Biology subjects

Lim, S.

Publications and source records attributed to Lim, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Proteomic clocks reveal organ-specific signatures of aging in mice

Aging alters protein abundance throughout the body, but whether proteomic aging signatures are shared across organs, and how they are represented in RNA, remains unclear. Plasma proteomics does not measure protein abundance directly within individual organs. Here, we developed organ-specific proteomic aging clocks for eight mouse organs. The clocks retained age associations across independent cohorts but transferred poorly between organs of the same mice, indicating predominantly organ-specific aging signatures. This organ specificity extended to RNA. Applied to the transcriptomes of the genes encoding each clock's protein features, with protein-derived weights unchanged, the scores retained age associations in independent transcriptomic cohorts, with stronger associations in the corresponding organs. The strength of age associations across molecular layers also differed by organ: in paired analysis, protein scores tracked age more strongly than RNA scores in brain and skeletal muscle, with no statistically detectable difference in kidney, liver or lung, suggesting a greater post-transcriptional contribution to proteomic aging in brain and muscle. In spatial transcriptomes, protein-derived RNA scores rose with age within tissue niches, usually without detectable changes in relative niche abundance. In the brain, white matter and glia-enriched microdomains showed elevated proteomic scores compared to other regions since young age. Together, these findings support organ-specific regulation of proteomic aging and identify molecular remodeling within persistent tissue compartments.

molecular biology↗

bioRxiv2026

Human Metalloenzymes Are Disproportionately Linked to Disease: The HEAM Structural Annotation Resource

Catalytic metal-binding sites are established drug targets for many human diseases, yet systematic study of how metal function relates to disease is limited by the lack of a resource combining structural localization of human metal-binding sites with catalytic annotation at scale. We introduce HEAM (the Human Enzymatic Annotation of Metalloproteins), a structurally annotated dataset of 1,933 human metalloenzymes and 1,171 non-catalytic metalloproteins. HEAM incorporates annotated metals into protein structures at their corresponding binding sites and classifies each site as catalytic or non-catalytic using the machine learning classifier MAHOMES II. HEAM more than quadruples the human site-level coverage of the largest prior metalloenzyme database. Using HEAM, we show that human metalloproteins are significantly enriched in KEGG disease pathways relative to the human proteome, and that metalloenzymes specifically are significantly enriched in ClinVar pathogenic variants -- a distinction obscured in prior, catalytically unresolved metalloprotein datasets. These pathogenic variants are concentrated in metabolic disorders, indicating that metabolic dysfunction is a substantial contributor to metalloenzyme-linked disease. By pairing 3D structural annotation with catalytic classification, HEAM provides both a foundation for structure-guided drug design against metal-binding sites and a resource for determining how catalytic and non-catalytic metal function contribute to human disease.

biophysics↗

bioRxiv2026

AnnFlux: object-conditioned neural stochastic differential equations for single-cell perturbation dynamics

Single-cell perturbation profiling measures responses to genetic and chemical interventions, yet most models learn a static map, ignoring how populations move over time and how perturbations combine. AnnFlux, an object-conditioned stochastic differential equation, learns a drift field in latent cell-state space. Conditioning on the perturbing object makes the field queryable one object at a time, yielding per-object drifts comparable across genes and drugs. By learning a drift field tailored to each perturbation context, it interpolates a held-out timepoint in an epithelial-mesenchymal transition time course and predicts unseen perturbations. Beyond point estimates, AnnFlux improves distributional fidelity and predicts responses to held-out perturbation combinations. An IFN-response signature predicted by AnnFlux was associated with TLS proximity in an independent pan-cancer spatial atlas. This framework maps perturbation-driven cell-state evolution as continuous trajectories and represents unseen perturbations using prior-knowledge embeddings.

bioinformatics↗

bioRxiv2026

Safety learning produces rapid fear suppression and distinct amygdala-prefrontal engagement

