Biology subjects

Yu, Y.

Publications and source records attributed to Yu, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Keratinizing Squamous and Intestinal Metaplasia in Urothelium of Long-term Catheterized Patients with Chronic Urinary Tract Infections

Purpose: Many spinal cord-injured (SCI) patients are recurrently catheterized to manage urinary retention and incontinence. Microbial pathogens colonize urethral catheter surfaces, persist in form of biofilms and trigger inflammatory responses in the bladder. Our objective was to determine bladder tissue changes due to long-term inflammation using LC-MS proteomics. Experimental Design: We examined longitudinal proteomic profiles from eight SCI patients, sampled over 2 to 6 months, in a patient-specific manner as well as compared with trauma patients diagnosed with acute urinary tract infections (UTI). The sample sources were urinary pellets (UP) from all subjects, and catheter biofilm extracts (CB) from SCI patients. Bioinformatic protein network and western blot analyses served to corroborate evidence of protein and cell differentiation changes in the urinary tract of SCI patients. Results: More than 50 proteins with functional or structural roles in the epidermal cornified envelope were quantitatively increased in proteomic profiles from three SCI patients. This included stress response keratins and proteins involved in keratin crosslinking, e.g. transglutaminases and small proline-rich proteins, suggesting occurrence of keratinizing squamous epithelial metaplasia. Protein co-expression network analyses supported epithelial trans-differentiation changes. Nearly thirty proteins with gastro-intestinal epithelial cell expression specificity were enriched in proteomic profiles from a single SCI patient. Among those were mucin-2 and sucrase-isomaltase, secreted proteins detected in samples for only that patient via western blots. This finding was consistent with intestinal epithelial metaplasia. Conclusions and clinical relevance: Without using invasive cystoscopy and histopathology, proteomic analyses from easily available UP and CB samples allowed us to infer cell differentiation changes in urothelial tissues of SCI patients with chronic infections, supporting a concept of clinical relevance for urothelial metaplasia screening, a precursor stage of urothelial neoplasms.

molecular biology↗

bioRxiv2026

An RGS5-positive fibroblast to WNT-like epithelial TGFB axis in colorectal liver metastasis, replicated in an independent paired cohort

Background: Colorectal cancer liver metastasis is accompanied by coordinated stromal remodelling, but which fibroblast and malignant epithelial states are coupled, and whether any such coupling holds outside the dataset it was found in, is rarely tested. Methods: Paired primary tumours and liver metastases from six patients were analysed by single-cell RNA sequencing with copy-number-based identification of malignant cells, trajectory inference, cell-cell communication and ligand-target modelling, and spatial transcriptomics of three metastatic sections. Protein staining was taken from an independent tissue resource. Every step was then repeated in a second, independent paired cohort of five patients and two further metastatic sections, with cell identities fixed by transfer from the discovery data rather than refitted. Results: An RGS5-positive fibroblast/pericyte-like state (F2) and a WNT-like malignant epithelial state (C2) were both enriched in metastases and occupied late trajectory positions. Communication analysis nominated a directional F2-to-C2 programme with transforming growth factor beta as the highest-ranked pathway and its receptor TGFBR2 enriched in C2. Spatial data placed the two states in the same neighbourhoods. Hub genes of the programme were TPT1, SLC1A5, SOX4 and TSC22D1, and protein staining was higher in tumour than normal colon for the three with available data. In the independent cohort the same states reappeared: F2 was enriched in metastases (Ro/E 1.22 versus 0.52; 32% versus 14% of fibroblasts), C2-like cells were the most metastasis-enriched epithelial state (1.10 versus 0.86), 92% of the cells that cohort's own authors called MCAM-positive fibroblasts mapped to F2, and F2 was again the strongest TGFB sender to C2-like epithelium at both sites. Conclusions: A directional TGFB axis from an RGS5-positive fibroblast state to a WNT-like epithelial state is a reproducible feature of colorectal liver metastasis rather than a property of one dataset. Its spatial expression varies between sections, which constrains how tightly the two states can be said to be co-localised.

