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PubMed2003

Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome.

The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseudogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.

Acetyltransferases↗
PubMed2026

Predicting cellular responses to perturbation across diverse contexts with State.

While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.

Machine Learning↗
PubMed2026

Ossicle occurrence characteristics and related molecular mechanisms in the sea cucumber Apostichopus japonicus.

To investigate the morphogenetic pattern and molecular mechanism of ossicle formation in the sea cucumber Apostichopus japonicus, this study systematically examined the morphological development and temporal sequence of spicules using the NaClO maceration method, in-situ squash preparation and microscopic observation. Comparative transcriptome sequencing was performed between doliolaria and pentactula larvae to screen differentially expressed genes (DEGs) related to ossicles formation, followed by pathway enrichment analysis. The function of the candidate key gene papilin-like was verified using siRNA-mediated gene silencing. The results were as follows: 1) Ossicles of A. japonicus first appeared at the late auricularia stage, initiating as X-shaped ossicles at the base of the oral tentacles. The number of X-shaped ossicles increased dramatically during the doliolaria stage. X-shaped ossicles were gradually replaced by table-shaped and rosette-shaped ossicles at the pentactula stage, suggesting that X-shaped ossicles may differentiate into these two ossicle types. The morphology of table-shaped ossicles showed a "simple-complex-simple" pattern with development. 2) Key genes related to ossicles formation, including CA1, COL1A2, and papilin-like, were identified by transcriptome analysis. After papilin-like knockdown, abnormal morphologies were observed in table-shaped ossicles of 1-year-old A. japonicus, such as spine-like protrusions on the outer margin of the disc and loss of table legs, confirming its crucial roles in maintaining ossicle morphology. This study clarified the morphological development pattern of ossicles in A. japonicus and identified a key regulatory gene (papilin-like) involved in ossicle morphogenesis, providing preliminary insights into the underlying molecular regulatory mechanism. These findings enrich our understanding on ossicles formation in echinoderms, and provide important morphological and molecular biological information for further studies on the developmental mechanism of ossicles in A. japonicus.

Animals↗
PubMed2026

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Transcriptome-wide association studies (TWAS) are a powerful approach for studying the genes underlying complex traits by directly integrating GWAS and gene expression datasets. In cattle, they have been previously applied to identify genes driving fertility, milk production, and health. However, these studies have also highlighted several challenges, from difficulties in reproducing these complex analyses to limitations from poor genotype calls, especially when called directly from RNA sequencing data. To address these and other challenges, for the H2020 BovReg Project, we have developed a streamlined, species-agnostic, and reusable Nextflow TWAS workflow to integrate transcriptomic and GWAS summary statistic datasets. Our workflow first generates accurate genotype calls and gene expression prediction models from transcriptomic datasets and then applies these tools to impute gene expression levels into GWAS cohorts, enabling the association of genes with traits of interest. We explore optimal strategies for calling genetic variants directly from transcriptomic data and illustrate that using imputation approaches specifically designed for low-pass sequencing data can improve variant calling over previously adopted methods. We demonstrate the utility of our TWAS workflow by applying it to both novel and publicly available GWAS cohorts for cattle, detecting novel gene-trait associations for complex traits. Using a new transcriptome annotation of the cattle genome generated for the BovReg project we also illustrate how previously un-assayable associations can be detected. The results and the workflow we present, provide a new resource for the community and contribute to a better understanding of the molecular drivers of complex traits in cattle with the goal of eventually leveraging this information in future breeding decisions.

Animals↗
PubMed2026

Comparative Multiomics Analysis of Cerebral Organoid-Derived Exosomes during Organoid Maturation.

Cerebral organoids derived from human pluripotent stem cells recapitulate key features of early brain development and provide a physiologically relevant model for neurogenesis. Exosomes secreted by these organoids carry bioactive cargo and offer a noninvasive means to monitor maturation and intercellular communication. We performed comprehensive multiomics profiling of exosomes collected from cerebral organoids at defined developmental stages to evaluate their utility as biomarkers of neuronal differentiation. Metabolomic analysis revealed a progressive decline in amino acids, including glutamic acid, consistent with increased metabolic demand during neurogenesis. Lipidomic and neurosteroid profiling showed dynamic increases in phosphatidylethanolamine and pregnenolone, reflecting synaptic membrane formation and signaling. Transcriptomic and proteomic analyses identified stage-specific neurodevelopmental signatures, with key markers mirroring those of parent organoids. Collectively, cerebral organoid-derived exosomes faithfully reflect organoid maturation and provide a robust platform for tracking in vitro brain development.