Animals detect and evaluate signs of danger and safety in their environment to ensure survival, yet the neural mechanisms that distinguish safety learning from other forms of conditioned inhibition, remain poorly understood. Here, we directly compared fear and safety learning in male rats. Fear conditioned rats showed high freezing to the tone and the conditioning context, whereas safety conditioned rats showed significant tone-specific reduction in freezing. This safety cue could also generalize to a novel, previously unassociated threat context leading to suppressed freezing when presented demonstrating that inhibitory actions of safety cues are not tied to its original training environment but can modify fear expression across settings. Fear and safety learning also produced unique patterns of neuronal activation and glutamatergic receptor expression in the medial prefrontal cortex (mPFC), basolateral amygdala (BLA), and central amygdala (CeA), as measured by c-Fos immunohistochemistry and Western blotting. Fear conditioning induced greater Fos expression in the BLA and CeA, as well as elevated amygdalar NMDA receptor (GluN1) levels, whereas safety learning increased amygdalar PSD-95 and AMPA receptor (GluA1) expression. Both safety and fear learning increased mPFC Fos expression without affecting glutamatergic receptors levels. Finally, safety conditioning was associated with lower tone-evoked freezing than fear conditioned rats across early extinction sessions and was accompanied by distinct patterns of prefrontal and amygdala activation across extinction. Together, these findings suggest that safety learning engages neural and behavioral mechanisms distinct from fear learning and extinction, while modifying amygdala-prefrontal circuits towards more rapid fear suppression.

neuroscience↗

bioRxiv2026

HPAI H5N1 risk in Australia: a model for the prediction of poultry outbreaks

The panzootic highly pathogenic avian influenza (HPAI) H5N1 virus has now been detected on the Australian mainland, with incursions from the sub-Antarctic region posing an increasing threat to domestic wildlife and poultry populations. Our study aimed to predict the risk of HPAI H5N1 poultry outbreaks across Australia at the local government area (LGA) level using a range of influential risk factors. We first used a Maximum Entropy (MaxEnt) model to estimate the environmental suitability for HPAI H5N1 occurrence across Australia. The resulting suitability layer was then integrated with five additional predictor layers, including abundance data for two Southern Ocean wild birds, one of which has introduced HPAI H5N1 into Australia; abundance data for 28 native Australian wild birds; native bird flyways across Australia; Australian chicken density; and poultry farm density. The six layers were aggregated and averaged to generate an HPAI H5N1 risk map for poultry outbreaks across Australian LGAs. Although most incursions have occurred in Western Australia (WA) and South Australia (SA), we identified New South Wales (NSW) and Victoria (VIC) as having the highest predicted risk of HPAI H5N1 poultry outbreaks. Additional high-risk areas were identified in WA, SA, and Tasmania (TAS). In contrast, the Northern Territory (NT) and large parts of Queensland (QLD), WA, and SA were predicted to be at low risk. These findings provide a spatially explicit framework to support targeted surveillance, preparedness, and biosecurity measures aimed at mitigating the impact of future HPAI H5N1 outbreaks in Australian poultry.

bioinformatics↗

bioRxiv2026

Mtf2 Safeguards Naïve Pluripotency by Restricting Trophectoderm Competence

The first fate decision in mammalian embryos segregates embryonic and extraembryonic lineages, establishing a largely irreversible transition from totipotency to pluripotency. The mechanisms that enforce this unidirectionality remain incompletely understood. Here, we identify the PRC2.1 subunit Mtf2 as a key epigenetic barrier that safeguards lineage fidelity in naive embryonic stem cells (ESCs). Despite globally elevated H3K27me3 levels in naive ESCs, loss of Mtf2 derepresses trophectoderm (TE) regulators, enhances cellular plasticity, generating both ICM- and TE-like populations. Mtf2-deficient cells readily acquire expanded potential with extraembryonic competence and form blastoids without trophoblast stem cell supplementation, whereas loss of the PRC2.2 subunit Jarid2 has minimal effects. Integrated CUT&Tag and transcriptomic analyses reveal that Mtf2 directly represses TE-associated genes in naive ESCs. Thus, reduced Mtf2 occupancy permits their activation in expanded potential states. Collectively, our findings establish Mtf2 as a central epigenetic barrier that stabilizes naive pluripotency and restricts inappropriate acquisition of extraembryonic fate.

