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PubMed2026

Biological Mechanisms Underlying the Cardiovascular Effects of Branched-Chain Amino Acids: A Proteome-Wide Mendelian Randomization Study.

BACKGROUND: Ischemic heart disease (IHD) is the leading cause of morbidity and mortality. Branched-chain amino acids (BCAAs) are associated with higher IHD risk, but the underlying biological pathways remain unclear. OBJECTIVES: This study aims to explore these pathways using 2-step proteome-wide Mendelian randomization. METHODS: We examined the associations between genetic proxies for BCAAs and 2922 proteins in the United Kingdom Biobank Pharma Proteomics Project, supplemented by a meta-analysis with data from deCODE to identify proteins associated with BCAAs. Next, we tested their effects on IHD risk using Coronary Artery Disease Genome-wide Replication and Meta-analysis plus Coronary Artery Disease Genetics Consortium (122,733 cases and 424,528 controls) and replicated in FinnGen (31,640 cases and 187,152 controls). We conducted sensitivity analyses using genetic instruments from deCODE. Proteins associated with IHD risk and, in a consistent direction, with genetically predicted BCAAs were considered potential mediators. RESULTS: Genetic proxies for BCAAs were associated with 40 proteins. Among these, 6 proteins showed consistent evidence of mediation, including complement C1s subcomponent, coagulation factor II, granulin, proprotein convertase subtilisin/kexin type 9, sex hormone-binding globulin, and V-set and transmembrane domain-containing protein 2-like. These proteins are involved in inflammation, coagulation, lipid metabolism, and cellular stress response. All associations were robust across different analytical methods and replicated in independent datasets. Mediation analysis showed that these proteins accounted for 6.5% to 32.1% of the association between BCAAs and IHD risk. CONCLUSIONS: This study identified 6 proteins that potentially link BCAAs to IHD, implicating pathways related to inflammation, coagulation, lipid metabolism, and cellular stress responses. To our knowledge, these findings provide novel mechanistic insights into the BCAA-IHD relationship and highlight potential protein targets for future prevention and intervention strategies.

Amino Acids, Branched-Chain↗

PubMed1977

Reductive dechlorination of DDT by haem proteins.

DDT1 is converted to DDD by reduced myoglobin (rapidly), cytochrome c oxidase, and a haem-containing undecapeptide derived from cytochrome c. Cytochrome c itself is inactive. This demonstrates that an accessible haem site is necessary for the reaction. Spectrophotometric evidence is presented for an interaction between DDT and the undecapeptide. These results cast light on one of the biological pathways for the breakdown of DDT.

Animals↗

PubMedDate not supplied

An IgG autoantibody which inactivates C1-inhibitor.

Antibodies are considered to play a specific pathogenic role in certain disease states such as myasthenia gravis, Graves' disease and autoimmune haemolytic anaemia. Autoantibodies which interfere with the function of enzyme cascade systems have also been described in diseases such as acquired haemophilia (anti-factor VIII antibodies) and glomerulonephritis (C3 nephritic factor). The identification of these autoantibodies is crucial to an understanding of the aetiology of such diseases and is also of importance in revealing the inter-relationships of the immune system with other biological pathways. This is the first report of an immunoglobulin G (IgG) autoantibody reactive with C1-inhibitor (C1-Inh), a pivotal inhibitor of the inflammatory response which is known to inactivate proteins of the complement, kinin, fibrinolytic and 'contact phase' systems. This autoantibody was isolated from a patient with a novel variant of acquired angioedema and C1-Inh dysfunction. This finding highlights the involvement of the immune system in the pathogenesis of disorders characterized by the presence of dysfunctional inflammatory response proteins.

Autoantibodies↗

PubMed1992

The murine pallid mutation is a platelet storage pool disease associated with the protein 4.2 (pallidin) gene.