Humans↗
PubMed2026

Longitudinal dynamics of gene expression and metabolomics in an aging population cohort.

Multiomic profiling provides a comprehensive physiological overview at the molecular level, but understanding of its spatiotemporal dynamics remains limited in human populations. We profiled longitudinal whole-blood gene expression and metabolite levels in 335 females over 8 years. Levels of 5061 genes and 181 metabolites changed over time, with individual trajectories often diverging from population-level trends. Longitudinally variable genes showed cell type specificity and enrichment for aging-relevant pathways, including cardiometabolic and neurodegenerative disorders. Longitudinal trajectories were further shaped by genetics, circadian rhythm, seasonality, and environmental pollutant exposures. Integrative analyses revealed extensive static and time-variable cross-omic connectivity. Longitudinal profiling offers insight into the temporal evolution of age-related conditions at the molecular level, and understanding individual variation within these longitudinal patterns will be essential for future precision medicine approaches.

Female↗
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↗
PubMed2026

Genetics-Informed Mapping Identifies a CRIM1-Associated Endocardial Inflammatory Remodeling State in Acute Myocardial Infarction.

BACKGROUND Acute myocardial infarction (AMI) reflects inherited susceptibility and inflammatory remodeling, but the cellular contexts linking genetic risk to disease remain unclear. MATERIAL AND METHODS We integrated a meta-transcriptome-wide association study (TWAS) with a human cardiac single-nucleus RNA-sequencing atlas contained 11 individuals (5 AMI and 6 donor) to identify genetics-informed cellular programs. Composite program states were defined by global score quartiles. A fixed 5-gene panel was evaluated for nucleus-level endocardial low-transcriptional-state (Endo_LTS) vs endocardial high-transcriptional-state (Endo_HTS) discrimination within the AMI endocardium using 5-fold leave-1-patient-out cross-validation. Functional follow-up used CRIM1 silencing in hypoxia-treated human induced pluripotent stem cell (hiPSC)-derived endocardial endothelial-like cells and complementary peripheral blood analyses. RESULTS The endocardium exhibited the most prominent infarction-associated increase in TWAS-anchored program activity, with expansion of program-high states and higher CytoTRACE scores. A consensus 5-gene panel (RPS8, PLEC, CFDP1, CRIM1, TNS2) was identified. Among 2163 AMI endocardial nuclei from 5 patients, the state classifier included 364 Endo_LTS and 751 Endo_HTS nuclei; 1048 Endo_MTS nuclei were excluded. Pooled out-of-fold ROC-AUCs ranged from 0.665 to 0.831. The panel also showed discriminatory value in an independent peripheral-blood AMI-vs-control cohort. CRIM1 was prioritized as a candidate linked to the remodeling program. CRIM1 silencing attenuated ACTA2/alpha-SMA, vimentin, LDHA, CCL2, and VEGFA and partially restored CD31, whereas TGF-ß remained elevated. CONCLUSIONS These findings identify a genetics-informed endocardial inflammatory remodeling state in AMI and define a 5-gene surrogate of its activated state. CRIM1 is prioritized as a candidate linked to selected inflammatory, metabolic, and structural outputs. Persistent TGF-b elevation after CRIM1 silencing argues against a simple linear regulatory model and indicates that further mechanistic validation is required.

Humans↗
PubMed2024

Integrative omics analysis identifies biomarkers of septic cardiomyopathy.

Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were mainly enriched in pathways associated with the complement and coagulation cascades. On this basis, the integrated analysis was performed between transcriptome and proteome. The potential biomarkers were further verified by RT-qPCR and WB. The current proteotranscriptomic research has furnished a valuable dataset and fresh perspectives that will enhance our comprehension of the development of SCM. This, in turn, is expected to expedite the formulation of novel approaches for the prevention and management of SCM in patients.

Cardiomyopathies↗
PubMed2026

Integrative Cross-platform Analysis of Kinase Inhibitor Effects on Statin-relevant Cardioprotective Pathways in Human Cardiomyocytes.

