Biology subjects

Zhang, Z.

Publications and source records attributed to Zhang, Z..

At least 145 records · Page 8Linked to original sources

bioRxiv2026

Gene-Specific Analysis of Clonal Hematopoiesis Identifies ASXL1 as a Risk Factor for Lung Cancer

IntroductionClonal hematopoiesis of indeterminate potential (CHIP) is a recognized risk factor for hematologic malignancies, but its contribution to different types of solid cancers remains incompletely defined. MethodsHere, we performed a systematic, gene-specific analysis of CHIP across 19 common solid cancer types using two large population-based cohorts, the UK Biobank and All of Us with Cox proportional hazards models and nested case-control logistic models. ResultsWe demonstrate that the relationship between CHIP and solid tumors is highly cancer-type specific, with lung cancer exhibiting the strongest association. In lung cancer, this association is largely driven by ASXL1-mutant clones. Specifically, high variant allele fraction (high-VAF) ASXL1 conferring a significantly increased risk (hazard ratio = 3.2), and the associations remained robust after adjustment for age, sex, body mass index (BMI), smoking status, and genetic ancestry. Notably, ASXL1 CHIP was substantially enriched among smokers, and its association with lung cancer risk was restricted to ever-smokers, highlighting a key interaction between CHIP and environmental exposure. The enrichment of ASXL1 CHIP in lung cancer was further validated in two independent cancer-only cohorts, including MSK-IMPACT and TCGA. In addition, rare germline variant association analysis revealed that germline variation in ASXL1 had the strongest association with lung cancer susceptibility among all solid tumors. ConclusionsCollectively, our findings support a model in which smoking-associated expansion of ASXL1-mutant clones contributes to lung cancer development and suggest that gene-specific CHIP metrics may enhance risk stratification and early detection strategies.

bioinformatics↗

bioRxiv2026

Integrated optimization of experimental and computational workflows improves genome recovery in long-read gut metagenomics

Short-read metagenomic sequencing has been widely applied in microbial research due to its high quality and decreasing cost. However, short reads are inherently fragmented, which limits assembly contiguity and the recovery of complete microbial genomes. In contrast, long-read sequencing, with significantly longer read lengths, helps overcome these limitations. Achieving complete and accurate genome recovery is the core goal of metagenomics. To address this challenge, we systematically evaluated and optimized the long-read metagenomic workflow, from sample processing to computational assembly, using the CycloneSEQ platform.

bioinformatics↗

bioRxiv2026

dbGIST: An LLM-Assisted Multi-Omics Resource for Target Exploration and Cross-Dataset Validation in Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract, yet GIST-specific omics evidence remains scattered across small cohorts and is not represented as a dedicated disease project in major cancer genomics resources, limiting reproducible target exploration. Here, we present dbGIST (https://www.dbgist.com), a dedicated GIST-focused multi-omics resource built to make dispersed GIST evidence searchable, analyzable, and reusable. dbGIST harmonizes data from 37 centers and 1,991 samples, including pathologically verified in-house cohorts, across genomics, bulk transcriptomics, proteomics, phosphoproteomics, and single-cell transcriptomics, and couples these data with curated clinical annotations covering survival, mutation status, risk stratification, metastasis or recurrence, mitotic index, tumor site and size, and imatinib response. The platform supports cohort-level molecular-clinical association, survival, enrichment, immune-infiltration, drug-sensitivity, and single-cell analyses through interactive visualizations, downloadable source data, and public APIs for programmatic access to reusable analysis outputs and visualization-ready data. An optional LLM-assisted interface helps users navigate analyses and interpret outputs. Using MCM7 as a case study, dbGIST linked a resource-derived candidate to survival, risk features, metastatic or recurrent disease, imatinib-response phenotypes, proliferative cell states, and in vitro GIST-cell behavior. dbGIST therefore provides a traceable and interoperable resource for target exploration and precision oncology research in GIST.