Pallid is one of 12 independent murine mutations with a prolonged bleeding time that are models for human platelet storage pool deficiencies in which several intracellular organelles are abnormal. We have mapped the murine gene for protein 4.2 (Epb4.2) to chromosome 2 where it co-localizes with pallid. Southern blot analyses suggest that pallid is a mutation in the Epb4.2 gene. Northern blot analyses demonstrate a smaller than normal Epb4.2 transcript in affected pallid tissues, such as kidney and skin. This is the first gene defect to be associated with a platelet storage pool deficiency, and may allow the identification of a novel structure or biological pathway that influences granulogenesis.

Animals↗

PubMed2026

Dissecting pleiotropy between major depressive disorder and physical disease comorbidities.

Major depressive disorder (MDD) is characterized by substantial comorbidity with medical conditions. To achieve better outcomes for patients with MDD, an improved understanding of the mechanisms underlying pervasive comorbidities is required. Here, to this end, we mapped patterns of pleiotropy by defining four clusters of physical diseases (cardiovascular, metabolic, gastrointestinal and immune) and analyzed their genetic relationships with MDD using genomic structural equation modeling. Three disease clusters exhibited independent associations with MDD and accounted for 47% of MDD h2SNP, with the gastrointestinal disease cluster having the strongest association (β = 0.63, s.e. = 0.05, P = 3.04 × 10-30). In addition, we identified independent loci associated with the shared genetic liability between each disease cluster and MDD, revealing different pleiotropic components. Characterization of these loci revealed previously unidentified associations with MDD and physical disease traits, along with unique biological pathways, drug groups, cell types and genes associated with each disease-MDD cluster. Our findings reveal genetic connections implicating the gut-brain axis as a key mechanism underlying the comorbidity of physical diseases in MDD. This work advances our understanding of MDD by highlighting unique and shared genetic components across different disease systems.

Major Depressive Disorder↗

PubMed2025

Horse model of spontaneous atrial fibrillation share proteomic changes with humans.

Horses and humans are among the few mammals susceptible to spontaneous atrial fibrillation (AF), both suffering from high recurrence rates after treatment. Treatment resistance is often attributed to progressive atrial remodeling, but current treatment options fail to effectively address this aspect. Here, we introduce a novel horse model of spontaneous AF to investigate the biological pathway changes in early stages of the disease. Through data-independent acquisition mass spectrometry on biopsies from the right and left atrium and left ventricular chamber of horses with early-stage persistent AF (n = 8) and controls (n = 8), we identify several differentially regulated proteins across all three chambers. Pathway enrichment analyses and histological stainings highlight a significant role of atrial extracellular matrix (ECM) remodeling in early AF. Other key proteomic changes relate to metabolism, contractility, and protein-folding, and overlap with findings from publicly available human datasets. Our results demonstrate that horses and humans share several AF-related proteomic changes, providing translational insights into the early atrial remodeling processes that are likely to contribute to treatment resistance. These protein-level changes could serve as biomarkers or pharmacological targets for preventing AF-associated atrial remodeling and improve treatment outcomes across species.

Atrial Fibrillation↗

PubMed1985

Characterization of rate-controlling steps in vivo by use of an adjustable expression vector.

Citrate synthase (EC 4.1.3.7) was varied from 10% to 5000% the level found in wild-type Escherichia coli by means of recombinant DNA techniques. When acetate was the sole carbon source, cell growth and carbon flow through the Krebs cycle were greatly affected by the under-production of citrate synthase. In contrast, when glucose was the main nutrient, the same underproduction of citrate synthase had little effect on either growth or carbon flux. When the enzyme was overproduced 50-fold, cultures would grow on glucose but cell division could be abruptly stopped by adding acetate to the medium. These results indicate that the regulatory properties of citrate synthase are highly dependent on the carbon-source composition of the medium. Furthermore, recombinant DNA technology can be used to alter rate-controlling steps in biological pathways and elucidate the regulatory properties of metabolic systems.

Citrate (si)-Synthase↗

PubMed1985

Diet and cancer: value of different types of epidemiological studies.