BACKGROUND/AIM: Kinase inhibitors (KIs) can cause cardiotoxicity through mechanisms overlapping with statin cardioprotective pathways, yet their effects on these pathways in cardiomyocytes remain uncertain. We evaluated six literature-defined statin-relevant gene sets using transcriptomic and proteomic data. MATERIALS AND METHODS: Pre-ranked gene set enrichment analysis was performed for 23 KIs in primary cardiac cells (GSE146096; n=319) and iPSC-derived cardiomyocytes (GSE217421; n=541), with cross-platform analysis of 21 KIs by shotgun proteomics (PXD014791; n=300). Pathway-specific concordance was assessed by Spearman correlation with Benjamini-Hochberg correction; protein scores were estimated after adjustment for cell line. RESULTS: KI effects were heterogeneous. The anti-fibrotic pathway showed nominal concordance across the two transcriptomic datasets (ρ=0.495, p=0.016, q=0.098; 91% direction concordance) and significant cell-line-adjusted transcriptomic-proteomic concordance (ρ=0.644, p=0.0016, q=0.0081). Nilotinib reproducibly upregulated NF-κB pathway genes [normalized enrichment score (NES)=+2.29 and +2.18 in discovery and validation], with targeted inter-gene-correlation-adjusted testing supporting higher NF-κB expression than under rosuvastatin (CAMERA p=3.54×10-8). No global cross-omics summary remained significant after harmonizing pathway universes and accounting for repeated pathways. CONCLUSION: KI effects on statin-relevant pathways were pathway-specific. Anti-fibrotic concordance and nilotinib-associated NF-κB upregulation are hypothesis-generating candidates for experimental validation.

Humans↗
PubMed2026

An integrated proteomics and transcriptomics analysis highlights concordance between protein turnover and carbohydrate transport and metabolism as key functional categories during the growth of Trichophyton rubrum.

Dermatophytes are a class of keratinophilic skin fungi that invade host skin, hair, and nails to acquire nutrients. An integrated multi-omics approach utilizing liquid chromatography-tandem mass spectrometry and RNA-seq after growth in a protein-rich soy medium was employed to capture the major subset of secreted protein families of Trichophyton rubrum. The secretome consisted mainly of proteases and cell wall-degrading enzymes, with subtilisins (Sub6 and Sub7), metallopeptidase (LAP2), and chitinase having the most abundant peptides. Transcriptional profiling indicated fungal adaptation in protein-rich media to process the protein nutrients through modulation of metabolism and general cellular function pathways. Correlation analysis between proteomics and transcriptomics data using functional KOG categories shows high concordance of KOG categories O (posttranslational modification, protein turnover, and chaperones), P (inorganic ion transport and metabolism), and G (carbohydrate transport and metabolism), as per cosine similarity analysis.IMPORTANCEDermatophytes are keratinophilic skin fungal pathogens that invade host skin, hair, and nails to acquire nutrients. There is an epidemic-like increase in infections, as well as an increase in antimicrobial resistance among dermatophytes, as witnessed over the last decade. There is hence a need to understand the key pathways and virulence factors required during growth and infection. We present an integrated multi-omics analysis (proteomics and transcriptomics data) using a vector-based similarity approach to show high concordance of KOG functional categories belonging to posttranslational modification, protein turnover, carbohydrate transport, and metabolism.

Proteomics↗
PubMed2026

Orange juice and hesperidin increase flavanone exposure without detectable short-term vascular benefits: a randomized crossover trial.

