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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Knockout of αβ but not γδ T cells in chickens is associated with high cytotoxicity and deficiency of regulatory and helper T cells

The availability of genetically modified mice has facilitated the study of mammalian T cells. No model has yet been developed to study these cells in chicken, an important livestock species with a high availability of {gamma}{delta} T cells. To investigate the role of {gamma}{delta} and {beta} T cell populations in birds, we generated chickens lacking these T cell populations. This was achieved by genomic deletion of the constant region of the T cell receptor {gamma} or {beta} chain, leading to a complete loss of either {gamma}{delta} or {beta} T cells. Our results show that a deletion of {beta} T cells but not {gamma}{delta} T cells resulted in a severe phenotype in knockout chickens. The {beta} T cell knockout chickens exhibited granulomas associated with inflammation of the spleen and the proventriculus. Immunophenotyping of {beta} T cell knockout chickens revealed a significant increase in monocytes and the absence of CD4+ T cells and FoxP3+ regulatory T cells compared to wild type chickens. In addition, we observed a significant decrease in immunoglobulins, B lymphocytes, and changes in the bursa morphology. Our data reveal the consequences of T cell knockouts in chickens and provide new insights into their function in vertebrates. Significance statementThe lack of genetically modified chickens has severely limited research in avian immunology compared to other animal models. Here, we report the generation of two T cell knockout chicken lines that will contribute significantly to the understanding of T cell biology as a very important research model as well as an important livestock species. The generated animals reveal the function of different T cell populations in chickens and will help to better understand the role of these cells during the interaction with various pathogens in birds.

immunology↗

bioRxiv2024

Connectomic Analysis of Mitochondria in the Central Brain of Drosophila

Mitochondria are integral to the metabolism and cell biology of a neuron. Electron microscopy images of fly brain volumes, taken for connectomics, can be analyzed for mitochondria as well as the cells and synapses already reported. Here, from the Drosophila Hemibrain connectome dataset, we extract, classify, and measure approximately 6 million mitochondria, with the majority located among more than 20 thousand neurons and over 5500 cell types. Each mitochondrion is annotated with its location, orientation, voxel size, and appearance (dark and dense, light and sparse, or medium), and each synapse is linked to its closest mitochondrion. Using these data, we show how the most basic characteristics of mitochondria--volume, distance from synapses, and appearance--vary considerably between cell types and between brain regions. Mitochondria are larger and closer at presynapses than at postsynapses, and presynapses typically have a mitochondrion within one micron. However, cells important for learning and memory, Kenyon cells, have unusually small and sparse mitochondria that are placed with unusual precision near only half of presynapses. We find that mitochondria occupy a greater fraction of cell volume in inhibitory neurons than excitatory cells, dopaminergic neurons have a distinct synaptic positioning of mitochondria, and that glutamatergic neurons have a high fraction of mitochondria with a light appearance. We also find that synapses with more postsynaptic partners have larger presynaptic mitochondria and more distant postsynaptic mitochondria. Finally, we have extended the data model of our public web interface, neuPrint, to record our mitochondria data as searchable subcellular components of neurons in a connectomic database. These results indicate wide-ranging principles for how mitochondrial organization varies by cell type in a dataset of unprecedented size and coverage.

neuroscience↗

bioRxiv2025

Plasmonic imaging of living pancreatic beta-cell networks

We present a novel plasmonic imaging technique for real-time, label-free tracking of bioelectrical interactions in live pancreatic beta-cell networks. Surface plasmon resonance microscopy (SPRM) is utilized to reveal synchronized glucose-induced intensity oscillations that are suppressed by calcium channel blockers. These oscillations are observed at the subcellular scale with a resolution of 1 m. The technique can also uncover the extracellular spread of these oscillations beyond the cells. We further combine SPRM with network analysis to quantify coordinated electrical activity within the living cell network using both amplitude and phase-based metrics. Our results demonstrate a new method for studying electrical communication in pancreatic beta-cells, which could be crucial for understanding dysregulation in diabetes and advancing treatment development. This technique holds promise for investigating electrical connectivity in biological cell networks with applications in neuroscience, cardiac science, and bioelectricity in cancer, microbiology, development and regeneration. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/649945v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1dc0ce1org.highwire.dtl.DTLVardef@1d1c333org.highwire.dtl.DTLVardef@6684acorg.highwire.dtl.DTLVardef@906170_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