Developmental Biology↗

bioRxiv2026

Mirror-image mRNA display uncovers isoform-selective D-peptide macrocycles targeting a cryptic KRAS pocket

Activating KRAS mutations drive millions of cancers diagnosed worldwide,1 yet for decades this oncoprotein was deemed "undruggable", reflecting the challenge of discovering molecules capable of perturbing its complex biological functions, and of translating these discoveries into effective cancer therapeutics.2 Recent advances propelled by innovative screening have identified diverse modalities that bind at or near the switch-II pocket (SII-P) of RAS proteins, including molecular glues,3 macrocyclic peptides,4 fragment-derived small molecules,5 and approved therapies that covalently target KRASG12C.6,7 Unfortunately, resistance to approved therapies has emerged,8,9 highlighting the need for molecules that engage new or underexploited binding sites on RAS oncoproteins with mechanisms complementary to established SII-P inhibitors.10,11 Here we show that mirror-image mRNA display12 enabled the discovery of all-D macrocyclic peptide ligands targeting a cryptic RAS back pocket (CRB-P).13 These ligands engage KRAS(OFF) and KRAS(ON) with equal affinity, exploit a single-residue difference among isoforms to bind KRAS selectively, and successfully inhibit oncogenic signaling in KRAS-mutant cells through a mechanism distinct from SII-P binders. Mirror-image screening directly afforded nanomolar peptide ligands stable toward cellular proteolysis and delivered probes targeting distinct epitopes not accessible by homochiral peptide-display methods. Together, these findings establish the CRB-P as a specifically druggable and mechanistically differentiated site on KRAS with potential for combination with emerging RAS-targeting therapies and substantiate mirror-image mRNA display as a strategy for discovering stable all-D macrocyclic peptides targeting previously inaccessible epitopes on challenging targets.

cancer biology↗

bioRxiv2026

Systematic histone mutagenesis reveals nucleosome-dependent maintenance of three-dimensional chromosome architecture and virulence in Cryptococcus neoformans

BackgroundGenomic stability is maintained through the coordinated regulation of DNA repair, dNTP pool balance, and histone dynamics--the three pillars of the DNA damage response. Because histones constitute the fundamental 3D physical scaffold of the genome, their precise regulation is essential for the spatial organization that dictates environmental fitness. In the radiotolerant pathogen Cryptococcus neoformans, the Rad53-Bdr1 pathway is a central DDR mediator; however, the mechanisms linking this checkpoint to histone dynamics remain poorly understood. Because conventional one-dimensional analyses cannot capture how spatial chromatin folding shapes transcriptional reprogramming, we integrated high-throughput chromosome conformation capture (Hi-C) with transcriptomic profiling to address this gap. ResultsWe demonstrate that HTA1 and HTB1, encoding H2A and H2B, are essential for viability, whereas H3 and H4 paralogs exhibit functional redundancy. Although most core histones are regulated by Rad53, HHT1 and variant HTZ1 are expressed independently of the Rad53. Notably, loss of the H3 paralog HHT2 induces growth defects under diverse stress conditions. Integrated RNA sequencing and Hi-C analyses reveal that HHT2 deletion drives transcriptional reprogramming of stress-responsive genes, coinciding with large-scale chromatin rearrangements such as A/B compartment switching and topologically associating domain boundary shifts. Furthermore, HHT2 loss impairs virulence factor formation and attenuates virulence. ConclusionOur findings identify core histones as essential architects of the 3D genome in C. neoformans. By establishing a causal link between chromatin structural collapse and transcriptional reprogramming, this study highlights 3D genome architecture as a decisive physical switch linking nucleosome-level dynamics to global transcriptional programs required for environmental survival.

