutant AML subtype. To therapeutically exploit the regulatory node, we engineer a myeloid leukemia-preferentially targeted lipid nanoparticle (LNP) formulation and demonstrate effective delivery of siRNAs against endogenous targets into leukemia cells in experimental animals. LNP-siPAN3-AS1 alone or in combination with a clinically used FLT3 inhibitor, Gilteritinib, reduces leukemia burden and significantly delay leukemogenesis in vivo. Overall, our study uncovers a therapeutic vulnerable lncRNA-centric circuitry and provides compelling preclinical evidence for the development and application of a novel RNA targeting-LNP based therapy for treatment of myeloid leukemia. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/744058v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@10c9d42org.highwire.dtl.DTLVardef@12864e7org.highwire.dtl.DTLVardef@f87f6aorg.highwire.dtl.DTLVardef@750cbf_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2026

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

bioinformatics↗

bioRxiv2026

Corilagin attenuates high glucose-induced neurotoxicity and mitochondrial dysfunction through restoration of the AMPK-SIRT1-PGC1α-TFAM signaling axis

Mitochondrial dysfunction and oxidative stress represent two interconnected, primary causes for Diabetic Neuropathy (DN); however, the majority of currently available anti-diabetic therapies have focused on glucose control as opposed to neurodegenerative downstream effects. Corilagin, is an ellagitannin having high anti-oxidant properties; however, it has not been evaluated against hyperglycemia induced neuronal injury. The present study demonstrates the ability of Corilagin to protect against mitochondrial dysfunction via models of diabetic nephropathy and cerebral ischemia. High glucose (50 mM, 24 hr) was utilized to induce diabetes like conditions in the SH-SY5Y human neuroblastoma Cell Line. High glucose induced significant decreases in cell viability, increases in intracellular and mitochondrial reactive oxygen species, depletion of reduced glutathione reserves, induces apoptosis, and causes mitochondrial depolarization and fragmentation. Corilagin pre-treatment attenuated each of these high-glucose induced effects by protecting against mitochondrial membrane potential loss and maintaining mitochondrial network morphology while reducing apoptotic cell fraction relative to glucose alone. Additionally, these protective effects were accompanied by restoration of AMPK phosphorylation and up-regulation of SIRT1, PGC1 and TFAM, components that are part of the principal signaling pathway that regulates mitochondrial biogenesis; therefore, therefore, this pathway may contribute mechanistically to the cyto-protective effect of Corilagin. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/740444v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1be4a92org.highwire.dtl.DTLVardef@11d6e9org.highwire.dtl.DTLVardef@1346757org.highwire.dtl.DTLVardef@16c9f1e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Proposed mechanism underlying the neuroprotective effects of Corilagin against high glucose-induced mitochondrial dysfunction.High glucose suppresses AMPK phosphorylation, leading to downregulation of the SIRT1-PGC-1-TFAM signaling axis, increased intracellular and mitochondrial reactive oxygen species (ROS), glutathione depletion, apoptosis, mitochondrial depolarization, and mitochondrial fragmentation. Corilagin pretreatment restores AMPK activation and the downstream SIRT1-PGC-1-TFAM pathway, thereby reducing oxidative stress, preserving intracellular glutathione, preventing apoptosis, maintaining mitochondrial membrane potential, and protecting mitochondrial network integrity. C_FIG

cell biology↗

bioRxiv2026

On the Polymorph-Selection Determinants of α-Synuclein Amyloid Fibrils Studied at Atomic Resolution

Alpha-synuclein is an intensely studied intrinsically disordered protein whose aggregation into amyloid fibrils is connected to the progression of several neurodegenerative diseases, most commonly Parkinsons Disease. A remarkable feature that has emerged from this research is how easy it is to induce the protein to aggregate in vitro into a wide range of amyloid fibrils that appear to resemble the aggregates found in Lewy bodies in diseases like Parkinsons while at the same time how difficult it is to produce aggregates whose fold truly represents the disease-associated amyloids at the atomic level. In an effort to produce the disease-relevant fibrils in vitro we have analyzed over 60 independent samples by cryo-electron microscopy using helical reconstruction to obtain atomic resolution models for most of the samples. While not yet achieving our original goal, we have found that several overlooked parameters influence the structural outcomes of alpha-synuclein aggregation, in particular protein purity, preparation of the monomeric starting material and agitation method.

