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PubMed2026

Multi-season analysis reveals hundreds of drought-responsive genes in sorghum.

Persistent drought affects global crop production and is becoming more severe in many parts of the world in recent decades. Deciphering how plants respond to drought will facilitate the development of flexible mitigation strategies. Sorghum bicolor L. Moench (sorghum), a major cereal crop and an emerging bioenergy crop, exhibits remarkable resilience to drought. To better understand the molecular traits that underlie sorghum's remarkable drought tolerance, we undertook a large-scale sorghum gene expression profiling effort, totaling nearly 1500 transcriptome profiles, across a 3-year field study with replicated plots in California's Central Valley. This study included time-resolved gene expression data from roots and leaves of two sorghum genotypes, BTx642 and RTx430, with different pre-flowering and post-flowering drought-tolerance adaptations under control and drought conditions. Quantification of genotype-specific drought tolerance effects was enabled by de novo sequencing, assembly, and annotation of both BTx642 and RTx430 genomes. These reference-quality genomes were used to construct a pangene set for characterizing conserved and genotype-specific expression. By integrating time-resolved transcriptomic responses to drought in the field across three consecutive years, we identified a set of 726 drought-responsive genes that responded similarly in all 3 years of our field study. Functional enrichment analysis identified abiotic stress, secondary cell wall-related processes and metabolism as particularly affected under both types of drought stress. We also found that some glyoxylate cycle pathway genes, including malate synthase and isocitrate lyase, are differentially regulated particularly during post-flowering drought stress, implicating this pathway as potentially important for drought responsiveness. This expansive dataset represents a unique resource for sorghum and drought research communities and provides a methodological framework for the integration of multi-faceted time-resolved transcriptomic datasets.

Sorghum↗

PubMed2026

Progressive salinity drives flavonoid branch reprogramming in Anoectochilus roxburghii.

Flavonoids play critical roles in plant adaptation to abiotic stress; however, how salt stress modulates metabolic flux distribution within flavonoid branches remains poorly understood, particularly in non-model medicinal plants. Here, we integrated targeted metabolomics, transcriptomics, and proteomics to examine flavonoid regulation in Anoectochilus roxburghii under 0, 50, 100, and 200 mmol·L- 1 NaCl. Metabolite profiling showed that salinity reshaped flavonoid composition rather than uniformly increasing flavonoid abundance. A metabolite-derived branch bias index (MI), representing the balance between reductive branch metabolites and flavonol products, increased under salt treatment, peaked at 100 mmol·L- 1 NaCl, and declined at 200 mmol·L- 1, indicating maximal branch bias under moderate stress followed by partial rebalancing under severe stress. Transcriptomic analysis showed induction of upstream phenylpropanoid and flavonoid entry genes, including PAL, 4CL, and CHS, whereas F3H was suppressed and FLS showed no induction. Furthermore, several short-chain dehydrogenase/reductase homologs (IFR-like SDR homologs) were upregulated, and the transcript-derived reductive branch index (EI) increased progressively across the salt gradient. EI was positively associated with MI, although the relationship was not strictly proportional under severe stress (200 mmol·L- 1 NaCl). Proteomic profiling further provided supportive evidence for sustained activation of upstream flavonoid biosynthesis, such as salt-induced accumulation of chalcone synthase (CHS) protein, complementing the transcriptomic and metabolomic datasets. Together, these results indicate that salt stress reorganizes flavonoid metabolism in A. roxburghii through persistent upstream activation and branch-specific regulation, favoring the reductive branch under moderate salinity.

Orchidaceae↗

PubMed2025

Molecular hallmarks of excitatory and inhibitory neuronal resilience to Alzheimer's disease.