cancer biology↗

bioRxiv2026

Full-length transcriptomics and proteomics reveal how genome minimization reshapes gene expression in synthetic bacteria

JCVI-syn1.0 (Syn1.0) and JCVI-syn3A (Syn3A), genetically synthetic and genome-reduced versions of the naturally occurring bacterium Mycoplasma mycoides, are landmark platforms for defining the gene set required for life, yet how their genomes are expressed at the RNA level remains uncharacterized. We combined full-length PacBio and native long-read RNA sequencing with short-read quantification and complementary proteomics to map transcription, RNA processing, and protein abundance in both cells. In Syn1.0, full-length sequencing resolved 459 operons encompassing 911 genes, revealing pervasive RNA processing with a strong 3' bias. Analysis of the division and cell-wall cluster showed how transcriptional context explained the restoration of genes required for normal cell division in Syn3A. Most antisense and intergenic transcription in Syn1.0 reflected low-level transcriptional noise arising from inherited mis-annotation, read-through, and synthetic sequences. Much of that transcription was lost in Syn3A after genome minimization. Reducing the genome unexpectedly altered the expression of several retained genes by deleting promoters, most prominently reducing expression of the nucleoid protein HupA and central-carbon enzymes. Meanwhile, one third of the coding mRNA pool was allocated to a single 21-gene ribosomal-protein operon, while several other ribosomal proteins had reduced transcript abundance, which possibly led to imbalanced ribosome assembly. Expression of RNA polymerase and central-carbon metabolism declined at both mRNA and protein levels whereas RNase Y degradosome abundance increased. These shifts in the synthetic evolution of Syn3A suggest plausible mechanisms for its reduced chromosome contacts and slower growth. Together, these results show that genome minimization alters not only gene content but also the transcriptional context and resource allocation of retained genes. The analysis and visualization that are shared via Jupyter Notebook provide the RNA-level foundation for whole-cell modeling of the minimal cell.

synthetic biology↗

bioRxiv2026

Increased receptor binding capability of the SARS-CoV-2 saltational variant PJ.2.1

The recently identified SARS-CoV-2 saltational variant PJ.2.1, a highly mutated descendant of MC.10.1, has achieved rapid intercontinental spread since its detection in May 2026. Here, we show that PJ.2.1 exhibits exceptionally high human ACE2-binding capability, significantly outperforming contemporary variants such as NB.1.8.1, XFG, and BA.3.2.2. However, despite acquiring over 25 spike mutations, PJ.2.1 remains antigenically similar to the circulating JN.1 family and does not display strong humoral immune evasion. Neutralization profiling indicates that PJ.2.1 possesses heightened neutralization sensitivity to human plasma and RBD-targeting antibodies, especially against cryptic-site-targeting class 4 and class 5 antibodies. This distinct sensitivity profile strongly suggests an altered spike structural dynamic that favors a receptor-accessible "up" conformation. While PJ.2.1 currently lacks the extreme immune evasion capabilities of other contemporary strains, its robust baseline receptor binding strength mirrors the early evolutionary trajectory of BA.2.86 to JN.1. This high receptor binding affinity provides a structural buffer that could facilitate the rapid acquisition of potent immune-evasive mutations, necessitating continued genomic, epidemiological, and virological surveillance of PJ.2.1.

microbiology↗

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

bioRxiv2026

NACraft: Programmatic nucleic-acid aptamer design via all-atom structure-model feedback