biophysics↗

bioRxiv2026

Autoreactive antibody production by intrarenal B cells in mouse kidney allograft rejection

Antibody-mediated rejection (AMR) is a major cause of kidney allograft failure, with donor HLA-specific antibodies (DSA) recognized as primary drivers of AMR pathology. However, a significant proportion of AMR diagnoses lack detectable DSA, thus implicating DSA-independent mechanisms. In support, we previously reported on the accumulation of autoreactive B cells in rejecting renal biopsies, which raised the possibility that autoreactive IgG produced within the allograft contributes to graft pathology. In this study, we used mouse models to show that rejecting kidney allografts preferentially promoted a breach in autoreactive B cell tolerance, leading to the local production of autoreactive antibodies. Notably, autoreactive IgG responses were uncoupled from DSA production, indicative of distinct regulation. Organoid cultures confirmed that autoantibody production was observed in the rejecting kidney, whereas DSAs were produced in both the lymph node and graft. Intrarenal B cells expressing Nur77 were enriched for autoreactivity, consistent with in situ antigen recognition. Finally, autoreactive antibodies are pathogenic, since inhibiting autoantibody production with transient anti-IL-15 and CTLA-4Ig led to preserved kidney allografts. These unique features of in situ autoantibody responses may be relevant to diverse diseases with chronic tissue inflammation beyond transplantation.

immunology↗

bioRxiv2026

HEALTHY AGING AS INFORMATION DIVERGENCE IN THE MULTIPLEX BRAIN

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding the co-evolution of the human brains structural scaffold and functional traffic across the adult lifespan remains a fundamental challenge in neuroscience. While age-related degradation in grey matter and functional activation is well-documented, the joint trajectory of the structural (SC) and functional (FC) connectomes is often overlooked due to the lack of an integrative framework. Here, we model the brain as a multiplex network to quantify the information-theoretic interdependencies between these two layers in a cross-sectional cohort of 589 healthy individuals (ages 18-88) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Using Jensen-Shannon Divergence and relative entropy metrics, we identify a fundamental organizing principle of healthy aging characterized by: a progressive information divergence where functional dynamics increasingly "untether" from their underlying structural constraints. Our results reveal that this decoupling follows a robust linear trajectory, yet is highly spatially hetero-geneous. Meso-scale community analysis using the Multiplex Map Equation identifies subcortical hubs--specifically the putamen, pallidum, caudate, and thalamus--as the primary epicenters of age-related SC-FC divergence. This topological shift toward functional independence in subcortical "switchboards" provides a mechanistic connectomic signature for the well-documented decline in fluid intelligence and motor adaptation that accompanies aging. In striking contrast, the limbic core (hippocampus and entorhinal cortex) exhibits remarkable stability across the lifespan, suggesting a biological imperative to preserve high-fidelity memory circuits amidst global communicative rewiring. By framing healthy aging as a systematic subcortical untethering alongside limbic resilience, our work provides a powerful new multiplex baseline to distinguish normative cognitive decline from the early topological signals of neurodegenerative disease. Significance StatementHealthy aging involves a fundamental reorganization of the brains information architecture. By modeling the brain as a multiplex network and leveraging information-theoretic measures, we demonstrate that adult lifespan development is characterized by a progressive "untethering" of functional dynamics from their underlying structural constraints. This decoupling is driven by a striking geometric paradox within subcortical hubs: while physical structural scaffolds undergo severe network constriction and isolation, their functional boundaries expand and blur in a state of neural de-differentiation. This architectural mismatch is mediated by a synchronized prefrontal-to-limbic pathway shift that operates collinearly with chronological age and mirrors the selective decline of fluid intelligence and motor adaptation that accompanies aging. Our work establishes a rigorous multiplex framework that moves beyond descriptive accounts of neural tissue loss to provide a normative baseline for distinguishing healthy brain aging from neurodegeneration.