BACKGROUND: A significant proportion of individuals maintain cognition despite extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals could reveal therapeutic targets for AD. METHODS: This study defines molecular and cellular signatures of cognitive resilience by integrating bulk RNA and single-cell transcriptomic data with genetics across multiple brain regions. We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk RNA sequencing (n = 631 individuals) and multiregional single-nucleus RNA sequencing (n = 48 individuals). Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole-genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition. RESULTS: Transcriptomics and polygenic risk analysis position resilience as an intermediate AD state. Only GFAP and KLF4 expression distinguished resilience from controls at tissue level, whereas differential expression of genes involved in nucleic acid metabolism and signaling differentiated AD and resilient brains. At the cellular level, resilience was characterized by broad downregulation of LINGO1 expression and reorganization of chaperone pathways, specifically downregulation of Hsp90 and upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. MEF2C, ATP8B1, and RELN emerged as key markers of resilient neurons. Excitatory neuronal subtypes in the entorhinal cortex (ATP8B+ and MEF2Chigh) exhibited unique resilience signaling through activation of neurotrophin (BDNF-NTRK2, modulated by LINGO1) and angiopoietin (ANGPT2-TEK) pathways. MEF2C+ inhibitory neurons were over-represented in resilient brains, and the expression of genes associated with rare genetic variants revealed vulnerable somatostatin (SST) cortical interneurons that survive in AD resilience. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience. CONCLUSIONS: We have defined molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preserved neuronal function, balanced network activity, and activation of neurotrophic survival signaling. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable interneurons likely provides compensation against AD-associated hyperexcitability. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

Humans↗

PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

PubMed2025

The protective role of γδ T cells in endometrial cancer.

Γδ T cells are non-conventional T cells that are not MHC restricted and have T cell receptors (TCRs) that are stimulated by phosphoantigens, stress-induced proteins, lipids, and other antigens. These cells are prognostic across cancer types in The Cancer Genome Atlas (TCGA) but have not been well studied in endometrial cancer, which has a rising incidence and mortality rate. Endometrial cancer patients have variable responses to checkpoint inhibitors which are related to the molecular subtype of their cancer. As such, there is a pressing need to understand the immune microenvironment in endometrial cancer. This study addresses this gap in knowledge by investigating γδ T cell repertoires and transcriptomes in this disease site. γδ T cell repertoires were obtained for 543 endometrial cancer patients within the TCGA and from 5 endometrial cancer patients in the single cell dataset SRP349751 using TRUST4. GLIPH2 was used to identify TCRs predicted to bind the same antigen. Transcriptomes were investigated in the single cell dataset. DNA Polymerase Epsilon Exonuclease (POLE) and Microsatellite Instability High (MSI-H) endometrial cancer subtypes had the most γδ T cell infiltration. Vδ1 and Vδ3 γδ T cell infiltration was prognostic independent of stage and molecular subtype. GLIPH2 analysis revealed TCRδ motifs for TDK, YTD, and GEL were public across all four molecular subtypes and were present in the single cell data set. Vδ1 γδ T cell transcriptomes were associated with cytotoxicity and recent TCR stimulation. These data support further investigation of immunotherapies targeting γδ T cells in endometrial cancer.

Humans↗

PubMed2025

Integration of multiple omics reveals key targets and cellular mechanisms for intervention in sarcopenia.

BACKGROUND: Sarcopenia, an age-related syndrome characterized by progressive loss of muscle mass, strength, and function, presents a significant global health burden with limited therapeutic interventions. This study integrates genomic causality, multi-tissue omics, and cellular mediation analyses to identify and prioritize mechanistically grounded therapeutic targets. METHODS: A multi-tiered analytical framework was applied, beginning with two-sample Mendelian randomization (MR) to infer causal relationships between 4907 plasma proteins (cis-pQTLs from 35,559 individuals) and sarcopenia traits in Pan-UK Biobank participants. Bayesian colocalization and transcriptomic validation in human sarcopenia muscle biopsies were employed to prioritize targets. Cellular mediation analysis quantified contributions of immune and stromal cell subtypes to protein-trait pathways using transcriptomic deconvolution. RESULTS: MR identified 1237 plasma proteins causally associated with sarcopenia traits, with six targets (HGFAC, GATM, HMOX2, F2, LMAN2L, HPGDS) validated through colocalization, transcriptomic expression, and sarcopenia-related dysregulation. Cellular mediation revealed immune mechanisms underlying HGFAC's effects, with CD4+ regulatory T cells mediating 3.49 % of its impact on sarcopenia traits. Prothrombin exhibited muscle-protective effects independent of coagulation. CONCLUSION: This study establishes a causal map linking plasma proteins to sarcopenia through immune-stromal interactions. The integration of MR, multi-omics validation, and cellular mediation prioritizes six proteins as actionable targets, supporting repurposing of thrombin inhibitors and development of immunometabolic therapies. The framework bridges genomic causality with cellular pathophysiology, advancing precision strategies for age-related muscle decline.

Humans↗

PubMed2026

Molecular and immune profiling of HER2-low, HER2 ultra-low, and HER2-null male breast cancer.