microbiology↗

bioRxiv2026

Codon degeneracy contributes to divergent fitness effects of rare tRNAs with A-starting anticodons

Transfer RNA (tRNA) repertoires vary greatly across genomes, shaped by genetic drift and selection. A peculiar pattern across prokaryotes is the near-complete absence of tRNAs with unmodified adenine at the 34th (wobble) position (i.e., tRNAANN). Each of these tRNAs are just a single mutation away from several other tRNAs. Hence, their persistent absence suggests fundamental but hitherto unclear constraints. We engineered 36 Escherichia coli strains expressing tRNAs carrying each theoretically possible ANN anticodon to determine their functionality and fitness effects. Notably, there was no evidence of broad toxicity due to these tRNAs. All five tRNAANN tested underwent post-transcriptional maturation and all seven tested compensated for the deletion of their respective native tRNABNN (carrying G, C or U at the 34th position), demonstrating that tRNAANN are translationally active. Furthermore, tRNAANN from four-fold degenerate (4D) codon boxes were unmodified and were generally neutral or beneficial, whereas tRNAANN from two-fold degenerate (2D) boxes underwent A34-to-I34 modification and were more likely to impair fitness. We suggest superwobbling by tRNAANN -- decoding an entire four-codon set -- as one mechanism underlying these differential fitness effects. Maximal degeneracy in 4D boxes buffers or exploits tRNAANN superwobbling via synonymous decoding, whereas constrained degeneracy in 2D boxes renders it deleterious, likely through amino acid misincorporation. Thus, these differential fitness effects, sharpens the paradox of neutral or beneficial yet absent 4D tRNAANN, while beginning to empirically unravel underlying causes for the absence of 2D tRNAANN.

evolutionary biology↗

bioRxiv2026

PALMS: A Computational Implementation for Pavlovian Associative Learning Models Simulation

In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the cycle of theory development and refinement, assisting researchers in formulating the precise definitions that models require, and making accurate predictions. This manuscript introduces a computational implementation of Pavlovian learning models in a Python environment, termed Pavlovian Associative Learning Models Simulation (PALMS). In addition to the canonical Rescorla-Wagner model, attentional approaches are implemented, including Pearce-Kaye-Hall, Mackintosh Extended, Le Pelleys Hybrid, and a novel extension of the Rescorla-Wagner model featuring a unified variable learning rate that synthesises Mackintoshs and Pearce and Halls opposing conceptualisations. To our knowledge, only the first attentional model has been previously specified computationally in a general design tool. PALMS integrates a graphical interface that permits the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. It uniquely enables the simulation of experiments involving hundreds of stimuli, such as those used with human participants, and the computation of configural cues and configural-cue compounds across all models, thereby substantially broadening their predictive capabilities. A comprehensive description of the models implementation and the environment functionalities is provided in the paper; these include efficient and accurate operation and instant visualisation of predicted results across different models within a single architecture and environment. We evaluate PALMS by simulating five published experiments in the associative learning literature that assessed the predictive scope of existing models, and we show that this implementation provides neuroscientists with a useful tool for identifying critical variables, refining experimental designs, making precise predictions, comparing model fitness, and formulating new theoretical approaches. PALMS is licensed under the open-source GNU Lesser General Public License 3.0. The environment source code and the latest multiplatform release build are accessible as a GitHub repository at https://github.com/cal-r/PALMS-Simulator. Author summaryResearch on associative learning is multidisciplinary, encompassing disciplines such as neuroscience, AI, psychology, psychiatry, behavioural sciences, planning, and marketing. Unlike static formalisms, precise computational definitions specify how a model operates, enabling model simulation, swift and error-free prediction calculations, which are essential for testing theories, comparing predictions, holding models accountable, and providing a common language across fields. We introduce Pavlovian Associative Learning Models Simulation (PALMS), a user-friendly, open-source Python environment for simulating classical conditioning and studying the role of attention in learning. PALMS implements the prescriptive Rescorla-Wagner and attentional models: Pearce-Kaye-Hall, Mackintosh Extended, Le Pelleys Hybrid, and a new hybrid model with a unified variable learning rate that blends Mackintosh and Pearce-Halls conflicting views. Its graphical interface makes it easy for neuroscientists to enter experiments. Our computational implementation supports simulations with hundreds of stimuli, configural cues, and compounds, broadening the models predictive power. Designed for efficiency, it offers instant visual results and useful features. We evaluate PALMS by simulating five published experiments, highlighting its value for model comparison and refinement, and, more generally, as a tool to assist research.