neuroscience↗

bioRxiv2026

Accounting for recurrent mutation in the frequency spectrum of rare alleles

As whole-genome and whole-exome datasets increase in size, they uncover alleles at lower and lower frequencies in the population. Samples of rare alleles often include recurrent mutations, where derived alleles are identical by state and not by descent. As a result, the site frequency spectrum (SFS) becomes challenging to analyze because it is strongly dependent on the mutation rate. To overcome this hurdle, we define the single mutation frequency spectrum (SMFS), which is the frequency spectrum of alleles descendant from a single mutational event. For rare alleles, the SFS with recurrent mutation is then a weighted sum of the convolutions of the SMFS with itself. This simple, yet powerful, model decouples recurrent mutation from the population genetic processes giving rise to the SMFS, such as genetic drift and selection. We show how both forward-in-time and backward-in-time models with recurrent mutations can be recast in terms of the SMFS. We then develop a method for combinatorial hierarchic estimation of the SMFS (which we name CHES). We apply this simple, yet robust, method to a human exome sequencing dataset to show that the SMFS with recurrent mutation can account for SFS differences between low and high mutation rate sites. The inferred SMFS shows an approximate scaling law with allele frequencies inconsistent with both a constant population size and an exponentially growing population model. Lastly, we use our model to compare the expected and observed proportions of missense and stop-gain mutations in the human exome, using this disparity to infer the strength of selection on these classes of mutations. Our combined results show how the SMFS can explain the dependency of the SFS on the mutation rate and how it reflects human demographic and evolutionary history.

genetics↗

bioRxiv2026

Chemical toxicity of microplastics is stronger than particle effects in D. magna

Microplastics (MP) are ubiquitous environmental contaminants with diverse physicochemical characteristics. Many studies have shown that size, shape, and polymer type are responsible for their toxicity, but this also seems to differ among MP from the same plastic type. One parameter likely contributing to these differences is plastic chemicals, a broad class of compounds intentionally or unintentionally added to plastics during their production and manufacturing. However, knowledge on the composition of plastic chemicals and their effects remains scarce. Therefore, to elucidate the chemical aspect of MP toxicity, we exposed Daphnia magna individuals to MP (PET, PBS, and PDLLA), cellulose, extracted particles (eMP), and methanol-based extracts of these particles for 10 days. Chemicals within such extracts were analyzed via GC-MS. This study was conducted with reduced food availability to investigate plastic effects in an environmentally relevant scenario. The introduction of a high-food control suggests that a more realistic feeding regime might exacerbate the plastic effects of the selected treatments. Our results indicated that, depending on the polymer type, plastic chemicals determine MP toxicity, which varies according to the endpoint investigated (i.e., body length, reproduction, levels of ROS and LPO). Body length, in particular, was significantly impaired by PET and PDLLA extracts, whereas reproduction was affected by most treatments. The investigated biochemical parameters (ROS and LPO) were not affected by the exposure. These results suggest that MP toxicity strongly depends on their chemical composition, whereas adverse effects due to physical properties are present independently of chemical composition across all MP types. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/724551v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@b6ecc8org.highwire.dtl.DTLVardef@17366d7org.highwire.dtl.DTLVardef@164d3f4org.highwire.dtl.DTLVardef@973af0_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2026

An Indole Dimer Antifungal Metabolite from a Rice Seed Endophyte Inhibits Ergosterol Biosynthesis in Fungal Pathogens

