The circulating blood proteome holds immense potential for biomarker discovery and understanding disease mechanisms. Notable advances in mass spectrometry and affinity-based technologies have been made, but data integration across studies and platforms is hindered by the absence of unified analytical standards. This limitation impedes comprehensive exploration of human biology across diverse phenotypes and cohorts as well as the translation of findings into clinical applications. The disparities between datasets, stemming from a combination of factors related to differences in sample collection, pre-analytical handling, measurement methods and instrumentation, further complicate data integration. In this Perspective, we outline key challenges in blood-based proteomics and propose actionable strategies. Central to our recommendations are high-quality, technology-agnostic reference samples, which can bridge disparate datasets and enable robust cross-study comparisons. By fostering interconnected investigations across proteomic technologies, blood sample collections, clinical phenotypes and different populations, these references will accelerate the field and its translation.

Here we report the results from exploratory analysis using a Bayesian network approach of data originally derived from a large North European study of type 2 diabetes (T2D) conducted by the IMI DIRECT consortium. 3029 individuals (795 with T2D and 2234 without) within 7 different study centres provided data comprising genotypes, proteins, metabolites, gene expression measurements and many different clinical variables. The main aim of the current study was to demonstrate the utility of our previously developed method to fit Bayesian networks by performing exploratory analysis of this dataset to identify possible causal relationships between these variables. The data was analysed using the BayesNetty software package, which can handle mixed discrete/continuous data with missing values. The original dataset consisted of over 16,000 variables, which were filtered down to 260 variables for analysis. Even with this reduction, no individual had complete data for all variables, making it impossible to analyse using standard Bayesian network methodology. However, using the recently proposed novel imputation method implemented in BayesNetty we computed a large average Bayesian network from which we could infer possible associations and causal relationships between variables of interest. Our results confirmed many previous findings in connection with T2D, including possible mediating proteins and genes, some of which have not been widely reported. We also confirmed potential causal relationships with liver fat that were identified in an earlier study that used the IMI DIRECT dataset but was limited to a smaller subset of individuals and variables (namely individuals with complete data at predefined variables of interest). In addition to providing valuable confirmation, our analyses thus demonstrate a proof-of-principle of the utility of the method implemented within BayesNetty. The full final average Bayesian network generated from our analysis is freely available and can be easily interrogated further to address specific focussed scientific questions of interest.

Introduction Idiopathic Multicentric Castleman Disease (iMCD) is a polyclonal lymphoproliferative disorder involving cytokine storms that can lead to organ failure and death. The cause of iMCD is unknown, but some clinical evidence suggests an autoimmune etiology. For example, connective tissue disorders (CTDs) and iMCD share many clinical features, and autoantibodies have been anecdotally reported in individual iMCD patients. This study investigates whether common autoantibodies are shared across iMCD patients.Methods We assembled custom bead-based protein arrays consisting of 52 autoantigens traditionally associated with CTDs and 38 full-length cytokines and screened serum samples from 101 iMCD patients for IgG autoantibodies. We also screened samples with a 1,103-plex array of recombinant human protein fragments to identify additional autoantibody targets. Finally, we performed receptor blocking assays on select samples with anti-cytokine autoantibodies (ACAs) identified by array.Results We found that an increased proportion of iMCD patients (47%) tested positive for at least one CTD-associated autoantibody compared to healthy controls (HC) (17%). Commonly detected CTD-associated autoantibodies were associated with myositis and overlap syndromes as well as systemic lupus erythematosus (SLE) and Sj & ouml;gren's Syndrome (SS). ACAs were also detected in a greater proportion of iMCD patients (38%) compared to HC (10%), while the protein fragment array identified a variety of other autoantibody targets. One iMCD sample tested positive for receptor blocking against interferon-omega (IFN omega).Discussion IgG autoantibodies binding autoantigens associated with common CTDs and cytokines are elevated in iMCD patients compared to HC, suggesting that autoimmunity may be involved in iMCD pathogenesis.

Aims/hypothesis: Type 1 diabetes manifests after irreversible beta cell damage, highlighting the crucial need for markers of the presymptomatic phase to enable early and effective interventions. Current efforts to identify molecular markers of disease-triggering events lack resolution and convenience. Analysing frequently self-collected dried blood spots (DBS) could enable the detection of early disease-predictive markers and facilitate tailored interventions. Here, we present a novel strategy for monitoring transient molecular changes induced by environmental triggers that enable timely disease interception.

