Biology subjects

Mathur, S.

Publications and source records attributed to Mathur, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Production of a resilient biosurfactant using agro-wastes and Paenibacillus dendritiformis isolated from petroleum-contaminated soil: a break-even analysis at pilot scale

Wastes from mustard oil mills and dairy were used as partial replacement of pure reagents in modified Bushnell and Haas broth media for production of biosurfactant by Paenibacillus dendritiformis isolated from petroleum-contaminated soil. The process was optimised by construction of a response model using Taguchi experimental design, leading to a 28.23% increase in performance characteristics and a 13.23 % increase in yield of biosurfactant. The optimized production medium consisted of carbon @7.9 %, nitrogen @ 1.1% and agitation rate 180 r.p.m. This was provided by adding glucose @ 2%, ammonium nitrate @0.2%, mustard oil seed meal @10% and whey@ 10% in modified Bushnell Haas broth. The biosurfactant produced retained its properties during exposure to a wide range of pH values (5-11), high temperatures (up to 121C), high salinities of up to 5% and also five -fold concentration. Approximately three- fold increase in yield of the biosurfactant (25g/L to 68.5g/L) was obtained upon scaling-up the production from 100ml to 1500 ml in a lab scale fermenter with agitation @ 150r.p.m. and D.O. levels maintained at 6mg/L. Power calculations and the cost thereof was calculated (Rs 10.24 /L) and summed up with the cost of raw materials Rs 8.40/L and found to be Rs 18.64/L of product .Since 68.5g product is present in 1L, the cost is calculated to be Rs 271/- per kg. Comparison with selling price of currently used chemical surfactants like Linear Alkyl Benzene sulphonic acid (LAB) and sodium Lauryl sulphate and Sodium Lauryl ether sulphate (SLS and SLES) reveals that prices of these products varies from Rs 120 to 600 per kg depending on manufacturer and quality, order quantity. From the above studies it can be seen that production and use of this multiutility biosurfactant has, besides a clear advantage over chemically produced surfactants in terms of sustainability, a fairly good comparison with selling price of currently used chemical surfactants. The present study on the production costs (excluding capital costs) successfully demonstrates a break even analysis into the production of biosurfactants.

microbiology↗

bioRxiv2026

Order invariance of fitness makes evolution predictable despite strong history-dependent effects

Environmental change is ubiquitous in nature, yet understanding its effect on the predictability of evolution remains a challenge. Previous work in microbes has shown that growth and fitness in a particular environment can depend strongly on the environments encountered previously. If such history-dependent effects are large, then any sequence of environments must be thought of as unique, and even a modest number of environments could generate a combinatorially large number of evolutionary paths. This would make evolution in changing environments difficult to predict. Here, we use cycles of three environments, which is the smallest number of environments which allow for comparisons of different environmental orderings -- X [->] Y [->] Z [->] X vs X [->] Z [->] Y [->] X -- to empirically test how environmental order affects the fitness of S. cerevisiae mutants. We measured the fitness of ~900 adaptive mutants in distinct three-environment cycles, and find large history-dependent effects which cause fitness in individual environmental components to differ drastically across the two directions of the cycle. However, these effects tend to compensate for each other, resulting in similar overall fitness across the whole cycle. This is true even when we specifically conduct experimental evolution in both directions of the same three-environment cycle. Of the ~450 mutants we isolated, only a small number of strains showed higher fitness in the direction in which they evolved. By splitting the contribution of each environment on fitness into its effect on the current and next component, we show that the invariance of overall fitness to environment order emerges because the effect of an environment on the next component is largely independent of the identity of the subsequent component, and can be captured by an additive model. Despite this general pattern of compensation, we do find instances of non-additivity, some of which are partly due to history-dependent effects lasting beyond two growth components. Overall, these results suggest that despite large history-dependent effects, fitness across a sequence of environments can be surprisingly predictable.

evolutionary biology↗

bioRxiv2026

Aerobic GenX Defluorination by a Wastewater Cladosporium halotolerans Strain with a Genomically Expanded Haloacid Dehalogenase Repertoire

