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PubMed2025

Hepatocyte dedifferentiation in 2D culture reveals extensive transcriptomic and proteomic rewiring.

BACKGROUND: Primary hepatocytes are commonly used in vitro to model liver metabolism, but prolonged culturing results in dedifferentiation and potentially limits the applicability of this model. METHODS: We characterized the transcriptome and proteome of full liver and primary hepatocytes as either freshly isolated cells or after 24 hours of 2D-culturing. RESULTS: We found that 2D-culturing for 24 hours changes more than 10,000 genes and 3000 proteins compared with freshly isolated cells, accompanied by a decrease in transcriptional heterogeneity and a loss of zonal markers. Moreover, there were changes in proteins associated with the extracellular matrix, in mitochondrial and ribosomal protein abundances, as well as an increase in the abundance of acute-phase response proteins. CONCLUSION: Collectively, primary mouse hepatocytes in culture rewire the transcriptome and proteome, which may affect the utility of this model to study physiological and molecular mechanisms related to the liver. We developed the Shiny app "Hepamorphosis" (https://cbmr.ku.dk/research/resources/shiny-apps/), which allows users to explore RNA/protein correlations, zonation profiles, and cell-type-specific transcription in full liver and cultured hepatocytes.

Hepatocytes↗
PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗
PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗
PubMed2026

Longitudinal development of infant oral ecosystem: salivary metabolomic, bacteriome, and virome dynamics in early infancy.

This prospective cohort study investigated the longitudinal development of the salivary bacteriome, virome, and metabolome during early infancy. We assessed the associations between oral bacteria, viruses, and metabolites from 10 mother-infant dyads, with oral samples collected at 1 and 2 years of age. Forty saliva and plaque samples underwent untargeted metabolomic analysis, and infant saliva samples underwent metagenomic sequencing. Maternal salivary and plaque metabolomic profiles remained largely stable, whereas infant profiles were clearly separated from maternal profiles and changed with age. Notably, infant dental plaque metabolism underwent more substantial changes from year 1 to year 2 than saliva, with age-dependent metabolite shifts mainly involving energy, amino acid, nucleotide, and lipid metabolic pathways. Our findings also revealed significant developmental shifts in salivary bacteriome, virome, and functional pathway profiles during early childhood. The most abundant oral bacteria in early life, comprising over 75% of total abundance, included Veillonella, Streptococcus, Rothia, Prevotella, Neisseria, and Actinomyces species. While human viruses like Roseolovirus were detected, bacteriophages constituted the majority of the virome. Comparing infants at year 1 and year 2, we identified differentially abundant bacteria, viruses, metabolic functional pathways, and specific metabolites. We observed associations between bacteria and viruses, noting that these cross-kingdom relationships attenuated as infants grew. The study results underscore the complex and dynamic development of the oral microbiome, virome, and metabolome during early childhood.IMPORTANCEThe human oral cavity undergoes substantial microbial and metabolic development during early childhood, yet the temporal changes in the infant oral ecosystem remain incompletely understood. In this study, we longitudinally profiled the salivary metabolome, bacteriome, and virome of infants at 1 and 2 years of age. We demonstrated that the infant oral metabolome undergoes substantial developmental shifts, particularly in pathways related to energy, amino acid, and lipid metabolism; whereas maternal metabolic profiles remained stable over the same period. Furthermore, our results revealed the dynamic assembly of infant salivary virome and bacteriome and their associations with the functional pathways and metabolites. These findings provide new insights into the complex and dynamic development of the oral microbiome, virome, and metabolome in early infancy.

bacteriome↗
PubMed2026

Integrative analysis of rumen microbiota activity and host metabolism following methanogenesis inhibition in dairy cattle.