The increasing prevalence of fungal phytopathogens and the widespread emergence of fungicide resistance necessitate alternative antifungal strategies with reduced environmental impact. Here, we report the isolation and characterization of a novel antifungal metabolite, SM06, produced by the rice seed-associated endophytic bacterium Phytobacter sp. RSE02. SM06 exhibited broad-spectrum antifungal activity against plant and human pathogenic fungi, including Curvularia lunata, Fusarium oxysporum, and Candida albicans. In vitro assays and micromorphological analyses revealed that SM06, an indole dimer, disrupts fungal cell membrane integrity, while in planta experiments demonstrated significant suppression of brown leaf spot disease in tomato and rice. Molecular docking suggested that SM06 binds to lanosterol 14-demethylase (ERG11), a key enzyme in fungal sterol biosynthesis. Consistent with this prediction, LC-MS-based analyses confirmed a significant reduction in ergosterol content in SM06-treated fungal cells. Together, these findings identify SM06 as a biologically active antifungal metabolite produced by a plant-associated bacterium and highlight its potential application in sustainable fungal disease management. IMPORTANCEFungal diseases cause major losses in crop-production and contribute to the growing challenge of antifungal resistance, underscoring the need for sustainable alternatives to chemical fungicides. This study identifies SM06, a novel indole dimer produced by the rice seed endophyte Phytobacter sp. RSE02, with strong antifungal activity against economically important plant pathogens and clinically relevant fungi. Through integrated chemical, cellular, and in planta analyses, we demonstrate that SM06 disrupts fungal membrane integrity by inhibiting ergosterol biosynthesis. The compound is biocompatible, stable, and effective in plant disease suppression, highlighting its translational potential for crop protection. These findings reveal seed endophytes as an important yet underexplored sources of antifungal metabolites and provide a mechanistic foundation for developing eco-friendly biocontrol strategies with implications beyond agriculture. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/697688v1_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@c7da68org.highwire.dtl.DTLVardef@d3f1a1org.highwire.dtl.DTLVardef@9cab82org.highwire.dtl.DTLVardef@1c01141_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

A Highly Thermostable and Novel GH5 Endoglucanase from Bacillus sp. Strain BS with Enhanced Biomass Saccharification Potential in Seawater

The efficient enzymatic conversion of lignocellulosic biomass to glucose is crucial for biofuel production, with enzyme costs being a significant barrier to commercialization. Multifunctional enzymes that reduce enzyme components can improve efficiency. In this study, we amplified and sequenced a gene from Bacillus sp. strain BS, which showed 89.8% identity with bacterial endoglucanase Q6YK34. This putative novel endoglucanase, BsEG2, is a GH5-like endoglucanase, with optimal activity at pH 6.0 and 55 {square}, and efficiently hydrolyzes amorphous and crystalline cellulose, including pre-treated and untreated sugarcane bagasse. Kinetics on carboxymethyl cellulose (CMC) indicate high substrate binding efficiency (apparent Vmax of 148.9 {micro}M min-1, a Km of 24.9 mg mL-1, and a kcat of 517 s-1). BsEG2 hydrolyzes long cellulosic chains, oligosaccharides, and cellobiose to produce glucose, demonstrating multifunctionality. It exhibits remarkable thermal stability, retaining over 90% activity after 15 days at 55 {degrees}C. BsEG2 also retains activity in high salts and ionic liquids, and is not inhibited by cellobiose, even at 200 mM, unlike other endoglucanases and cellobiohydrolases. Furthermore, BsEG2 functions in seawater, reducing freshwater footprint in biofuel production. BsEG2 is a promising candidate for a cost-effective enzyme formulation for glucose production from lignocellulosic biomass, supporting downstream glucose-fed metabolic pathways.

biochemistry↗

bioRxiv2025

Genomic characterization of type I-E* CRISPR-Cas systems in Klebsiella pneumoniae: association of spacer diversity and antimicrobial resistance

BackgroundKlebsiella pneumoniae, known to acquire antimicrobial resistance genes (ARGs) through horizontal gene transfer, possess type I CRISPR-Cas systems. This study investigated the association of multidrug resistance (MDR) and CRISPR-Cas in septicemic K. pneumoniae, along with comparative analysis of CRISPR-positive global K. pneumoniae. MethodsArchived K. pneumoniae from septicemic neonates (2008-2020) were assessed for CRISPR-Cas system, cas gene expression and correlation with ARG carriage. Whole-genome sequencing was performed and diversity of spacers, protospacers adjacent motifs (PAMs), targeted plasmids, were evaluated. Comparative analyses were performed for CRISPR-positive global K. pneumoniae. ResultsTwenty-five percent of study K. pneumoniae possessed complete type I-E* CRISPR-Cas comparable to 22% of global K. pneumoniae, which were distributed equally to type I-E & I-E*. CRISPR-positive study strains (83%) were MDR, and exhibited no significant association of CRISPR-Cas with MDR phenotype. Spacers noted in non-MDRs were diverse, showed increased average count and identified more diverse PAMs compared to spacers from MDR strains. Plasmids harboring ARGs such as blaNDM, blaCTX-M, qnrS1, mcr-8, etc. were frequently targeted by spacers from non-MDRs. Presence of CRISPR-Cas system in epidemic MDR clones such as ST14/15/23 in I-E* (local, global) and ST147 in I-E (global), indicated a lineage-specific association with CRISPR-Cas subtypes. ConclusionThis study demonstrated the co-existence of CRISPR-Cas systems within both MDR and non-MDR K. pneumoniae, with no significant correlation with either group. However, presence of diverse spacers in non-MDR strains that identified more PAMs and targeted ARG-bearing plasmid implied role of CRISPR-Cas system in limiting ARG acquisition.