Per- and polyfluoroalkyl substances (PFAS) such as GenX (HFPO-DA) are aerobically recalcitrant contaminants for which biological treatment options remain scarce; the best-characterized microbial degraders require strictly anaerobic conditions and external cofactors. We isolated Cladosporium halotolerans strain CsHGX-1 from activated sludge at a municipal wastewater treatment plant using GenX as the sole carbon source. Whole-genome sequencing (32.4 Mb; 11,201 genes) revealed a PFAS-degradation gene repertoire substantially expanded relative to congeneric Cladosporium species, including 26 dehalogenases (four type-II haloacid dehalogenases, HADs), 141 cytochrome P450s, and 558 esterases/hydrolases. Under aerobic conditions with GenX (50 mg L-1) as the sole carbon source, strain CsHGX-1 removed 47.6 {+/-} 1.7% of GenX within 48 h, accompanied by fluoride release (0.079 {+/-} 0.018 mM) that was absent in abiotic controls, confirming genuine C-F bond cleavage. Time-resolved RNA sequencing (0, 6, 24, 48 h; n = 6 biological replicates) revealed a phase-structured transcriptional program: oxidative genes, including cytochrome P450s, peaked first (6 h; up to 25.4-fold), hydroxylation and reactive-oxygen-species-management genes peaked next (24 h; up to 33.5-fold), and the three type-II HAD genes peaked last (48 h; up to 50.9-fold), coincident with fluoride accumulation. A parallel resazurin metabolic assay over 5 days confirmed sustained catabolic activity in GenX-exposed cultures relative to controls (1.37-1.52-fold; p [≤] 0.003). These findings identify strain CsHGX-1 as, to our knowledge, the first Ascomycete fungus for which genomic and transcriptomic evidence links oxidative activation to haloacid-dehalogenase-mediated defluorination of an aerobically recalcitrant PFAS, extending the known diversity of fungal PFAS degraders beyond Basidiomycota white-rot taxa. IMPORTANCEGenX is a PFAS "replacement" chemical that the U.S. Environmental Protection Agency added to its list of hazardous constituents in 2024, yet no aerobic biological treatment exists for it: every well-characterized microbial degrader requires oxygen-free conditions and added cofactors. We show that a fungus recovered from ordinary wastewater sludge breaks down GenX while using oxygen, the same conditions already used in conventional treatment plants, with no nutrient or reductant supplementation. Genome sequencing showed why this strain is unusual: it carries far more dehalogenase and cytochrome P450 genes than its close relatives. Time-course RNA sequencing showed these genes switch on in a defined order, oxidation first, then carbon-fluorine bond cleavage, matching the appearance of free fluoride in the culture. This links genome content to a functional outcome in an Ascomycete fungus, suggesting aerobic fungal defluorinators may already be present, unrecognized, in engineered wastewater systems.

microbiology↗

bioRxiv2026

SCD1 inhibition synergizes with TMZ to improve IDH1 MUT glioma survival of mice via lipotoxic stress

Mutations in the isocitrate dehydrogenase enzyme (IDH1) are prevalent in low-grade gliomas, such as oligodendrogliomas. Other than surgery, radiation, and chemotherapy, few options for treatment exist. The standard of care for patients currently consists of maximal safe resection, as well as the potential use of radiation and chemotherapy, typically followed by radiographic surveillance. Although significant advances have been achieved, these have not translated into meaningful improvements in overall survival, warranting the development of novel therapies. Herein, we demonstrate that the inhibition of SCD1 in vitro leads to decreased colony formation and is more specific to IDH1MUT glioma than the IDH1WT cells, We further identified MF-438 as an active inhibitor of this enzyme and showed that this inhibitor is linked with iron transport and ferroptosis. MF438 depleted oleic (C18:1) and palmitoleic acid (C16:1) levels, driving saturated phosphatidylcholine (PC) accumulation and ER stress (INSIG1, SEL1L upregulation). TS603 exhibited selective polyunsaturated phosphatidylcholine reduction with downregulation of GPX4, FTH1, and KEAP1, and upregulation of NCOA4, SLC11A2, ALDH7A1, and DPP4, consistent with ferroptosis priming via ferritinophagy-driven expansion of the labile iron pool, as confirmed by FerroOrange flow cytometry. Neutral lipid metabolism genes (LPIN1, LDLR, PNPLA3, ACSL1), intracellular lipid transport genes (TMEM41B, OSBP, STARD4), and lipid droplet organization genes (SQLE, CHKA, AUP1) were coordinately upregulated in TS603 after treatment with MF-438. MF438+TMZ activated the integrated stress response (ATF3, DDIT3, IRF1, CDKN1A, GADD45B) and synergistically suppressed TS603 neurosphere growth (p=0.0326). In vivo combination between MF-438 and TMZ showed improved survival versus the TMZ-alone group, in an IDH1-mutant oligodendroglioma model. Tissue analyses showed significant reduced Ki67 expression, a marker of cellular proliferation, in the combination treatment. Collectively, these findings suggest that targeting lipid metabolism may enhance the efficacy of standard-of-care therapy in IDH1-mutant oligodendroglioma.

