Explore the vast range of titles available.

With the growing number of genetic association studies, the genotype-phenotype atlas has become increasingly more complex, yet the functional consequences of most disease associated alleles is not understood. The measurement of protein level variation in solid tissues and biofluids integrated with genetic variants offers a path to deeper functional insights. Here we present a large-scale proteogenomic study in 5,368 individuals, revealing 4,035 independent associations between genetic variants and 2,091 serum proteins, of which 36% are previously unreported. The majority of both cis - and trans -acting genetic signals are unique for a single protein, although our results also highlight numerous highly pleiotropic genetic effects on protein levels and demonstrate that a protein’s genetic association profile reflects certain characteristics of the protein, including its location in protein networks, tissue specificity and intolerance to loss of function mutations. Integrating protein measurements with deep phenotyping of the cohort, we observe substantial enrichment of phenotype associations for serum proteins regulated by established GWAS loci, and offer new insights into the interplay between genetics, serum protein levels and complex disease. Circulating proteins have been linked to many conditions, and understanding their genetic control can lead to understanding of disease. Here, the authors associate common genetic variants with protein levels, finding overlap of genetic associations with circulating proteins and complex disease.

Understanding the function of human blood serum proteins in disease has been limited by difficulties in monitoring their production, accumulation, and distribution. Emilsson et al. investigated human serum proteins of more than 5000 Icelanders over the age of 65. The composition of blood serum includes a complex regulatory network of proteins that are globally coordinated across most or all tissues. The authors identified modules and functional groups associated with disease and health outcomes and were able to link genetic variants to complex diseases. Science , this issue p. 769 A deep proteome analysis of human serum reveals the relationship between disease and genetics. Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

Increased potential for branched-chain amino acid and lipopolysaccharide biosynthesis in the gut microbiome of insulin-resistant individuals suggests that changes in the serum metabolome induced by dysbiosis, and driven by only a handful of species, contribute to the development of diabetes. Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders. Gut microbes and host insulin sensitivity This study explores the relationship between the microbial gut metabolome and insulin resistance, finding that there is increased potential for lipopolysaccharide and branched-chain amino acid (BCAA) biosynthesis in insulin-resistant individuals. This suggests that changes in the serum metabolome induced by dysbiosis contribute to the development of diabetes. Positive correlations between microbial functions and insulin resistance are largely driven by just a few species, notably Prevotella copri and Bacteroides vulgatus . Challenging mice with P. copri led to increased circulating serum levels of BCAAs and an aggravation of glucose intolerance.

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins’ emerging role as biomarkers and potential causative agents of a wide range of diseases. Finding the genetic basis of protein expression can elucidate the genetic mechanisms of disease. Here, the authors link low-frequency and common DNA sequence variants to thousands of serum proteins, finding genetic overlap between circulating proteins and a wide range of common diseases.

The first individual genome from the Clovis culture is presented; the origins and genetic legacy of the people who made Clovis tools have been under debate, and evidence here suggests that the individual is more closely related to all Native American populations than to any others, refuting the hypothesis that the Clovis people arrived via European (Solutrean) migration to the Americas. Ancient genome maps Native American ancestry The Clovis complex is an archaeological culture distributed widely in North America. Dating to around 13,000 years ago it is characterized by distinct stone tools including a spear blade known as the Clovis point. Just who made these tools has been a subject of much speculation based on sparse information. There is now more to go on with the publication of the first genome sequence of an ancient North American individual. The genome is that of a male infant (Anzick-1) from the Clovis burial at the Anzick site in Montana. The partial skeleton, buried about 12,600 years ago, was found in association with scores of ochre-painted stone tools. Its genome is from a population from which contemporary Native Americans are descended and is more closely related to all indigenous American populations than to any others. These findings refute the hypothesis that the Clovis people migrated from Europe, are consistent with a human occupation of the Americas a few thousand years before Clovis, and suggest that contemporary Native Americans are descendants of the first people to settle successfully in the Americas. Clovis, with its distinctive biface, blade and osseous technologies, is the oldest widespread archaeological complex defined in North America, dating from 11,100 to 10,700 14 C years before present ( bp) (13,000 to 12,600 calendar years  bp ) 1 , 2 . Nearly 50 years of archaeological research point to the Clovis complex as having developed south of the North American ice sheets from an ancestral technology 3 . However, both the origins and the genetic legacy of the people who manufactured Clovis tools remain under debate. It is generally believed that these people ultimately derived from Asia and were directly related to contemporary Native Americans 2 . An alternative, Solutrean, hypothesis posits that the Clovis predecessors emigrated from southwestern Europe during the Last Glacial Maximum 4 . Here we report the genome sequence of a male infant (Anzick-1) recovered from the Anzick burial site in western Montana. The human bones date to 10,705 ± 35 14 C years  bp (approximately 12,707–12,556 calendar years  bp ) and were directly associated with Clovis tools. We sequenced the genome to an average depth of 14.4× and show that the gene flow from the Siberian Upper Palaeolithic Mal’ta population 5 into Native American ancestors is also shared by the Anzick-1 individual and thus happened before 12,600 years  bp . We also show that the Anzick-1 individual is more closely related to all indigenous American populations than to any other group. Our data are compatible with the hypothesis that Anzick-1 belonged to a population directly ancestral to many contemporary Native Americans. Finally, we find evidence of a deep divergence in Native American populations that predates the Anzick-1 individual.