Orange juice is a major dietary source of hesperidin, a citrus flavanone with vascular protective effects in experimental models. However, whether nutritionally realistic intake levels induce measurable benefits in humans remains unclear. We investigated the effects of orange juice and hesperidin supplementation, at realistic dietary doses, on vascular function, flavanone bioavailability, and molecular responses. Thirty-seven centrally overweight men completed a randomized, double-blind, controlled, three-period crossover trial with three 6-week interventions separated by washout periods. Participants consumed daily 330 mL of 100% orange juice (OJ), an isoenergetic control beverage (CON), or a hesperidin-enriched control beverage (HESP, 210 mg day-1). Fasting vascular, metabolic and anthropometric parameters were assessed before and after each intervention, with flow-mediated dilation (FMD) as the primary endpoint. Postprandial FMD, circulating flavanone metabolites and oxylipin profiles were evaluated following a standardized high-fat meal challenge, and flavanone bioavailability was assessed by 24 h urinary excretion. Whole-blood transcriptomics were performed in a subset (n = 9). Plasma exposure to phase II hesperetin metabolites (AUC0-6 h) and 24 h urinary excretion were comparable after OJ and HESP, indicating effective hesperidin delivery and limited matrix effects on bioavailability. Neither intervention significantly affected fasting or postprandial FMD, vascular, metabolic or anthropometric parameters, or oxylipin profiles versus CON. Marked interindividual variability was observed in vascular responses and flavanone bioavailability, although treatment effects were unrelated to baseline endothelial function or flavanone exposure. Exploratory transcriptomic analyses suggested modulation of pathways involved in vascular biology following OJ and HESP. Under nutritionally realistic conditions, orange juice and hesperidin induced measurable biological engagement without detectable short-term vascular benefits, highlighting the complexity of linking flavanone exposure to functional vascular outcomes in humans.

Humans↗
PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗
PubMed2025

Integrated Multiomics Analysis of Microsatellite Instability-High Colorectal Cancer Identifies a Subtype With Poor Outcome.

Up to 50% of patients with metastatic microsatellite instability-high (MSI-H) colorectal cancer (CRC) are resistant to immunotherapy and experience progression or recurrence after treatment. We integrated the genomic, epigenomic, transcriptomic, and proteomic data for 99 patients in a Chinese MSI-H CRC cohort. Proteomic profiling of primary tumors clearly classified MSI-H tumors into 2 subtypes. We found that the 2 subtypes have different mutational signatures, enriched pathways, gene fusion networks, and clinical outcomes. Notably, NCAM1 could serve as a potential biomarker for checkpoint inhibitor response in MSI-H CRC. Thus, there is an urgent need to stratify the MSI-H group into different subtypes and adopt more targeted therapies to prolong patient survival.

Humans↗
PubMed2002

Transcriptome analysis of all two-component regulatory system mutants of Escherichia coli K-12.

We have systematically examined the mRNA profiles of 36 two-component deletion mutants, which include all two-component regulatory systems of Escherichia coli, under a single growth condition. DNA microarray results revealed that the mutants belong to one of three groups based on their gene expression profiles in Luria-Bertani broth under aerobic conditions: (i) those with no or little change; (ii) those with significant changes; and (iii) those with drastic changes. Under these conditions, the anaeroresponsive ArcB/ArcA system, the osmoresponsive EnvZ/OmpR system and the response regulator UvrY showed the most drastic changes. Cellular functions such as flagellar synthesis and expression of the RpoS regulon were affected by multiple two-component systems. A high correlation coefficient of expression profile was found between several two-component mutants. Together, these results support the view that a network of functional interactions, such as cross-regulation, exists between different two-component systems. The compiled data are avail-able at our website (http://ecoli.aist-nara.ac.jp/xp_analysis/ 2_components).

Bacterial Proteins↗
PubMed2024

Identification and analysis of key genes related to efferocytosis in colorectal cancer.

UNLABELLED: The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis. BACKGROUND: It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC). METHODS: In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS: There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187. CONCLUSION: Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.

Humans↗
PubMed2025

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis.

The integration of cell transcriptomics and spatial position to organize differentiation trajectories remains a challenge. Here, we introduce SpaTrack, which leverages optimal transport to reconcile both gene expression and spatial position from spatial transcriptomics into the transition costs, thereby reconstructing cell differentiation. SpaTrack can construct detailed spatial trajectories that reflect the differentiation topology and trace cell dynamics across multiple samples over temporal intervals. To capture the dynamic drivers of differentiation, SpaTrack models cell fate as a function of expression profiles influenced by transcription factors over time. By applying SpaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Diverse malignant lineages expanding within a primary tumor are uncovered. One lineage, characterized by upregulated epithelial mesenchymal transition, implants at the metastatic site and subsequently colonizes to form a secondary tumor. Overall, SpaTrack efficiently advances trajectory inference from spatial transcriptomics, providing valuable insights into differentiation processes.

Animals↗