Cancer Biology↗

bioRxiv2026

Putative G-Quadruplex Structures in Cancer-Dysregulated Circulating lncRNAs and their G4-mediated Identification of Protein Interacting Partners

Circulating long non-coding RNAs (lncRNAs) have emerged as compelling cancer biomarkers. However, the structural features that mediate their extracellular stability and protein interactions remain largely unexplored. Here, we present the first systematic investigation of G-quadruplex (G4) motifs within cancer-dysregulated circulating lncRNAs and exploit these structures as molecular handles to identify associated RNA-binding protein (RBP) networks. From 283 circulating lncRNAs curated from the Lnc2Cancer 3.0 database, putative G-quadruplex-forming sequences (PQSs) were identified computationally using QGRS Mapper and G4Hunter, yielding four prioritized candidates -- AGAP2-AS1, LINC00683, DLG1-AS1, and KRTAP5-AS1 -- spanning 2G to 4G architectures. In vitro transcribed PQSs were validated for parallel G4 formation by circular dichroism spectroscopy, native polyacrylamide gel electrophoresis with thioflavin T staining, and reverse transcriptase stop assays, conducted under both standard buffer and simulated body fluid conditions to approximate the circulatory milieu. Electrophoretic mobility shift assays and isothermal titration calorimetry demonstrated nanomolar-affinity interactions between the G4-containing RNA and human serum albumin (HSA), the most abundant circulating protein. Cross-referencing G4-interacting proteins from the G4IPDB database with lncRNA-protein associations from LncTarD and NPInter, combined with RPISeq interaction predictions, identified ten candidate RBPs. A STRING-based protein-protein interaction (PPI) network was constructed at a confidence threshold of [≥]0.7 and refined iteratively using experimental stability data to exclude proteins associated exclusively with the structurally weaker KRTAP5-AS1. The resulting network, centered on ELAVL1, IGF2BP1, hnRNPA2B1, and FUS, highlights a coordinated post-transcriptional regulatory module relevant to oncogenesis. This work establishes a novel, experimentally validated framework wherein G4 motifs serve as entry points for decoding the protein interactome of circulating lncRNAs, with implications for cancer diagnostics and RNA-targeted therapeutic strategies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/728349v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1bba7dborg.highwire.dtl.DTLVardef@1095a53org.highwire.dtl.DTLVardef@1092eddorg.highwire.dtl.DTLVardef@1e3b7e7_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

A novel lipid-triggered allosteric site modulates LC3-LIR receptor binding activity

