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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Brain dynamics of memory encoding for simple versus complex musical sequences

Memory encoding is the foundational process by which the brain transforms sensory input into lasting representations. While the neural mechanisms of auditory memory have been extensively studied, how musical complexity modulates the neural activity during memory encoding remains poorly understood. Here, we used magnetoencephalography (MEG) to investigate the encoding of simple (tonal) versus complex (atonal) musical melodies in 67 participants. Behaviorally, the latter melodies were consistently rated as more complex and associated with lower recognition accuracy across three testing sessions (same day, one day later, and ten days after the encoding task). At the neural level, source-localized analyses revealed distinct spatiotemporal dynamics: simple melodies elicited stronger activity in auditory cortices (left and right Heschl's gyrus) and cingulate regions (medial and anterior cingulate gyrus), while complex melodies recruited the left hippocampus more extensively across multiple tones. These findings demonstrate that musical complexity shapes neural encoding processes from the outset, with tonal sequences benefiting from efficient sensory processing and atonal sequences requiring greater memory-related recruitment. Our study provides novel insights into how the human brain encodes complex auditory information, providing a framework for understanding the neural basis of memory formation for temporally structured stimuli.

neuroscience↗

bioRxiv2026

OsPATROL1 overexpression accelerates stomatal opening to enhance photosynthetic induction and growth under fluctuating light in rice

Slow stomatal opening after increases in irradiance constrains carbon gain under fluctuating light, yet stomatal kinetics remain an underexplored target for crop improvement. Here, we investigated Oryza sativa PROTON ATPASE TRANSLOCATION CONTROL 1 (OsPATROL1), which encodes a Munc13-like protein implicated in stomatal regulation in Arabidopsis thaliana. OsPATROL1 overexpression had modest, condition-dependent effects on steady-state gas exchange and did not alter stomatal morphology or biochemical traits. In contrast, it consistently accelerated stomatal opening and photosynthetic induction, reducing the stomatal conductance time constant during induction by 41-43%. During 12 h of simulated natural fluctuating light, OsPATROL1-overexpressing plants maintained higher stomatal conductance and net CO2 assimilation rate, increasing cumulative assimilation by 8-12% while maintaining their intrinsic water-use efficiency (iWUE). Under artificial fluctuating light, overexpression alleviated growth reductions relative to steady light. Under glasshouse conditions, total biomass increased by 34-44%, accompanied by greater tiller number, root biomass, bleeding sap rate, and leaf nitrogen content. Taken together, these results indicate that OsPATROL1 overexpression accelerates stomatal opening, enhances photosynthetic induction and daytime carbon gain without compromising iWUE, and is associated with greater growth.

plant biology↗

bioRxiv2026

Convergent stochastic assembly governs reef biofilm microbiomes across ecologically distinct benthic substrates

Understanding the processes that shape microbial biodiversity and community structure is a key objective of the field of microbial ecology. The processes driving assembly of benthic biofilm bacteria on functionally important reef substrates, such as crustose coralline algae (CCA) and calcium carbonate, are not well understood, despite their critical contributions to the maintenance of biodiversity and ecosystem function on reefs. To characterize the patterns of community assembly and biogeography on these substrates, climax biofilm bacterial communities from 11 reef sites were collected, and full 16S small subunit rRNA genes were sequenced. Though CCA- and carbonate-associated communities demonstrated different diversity, composition, and correlations with environmental conditions, communities on both substrates were assembled according to similar processes. Stochastic processes dominated assembly on both substrates, primarily drift with moderate influence from dispersal limitation and selection. Sub-communities of habitat generalists and specialists, as well as rare and abundant taxa, experienced disparate patterns of assembly that remained consistent between substrates, highlighting the importance of individual taxa traits in shaping community assembly. These results provide insight into the factors shaping benthic biofilm bacterial assembly and biogeography in a tropical reef ecosystem and contribute to understanding of reef resilience in the face of environmental change.

ecology↗

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

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

genetics↗

bioRxiv2026

GDF15 contributes to inflammasome-associated excessive mechanoresponses of hyperlipidemic PdL fibroblasts