Vibrio deploys Type 2 secreted lipase to esterify cholesterol with host fatty acids and mediate cell egress

Pathogens find diverse niches for survival inside host cells where replication occurs in a relatively protected environment. Vibrio parahaemolyticus, a facultative intracellular pathogen, uses its type 3 secretion system 2 (T3SS2) to invade and replicate inside host cells. However, after extensive analysis, the T3SS2 pathogenicity island appeared to lack a mechanism for egress of this bacterium from the invaded host cell. Using a combination of cell biology, microbial genetics and lipid biochemistry, we found that VPA0226, a constitutively secreted lipase, is required for escape of Vibrio parahaemolyticus from host cells. Remarkably, this lipase must be delivered into the host cytoplasm where it preferentially uses fatty acids associated with innate immune response (i.e. arachidonic acid, 20:4) to esterify cholesterol, weakening the plasma membrane and allowing egress of the bacteria. This study reveals the resourcefulness of microbes and the interplay between virulence systems to evolve an ingenious scheme for survival and escape. Impact StatementConsidering the course of a pathogens evolution, there appears to be interplay between secretion systems, providing unique, synergistic mechanisms to support a successful lifestyle for possibly pathogenesis, symbiosis and/or parasitosis.

microbiology↗

bioRxiv2024

Mixed Wolbachia infections resolve rapidly during in vitro evolution

The intracellular symbiont Wolbachia pipientis evolved after the divergence of arthropods and nematodes, but it reached high prevalence in many of these taxa through its abilities to infect new hosts and their germlines. Some strains exhibit long-term patterns of co-evolution with their hosts, while other strains are capable of switching hosts. This makes strain selection an important factor in symbiont-based biological control. However, little is known about the ecological and evolutionary interactions that occur when a promiscuous strain colonizes an infected host. Here, we study what occurs when two strains come into contact in host cells following horizontal transmission and infection. We focus on the faithful wMel strain from Drosophila melanogaster and the promiscuous wRi strain from Drosophila simulans using an in vitro cell culture system with multiple host cell types and combinatorial infection states. Mixing D. melanogaster cell lines stably infected with wMel and wRi revealed that wMel outcompetes wRi quickly and reproducibly. Furthermore, wMel was able to competitively exclude wRi even from minuscule starting quantities, indicating that this is a nearly deterministic outcome, independent of the starting infection frequency. This competitive advantage was not exclusive to wMels native D. melanogaster cell background, as wMel also outgrew wRi in D. simulans cells. Overall, wRi is less adept at in vitro growth and survival than wMel and its in vivo state, revealing differences between cellular and humoral regulation. These attributes may underlie the observed low rate of mixed infections in nature and the relatively rare rate of host-switching in most strains. Our in vitro experimental framework for estimating cellular growth dynamics of Wolbachia strains in different host species, tissues, and cell types provides the first strategy for parameterizing endosymbiont and host cell biology at high resolution. This toolset will be crucial to our application of these bacteria as biological control agents in novel hosts and ecosystems. Author SummaryWolbachia pipientis is one of the most common bacterial endosymbionts due to its ability to manipulate host reproduction, and it has become a useful biological control tool for mosquito populations. Wolbachia is passed from mother to offspring, however the bacterium can also "jump" to new hosts via horizontal transmission. When a Wolbachia strain successfully infects a new host, it often encounters a resident strain that it must either replace or co-exist with as a superinfection. Here, we use a Drosophila melanogaster cell culture system to study the dynamics of mixed Wolbachia infections consisting of the high-fidelity wMel and promiscuous wRi strains. The wMel strain consistently outcompetes the wRi strain, regardless of wMels initial frequency in D. melanogaster cells. This competitive advantage is independent of host species. While both strains significantly impede host cell division, only the wMel strain is able to rapidly expand into uninfected cells. Our results suggest that the wRi strain is pathogenic in nature and a poor cellular symbiont, and it is retained in natural infections because cell lineages are not expendable or replaceable in development. These findings provide insights into mixed infection outcomes, which are crucial for the use of the bacteria in biological control.

