Biology subjects

Sun, N.

Publications and source records attributed to Sun, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Measurement-Dependent Parameter Recoverability in Neural Cardiovascular LPN Inference

Estimating cardiovascular lumped-parameter network (LPN) parameters from limited hemodynamic observations is a potentially non-unique inverse problem in cardiovascular model personalization. We investigated how parameter recoverability depends on measurement availability and noise, and whether poor recovery reflects limitations of neural inference or insufficient observational information. We developed Mada (measurement-availability and difficulty-aware), a probabilistic neural framework that provides parameter estimates and predictive uncertainty under incomplete observations. Using a closed-loop, four-chamber cardiovascular LPN with time-varying elastance, we generated a synthetic population of 650,000 cases comprising 20 chamber-mechanics and vascular parameters and up to 30 hemodynamic and timing observations. Under complete observations, Mada achieved macro R2 = 0.862, maintaining competitive accuracy across neural baselines while providing parameter-specific uncertainty. Recoverability varied substantially across parameters, with atrial active elastances remaining particularly difficult. For mask-aware Mada, macro R2 ranged from 0.066 with basic clinical measurements to 0.751 with a rich clinical set and 0.842 with all measurements, while uncertainty generally increased as informative measurements were removed. Bounded direct optimization achieved substantially smaller hemodynamic residuals but did not consistently recover the generating parameters. Complementary profile and local-sensitivity analyses revealed case-specific parameter compensation and weak observational directions, while target-specific fine-tuning offered little improvement for difficult atrial parameters. Increasing measurement noise degraded both parameter recovery and predictive-interval coverage, and similar trends were observed with a simpler probabilistic ResMLP. These findings demonstrate that accurate reconstruction of observed hemodynamics does not necessarily imply accurate recovery of the underlying LPN parameters, highlighting the importance of assessing parameter recoverability and predictive uncertainty when physiologically meaningful parameter estimates are sought.

bioengineering↗

bioRxiv2026

Live imaging reveals oocyte membrane pre-remodeling forming permissive space for asymmetric division

Abstract Successful oocyte maturation requires first polar body (PB1) extrusion, yet how the oocyte prepares for this asymmetric event remains unclear. Live membrane imaging of mouse oocytes reveals pronounced regional heterogeneity in plasma-membrane dynamics, generating an early polarized pattern across the surface. Whereas most of the oocyte surface and its microvilli remain relatively stable, a discrete membrane domain collapses and retracts around germinal vesicle breakdown (GVBD), separating from the zona pellucida to establish a pre-extrusion space (PES). Transient oocyte comb-like protrusions arise within the PES and subsequently collapse, converting the protrusion-filled region into an open cavity before PB1 extrusion. Mechanistically, early ERK28 dependent dissociation of the cortical scaffold ZO-1 is required for this space to form. ERK2 phosphorylates ZO-1, suppresses its phase separation, and promotes its removal from the cortex. Blocking MEK-ERK signaling prevents ZO-1 dissociation, abolishes pre-extrusion space formation, and impairs PB1 extrusion, whereas ERK activation restores these events in aged oocytes. Together, our findings identify a membrane collapse-based pre-remodeling mechanism that licenses PB1 extrusion and reveal ERK-ZO-1 signaling as a key regulator of oocyte maturation.

cell biology↗

bioRxiv2026

Multi-omics reveals accumulation mechanisms underlying differences in sesquiterpenoid content across different growth environments of Atractylodes macrocephala rhizome

This study aimed to explore mechanisms driving differential sesquiterpenoid accumulation in Atractylodes macrocephala rhizome (AMR) under different growth environments. Root and rhizosphere soil samples were collected from wild, imitated wild, one- and two-year cultivated AMR. Combined metabolomic, transcriptomic and microbiomic profiling was carried out to investigate alterations in sesquiterpenoid levels, root-associated microbiota, and terpenoid biosynthetic genes under different growth environments. Metabolomics revealed that wild AMR exhibited higher pyruvate consumption and accumulated more atractylone and atractylenolides. Transcriptomic analysis demonstrated that the genes encoding key MEP pathway enzymes (DXS, DXR, MCT, HDS) and the genes encoding the sesquiterpenoid biosynthetic enzyme GDS were significantly upregulated in wild AMR. In contrast, the genes encoding triterpenoid synthetic enzymes (SQS, SQE, {beta}-AS) were downregulated, redirecting metabolic precursors toward sesquiterpenoid synthesis. Microbial community structures differed substantially across groups. Further analysis suggested rhizosphere microorganisms were important contributors to AMR medicinal quality, and sesquiterpenoid metabolites accumulated in AMR roots, together with the rhizosphere microenvironment, jointly shape specific rhizosphere fungal assembly. This study systematically revealed the synergistic regulatory relationships among sesquiterpenoids metabolism, gene expression, growth environment, and root-associated microbes, providing multi-omics evidence for clarifying the mechanism by which these factors coordinately regulate the quality of AMR.