Membrane recruitment is a fundamental regulator of protein function. However, the allosteric mechanisms by which lipid binding controls protein activity remain poorly understood. In autophagy, the ubiquitin-like protein LC3 is lipid-anchored to autophagosomes, where it is essential for receptor recruitment and vesicle formation. While LC3-receptor interactions are structurally well defined, how membrane engagement governs LC3 functional dynamics has remained enigmatic. Here, we uncover that membrane binding triggers a major conformational transition in LC3, exposing functional pockets that are occluded in its cytosolic form. We demonstrate that this shift is mediated by dynamic coupling between the allosteric site (3- loop5-{beta}3-loop6) and the functional binding pockets. To conclusively test this mechanism, we utilised an ensemble-based protein design strategy guided by molecular dynamics to engineer the allosteric site. From a series of mutants, two variants emerged that stabilized LC3 conformation in either active or inactive state on the membrane. X-ray crystal structures of mutant LC3, biophysical assays, super-resolution microscopy, and TEM confirmed that the activated allosteric site mutant facilitates receptor binding and cargo capture. In contrast, the inactive variant is functionally inert on the membrane. Our work identifies a fundamental lipid-triggered allosteric site in LC3 that is critical for autophagy regulation and broader implication of membrane-dependent reprogrammable protein activities.

biophysics↗

bioRxiv2025

Identification of Patient Trajectories in Timeseries Clinical Transcriptomics Data

In clinical trials, it is common for only a subset of patients to respond to a given therapy. Such variability may arise from genetic differences, environmental influences, or the presence of distinct disease endotypes. Longitudinal transcriptomic profiles collected in phase 2a/2b studies provide a unique opportunity not only to investigate drug-induced biological mechanisms in humans, but also to understand why certain individuals fail to respond and to uncover previously unrecognized disease endotypes--ultimately informing the development of targeted therapeutics. However, analyzing these datasets is challenged by substantial patient heterogeneity, variability in disease severity at each visit, and the coarse temporal resolution due to sparse sampling. To address these limitations, we introduce a classification-guided autoencoder framework that jointly optimizes gene-expression reconstruction and classification objective to learn disease-relevant sample embeddings. Sample embeddings from all patients are leveraged to establish a continuous representation of disease dynamics, which can be clustered to delineate discrete disease states. We then construct a patient-sample graph in the learned latent space and apply a multi-commodity-flow based algorithm to infer patient trajectories through these states, enabling the identification of different patient trajectories. We evaluated our approach on three interventional datasets--ulcerative colitis, psoriasis, and atopic dermatitis, each containing both responders and non-responders. The method recapitulates known pathway perturbations associated with anti-IL17 and anti-IL6 therapies, identifies intermediate states reflecting disease/treatment progression, and reveals biologically meaningful patient trajectories within the patient population. Code and data are available in a public GitHub repository - https://github.com/Sanofi-Public/EndotypeDetection-Timeseries

bioinformatics↗

bioRxiv2025

Phylogenomic and comparative genome analysis of the Juglandaceae family reveals black walnut (Juglans nigra) as an ancient lineage of the Rhysocaryon section.

Accelerated climate change causes heatwaves, droughts, and severe freezes, posing threats to tree regeneration and to global biodiversity. Deeper genomic insights are crucial for breeding, conserving genetic resources, guiding management, and building resilience to these threats. Juglans, a globally distributed genus of timber and nut-producing trees, still has unresolved species relationships; comprehensive genomic data would clarify adaptation to rapid climate change and inform assisted migration strategies. We analyzed 20 Juglans species from North and South America and Asia using whole-genome data, chloroplast genome data, cold-hardiness gene families, and environmental variables related to freezing tolerance. Phylogenomic analyses indicate that black walnuts (J. nigra, section Rhysocaryon) may have diverged ~28.7 MYA (late Oligocene). J. nigra reveals higher nucleotide diversity with more genes than other Juglans, whereas J. hindsii clusters with Central/South American species, consistent with a more recent divergence within Rhysocaryon. As expected, Juglans species originating from mild-winter regions are distinct from those in cold-climate regions in genome-wide and cold-hardiness gene analyses, with introgression occurring mainly from temperate into tropical lineages. Juglans species from milder or warmer winters show CBF gene deletions, whereas cold-adapted species lack such gene deletions. These results support J. nigra as an ancient lineage within Rhysocaryon, consistent with subsequent colonization of Central/South America and longer evolutionary histories. Genome analyses further suggest Rhysocaryon is ancestral to Cardicaryon, Trachycaryon, and Dioscaryon. These findings illuminate the demographic history of the Juglans, particularly Rhysocaryon, and have direct implications for gene conservation, breeding, and assisted migration.