evolutionary biology↗

bioRxiv2017

cytoNet: Network Analysis of Cell Communities

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNets capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin 4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains. Availability and ImplementationQutubLab.org/how | cytoNet contact: cytoNetProject@gmail.com Brain Initiative Alliance Toolmaker cytoNet site: https://www.braininitiative.org/toolmakers/resources/cytonet/ Author / Lay SummarycytoNet provides an online tool to rapidly characterize relationships between objects within images and video frames. To study complex tissue, cell and subcellular topologies, cytoNet integrates vision science with the mathematical technique of graph theory. This allows the method to simultaneously identify environmental effects on single cells and on network topology. cytoNet has versatile use across neuroscience, stem cell biology and regenerative medicine. cytoNet applications described in this study include: (1) characterizing how sensing pain alters neural circuit activity, (2) quantifying how vascular cells respond to neurotrophic stimuli overexpressed in the brain after injury or exercise, (3) delineating features of fat tissue that may confer resistance to obesity and (4) uncovering structure-function relationships of human stem cells as they transform into neurons.

bioengineering↗

bioRxiv2022

A QuantCrit investigation of society's educational debts due to racism, sexism, and classism in biology student learning

We investigated the intersectional relationships between racism, sexism, and classism in inequities in student conceptual knowledge in introductory biology courses using a quantitative critical framework. Using Bayesian hierarchical linear models, we examined students conceptual knowledge as measured by the Introductory Molecular and Cell Biology Assessment. The data came from the LASSO database and included 6,547 students from 87 introductory courses at 11 institutions. The model indicated that students with marginalized identities by race, gender, and class tended to start with lower scores than continuing-generation, White men. We conceptualized these differences as educational debts society owed these students due to racism, sexism, and classism. Instruction added to these educational debts for most marginalized groups, with the largest increases for students with multiple marginalized identities. After instruction, society owed Black and Hispanic, first-generation women an educational debt equal to 60-80% of the average learning in the courses. These courses almost all (85/87) used collaborative learning and half (45/87) supported instruction with learning assistants. While research shows collaborative learning better serves students than lecture-based instruction, these results indicate it does not repay educational debts due to racism, sexism, and classism.

scientific communication and education↗

bioRxiv2022

scFormer: A Universal Representation Learning Approach for Single-Cell Data Using Transformers

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell sequencing has emerged as a promising technique to decode cellular heterogeneity and analyze gene functions. With the high throughput of modern techniques and resulting large-scale sequencing data, deep learning has been used extensively to learn representations of individual cells for downstream tasks. However, most existing methods rely on fully connected networks and are unable to model complex relationships between both cell and gene representations. We hereby propose scFormer, a novel transformer-based deep learning framework to jointly optimize cell and gene embeddings for single-cell biology in an unsupervised manner. By drawing parallels between natural language processing and genomics, scFormer applies self-attention to learn salient gene and cell embeddings through masked gene modelling. scFormer provides a unified framework to readily address a variety of downstream tasks such as data integration, analysis of gene function, and perturbation response prediction. Extensive experiments using scFormer show state-of-the-art performance on seven datasets across the relevant tasks. The scFormer model implementation is available at https://github.com/bowang-lab/scFormer.

bioinformatics↗

bioRxiv2023

Unique proteomic signature of JCPyV-infected human astrocytes: from cells to extracellular vesicles

JC polyomavirus (JCPyV) is an opportunistic virus that remains in a latent state in the kidneys of more than half of the human adult population. In rare cases of severe immune suppression, the virus is able to establish a lytic infection of glial cells in the brain, resulting in a debilitating, demyelinating disease known as progressive multifocal leukoencephalopathy (PML). Because of the exceptional species and tissue specificity of the virus, appropriate models of JCPyV infection in the brain are lacking, thus hampering progress towards the development of novel antiviral strategies and biomarkers of disease activity. While PML has traditionally been characterized as a lytic infection of oligodendrocytes, more recent findings suggest an important role for astrocytes during the initial stages of disease. Here, using human induced pluripotent stem cell (hiPSC) derived-astrocytes coupled with a multiparametric approach, we show that 1. JCPyV readily infects and replicates in astrocytes, 2. JCPyV strongly dysregulates the cell biology and 3. these findings adequately reflect ex vivo findings. We perform an in-depth characterization of the effect of JCPyV on the cell proteome over time, demonstrating a strong dysregulation of the cell cycle and activation of the DNA damage response. Furthermore, we show that the proteomic signature observed for infected astrocytes is extended to excreted vesicles, underlining their potential to gain valuable insights into JCPyV propagation in the brain.