Enteric methane emission from dairy cattle is an environmental challenge. The most efficient mitigation strategies nowadays include the use of methanogenesis inhibitors that specifically target the rumen methanogens. Specific inhibitors, such as 3-nitrooxypropanol (3-NOP), reduce methane emissions without negative effects on the products of fermentation that serve as energy metabolites for the host. However, the concomitant effects of methanogenesis inhibition on rumen microbiota and host metabolism are poorly characterized. Thus, the objective of this study was to explore the association between rumen microbiota and host metabolism when methanogenesis is inhibited. Thirteen dairy cows were used as controls, and 12 were supplemented with 3-NOP for 6 weeks. Rumen microbiota composition and activity were characterized using metagenomics and metatranscriptomics. The host metabolism was assessed in a previous publication by a metabolomic analysis of the plasma. Microbiota data were used as explanatory variables of the metabolome data in a multiblock sparse partial least squares analysis. Overall, the association between rumen microbiota and host metabolism was moderate. Notwithstanding this, a few downregulated transcripts related to glycolysis, hydrogen transfer, and protein synthesis, together with a decrease in the proportion of taxa of the Oscillospirales order, showed a correlation with host one-carbon metabolites (|r| > 0.6). These associations raised novel hypotheses that remain to be elucidated, especially with regard to the effects of dihydrogen on the accumulation of microbial glycolysis and methanogenesis metabolite intermediates.IMPORTANCEDairy cattle produce a substantial amount of methane, a potent greenhouse gas. Several strategies have been designed to reduce methane production by targeting the rumen microbiota. One such strategy specifically inhibits methanogens with a molecule called 3-nitrooxypropanol. This study uses an integrative data analysis approach, combining rumen microbiota and host metabolome information, to explore the consequences of inhibiting methanogenesis on the holobiont. This provides additional holistic insight into the effect of methane mitigation strategies on dairy cattle.

Animals↗
PubMed2025

CountASAP: a lightweight, easy to use python package for processing ASAPseq data.

BACKGROUND: Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. RESULTS: To address this lack of a dedicated tool for ASAPseq data processing, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences. We directly compare the performance and user-friendliness of CountASAP to existing tools using similarly-structured data from a more common sequencing experiment: cellular indexing of transcriptomes and epitopes by sequencing (CITEseq). Further benchmarking against existing tools helps to identify proper defaults for CountASAP and assess the agreement of outputs from all tested software. A final test using a novel ASAPseq dataset provides evidence that CountASAP can generate biologically meaningful results that correlate well with paired chromatin accessibility data. CONCLUSIONS: CountASAP shows good agreement with existing, well-tested data processing tools in the analysis of similarly-structured benchmarking data. CountASAP runs efficiently on a standard laptop, has user-friendly documentation, a one-step installation, and represents the first and only tool designed specifically for the processing of ASAPseq data.

Software↗
PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗
PubMed2026

Adaptive genomic evolution and WD40-regulated temporal dynamics of anthocyanins support leaf photoplasticity in Parrotia subaequalis.

BACKGROUND: Parrotia subaequalis, a Tertiary relict endemic to China, plays a significant role in phylogeny and adaptive evolution as a key species in the early differentiation of angiosperms. It has abundant leaf colors and great potential as an ornamental tree. RESULTS: This study assembled the first chromosome-level genome of P. subaequalis (Contig N50 = 2.15 Mb), revealing transposable element proliferation, key paleopolyploid events and dynamic gene family evolution, including the expansion of secondary metabolite transport and synthesis genes (such as WD40, 2OG-FeII_Oxy) and the contraction of gene families related to flower morphogenesis (such as F-box-like, K-box). Through integrative transcriptomics and targeted metabolomics approaches, we further revealed that the color transition of young leaves from red to green was driven by temporal accumulation differences of malvidin-3,5-O-diglucoside, whose biosynthesis is progressively down-regulated during leaf development. WGCNA revealed that a subset of WD40 genes (light-signaling, TTG1/HOS15-like, etc.) coexpresses with anthocyanin biosynthetic genes, like 4CLL9, GT1, in anthocyanin-related modules enriched for auxin signaling and hydrolase activity, suggesting a potential link between WD40 expansion and photoprotective plasticity. Relevant regulatory networks were found to complement the species-specific gene pool related to leaf color regulation. CONCLUSION: This genomic resource of P. subaequalis advanced our understanding of early angiosperm adaptation through neofunctionalized regulatory networks and established a foundation for molecular breeding aimed at enhancing environmental resilience while preserving ornamental traits.

