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9 recordsLinked to original sources
PubMed2016

Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.

Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

Atlases as Topic↗
PubMed2026

Essence: A benchmarking-validated transformer framework for early diagnosis of Parkinson's disease using cerebrospinal fluid protein biomarkers.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. The lack of objective molecular biomarkers limits early diagnosis and personalized treatment. Here, we propose Essence, a benchmarking-validated framework integrating cerebrospinal fluid (CSF) proteomics with traditional and deep learning models to identify robust protein signatures for PD. Using data from two independent cohorts, 1266 high-confidence proteins are quantified, among which 178 exhibit differential abundance between PD and healthy controls (HC). Through systematic benchmarking of ten machine learning algorithms and four neural architectures, the Transformer model consistently outperforms alternatives across multiple feature selection strategies, achieving an area under the receiver operating characteristic curve (AUC) of 1.0000 with only 35 features. Functional analyses of the top-ranked 35 proteins reveal enrichment in neuroinflammatory, synaptic, and oxidative stress-related pathways. Importantly, spatial transcriptomic profiling based on the Allen Brain Atlas shows region-specific expression of these biomarkers in PD-relevant brain structures, including the striatum, subthalamic nucleus, hippocampus, and white matter tracts. This anatomical alignment supports the functional relevance of the identified markers and highlights their potential utility in early-stage diagnosis and mechanistic understanding of PD.

Benchmarking↗
PubMed2021

Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Animals↗
PubMed2025

The new frontier in understanding human and mammalian brain development.

Neurodevelopmental disorders that cause cognitive, behavioural or motor impairments affect around 15% of children and adolescents worldwide1, with diagnoses of profound autism and attention deficit hyperactivity disorder increasing in the USA and contributing to a major economic burden2,3. Yet the origins and mechanisms of these conditions remain poorly understood, limiting progress in therapies. Comprehensive cell atlases of the developing human brain, alongside those of model organisms such as mice and non-human primates, are now providing high-resolution measures of gene expression, cell-type abundance and spatial distribution. In this Perspective, we highlight recent studies that have identified novel developmental cell populations, revealed conserved and divergent patterns of cell genesis, migration and maturation across species, and begun testing hypotheses that link them to processes ranging from transcriptional control of cell fate specification to the emergence of complex behaviours. We present remaining conceptual and technical challenges and provide an outlook on how further studies of human and mammalian brain development can empower a deeper understanding of neurodevelopmental and neuropsychiatric disorders. Future efforts expanding to additional developmental stages, including adolescence, as well as whole-brain, multimodal and cross-species integration, will yield new insights into how development shapes the brain. These atlases promise to serve as essential references for unravelling mechanisms of brain function and disease vulnerability, and for advancing precision medicine.

Humans↗
PubMed2026

Spatially resolved single-cell atlas reveals the macroevolutionary trajectory of animal hearts.

Animal hearts display diverse anatomical structures during adaptive evolution. Here, we present a multiomics atlas of adult hearts from 27 species across chordates, arthropods, and mollusks. Joint analysis indicates that Bilateria hearts share a core gene repertoire, taking a stepwise "add-on" approach as a universal evolutionary strategy. The "proto-heart" is populated by key cell types, including cardiomyocytes, fibroblasts, endothelial cells, and neural cells, which maintained core signatures while evolving with shifts in living environments and corresponding adaptations in the cardiovascular system. Additionally, we reveal an evolutionarily conserved cardiomyocyte state dynamic potentially linked to cardiac development and stress responses. Finally, we identify a common molecular program underpinning chamber evolution from a ventricular foundation. This work establishes a resource for understanding the intrinsic mechanisms of heart evolution.

Animals↗
PubMed2025

Transcriptome atlases of rat brain regions and their adaptation to diabetes resolution following gastrectomy in the Goto-Kakizaki rat.

Brain regions drive multiple physiological functions through specific gene expression patterns that adapt to environmental influences, drug treatments and disease conditions. To generate a detailed atlas of the brain transcriptome in the context of diabetes, we carried out RNA sequencing in hypothalamus, hippocampus, brainstem and striatum of the Goto-Kakizaki (GK) rat model of spontaneous type 2 diabetes, which was applied to identify gene transcription adaptation to improved glycemic control following vertical sleeve gastrectomy (VSG) in the GK. Over 19,000 distinct transcripts were detected in the rat brain, including 2794 which were consistently expressed in the four brain regions. Region-specific gene expression was identified in hypothalamus (n = 477), hippocampus (n = 468), brainstem (n = 1173) and striatum (n = 791), resulting in differential regulation of biological processes between regions. Differentially expressed genes between VSG and sham operated rats were only found in the hypothalamus and were predominantly involved in the regulation of endothelium and extracellular matrix. These results provide a detailed atlas of regional gene expression in the diabetic rat brain and suggest that the long term effects of gastrectomy-promoted diabetes remission involve functional changes in the hypothalamus endothelium.

Animals↗
PubMed2025

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

MOTIVATION: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling. RESULTS: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method. AVAILABILITY AND IMPLEMENTATION: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.