neuroscience↗

bioRxiv2024

A novel induced pluripotent stem cell model of schwann cell differentiation reveals NF2-related gene regulatory networks of the extracellular matrix

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2-related schwannomatosis. We developed a novel human induced pluripotent stem cell (hiPSC) model to study Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann cells (SCs) representing distinct stages of development. To validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we identified gene co-expression modules that are dynamically regulated during hiPSC to SC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets, we identified new genes potentially associated with NF2 expression. Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

bioinformatics↗

bioRxiv2025

Comprehensive profiling of N6-methyladnosine (m6A) readouts reveals novel m6A readers that regulate human embryonic stem cell differentiation

N6-methyladenosine (m6A) methylation has emerged as a prevalent RNA modification that extensively impacts various physiological and pathological processes via various post-transcriptional readout effects in mammals. High-throughput methylome profiling has outlined the landscape of m6A modification sites, but their downstream readouts require comprehensive investigation. To this end, we systematically assessed the effects of m6A on mRNA half-life, translation efficiency, and alternative splicing across five cell lines (A549, HEK293T, HUVEC, JURKAT, and human embryonic stem cells (hESCs)) using actinomycin D-disrupted temporal transcriptome sequencing, ribosome sequencing, and ultra-high-depth transcriptome sequencing, respectively. Our analysis, coupled with the integration of public and re-profiled m6A methylome data, revealed high cell type specificity in m6A readouts where m6A level alone is insufficient to predict m6A readouts. Machine learning models focused on the RNA binding protein (RBP) binding context of m6A sites demonstrated substantial predictive ability of m6A readouts while prioritizing putative m6A readers from their informative RBP features. Four novel m6A readers (DDX6, FUBP3, FXR2, and L1TD1) were identified and validated through m6A RNA pull-down assays and transcriptome-wide RBP binding site mapping. Notably, FUBP3, FXR2 and L1TD1 were found to regulate hESC differentiation without impairing self-renewal, underscoring their critical roles in stem cell biology. Together, this study bridges the gap in understanding m6A functional readouts and lays the groundwork for future research on m6A-mediated stem cell fate decisions.

genomics↗

bioRxiv2021

Chloroplast movement and positioning protein CHUP1 is required for focal immunity against Phytophthora infestans

Communication between cellular organelles is essential for mounting effective innate immune responses to eliminate pathogens. In plants, the transport of cellular organelles to pathogen penetration sites and their assembly around the host membrane delineating plant-pathogen interface are well-documented. However, whether organelles associate with these specialized plant-pathogen membrane interfaces and the extent to which this process contributes to immunity remain unknown. Here, we discovered defense-related membrane contact sites (MCS) comprising a membrane tethering complex between chloroplasts and the extrahaustorial membrane (EHM) surrounding the pathogen haustorium. The assembly of this membrane tethering complex relies on the association between the chloroplast outer envelope protein CHLOROPLAST UNUSUAL POSITIONING 1 (CHUP1), and its plasma membrane-associated partner, KINESIN-LIKE PROTEIN FOR ACTIN-BASED CHLOROPLAST MOVEMENT 1 (KAC1). Our biochemical assays revealed that CHUP1 and KAC1 interact, while infection cell biology demonstrated their co-accumulation in foci where chloroplasts contact the EHM. Genetic depletion of CHUP1 or KAC1 reduces the deposition of callose--a cell wall material typically deployed to fortify pathogen penetration resistance--around the haustorium, without affecting other core immune processes. Our findings suggest that the chloroplast-EHM attachment complex positively regulates plant focal immunity, revealing the key components and their potential roles in the targeted deposition of defense components at the pathogen interface. These results advance our understanding of organelle-mediated immune responses and highlight the significance of MCS in plant-pathogen interactions.

plant biology↗

bioRxiv2026

Impact of the sphingolipid metabolizing enzyme β-galactosylceramidase on mitochondrial sphingolipid profile and energetic metabolism in human melanoma cells