plant biology↗

bioRxiv2026

ProxiNet transfers spatially learned cellular proximity to dissociated single-cell transcriptomes

Spatial transcriptomics reveals cellular organization within intact tissues, whereas dissociated single-cell RNA sequencing provides broad transcriptomic coverage but loses information about cellular proximity and neighborhood structure. Here, we developed ProxiNet, a spatially supervised framework that learns transcriptomic signatures of pairwise cellular proximity from spatial reference datasets and transfers these relationships to dissociated single-cell transcriptomes. ProxiNet predicted cellular proximity across brain regions and spatial technologies, including zero-shot cross-technology transfer, and gradient-based attribution identified genes and broader transcriptional programs associated with proximity predictions. In spatial datasets with known coordinates, ProxiNet-derived cellular neighborhoods recovered reproducible tissue organization and anatomical structure, providing independent spatial validation of the inferred proximity relationships. Applying the spatially calibrated model to dissociated scRNA-seq revealed heterogeneous cellular neighborhoods with distinct cell-type compositions and candidate communication programs. In an Alzheimer dataset, ProxiNet further identified age-associated remodeling of inferred neighborhoods, including an AD-associated neighborhood at 8 months characterized by distinct astrocyte and neuronal transcriptional states and candidate intercellular communication programs. Together, these results establish pairwise cellular proximity as an interpretable and transferable representation for extending spatially learned tissue organization to dissociated single-cell transcriptomes.

bioinformatics↗

bioRxiv2026

Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts

Genetic perturbations can reshape cell populations by altering the relative abundance of specific cell types and states within the profiled population, including increases, decreases and states that become detectable after perturbation. Pooled single-cell screens, such as Perturb-seq, measure such responses at scale. However, only a small fraction of possible perturbations can be tested experimentally. A central challenge is therefore to predict compositional shifts induced by unseen perturbations. Many perturbation-prediction methods do not directly optimize for this outcome; instead, they predict gene-expression responses and infer cell-type and cell-state composition downstream. Surprisingly, we find that even models that accurately predict perturbation-induced changes in average gene expression perform poorly at forecasting these compositional shifts. To address this gap, we present Pop-Corn, a method that directly predicts how a perturbation reshapes cell-type composition without reconstructing gene expression. In the primary T-cell benchmark, Pop-Corn predicted the overall cell-state composition of held-out perturbations more accurately than the evaluated expression-prediction pipelines, while better preserving the diversity of observed cell states. We further extend Pop-Corn to intact tissue, where it predicts perturbation-induced cell-type proportion changes in local cellular neighborhoods and uses attention patterns to generate hypotheses about context-dependent cellular interactions. Retrospective virtual screens support the use of Pop-Corn to prioritize perturbations for experimental follow-up according to their predicted effects on cell-state composition.

bioinformatics↗

bioRxiv2026

Context-dependent regulatory networks connect Alzheimer's disease genetics to microglial inflammatory responses

Inflammation is central to Alzheimer's disease (AD) pathogenesis. Microglia, the resident innate immune cells of the brain, exhibit diverse inflammatory states and are enriched for AD-associated genetic variants within active cis-regulatory elements (CREs). However, the interplay among genetic variants, transcription factor (TF)-CRE-gene programs, and microglial responses across inflammatory and disease contexts remain poorly understood. Here, we develop context-dependent epigenomic networks (cEpiNets), integrating bulk and single-nucleus assay for transposase-accessible chromatin using sequencing (ATAC-seq) to reconstruct regulatory programs across inflammatory, genetic perturbation, and disease contexts. Leveraging TF footprinting and graph embedding, cEpiNets identifies shared and context-specific programs and predicts regulatory circuits in unseen biological contexts. In a SORL1-marked inflammatory microglial state that expands during AD progression, cEpiNets annotates AD risk variants at the SORL1 locus and identifies variants associated with cellular state abundance across donors. Cross-context analysis further identifies ZBTB14, whose inflammation-associated program connects AD risk variant-harboring CREs to target genes and widespread TF remodeling in AD. Donor-level ZBTB14 footprint activity is negatively associated with AD pathology, while combined IFN{gamma}/TNF stimulation represses ZBTB14 and activates a subset of inferred targets. Collectively, cEpiNets bridges genetic variation, regulatory programs, and disease-associated cellular phenotypes to facilitate mechanistic interpretation of complex disease genetics.

