Whole-genome sequence association analysis of blood proteins in a longitudinal wellness cohort

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Genetic architecture of heart mitochondrial proteome influencing cardiac hypertrophy

Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Targeted proteomics using stable isotope labeled protein fragments enables precise and robust determination of total apolipoprotein(a) in human plasma

Lipoprotein(a), also known as Lp(a), is an LDL-like particle composed of apolipoprotein(a) (apo(a)) bound covalently to apolipoprotein B100. Plasma concentrations of Lp(a) are highly heritable and vary widely between individuals. Elevated plasma concentration of Lp(a) is considered as an independent, causal risk factor of cardiovascular disease (CVD). Targeted mass spectrometry (LC-SRM/MS) combined with stable isotope-labeled recombinant proteins provides robust and precise quantification of proteins in the blood, making LC-SRM/MS assays appealing for monitoring plasma proteins for clinical implications. This study presents a novel quantitative approach, based on proteotypic peptides, to determine the absolute concentration of apo(a) from two microliters of plasma and qualified according to guideline requirements for targeted proteomics assays. After optimization, assay parameters such as linearity, lower limits of quantification (LLOQ), intra-assay variability (CV: 4.7%) and inter-assay repeatability (CV: 7.8%) were determined and the LC-SRM/MS results were benchmarked against a commercially available immunoassay. In summary, the measurements of an apo(a) single copy specific peptide and a kringle 4 specific peptide allow for the determination of molar concentration and relative size of apo(a) in individuals.

Investigation of 91 proteins implicated in neurobiological processes identifies multiple candidate plasma biomarkers of stroke outcome

The inter-individual variation in stroke outcomes is large and protein studies could point to potential underlying biological mechanisms. We measured plasma levels of 91 neurobiological proteins in 209 cases included in the Sahlgrenska Academy Study on Ischemic Stroke using a Proximity Extension Assay, and blood was sampled in the acute phase and at 3-month and 7-year follow-ups. Levels were also determined once in 209 controls. Acute stroke severity and neurological outcome were evaluated by the National Institutes of Health Stroke Scale. In linear regression models corrected for age, sex, and sampling day, acute phase levels of 37 proteins were associated with acute stroke severity, and 47 with 3-month and/or 7-year outcome at false discovery rate < 0.05. Three-month levels of 8 proteins were associated with 7-year outcome, of which the associations for BCAN and Nr-CAM were independent also of acute stroke severity. Most proteins followed a trajectory with lower levels in the acute phase compared to the 3-month follow-up and the control sampling point. Conclusively, we identified multiple candidate plasma biomarkers of stroke severity and neurological outcome meriting further investigation. This study adds novel information, as most of the reported proteins have not been previously investigated in a stroke cohort.

Inflammatory biomarkers after an exercise intervention in childhood acute lymphoblastic leukemia survivors

Cancer survivors show increased risk for non-communicable diseases and chronic low-grade inflammation characterizes the development of such diseases. We investigated inflammatory plasma protein profiles of survivors of childhood acute lymphoblastic leukemia (ALL) in comparison to healthy controls and after an intervention with a home-based exercise program. Survivors of childhood ALL aged 16-30 years (n = 21) with a median age at diagnosis 4.9 (1.6-12.9) years and a median time of 15.9 years from diagnosis, and sex- and age-matched healthy controls (n = 21) were studied. Stored plasma samples were analyzed with Olink's 92-protein-wide Inflammation panel in 21 ALL long-term survivors at baseline, after a previous 16-week home-based exercise intervention (n = 17) and in 21 age- and sex-matched controls at baseline. Protein expression levels were compared between the groups. Inflammatory protein levels did not differ between the survivors and controls at baseline. Significantly reduced levels after the intervention were found in 11 proteins related to either vascular inflammation, insulin resistance, or both: tumor necrosis factor superfamily member 14 (TNFSF14), oncostatin M (OSM), monocyte chemoattractant protein 1 (MCP-1), MCP-2, fibroblast growth factor 21 (FGF-21), chemokine (C-C motif) ligand 4 (CCL4), transforming growth factor alpha (TGF-α), tumor necrosis factor-related apoptosis-inducing ligand 10 (TRAIL), adenosine deaminase (ADA), chemokine (C-X-C motif) ligand 6 (CXCL6), and latency-associated peptide transforming growth factor beta 1 (LAP TGF-β1). The ALL survivors were not significantly more affected by inflammation than controls at baseline. The survivors' 16-week exercise intervention led to significant reduction in inflammatory protein levels. Physical exercise should be promoted for survivors of childhood cancer.