genomics↗

bioRxiv2025

Clinical risk factors of intensive care unit acquired weakness predicted by human muscle microtissue response to humoral factors

1.ICUAW is an acquired phenomenon in the critically ill patient that is characterized by severe muscle weakness and atrophy. It results from both myopathic and neuropathic injury and is associated with heightened patient morbidity and mortality. Critical illness survivors exhibit variable functional outcomes following ICUAW, ranging from full recovery to persistent weakness with a significant negative impact on quality of life. Despite clinical importance, the mechanistic understanding of ICUAW remains incomplete, in part due to a paucity of tractable, species-specific experimental models. Previous studies have suggested a potential causative role of bloodborne factors, though this remains a matter of debate. To address these technological and knowledge gaps, we leveraged a 3-D human skeletal muscle microtissue (hMMT) culture platform to investigate the ability of humoral factors to directly impact pathogenesis of the myopathic component of ICUAW; critical illness myopathy (CIM). Blood serum collected from ICU patients within 72 hours of ICU admission was used to treat hMMTs for 6 days starting at a time-point when the myotubes were multinucleated, striated, and capable of generating force. hMMTs treated with serum from patients that survived the ICU stay exhibited several hallmarks of CIM including; significant reductions in myotube diameter (atrophy) and striation density, alongside functional declines in calcium release, peak force, and force kinetics. The in vitro profiles of ICU serum treated hMMTs displayed significant associations with known ICUAW and CIM clinical risk factors (e.g. age, length of ICU stay) such that individual patient risk factors could be predicted from serum-induced hMMT responses. An evaluation of hMMT responses enabled delineation of ICU non-survivors from healthy control and ICU survivors. This study offers an enabling technology for studies of CIM biology in the context of human cells while also delivering compelling evidence that humoral factors directly influence ICU-associated skeletal muscle pathogenesis. Together, these advances may inform strategies for preventing or treating ICUAW.

bioengineering↗

bioRxiv2025

Finetuning Foundation Models for Temporal Clinical Transcriptomics Data

BackgroundTimeseries clinical transcriptomic datasets offer the opportunity to gain insights into the dynamics of disease mechanisms/treatment responses. However, their utility in uncovering temporal patterns is often limited by high noise levels and small sample sizes. Leveraging foundational gene embeddings and incorporating interaction information can help address these challenges, improve gene network analysis, and enable the detection of subtle changes that drive disease progression or drug response. ResultsWe finetuned gene embeddings from foundation models using healthy tissue gene expression data and used them in temporal GNNs to model gene expression of responder and non-responders to treatment in 3 disease datasets - ulcerative colitis, Crohns disease and psoriasis. Application of our method to these datasets confirmed known mechanisms associated with drug action, and also identified key differences between activated and repressed pathways for responders and non responders including B-Cell activation and mitochondria related activity in ulcerative colitis patients. ConclusionFinetuning gene embeddings from foundation models provide a richer context to model gene expression data compared to using them in their naive state. Even with smaller sample sizes, results from GNN-based temporal models outperform traditional methods by detecting known mechanisms of response and unraveling role of genes and mechanisms not known to be associated with response and non-response. Code AvailabilityCode and data are available in a public GitHub repository - https://github.com/Sanofi-Public/GNN-Timeseries

bioinformatics↗

bioRxiv2025

An empirical long-term competition among natural yeast isolates reveals that short-term fitness largely but not entirely predicts long-term outcomes

The fitness of an organism determines its likelihood of succeeding in short-term competition, but many other factors can influence its long-term success. In this study, we investigate the relative contribution of initial fitness to a strain's long-term success in a naturally diverse population. Specifically, we compete a pool of 282 genetically barcoded S. cerevisiae isolates for up to 720 generations in six distinct environments. We find that the strains that remain at detectable frequency until the end of the competition uniformly come from the initially fittest top 5% of strains, rendering initial fitness a strong predictor of long-term success. However, small fitness differences among the top strains matter little for their long-term evolutionary fate. Indeed, we often see heterogeneity in the competition outcomes across replicates, suggesting that stochastic, large-effect adaptive mutations overwrite small differences in the initial fitness of the highest fitness strains. In addition, our results hint that evolvability differences might result in certain strains consistently over- or underperforming in the competition. We further demonstrate that the "finalists" of our competition accumulate a diverse spectrum of de novo genetic changes: single nucleotide mutations, indels, whole chromosome losses and amplifications, and widespread losses of heterozygosity that occasionally span hundreds of kilobases. Taken together, we show that for a pool of natural strains, high initial fitness is necessary, though not sufficient, to succeed long-term, and that adaptive evolution can drive unexpected outcomes in a long-term competition in a novel environment.