bioinformatics↗

bioRxiv2026

Pretraining Enhances Megabase-Scale Gene Expression Prediction with GeneUnet

Predicting gene expression from DNA sequence across diverse genomic tracks is essential for understanding gene regulation and interpreting non-coding variants. Existing supervised methods are limited to few species and fail to exploit conserved regulatory mechanisms, while DNA foundation models capture cross-species information but remain constrained to kilobase-scale contexts insufficient for this task. Here we introduce GB.GeneUnet, an 837M-parameter transformer-based U-Net pretrained on 6 trillion tokens from multi-species genomes in OpenGenome2, extending genomic context to 1 Mb with up to 100x inference speedup over GeneMoE, a preliminary MoE transformer baseline of similar model size pretrained on the same data. Fine-tuned for gene expression prediction, GB.GeneUnet achieves state-of-the-art performance on the Borzoi benchmark at 524 kb context, and attains performance comparable to AlphaGenome at 1 Mb context while requiring a lighter fine-tuning procedure. Together, these results establish a scalable framework linking multi-species pretraining to ultra-long-context gene expression modeling.

genomics↗

bioRxiv2026

MicroGrowAgents: An Agentic AI System for Microbial Cultivation Engineering

Microbial cultivation optimization remains labor-intensive and inefficient, requiring extensive experimental screening to identify suitable growth conditions. Traditional one-factor-at-a-time approaches are particularly ineffective for exploring complex, multidimensional nutrient parameter spaces. We present MicroGrowAgents, an agent-assisted system for auditable design of candidate growth media through integration of knowledge graphs, metabolic modeling, and optimal experimental design. The system comprises 29 documented agents implementing 58 skills across seven functional categories that query structured biological knowledge (KG-Microbe: 864,363 validated species), Bakta-annotated genomes (667,000+ annotated features), curated organism-specific FACTS sheets, and a DOI-linked publication corpus, and apply this evidence to three ends: recommending candidate ingredients and concentration ranges, specifying the factors of a statistically optimal MaxPro experimental design, and interpreting cultivation outcomes against known biochemistry. We applied the approach to Methylorubrum extorquens AM1 (formerly Methylobacterium extorquens; reclassified per LPSN) by cultivating 69 designed media plus a default media baseline (70 total tested conditions) in quadruplicate and assessing two concurrent objectives: biomass turbidity (740 nm) and apparent residual-Nd depletion (residual Nd by arsenazo III). Monte-Carlo resampling of the replicate-level uncertainty (1000 iterations) identified MPOB_058 as the single MC-stable Pareto-optimal condition (membership frequency 0.922); paired-control biology analysis flags MPOB_058 as chemistry-confounded and nominates MPOB_008 as the cleaner biological-signal anchor (its lower abiotic-drift contribution to the residual-Nd measurement makes it the better candidate for confirming biological lanthanide handling in a follow-up round), with MPOB_019 borderline-stable, providing a prioritized anchor set for confirmation in subsequent rounds rather than a single declared optimum. The integration of chemical similarity search (208,000+ embeddings), metabolic gap analysis, and multi-modal reasoning enables evidence-based hypothesis generation that reduces experimental burden while accelerating discovery of growth-promoting conditions. On the Biolog Odin platform, the MC-stable composite candidate MPOB_058 grew 79% more integrated biomass (area under the 740 nm growth curve) and 46% faster (maximum specific growth rate _max; Gompertz fits, R{superscript 2} > 0.99) than the standard MP base medium for this organism (the unsupplemented starting-point recipe carried as the on-plate control). This biomass and growth-rate advantage is a direct kinetic measurement; the conditions apparent residual-Nd depletion ranking, by contrast, awaits confirmation in a subsequent round, because MPOB_058s apparent Nd depletion is partly abiotic. Unlike general-purpose AI co-scientists, MicroGrowAgents grounds every recommendation in inspectable evidence -- structured provenance manifests with per-session input checksums, schema- and ontology-validated outputs, and 90.5% literature citation coverage (143 of 158 curated DOIs with evidence-supports-claim verification) -- separating deterministic design and analysis from agentic interpretation so that recommendations are transparent, explainable, and auditable, while passing 7 of 9 bbop-skills agentic-system criteria in a Claude-Code self-audit (the two unmet criteria are MCP-standardized tool exposure and full input-data cryptographic hashing, both tracked roadmap items). The Bigger PictureIdentifying growth conditions for poorly characterized microbes is one specific instance of a recurring problem in the natural sciences: how to integrate heterogeneous prior knowledge -- structured databases, published literature, mechanistic models -- with statistically efficient experimental design so that limited wet-lab time is spent where it most reduces the search. We show that a multi-agent AI system can perform that integration from organism input to wet-lab design, with every recommendation traceable from its source evidence through to the experimental design that tested it. The same architecture (specialized agents over structured knowledge bases, mechanistic models, and statistical design, with checksummed provenance recorded as the unit of evidence) is in principle transferable to other DBTL loops against under-explored search spaces such as catalyst discovery, materials formulation, cell-line engineering, drug-combination screens, or environmental remediation. We demonstrate it here only for microbial cultivation and flag cross-domain transfer as future work rather than a demonstrated capability. Our cultivation results on Methylorubrum extorquens AM1 are a concrete demonstration that this pattern works on real laboratory data: an uncertainty-aware shortlist of candidate media emerged from Monte-Carlo stability analysis of two competing objectives, with the leading composite candidate growing 79% more integrated biomass and 46% faster than the standard MP base medium for this organism; this biomass and growth-rate advantage is directly measured, while its apparent residual-Nd depletion advantage awaits confirmation in a subsequent round, since the apparent Nd depletion is partly abiotic. But the lesson we want to port across research domains is the architecture and its provenance discipline, not the specific organism. MicroGrowAgents is released under BSD-3 with checksummed YAML provenance manifests so that any group adopting these agents inherits a workflow designed to be re-runnable from its recorded inputs.

