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PubMed2025

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals.

BACKGROUND: Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability. RESULTS: Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246 K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86 K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate. CONCLUSIONS: This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.

Humans↗

PubMed2026

Experimental insights in taxon-specific functional responses to droughts in glacier-fed stream biofilms.

BACKGROUND: Glacier-fed streams are predicted to face increasingly frequent and intense droughts. However, the impacts of drought events on benthic biofilm, including bacteria, eukaryotes, and viruses, the dominating life form in glacier-fed streams, remain poorly understood. RESULTS: Using streamside flume mesocosms in the Swiss Alps, we grew glacier-fed stream biofilms over 103 days and exposed them to three droughts. Using a multi-omics approach (metagenomics, metatranscriptomics, and metaproteomics), we assessed the effects of a series of droughts on the taxonomy and metabolic activity of bacterial, eukaryotic, and viral metagenome-assembled genomes (MAGs). We found that the first drought (6 h) caused only minor changes, including mild upregulation of heterotrophic metabolism and signs of stress in diatoms. In contrast, the second drought (24 h) significantly altered both the composition and functionality of the microbiome, shifting phototrophic dominance from diatoms to Cyanobacteriota, while maintaining overall phototropic biomass and further upregulating the heterotrophic metabolism. Interestingly, a third 24 h drought had no detectable transcriptomic effect between pre- and post-drought conditions, suggesting a certain level of adaptive responses to droughts, but with the low diatom abundance being maintained. CONCLUSIONS: These findings indicate that glacier-fed biofilm microorganisms initially resisted short-term drought, but a second longer drought caused important shifts in their community structure, activity, and function. Climate-induced increases in drought frequency or duration may therefore have a lasting impact on microbial ecosystem functioning in glacier-fed streams. Video Abstract.

Biofilms↗

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

Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.

Cancer of Unknown Primary (CUP) presents substantial diagnostic and therapeutic challenges owing to its heterogeneous nature and the absence of an identifiable primary tumor site. This review provides a structured search of the pathogenesis, epidemiological characteristics, and limitations of traditional diagnostic and therapeutic approaches for CUP, with an emphasis on the evolution of Tissue of Origin (TOO) identification techniques. Recent advances in precision medicine have accelerated the development of machine learning-based TOO identification tools, representing a paradigm shift in CUP diagnostics. Deep learning (DL) algorithms that integrate multi-omics data (such as genomics and transcriptomics) with clinical features have markedly enhanced the accuracy of tracing tumor origin, and artificial intelligence (AI) driven TOO models are increasingly being incorporated into clinical practice, offering new insights for pathological diagnosis, treatment selection, and prognostic evaluation. Nevertheless, several challenges remain, including issues of data standardization, model generalizability, and interpretability. Ethical considerations related to data privacy, algorithmic fairness, and clinical implementation also warrant careful attention. Future research should focus on establishing standardized multi-center databases, developing more interpretable AI models, and fostering multidisciplinary collaborative strategies for CUP management. Through continued refinement of technical solutions and regulatory guidelines, TOO identification is anticipated to progress from research to routine clinical application, ultimately supporting precise and personalized care for patients with CUP.

Artificial intelligence↗

PubMed2025

Emerging biomarkers in ischemic stroke.

Ischemic stroke is a devastating global public health problem and the leading cause of acute death and chronic disability. Despite being the diagnostic cornerstone, limitations in neuroimaging, including availability, cost, and therapeutic window, have rekindled interest in biomarker-based approaches. Biomarkers will be employed to facilitate the eventual prediction, early diagnosis, and prognosis of strokes, as well as to inform person-centered medicine. This review summarizes recent advances in the search for biomarkers related to inflammatory, endothelial, metabolic, and neuroaxonal pathways. Interleukin-6 (IL-6), asymmetric dimethylarginine (ADMA), endothelial microparticles (EMP), and homocysteine serve as predictive biomarkers corresponding to vascular risk and inflammatory priming. Glial fibrillary acidic protein (GFAP), D-dimer, and neuron-specific enolase (NSE) are diagnostic markers that can already subtype stroke and estimate lesion burden. Prognostic biomarkers, such as serum neurofilament light chain (sNfL), N-terminal pro-B-type natriuretic peptide (NT-pro-BNP), and growth differentiation factor 15 (GDF-15), are associated with infarct size and long-term outcomes. The -omic sciences (genomic, proteomic, and metabolomic) have discovered defined molecular signatures and panels with high specificity to describe heterogeneity in stroke. Cerebrospinal fluid (CSF) biomarkers and newer imaging modalities, such as those provided through positron emission tomography/computed tomography (PET/CT), offer valuable adjuncts to blood biomarkers in the diagnosis of conditions. Translational potential is hindered by heterogeneity in the transcriptional landscape.