Methods: Whole blood (10 μl) was sampled regularly (every 1–5 days) from adult NOD mice infected with Coxsackievirus B3 (CVB3) or treated with vehicle alone. Blood samples (5 μl) were dried on filter discs. DBS samples were analysed by proximity extension assay. Generalised additive models were used to assess linear and non-linear relationships between protein levels and the number of days post infection (p.i.). A multi-layer perceptron (MLP) classifier was developed to predict infection status. CVB3-infected SOCS-1-transgenic (tg) mice were treated with immune- or non-immune sera on days 2 and 3 p.i., followed by monitoring of diabetes development.

Results: Frequent blood sampling and longitudinal measurement of the blood proteome revealed transient molecular changes in virus-infected animals that would have been missed with less frequent sampling. The MLP classifier predicted infection status after day 2 p.i. with over 90% accuracy. Treatment with immune sera on day 2 p.i. prevented diabetes development in all (100%) of CVB3-infected SOCS-1-tg NOD mice while five out of eight (62.5%) of the CVB3-infected controls treated with non-immune sera developed diabetes.

Conclusions/interpretation: Our study demonstrates the utility of frequently collected DBS samples to monitor dynamic proteome changes induced by an environmental trigger during the presymptomatic phase of type 1 diabetes. This approach enables disease interception and can be translated into human initiatives, offering a new method for early detection and intervention in type 1 diabetes.

Data and code availability: Additional data available at https://doi.org/10.17044/scilifelab.27368322 . Additional visualisations are presented in the Shiny app interface https://mouse-dbs-profiling.serve.scilifelab.se/

Background: Protein quantitative trait loci (pQTLs) remain underexplored in asthma but might provide valuable insights into the underlying molecular mechanisms. This study aimed to investigate associations between genetic variation and inflammation-related plasma proteins and to assess differences in the levels of genetically determined proteins in subjects with signatures of type-2 inflammation and/or asthma. Methods: A pQTL mapping of 92 inflammation-related plasma proteins was conducted in young adults from the Swedish BAMSE cohort (n = 1538). Replication of sentinel pQTLs was attempted, and the overlap and colocalization of pQTLs with expression quantitative trait loci (eQTLs) were investigated using publicly available data. Proteins with significant pQTLs were tested for association with type-2 signatures defined as high levels of fractional exhaled nitric oxide, blood eosinophils, and/or sensitization to airborne allergens in subjects with or without asthma in BAMSE. Results: Forty-five sentinel pQTLs (33 cis, 12 trans) for 39 inflammation-related proteins were identified (p ≤ 7.14 × 10⁻¹¹), and a high proportion of these were validated in independent populations. A high likelihood for colocalization of cis-pQTLs and cis-eQTLs was observed for 19 proteins in different tissues. Six of the 39 circulating proteins with significant pQTLs were associated with type-2 signatures and/or asthma, and matrix metalloproteinase-10 (MMP-10) showed the most significant associations. Conclusions: These findings underscore the existence of a genetic component influencing the plasma levels of proteins involved in inflammatory processes, including MMP-10, which is suggested to have a role in high type-2 inflammation in asthma subjects.

Background: Circulating proteins are routinely quantified from liquid biopsies to deduce health and disease. Among these are endocrine protein hormones, which regulate human growth, development, metabolism, and reproduction. Most commonly, these proteins are analyzed in plasma or serum prepared from venous blood draws. Recently, devices for quantitative capillary sampling from a finger prick have emerged, but their utility for clinical testing remains to be explored. Methods: To study the analytical capabilities of quantitative dried blood spots (qDBS), we quantified the luteinizing hormone subunit beta (LHB), follicle-stimulating hormone subunit beta (FSHB), thyroid-stimulating hormone subunit beta (TSHB), prolactin (PRL), and growth hormone 1 (GH1) by multiplexed immunoassays. We determined the performance of the endocrine hormone assays in paired qDBS and EDTA plasma samples from 100 donors (90% females) aged 4 to 78. Lastly, we compared the protein levels with those from an accredited clinical chemistry laboratory. Results: The multiplexed analysis showed precise protein quantifications in qDBS (mean CV = 8.3%), high concordance with plasma levels (r = 0.88 to 0.99), and accuracy being matrix- and protein-dependent (recovery: 80–225%). Using the current protocol and sample dilutions, reported protein concentrations were 1.2 to 7.5 times higher in plasma than in qDBS eluates. Concentrations from multiplexed plasma assays agreed with the clinical data (r = 0.87 to 0.99) and decreased slightly when comparing clinical plasma data with multiplexed qDBS assays (r = 0.76 to 0.98). Significant increases in age-related FSHB and LHB levels were observed in females in all specimens and assays (p < 0.01). Conclusions: This study shows the suitability of modern qDBS devices for quantifying clinically informative proteins in multiplexed assays and highlights the need for future work on specimen-specific optimization and standards. Volumetric DBS sampling offers new routines for accurate protein quantification for precision medicine.