synthetic biology↗

bioRxiv2026

Dissecting inter-focal heterogeneity and convergent immunosuppressive ecosystems in multifocal hepatocellular carcinoma through single-cell and spatial multi-omics

Background Multifocal hepatocellular carcinoma (HCC) comprises spatially separated tumor foci with distinct evolutionary trajectories, posing a major therapeutic challenge. Whether divergent lesions retain shared immune-evasion mechanisms beyond lesion-specific heterogeneity and offer cross-focal therapeutic opportunities remains unknown. Objective To resolve malignant and microenvironmental heterogeneity across tumor foci and identify conserved immunosuppressive programs underlying multifocal HCC progression. Design Matched tumour foci and peritumoural tissues from patients with multifocal HCC underwent integrated single-cell and spatial multi-omics profiling, including scFAST-seq (n=15), scATAC-seq (n=12), and Stereo-seq (n=14). In-house RNA-seq (n=13), public scRNA-seq (HRA001748) and RNA-seq datasets (TCGA-LIHC, OEP000321) were used for validation. Multiplex immunohistochemistry (n=96) and functional assays validated key findings. Results Multifocal HCC exhibited extensive inter-focal heterogeneity in malignant genomic, transcriptional, epigenetic, and evolutionary states, accompanied by diverse immune and stromal landscapes. Despite this divergence, spatially separated tumor foci converged on shared malignant states marked by elevated TMED2 expression and conserved immunosuppressive ecosystems characterized by regulatory T-cell (Treg) and SPP1+/SPRED1+ macrophage enrichment, impaired cytotoxic CD8+ T-cell immunity, and recurrent stromal-vascular remodeling. Mechanistically, malignant hepatocytes engaged SPP1+/SPRED1+ macrophages through C3/C5 complement signaling across divergent tumor foci, while macrophages and Tregs contributed to CD8+ T-cell dysfunction through CD274-PDCD1, CD80/CD86-CTLA4/CD28, and MHC-I-CD8 interactions. A tumor-intrinsic TMED2-ADAR1-dependent A-to-I RNA editing program promoted C3/C5 complement expression and secretion, linking malignant heterogeneity to conserved tumor-immune signaling. Conclusions Despite divergent malignant evolution, multifocal HCC converges on shared immunosuppressive ecosystems. The TMED2-ADAR1-C3/C5 complement axis represents a conserved tumor-immune vulnerability that may enable therapeutic strategies targeting common immune-evasion mechanisms across heterogeneous tumor foci.

