Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator

Connecting dementia risk loci to the CSF proteome identifies pathophysiological leads for dementia

Genome-wide association studies have successfully identified many genetic risk loci for dementia, but exact biological mechanisms through which genetic risk factors contribute to dementia remains unclear. Integrating CSF proteomic data with dementia risk loci could reveal intermediate molecular pathways connecting genetic variance to the development of dementia. We tested to what extent effects of known dementia risk loci can be observed in CSF levels of 665 proteins [proximity extension-based (PEA) immunoassays] in a deeply-phenotyped mixed memory clinic cohort [n = 502, mean age (standard deviation, SD) = 64.1 (8.7) years, 181 female (35.4%)], including patients with Alzheimer's disease (AD, n = 213), dementia with Lewy bodies (DLB, n = 50) and frontotemporal dementia (FTD, n = 93), and controls (n = 146). Validation was assessed in independent cohorts (n = 99 PEA platform, n = 198, mass reaction monitoring-targeted mass spectroscopy and multiplex assay). We performed additional analyses stratified according to diagnostic status (AD, DLB, FTD and controls separately), to explore whether associations between CSF proteins and genetic variants were specific to disease or not. We identified four AD risk loci as protein quantitative trait loci (pQTL): CR1-CR2 (rs3818361, P = 1.65 × 10-8), ZCWPW1-PILRB (rs1476679, P = 2.73 × 10-32), CTSH-CTSH (rs3784539, P = 2.88 × 10-24) and HESX1-RETN (rs186108507, P = 8.39 × 10-8), of which the first three pQTLs showed direct replication in the independent cohorts. We identified one AD-specific association between a rare genetic variant of TREM2 and CSF IL6 levels (rs75932628, P = 3.90 × 10-7). DLB risk locus GBA showed positive trans effects on seven inter-related CSF levels in DLB patients only. No pQTLs were identified for FTD loci, either for the total sample as for analyses performed within FTD only. Protein QTL variants were involved in the immune system, highlighting the importance of this system in the pathophysiology of dementia. We further identified pQTLs in stratified analyses for AD and DLB, hinting at disease-specific pQTLs in dementia. Dissecting the contribution of risk loci to neurobiological processes aids in understanding disease mechanisms underlying dementia.

Identification of an Allele-Specific Transcription Factor Binding Interaction that May Regulate PLA2G2A Gene Expression

The secreted phospholipase A2 (sPLA2) isoform, sPLA2-IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA2-IIA. The work in the manuscript leveraged 4 publicly available datasets to investigate the mechanism by which rs11573156 influences sPLA2-IIA levels via bioinformatics and modeling analysis. Through genotype-tissue expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA2-IIA, PLA2G2A. SNP2TFBS was used to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified eQTL SNPs. Subsequently, candidate TF-SNP interactions were cross-referenced with the ChIP-seq results in matched tissues from ENCODE. SP1-rs11573156 emerged as the significant TF-SNP pair in the liver. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was likely affected by tissue SP1 protein levels. Using an ordinary differential equation based on Michaelis-Menten kinetic assumptions, we modeled the dependence of PLA2G2A transcription on SP1 protein levels, incorporating the SNP influence. Collectively, our analysis strongly suggests that the difference in the binding dynamics of SP1 to different rs11573156 alleles may underlie the allele-specific PLA2G2A expression in different tissues, a mechanistic model that awaits future direct experimental validation. This mechanism likely contributes to the variation in circulating sPLA2-IIA protein levels in the human population, with implications for a wide range of human diseases.

Profound perturbations are found in the proteome and metabolome in children with obesity after weight loss intervention

Background and aims

The mechanisms occur in children with obesity after lifestyle intervention remain poorly explained. Here, we investigated the serum proteomes and metabolomes of children with obesity who had undergone 30 days of weight loss intervention.