cancer biology↗

bioRxiv2026

Spatio-temporal machine learning for multi-horizon prediction of bluetongue outbreaks

Reliable early warning of infectious disease outbreaks remains a major challenge for surveillance systems, particularly for vector-borne pathogens whose transmission depends on interactions among hosts, vectors, and climate-sensitive environmental conditions. Data-driven forecasting offers a promising approach for predicting outbreak risk using surveillance and environmental data. This study develops a logit-weighted ensemble (LWE), a machine-learning framework that predicts outbreak occurrence 1-6 months ahead at the administrative unit-month scale using routinely available outbreak notifications and gridded climate data. Bluetongue virus (BTV), an arbovirus of ruminants transmitted by Culicoides biting midges, provides a well-characterised system in which transmission is strongly shaped by climate, making it a useful system for applying and testing this approach. The framework is evaluated using surveillance data collected between 2005 and 2024 from France, Greece, and Italy, selected for their long-running and high-quality outbreak surveillance records. Across all three countries, the LWE achieved the strongest and most stable predictive performance under a recall-focused evaluation that prioritises correctly identifying outbreak months. It outperformed or matched 14 benchmark models, with differences becoming more pronounced at longer lead times (month +3 onward), when predictions are more uncertain and outbreaks are relatively rare. Predictability varied across countries, with the highest performance in Greece, strong performance in France, and lower, more variable performance in Italy, reflecting differences in how consistently outbreaks occur and spread across regions. Overall, the results demonstrate that horizon-aware, climate-informed forecasting can reliably identify months and locations at elevated risk of outbreak occurrence up to six months in advance, supporting surveillance planning and preparedness across heterogeneous European settings. The ensemble framework provides a robust and portable strategy for outbreak prediction using routinely collected surveillance and environmental data. Author SummaryPredicting infectious disease outbreaks before they occur remains a major challenge, particularly for diseases influenced by environmental conditions. In this study, we focus on bluetongue, a viral disease of livestock transmitted by biting midges, where transmission is strongly affected by climate and seasonal patterns. We develop a method that uses routinely collected outbreak reports and climate data to estimate where and when outbreaks are more likely to occur, up to six months in advance. We apply this approach across three European countries with a history of bluetongue outbreaks. We find that combining climate information with recent outbreak patterns can provide useful early signals of increased risk. Predictions are most accurate at shorter timeframes, but longer-range forecasts can still support planning and preparedness. Because our approach uses widely available data, it could be applied in other regions or to similar environmentally driven diseases. However, it does not include factors such as vaccination, animal movement, or detailed information on vector populations, which may also influence how outbreaks develop. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/726753v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@45e41borg.highwire.dtl.DTLVardef@82c787org.highwire.dtl.DTLVardef@1f97888org.highwire.dtl.DTLVardef@1586747_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2026

Bayesian Estimation of Mosaic Loss of Chromosome Y from Bulk RNA Sequencing Data

Mosaic loss of chromosome Y (LOY) is a common age-associated somatic alteration in men and is typically measured from DNA-based assays. Many cohorts, however, contain bulk RNA-seq data without matched DNA-based LOY measurements. We developed a Bayesian framework to estimate the fraction of cells with LOY from male bulk RNA-seq by modeling reduced Y-linked gene expression relative to expected expression after adjustment for age, expression covariates, and autosomal/X-linked control genes. In 377 male GTEx samples, individual Y-linked genes showed negative correlations with separately obtained DNA-based LOY measurements, supporting a shared Y-expression depletion signal. The primary fast empirical Bayes estimator achieved a Pearson correlation of 0.678 with measured LOY, a mean absolute error of 1.79%, a root mean squared error of 3.72%, and 95.2% empirical coverage of measured LOY. Performance was strongest for identifying large LOY events, with an AUC of 0.964 for measured LOY greater than 20%, while fine ranking among low-LOY samples remained uncertain. A mixture/PCA hierarchical Bayesian sensitivity model provided similar validation performance and interpretable posterior quantities but did not improve point estimation. Leave-one-Y-gene-out and prior-sensitivity analyses showed that the signal was distributed across multiple Y-linked transcripts and that prior shrinkage affected calibration. In an external whole-blood RNA-seq dataset without measured LOY, estimated LOY showed a modest age-related increase, but ex vivo immune stimulation shifted RNA-derived LOY estimates and reduced multiple Y-linked transcripts, indicating transcriptional confounding. These results show that bulk RNA-seq contains usable information about LOY, especially for larger events, but RNA-derived LOY should be interpreted as a probabilistic transcriptome-based estimate rather than a direct substitute for DNA-based mosaicism measurement.