Ischemic stroke↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed2026

Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.

Agentic artificial intelligence is extending bioinformatics beyond conversational assistance by enabling systems to select tools, execute code, revise analytical plans, and interpret biological data. These capabilities may accelerate research, but they also redistribute decisions that determine whether biological conclusions are valid. We conducted a targeted, structured PubMed search in July 2026 and identified 11 peer-reviewed agentic bioinformatics systems for descriptive review based on predefined eligibility criteria for analytical decision-making, tool or code execution, iterative evaluation, or coordinated agent activity. The evidence base covered single-cell transcriptomics, microbial genomics, cancer genomics, and omics applications, together with methodological literature on reproducibility and biological validation. We examined how current systems report delegated authority, provenance, validation, evidence, abstention, and human oversight. Existing platforms implement safeguards such as sandboxed execution, restricted commands, interaction logs, evidence identifiers, automated checks, critic agents, quality scores, and expert assessment. However, published reports rarely provide a connected account linking the original biological question to samples, reference resources, analytical decisions, computational actions, statistical results, supporting evidence, validation outcomes, and final claims. We distinguish inherited bioinformatics errors, errors amplified through autonomous action, and emergent failures arising from memory, retrieval, tool interaction, or agent coordination. We further propose a multidimensional decision-rights profile, consequence-sensitive validation gates, and a claim-to-evidence provenance architecture organized through the Traceable History of Research Evidence, Agent Actions, and Decisions in Bioinformatics (THREAD-Bio) framework. Illustrative cases show that technically successful execution may still support misleading inference. Trustworthy agentic bioinformatics therefore requires claims to remain reconstructible, challengeable, validated, and proportionate to the evidence.

accountable autonomy↗

PubMed2025

Cardiovascular Complications Are Increased in Inflammatory Bowel Disease: A Path Toward Achievement of a Personalized Risk Estimation.

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis-including genomic, proteomic, transcriptomic, and microbiome profiling-will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models.

Crohn’s disease↗

PubMed2021

Early-Onset Atrial Fibrillation and the Prevalence of Rare Variants in Cardiomyopathy and Arrhythmia Genes.

IMPORTANCE: Early-onset atrial fibrillation (AF) can be the initial manifestation of a more serious underlying inherited cardiomyopathy or arrhythmia syndrome. OBJECTIVE: To examine the results of genetic testing for early-onset AF. DESIGN, SETTING, AND PARTICIPANTS: This prospective, observational cohort study enrolled participants from an academic medical center who had AF diagnosed before 66 years of age and underwent whole genome sequencing through the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine program. Participants were enrolled from November 23, 1999, to June 2, 2015. Data analysis was performed from October 24, 2020, to March 11, 2021. EXPOSURES: Rare variants identified in a panel of 145 genes that are included on cardiomyopathy and arrhythmia panels used by commercial clinical genetic testing laboratories. MAIN OUTCOMES AND MEASURES: Sequencing data were analyzed using an automated process followed by manual review by a panel of independent, blinded reviewers. The primary outcome was classification of rare variants using American College of Medical Genetics and Genomics criteria: benign, likely benign, variant of undetermined significance, likely pathogenic, or pathogenic. Disease-associated variants were defined as pathogenic/likely pathogenic variants in genes associated with autosomal dominant or X-linked dominant disorders. RESULTS: Among 1293 participants (934 [72.2%] male; median [interquartile range] age at enrollment, 56 [48-61] years; median [interquartile range] age at AF diagnosis, 50 [41-56] years), genetic testing identified 131 participants (10.1%) with a disease-associated variant, 812 (62.8%) with a variant of undetermined significance, 92 (7.1%) as heterozygous carriers for an autosomal recessive disorder, and 258 (20.0%) with no suspicious variant. The likelihood of a disease-associated variant was highest in participants with AF diagnosed before the age of 30 years (20 of 119 [16.8%; 95% CI, 10.0%-23.6%]) and lowest after the age of 60 years (8 of 112 [7.1%; 95% CI, 2.4%-11.9%]). Disease-associated variants were more often associated with inherited cardiomyopathy syndromes compared with inherited arrhythmias. The most common genes were TTN (n = 38), MYH7 (n = 18), MYH6 (n = 10), LMNA (n = 9), and KCNQ1 (n = 8). CONCLUSIONS AND RELEVANCE: In this cohort study, genetic testing identified a disease-associated variant in 10% of patients with early-onset AF (the percentage was higher if diagnosed before the age of 30 years and lower if diagnosed after the age of 60 years). Most pathogenic/likely pathogenic variants are in genes associated with cardiomyopathy. These results support the use of genetic testing in early-onset AF.