Orthodontic tooth movement relies on a tightly regulated pro-inflammatory and pro resorptive mechanoresponse of local periodontal ligament fibroblasts (PdLFs). Dysregulation is linked to complications such as root resorption and tooth loss. Hyperlipidemic conditions promote excessive PdL mechanoresponses, with growth differentiation factor 15 (GDF15) acting as potential regulator. This study examined the contribution of the inflammasome/pyroptosis pathway as underlying mechanism for dysregulated mechanoresponses. Human PdLFs were treated with palmitic acid (PA) or oleic acid (OA) for six days before 24 hours of compressive loading. PA increased CASP1, CASP4, and CASP3 activity, secretion of IL-1{beta}, IL-18, and HMGB1, and LDH release. Pharmacological blockade and siRNA-mediated knockdown of inflammasome- and pyroptosis-related targets revealed that NLRP3, CASP1, CASP4, and GSDMD partially contributed to monocyte and osteoclast overactivation. Silencing PA-increased GDF15, partially normalized the phenotype, at least in part by inflammasome/pyroptosis regulation. GDF15 acted through extracellular, and a nuclear signaling route, each accounting partially to this phenotype. Together, GDF15 partially regulates the PA-induced, pyroptosis-associated overactivated mechanoresponse alongside pyroptosis-independent mechanisms suggesting it as an interesting target for potential clinical interventions.

cell biology↗

bioRxiv2026

Compression Sequencing enables ultra-sensitive and scalable scRNA-seq

Current sequencing methods are inefficient and bottlenecked by repeated sampling of highly abundant molecules, which dominate sequencing reads, limit assay throughput and sensitivity for rare targets. For example, single-cell RNA sequencing (scRNA-seq) can profile up to millions of cells, but remains severely constrained by sequencing cost, resulting in shallow gene coverage and high dropout rate. Here we report an information science-inspired method, Compression Sequencing, that tackles this fundamental inefficiency and enables highly improved (>100x) sequencing power. Our method works by performing an accurate and unbiased logarithmic transform on molecular abundances over a wide (5 logs) dynamic range, thus suppressing high-abundance targets and enriching rare ones, while maintaining quantitative accuracy. Applied to scRNA-seq libraries, our method allows ultra-sensitive detection of low-abundance transcripts (2-5x more UMIs), ultra-low sequencing cost (200x reduction), preserves accurate cell types and differential expression analysis over a 500-2,000 gene panel. In AML clinical samples, Compression Sequencing reproduces clinical diagnosis and additionally allows transcriptomic profiling at affordable cost (est. $10 per sample). Our approach thus enables ultra-sensitive and scalable single-cell analysis for large-scale functional genomics studies, drug discovery screens, AI cell model training, as well as affordable single-cell disease diagnostics.

bioengineering↗

bioRxiv2026

The Microbiota Dictates Vendor-Derived Differences in a Murine Clostridioides difficile Infection Model

Clostridioides difficile infection (CDI) is the leading cause of healthcare-associated infectious diarrhea and remains a major burden to healthcare systems worldwide. The development of novel therapeutics for CDI requires robust and reproducible preclinical models. However, the microbiota has emerged as a major source of variability in animal studies. Here, we found that genetically similar mice obtained from two commercial vendors, Jackson Laboratory (JAX) and Charles River Laboratories (CRL), exhibited marked differences in susceptibility to CDI, with JAX mice developing fulminant disease and CRL mice remaining resistant. Using full-length 16S rRNA gene sequencing, we show that JAX and CRL mice harboured distinct gut microbiota, and that cohousing susceptible JAX mice with resistant CRL mice was sufficient to shift the JAX microbiota toward the CRL community structure and confer resistance to CDI. Differential abundance analysis identified taxa distinguishing resistant and susceptible mice, providing candidates for future mechanistic investigation. These findings demonstrate that vendor-derived variation in the gut microbiota drives differential susceptibility to CDI in mice, and that this phenotype is transferable via cohousing, highlighting the importance of accounting for the microbiota when designing and interpreting animal models of infectious disease.

microbiology↗

bioRxiv2026

The trade-off between parsimony and model complexity for understanding biomedical mechanisms from mathematical models