Methods and results

Serum samples and clinical parameters were collected before and after lifestyle alteration interventions. Proteomic and metabolomic profiling was used to identify the differentially expressed proteins and differentially abundant metabolites in response to weight loss intervention. Lifestyle alteration interventions significantly decreased BMI, waist circumference, hip circumference and body fat, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high non-HDL cholesterol, but not TG and high-density lipoprotein cholesterol (HDL), in children with obesity. By comparing the multiomics data, we identified 43 proteins and 165 metabolites that were significantly differentially expressed in children with obesity before and after lifestyle alteration interventions. Using integrated -omics analysis, we obtained 7 KEGG pathways that were organically integrated based on the correlations between differentially expressed proteins (DEPs) and metabolites (DMs). Further interaction analysis identified 7 proteins as candidate DEPs and 9 metabolites as candidate DMs. Interestingly, we found that some of these candidate DEPs and candidate DMs were significantly correlated with clinical parameters.

Conclusion

Our results provide valuable proteome and metabolome data resources for better understanding weight loss-associated responses in children with obesity. In addition, we analyzed the number of significantly differentially expressed proteins and metabolites, shed new light on weight loss pathogenesis in children with obesity, and added potential therapeutic agents for obese children.

Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

ATRX guards against aberrant differentiation in mesenchymal progenitor cells

Alterations in the tumor suppressor ATRX are recurrently observed in mesenchymal neoplasms. ATRX has multiple epigenetic functions including heterochromatin formation and maintenance and regulation of transcription through modulation of chromatin accessibility. Here, we show in murine mesenchymal progenitor cells (MPCs) that Atrx deficiency aberrantly activated mesenchymal differentiation programs. This includes adipogenic pathways where ATRX loss induced expression of adipogenic transcription factors and enhanced adipogenic differentiation in response to differentiation stimuli. These changes are linked to loss of heterochromatin near mesenchymal lineage genes together with increased chromatin accessibility and gains of active chromatin marks. We additionally observed depletion of H3K9me3 at transposable elements, which are derepressed including near mesenchymal genes where they could serve as regulatory elements. Finally, we demonstrated that loss of ATRX in a mesenchymal malignancy, undifferentiated pleomorphic sarcoma, results in similar epigenetic disruption and de-repression of transposable elements. Together, our results reveal a role for ATRX in maintaining epigenetic states and transcriptional repression in mesenchymal progenitors and tumor cells and in preventing aberrant differentiation in the progenitor context.

Sex-biased genetic regulation of inflammatory proteins in the Dutch population

BACKGROUND

Significant differences in immune responses, prevalence or susceptibility of diseases and treatment responses have been described between males and females. Despite this, sex-differentiation analysis of the genetic architecture of inflammatory proteins is largely unexplored. We performed sex-stratified meta-analysis after protein quantitative trait loci (pQTL) mapping using inflammatory biomarkers profiled using targeted proteomics (Olink inflammatory panel) of two population-based cohorts of Europeans.

RESULTS

Even though, around 67% of the pQTLs demonstrated shared effect between sexes, colocalization analysis identified two loci in the males (LINC01135 and ITGAV) and three loci (CNOT10, SRD5A2, and LILRB5) in the females with evidence of sex-dependent modulation by pQTL variants. Furthermore, we identified pathways with relevant functions in the sex-biased pQTL variants. We also showed through cross-validation that the sex-specific pQTLs are linked with sex-specific phenotypic traits.

CONCLUSION

Our study demonstrates the relevance of genetic sex-stratified analysis in the context of genetic dissection of protein abundances among individuals and reveals that, sex-specific pQTLs might mediate sex-linked phenotypes. Identification of sex-specific pQTLs associated with sex-biased diseases can help realize the promise of individualized treatment.

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

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression

The secreted phospholipase A 2 (sPLA 2 ) isoform, sPLA 2 -IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA 2 -IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA 2 -IIA, PLA2G2A . SNP2TFBS ( https://ccg.epfl.ch/snp2tfbs/ ) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcription's dependence on SP1 protein levels, incorporating the SNP's influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.

Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome

Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

ATRX guards against aberrant differentiation in mesenchymal progenitor cells

Alterations in the tumor suppressor ATRX are recurrently observed in several cancer types including sarcomas, which are mesenchymal neoplasms. ATRX has multiple epigenetic functions including heterochromatin formation and maintenance and regulation of transcription through modulation of chromatin accessibility. Here, we show in murine mesenchymal progenitor cells (MPCs) that Atrx deficiency aberrantly activated mesenchymal differentiation programs. This includes adipogenic pathways where ATRX loss induced expression of adipogenic transcription factors (Pparγ and Cebpα) and enhanced adipogenic differentiation in response to differentiation stimuli. These changes are linked to loss of heterochromatin near mesenchymal lineage genes together with increased chromatin accessibility and gains of active chromatin marks at putative enhancer elements and promoters. Finally, we observed depletion of H3K9me3 at transposable elements, which are derepressed including near mesenchymal genes where they could serve as regulatory elements. Our results demonstrate that ATRX functions to buffer against differentiation in mesenchymal progenitor cells, which has implications for understanding ATRX loss of function in sarcomas.

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

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease

The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.

Molecular profile and response to energy deficit of leptin-receptor neurons in the lateral hypothalamus

Leptin exerts its effects on energy balance by inhibiting food intake and increasing energy expenditure via leptin receptors in the hypothalamus. While LepR neurons in the arcuate nucleus of the hypothalamus, the primary target of leptin, have been extensively studied, LepR neurons in other hypothalamic nuclei remain understudied. LepR neurons in the lateral hypothalamus contribute to leptin's effects on food intake and reward, but due to the low abundance of this population it has been difficult to study their molecular profile and responses to energy deficit. We here explore the transcriptome of LepR neurons in the LH and their response to energy deficit. Male LepR-Cre mice were injected in the LH with an AAV carrying Cre-dependent L10:GFP. Few weeks later the hypothalami from fed and food-restricted (24-h) mice were dissected and the TRAP protocol was performed, for the isolation of translating mRNAs from LepR cells in the LH, followed by RNA sequencing. After mapping and normalization, differential expression analysis was performed with DESeq2. We confirm that the isolated mRNA is enriched in LepR transcripts and other known neuropeptide markers of LepR^LH neurons, of which we investigate the localization patterns in the LH. We identified novel markers of LepR^LH neurons with association to energy balance and metabolic disease, such as Acvr1c, Npy1r, Itgb1, and genes that are differentially regulated by food deprivation, such as Fam46a and Rrad. Our dataset provides a reliable and extensive resource of the molecular makeup of LH LepR neurons and their response to food deprivation.

Metabolic Messengers: fibroblast growth factor 1

While fibroblast growth factor (FGF) 1 is expressed in multiple tissues, only adipose-derived and brain FGF1 have been implicated in the regulation of metabolism. Adipose FGF1 production is upregulated in response to dietary stress and is essential for adipose tissue plasticity in these conditions. Similarly, in the brain, FGF1 secretion into the ventricular space and the adjacent parenchyma is increased after a hypercaloric challenge induced by either feeding or glucose infusion. Potent anorexigenic properties have been ascribed to both peripheral and centrally injected FGF1. The ability of recombinant FGF1 and variants with reduced mitogenicity to lower glucose, suppress adipose lipolysis and promote insulin sensitization elevates their potential as candidates in the treatment of type 2 diabetes mellitus and associated comorbidities. Here, we provide an overview of the known metabolic functions of endogenous FGF1 and discuss its therapeutic potential, distinguishing between peripherally or centrally administered FGF1.