Using the Plasma Proteome for Risk Stratifying Patients with Pulmonary Arterial Hypertension

Rationale: NT-proBNP (N-terminal pro-brain natriuretic peptide), a biomarker of cardiac origin, is used to risk stratify patients with pulmonary arterial hypertension (PAH). Its limitations include poor sensitivity to early vascular pathology. Other biomarkers of vascular or systemic origin may also be useful in the management of PAH. Objectives: Identify prognostic proteins in PAH that complement NT-proBNP and clinical risk scores. Methods: An aptamer-based assay (SomaScan version 4) targeting 4,152 proteins was used to measure plasma proteins in patients with idiopathic, heritable, or drug-induced PAH from the UK National Cohort of PAH (n = 357) and the French EFORT (Evaluation of Prognostic Factors and Therapeutic Targets in PAH) study (n = 79). Prognostic proteins were identified in discovery-replication analyses of UK samples. Proteins independent of 6-minute-walk distance and NT-proBNP entered least absolute shrinkage and selection operator modeling, and the best combination in a single score was evaluated against clinical targets in EFORT. Measurements and Main Results: Thirty-one proteins robustly informed prognosis independent of NT-proBNP and 6-minute-walk distance in the UK cohort. A weighted combination score of six proteins was validated at baseline (5-yr mortality; area under the curve [AUC], 0.73; 95% confidence interval [CI], 0.63-0.85) and follow-up in EFORT (AUC, 0.84; 95% CI, 0.75-0.94; P = 9.96 × 10^-6). The protein score risk stratified patients independent of established clinical targets and risk equations. The addition of the six-protein model score to NT-proBNP improved prediction of 5-year outcomes from AUC 0.762 (0.702-0.821) to 0.818 (0.767-0.869) by receiver operating characteristic analysis (P = 0.00426 for difference in AUC) in the UK replication and French samples combined. Conclusions: The plasma proteome informs prognosis beyond established factors in PAH and may provide a more sensitive measure of therapeutic response.

Polymorphisms in alpha 7 nicotinic acetylcholine receptor gene, CHRNA7, and its partially duplicated gene, CHRFAM7A, associate with increased inflammatory response in human peripheral mononuclear cells

The vagus nerve can, via the alpha 7 nicotinic acetylcholine receptor (α7nAChR), regulate inflammation. The gene coding for the α7nAChR, CHRNA7, can be partially duplicated, that is, CHRFAM7A, which is reported to impair the anti-inflammatory effect mediated via the α7nAChR. Several single nucleotide polymorphisms (SNPs) have been described in both CHRNA7 and CHRFAM7A, however, the functional role of these SNPs for immune responses remains to be investigated. In the current study, we set out to investigate whether genetic variants of CHRNA7 and CHRFAM7A can influence immune responses. By investigating data available from the Swedish SciLifeLab SCAPIS Wellness Profiling (S3WP) study, in combination with droplet digital PCR and freshly isolated PBMCs from the S3WP participants, challenged with lipopolysaccharide (LPS), we show that CHRNA7 and CHRFAM7A are expressed in human PBMCs, with approximately four times higher expression of CHRFAM7A compared with CHRNA7. One SNP in CHRFAM7A, rs34007223, is positively associated with hsCRP in healthy individuals. Furthermore, gene ontology (GO)-terms analysis of plasma proteins associated with gene expression of CHRNA7 and CHRFAM7A demonstrated an involvement for these genes in immune responses. This was further supported by in vitro data showing that several SNPs in both CHRNA7 and CHRFAM7A are significantly associated with cytokine response. In conclusion, genetic variants of CHRNA7 and CHRFAM7A alters cytokine responses. Furthermore, given that CHRFAM7A SNP rs34007223 is associated with inflammatory marker hsCRP in healthy individuals suggests that CHRFAM7A may have a more pronounced role in regulating inflammatory processes in humans than previously been recognized.

Integrative genetic and immune cell analysis of plasma proteins in healthy donors identifies novel associations involving primary immune deficiency genes

Background

Blood plasma proteins play an important role in immune defense against pathogens, including cytokine signaling, the complement system, and the acute-phase response. Recent large-scale studies have reported genetic (i.e., protein quantitative trait loci, pQTLs) and non-genetic factors, such as age and sex, as major determinants to inter-individual variability in immune response variation. However, the contribution of blood-cell composition to plasma protein heterogeneity has not been fully characterized and may act as a mediating factor in association studies.

Methods

Here, we evaluated plasma protein levels from 400 unrelated healthy individuals of western European ancestry, who were stratified by sex and two decades of life (20–29 and 60–69 years), from the Milieu Intérieur cohort. We quantified 229 proteins by Luminex in a clinically certified laboratory and their levels of variation were analyzed together with 5.2 million single-nucleotide polymorphisms. With respect to non-genetic variables, we included 254 lifestyle and biochemical factors, as well as counts of seven circulating immune cell populations measured by hemogram and standardized flow cytometry.