genomics↗

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

NeuroPupil: A generalization-first framework for scalable and biologically informative cross-species pupillometry

Quantitative pupillometry provides a noninvasive window into brain state and neurological function, but its broader use across experimental and clinical settings is limited by challenges in achieving accurate, scalable, and generalizable measurements. Here, we present NeuroPupil, a deep learning framework for high-throughput, cross-species pupillometry that emphasizes robust generalization across subjects, behavioral contexts, and imaging conditions. Through systematic benchmarking of training strategies and network architectures, we identify pooled multi-subject training combined with an optimized U-Net architecture as a key determinant of reliable and transferable pupil tracking performance. Across diverse mouse and human datasets, NeuroPupil achieves improved accuracy and substantial gains in computational efficiency compared to existing approaches, enabling practical analysis of large-scale datasets. We further demonstrate that improved pupil tracking fidelity enhances downstream biological inference: NeuroPupil-derived pupil features significantly improve prediction of distributed cortical activity in behaving mice and preserve diagnostically relevant temporal structure in human clinical recordings. These findings highlight the importance of precise and scalable measurement for linking pupil dynamics to brain activity and clinical phenotypes. By integrating benchmarking, scalability, and accessible software tools, NeuroPupil provides a reproducible framework for large-scale pupillometry and facilitates its application in systems and translational neuroscience.

neuroscience↗

bioRxiv2026

OpenBase: a universal framework for high-accuracy single-molecule detection of diverse non-canonical DNA bases using nanopore sequencing

Nanopore sequencing holds great potential for the direct detection of non-canonical DNA bases from electrical signals, yet current approaches remain limited to a few classical epigenetic marks. Here we present OpenBase, an open and universal framework that standardizes training-data generation and deep learning based-modeling for single-molecule identification of diverse non-canonical DNA bases. OpenBase transforms nanopore sequencing into a broadly accessible platform for exploring the chemical diversity of DNA.

bioinformatics↗

bioRxiv2026

Metagenome-scale Modeling to Assess Microbiome Metabolic Complementarity for Precision Microbiota Transplantation Therapies

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable, in part because existing computational models do not fully capture the metabolic compatibility between donor and recipient communities. Here, we present a metagenome-scale metabolic modeling framework that quantifies metabolic niche complementarity between donor and recipient microbiomes to predict transplantation outcomes. Using MICOM-derived community metabolic models, we show that donor taxa whose metabolic flux profiles are more dissimilar from the recipient community engraft at significantly higher rates in both murine and human FMT cohorts. In a human IBS trial, metabolic models accurately predicted post-FMT community composition via leave-one-out cross-validation and recapitulated disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to demonstrate a platform for personalized donor screening. This screen identified super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition, as quantified by a flux-derived ecological network balance index that strongly predicted engraftment potential. This framework provides a mechanistic, scalable tool for rational donor-recipient matching that could guide personalized microbiome-based therapies.

microbiology↗

bioRxiv2026

Membrane-anchored influenza neuraminidase vaccine drives human-like broadly protective B cell responses