BACKGROUND: HER2 expression is described along a biological continuum from null to positive and serves as a critical biomarker for therapeutic guidance in breast cancer (BC). While HER2-low and ultra-low categories have emerged as actionable targets for antibody-drug conjugates (ADCs) in female BC, their molecular and immune characteristics remain largely unexplored in male breast cancer. METHODS: We profiled 214 male breast tumors using next-generation sequencing and whole-transcriptome sequencing to assess mutational, transcriptomic, and immune landscapes. Tumor mutational burden (TMB) was defined as high if > 10 mutations/Mb. Immune cell fractions were inferred using Quantiseq deconvolution. RESULTS: Among 214 samples, 66 (30.8%) were HER2-null, 53 (24.8%) HER2 ultra-low, 80 (37.4%) HER2-low, and 15 (7.0%) HER2-positive. HER2 ultra-low tumors exhibited a higher prevalence of PIK3CA mutations (39.2% vs 22.6%, p ≤ 0.05) compared to HER2-null. No significant differences were observed in TMB-high frequency or PD-L1 expression across subgroups. Immune composition differed primarily between HER2-null and HER2-expressing subgroups: HER2-ultra-low tumors showed higher B-cell infiltration, whereas HER2-null tumors were enriched in neutrophils. Transcriptomic analysis revealed upregulation of selected stemness-associated genes (NANOG, KLF4, POU5F1) and CEACAM1 in HER2-null tumors, while HER2-low and HER2-ultra-low tumors were largely similar across most molecular and immune readouts in this cohort. CONCLUSIONS: HER2-null male breast cancer appears to represent the most biologically divergent subgroup within the HER2-negative spectrum, whereas HER2-low and HER2-ultra-low tumors were largely similar in this cohort. These findings support further investigation of HER2-null disease as a distinct biological state and provide hypothesis-generating data for biomarker development in this rare population.

Male↗

PubMed2026

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.

Humans↗

PubMed2025

An adjuvant database for preclinical evaluation of vaccines and immunotherapeutics.

Adjuvants are immunostimulators used to enhance vaccine efficacy against infectious diseases. However, current methods for evaluating their efficacy and safety are limited, hindering large-scale screening. To address this, we developed a prototype Adjuvant Database (ADB) containing transcriptome data, generated using the same protocols as the widely used Open TG-GATEs (OTG) toxicogenomics database, covering 25 adjuvants across multiple species, organs, time points, and doses. This enabled cross-database integration of ADB and OTG. Transcriptomic patterns successfully distinguished each adjuvant regardless of organs or species. Using both databases, we built machine learning models to predict adjuvanticity and hepatotoxicity. Notably, we identified colchicine's adjuvant activity and FK565's liver toxicity through data-driven analysis. Overall, ADB combined with OTG offers a framework for transcriptomics-based, data-driven screening of adjuvant candidates.

Animals↗

PubMed2025

Rare variants in MIR184 are a novel genetic cause of Fuchs endothelial corneal dystrophy.

PURPOSE: To identify novel genetic causes of Fuchs endothelial corneal dystrophy (FECD) within a genetically unsolved patient cohort lacking repeat expansions in the TCF4 gene (Exp-). METHODS: A rare variant analysis framework (CoCoRV) was applied to exome data, in combination with in silico modeling, luciferase reporter, and RNA-seq analysis to characterize transcriptome-wide consequences of identified variants. RESULTS: A gene burden analysis identified MIR184, a microRNA encoding gene, to be enriched for rare pathogenic variants within the studied Exp- FECD cohort. In total, 2 noncoding rare variants were identified in 4 unrelated FECD probands: NR_029705.1:n.58G>A and n.73G>T. Both variants altered highly conserved mature sequence residues, were predicted to induce hairpin structural changes, and were experimentally determined to disrupt microRNA-mRNA interactions. RNA-seq of transfected human corneal endothelial cells revealed that the mutants elicited distinct transcriptomic profiles. Enriched KEGG pathways included PI3K-Akt signaling, focal adhesion, and immune response, revealing shared pathogenic mechanisms between MIR184-associated FECD and the more common TCF4 repeat expansion-mediated form of disease. CONCLUSION: MIR184 variants are a novel rare genetic cause of FECD, and common pathways of transcriptomic dysregulation are shared across genetically distinct subtypes of the disease. These pathways may serve as future gene agnostic targets for therapeutic interventions.