Anthocyanins↗
PubMed2026

Thyroid-stimulating hormone receptor mediates peripheral-central neuroimmune crosstalk in autoimmune thyroid diseases.

BACKGROUND: Organ-specific autoimmune diseases, particularly Graves' disease (GD) and its extrathyroidal manifestation, Graves' orbitopathy (GO), are characterized by systemic autoimmunity that may extend its impact to the central nervous system (CNS). While thyroid-stimulating hormone receptor (TSHR) is the primary driver of pathological remodeling in the thyroid and orbital tissues, emerging evidence suggests it is also expressed in the brain and may participate in neuroimmune signaling. However, the molecular mechanisms linking peripheral TSHR-driven autoimmunity to these extended systemic features remain unclear. Thus, GD and GO provide a unique window to investigate how peripheral autoantibodies influence CNS involvement as part of its broader pathological spectrum. METHODS: Genome-wide association studies (GWAS) and post-GWAS analyses were integrated with bulk RNA sequencing, single-cell and spatial transcriptomics, and brain imaging phenotypes to comprehensively characterize peripheral and central alterations in GD and GO. Mendelian randomization was applied to test causal relationships between genetic variants and brain signatures. Structural biology analyses were further conducted including protein-protein docking, small-molecule docking, and normal mode dynamics to identify prospective modulators of TSHR. Immunofluorescence staining was performed in a GO mouse model to validate the colocalization of potential interacted proteins in the specific brain region. RESULTS: Brain imaging-derived phenotypes (IDPs) alterations in GO and GO were systematically analyzed to identify neuroanatomical and functional alterations. TSHR was further identified as a shared genetic driver across peripheral and central compartments. TSHR was expressed in spiny projection neurons, microglia, and peripheral T cells, with cell-cell communication analyses highlighting TSHR-mediated interactions among neurons, endothelial cells, and microglia. Immunofluorescence staining in a GO mouse model confirmed the colocalization of TSHR with FN1 and GNAS in the basal ganglia, providing tissue-level validation of the computationally predicted ligand-receptor interactions. Immune profiling further showed immune alterations in GD and GO. Structural modeling supported plausible physical interfaces between TSHR and interacting proteins, and small-molecule screening identified three repurposable compounds - venetoclax, irinotecan, and dutasteride - with predicted favorable docking scores and stable binding poses in our simulations. CONCLUSIONS: These findings demonstrate that TSHR acts as a molecular hub mediating peripheral-central neuroimmune crosstalk in GD and GO. The results support a broader "disease-molecule axis" framework that links genetic susceptibility with multi-level immune and neural mechanisms. This work provides mechanistic insights relevant to the development of TSHR-targeted therapies, with implications for both peripheral immune modulation and central regulation. However, the limited sample size, lack of longitudinal follow-up, and absence of in vivo validation warrant cautious interpretation and further investigation.

Receptors, Thyrotropin↗
PubMed2024

ASXL1 truncating variants in BOS and myeloid leukemia drive shared disruption of Wnt-signaling pathways but have differential isoform usage of RUNX3.

BACKGROUND: Rare variants in epigenes (a.k.a. chromatin modifiers), a class of genes that control epigenetic regulation, are commonly identified in both pediatric neurodevelopmental syndromes and as somatic variants in cancer. However, little is known about the extent of the shared disruption of signaling pathways by the same epigene across different diseases. To address this, we study an epigene, Additional Sex Combs-like 1 (ASXL1), where truncating heterozygous variants cause Bohring-Opitz syndrome (BOS, OMIM #605039), a germline neurodevelopmental disorder, while somatic variants are driver events in acute myeloid leukemia (AML). No BOS patients have been reported to have AML. METHODS: This study explores common pathways dysregulated by ASXL1 variants in patients with BOS and AML. We analyzed whole blood transcriptomic and DNA methylation data from patients with BOS and AML with ASXL1-variant (AML-ASXL1) and examined differential exon usage and cell proportions. RESULTS: Our analyses identified common molecular signatures between BOS and AML-ASXL1 and highlighted key biomarkers, including VANGL2, GRIK5 and GREM2, that are dysregulated across samples with ASXL1 variants, regardless of disease type. Notably, our data revealed significant de-repression of posterior homeobox A (HOXA) genes and upregulation of Wnt-signaling and hematopoietic regulator HOXB4. While we discovered many shared epigenetic and transcriptomic features, we also identified differential splice isoforms in RUNX3 where the long isoform, p46, is preferentially expressed in BOS, while the shorter p44 isoform is expressed in AML-ASXL1. CONCLUSION: Our findings highlight the strong effects of ASXL1 variants that supersede cell-type and even disease states. This is the first direct comparison of transcriptomic and methylation profiles driven by pathogenic variants in a chromatin modifier gene in distinct diseases. Similar to RASopathies, in which pathogenic variants in many genes lead to overlapping phenotypes that can be treated by inhibiting a common pathway, our data identifies common pathways for ASXL1 variants that can be targeted for both disease states. Comparative approaches of high-penetrance genetic variants across cell types and disease states can identify targetable pathways to treat multiple diseases. Finally, our work highlights the connections of epigenes, such as ASXL1, to an underlying stem-cell state in both early development and in malignancy.