evolutionary biology↗

bioRxiv2025

Reduced CSF1R expression in myeloid cells has limited impact on chronic lymphocytic leukemia progression

Targeting the colony-stimulating factor 1 receptor (CSF1R) to remove tumor-associated macrophages is being explored as cancer therapy. This strategy may be relevant for chronic lymphocytic leukemia (CLL), which strongly depends on support from myeloid cells. However, it is unclear how CSF1R expression affects the CLL microenvironment and leukemic progression. To examine this question, we created CLL mice with Csf1r haploinsufficiency to investigate changes in myeloid cells, Csf1r expression, and leukemia progression. Reducing Csf1r expression on circulating monocytes did not change the overall numbers of monocytes or macrophages in blood and lymphoid tissues. Mice with lower Csf1r levels had less leukemia during early disease, but this effect faded with disease progression, and their overall survival was similar to controls. Furthermore, reduced Csf1r expression on macrophages did not affect the survival or migration of patient-derived CLL cells. In summary, our results show that while the CSF1R pathway is important for maintaining the myeloid cells that support CLL, simply reducing CSF1R expression has only a limited effect on disease progression. Attempts to target the CSF1R for leukemic therapy might benefit from a stronger depletion of macrophages or the combination with other agents.

cancer biology↗

bioRxiv2025

Mapping the miRNA Landscape of Gallbladder Cancer: Genome-Wide Insights and Functional Implications

Gallbladder cancer (GBC), the sixth most aggressive gastrointestinal cancer, exhibits complex molecular dysregulation involving microRNAs (miRNAs) that drive tumor progression. This study has comprehensively profiled genome-wide miRNA expression in GBC cells revealing 971 known miRNAs, and 27 novel miRNAs. We identified 388 significantly differentially expressed (DE) miRNAs (191 upregulated and 197 downregulated), providing valuable insights into the various complex molecular mechanisms underlying GBC pathogenesis. These DE miRNAs target key biological processes, including hormone-induced signaling pathways, cell adhesion molecules, apoptosis, ferroptosis, and cancer-associated pathways, particularly those linked to gastric cancer. Profiling of DE miRNAs in GBC cells presents opportunities for novel miRNA-based diagnostic and prognostic approaches in GBC. Notably, among the DE miRNAs, the miR-17[~]92 cluster was significantly suppressed in GBC cells. We also show that miR-17[~]92 cluster targets and destabilizes an S-phase licensing factor Cdt2 (an oncogene) in GBC cells. miR-17[~]92 mediated negative regulation of Cdt2 induces S-phase arrest, leading to checked proliferation and invasive capabilities of GBC cells specifically, without any significant effect on non-cancerous cells, presenting miR-17[~]92 as a potential miRNA therapeutic for GBC. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/679165v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@243970org.highwire.dtl.DTLVardef@36f578org.highwire.dtl.DTLVardef@2dba37org.highwire.dtl.DTLVardef@1c9e1b_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

cancer biology↗

bioRxiv2025

Machine Learning-Guided Synthetic Microbial Communities Enable Functional and Sustainable Degradation of Persistent Environmental Pollutants