cancer biology↗

bioRxiv2026

Epigenetically constrained astrocyte states underlie prefrontal cortex vulnerability in Down syndrome associated Alzheimer disease

Down syndrome (DS), caused by trisomy 21, confers a near-universal risk for Alzheimers disease (AD), yet individuals exhibit marked variability in cognitive decline, suggesting the presence of cellular mechanisms that modulate vulnerability and resilience. However, these mechanisms remain poorly defined in the human brain. Here, we integrate matched single-nucleus RNA-seq and ATAC-seq profiles from the prefrontal cortex (PFC) and amygdala (AMY) of age-matched individuals with DS with and without AD (DSAD), enabling direct comparison within a shared genetic background. We identify basal astrocytes in the PFC as a selectively vulnerable cell state in DSAD, characterized by both reduced abundance and coordinated transcriptional and regulatory reprogramming. This state exhibits a shift away from homeostatic support functions, with decreased cytokine signaling and lipid-handling programs, alongside increased steroid- and nuclear receptor-associated activity. Concomitantly, chromatin accessibility profiling reveals reduced engagement of immune- and stress-responsive transcription factor programs, including AP-1, STAT, and BACH families, with linked regulatory perturbations at loci such as ABCA1, DAB2IP, and IL1RAP. Together, these findings define a previously unrecognized astrocyte state marked by epigenetic constraint and diminished responsiveness to stress and inflammatory signals, distinguishing it from classical reactive astrocyte phenotypes. Our results nominate PFC basal astrocytes as a key locus of vulnerability in DSAD and suggest that failure to mount appropriate astrocyte responses, rather than overt activation alone, may contribute to neurodegenerative progression.

bioinformatics↗

bioRxiv2026

Potential risk for hearing from prolonged exposure to sound at conversation levels

Prolonged exposure to loud and moderate noise impairs hearing; the lower the noise level, the lower risk of hearing loss is. To date, little is known about how low the noise level can be safe to hearing. This study investigated the risk of exposure to tone at typical conversational levels by measuring auditory brainstem response (ABR). We show that exposing C57 mice to continuous pure tone at 65 dB SPL for 1 hour (TE65) leads to an increase in ABR threshold that is specific to the exposure frequency. Tone exposure also increased the latencies and decreased the amplitude in Waves I and II but not in Waves III and V. Significantly, the changes in amplitude and latency were highly correlated in Wave I and such correlation gradually degraded from Wave I through to Wave V. Our findings suggest that exposure to low level sound can impair hearing and alter the auditory information process in the brain if it is persistent and presented over a sufficient period of time. Significant StatementOur findings established the risk of hearing impairment following the exposure to continuous tone at normal or conversational voice levels. This finding challenges current public health guidelines for hearing protection. Although further clarification is required, our studies prompt that the regular use of ABR testing is a potential protocol for diagnosing hearing impairment in patients experiencing hidden hearing loss (HHL).

neuroscience↗

bioRxiv2025

Convergent Signals Encode Persistent Memory of Resilient States

Caloric restriction reduces metabolic disease and associated comorbidities. Yet, the molecular mechanisms encoding cellular memory of these benefits remain unclear. Here, we use a functional genomics approach to integrate evolutionary cues, single-cell sequencing, and metabolomics, identifying convergent signals that encode epigenetic memory of adaptive states in visceral adipose tissue natural killer (NK) cells. Cross-species analysis shows that genomic hubs linked to human accelerated regions (HARs) are conserved and active during dietary restriction, forming transcriptional compartments that regulate oxidative stress response and DNA repair genes. Targeted screens and multimodal profiling across human data and mouse models reveal convergent signals needed for persistent adaptation: cooperative transcriptional regulators (NRF2, CIRBP, NR4A2) at key HAR-linked genomic hubs, innate immune mediators (IL15-IL2RB), and metabolic cofactors from linoleic acid oxidation. Activity of these convergent signals reduces DNA damage and methylation and enhances epigenetic plasticity and cytotoxic functions, thereby decreasing tissue fibrosis and senescence while maintaining both local and systemic metabolic plasticity. Mechanistically, linoleic acid metabolism supplies acetyl-CoA to sustain H3K27ac epigenetic marks that maintain coordinated repair and cytotoxic programs after programming. IL15-IL2RB signaling links tissue metabolic state to NK cell function for coherent adaptive responses. We show that engineering of NK cells with this multimodal programming leads to long-term preservation of therapeutic phenotypes in metabolic aging models, rescuing systemic dysfunction through damaged cells clearance and leptin sensitivity. Overall, these findings establish that convergent signals encode persistent cellular phenotypes linked to metabolic plasticity, suggesting design principles for engineering therapeutic memory of cell function. One-Sentence SummaryA multimodal approach reveals how dietary and evolutionary cues can be harnessed to engineer resilient phenotype memory.