Results

Collectively, we found 152 significant associations involving 49 proteins and 20 non-genetic variables. Consistent with previous studies, age and sex showed a global, pervasive impact on plasma protein heterogeneity, while body mass index and other health status variables were among the non-genetic factors with the highest number of associations. After controlling for these covariates, we identified 100 and 12 pQTLs acting in cis and trans, respectively, collectively associated with 87 plasma proteins and including 19 novel genetic associations. Genetic factors explained the largest fraction of the variability of plasma protein levels, as compared to non-genetic factors. In addition, blood-cell fractions, including leukocytes, lymphocytes, monocytes, neutrophils, eosinophils, basophils, and platelets, had a larger contribution to inter-individual variability than age and sex and appeared as confounders of specific genetic associations. Finally, we identified new genetic associations with plasma protein levels of five monogenic Mendelian disease genes including two primary immunodeficiency genes (Ficolin-3 and FAS).

Conclusions

Our study identified novel genetic and non-genetic factors associated to plasma protein levels which may inform health status and disease management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01032-y.

Exploring Human Milk Dynamics: Interindividual Variation in Milk Proteome, Peptidome, and Metabolome

Human milk is a dynamic biofluid, and its detailed composition receives increasing attention. While most studies focus on changes over time or differences between maternal characteristics, interindividual variation receives little attention. Nevertheless, a comprehensive insight into this can help interpret human milk studies and help human milk banks provide targeted milk for recipients. This study aimed to map interindividual variation in the human milk proteome, peptidome, and metabolome and to investigate possible explanations for this variation. A set of 286 milk samples was collected from 29 mothers in the third month postpartum. Samples were pooled per mother, and proteins, peptides, and metabolites were analyzed. A substantial coefficient of variation (>100%) was observed for 4.6% and 36.2% of the proteins and peptides, respectively. In addition, using weighted correlation network analysis (WGCNA), 5 protein and 11 peptide clusters were obtained, showing distinct characteristics. With this, several associations were found between the different data sets and with specific sample characteristics. This study provides insight into the dynamics of human milk protein, peptide, and metabolite composition. In addition, it will support future studies that evaluate the effect size of a parameter of interest by enabling a comparison with natural variability.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Genome-wide association study and genomic heritabilities for blood protein levels in Lori-Bakhtiari sheep

Serum protein levels are related to physiological and pathological status of animals and could be affected by both genetic and environmental factors. This study aimed to evaluate genetic variation of serum protein profile in sheep. Blood samples were randomly collected from 96 Lori-Bakhtiari ewes, a heavy meat-type breed. Total protein, albumin, globulin, α1, α2, β and γ globulins and IgG levels were measured in blood serum. The samples were genotyped using the Illumina OvineSNP50 BeadChip. The studied traits adjusted for age, birth type, birth season and estimate of breeding value for body weight were considered as pseudo-phenotypes in genome-wide association analysis. In the GWAS model, the first five principal components were fitted as covariates to correct the biases due to possible population stratification. The Plink, R and GCTA software were used for genome-wide association analysis, construction of Q-Q and Manhattan plots and estimation of genetic variances, respectively. Noticeable genomic heritabilities ± SE were estimated for total and γ globulins (0.868 ± 0.262 and 0.831 ± 0.364, respectively), but other protein fractions had zero or close to zero estimates. Based on the Bonferroni adjusted p values, four QTLs located on 181.7 Mbp of OAR3, 107.7 Mbp of OAR4, 86.3 Mbp of OAR7 and 83.0 Mbp of OAR8 were significantly associated with α1, β, β and γ globulins, respectively. The results showed that the PKP2, IGF2R, SLC22A1 and SLC22A2 genes could be considered as candidate genes for blood serum proteins. The present study showed significant genetic variations of some blood protein fractions.

Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms

Background

COVID-19 has caused millions of deaths globally, yet the cellular mechanisms underlying the various effects of the disease remain poorly understood. Recently, a new analytical platform for comprehensive analysis of plasma protein profiles using proximity extension assays combined with next generation sequencing has been developed, which allows for multiple proteins to be analyzed simultaneously without sacrifice on accuracy or sensitivity.

Methods

We analyzed the plasma protein profiles of COVID-19 patients (n = 50) with mild and moderate symptoms by comparing the protein levels in newly diagnosed patients with the protein levels in the same individuals after 14 days.

Findings

The study has identified more than 200 proteins that are significantly elevated during infection and many of these are related to cytokine response and other immune-related functions. In addition, several other proteins are shown to be elevated, including SCARB2, a host cell receptor protein involved in virus entry. A comparison with the plasma protein response in patients with severe symptoms shows a highly similar pattern, but with some interesting differences.