Gene-Environment Interaction↗
PubMed2026

Cardiovascular risks in psychiatric disorders and psychiatric risks in cardiovascular disorders: implications for prevention and clinical management - a large-scale umbrella review encompassing 76 meta-analyses.

OBJECTIVE: Psychiatric and cardiovascular disorders often co-occur, complicating their assessment and management. No umbrella review(UR) has summarized the meta-analytic evidence on the co-occurrence of psychiatric and cardiovascular disorders and assessed its credibility. METHODS: Meta-analytic systematic reviews of observational studies documenting the prevalence, risk factors, and outcomes associated with the co-occurrence of cardiovascular and psychiatric disorders, indexed from inception through March.16.2026, and meeting established diagnostic criteria, were included. Meta-analytic association and prevalence estimates were recalculated and graded based on established or adapted criteria. The AMSTAR-2 assessed the quality of the meta-analyses, while several subgroup analyses and meta-regressions aimed to explain the heterogeneity. RESULTS: We included 76 meta-analyses yielding 131 meta-analytic estimates. Based on pre-existing meta-analytic evidence, 22/24 prevalence estimates (91.7%) met moderate/strong credibility criteria. Strong credibility emerged for: orthostatic hypotension in Lewy body(58%;95%C.I. = 50-66%) and Alzheimer's dementias(28.0% = 95%C.I. = 17.0-40.0%); pericardial effusion in anorexia nervosa(25.0%;95%C.I. = 17.0-34.0%); in heart failure(HF): major depressive disorder(MDD)(41.9%;95%C.I. = 36.7-47.1%), mild cognitive impairment(MCI)(41.4%;95%C.I. = 38.3-45.6%), anxiety(32.0%;95%C.I. = 26.5-37.6%), MDD + anxiety(24.7%;95%C.I. = 17.9-34.3%), and dementia(19.8%;95%C.I. = 12.9-27.8%); in atrial fibrillation(AF): MCI(26.0%;95%C.I. = 21.0-30.0%), anxiety in patients undergoing pulmonary vein isolation(PVI)(25.0%;95%C.I. = 12.0-46.0%), MDD in PVI patients (20.0%;95%C.I. = 13.0-29.0%); in coronary artery disease: MDD + anxiety(19.8%;95%C.I. = 16.0-24.6%): in schizophrenia spectrum disorders: clozapine-associated-cardiomyopathy(0.6%;95%C.I. = 0.2-2.3%); clozapine-associated-cardiomyopathy absolute death rates (0.0003;95%C.I. = 0.0001-0.0012); clozapine-associated-cardiomyopathy case fatality rate (0.078;95%C.I. = 0.018-0.285). Several additional disorders were multimorbid in>5% of people, yet with a lower credibility rating. No re-pooled risk factors/outcomes reached strong credibility criteria. CONCLUSIONS: The present study provides an atlas of cardiovascular and psychiatric multimorbidity across varying levels of credibility, reinforcing the need for an integrated, multidisciplinary approach to patient care and for more research on actionable risk/protective factors and outcomes.

Humans↗
PubMed2026

Dataset Readiness Assessment With Large Language Model (DRAFT-LLM): A Multi-Axis Audit Guided by LLM.

This article details the Dataset Readiness Assessment for Training (DRAFT), a systematic method for determining whether a high-dimensional biological dataset is suitable for developing reliable, equitable (i.e., the extent to which model performance, error patterns, and potential benefits or harms are evaluated and found to be acceptably distributed across relevant demographic, biological, clinical, and contextual subgroups), and scientifically meaningful machine-learning models, and DRAFT Large Language Model (DRAFT-LLM), its optional human-in-the-loop extension for calibrating study-specific audits through structured, critically reviewed LLM guidance. Standard model validation often fails to detect when apparent performance is driven by spurious correlations, technical artifacts, or hidden stratification, leading to irreproducible and inequitable findings. DRAFT-LLM addresses this gap by shifting the focus from model tuning to structured dataset auditing, organized around Support Protocols 1 to 4 that capture the scientific intent, data structure, and governance constraints of a given study. These Support Protocols: (1) elicit and formalize investigator input into a study intake and dataset card; (2) compute standardized dataset statistics and structural summaries suitable for downstream analysis and LLM context; (3) configure the language model using form-based responses, safety guardrails, and governance rules; and (4) generate personalized instructions, prompts, and code templates for running DRAFT audits. Basic Protocols 1 to 3 are instantiated from this support layer for generalization, equity, and stability: they are reusable execution patterns whose concrete behavior is determined by the cards, statistics, and configurations defined in the Support Protocols. DRAFT-LLM and DRAFT are demonstrated in this article through an end-to-end case study on The Cancer Genome Atlas (TCGA). © 2026 Wiley Periodicals LLC. Support Protocol 1: Study intake and dataset card construction Support Protocol 2: Dataset structure and advanced summary statistics for LLM context Support Protocol 3: LLM configuration using structured form responses Support Protocol 4: Generation of personalized instructions for DRAFT audits Basic Protocol 1: Generalization audit Basic Protocol 2: Equity audit Basic Protocol 3: Stability audit.

Large Language Models↗