immunology↗

bioRxiv2025

Haruka Resolves Perturbation Response Heterogeneity in Spatial Cell Niches

Understanding how tissues remodel in response to perturbations requires computational tools that can untangle condition-specific changes from the conserved tissue architecture. We present Haruka, a spatially aware contrastive learning framework that identifies salient (condition-specific) and background (shared) spatial domains across tissue slices and experimental conditions. Haruka integrates contrastive variational inference with an auxiliary microenvironment reconstruction task, enabling the model to learn spatial-context-informed embeddings that capture both perturbation effects and local neighborhood context. Benchmarking on simulated and real datasets demonstrates superior detection of spatially heterogeneous perturbation responses compared to existing methods. Applied to diverse spatial omics platforms, Haruka distinguished immunotherapy responders in melanoma, traced fibrosis progression in human lung tissue, and mapped treatment-resistant microenvironments in KRASG12D-mutated lung cancer. Extended to spatially resolved multiomics data co-profiling transcriptomics and chromatin accessibility from an LPC-induced neuroinflammation mouse model, using a modality-parallel design that independently models each omics layer before integration, Haruka resolved condition-specific regulatory programs encoded across chromatin and transcriptome layers, linking spatial domain identity to transcription factor network architecture and human psychiatric disease genetics. Thus, Haruka provides a generalizable framework for spatial contrastive analysis, enabling systematic dissection of tissue organization, cellular plasticity, and microenvironmental remodeling across disease contexts and therapeutic perturbations.

bioinformatics↗

bioRxiv2025

Benchmarking Perturbation Tools for the Noncoding Genome

Deciphering the functionality of the noncoding genome which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancer, dual-SpCas9 outperforms other methods with superior efficiency of destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model DeepDC with associated web server is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

genetics↗

bioRxiv2025

A unified network systems approach uncovers a core novel program underlying T follicular helper cell differentiation

T follicular helper (Tfh) cells are central to the adaptive immune response and exhibit remarkable functional diversity and plasticity. The complex nature of Tfh cell populations, inconsistent findings across experimental systems and potential differences across species have fueled ongoing debate regarding core regulatory pathways that govern Tfh differentiation. Many studies have experimentally investigated individual proteins and circuits involved in Tfh differentiation in limited contexts, each providing only a partial understanding of the process. To address this, we adopted a novel multi-scale network systems approach that incorporates both regulatory and protein-protein interactions. Our approach integrates diverse data types, captures regulation across multiple levels of immune system organization, and recapitulates known drivers. Further, we discover a core Tfh gene set that is conserved across tissue types and disease contexts, and is consistent across data modalities - bulk, single-cell and spatial. While components of this set have been individually reported, a novel aspect of our work lies in the discovery, characterization, and connectivity of this core signature using a single unbiased approach. Using this method, we also uncover a novel function of IL-12, a molecule with reported conflicting functions, in the regulation of Tfh differentiation. Notably, we find that, in both humans and mice, IL-12 is permissive for the differentiation of Tfh precursors, but blocks subsequent differentiation into GC Tfh cells. Overall, this work elucidates novel networks with unexplored roles in governing Tfh cell differentiation across species and tissues, paving the way for novel -therapeutic interventions.

immunology↗

bioRxiv2025

Rapid and Reproducible Multimodal Biological Foundation Model Development with AIDO.ModelGenerator