animal behavior and cognition↗

bioRxiv2026

Historical rice samples from the 1950s reveal pre-modern-breeding population structure in indica landraces of mainland Southeast Asia

Understanding crop population history requires genetic material that predates modern breeding. Here, we analyzed historical seeds of rice landraces assembled in Southeast Asia from 1957 to 1958. Target capture resequencing of coding regions yielded high-quality genomic data from 66 historical accessions (seven from North Vietnam, 30 from South Vietnam, 13 from Cambodia, two from Laos, and 14 from Thailand). When integrated with published rice panels, all historical accessions were assigned to indica, and the previously described major regional structure was recovered. Within the historical collection, South Vietnamese, Cambodian, and Thai accessions formed a largely continuous group, whereas North Vietnamese accessions were clearly distinct and comprised two differentiated groups corresponding to the traditional growth seasons (fifth- and tenth-month rice). Notably, the fifth-month rice accessions were assigned to the recently reported Vietnam-I5 cluster. Admixture graph, f-statistics, and qpAdm analyses further indicated that Vietnam-I5 is closely related to a China-associated lineage; however, this relationship was not fully explained by sampled indica, japonica, or aus proxies. Together, these results show that major components of the present-day indica regional structure were already present before modern cultivar replacement, and highlight northern Vietnam as a historical zone of lineage differentiation between Chinese and mainland Southeast Asian rice. HighlightGenomic analyses of historical indica rice seeds reveal that a distinctive northern Vietnamese lineage and a genetic continuum across southern Vietnam, Cambodia, and Thailand predated modern cultivar replacement.

plant biology↗

bioRxiv2026

Counting strands in outer membrane beta-barrels

Beta-barrel structures are critical components of bacterial outer membranes, where they facilitate transport, cell signaling, antibiotic resistance, and structural integrity. A key feature of beta-barrels is their strand count, which influences pore diameter, binding site locations, and functional properties. However, because of breaks in strands and the presence of strands in periplasmic domains and plug domains, manual counting is inefficient and current algorithms do not accurately determine barrel strand count. To address this, we refined our previous beta-barrel structural assessment tool, PolarBearal, to improve strand number identification in large-scale datasets. To enhance the accuracy of barrel strand number labeling, our updated algorithm integrates three structural criteria, namely inter-residue vector angles, hydrogen-bonding distances, and strand connectivity. Using this algorithm, we labeled strand numbers for 571,760 predicted outer membrane beta-barrel structures obtained from the AlphaFold2 database. Our algorithm has 97% accuracy in strand number assignments, and the resulting dataset facilitates assessment of the homogeneity of strand counts for different types of outer membrane proteins. The strand labeling also provides insights on beta-barrel strand distribution and evolutionary patterns, supporting further research in protein structure prediction and design. SignificanceThis work contributes an accurate, automated method for counting beta-barrel strands in bacterial outer membrane proteins. Methodological ImpactThe algorithm achieves 97% accuracy in strand counting, solving a technical problem that has hindered large-scale structural analysis. Previous manual methods were inefficient, and existing algorithms failed to handle the structural complexities of real beta-barrel proteins, including strand breaks and additional domains. Scale of ContributionBy annotating over 571,000 predicted structures from the AlphaFold2 database, this work represents the largest systematic characterization of beta-barrel strand distributions to date. This labeled dataset provides a resource for the structural biology community. Broader ApplicationsThis tool enables researchers understand structure-function relationships in outer membrane proteins, supporting advances in protein design, drug development targeting bacterial membranes.