Humans↗

PubMed2025

Integrated proteomic network analysis reveals PTPRC as a central hub protein orchestrating co-expression modules and metabolic dysregulation in renal carcinoma: PTPRC protein molecular action.

The occurrence of renal carcinoma is closely related to a variety of molecular mechanisms and metabolic disorders. PTPRC (protein tyrosine phosphatase receptor C), as an important regulatory protein, was studied to reveal the role of PTPRC in renal carcinoma through comprehensive proteomic network analysis, especially its core position in the coordination of co-expression modules and metabolic disorders. This study was the first to download and process multiple publicly available renal cancer transcriptome data to conduct differential gene expression analysis across datasets. Functional enrichment and disease ontology analysis were performed on the transcriptome of renal cancer, and weighted gene co-expression network (WGCNA) was constructed. The results showed that comprehensive principal component analysis revealed significant differences in the transcriptome of renal cancer, and functional annotation revealed specific pathways associated with renal cancer. WGCNA analysis identified tumor-associated co-expression modules, while multi-omics analysis further identified core regulatory networks including PTPRC. As a central hub protein, PTPRC plays an important coordinating role in the co-expression module and metabolic dysregulation of renal carcinoma. This discovery provides a new perspective for understanding the molecular mechanism of kidney cancer.

Humans↗

PubMed2025

Proteomic hub proteins CDKN2B, TRAPPC2L, WFS1, and ARPP19 drive biochemical recurrence and metastatic progression in prostate cancer: Protein macromolecule action.

The biological characteristics and metastasis mechanism of prostate cancer are complex, involving the important role of many proteins in cell transcriptional regulation. This study focused on the role of the proteomic hub proteins CDKN2B, TRAPPC2L, WFS1 and ARPP19 in the biochemical recurrence and metastasis progression of prostate cancer. Cross-platform transcriptome integration and differential expression analysis were used to evaluate transcriptome characteristics in a prostate cancer cohort. Functional enrichment analysis was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, and weighted gene co-expression network analysis (WGCNA) was used to investigate cancer progression subtypes. It was found that prostate cancer progression showed significant transcriptome heterogeneity, and low-expression genes dominated. We reveal the important role of epithelial-immune interactions and inflammatory signaling in transcriptional remodeling in prostate cancer. The co-expression network topology analysis showed that the immune-metabolic center module plays a central role in cancer progression. CDKN2B was identified as a key transcriptional determinant in prostate cancer typing, while TRAPPC2L and WFS1 acted as core transcriptional regulators, driving metastatic heterogeneity. ARPP19 and LOC650152 also show important transcriptional driving effects in advanced prostate cancer.

Humans↗

PubMed2025

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies. OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data. METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes. RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts. CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

Humans↗

PubMed2026

Multimodal computational framework resolves B cell maturation in autoimmunity and ageing.

Identification of the origin of pathogenic immune cells is crucial for therapeutic interventions and diagnosis but pseudotime methods struggle to trace immune cells accurately. Current trajectory inference methods for B cell development and response in health and disease either ignore or underutilize antigen receptor sequence information, limiting their ability to resolve developmental pathways, particularly for pathogenic populations. Widely used methods such as Monocle 3 reconstruct developmental paths from transcriptomic similarity alone, discarding the features from immune receptors. Dandelion has combined the immune receptor features with transcriptomics but it struggles to simulate the trajectory path of B cells. Here we present ClonoTrace, a computational framework that integrates BCR sequence features with transcriptomic trajectory inference through gated fusion of multimodal embeddings. In fetal B cell development and germinal centre development, ClonoTrace demonstrates closer concordance with the canonical reference ordering than Monocle 3 and Dandelion. Applied to systemic lupus erythematosus, ClonoTrace indicates a memory B cell extrafollicular maturation route alongside the naïve B cell route, accompanied by induction of ZEB2 with a concomitant decline of BACH2 along the trajectory, as a candidate alternative route to pathogenic double negative 2 B cells (DN2) in systemic lupus erythematosus (SLE) patients. In healthy ageing, ClonoTrace resolved three candidate age-related B cell maturation routes, from naïve, IgM+ memory and switched-memory B cells, each passing through a DN2-associated transcriptional state that is ordered before age-associated B cells along the inferred trajectory. ClonoTrace's fate probability algorithm indicated that IgM+ memory B cell to ABC transition as the leading candidate age-associated transition, which may be distinct from SLE DN2 maturation. ClonoTrace provides a generalizable framework for receptor-informed trajectory inference, describing candidate developmental routes of pathogenic B cell populations in autoimmunity and ageing.