Adult↗

PubMed2022

Mortality Among Patients With Early-Onset Atrial Fibrillation and Rare Variants in Cardiomyopathy and Arrhythmia Genes.

IMPORTANCE: Patients with early-onset atrial fibrillation (AF) are enriched for rare variants in cardiomyopathy and arrhythmia genes. The clinical significance of these rare variants in patients with early-onset AF is unknown. OBJECTIVE: To assess the association between rare variants in cardiomyopathy and arrhythmia genes detected in patients with early-onset AF and time to death. DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort study included participants with AF diagnosed before 66 years of age who underwent whole-genome sequencing through the National Heart, Lung and Blood Institute's Trans-Omics for Precision Medicine program. Participants were enrolled from November 23, 1999, to June 2, 2015. Data were analyzed from February 26 to September 19, 2021. EXPOSURES: Rare variants identified in a panel of 145 genes that are included in cardiomyopathy and arrhythmia panels used by commercial clinical genetic testing laboratories. MAIN OUTCOMES AND MEASURES: The primary study outcome was time to death and was adjudicated from medical records and the National Death Index. Multivariable Cox proportional hazards regression was used to evaluate the association of disease-associated variants with risk of death after adjustment for age at AF diagnosis, sex, race, body mass index, left ventricular ejection fraction, and an interaction term of age at AF diagnosis and disease-associated variant status. RESULTS: Among 1293 participants (934 [72%] male; median age at enrollment, 56.0 years; IQR, 48.0-61.0 years), disease-associated (pathogenic or likely pathogenic) rare variants were found in 131 (10%). During a median follow-up of 9.9 years (IQR, 6.9-13.2 years), 219 participants (17%) died. In univariable analysis, disease-associated variants were associated with an increased risk of mortality (hazard ratio, [HR], 1.5; 95% CI, 1.0-2.1; P = .05); the association remained significant in multivariable modeling when adjusted for age at AF diagnosis, sex, race, body mass index, left ventricular ejection fraction, and an interaction term between disease-associated variant status and age at AF diagnosis. The interaction demonstrated that disease-associated variants were associated with a significantly higher risk of mortality compared with no disease-associated variant when AF was diagnosed at a younger age (P = .008 for interaction). Higher body mass index (per IQR: HR, 1.4; 95% CI, 1.2-1.6; P < .001) and lower left ventricular ejection fraction (per IQR: HR, 0.8; 95% CI, 0.7-0.8; P < .001) were associated with higher mortality risk. There were 73 cardiomyopathy-related deaths, 40 sudden deaths, and 10 stroke-related deaths. Mortality among patients with the most prevalent genes with disease-associated variants was 26% (10 of 38 patients) for TTN, 33% (6 of 18) for MYH7, 22% (2 of 9) for LMNA, 0% (0 of 10) for MYH6, and 0% (0 of 8) for KCNQ1. CONCLUSIONS AND RELEVANCE: The findings suggest that rare variants in cardiomyopathy and arrhythmia genes may be associated with increased risk of mortality among patients with early-onset AF, especially those diagnosed at a younger age. Genetic testing may provide important prognostic information for patients with early-onset AF.

Atrial Fibrillation↗

PubMed2002

Correlated mutation analyses on very large sequence families.

The 'omics era' (the era of genomics, proteomics, and so forth) is marked by a flood of data that need to be interpreted to become useful information. Thanks to genome sequencing projects, large numbers of sequence families with more than a thousand members each are now available. Novel analytical techniques are needed to deal with this avalanche of sequence data. Sequence entropy is a measure of the information present in an alignment, whereas sequence variability represents the mutational flexibility at a particular position. Entropy versus variability plots can reveal the roles of groups of residues in the overall function of a protein. Such roles can be as part of the main active site, part of a modulator binding site, or transduction of a signal between those sites. Residues that are involved in a common function tend to stay conserved as a group, but when they mutate, they tend to mutate together. Correlated mutation analysis can detect groups of residue positions that show this behaviour. The combination of entropy, variability and correlation is a powerful tool to convert sequence data into useful information. This analysis can, for example, detect the key residues involved in cooperativity in globins, the switch regions in ras-like proteins and the calcium binding and signalling residues in serine proteases. We have extrapolated from these three classes of structurally and functionally well-described proteins to G-protein-coupled receptors (GPCRs). We can detect the residues in the main functional site in GPCRs that are responsible for G-protein coupling, the residues in the endogenous agonist binding site, and the residues in between that transduce the signal to and fro between these sites. The results are discussed in the light of a simple two-step evolutionary model for the development of functional proteins.