Mechanistic mathematical models have been used extensively to provide a deeper understanding of biological mechanisms, including unveiling the regulation of tumour growth and its response to various treatments. However, given the breadth of biological regulatory mechanisms, these models are frequently large and thus prone to potential issues with parameter identifiability. Statistical metrics like the Akaike and Bayesian information criteria can help identify a parsimonious model by balancing goodness of fit against model complexity. Yet simple models may fail to provide sufficient biological insight if they do not adequately capture known physiological processes or mechanisms. A modeller must therefore balance hypothesis generation and biological learning with model tractability. Here, we illustrate this balance using models of ovarian cancer growth and treatment response to cisplatin and immune checkpoint blockade in homologous recombination (HR)-deficient and HR-proficient immunocompetent mouse models. We develop a hierarchy of mathematical models of increasing complexity to describe tumour growth, treatment response, and immune dynamics. Our results highlight the limits of relying purely on statistical metrics for model selection, particularly when the goal is to obtain biological insight and underscore the importance of balancing model complexity to avoid overfitting and parameter unidentifiability.

systems biology↗

bioRxiv2026

Critical Fragility Emerges from Chromosomal Instability in Cancer

Genomic instability is a major driver of tumor evolution, promoting diversification and adaptation while simultaneously increasing the accumulation of deleterious alterations. How tumor populations balance these opposing effects remains poorly understood. Here, we introduce a computational framework that explicitly represents diploid genomes, functional gene classes, point mutations, and chromosome-segregation errors in spatially constrained and well-mixed tumor populations. We identify a viability boundary separating sustained tumor expansion from instability-induced population collapse. Within the viable regime, mutation and selection generate a stable distribution of genomic-instability classes that is accurately captured by an analytical replicator--mutator description. Near the viability boundary, tumor dynamics exhibit prolonged extinction transients and strong sensitivity to stochastic fluctuations, with important differences between solid and liquid architectures. Chromosomal alterations further modify growth by creating transient benefits through increased gene dosage and genetic redundancy, while ultimately increasing genomic fragility. Finally, simulated interventions show that eliminating low-instability subpopulations or increasing the global mutational burden can displace tumors beyond their viability boundary and trigger irreversible collapse. These results identify genome instability as both an evolutionary advantage and an intrinsic vulnerability, providing a quantitative framework for developing therapies that exploit the limits of tumor evolution.

cancer biology↗

bioRxiv2026

Salicylic acid-triggered apoplastic proteolysis releases cryptic phytocytokines with distinct immunogenic functions

Plants rely on an innate immune system to defend against pathogens through various molecular responses. In addition to classical damage- and pathogen-associated molecular patterns (DAMPs and PAMPs), plants produce endogenous signaling peptides termed phytocytokines that amplify and regulate immune responses following stress. Although most characterized phytocytokines originate from dedicated precursor proteins, the contribution of multifunctional proteins to phytocytokine generation remains poorly understood. Here, we show that salicylic acid (SA) rapidly remodels the maize apoplastic peptidome through an early, transient proteolytic program driven by apoplastic serine hydrolases. Time course peptidomics identified fourteen candidate phytocytokines, including two cryptic peptides, PC13 and PC14, released from the stress-associated zinc-finger protein ZmSAP7 and the migration inhibitory factor-like protein ZmMDL1, respectively. Both peptides activated immune-associated gene expression but triggered distinct transcriptional responses and exerted opposing effects on Ustilago maydis infection, with PC13 enhancing resistance and PC14 promoting susceptibility. Biochemical analysis demonstrated that PMSF-sensitive apoplastic serine proteases directly process ZmMDL1 to release PC14. Together, our findings uncover a SA-responsive proteolytic pathway that generates functionally distinct phytocytokines from multifunctional proteins, expanding the repertoire of immune signaling peptides and revealing an additional layer of regulation in plant defense.

plant biology↗

bioRxiv2026

Multimodal Protein Retrieval via Joint Representation Learning from Sequences and Cryo-EM Density Maps