Influenza neuraminidase (NA) is a promising target for universal flu vaccines, yet eliciting potent B-cell responses against its conserved epitopes remains challenging. Here, we developed a membrane-anchored, folding-domain-free NA (mNA) that elicited superior head-specific germinal center B cell and antibody responses compared to soluble tetrameric NA. In non-human primates, mNA immunization induced cross-reactive memory B cell (MBC) responses, expanding clones with the conserved "DR" motif in HCDR3, a hallmark of human broadly reactive NA antibodies. These MBCs conferred cross-inhibitory activity against diverse NA variants and in vivo cross-protection. Cryo-EM analysis revealed that the 554-C2 clone targets the conserved enzymatic pocket via the "DR" motif, while the 554-C1 clone recognizes previously uncharacterized epitopes at the interface between two adjacent N2 monomers, effectively reducing plaque formation by contemporary H3N2 strains. Our findings highlight the immunological advantages of membrane-anchoring, providing a robust strategy for designing next-generation vaccines against influenza and other pathogens.

immunology↗

bioRxiv2026

CHOP promotes the transition to chronic integrated stress response signaling with suppression of hepatocyte identity

The transcription factor CHOP promotes cell death during ER stress, but it is strongly induced even by moderate stresses that do not result in appreciable cell death. Its role during less severe stresses--especially in intact tissues in vivo--is poorly understood. Here, we both deleted and restored CHOP specifically in hepatocytes and challenged animals with ER stress in vivo. We found that CHOP influenced stress-dependent hepatocyte gene expression through two previously unappreciated mechanisms. It directly suppressed the expression of transcriptional master regulators of hepatocyte identity and metabolism. And more broadly, it exacerbated ER stress through the promotion of protein synthesis, which led to persistent activation of the integrated stress response (ISR) despite dephosphorylation of eIF2. This shift to second-phase ISR signaling was phenocopied by deletion of the protective UPR sensor ATF6, suggesting that it reflects a transition from an acute stress response to a chronic one. Our findings show that CHOP augments the capacity of the ISR and UPR to continue to mount a protective response even after eIF2 phosphorylation has been suppressed. In vivo, where ISR signaling intersects with hepatocyte gene regulatory networks, this transition favors lipid dysregulation, highlighting a pathway through which CHOP impacts tissue function independent of cell death.

cell biology↗

bioRxiv2026

Membrane Phase, Charge, and Curvature Regulate α-Synuclein Binding Dynamics

-Synuclein (Syn) is an intrinsically disordered protein whose interactions with lipid membranes are central to both its physiological function and its role in synucleopathies. While membrane charge, phase, and curvature are each known to influence Syn binding, these properties are typically examined independently, leaving their combined effects on both equilibrium and dynamic membrane association unresolved. Here, we systematically investigate how membrane phase and charge jointly regulate Syn binding, curvature sensitivity, and exchange dynamics using fluorescence microscopy, circular dichroism spectroscopy, and fluorescence recovery after photobleaching (FRAP), complemented by coarse-grained molecular dynamics simulations. Under zwitterionic conditions, Syn preferentially binds highly curved gel-phase membranes, driven by curvature-dependent enrichment of packing defects arising from faceted vesicle morphologies. Incorporation of anionic lipids selectively enhances binding in liquid-phase membranes while attenuating curvature-dependent partitioning in gel-phase membranes. Dynamic measurements reveal that membrane phase and charge also govern the stability of membrane-associated Syn, with gel-phase membranes and anionic lipids promoting kinetically stabilized states. Simulations show that curvature-induced defect formation is strongly amplified in gel-phase membranes but largely insensitive to charge. These findings establish that Syn-membrane interactions are governed by a cooperative interplay between membrane phase, curvature, and charge and highlight the importance of resolving both thermodynamic and kinetic contributions to protein-membrane binding.