Persistent environmental pollutants demand the use of diverse microbial metabolic capabilities for effective degradation. While naturally occurring consortia or single strains often fall short in efficiency, synthetic microbial communities (SynComs) hold greater promise for enhanced degradation. To address this challenge, we developed GENIA (Genomically and Environmentally Networked Intelligent Assemblies), a genome-informed and machine learning-guided framework for the rational design of SynComs capable of multi-pollutant degradation under simulated environmental conditions. Using a microfluidic high-throughput cultivation platform, 2,155 bacterial strains were isolated from xenobiotic-enriched environments and screened for pollutant-specific growth. Whole-genome sequencing and functional annotation of 45 prioritized strains revealed metabolic traits associated with the potential degradation of challenging persistent environmental pollutants as proof of concept, i.e., lignin oxidation, atrazine dechlorination, and PFAS defluorination. These genomic profiles were encoded into spline-based graph representations and integrated within the GENIA pipeline, which combines graph neural networks, pathway complementarity modeling, and functional redundancy minimization to predict optimal community assemblies. The resulting nine-member community--comprising Pantoea dispersa, Atlantibacter hermannii, Pseudomonas fulva, Paenibacillus polymyxa, Bacillus cabrialesii, Micrococcus luteus, Bacillus pseudomycoides, Bacillus licheniformis, and Pseudomonas pergaminensis--was predicted to exhibit broad catabolic capacity and minimal intra-community competition. Kinetic experiments in minimal medium demonstrated simultaneous multi-pollutant degradation: lignin (91.6% removal by day 5), atrazine (91.4% removal by day 3), and PFOS (93.1% removal within seven days), representing a 2-4-fold improvement over existing approaches. GENIA establishes a scalable and generalizable framework that integrates systems-level genomics, phenotypic screening, and predictive modeling to engineer ecologically coherent microbial consortia with application to complex environmental bioremediation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/677392v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@1beee39org.highwire.dtl.DTLVardef@a0a1corg.highwire.dtl.DTLVardef@11ddc4aorg.highwire.dtl.DTLVardef@169ba57_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

ZNFX1 uses two-component ubiquitin circuitry to quarantine viral RNA

The detection of viral RNA inside cells triggers a diverse range of antiviral responses, including global translation inhibition, interferon secretion and RNA sequestration. Mutations in the gene ZNFX1 cause severe paediatric immunodeficiencies, including chronic viral infection and autoinflammation. Here, we show that ZNFX1 is an RNA helicase with cryptic and unusual bifurcating E3 ubiquitin ligase activity. Nucleotide-dependent RNA binding stimulates ZNFX1 to generate complex ubiquitin chains via a two-component ubiquitin circuit wired in parallel, with ubiquitin flux occurring via either of two competing paths. One route produces K63-linked polyubiquitin that drives ZNFX1 aggregation and RNA entrapment; the other route produces K48-linked polyubiquitin that drives ZNFX1 turnover. RNA entrapment restricts RNA virus replication, and is reversible by deubiquitination. Patient ZNFX1 variants are defective for viral restriction, linking RNA entrapment to antiviral immunity in vivo.

biochemistry↗

bioRxiv2025

What Large Language Models Know About Plant Molecular Biology

Large language models (LLMs) are rapidly permeating scientific research, yet their capabilities in plant molecular biology remain largely uncharacterized. Here, we present MOBIPLANT, the first comprehensive benchmark for evaluating LLMs in this domain, developed by a consortium of 112 plant scientists across 19 countries. MOBIPLANT comprises 565 expert-curated multiple-choice questions and 1,075 synthetically generated questions, spanning core topics from gene regulation to plant-environment interactions. We benchmarked seven leading chat-based LLMs using both automated scoring and human evaluation of open-ended answers. Models performed well on multiple-choice tasks (exceeding 75% accuracy), although most of them exhibited a consistent bias towards option A. In contrast, expert reviews exposed persistent limitations, including factual misalignment, hallucinations, and low self-awareness. Critically, we found that model performance strongly correlated with the citation frequency of source literature, suggesting that LLM knowledge inherits the visibility distribution of the underlying scientific corpus. Consequently, models tend to be more reliable on consolidated topics and less reliable on under-cited or recently emerging ones. We also benchmarked agents equipped with web-search and additional tools in more complex tasks involving DNA sequence analysis. These agents were outperformed by domain specific models in sequence classification and regression tasks, indicating an opportunity for joint agentic systems that combine both the reasoning power of LLMs and the dedicated processing of DNA models. This understanding is key to guiding both the development of next-generation models and the informed use of current tools in the everyday work of plant researchers. MOBIPLANT is publicly available online in this link.