bioinformatics↗

bioRxiv2026

Compensatory evolution facilitates loss of prfB autoregulation in Pseudomonas fluorescens SBW25

Understanding why some traits are maintained whereas others are repeatedly lost is a central question in evolutionary biology. Here, we address this problem through an analysis of the evolutionary dynamics of autoregulation of the prfB gene, which encodes peptide-chain release factor 2 (RF2), a key factor in bacterial translation termination. RF2 recognizes UGA and UAA stop codons and catalyzes the release of the completed polypeptide. In many species, prfB contains an internal UGA stop codon, triggering premature translation termination by RF2 itself. Complete RF2 translation depends on a +1 programmed ribosomal frameshifting (PRF) event on the internal stop codon, which occurs more frequently when RF2 levels are low, resulting in autoregulation of prfB expression. While widespread, this autoregulatory mechanism has been lost in multiple bacterial lineages. We combined phylogenetics, experimental evolution and molecular genetics to investigate the evolutionary forces behind this loss. We found no significant correlation between PRF loss and stop codon usage using phylogenetically informed analyses, and PRF disruption in Pseudomonas fluorescens SBW25 had no detectable fitness effect. However, engineered mutations that reduced frameshifting caused fitness defects, which were compensated by two classes of mutation: (i) mutations that impair specific ribosomal proteins, and (ii) single-nucleotide deletions in prfB that adjust the reading frame and bypass the internal stop codon. These results suggest that compensatory mutations facilitate the loss of prfB autoregulation under RF2-limiting conditions. We discuss three potential scenarios that could account for this process.

evolutionary biology↗

bioRxiv2026

Leveraging TNFR2 for antitumour immunity: T reg depletion and myeloid reprogramming versus T cell costimulation

Although checkpoint inhibitors have revolutionized cancer treatment, responses are largely restricted to targeting CTLA-4 and PD-(L)1. TNFR2 has been identified as a target of interest, but a lack of understanding of whether agonists or blockers should be used, and whether Fc{gamma}R interactions promote effcacy, has hampered therapeutic antibody development. Here, we engineered two distinct -TNFR2 types: ligand-blocking Fc{gamma}R-engaging versus non-ligand-blocking agonist antibodies. Both showed potent anti-tumor effcacy across multiple syngeneic mouse tumor models and combined effectively with -PD-1. The ligand-blocking antibody depleted intratumoral T regs and reprogrammed the myeloid compartment, directing monocytes towards a pro-inflammatory macrophage and dendritic cell trajectory. Surprisingly, this effect depended on both inhibitory and activating Fc{gamma}Rs. In contrast, the agonist directly co-stimulated T and NK cells, through partially Fc{gamma}R-independent mechanisms. Both antibodies converged on activating tumor-specific CD8+ T cells mediating tumor rejection. Clinical assessment of both ligand-blocking (BI-1808) and agonist (BI-1910) -TNFR2 antibodies is currently ongoing. Graphical abstractO_LILigand-blocking anti-TNFR2 mAbs (BI-1808 and 3F10) deplete CCR8+ T regs and activate myeloid cells, remodeling the TME and leading to CD8+ T cell killing of cancer cells. T reg depletion and myeloid activation are mediated through interaction with activating Fc{gamma}Rs and the inhibitory Fc{gamma}RIIB, respectively. C_LIO_LIAgonist anti-TNFR2 mAbs (BI-1910 and 5A05) intrinsically agonize TNFR2 on T cells and NK cells, which is enhanced by Fc{gamma}RIIB crosslinking. The resulting broad lymphocyte activation mediates cancer cell killing. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/697572v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@3ecc17org.highwire.dtl.DTLVardef@c936a7org.highwire.dtl.DTLVardef@23189aorg.highwire.dtl.DTLVardef@243a05_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Cycling of sulfur redox intermediates drives microbial activity in the sulfate-methane transition zone of cold methane seeps