Amino Acid Sequence↗

PubMed2026

Nrsn1-Smarcc1 Coupling Regulates Neural Stem Cell Differentiation and Chronic-phase Recovery After Ischemic Stroke.

Stroke remains a leading cause of long-term neurological disability worldwide, largely due to irreversible neuronal loss and the limited regenerative capacity of the adult mammalian brain. Neural stem cells (NSCs) in the adult brain possess the potential to generate new neurons after injury, yet the molecular mechanisms regulating their neuronal differentiation following ischemic insult remain incompletely understood. Here, integrating single-cell multi-omics analyses with spatial transcriptomics, we systematically delineated cell type-specific spatiotemporal dynamics in the striatum of a mouse model of ischemia-reperfusion injury. We identified Neurensin 1 (Nrsn1) as a gene markedly upregulated during NSC-derived neuronal differentiation in the recovery phase. Mechanistically, Foxa2 directly activates Nrsn1 transcription, whereas Nrsn1 promotes neuronal differentiation by facilitating the nuclear translocation of the chromatin-remodeling factor Smarcc1 in vitro. In vivo, both endogenous NSCs and transplanted NSCs overexpressing Nrsn1 significantly enhanced neuronal regeneration and improved functional recovery in mice subjected to middle cerebral artery occlusion and reperfusion (MCAO/R). Collectively, these findings identify Nrsn1 as a key regulator of NSC neuronal differentiation and uncover a Nrsn1-Smarcc1 coupling mechanism that promotes neural regeneration after ischemic brain injury, highlighting a potential molecular target for strategies aimed at enhancing post-stroke recovery.

Foxa2↗

PubMed2025

Genetic Architecture of Idiopathic Inflammatory Myopathies From Meta-Analyses.

OBJECTIVE: Idiopathic inflammatory myopathies (IIMs, myositis) are rare systemic autoimmune disorders that lead to muscle inflammation, weakness, and extramuscular manifestations, with a strong genetic component influencing disease development and progression. Previous genome-wide association studies identified loci associated with IIMs. In this study, we imputed data from two prior genome-wide myositis studies and analyzed the largest myositis data set to date to identify novel risk loci and susceptibility genes associated with IIMs and its clinical subtypes. METHODS: We performed association analyses on 14,903 individuals (3,206 patients and 11,697 controls) with genotypes and imputed data from the Trans-Omics for Precision Medicine reference panel. Fine-mapping and expression quantitative trait locus colocalization analyses in myositis-relevant tissues indicated potential causal variants. Functional annotation and network analyses using the random walk with restart (RWR) algorithm explored underlying genetic networks and drug repurposing opportunities. RESULTS: Our analyses identified novel risk loci and susceptibility genes, such as FCRLA, NFKB1, IRF4, DCAKD, and ATXN2 in overall IIMs; NEMP2 in polymyositis; ACBC11 in dermatomyositis; and PSD3 in myositis with anti-histidyl-transfer RNA synthetase autoantibodies (anti-Jo-1). We also characterized effects of HLA region variants and the role of C4. Colocalization analyses suggested putative causal variants in DCAKD in skin and muscle, HCP5 in lung, and IRF4 in Epstein-Barr virus (EBV)-transformed lymphocytes, lung, and whole blood. RWR further prioritized additional candidate genes, including APP, CD74, CIITA, NR1H4, and TXNIP, for future investigation. CONCLUSION: Our study uncovers novel genetic regions contributing to IIMs, advancing our understanding of myositis pathogenesis and offering new insights for future research.

Humans↗

PubMed2026

A Multifaceted Interplay Among Hemophagocytosis, Interleukin-18, and Type I Interferon Distinguishes Still Disease From Other Autoinflammatory Diseases.