Foundation models (FMs) for DNA, RNA, proteins, cells, and tissues have begun to close long-standing performance gaps in biological prediction tasks, yet each modality is usually studied in isolation. Bridging them requires software that can ingest heterogeneous data, apply large pre-trained backbones from various sources, and perform multimodal benchmarking studies at scale. We present AIDO.ModelGenerator, an open-source toolkit that turns these needs into declarative experiment recipes through a structured experimental framework. AIDO.ModelGenerator provides (i) 300+ datasets covering DNA, RNA, protein, cell, spatial, and multimodal data types; (ii) 30+ pretrained FMs ranging from 3M to 16B parameters; (iii) 10+ plug-and-play use-cases covering inference, adaptation, prediction, generation, and zero-shot evaluation; and (iv) YAML-driven experiment recipes that enable exact reproducibility. On a sequence-to-expression prediction task, AIDO.ModelGenerator systematically builds and tests unimodal and multimodal models, achieving a new SOTA by combining DNA and RNA FMs that outperforms unimodal baselines by over 10%. In a Crohns disease case-study, the frameworks simulated knockout protocol ranks the clinically implicated target SOX4 6,000 positions higher than differential-expression baselines, illustrating its utility for therapeutic target discovery. We release code, tutorials, checkpoints, datasets, and API reference to accelerate multimodal FM research in the life sciences1.

bioinformatics↗

bioRxiv2025

Scutellarin inhibits ferroptosis by promoting cellular antioxidant capacity through regulating the Nrf2 signaling

Ferroptosis is a lytic form of regulated cell death that is driven by iron-dependent lipid peroxidation, and has been implicated in various diseases including acute kidney injury (AKI). Scutellarin is a flavonoid isolated from Erigeron breviscapus (Vant.) Hand.-Mazz. and possesses various pharmacological activities including anti-inflammatory and antioxidative properties. Yet it is unclear whether scutellarin can inhibit ferroptosis and mitigate related diseases. In this study, we found that scutellarin was able to inhibit ferroptosis in both human HK-2 cells and mouse bone marrow-derived macrophages stimulated with RSL3 or erastin. Mitochondrial dysfunction and reactive oxygen species generation were counteracted by scutellarin treatment, suggesting involvement of its antioxidative activity. Furthermore, scutellarin increased the nuclear levels of Nrf2 and the expression of its target genes including HO-1 and GPX4. Scutellarin-mediated inhibition of ferroptosis and an increase of these proteins was abrogated by the co-treatment with brusatol, an Nrf2 inhibitor, indicating an essential role for Nrf2 in this process. In a mouse model of folic acid-induced AKI, scutellarin mitigated acute renal damage as revealed by histopathological analysis and serum blood urea nitrogen and creatinine assays. Folic acid-induced acute renal injury was associated with increased ferroptosis as revealed by elevated levels of 4-hydroxynonenal (4-HNE), a surrogate marker of ferroptosis, which were diminished by scutellarin cotreatment. Specifically, the elevated 4-HNE levels in macrophages (MAC-2 positive) and other renal cells were suppressed by scutellarin. Collectively, scutellarin can inhibit ferroptosis both in cultured cells and in a mouse model of AKI by regulating Nrf2 signaling.

pharmacology and toxicology↗

bioRxiv2025

Cellular and Regional Vulnerability Shapes the Molecular Landscape of Psychosis in Alzheimers Disease

Approximately 40 percent of Alzheimers disease patients develop psychosis (AD+P), yet the molecular and cellular basis of these symptoms remains poorly understood. Here we profiled single-nucleus transcriptomes and epigenomes from 48 postmortem Alzheimers brains stratified by psychiatric diagnosis. Across cell types, AD+P was distinguished by transcriptional programs in upper-layer cortical pyramidal neurons consistent with re-engagement of developmental and structural plasticity pathways. These neurons exhibited greater loss in AD+P cortex, indicating that such programs emerge in a context of heightened vulnerability. Integrating these findings with functional perturbation screens in stem-cell-derived brain organoids, we found that activation of these programs alters cortico-cortical network connectivity and can exacerbate network dysfunction. Our data suggest that compensatory neuronal plasticity, shaped by glial inflammatory responses, may paradoxically contribute to circuit instability and selective vulnerability underlying neuropsychiatric symptoms in dementia. HighlightsO_LICell-type- and brain-region-specific transcriptional changes in AD with psychosis (AD+P) C_LIO_LIUpper-layer pyramidal dysfunction and metabolic vulnerability marks the pathophysiology of AD+P C_LIO_LICircuit wiring programs are evoked in AD+P as maladaptive compensatory responses C_LIO_LIAD+P-associated IL-6 signaling impairs neuronal network function in brain organoids C_LI

neuroscience↗