Microbial sulfate reduction is a cornerstone of marine sediment biogeochemistry, driving carbon remineralization and fueling the anaerobic oxidation of methane (AOM). Yet in zones of high methane flux, sulfate limitations may constrain the sulfate-reducing bacteria (SRB) and anaerobic methanotrophic archaea (ANME) that typically perform AOM. Although often overlooked, sulfur redox intermediates are readily utilized by diverse microorganisms, potentially driving AOM in sulfate-limited zones. To resolve the microbial mechanisms underlying cryptic sulfur cycling in such sediments, Monterey Canyon cold methane seeps were investigated through an integrated geochemical, isotopic, and metatranscriptomic approach. High-resolution electrochemical measurements confirmed intense sulfide production in seep sites, and long-term anoxic incubations were conducted with sediment from the SMTZ amended with elemental sulfur, thiosulfate, or sulfate as the sole sulfur source, with or without methane. Over 650 days, sulfide accumulation was greatest in elemental sulfur treatments, followed by thiosulfate and sulfate; in all cases methane addition enhanced sulfide production. Isotopic measurements showed modest S-isotope fractionation indicating that the large fractionations typical of slow sulfate reduction were muted by additional sulfur transformations. In elemental sulfur treatments, isotopic and geochemical patterns suggested that disproportionation was unlikely. Metatranscriptomes revealed broad expression of sox genes and abundant dsr/apr across treatments, along with thiosulfate-linked phs upregulation. While ANME-2c and SEEP-SRB2 activity increased with methane, transcriptomic and isotopic data together highlighted the roles of Desulfocapsaceae, Desulfobulbaceae, and Sulfurovaceae lineages in mediating sulfur transformations. Taken together, these results demonstrate how cryptic sulfur cycling may sustain microbial communities in sulfate-depleted deep-sea sediments and contribute to AOM.

ecology↗

bioRxiv2025

From minutes to bounds: A probabilistic UV-C control and a shape-only morphological fingerprint for postharvest Colletotrichum

Postharvest losses in high-value horticultural crops such as cacao and coffee are often driven by Colletotrichum spp. and other latent fruit pathogens. Ultraviolet-C (UV-C) is increasingly deployed as a chemical-free postharvest technology, yet prescriptions remain framed in minutes rather than in probabilistic guarantees of disease control and safety. We present a dual framework that (1) establishes conservative confidence bounds on survival and (2) validates a "shape-only" morphological fingerprint. This approach addresses the biological complexity of host-pathogen interactions by quantifying isolate-specific heterogeneity, rather than averaging it away. We utilized a large Colletotrichum dataset (n = 5,363) from cacao and coffee, spanning diverse treatments including UV-C, UV-B, and sonication. First, focusing on the Coffee UV-C cohort ([~]10 min), we quantified this heterogeneity; the most conservative Clopper-Pearson upper 95% bound on survival reached 1.000, highlighting partial survival events (e.g., isolate P24-88) under otherwise high-efficacy conditions. This probabilistic framework captures the "tail-risk" of biological resilience instead of assuming complete kill. Second, we trained machine learning models on the full dataset using only geometric features (e.g., aspect ratio, asymmetry), explicitly excluding all primary size metrics. Serving as a rapid physiological indicator of UV-induced stress, these "shape-only" models successfully predicted pathogen host-origin (Accuracy {approx} 0.93) and post-treatment survival (R{superscript 2} {approx} 0.74). The signals ability to generalize across UV-B and sonication confirms that geometry, not just growth reduction, carries a robust and transferable physiological stress signature. This work provides a device-agnostic, probabilistic control platform, replacing time-based heuristics with quantitative guarantees and a generalizable, shape-based diagnostic. HighlightsO_LIA conservative upper 95 % confidence bound (on survival = 1.000) was observed for the Coffee UV-C ([~]10 min) cohort, revealing strong isolate-specific heterogeneity rather than a universal kill guarantee C_LIO_LIIsolate-specific heterogeneity (e.g., localized P24-88 survival) was quantified rather than averaged away. C_LIO_LIA size-free morphological fingerprint predicted host-origin (Accuracy {approx} 0.93) and survival (R{superscript 2} {approx} 0.74). C_LIO_LIThe shape-only signal generalized across diverse stressors, including UV-C, UV-B, and sonication. C_LIO_LIA probabilistic, device-agnostic control framework replaces traditional time-based heuristics. C_LI