Mitochondrial plasticity, characterized by the dynamic balance between glycolysis and oxidative phos-phorylation in response to genetic and microenvironmental changes, is a hallmark of melanoma progression. Sphingolipids play a significant role in various aspects of cancer cell biology, including metabolic reprogramming. Previous observations had shown that the lysosomal sphingolipid-metabolizing enzyme {beta}-galactosylceramidase (GALC) rewires the lipid profile of mouse melanoma cells, exerting pro-oncogenic functions, gene silencing leading to a decreased oncogenic activity in murine and human melanoma cells. Here, we have focused on the mitochondrial sphingolipid composition and energetic metabolism in GALC knockout (KO) A2058 human melanoma cells. Targeted analysis of the mitochondrial sphingolipid profile, transcriptomic data, and mitochondrial structural and functional studies indicate that GALC loss drives a sphingolipid-mediated reprogramming of mitochondrial metabolism in absence of major structural alterations, characterized by bioenergetic insufficiency possibly due to ceramide- and sphingomyelin-driven impairment of respiratory chain function. Overall, these data indicate that GALC KO leads to a sphin-golipid-driven mitochondrial metabolic suppression and may provide novel information for the development of efficacious approaches in mitochondrial targeting melanoma therapies.

cancer biology↗

bioRxiv2021

Strategies for the site-specific decoration of DNA origami nanostructures with functionally intact proteins

DNA origami structures provide flexible scaffolds for the organization of single biomolecules with nanometer precision. While they find increasing use for a variety of biological applications, the functionalization with proteins at defined stoichiometry, high yield, and under preservation of protein function remains challenging. In this study, we applied single molecule fluorescence microscopy in combination with a cell biological functional assay to systematically evaluate different strategies for the site-specific decoration of DNA origami structures, focusing on efficiency, stoichiometry and protein functionality. Using an activating ligand of the T-cell receptor (TCR) as protein of interest, we found that two commonly used methodologies underperformed with regard to stoichiometry and protein functionality. While strategies employing tetravalent wildtype streptavidin for coupling of a biotinylated TCR-ligand yielded mixed populations of DNA origami structures featuring up to 3 proteins, the use of divalent (dSAv) or DNA-conjugated monovalent streptavidin (mSAv) allowed for site-specific attachment of a single biotinylated TCR-ligand. The most straightforward decoration strategy, via covalent DNA conjugation, resulted in a 3-fold decrease in ligand potency, likely due to charge-mediated impairment of protein function. Replacing DNA with charge-neutral PNA in a ligand conjugate emerged as the coupling strategy with the best overall performance in our study, as it produced the highest yield with no multivalent DNA origami structures and fully retained protein functionality. With our study we aim to provide guidelines for the stoichiometrically defined, site-specific functionalization of DNA origami structures with proteins of choice serving a wide range of biological applications.

biophysics↗

bioRxiv2022

Systematic Single Cell Pathway Analysis (SCPA) reveals novel pathways engaged during early T cell activation

Next generation sequencing technologies have revolutionized the study of T cell biology, capturing previously unrecognized diversity in cellular states and functions. Pathway analysis is a key analytical stage in the interpretation of such transcriptomic data, providing a powerful method for detecting alterations in important biological processes. Current pathway analysis tools are built on models developed for bulk-RNA sequencing, limiting their effectiveness when applied to more complex single cell RNA-sequencing (scRNA-seq) datasets. We recently developed a sensitive and distribution-free statistical framework for multisample distribution testing, which we implement here in the open-source R package Single Cell Pathway Analysis (SCPA). After demonstrating the effectiveness of SCPA over commonly used methods, we generate a scRNA-seq T cell dataset and characterize pathway activity over early cellular activation and between T cell populations. This revealed unexpected regulatory pathways in T cells, such as an intrinsic type I interferon system regulating T cell survival and a reliance on arachidonic acid metabolism throughout T cell activation. A systems level characterization of pathway activity in T cells across multiple human tissues also revealed alpha defensin expression as a hallmark of bone marrow derived T cells. Overall, our work here provides a widely applicable tool for single cell pathway analysis, and highlights unexpected regulatory mechanisms of T cells using a novel T cell dataset.

immunology↗

bioRxiv2022

A full computational model of cell motility: Early spreading, cell migration and competing taxis