Aligning protein sequences with cryo-EM density maps remains challenging due to limited paired data, structural heterogeneity, varying map resolutions, and the presence of multiple conformational states. In this work, we propose a multimodal representation learning framework that learns a shared latent space between protein sequences and cryo-EM density maps for cross-modal retrieval. Our approach combines pretrained protein sequence embeddings with a volumetric cryo-EM encoder trained using self-supervised representation learning and transfer learning. The resulting model enables bidirectional retrieval between sequences and density maps while learning biologically meaningful structural representations. Experimental results demonstrate strong retrieval performance across both sequence-to-map and map-to-sequence tasks, achieving median retrieval ranks of 2--3 within a database of 3,275 cryo-EM maps. The learned embedding space shows a clear separation between matched and unmatched sequence--map pairs and remains robust across varying cryo-EM resolutions. Additionally, the model generalizes across species, successfully retrieving conserved mouse protein structures using human sequence embeddings. Our findings demonstrate that joint latent-space learning provides a promising direction for connecting protein sequences with cryo-EM structural representations, with potential applications in structural retrieval, protein annotation, and multimodal biological representation learning.

bioengineering↗

bioRxiv2026

Paternal regulation of H3K4 methylation supports tumor suppressor networks in mammals intergenerationally

Paternally-inherited epigenetic information can influence phenotype in offspring (1). Here, we identify a critical mechanistic contribution of KDM6A (UTX), an X-linked histone modifier and tumor suppressor, in regulating transmissible epigenetic information in mammalian sperm. Paternal loss of KDM6A increases cancer risk in genetically wild type offspring, but how Kdm6a knockout sperm transmit this effect at the molecular level is unknown (2). We find that KDM6A functions in spermatogenesis to promote methylation of histone H3 lysine 4 (H3K4) via selective interaction with the COMPASS complex methyltransferase KMT2C (MLL3). KMT2C and KDM6A are coordinately recruited to promoters of active genes in spermatogenic cells, contrasting with recruitment to intergenic enhancers in other cell types (3, 4). Loss of KDM6A disrupts H3K4 methylation at promoters of tumor suppressor genes in spermatogonia, and some of these defects persist in epididymal sperm and correspond to impaired expression in preimplantation embryos. These genes are also misregulated in normal and malignant hematopoietic tissue of genetically wild type offspring, indicating that impaired H3K4 methylation in KDM6A-deficient male germ cells may preferentially alter regulation of tumor suppressor gene networks in development across generations.

genetics↗

bioRxiv2026

Clonal memory in human embryonic stem cells biases fate potential during endoderm differentiation

Cell fate decisions during development are shaped not only by extrinsic signals but also by heritable intrinsic states passed on across cell division. The extent to which this phenomenon, termed clonal memory, can explain the persistent heterogeneity observed from directed differentiation of human embryonic stem cells is unclear. Here, we combine lineage tracing with single-cell transcriptomics and chromatin accessibility profiling to track clonal behaviour across human embryonic stem cell differentiation towards definitive endoderm. Using a lentiviral barcoding system coupled with a split-well sampling strategy, we find that clonally related cells exhibit reproducible, probabilistic fate outcomes that cannot be explained by signalling environment alone. Fate-biased clones are transcriptionally indistinguishable at the pluripotent stage yet display distinct chromatin accessibility landscapes at lineage-specific cis-regulatory elements. Pre-existing accessibility at these lineage-specific regulatory regions distinguish clones that undergo successful endoderm differentiation from those that generate off-target mesoderm derivatives. Together, these findings provide an explanation for how off-target populations arise during directed differentiation, identifying heritable chromatin states within pluripotent cultures as a source of variability relevant to stem cell-derived in vitro models and cell therapies.

developmental biology↗

bioRxiv2026

Local mechanical heterogeneity drives epidermal cell delamination

Delamination within stratified epithelia like the skin epidermis describes the detachment and upward motion of cells originating from the basal layer. Despite its fundamental importance for tissue development, homeostatic regeneration and repair, the mechanisms that drive delamination remain a longstanding open question. Upward motion follows cell shape changes, which are inherently driven by physical forces, but their role is elusive. Here, we investigate delamination in stratifying keratinocytes by combining imaging, force measurements and theoretical modeling. We identify a local change in force balance between differentiating cells and their environment as the key step initiating delamination. Within a homogeneous cell layer with apically polarized contractility, differentiation leads to actomyosin remodeling, redistributing cellular force exertion to the basal side. Such mechanical heterogeneity then results in differentiating cells experiencing and inward basal and outward apical forces that manifest in the formation of a +1 force defect and promote shape changes culminating in upward motion. Simultaneously, delaminating cells actively pull on their underlying neighbors, generating convergent tissue flows which close the basal layer below. Together, we propose a general physical description of delamination initiation, which may act across various multilayered epithelia.