Protein-nucleic-acid interactions underpin diverse biological processes and provide a basis for molecular sensing, regulation and therapeutic intervention. However, the coupled dependence of aptamer function on nucleotide sequence, three-dimensional folding and target binding makes rational RNA and DNA binder design challenging. Here we present NACraft, a training-free and programmatic framework for all-atom nucleic-acid aptamer design based on backpropagation through structure-model feedback. By composing binding, sequence-similarity and anti-binding constraints, NACraft supports de novo generation, similarity-guided sampling and target-selective design within a unified optimization framework, without task-specific training or fine-tuning. Computational experiments showed that NACraft generated high-confidence candidates de novo across diverse protein targets, with further improvements achieved through similarity-guided design for both RNA and DNA complexes. Its target-selective design capability was further validated in silico, with 69.44% of paired candidates generated to favour the positive target EGFR over the off-target HER2. Under matched independent AlphaFold3 evaluation, NACraft achieved better performance than ODesign in 10 of 11 NA-12 targets and 17 of 20 protein target-length settings. Together, these results demonstrate the effectiveness and versatility of NACraft and extend structure-model hallucination toward programmatic nucleic-acid aptamer design. Codehttps://github.com/OTEAM-AI4S/NACraft

bioinformatics↗

bioRxiv2026

Systematic Benchmarking of AI-Based Molecular Generation Models for Structure-Based Drug Design

Generative artificial intelligence is accelerating molecular design, yet the relative suitability of available models for different targets and stages of preclinical drug discovery remains unclear. Here we benchmarked 12 molecular generation and optimization methods across 176 curated protein-ligand systems spanning diverse therapeutic target classes, with experimentally validated ligands providing reference chemical space. The evaluated methods encompassed pocket-conditioned 3D generation, diffusion and flow-based modeling, autoregressive construction, reference-conditioned optimization and synthesis-aware design. Performance was assessed using operational robustness, chemical validity, uniqueness, molecular and scaffold diversity, quantitative estimate of drug-likeness, synthetic accessibility, docking, physicochemical and ADMET properties, and computational resource requirements. The results revealed architecture-dependent trade off such as receptor-conditioned methods exploited binding-pocket geometry, flow-based approaches enabled efficient sampling, reference-conditioned methods favored analogue generation, and synthesis-aware approaches improved chemical feasibility, but no method consistently optimized all criteria. To address the functional potential of generated molecules, we further developed a state-aware functional classifier (SAFC) that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs. SAFC provided dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores. These findings support hybrid, stage specific deployment of generative models rather than reliance on any single architecture or evaluation metric. This study provides practical guidelines for generative AI based preclinical drug development processes.

bioinformatics↗

bioRxiv2026

A Centimeter-Scale, Peristaltic Human Intestinal Organoid with Integrated Neuro-Immune-Vascular Systems Recapitulates Enteritis and Orthotopic Colorectal Cancer

Human pluripotent stem cell-derived intestinal organoids hold great promise for disease modeling, drug screening, and regenerative medicine. However, conventional intestinal organoids are predominantly epithelial, small in scale, and lack the multicellular complexity required to recapitulate the pathophysiology of intestinal disorders such as inflammatory bowel disease (IBD) and colorectal cancer (CRC). Here, we report the development of Centimeter-Scale, purely 3D self-organized human intestinal organoids (IOs) from induced pluripotent stem cells (iPSCs) that encompass multiple tissue lineages, including epithelium, mesenchyme, smooth muscle, neurons, immune cells, and vasculature. These organoids achieve functional maturation by day 100+, exhibiting rhythmic peristaltic-like contractions, and by day 147 they display histological structures including lumens, crypt-like architecture, goblet cells, and smooth muscle. Importantly, for the first time, the neuro-muscle lineages arise spontaneously and autonomously in a purely 3D culture system, without any external stimulation (e.g., electrical, chemical, or mechanical), and mature to form functional neuromuscular junctions, driving macroscopically visible peristaltic-like contractions that mimic intestinal motility entirely through in vitro culture, without any xenotransplantation. Single-cell RNA sequencing at day 115 identified 12 cell subtypes across four major lineages, recapitulating the cellular diversity of the developing human intestine. Using this platform, we established an LPS/IFN-{gamma}-induced IBD model that recapitulated key pathological features, including epithelial disruption, immune cell infiltration, and IL-6 elevation. Transcriptomic analysis confirmed activation of the NF-{kappa}B and JAK2-STAT3 pathways, multi-modal cell death, and immune recruitment machinery, all consistent with clinical IBD pathology. Furthermore, we developed intestinal cancer models at 7 and 21 days showing abnormal hyperplasia, and a probiotic co-culture system demonstrating anti-inflammatory efficacy. Together, these results establish Centimeter-Scale intestinal organoids as a physiologically relevant, multicellular platform for modeling intestinal diseases and evaluating therapeutic interventions.