Diet and nutrition are increasingly recognized as likely to be major determinants of cancer, notably cancers of the gastrointestinal tract, breast, endometrium, ovary, and prostate. Dietary factors may collectively account for a greater proportion of all cancers that occur in contemporary Western society than does any other category of environmental exposure (1). With the development of knowledge of the protective properties of certain components of food, links with diet have been suggested for other cancer sites (2). The epidemiological evidence for the association of diet and cancer is, however, not uniformly convincing; also, the likely biological pathways are not always clear. In this paper, we comment on some current hypotheses in this area and examine the best epidemiological methods to test them.

Diagnosis-Related Groups↗

PubMed2026

Analytical challenges for mapping non-canonical and non-protein ubiquitin/Ubl modifications by mass spectrometry.

INTRODUCTION: Covalent modification by ubiquitin via Lys isopeptide bonds is fundamental for regulating protein turnover and function. Additionally, ubiquitin esterification occurs on Ser/Thr/Tyr residues in proteins and on non-proteinaceous substrates including ribose, saccharides, lipids, and small molecule drugs. Ubiquitin posttranslational modifications may therefore be much more widespread across cell biological pathways. Recent literature (PubMed) reflects the increased interest in analytical methods for mapping of non-canonical substrates modified by ubiquitin and ubiquitin-like (UBL) proteins. AREAS COVERED: Mass spectrometry (MS)-based methodologies involve advanced proteomic techniques to identify ubiquitin modifications on amino acids other than Lys, such as Ser, Thr, Tyr and Cys as well as protein N-termini. After digestion, standard MS workflows identify canonical ubiquitination by detecting a ubiquitin C-terminal tag attached to the amine side chains of Lys residues of substrate-derived peptides suitable for MS/MS sequencing. For non-canonical modifications on proteins and substrates other than proteins, specialized strategies are required, such as using antibodies to enrich N-terminally modified peptides in combination with using high-resolution MS/MS based on softer fragmentation technologies to detect esterification and possibly other types of substrate modifications. EXPERT OPINION: Enabling such technologies will reveal a previously unrecognized angle of the ubiquitin code's complexity in cells.

Humans↗

PubMed2026

Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.

INTRODUCTION: Inborn errors of metabolism (IEMs) are rare, heterogeneous disorders traditionally diagnosed through genetic testing, enzyme assays, and metabolite measurements. However, these tools often do not fully explain phenotypic variability, organ involvement, disease progression, or treatment response. Clinical proteomics provides a complementary functional layer by capturing changes in protein abundance, proteoforms, post-translational modifications (PTM), and biological pathways, offering insights beyond genotype- and metabolite-based approaches. AREAS COVERED: This review examines the role of high-resolution mass spectrometry and computational proteomics in biomarker discovery and clinical decision-making for IEMs. It focuses on their contribution to diagnosis, variant interpretation, patient stratification, and treatment monitoring. Disease-specific applications are discussed, with the strongest evidence in lysosomal storage disorders, mitochondrial diseases, congenital disorders of glycosylation, and selected neurodegenerative or renal metabolic conditions. The literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar, covering peer-reviewed articles available up to 2026, with emphasis on methodological advances and translational applications in clinical proteomics for IEMs. EXPERT OPINION: Proteomics will not replace established diagnostic tools, but it can help address clinically actionable questions in selected contexts. Translation into clinical practice will require standardized workflows, multicenter validation, clinically anchored endpoints, and integration with other omics approaches.

Humans↗

PubMed2026

Gut microbial diversity at baseline conditions the clinical, microbiome, and metabolic response to paraprobiotic Lactiplantibacillus plantarum LRCC5282 in overweight adults.