biophysics↗

bioRxiv2026

A configuration-resolved benchmark of differential abundance analysis methods for human gut 16S rRNA microbiome data

Tools for differential abundance testing of 16S rRNA data are conventionally treated as discrete methods, and benchmarks have accordingly sought to determine which tool performs best. However, each tool offers an array of configurations based on different normalisation, transformation, reference choice, and sensitivity filtering methods, and the specific impact of these configurations on performance has rarely been systematically investigated. We benchmarked five widely used tools (MaAsLin 2, MaAsLin 3, edgeR, ALDEx2, and ANCOM-BC2) across 18 configurations, using simulated communities and human gut profiles with implanted signals, at two taxonomic resolutions and across several design factors. Configuration accounted for as much performance variation as the choice of tool itself, with the ranking of two tools depending on which of their settings are compared. Individual parameters behaved as switches between opposite error regimes rather than as graded adjustments, and the settings carrying this weight are identifiable in advance. These behaviours were reproducible across data sources and resolutions. Our results define a configuration-aware framework for matching a tool and its settings to the cohort, study design, and feature resolution, establishing that a differential abundance result is interpretable only if the configuration used for the analysis is reported.

microbiology↗

bioRxiv2026

Rate of meristem initiation driven by the MADS-WUS axis contributes to floral survival and inflorescence evolution in grasses

Crop domestication has repeatedly shaped inflorescence architecture to improve floral production, but mechanisms coordinating the rate of floral initiation, maturation and survival remain unclear. Combining morphometry, modelling and molecular genetic analyses, we show that floral production in the indeterminate barley (Hordeum vulgare L.) inflorescence follows an "initiate fast-die young" strategy orchestrated by a main MADS-box gene, SPIKELET INITIATION AND FERTILITY (SIF). SIF accomplishes this duality by coordinately terminating the inflorescence meristem via WUSCHEL and activating the floral meristem via APETALA1 (Vrn-H1). Hereby, the ancestral SIF "slow" allele promotes a timely commitment to floral maturation, whereas the derived "fast" allele permits more floral initiations. Postdomestication selection of SIF alleles thus enables diversified reproductive strategies in barley populations to maintain yield traits in the field. Finally, we show that a lineage-specific SIF duplication contributed to meristem fate transition and inflorescence evolution during Triticeae cold adaptation. Our results establish developmental rate as a key driver of architectural innovation and reproductive success.

plant biology↗

bioRxiv2026

Increased substrate complexity drives re-diversification and functional reorganization in simplified methanogenic consortia

Anaerobic digestion is a sustainable process for methane production that relies on complex microbial networks. While simplified enriched consortia offer a promising strategy to improve process control, excessive simplification can disrupt key functions and microbial partnerships, reducing community resilience. In this study, we investigated whether simplified methanogenic communities could re-diversify and maintain methane production when exposed to more complex substrates, namely butyrate and glucose. We also evaluated the effect of vitamin and amino acid supplementation on sustaining key methanogens and beneficial microbial partners. Three methanogenic communities were monitored over three months for methane production and microbial diversity while receiving butyrate and/or glucose, with different vitamin or amino acid supplements. Exposure to more complex substrates successfully restored the diversity of acidogenic and acetogenic populations, even after prolonged feeding with simple substrates, highlighting both the resilience of the simplified communities and the ecological importance of low-abundance taxa. However, the transition reduced process stability and methane production, likely due to substrate overloading. The results further suggest that substrate complexification should be introduced stepwise, promoting acetogenesis before acidogenesis. This fundamental study brings new light on which factors must be considered in the long-term goal of designing tailored-made consortia for anaerobic digestion.

bioengineering↗