cell biology↗

bioRxiv2026

Semaglutide-induced satiation, nausea, and food reward suppression are mediated by GLP-1 receptors in the area postrema

The GLP-1-based obesity drug semaglutide lowers bodyweight primarily by increasing satiation and satiety, whilst also reducing food reward and commonly causing nausea. The brainstem dorsal vagal complex (DVC) has been identified as a key site of action for these phenotypic components of semaglutides anorectic effect. However, which GLP-1 receptor (GLP-1R) populations within the DVC are recruited to mediate these phenotypic components, and whether they are dissociable, are translationally important but unresolved questions. We addressed these using metabolic and behavioural phenotyping, combined with activity-dependent genetic labelling ( Sema-TRAP) and chemogenetic manipulation of semaglutide-recruited brainstem circuits. Semaglutide potentiated satiation and satiety, caused behavioural proxies of nausea, and suppressed motivation for Western diet, in a largely sex-independent manner. It activated a substantial proportion of GLP-1R-expressing neurons in the brainstem area postrema (AP), but surprisingly most semaglutide-activated neurons in the nucleus tractus solitarius (NTS) did not express GLP-1R. Chemogenetic reactivation of Sema-TRAP neurons in the NTS alone was sufficient to recapitulate the acute effects of semaglutide on satiation, nausea, food reward, and bodyweight. Knockdown of GLP-1R expression in the AP before Sema-TRAPing abolished the recruitment of Sema-TRAPNTS neurons which elicited all these effects, while leaving the effects of semaglutide on satiety and bodyweight intact. These data demonstrate that semaglutide recruits dissociable anorectic circuits to suppress eating via distinct behavioural mechanisms, with non-GLP-1R NTS neurons downstream of GLP-1RAP representing potential therapeutic targets to tune GLP-1-based obesity drugs towards a better-tolerated effect profile.

neuroscience↗

bioRxiv2026

GWAS and multi-omics uncover the SLC6A3 locus for canine stranger-directed social anxiety and implicate NRIP3 in dopaminergic network regulation

The social anxiety disorder (SAD) is one of the most common mental health disorders, often developing in adolescence. It can severely impair educational achievement, career progression, and social functioning. Certain individuals within the Chinese Kunming dog (CKD) population display spontaneous, SAD-like behaviors towards strangers, offering a valuable model for genetic investigation. Using whole-genome sequencing data, we conducted a genome-wide association study on this SAD-like behavior in 100 CKDs, identifying several genes previously linked to human psychiatric disorders. Notably, SLC6A3 reached genome-wide significance, whereas CADPS2 exceeded only the suggestive threshold and is therefore considered a network-supported candidate locus. By integrating weighted gene co-expression network analysis with public RNA-seq data from the VTA of 38 mice (after quality control), we further showed that Slc6a3 and Cadps2 are part of a co-expression network module associated with the dopamine system. These findings suggest that this specific module may play a functional role in the stranger-directed anxiety-related behaviours in CKDs. Furthermore, we identified Nrip3 as a key candidate transcriptional coregulator in this specific module. Subsequent virus-mediated overexpression and behavioural experiments showed that Nrip3 regulates gene expression within this network and induces a spectrum of behavioural alterations in mice, including social avoidance, anhedonia, and increased locomotor activity. Collectively, this study highlights the involvement of dopamine system genes in canine social anxiety and suggests Nrip3 as an upstream regulator of Slc6a3 associated with anxiety. The identification of conserved anxiety-related signatures between humans and dogs further supports the dog as a valuable translational model for psychiatric research.