genomics↗

bioRxiv2025

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data

High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which ignore gene-gene dependencies and nonlinear effects, while multivariate machine-learning (ML) methods often lack principled feature selection and uncertainty quantification. The Bayesian kernel machine regression (BKMR) framework offers an appealing alternative because it (a) captures non-linear gene-outcome relationships and higher-order interactions, and (b) enables automatic relevance determination (ARD) through sparsity-inducing priors. However, we show that the traditional latent Gaussian process (GP) formulation of BKMR is inadequate for discrete outcomes (e.g., cell-type labels), leading to biased inference and unstable variable selection. We propose a copula-based Bayesian kernel machine regression (CBKMR) model that uses outcome-appropriate discrete marginals while a Gaussian copula captures kernel-induced dependence across observations. To ensure scalability to modern single-cell datasets, we further introduce a nearest-neighbor GP-based variant, NNCBKMR, which reduces computational complexity from O(N 3) to nearly linear in N. Simulation studies show that CBKMR more accurately captures nonlinear effects and yields stronger marker-selection performance than BKMR and top ensemble ML methods (e.g., random forests, XGBoost). Applications to multiple scRNA-seq datasets demonstrate that CBKMR identifies concise marker panels that align closely with expert-annotated gene signatures while providingposterior uncertainty for principled decision-making.

bioinformatics↗

bioRxiv2025

Sustained poly(ADP-ribosyl)ation limits RPA loading and attenuates ATR-CHK1 signaling upon replication fork collapse

Poly(ADP-ribosyl)ation (PARylation) is a transient post-translational modification catalyzed by PARP enzymes and reversed by the glycohydrolase PARG. Inhibition of PARG causes sustained PARylation, a toxic state that could be exploited for cancer therapy. However, the mechanisms by which excessive PARylation drives toxicity and cell death remain incompletely understood. Here, we examined how persistent PARylation influences cellular responses to replication stress and DNA damage. We found that during replication stress, the decline of ATR checkpoint activity marks the transition from fork stalling to fork collapse, a context in which stabilized poly(ADP-ribose) reduces chromatin-bound RPA. This loss of RPA, a key activator of the checkpoint, further reduces ATR signaling, revealing a feedback loop between unrestrained PARylation and checkpoint control. Our results identify a checkpoint-dependent consequence of unregulated PARP activity during fork collapse and highlight the PARylation-RPA axis as a critical determinant of PARG inhibitor cytotoxicity.

cell biology↗

bioRxiv2025

Alpha-synuclein fibrils induce budding of mitochondrial-derived vesicles

-synuclein (-syn) aggregation is a hallmark of synucleinopathies, a class of neurodegenerative disorders such as Parkinsons disease (PD). Several lines of evidence indicate the involvement of mitochondria in the disease pathology. Despite extensive study, the link between -syn aggregation and mechanisms of mitochondrial toxicity remains not fully understood. Using high-resolution imaging with electron microscopy, we examined cells exposed to -syn fibrils vs control cells with a focus on mitochondria. We found that upon exposure to -syn fibrils, mitochondria increase in size, cristae structure gets defects, and mitochondria enhance the budding of mitochondrial-derived vesicles (MDVs). MDV formation reflects an evolutionarily conserved mechanism reminiscent of bacterial outer membrane vesicle (OMV) biogenesis. Structural proteomics analysis by mass spectrometry corroborates this microscopy observation by identifying changes in multiple proteins that regulate cristae structure, MDV formation, and trafficking. Our results provide a new link between -syn and mitochondria and identify novel pathways responding to -syn aggregates, particularly that -synuclein directly triggers MDV generation. The processes we detected could be of interest for diagnostics and potential therapeutic interventions.

neuroscience↗