plant biology↗

bioRxiv2025

Female sex hormones enhance gonococcal colonization at the endocervix by modifying cervical mucus

Neisseria gonorrhoeae is a human-exclusive pathogen that causes gonorrhea. Gonococci (GC) initiate female infections by colonizing the cervix, which can remain asymptomatic, cause cervicitis, or ascend to the upper female reproductive tract (FRT), leading to severe tissue damage. The FRT undergoes sex hormone-mediated changes during the menstrual cycle, which have long been implicated in the vulnerability to GC infection. One of the major changes is the increase and decrease in the production of the gel-forming mucin MUC5B by the endocervix in response to the level of estradiol (E2). This study examined the impact of sex hormones on GC infection of the human cervix, utilizing a human cervical tissue explant model. Tissue explants were treated without and with E2 alone or in combination with progesterone (E2+P4) to mimic various menstrual cycle phases. Treatment of E2 or E2+P4 enhanced GC colonization at the endocervix exclusively, but did not affect epithelial transmigration. While both treatments increased the number of GC microcolonies, E2+P4 increased GC colony size on the endocervical epithelium. These increases were independent of GC host receptors, carcinoembryonic antigen-related cell adhesion molecules. GC effectively diffused through cervical mucus to interact with the cervical epithelium under all hormone conditions and through mucin hydrogels with different MUC5B and MUC5AC compositions. Mucus gels collected from cervical explants and animal mucin mixtures enhanced GC aggregation in vitro. GC diffusion through mucin-hydrogels and aggregation in the presence of cervical mucus or animal mucins decreased as the MUC5B concentration increased. Our results suggest that female sex hormones promote GC colonization at the human endocervix by modulating the cervical mucus production, regulating womens susceptibility to GC infection, and further reveal the ability of GC to evade the mucus defense barrier for infection. Author SummaryNeisseria gonorrhoeae is a bacterial pathogen that primarily infects the human genital and female reproductive tracts, causing gonorrhea. While this bacterium can infect both men and women, the infection can lead to severe and permanent damage to womens reproductive systems. Currently, the relationship of gonococcal infection with the menstrual cycle is unknown. Here, we utilize a human cervical tissue explant model that mimics gonococcal infection in women to examine the impact of female sex hormones that drive the menstrual cycle on gonococcal infection. We found that estrogen alone or in combination with progesterone enhanced gonococcal colonization, increasing both the number and size of bacterial microcolonies on the cervical luminal surface, through regulating mucus production. Gonococci effectively penetrate through mucus layers to reach cervical epithelial cells and also prefer to aggregate with each other in the presence of mucus. Our results reveal that hormone-regulated mucus production changes the vulnerability of women to gonococcal infection, and that gonococci convert the mucus defense barrier into a colonization facilitator.

microbiology↗

bioRxiv2024

PyEvoCell: An LLM- Augmented Single Cell Trajectory Analysis Dashboard

MotivationSeveral methods have been developed for trajectory inference in single cell studies. However, identifying relevant lineages among several celltypes is a challenging task and requires deep understanding of various celltype transitions and progression patterns. Therefore, there is a need for methods that can aid researchers in the analysis and interpretation of such trajectories. ResultsWe developed PyEvoCell, a dashboard for trajectory interpretation and analysis that is augmented by large language model (LLM) capabilities. PyEvoCell applies the LLM to the outputs of trajectory inference methods such as Monocle3, to suggest biologically relevant lineages. Once a lineage is defined, users can conduct differential expression and functional analyses which are also interpreted by the LLM. Finally, any hypothesis or claim derived from the analysis can be validated using the veracity filter, a feature enabled by the LLM, to confirm or reject claims by providing relevant PubMed citations. Software Availability and ImplementationThe software is available at https://github.com/Sanofi-Public/PyEvoCell. It contains installation instructions, user manual, demo datasets, as well as license conditions (including limitation to non-commercial uses only). Supplementary informationSupplementary information is attached.

bioinformatics↗