genetics↗

bioRxiv2026

Plasma titration provides a physical ruler for cross-platform proteomics

Plasma proteomics is expanding across platforms and cohorts, and integrating these data for AI demands comparability at the protein level, not merely concordant associations1,2. Affinity and MS platforms use distinct probes (antibodies, aptamers, or peptides) and signal readouts, yielding contradictory cross-platform results3,4. Without a known quantitative truth, we cannot distinguish biology from measurement distortion, leaving no gold standard for integration. Here we introduce Plasmix--a plasma reference suite with predefined male:female ratios (M, 1:0; Y, 3:1; P, 1:1; X, 1:3; F, 0:1)--and show that preserving this quantitative titration gradient, not just technical repeatability, predicts cross-platform concordance and identifies protein measurements suitable for integration. Profiling Plasmix across five platforms (Olink, SomaScan, NULISA, AAgAtlas, and MS-DIA) and 12 protocols across 17 batches, we found discordance is dominated by signal generation, not sample identity, and platforms distort signals in a protein-specific manner. Crucially, proteins retaining the titration response showed stronger agreement in an independent cohort; anchoring to the Plasmix midpoint (P) via sample-to-reference ratios reduced distortions, extending harmonizable coverage by 10-20%. Plasmix thus provides a physical ruler to benchmark accuracy, identifying genuinely integrable measurements before pooling datasets or training AI models--a critical bottleneck for plasma proteomics.

molecular biology↗

bioRxiv2026

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for spatially Resolved Multi-omics, for rigorously analyzing spatial patterns in spatial multi-omics research. STORM incorporates a robust and efficient nonparametric test that quantifies local deviations in molecular feature measurements to detect spatial dependence across transcriptomic and proteomic data. It further estimates an interpretable spatial effect size, supports power calculations for both spatial locations and biological replicates, and enables formal group-level comparisons. In several simulated and experimental spatial multi-omics case studies, STORM demonstrates reliable performance in detecting spatial structures while offering quantitative support for study design decisions. Overall, STORM provides a principled statistical framework that unifies spatial hypothesis testing, effect size estimation, power analysis, and experimental design for spatial multi-omics data.

bioinformatics↗

bioRxiv2026

Omics-scale capture of electrophilic metabolites

The majority of metabolic pathways rely on the production of activated electrophilic intermediates, e.g., coenzyme-A esters, and their chemical structures and abundances are central to understanding enzyme function and biochemical mechanisms. However, most electrophilic metabolites are lost in traditional metabolomic analysis and thus remain poorly characterized. Here we introduce a biochemical probe, O-(trimethylammoniobutyl)-hydroxylamine (TAMOHA), that enables comprehensive profiling of electrophilic species such as coenzyme-A esters, ketones, and aldehydes. TAMOHA incorporates a highly nucleophilic hydroxyl amine that reacts quickly with electrophilic species upon tissue lysis, trapping them as stable derivatives that feature a tetraalkylammonium moiety whose constitutive charge and characteristic MS2 fragmentation fingerprint enable their highly sensitive detection. Using TAMOHA to survey the electrophilomes of E. coli, C. elegans, and mouse revealed several thousand electrophilic metabolites, most of which have not been characterized. We then demonstrate trapping of electrophilic metabolites with TAMOHA in the context of specific biochemical pathways, confirming previously proposed functions of two fatty acid metabolism enzymes, detecting formaldehyde production in mice, and providing new insights into the biosynthesis of ascaroside pheromones in C. elegans. We anticipate that use of TAMOHA for the profiling of electrophilic species will help clarify enzyme function and uncover previously elusive biochemical mechanisms in a wide range of biological systems. Table of Contents artwork O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/743530v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@79a416org.highwire.dtl.DTLVardef@3b937org.highwire.dtl.DTLVardef@74cae9org.highwire.dtl.DTLVardef@1b5a915_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Design of high-affinity miniprotein antagonist targeting IL-4Ra for IL-4/IL-13 signal blockade