Humans↗
PubMed2026

A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes and long-term metabolic and cardiovascular risk. However, oral glucose tolerance testing at 24-28 gestational weeks limits early risk stratification. Gut microbiota-associated metabolites may reflect early metabolic abnormalities, including those relevant to cardiometabolic health, but robust early-pregnancy biomarkers remain limited. METHODS: We conducted a multicenter nested case-control and prospective study involving 2,693 pregnant women. Untargeted metabolomics and metagenomics were integrated to identify GDM-associated metabolites and gut microbial alterations. Three consistently dysregulated metabolites, 3-hydroxydecanoic acid, γ-Glu-Leu, and propionic acid, were quantified by targeted LC-MS/MS. Candidate algorithms were compared using repeated 10-fold cross-validation, and a final generalized linear model was externally and prospectively validated. RESULTS: Women who later developed GDM showed an adverse early-pregnancy metabolic profile, including higher BMI, triglycerides, and platelet count. Untargeted metabolomics identified 14 persistently altered metabolites enriched in energy, oxidative stress, and amino acid metabolism pathways. Metagenomics revealed taxonomic restructuring and coordinated microbiota-metabolite associations. The three-metabolite model achieved AUCs of 0.838 (95% CI, 0.791-0.885) in training, 0.840 (95% CI, 0.769-0.911) in internal validation, 0.955 (95% CI, 0.925-0.985) and 0.917 (95% CI, 0.875-0.958) in two external cohorts, and 0.969 (95% CI, 0.937-1.000) in the prospective cohort. CONCLUSION: Early microbiota-associated metabolic dysregulation is detectable before routine GDM diagnosis. This compact three-metabolite panel may support early GDM risk stratification and provides metabolic evidence relevant to broader cardiometabolic risk assessment in pregnancy.

Humans↗
PubMed2026

Systematic discovery of retina-enriched Rik genes identifies 1190005I06Rik as a novel modulator of visual signalling.

BACKGROUND: High‑throughput transcriptome projects have revealed thousands of mammalian genes with little or no functional annotation. Among these are hundreds of loci assigned provisional “Rik” identifiers following discovery in the RIKEN cDNA annotation effort. Although often dismissed as genomic dark matter, such genes may encode tissue‑restricted proteins that modulate physiologic functions and influence disease. The retina is a highly specialised neural tissue and a common site of inherited disorders; understanding its molecular repertoire could illuminate novel therapeutic avenues. METHODS: We integrated bulk RNA‑seq from ten adult mouse tissues, evolutionary and domain analysis, single‑cell RNA‑seq, and CRISPR/Cas9 gene disruption to systematically catalogue protein‑coding Rik genes enriched in the retina and test the function of a representative gene. RESULTS: A rigorous differential expression analysis identified 44 Rik genes with robust retina‑specific expression compared with nine non‑retinal tissues. Many of these genes lack orthologues beyond rodents, while others show broad conservation, illustrating a continuum from lineage‑restricted to conserved retinopathy candidates. Single‑cell transcriptomics revealed that these genes are expressed across retinal cell types, with the highest aggregate expression in cone photoreceptors and inner interneurons. To evaluate physiological significance, we generated a 1190005I06Rik knockout mouse. Although retinal architecture appeared normal, loss of 1190005I06Rik enhanced electroretinogram b‑wave amplitudes and altered light‑avoidance behaviour, indicating that this previously uncharacterised gene acts as a negative modulator of visual signalling. CONCLUSIONS: We present a curated atlas of retina‑enriched Rik genes and demonstrate that 1190005I06RIK modulates retinal circuit function. This resource expands the molecular landscape of the retina and provides new candidates for the genetic basis of inherited retinal disease. Our findings underscore that unannotated genes may exert measurable effects on sensory processing and warrant systematic exploration in the context of human ocular disorders.