The gut microbiota is increasingly recognized as a target for obesity management; however, whether baseline gut microbial diversity conditions responsiveness to microbiota-targeted interventions remains unclear. We aimed to investigate whether baseline gut microbial diversity is associated with responsiveness to a paraprobiotic derived from Lactiplantibacillus plantarum LRCC5282 (LP5282-P) in overweight adults. In a 12-week, randomized, double-blind, placebo-controlled, multicenter trial of 120 overweight adults, LP5282-P produced no significant between-group differences in any clinical outcome across the overall per-protocol population. However, in the low-diversity subgroup, LP5282-P was associated with significant reductions in body weight, body mass index, and circulating leptin levels. These clinical changes were accompanied by compositional shifts in the gut microbiota, including higher relative abundances of Christensenellaceae, Faecalibacterium, and Alistipes. Fecal metabolite profiles showed elevated acetate and butyrate concentrations and altered bile acid composition. Within the low-diversity subgroup, changes in the relative abundances of Akkermansia and Eubacterium were inversely correlated with changes in body weight, body fat mass, and leptin levels. In contrast, the high-diversity subgroup exhibited no consistent response across the outcome domains examined. Overall, baseline gut microbial diversity was associated with differential responsiveness to LP5282-P, supporting its potential use as a stratification variable in future microbiota-targeted intervention trials. Further studies integrating direct measures of microbial activity and host response are warranted to elucidate the biological pathways underlying this diversity-dependent responsiveness. Trial registration: Clinical Research Information Service (CRIS), KCT0008119.

Humans↗

PubMed1990

Positron emission tomography.

While positron emission tomography (PET) represents the most advanced methodology using radiotracers, it is subject to two main constraints. The first is the physical accuracy with which the regional distribution, time course and concentration of the tracer can be determined. This is principally a function of the instrumentation. The second constraint is the biological accuracy, that a chosen tracer molecule defines the specific biological pathway under study. This paper discusses the application of PET, mainly to the brain, and future possible improvements to this powerful technique.

Brain↗

PubMed2026

Large-scale pleiotropic analysis across cancers reveals shared genetic mechanisms and identifies novel functional genes.

Pleiotropic genetic loci have been increasingly reported in cancer, and identifying genetic variants with pleiotropic associations can reveal shared biological pathways influencing multiple cancers. Using summary statistics from genome-wide association studies for 37 cancer types (N = 433 836), we identified extensive genome-wide and local genetic correlations among cancers. Through pairwise pleiotropic analysis, we identified 75 243 significant pleiotropic single nucleotide polymorphisms (SNPs) across 372 cancer pairs, among which 3472 were lead SNPs with potential regulatory functions. Using FUMA and MAGMA, we identified 2527 pleiotropic risk loci and 4272 candidate pleiotropic genes. Notably, genes such as TERT (5p15.33), POU5F1B (8q24.21), and FANCA (16q24.3) exhibited widespread pleiotropy across multiple cancer types. Pathway enrichment analysis highlighted the critical roles of pigment synthesis, metabolism, and apoptosis in skin-related cancers, while cross-cancer enrichment analysis emphasized pathways related to apoptosis, chromatin structure, and intermediate filaments. We also identified 33 novel functional genes harboring previously unreported cancer risk variants. Drug-gene interaction analysis revealed several repositionable FDA-approved drugs. Importantly, drug sensitivity assays demonstrated that bosutinib and cobimetinib exhibited promising therapeutic potential in breast cancer cell lines. Finally, we developed the PleioCancer database (https://gonglab.hzau.edu.cn/PleioCancer/), providing a comprehensive resource for cancer pleiotropy research. These findings have important implications for carcinogenesis cancer, prevention and treatment.

Humans↗

PubMed2026

shinyDeepGxP: a user-friendly R shiny app for predicting surface protein abundance from scRNA-seq expression using deep learning in blood cells.