OBJECTIVE: The unknown pathophysiology and the lack of specific features for systemic juvenile idiopathic arthritis and adult-onset Still disease (collectively known as Still disease; SD) delay diagnosis and appropriate treatment. The goal of this study was to identify features and mechanisms that distinguish SD from other systemic autoinflammatory diseases (SAID). METHODS: Using the SomaScan assay and RNA sequencing (RNA-Seq), we determined the plasma proteomes and immune cell microRNA (miRNA) and RNA transcriptomes of 372 patients with SAID, respectively. Proteomic findings were validated by enzyme-linked immunosorbent assays. SD (n = 72) and non-SD SAIDs (n = 300) were compared to identify distinguishing features of SD. We performed integrated and unbiased analyses of all data sets using weighted gene correlation network analysis to identify feature modules that characterize SD and stratify patients. RESULTS: Elevated plasma heme oxygenase 1 (HO-1) and interleukin-18 (IL-18) strongly correlate and characterize SD but do not associate with general inflammation. SD was characterized by ferroptosis in plasma, type I interferon (IFN) signaling in monocyte transcriptomes, and elevated natural killer cell miRNA-146a-5p, which is an IL-18 induced miRNA. Finally, we identified feature modules that distinguish SD from other SAIDs and stratified patients with SD into two distinct subgroups not attributable to disease activity or inflammation but hemophagocytosis. CONCLUSION: This unprecedented large omics data set of SAIDs revealed that complex interactions among hemophagocytosis, IL-18, and type I IFN signaling characterize SD. Furthermore, two distinct subgroups in patients with SD were distinguished by the degree of hemophagocytic activity. Finally, the large proteomics and RNA-Seq data sets generated in this study can serve as an invaluable resource for the further investigation of SD and other SAIDs.

Humans↗

PubMed2004

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques.

Ammonia↗

PubMed2026

Comprehensive In Silico Analysis Identifies MSTO1 and LIG1 as Candidate Biomarkers With Diagnostic and Prognostic Relevance in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and remains a major cause of cancer-related mortality worldwide. Its poor clinical outcomes are largely attributed to late-stage diagnosis and the limited accuracy of currently available diagnostic and prognostic biomarkers. Therefore, identifying novel molecular markers with improved sensitivity, specificity, and therapeutic relevance is essential for enhancing early detection and guiding personalized treatment strategies. AIMS: To identify and prioritize novel candidate HCC biomarkers with diagnostic and prognostic value and potential therapeutic vulnerability using integrated multi-omics, survival, functional dependency, and tumor microenvironment analyses. METHODS AND RESULTS: We examined the mRNA and protein expression levels of 8 DEGs in HCC tissues in the TCGA and CPTAC datasets using UALCAN, which showed that MSTO1 and LIG1 were overexpressed consistently in HCC relative to normal liver tissues. Moreover, elevated expression levels of these genes were significantly associated with higher tumor grade and advanced stage. Kaplan-Meier plotter survival data confirmed that increased expression of MSTO1 and LIG1 was associated with poorer overall survival. The DepMap CRISPR knockout data confirmed a functional dependency of both genes in HCC cell lines. CBioPortal analyses provided characterization of genomic alterations and enabled enrichment analysis of co-expressed genes, and the TCGA-UALCAN pan-cancer analyses supported the assessment of tissue specificity across tumor types. TIMER3 analyses linked candidate gene expression with immune cell infiltration patterns. Diagnostic performance by ROC analysis showed excellent discrimination for MSTO1 (AUC = 0.987) and good discrimination for LIG1 (AUC = 0.897). Multivariate Cox regression with Benjamini-Hochberg FDR correction across the eight genes supported MSTO1 as a candidate independent prognostic factor after adjustment for tumor stage, grade, etiology, age, and sex (HR = 1.29, p = 0.035), whilst LIG1 showed no independent prognostic value. Promoter methylation of MSTO1 and ADH4, assessed via UALCAN, showed that both genes were significantly differentially methylated in the promoter region of primary HCC tissues compared with normal liver tissues. Our study also confirmed the biological and clinical relevance of established HCC biomarkers: TERT, IRAK1, and ADH4. CONCLUSION: MSTO1 and LIG1 emerged as candidate diagnostic biomarkers in HCC. Additionally, MSTO1 showed a candidate prognostic association with overall survival that remained significant after adjusting for tumor stage, grade, and etiology, as well as patients' age, but not after further adjustment for AFP status. Functional data also highlighted MSTO1 as a candidate therapeutic dependency. On the other hand, LIG1 showed no independent prognostic association in either multivariate model. Their differential expression and functional essentiality in HCC cell lines highlighted their value for further experimental and independent-cohort validation before potential integration into biomarker development pipelines aimed at improving early detection and targeted therapy in HCC.

Humans↗

PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

PubMed2006

Bridging biological scales by state-space analysis and modeling using molecular, tissue cytometric and physiological data.

Combining data streams across different levels of biological organization such as molecular, cellular, and physiological responses support to a system-wide view in biology. Recently, an unbiased analysis of tissues that provides data-rich descriptors of tissue architecture, cell types, and cell states has become available. As tissues are centrally located in the biological hierarchy, these advancements give rise to a new class of state variables that are critical to elucidate both underlying cellular, molecular and emergent physiological properties. Concepts to statistically identify, correlate, and model relationships across scales are introduced, which rely on a state-space matrix derived by multi-omics data aggregation.

Computational Biology↗