Animals↗
PubMed2026

Reduced legacy precipitation decreases microbial community growth efficiency and alters soil organic carbon in a California grassland.

BACKGROUND: Changes in global patterns can leave a lasting legacy in semiarid grasslands by reshaping microbial growth dynamics and carbon cycling during the first wet-up in the autumn-a period known for intense microbial activity and significant carbon emissions. To study the lasting impacts of decreased winter rain, we implemented two precipitation regimes (100% vs. 50% mean annual precipitation) in California Mediterranean-climate grassland field plots. After the dry season, soils were rewetted in the laboratory with H218O and sampled at 0 h, 3 h, 24 h, 48 h, 72 h, and 168 h post rewet. We quantified CO2 efflux, measured microbial growth and mortality via quantitative 18O stable isotope probing and 16S rRNA gene amplicon sequencing, and characterized the soil organic carbon chemical composition, metagenomes, and metatranscriptomes. RESULTS: We found that reduced winter precipitation imposed a strong legacy effect on microbial turnover; despite maintaining similar respiration rates, microbial growth declined by ~1 order of magnitude, yielding decreased community growth efficiency (CGE = new biomass growth/respiration), and microbial mortality declined by ~2 orders of magnitude. Soil organic carbon also shifted from lipid-like, amino-sugar-like, and protein-like compounds (indicative of microbial necromass) to more oxidized lignin-like and tannin-like compounds (indicative of decomposing plant-derived compounds). Meta-omics revealed distinct metabolic strategies linked to CGE. At high-CGE, microbes appeared to consume more energetically favorable N-rich necromass (released via high microbial turnover); this allowed for increased amino acids and peptidoglycan biosynthesis and greater aromatic compound degradation, fueling further energy production and growth efficiency. At low CGE, communities had elevated carbohydrate metabolism and lipid turnover, consistent with increased investment in plant detritus degradation and membrane repair and maintenance rather than growth. CONCLUSIONS: Together, our findings demonstrate that reduced winter rainfall decreases microbial turnover following rewetting without a concurrent reduction in CO2 emissions. This shift results in persistently lower CGE, which has the potential to increase soil carbon loss as CO2. If such conditions are maintained over multiple years, these changes could reshape soil organic carbon stocks and alter the balance of grassland ecosystems under future climate scenarios. While our data suggest that sustained reductions in CGE may drive SOC decline, the magnitude and persistence of these effects depend on long-term environmental dynamics and warrant further investigation. Video Abstract.

Soil Microbiology↗
PubMed2026

Integrated analysis of plasma metabolomics and proteomics reveals the biological characteristics of damp-heat and stasis-toxin syndrome in colorectal cancer.