biophysics↗

bioRxiv2026

GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics

Spatial transcriptomics (ST) measures gene expression together with spatial coordinates, enabling spatial domain identification of coherent tissue regions. Many recent approaches rely on graph-based modeling to combine spatial neighborhoods and transcriptomic (gene-expression) similarity, yet neighborhood construction is often unreliable under sparsity and technical noise. As a result, spurious cross-domain shortcut edges can persist in static graphs and propagate misleading signals during message passing, ultimately blurring domain boundaries and weakening cluster separability. In this paper, we propose GatorDuo, a topology-aware dual-graph contrastive self-supervised framework for robust spatial domain identification that couples gene-expression similarity with spatial proximity through complementary neighborhood graphs. GatorDuo introduces global-consistency-based graph refinement that uses a pseudo-label agreement mask to suppress cross-domain shortcut edges in both views, thus stabilizing neighborhood topology for representation learning. To avoid manual tuning of domain resolution, GatorDuo further employs a contextual bandit reinforcement-learning strategy to adaptively select the clustering granularity (the number of clusters) used for refinement. The refined view-specific embeddings are integrated via a hybrid-routing Mixture-of-Experts (MoE) module to generate a unified embedding, optimized with contrastive objectives augmented by an MoE-alignment term. Across eight public benchmarks spanning sequencing- and imaging-based ST at spot and single-cell resolution, and compared with ten representative baselines, GatorDuo consistently delivers strong and robust spatial domain identification performance across multiple clustering metrics, while yielding informative unified embeddings that can support downstream biological analyses.

bioinformatics↗

bioRxiv2026

Antiphage defence systems Druantia III and Zorya II synergise via shared DNA intermediates in a phage-specific manner

Most bacteria encode multiple antiphage defence systems, but how these systems interact, remains poorly understood. Here, we define the mechanism of Druantia III and explore its synergy with Zorya II. Druantia III is a late-acting defence where DruH is the likely infection sensor and DruE is a helicase-nuclease effector that engages ssDNA-containing replication intermediates. Druantia III and Zorya II are each sensitive to the loss of RecD, whereas their synergy requires an intact RecBCD complex, indicating that the combined response depends on a shared DNA-processing hub. During T3 infection, Zorya apparently preserves this hub by limiting accumulation of the phage RecBCD-inhibitor Gp5.9, whereas DruE together with RecBCD promote the formation of DNA structures permissive for ZorE cleavage. During Bas37 infection, Druantia provides the dominant pathway and recruits ZorE in a non-canonical, ZorAB-independent manner. These findings show how shared DNA intermediates can connect defence systems into a coordinated antiphage response.

microbiology↗

bioRxiv2026

Engineering a pacemaker-driven human mini-heart guided by spatial multi-omics of sinoatrial node development

The human sinoatrial node (SAN) functions as the primary pacemaker of the heart and coordinates the hierarchical electrical activity that drives cardiac contraction. However, experimental systems capable of reconstructing pacemaker-driven cardiac organization in human tissues remain limited. Here we integrate spatial multi-omics of the human fetal SAN with stem-cell engineering to generate pacemaker organoids ("Sinoids") and assemble them into a pacemaker-driven human mini-heart composed of sinoatrial, atrial and ventricular cardiac modules. High-resolution spatial transcriptomics and single-nucleus multi-omic analyses of human fetal SAN tissues identify regulatory pathways guiding pacemaker lineage specification, which we leverage to engineer human pluripotent stem cell-derived SAN organoids with robust pacemaker identity and electrophysiological activity. When integrated with atrial and ventricular cardioids, Sinoids initiate and coordinate electrical activation across assembled cardiac tissues, establishing directional propagation of electrophysiological signals within structured mini-heart organoids. Combining AI-guided perturbation modeling with functional validation further identifies conserved regulatory pathways controlling pacemaker specification and regionalization, including YAP-TEAD and NRG-ERBB signaling. Together, these results establish a multi-omic-guided strategy for engineering pacemaker tissues and reconstructing cardiac conduction hierarchy in vitro. The pacemaker-driven mini-heart platform provides a modular human cardiac system for studying pacemaker biology, modeling arrhythmia mechanisms and enabling electrophysiological drug discovery.