BackgroundPrevious case-control studies have reported aberrations of the gut microbiota in individuals with prediabetes. The primary objective of the present study was to explore the dynamics of the gut microbiota of individuals with prediabetes over 4 years with a secondary aim of relating microbiota dynamics to temporal changes of metabolic phenotypes.MethodsThe study included 486 European patients with prediabetes. Gut microbiota profiling was conducted using shotgun metagenomic sequencing and the same bioinformatics pipelines at study baseline and after 4 years. The same phenotyping protocols and core laboratory analyses were applied at the two timepoints. Phenotyping included anthropometrics and measurement of fasting plasma glucose and insulin levels, mean plasma glucose and insulin under an oral glucose tolerance test (OGTT), 2-h plasma glucose after an OGTT, oral glucose insulin sensitivity index, Matsuda insulin sensitivity index, body mass index, waist circumference, and systolic and diastolic blood pressure. Measures of the dynamics of bacterial microbiota were related to concomitant changes in markers of host metabolism.ResultsOver 4 years, significant declines in richness were observed in gut bacterial and viral species and microbial pathways accompanied by significant changes in the relative abundance and the genetic composition of multiple bacterial species. Additionally, bacterial-viral interactions diminished over time. Despite the overall reduction in bacterial richness and microbial pathway richness, 80 dominant core bacterial species and 78 core microbial pathways were identified at both timepoints in 99% of the individuals, representing a resilient component of the gut microbiota. Over the same period, individuals with prediabetes exhibited a significant increase in glycemia and insulinemia alongside a significant decline in insulin sensitivity. Estimates of the gut bacterial microbiota dynamics were significantly correlated with temporal impairments in host metabolic health.ConclusionsIn this 4-year prospective study of European patients with prediabetes, the gut microbiota exhibited major changes in taxonomic composition, bacterial species genetics, and microbial functional potentials, many of which paralleled an aggravation of host metabolism. Whether the temporal gut microbiota changes represent an adaptation to the progression of metabolic abnormalities or actively contribute to these in prediabetes cases remains unsettled.Trial registrationThe Diabetes Research on Patient Stratification (DIRECT) study, an exploratory observational study initiated on October 15, 2012, was registered on ClinicalTrials.gov under the number NCT03814915.

Background: Metformin is one of the most used drugs worldwide. Given the increasing use of proteomics in trials, bioresources, and clinics, it is crucial to understand the influence of metformin on the levels of the circulating proteome. Methods: We analysed a combined longitudinal proteomics dataset from the IMPOCT, RAMP and S3WP-T2D clinical trials in 98 participants before and after metformin exposure. This discovery analysis contained 372 proteins measured by proximity extension assays (Olink). We followed up experiment–wise statistically significant findings in two cross-sectional cohorts of people with type 2 diabetes comparing metformin treated and untreated individuals: IMI-DIRECT (784 participants, 372 proteins, Olink) and IMI-RHAPSODY (1175 participants, 1195 proteins, SomaLogic). Findings: Overall, 23 protein analytes were robustly associated with exposure to metformin in the discovery and replication. This includes 11 protein-metformin associations that replicated in both replication sets and platforms (REG4, GDF15, REG1A, t-PA, TFF3, CDH5, CNTN1, OMD, NOTCH3, THBS4 and CD93), with the remaining 12 protein-metformin associations replicated using the Olink platform (EPCAM, SPINK1, SAA-4, COMP, ITGB2, ADGRG2, FAM3C, MERTK, COL1A1, HAOX1, VCAN, TIMD4) but not measured on the SomaLogic platform. Gene-set enrichment analysis revealed that the metformin exposure was associated with intestinal associated proteins. Interpretation: These data highlight the need to account for exposure to metformin, and potentially other drugs, in proteomic studies and where protein biomarkers are used for clinical care. Funding: Innovative Medicines Initiative Joint Undertaking 2, under grant agreement no. 115881 (RHAPSODY) and the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution as well as the Swiss State Secretariat for Education Research' and Innovation (SERI), under contract no. 16.0097 (RHAPSODY).

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" (http://comics.metabolomix.com), providing interactive data exploration and hypotheses generation possibilities.