OBJECTIVE: To investigate the biological attributes of core syndromes in colorectal cancer, namely, the damp-heat and stasis-toxin syndrome (SRYD). METHODS: Between October 2021 and October 2022, a cohort comprising 40 patients with colorectal cancer (CRC) diagnosed with damp-heat and stasis-toxin syndrome (SRYD group), 40 patients with CRC without this syndrome (non-SRYD group), and 40 healthy controls (Normal group) was recruited at Jiangsu Province Hospital of Chinese Medicine. Untargeted metabolomics analysis was conducted on plasma samples from all 120 participants, while differential protein analysis using four-dimensional data-independent acquisition proteomics was performed on 20 randomly selected samples per group. A combined analysis of proteomics and metabolomics data followed, and the identified potential diagnostic biomarkers were subsequently used to train and validate multiple machine learning models. RESULTS: Proteomic analysis revealed 130 differential proteins in the colorectal cancer with damp-heat and stasis-toxin syndrome (CRC-SRYD) group, enriched in pathways including complement and coagulation cascades, as well as nuclear factor kappa-B (NF-κB) signaling. Metabolomic analysis identified 584 differential metabolites within the same group, showing enrichment in pathways such as primary bile acid biosynthesis, central carbon metabolism in cancer, and glucagon signaling. Integrated pathway analysis indicated heightened activity of the NF-κB signaling pathway in the CRC-SRYD group. A biomarker panel, comprising 6 proteins and 9 metabolites selected through the ReliefF algorithm, was used to construct a diagnostic model with random forest, achieving an accuracy of 93.33%, sensitivity of 80.00%, and specificity of 100%. CONCLUSION: This study systematically elucidates plasma metabolomic and proteomic alterations in patients with CRC, establishing a robust diagnostic model for CRC syndrome (CRC-SRYD). Further investigation is warranted to clarify the underlying molecular mechanisms and biological foundations.

Humans↗
PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗
PubMed2026

A regulatory network underlying idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease in which genetic susceptibility interacts with epithelial, immune, and mesenchymal remodeling. Although the chromosome 11p15.5 locus contains established IPF susceptibility signals near MUC5B and TOLLIP, the broader regulatory architecture of this region remains incompletely resolved. METHODS: We integrated IPF genome-wide association study summary statistics with methylation, expression, and protein quantitative trait loci using summary-data-based Mendelian randomization (SMR). SMR-prioritized candidates were evaluated in independent transcriptomic and methylation cohorts and further contextualized using microRNA, transcription-factor, protein-interaction, machine-learning, single-cell, and spatial transcriptomic analyses. Fibrosis-associated expression patterns were assessed in a bleomycin-induced pulmonary fibrosis rat model. RESULTS: The analyses recovered the established MUC5B and TOLLIP signals and prioritized BRSK2 as a comparatively underexplored candidate supported by eQTL-based SMR and independent molecular evidence. The BRSK2 pQTL association did not pass the HEIDI test and was therefore not interpreted as convergent protein-level genetic evidence. Network analyses linked BRSK2 to cell-cycle, metabolic-stress, and senescence-related programs, while cross-cohort machine learning prioritized FOXA2, CDC25B, and NFE2 as informative network features. Single-cell and spatial analyses localized BRSK2 preferentially to fibroblast and myofibroblast compartments and to regions with greater histological fibrosis severity. In fibrotic rat lungs, BRSK2 expression increased, whereas FOXA2 and CDC25B decreased at the transcript and protein levels. CONCLUSIONS: These findings refine the molecular landscape of the chromosome 11p15.5 IPF susceptibility locus and prioritize BRSK2 as a candidate component of an IPF-associated profibrotic fibroblast state. Its causal contribution, direct regulatory relationships, and therapeutic tractability require targeted mechanistic validation.

Idiopathic Pulmonary Fibrosis↗
PubMed2026

Bioinformatics in crop research: using genomic data for crop improvement.

Sustainable crop development aims to maintain or increase yields while reducing environmental impact and managing the challenges imposed by climate change. As the global population grows and arable land becomes scarcer, the integration of molecular breeding with bioinformatics has emerged as an effective strategy for long-term crop improvement. Bioinformatics enables researchers to analyze and interpret the vast quantities of genetic data generated by high-throughput sequencing, making it possible to identify molecular markers, candidate genes, and regulatory networks linked to specific agronomic traits, which breeders then translate into focused, ecologically sustainable breeding programs. This approach has enabled major progress across several fronts: the identification of genes conferring resistance to biotic stressors (pests, pathogens) and abiotic stressors (drought, salinity, heat); the development of nutrient-efficient, low-input crop varieties; the improvement of agronomic performance and nutritional quality through identification of yield- and quality-related genes; and the conservation and deployment of genetic diversity to safeguard long-term breeding sustainability. By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.

bioinformatics↗