microbiology↗

bioRxiv2025

Decoding pathogen ecological memory from chemical-stress phenotypes

Can standardized chemical stress reveal a reproducible, taxonomy-agnostic stress-response fingerprint that is predictive of sample-source labels (crop-of-isolation) in fungal isolates? Six coffee{square}associated Colletotrichum isolates were profiled using four phenolic{square}branched compounds and compared them with a previously characterized cacao panel. Quantitative morphology, hyperspectral imaging (HSI), and supervised machine learning (ML) yielded panel-specific fingerprints under uniform, isotropic in vitro conditions. Circularity, a measure of edge symmetry, was the most informative morphological feature, and ML classified the crop-of-isolation label (coffee vs cacao, in this panel) with 86.7% accuracy in within-panel cross-validation. HSI detected dose-dependent spectral shifts in a targeted subset of isolates and compounds, including changes near 1930 nm in the short-wave infrared, a moisture-sensitive region that warrants robustness checks (e.g., band masking or preprocessing sensitivity) prior to biochemical attribution. Multi{square}locus phylogeny showed the coffee isolates are polyphyletic, so the predictive signal should be interpreted conservatively as a taxonomy-agnostic phenotype fingerprint associated with crop background in this mixed-lineage panel, acknowledging that crop labels are partially confounded with phylogenetic structure. We propose a "chemical priors" framework as a working hypothesis, in which long-term environmental exposure may imprint stress-response pathways that become legible under simple, standardized probes. This integrative workflow supports scalable screening of eco-friendly antifungals and sensor-driven decision support for high-throughput phenotype-based screening workflows. HighlightsO_LITaxonomy-agnostic workflow integrates hyperspectral imaging and morphology. C_LIO_LIMachine learning predicts crop-source labels with 86.7% accuracy in mixed lineages. C_LIO_LISystem exhibits robustness, maintaining >86% accuracy even after feature ablation. C_LIO_LINon-linear ML captures structure missed by linear stats (1.7% variance). C_LIO_LIEnables rapid, sensor-driven antifungal screening without prior DNA sequencing. C_LI

microbiology↗

bioRxiv2025

An Eligibility-Aware Pipeline for Robust ITS Diagnostics in Fungi: A Cacao Case Study with Generalizable Rules

Accurate fungal ITS diagnostics rely on public sequence archives, but heterogeneous record lengths, especially frequent truncation before the LSU/28S segment, cause naive in silico benchmarking to conflate primer performance with database incompleteness. To resolve this persistent "denominator error," we present an open and fully reproducible "eligibility-aware" framework. Our pipeline first establishes eligibility by confirming both primer sites are present before applying bench-realistic performance rules, including a strict penalty for 3-terminal mismatches. It further provides mechanistic insights by analyzing binding-site conservation and uses a rarefaction-based approach to guide efficient quality control as databases grow. We demonstrate the frameworks utility using the cacao pathosystem, a context where rapid differentiation of the fungal pathogen Moniliophthora from symptomatically similar oomycetes is critical. The result is a robust, field-ready diagnostic decision tree operable under a single touchdown (TD) PCR program. By providing a transparent and barcode-agnostic template, our eligibility-aware approach offers a significant methodological advance for designing and validating molecular assays in mycology and beyond.

microbiology↗