IL-4Ra is a key regulatory receptor for type 2 inflammatory responses, signal transduce from IL-4 and IL-13 through binding with IL-13Ra or the gamma c chain to activate the downstream JAK1-STAT6 pathway. IL-4Ra is currently the most successful "golden target" in the field of allergic disease therapeutics. Its representative monoclonal antibody drug, dupilumab, through the dual blockade mechanism of IL-4/IL-13 has pioneered a new era of precision therapy for type 2 inflammation. In our manuscript, we employed large-scale deep learning-based computational design methods to de novo design mini-protein antagonists specific for both human and mouse IL-4Ra. The binding affinity was improved from 22.1 nM to 569 pM through partial diffusion. The design accuracy and binding specificity were verified through X-ray crystallography and biochemical studies. In vitro IL4/IL13 signal blockade assays revealed that de novo designed monomeric mini-protein antagonist exhibited comparable blockade ability to bivalent dupilumab. In vivo pharmacokinetic half-life studies demonstrated that fusion to an HSA-binding domain extended the half-life of the mini-protein antagonist from 2.7 hours to 60.6 hours. The IL-4Ra mini-protein antagonist had excellent expression levels, solubility and thermal stability. The IL4/IL13 signal blockade ability remained unchanged even after being heating to 95 degrees. In conclusion, through large-scale cluster computing and deep learning-based de novo design, we developed well-performed IL-4Ra mini-protein antagonist, and demonstrates certain potential for drug development.

synthetic biology↗

bioRxiv2026

Genomic and eco-geographic features of locally adapted inversions in wild sunflowers

Chromosomal inversions are increasingly recognized as important drivers of local adaptation and ecological divergence because they suppress recombination and maintain adaptive allele combinations despite ongoing gene flow. However, the eco-evolutionary conditions favouring the establishment of such indirectly adaptive inversions, as well as the genomic features that distinguish them from other inversions remain incompletely understood. In this study, we investigated these questions in a wild sunflower system comprising two species: Helianthus debilis and Helianthus praecox, which exhibit diverse ecotypes and varying degrees of geographic overlap across Texas and Florida in the USA. To resolve the evolutionary relationships between and within these species, and identify potentially adaptive inversions, we generated haplotype-resolved reference assemblies and integrated comparative and population genomic analyses. We identified three major genetic clusters that only partially corresponded to taxonomic classifications. We further detected 156 inversions across the genome, 11 of which showed signatures consistent with a role in local adaptation. Notably, nine of the 11 putatively adaptive inversions were found in sympatric Texas populations. Together with a similar enrichment of inversions in genome assemblies from sympatric versus allopatric populations, our results suggest that inversions are more likely to evolve in heterogeneous environments with ongoing gene flow than in allopatry. Lastly, locally adaptive inversions were generally larger, contained more genes, and showed greater sequence divergence between haplotypes than other types of inversions. Our findings provide empirical support for the role of gene flow in promoting inversion establishment and identify genomic characteristics associated with indirectly adaptive inversions.

evolutionary biology↗

bioRxiv2026

Sound unmasks short-latency tectal visual signals that are independent of the primary visual cortex