Cell motility represents one of the most fundamental function in mechanobiology. Cell motility is directly implicated in development, cancer or tissue regeneration, but it also plays a key role in the future of tissue and biomedical engineering. Here, we derived a computational model of cell motility that incorporates the most important mechanisms toward cell motility: cell protrusion, polarization and retrograde flow. We first validate our model to explain two important types of cell migration, i.e. confined and ameboid cell migration, as well as all phases of the latter cell migration type, i.e. symmetric cell spreading, cell polarization and latter migration. Then, we use our model to investigate durotaxis and chemotaxis. The model predicts that chemotaxis alone induces larger migration velocities than durotaxis and that durotaxis is activated in soft matrices but not in stiff ones. More importantly, we analyze the competition between chemical and mechanical signals. We show that chemotaxis rules over durotaxis in most situations although durotaxis diminishes chemotaxis. Moreover, we show that inhibiting the effect of GTPases in actin polymerization at the cell front may allow durotaxis to take control over chemotaxis in soft substrates. Understanding how the main forces in cell motility cooperate, and how a precise manipulation of external cues may control directed cell migration is not only key for a fundamental comprehension of cell biology but also to engineer better biomimetic tissues. To this end, we provide a freely-available platform to predict all phases and modes of cell motility analyzed in this work.

biophysics↗

bioRxiv2025

EVA: a Foundation Model Advancing Translational Drug Development in Immuno-Inflammation

Drug development is a lengthy and high-risk process, with most investigational drug candidates failing in phase II randomized clinical trials (RCT) due to insufficient efficacy. It makes early prediction of trial outcomes crucial for reducing attrition and guiding strategic decisions, especially in immunology and inflammation (I&I) diseases. Herein, we present EVA, the first pre-trained foundation model in complex inflammatory diseases tailored to support drug development. EVA learns generalizable patterns from large-scale data of cell biology and immunology, enabling superior predictive performance and generalization compared to traditional approaches. EVA is pre-trained on tens of millions of single-cell RNA-seq samples and tens of thousands of bulk RNA-seq samples from I&I diseases patients, enabling it to learn disease-relevant transcriptomic patterns in this therapeutic area. By fine-tuning EVA in few-shot settings on both preclinical (mouse) and clinical (human) data and harnessing its wide pre-training knowledge, EVA predicts drug responses in I&I with high precision at both cohort and patient levels, as illustrated by accurate forecasting of anti-TNF therapeutic activity in ulcerative colitis. By deciphering its decision process, we further highlight that EVAs ability to stratify patients based on predicted drug response can also be leveraged to discover drug response biomarkers as early as preclinical stages. EVAs applications in precision immunology encompass therapeutic target validation prior to clinical entry, identification of patient subpopulations most likely to benefit from treatment, and comparative efficacy analysis against competitor compounds. EVAs versatility makes it an invaluable tool for strategic decision-making throughout the drug development pipeline: by leveraging it to prioritize the most promising drug candidates and optimize RCT designs, it can contribute to reduce late-stage failures and accelerate the delivery of effective therapies. Overall, this work represents a significant advancement in utilizing a pre-trained foundation model for precision drug development in complex inflammatory diseases. Graphical abstractEVA is a pre-trained foundation model specific to immune-mediated inflammatory diseases. It enables the prediction of therapeutic efficacy in patients leveraging data from preclinical disease models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/651839v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1c783f1org.highwire.dtl.DTLVardef@1a760d9org.highwire.dtl.DTLVardef@1c7748aorg.highwire.dtl.DTLVardef@1b44f82_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Asymmetry and redundancy of STAT5 paralogs across CD8+ T cell differentiation states

Fostering STAT5 signaling is key to immunotherapies that leverage CD8+ T cell biology. Using mouse models, we demonstrate that the two mammalian STAT5 paralogs, STAT5A and STAT5B, are at once redundant and functionally distinct in CD8+ T cells. Specifically, we establish that they are asymmetric paralogs, exhibiting both widespread homology at molecular level and functional asymmetry at cellular level, with STAT5B emerging as dominant. In fact, compared to STAT5A, STAT5B deficiency had greater impact on nearly all parameters tested. As a mechanism, we determined STAT5B is twice as abundant, accounting for two-thirds of the total STAT5 pool. We also defined both cytokine- and cell state-restricted STAT5B functions, and a core gene signature that highlights universal effects. Together, these studies affirm the centrality of STAT5 in CD8+ T cells, reveal common and circumscribed activities, and present a unifying model for paralog redundancy that foregrounds and explains the dominance of STAT5B. Summary: STAT5 paralog dominance and redundancy in CD8+ T cells

immunology↗