bioinformatics↗

bioRxiv2026

Deciphering the epitranscriptomic code of RNA degradation with nanopore direct RNA sequencing

The precise regulation of RNA degradation is crucial for gene expression homeostasis, yet how multiple molecular features coordinate on a single transcript remains poorly understood. Here, we use nanopore direct RNA sequencing (DRS) to simultaneously track alternative isoforms, m6A modifications, and poly(A) tail dynamics at single-molecule resolution during a time course of RNA decay. We show that m6A regulates RNA degradation in a stoichiometry-dependent manner, where modification levels quantitatively modulate decay kinetics. Mechanistically, m6A is functionally coupled to deadenylation, promoting accelerated poly(A) tail shortening and coordinated RNA turnover. At the isoform level, we identify regional m6A clusters (RMCs) as structural elements that associate with isoform-selective degradation and remodel protein-coding potential. Furthermore, transcript splicing architecture is associated with distinct m6A deposition patterns, suggesting that gene structure encodes RNA decay kinetics through m6A-mediated regulation. A machine learning model integrating these multi-modal features highlights the central contribution of m6A and deadenylation in shaping RNA decay, while revealing substantial regulatory heterogeneity across transcripts. Collectively, our study deciphers the multi-layered, cooperative principles of RNA degradation and provides an epitranscriptomic perspective for understanding how RNA fate is encoded at the single-molecule resolution.

bioinformatics↗

bioRxiv2026

3'UTR Insertion of a Directed-Evolved RNA Element for Enhanced Translation

Translation efficiency remains a major limitation for RNA therapeutics. Conventional optimization targets the 5 untranslated region (5 UTR), while the 3 UTR is viewed mainly as a stabilizing element. Here, we demonstrate that the 3 UTR can be rationally engineered to actively enhance translation. Using an intracellular directed-evolution platform based on the SINEB2 element, we identified RNA modules P51 and its compact variant P51t3,which markedly increased protein output without affecting mRNA levels. P51t3 consistently boosted expression two- to six-fold across plasmid, in vitro transcribed mRNA, and recombinant AAV systems. Mechanistic studies revealed that P51t3 binds ribosomal protein RPL39, recruiting 60S subunits to the initiation site through the natural closed-loop translation model. By integrating evolutionary selection with 3 UTR design, this work redefines the 3 UTR as an active translational enhancer and provides a broadly applicable regulatory element for next-generation mRNA and gene-delivery therapeutics.

molecular biology↗

bioRxiv2026

Neuromodulation of Foxp2+ hypothalamic neurons induces therapeutic hypothermia

The inability to selectively trigger therapeutic hypothermia independent of environmental cooling has hindered causal analysis of its broader physiological benefits. Here, we employed P57, a natural product that pharmacologically induces therapeutic hypothermia circumventing external cold stress, to identify the neuronal substrates underlying hypothermia-induced antitumor effects. Integrating functional ultrasound imaging, activity-dependent neuronal labeling, and single-nucleus RNA sequencing, we identify a previously unrecognized population of Foxp2 neurons in the medial preoptic area (MPA) that are selectively activated by P57. Inhibition of MPA Foxp2+ neurons abolishes P57-induced hypothermia, whereas the paraventricular hypothalamus (PVH) serves as a critical downstream node. Importantly, targeted activation of MPA Foxp2 neurons is sufficient to induce sustained hypothermia, suppress systemic metabolism, and inhibit tumor growth, providing causal evidence that neuronally driven reductions in core body temperature can exert antitumor effects. Together, these findings establish MPA Foxp2 neurons as a controllable node for therapeutic hypothermia induction and demonstrate that neuromodulation of defined neuronal populations can achieve therapeutic benefit by directly controlling physiological states.

neuroscience↗