Optimal behavior requires active coordination between exogenous sensory events and internal brain states. Understanding such coordination is rendered challenging given the massive parallelism, followed by equally massive convergence, of sensory signals, even within a single sensory modality, when conveying information to the motor periphery. Here, using audiovisual stimulation and saccadic orienting, we show how multisensory stimulation differentially recruits visual processing pathways bypassing the primary visual cortex (V1), which may otherwise appear dormant. We reversibly inactivated primate V1 and explored the influences on short-latency reflexive saccadic resetting. Having recently shown that such resetting is eliminated by V1 inactivation, randomly interleaving task-irrelevant simultaneous sounds not only recovered the phenomenon, but it was also associated with V1-independent neuronal modulations in both the superior (SC) and inferior (IC) colliculi. These results show that even when the geniculostriate pathway is dominant, like in primates, alternative visual pathways still alter behavior under the right sensory contingencies.

neuroscience↗

bioRxiv2026

MAXWELL: Calibrating the probabilistic outputs of protein language models to the mutation-induced stability change landscape

Designing mutations that enhance protein stability is a central goal in protein engineering. However, experimentally screening large numbers of candidate mutations is costly and time-consuming, creating a strong need for computational methods that can identify potentially stabilizing mutations. Among these approaches, protein language models are particularly promising because they learn context-dependent amino acid preferences from large-scale sequence and structure datasets. Nevertheless, most existing stability prediction methods use these models primarily as feature extractors and do not fully exploit the amino acid probability distributions they encode. Here, we introduce MAXWELL (Matrix-wise Landscape Learning), a novel post-training method that calibrates the probabilistic outputs learned by protein language models during pretraining to generate mutational landscapes that quantify the effects of individual amino acid substitutions on protein stability. When applied to ProteinMPNN, MAXWELL yields a state-of-the-art predictor of the effects of protein mutations on stability, outperforming ThermoMPNN and other representative methods on a curated benchmark of experimentally measured stability changes. We next applied MAXWELL to the design of ten single-point mutations in the DhaA dehalogenase, seven of which (70%) increased thermal stability. Among them, G171W showed the largest improvement, with a measured {Delta}Tm of 4.91 {degrees}C. These experimental results establish MAXWELL as a novel post-training strategy for protein language models and a practical framework for designing stabilizing mutations. Repositoryhttps://github.com/ai4protein/Venus-MAXWELL

bioinformatics↗

bioRxiv2026

Sex-Specific Modulation of Gene Expression by 17beta-Estradiol in Human Meniscal Cells: Pathways to Targeted Osteoarthritis Therapies

Osteoarthritis (OA) disproportionately affects women, and estrogen has been implicated in cartilage and joint homeostasis, yet its effects on meniscal fibrochondrocytes (MFCs), and whether those effects differ by sex, remain poorly defined. We reanalyzed a publicly available RNA-sequencing dataset (Gene Expression Omnibus, GSE199087) comprising human MFCs from a male and a female donor treated with 17{beta}-estradiol (E2) or vehicle. Differential expression, Gene Set Enrichment Analysis, Enrichr, and iLINCS were integrated to identify the transcriptional programs modulated by E2 in each sex. In female MFCs, E2 upregulated pathways governing DNA replication, cell-cycle progression, and genomic maintenance (e.g., MCM8, BRCA1, RAD51), while downregulating inflammatory and extracellular matrix-degrading genes, including MMP1, IL12A, CXCL12, and MYD88. In male MFCs, E2 instead upregulated chromatin remodeling and developmental signaling programs, led by SRCAP, ERCC6, and NOTCH1, and downregulated antigen presentation and mitochondrial genes. These divergent responses indicate that E2 engages distinct, sex-specific transcriptional programs in MFCs, with the female profile favoring proliferation and matrix preservation. Although derived from a limited sample and therefore hypothesis-generating, these findings nominate candidate mechanisms underlying sex differences in meniscal biology and OA susceptibility, and warrant validation in larger, sex-balanced cohorts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/740612v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@16026fforg.highwire.dtl.DTLVardef@1a048acorg.highwire.dtl.DTLVardef@3246corg.highwire.dtl.DTLVardef@4c598b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