Interpretation

The study presented here demonstrates the usefulness of “next generation plasma protein profiling” to identify molecular signatures of importance for disease progression and to allow monitoring of disease during recovery from the infection. The results will facilitate further studies to understand the molecular mechanism of the immune-related response of the SARS-CoV-2 virus.

Funding

This work was financially supported by Knut and Alice Wallenberg Foundation.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Cancer diagnostics based on plasma protein biomarkers: hard times but great expectations

Cancer diagnostics based on the detection of protein biomarkers in blood has promising potential for early detection and continuous monitoring of disease. However, the currently available protein biomarkers and assay formats largely fail to live up to expectations, mainly due to insufficient diagnostic specificity. Here, we discuss what kinds of plasma proteins might prove useful as biomarkers of malignant processes in specific organs. We consider the need to search for biomarkers deep down in the lowest reaches of the proteome, below current detection levels. In this regard, we comment on the poor molecular detection sensitivity of current protein assays compared to nucleic acid detection reactions, and we discuss requirements for achieving detection of vanishingly small amounts of proteins, to ensure detection of early stages of malignant growth through liquid biopsy.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Bacteroides Fragilis Polysaccharide A Ameliorates Abnormal Voriconazole Metabolism Accompanied With the Inhibition of TLR4/NF-κB Pathway

The antifungal agent voriconazole (VRC) exhibits extreme inter-individual and intra-individual variation in terms of its clinical efficacy and toxicity. Inflammation, as reflected by C-reactive protein (CRP) concentrations, significantly affects the metabolic ratio and trough concentrations of voriconazole. Bacteroides fragilis (B. fragilis) is an important component of the human intestinal microbiota. Clinical data have shown that B. fragilis abundance is comparatively higher in patients not presenting with adverse drug reactions, and inflammatory cytokine (IL-1β) levels are negatively correlated with B. fragilis abundance. B. fragilis natural product capsular polysaccharide A (PSA) prevents various inflammatory disorders. We tested the hypothesis that PSA ameliorates abnormal voriconazole metabolism by inhibiting inflammation. Germ-free animals were administered PSA intragastrically for 5 days after lipopolysaccharide (LPS) stimulation. Their blood and liver tissues were collected to measure VRC concentrations. PSA administration dramatically improved the resolution phase of LPS-induced hepatic VRC metabolism and inflammatory factor secretion. It reversed inflammatory lesions and alleviated hepatic pro-inflammatory factor secretion. Both in vitro and in vivo data demonstrate that PSA reversed LPS-induced IL-1β secretion, downregulated the TLR4/NF-κB signaling pathway and upregulated CYP2C19 and P-gp. To the best of our knowledge, this study is the first to show that PSA from the probiotic B. fragilis ameliorates abnormal voriconazole metabolism by inhibiting TLR4-mediated NF-κB transcription and regulating drug metabolizing enzyme and transporter expression. Thus, PSA could serve as a clinical adjunct therapy.

Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes

BACKGROUND

Comprehensive proteomics profiling may offer new insights into the dysregulated metabolic milieu of type 2 diabetes, and in the future, serve as a useful tool for personalized medicine. This calls for a better understanding of circulating protein patterns at the early stage of type 2 diabetes as well as the dynamics of protein patterns during changes in metabolic status.

METHODS

To elucidate the systemic alterations in early-stage diabetes and to investigate the effects on the proteome during metabolic improvement, we measured 974 circulating proteins in 52 newly diagnosed, treatment-naïve type 2 diabetes subjects at baseline and after 1 and 3 months of guideline-based diabetes treatment, while comparing their protein profiles to that of 94 subjects without diabetes.

FINDINGS

Early stage type 2 diabetes was associated with distinct protein patterns, reflecting key metabolic syndrome features including insulin resistance, adiposity, hyperglycemia and liver steatosis. The protein profiles at baseline were attenuated during guideline-based diabetes treatment and several plasma proteins associated with metformin medication independently of metabolic variables, such as circulating EPCAM.

INTERPRETATION

The results advance our knowledge about the biochemical manifestations of type 2 diabetes and suggest that comprehensive protein profiling may serve as a useful tool for metabolic phenotyping and for elucidating the biological effects of diabetes treatments.

FUNDING

This work was supported by the Swedish Heart and Lung Foundation, the Swedish Research Council, the Erling Persson Foundation, the Knut and Alice Wallenberg Foundation, and the Swedish state under the agreement between the Swedish government and the county councils (ALF-agreement).

Integration of molecular profiles in a longitudinal wellness profiling cohort

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, the authors sample a longitudinal wellness cohort and analyse blood molecular profiles as well as gut microbiota composition.