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes. Four T2D subtypes were previously identified: severe insulin deficient, severe insulin resistant, mild obesity-related, and mild age-related diabetes. Here, the authors show that these subtypes can be translated to an Arabic population and identify distinct subtype-specific metabolic and proteomic signatures.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression. There is an urgent need for biomarkers for type 2 diabetes progression that provide a deeper understanding of the disease process. Here, the authors identify biomarkers in three molecular classes, replicate them in other cohorts and explore top protein biomarkers in detail in functional studies.

Age-related macular degeneration (AMD) is one of the most common causes of visual impairment in the elderly, with a complex and still poorly understood etiology. Whole-genome association studies have discovered 34 genomic regions associated with AMD. However, the genes and cognate proteins that mediate the risk, are largely unknown. In the current study, we integrate levels of 4782 human serum proteins with all genetic risk loci for AMD in a large population-based study of the elderly, revealing many proteins and pathways linked to the disease. Serum proteins are also found to reflect AMD severity independent of genetics and predict progression from early to advanced AMD after five years in this population. A two-sample Mendelian randomization study identifies several proteins that are causally related to the disease and are directionally consistent with the observational estimates. In this work, we present a robust and unique framework for elucidating the pathobiology of AMD. Age related macular degeneration is a common cause of visual impairment in the elderly, but the etiology is not fully understood. Here, the authors use genetic data, serum proteomics, and AMD phenotypic data from a large Icelandic cohort to discover proteins altered in, causally related to AMD or signifying progression of advanced AMD.

Background A decline in forced expiratory volume (FEV1) is a hallmark of respiratory diseases that are an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1. Methods Data from the population-based AGES-Reykjavik study were used. Serum proteomic measurements were done using 4782 DNA aptamers (SOMAmers). Data from 1479 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional two-sample Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102). Results In observational analyses, 530 SOMAmers were associated with FEV1 after multiple testing adjustment (FDR < 0.05). The most significant were Retinoic Acid Receptor Responder 2 (RARRES2), R-Spondin 4 (RSPO4) and Alkaline Phosphatase, Placental Like 2 (ALPPL2). Of the 257 SOMAmers with genetic instruments available, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta (ERO1B) and Apolipoprotein M (APOM). THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments. Conclusions In summary, this large scale proteogenomic analyses of FEV1 reveals circulating protein markers of FEV1, as well as several proteins with potential causality to lung function.

Recent studies indicate that the interplay between diet, intestinal microbiota composition, and intestinal permeability can impact mental health. More than 10% of children and adolescents in Iceland suffer from mental disorders, and rates of psychotropics use are very high. The aim of this novel observational longitudinal case-control study, “Meals, Microbiota and Mental Health in Children and Adolescents (MMM-Study)” is to contribute to the promotion of treatment options for children and adolescents diagnosed with mental disorders through identification of patterns that may affect the symptoms. All children and adolescents, 5–15 years referred to the outpatient clinic of the Child and Adolescent Psychiatry Department at The National University Hospital in Reykjavik, Iceland, for one year ( n≈1 50) will be invited to participate. There are two control groups, i.e., sex-matched children from the same postal area ( n≈1 50) and same parent siblings (full siblings) in the same household close in age +/- 3 years ( n <150). A three-day food diary, rating scales for mental health, and multiple questionnaires will be completed. Biosamples (fecal-, urine-, saliva-, blood samples, and buccal swab) will be collected and used for 16S rRNA gene amplicon sequencing of the oral and gut microbiome, measurements of serum factors, quantification of urine metabolites and host genotype, respectively. For longitudinal follow-up, data collection will be repeated after three years in the same groups. Integrative analysis of diet, gut microbiota, intestinal permeability, serum metabolites, and mental health will be conducted applying bioinformatics and systems biology approaches. Extensive population-based data of this quality has not been collected before, with collection repeated in three years’ time, contributing to the high scientific value. The MMM-study follows the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) guidelines. Approval has been obtained from the Icelandic National Bioethics Committee, and the study is registered with Clinicaltrials.gov. The study will contribute to an improved understanding of the links between diet, gut microbiota and mental health in children through good quality study design by collecting information on multiple components, and a longitudinal approach. Furthermore, the study creates knowledge on possibilities for targeted and more personalized dietary and lifestyle interventions in subgroups. Trial registration numbers : VSN-19-225 & NCT04330703 .