Proteomics

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

  • Design: A longitudinal multi-omics twin study (NTR and FinnTwin12 cohorts) examining the association between plasma protein levels and changes in BMI (trajectories) over approximately a decade.
  • Key Finding: The association between the plasma proteome and BMI trajectories is largely explained by shared genetic factors and common environmental influences, pointing to a common underlying etiology.
  • Causal Link: Mendelian Randomization analysis identified Apolipoprotein B (ApoB) as a key protein, providing evidence that genetically determined ApoB levels have a causal effect on BMI, but not the reverse.
23 January 2026

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

  • Goal: To develop and validate PIMMS (Proteomics Imputation Modeling Mass Spectrometry), a deep learning-based framework for imputing missing values in label-free quantitative (LFQ) mass spectrometry data.
  • Method: PIMMS leverages three self-supervised models—Collaborative Filtering (CF), Denoising Autoencoder (DAE), and Variational Autoencoder (VAE)—which significantly outperformed 27 other imputation methods, including median imputation and R-based KNN, on simulated Missing Not At Random (MNAR) data.
  • Impact: Applying PIMMS-VAE to a clinical ALD cohort identified 30 additional significantly differentially abundant proteins (+13.2%) compared to non-imputed data, demonstrating that DL imputation can enhance the biological conclusions derived from proteomics analysis.
23 January 2026

Blood protein assessment of leading incident diseases and mortality in the UK Biobank

  • Objective: To identify and validate protein biomarkers for the 10-year incidence of 23 age-related diseases and all-cause mortality using proteomics data from 47,600 UK Biobank participants.
  • Key Result: Multi-protein risk scores (ProteinScores), developed using penalized Cox regression, significantly improved the Area Under the Curve (AUC) for the 10-year onset prediction of six major outcomes, including all-cause mortality, coronary artery disease (CAD), and Type 2 diabetes (T2D), even after adjusting for 24 comprehensive clinical and lifestyle factors.
  • Implication: The ProteinScores capture independent biological information related to underlying aging and systemic disease risk not found in standard clinical measures, validating the use of multi-protein panels for enhanced personalized risk stratification.
23 January 2026

Proteomic signatures improve risk prediction for common and rare diseases

  • Objective: This large-scale study demonstrated the ability of plasma proteomic signatures to enhance the 10-year incidence risk prediction for 218 common and rare diseases in the UK Biobank (UKB-PPP) cohort.
  • Result: Sparse proteomic models (using 5–20 proteins) significantly improved the C-index over models based on basic clinical information for 67 diseases, including hard-to-diagnose conditions like multiple myeloma and motor neuron disease.
  • Robustness/Confounding: The analysis highlighted that residual confounding is a major issue, as over 80% of initial associations attenuated after adjusting for demographic and clinical factors, underscoring the necessity of understanding protein determinants to ensure findings are biologically relevant and not spurious.
23 January 2026

Mapping the proteo-genomic convergence of human diseases

  • Objective: To construct a proteo-genomic map by systematically linking genetic risk for hundreds of human diseases and traits to variations in the levels of ~3,000 plasma proteins using data from >54,000 individuals.
  • Key Result: Identified 2,228 instances of genetic colocalization across 1,440 proteins and 498 diseases/traits, indicating that a substantial portion of genetic disease risk is mediated by altered protein levels.
  • Causal Inference: Through Mendelian Randomization (MR), the study robustly predicted 44 proteins to be causally linked to 37 diseases/traits (e.g., CRP and CAD), thereby providing high-priority candidates for drug targets.
23 January 2026

Current landscape of plasma proteomics from technical innovations to biological insights and biomarker discovery