MOTIVATION: Understanding accurate immune cell heterogeneity and function in single-cell datasets requires access to protein-level information, which is often unavailable due to experimental limitations. RESULTS: We present shinyDeepGxP, an interactive web application featuring our deep learning model, DeepGxP, for predicting surface protein abundance from single-cell RNA-sequencing (scRNA-seq) data. This platform makes DeepGxP accessible to researchers without programming skills. Users can upload scRNA-seq count matrices and use "Predict Protein" to predict the abundance of 224 biologically relevant surface proteins. shinyDeepGxP provides visualizations to help identify distinct cell populations based on predicted protein profiles. Moreover, users can choose "Explore Model" to reveal key RNA predictors and their associated biological pathways for each protein. Overall, shinyDeepGxP is a user-friendly, freely available web tool that provides protein-level detail for RNA-only single-cell datasets, enabling multimodal discovery without additional experiments. AVAILABILITY AND IMPLEMENTATION: shinyDeepGxP can be launched on https://shiny.crc.pitt.edu/deepgxp/.

Journal Article↗

PubMed2025

Meta-analysis models with group structure for pleiotropy detection at gene and variant level using summary statistics from multiple datasets.

Genome-wide association studies (GWASs) have highlighted the importance of pleiotropy in human diseases, where one gene can impact 2 or more unrelated traits. Examining shared genetic risk factors across multiple diseases can enhance our understanding of these conditions by pinpointing new genes and biological pathways involved. Furthermore, with an increasing wealth of GWAS summary statistics available to the scientific community, leveraging these findings across multiple phenotypes could unveil novel pleiotropic associations. Existing selection methods examine pleiotropic associations one by one at a scale of either the genetic variant or the gene, and thus cannot consider all the genetic information at the same time. To address this limitation, we propose a new approach called MPSG (Meta-analysis model adapted for Pleiotropy Selection with Group structure). This method performs a penalized multivariate meta-analysis method adapted for pleiotropy and takes into account the group structure information nested in the data to select relevant variants and genes (or pathways) from all the genetic information. To do so, we implemented an alternating direction method of multipliers algorithm. We compared the performance of the method with other benchmark meta-analysis approaches such as GCPBayes, PLACO, and ASSET by considering as inputs different kinds of summary statistics. We provide an application of our method to the identification of potential pleiotropic genes between breast and thyroid cancers.

Humans↗

PubMed2026

Using deep learning models as a genetic architecture for the simulation of breeding schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.

Deep Learning↗

PubMed2025

A genome-wide association study identified 10 novel genomic loci associated with intrinsic capacity.

BACKGROUND: Intrinsic capacity (IC) is a multidimensional concept within the World Health Organization framework for healthy aging. It refers to the composite of an individual's physical and mental capacities that enable them to maintain well-being, functional ability, and engagement in valued activities throughout life. While substantial evidence supports the biological basis of IC and its subdomains, the extent to which genetic factors influence IC remains largely unexplored, with no studies currently available. METHODS: Using datasets from the UK Biobank (UKB; N = 44 631) and the Canadian Longitudinal Study on Aging (CLSA; N = 13 085), we implemented the restricted maximum likelihood method to estimate SNP-based heritability (h2snp), followed by a Genome-Wide Association Study (GWAS) to identify genetic variants associated with IC, and post-GWAS analyses to pinpoint biological implications. RESULTS: The h2snp for IC was estimated at 25.2% in UKB and 19.5% in CLSA. Our GWAS identified 38 independent SNPs for IC across 10 genomic loci and 4289 candidate SNPs, mapped to 197 genes. Post-GWAS analysis revealed the role of these genes in cellular processes such as cell proliferation, immune function, metabolism, and neurodegeneration, with high expression in muscle, heart, brain, adipose, and nerve tissues. Of the 52 traits tested, 23 showed significant genetic correlations with IC, and a higher genetic loading for IC was associated with higher IC scores. CONCLUSIONS: Overall, this study provides comprehensive evidence on the genetic architecture of IC, identifying novel genetic variants and biological pathways, advancing our current knowledge and laying the foundation for ongoing and future research on healthy aging.

Adult↗

PubMed2026

The causal relationship between multiple cardiovascular diseases and glioblastoma: A Mendelian randomization study.

Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P < .05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio < 1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P < 5 × 10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.

Causality↗