Humans↗

PubMed2026

Genome-Wide Impact of Human DBR1 Depletion on RNA Processing Networks Reveal a Connection Between Pre-mRNA Splicing, mRNA Surveillance and Stress Granule Dynamics.

The RNA lariat debranching enzyme DBR1 is essential for intron turnover and RNA metabolism, yet its broader impact on transcriptome regulation remains incompletely defined. To elucidate the consequences of DBR1 depletion, we performed transcriptome-wide RNA sequencing of DBR1-knockdown and wild-type HEK293 cells. Differential expression analysis revealed widespread perturbations in pathways linked to RNA splicing, mRNA surveillance, translational control, and stress-granule biology. Many of the most significantly altered transcripts encode splicing factors and RNA quality-control components, underscoring DBR1's influence on post-transcriptional regulation. Alternative splicing analysis showed changes across multiple event types, with exon skipping accounting for >50% of events, followed by mutually exclusive exons, alternative 5' and 3' splice sites, and retained introns, indicating that DBR1 depletion induces pervasive splicing defects. Direct spliceosome inhibition using isoginkgetin (blocks tri-snRNP recruitment) and pladienolide B (targets SF3B1) reproduced the DBR1-KD mis-splicing patterns of cell signaling genes and factors involved in RNA metabolism, supporting a functional link between DBR1 activity and alternative splicing. Notably, DBR1 knockdown revealed a subset of transcripts that are both NMD-sensitive and enriched within stress granules. Consistent with this observation, G3BP1 immunopurification and confocal microscopy further support a role for DBR1 and UPF1 in stress-granule dynamics, suggesting that these factors may participate at distinct stages to influence mRNA fate under stress conditions. Together, these findings indicate that DBR1 functions beyond lariat RNA turnover as a common regulator of RNA processing, transcriptome stability, and stress granule homeostasis, revealing intricate crosstalk between RNA splicing and RNA quality control pathways in human cells.

Humans↗

PubMed2025

Epstein-Barr Virus-Associated Gastric Cancer: A Histopathologic Study With Comprehensive Molecular Profiling.

A subset of gastric cancers (GCs) is linked to Epstein-Barr virus (EBV) infection. This study aims to characterize the histopathological and molecular features of EBV-associated GCs (EBVaGCs), focusing on predictive biomarkers and genomic and transcriptomic analysis. A total of 35 primary EBVaGCs were considered. The presence of EBV was confirmed with in situ hybridization. Immunohistochemical analyses for HER2, PD-L1, claudin 18.2, and mismatch repair proteins were performed. Genomic and transcriptomic profiles were assessed using AmoyDx Master Panel, which can identify single-nucleotide variants, InDels, and copy number variations on 571 hot genes, as well as microsatellite status, tumor molecular burden, and homologous recombination deficiency at the DNA level; however, at the RNA level, it identifies rearrangements/fusions in 45 genes and also quantifies the expression of 2396 cancer-related transcripts. The following histotypes were identified: carcinoma with lymphoid stroma (CLS; 69%), tubular (20%), and mixed (11%). Most cases were associated with atrophic gastritis (71%), and only 11% with dysplasia. The vast majority (94%) of EBVaGCs expressed EBV-encoded RNA in all tumor cells. Mismatch repair deficiency and HER2 overexpression were each observed in 6% of cases, whereas all tumors had a PD-L1-combined positive score ≥10. Sixty-six percent of cases showed moderate/strong claudin 18.2 expression in ≥75% of cancer cells. The most frequently altered genes were PIK3CA (41%) and ARID1A (17%). Transcriptomic analysis revealed substantial differential gene expression between EBVaGCs and EBV-negative controls, with upregulation of genes involved in antigen presentation, natural killer cell-mediated cytotoxicity, and cytokine-cytokine receptor interaction in EBVaGCs. Within EBVaGC, CLS showed higher expression of immune-related transcripts and higher PD-L1 expression than other histotypes. This study establishes EBVaGC as a distinct molecular class, with a distinctive profile of genomic alterations and expression of predictive biomarkers, and also with a unique immune microenvironment with enhanced cytotoxic activity. The findings highlight EBV's role in early tumor development and EBVaG-CLS as a distinct subgroup within EBVaGC, characterized by unique morphologic features and a pronounced immune activation profile.

Humans↗

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↗

PubMed2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning↗