  • Topic: A systematic benchmarking and technical evaluation of the current landscape of plasma proteomics, directly comparing different affinity-based and mass spectrometry platforms.
  • Method: Eight state-of-the-art proteomics platforms were applied to the same human plasma cohort to compare performance across over 13,000 proteins, assessing metrics like coverage, sensitivity, and reproducibility.
  • Impact: The study provides a critical resource for researchers, detailing the trade-offs in protein coverage and identifying the complementary strengths and limitations of each platform to guide informed decision-making for biomarker discovery.
23 January 2026

Proteomic prediction of disease largely reflects environmental risk exposure

  • Core Finding: The high disease predictive value of the plasma proteome primarily reflects its sensitivity as a quantitative readout of environmental risk factors (like smoking and alcohol intake), rather than identifying proteins that are causal drivers of disease.
  • Causality Assessment: Using Mendelian Randomization (MR) on thousands of protein-disease associations in the UK Biobank, the study found only 8% showed suggestive evidence of a causal relationship, confirming that causal drivers are rare and disease-specific.
  • Environmental Biomarkers: The vast majority of non-causal proteins, particularly those broadly associated with multiple diseases, were found to be exposure-associated. The developed Proteomic Score for Smoking (SmokingPS) achieved an AUC of 0.96, validating the proteome’s role as an objective, quantitative index of lifestyle behaviors.
23 January 2026

Decoding the functional impact of the cancer genome through protein-protein interactions

  • Core Concept: This review establishes that oncogenic driver mutations exert their functional impact largely by rewiring molecular signaling networks through the alteration of Protein-Protein Interactions (PPIs).
  • Mechanism: Mutations at the protein surface, particularly at PPI interfaces, can lead to the creation of neomorphic PPIs (neoPPIs) or the loss of existing hypomorphic PPIs (hypoPPIs), necessitating a mutation-focused analysis.
  • Therapeutic Implication: The mutation-directed PPIs are presented as a new class of targets for precision oncology, paving the way for the development of small-molecule modulators that can selectively disrupt oncogenic neoPPIs or restore tumor-suppressive hypoPPIs.
23 January 2026

Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions

  • Novel Method: Introduces Ratio-QTLs (rQTLs), a proteogenomic method that analyzes the genetic determinants of ratios between pairs of plasma protein levels rather than individual protein levels.
  • Key Finding: The rQTL approach provided an enormous increase in statistical power, strengthening associations at known pQTL loci by several hundred orders of magnitude (p-gain) and enabling the discovery of new cis-pQTLs.
  • Biological Relevance: rQTLs were 7.6-fold enriched in established Protein-Protein Interactions (PPIs), confirming the method’s unique ability to uncover genetic variants that regulate the functional interaction or shared biological regulation between two proteins.
23 January 2026

Machine learning-guided deconvolution of plasma protein levels

  • Objective: Used machine learning (ML) to systematically identify and quantify the contribution of over 1,800 characteristics (health, genetic, technical) to the variation in approximately 3,000 plasma protein levels across 43,240 UK Biobank individuals.
  • Key Result: A median of 20 factors explained an average of 19.4% of protein variance. Modifiable characteristics (median: 10.0%) were found to explain significantly more variation than genetic factors (median: 3.9%).
  • Implication: The study provides a crucial resource (knowledge graph, R package) and framework for understanding protein origins, clustering proteins by their drivers (e.g., disease, pre-analytical factors), and guiding the identification of biologically relevant biomarkers and drug target engagement markers.
23 January 2026

PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins

  • Novel Method: Introduces PWAS (Proteome-Wide Association Study), a protein-centric method that detects gene-phenotype associations by quantifying the cumulative functional damage caused by coding-region variants on the resulting protein product using a machine learning model called FIRM.
  • Key Advantage: PWAS is specifically designed to model and detect non-additive heritability, demonstrating its power in identifying associations under the recessive inheritance model, which is often missed by standard GWAS.
  • Discovery Power: In analysis using the UK Biobank, PWAS uncovered numerous gene-phenotype associations unique from standard GWAS, including detecting the known colorectal cancer gene MUTYH with high significance under its characteristic recessive mode.
23 January 2026
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