Feature selection for kernel methods in systems biology

The substantial development of high-throughput biotechnologies has rendered large-scale multi-omics datasets increasingly available. New challenges have emerged to process and integrate this large volume of information, often obtained from widely heterogeneous sources. Kernel methods have proven successful to handle the analysis of different types of datasets obtained on the same individuals. However, they usually suffer from a lack of interpretability since the original description of the individuals is lost due to the kernel embedding. We propose novel feature selection methods that are adapted to the kernel framework and go beyond the well-established work in supervised learning by addressing the more difficult tasks of unsupervised learning and kernel output learning. The method is expressed under the form of a non-convex optimization problem with a ℓ₁ penalty, which is solved with a proximal gradient descent approach. It is tested on several systems biology datasets and shows good performances in selecting relevant and less redundant features compared to existing alternatives. It also proved relevant for identifying important governmental measures best explaining the time series of Covid-19 reproducing number evolution during the first months of 2020. The proposed feature selection method is embedded in the R package mixKernel version 0.8, published on CRAN. Installation instructions are available at http://mixkernel.clementine.wf/.

Low-frequency Coding Variants Associated With Body Mass Index Affect the Success of Bariatric Surgery

CONTEXT

A recent study identified 14 low-frequency coding variants associated with body mass index (BMI) in 718 734 individuals predominantly of European ancestry.

OBJECTIVE

We investigated the association of 2 genetic scores (GS) with i) the risk of severe/morbid obesity, ii) BMI variation before weight-loss intervention, iii) BMI change in response to an 18-month lifestyle/behavioral intervention program, and iv) BMI change up to 24 months after bariatric surgery.

METHODS

The 14 low-frequency coding variants were genotyped or sequenced in 342 French adults with severe/morbid obesity and 574 French adult controls from the general population. We built risk and protective GS based on 6 BMI-increasing and 5 BMI-decreasing low-frequency coding variants that were polymorphic in our study.

RESULTS

While the risk GS was not associated with severe/morbid obesity status, BMI-decreasing low-frequency coding variants were significantly less frequent in patients with severe/morbid obesity than in French adults from the general population. Neither the risk nor the protective GS was associated with BMI before intervention in patients with severe/morbid obesity, nor did they affect BMI change in response to a lifestyle/behavioral modification program. The protective GS was associated with a greater BMI decrease following bariatric surgery. The risk and protective GS were associated with a higher and lower risk of BMI regain after bariatric surgery.

CONCLUSION

Our data indicate that in populations of European descent, low-frequency coding variants associated with BMI in the general population also affect the outcomes of bariatric surgery in patients with severe/morbid obesity.

Tent5a modulates muscle fiber formation in adolescent idiopathic scoliosis via maintenance of myogenin expression

OBJECTIVE

Paravertebral muscle asymmetry may be involved in the pathogenesis of adolescent idiopathic scoliosis (AIS), and the Tent5a protein was recently identified as a novel active noncanonical poly(A) polymerase. We, therefore, explored the function of the AIS susceptibility gene Tent5a in myoblasts.

MATERIALS AND METHODS

RNA-seq of AIS paravertebral muscle was performed, and the molecular differences in paravertebral muscle were investigated. Twenty-four AIS susceptibility genes were screened, and differential expression of Tent5a in paravertebral muscles was confirmed with qPCR and Western blot. After the knockdown of Tent5a, the functional effects of Tent5a on C2C12 cell proliferation, migration, and apoptosis were detected by Cell Counting Kit-8 assay, wound-healing assay, and TUNEL assay, respectively. Myogenic differentiation markers were tested with immunofluorescence and qPCR in vitro, and muscle fiber formation was compared in vivo.

RESULTS

The AIS susceptibility gene Tent5a was differentially expressed in AIS paravertebral muscles. Tent5a knockdown inhibited the proliferation and migration of C2C12 cells and inhibited the maturation of type I muscle fibers in vitro and in vivo. Mechanistically, the expression of myogenin was decreased along with the suppression of Tent5a.

CONCLUSIONS

Tent5a plays an important role in the proliferation and migration of myoblasts, and it regulates muscle fiber maturation by maintaining the stability of myogenin. Tent5a may be involved in the pathogenesis of AIS by regulating the formation of muscle fiber type I.

Proteome-wide associations with short- and long-term weight loss and regain after Roux-en-Y gastric bypass surgery

OBJECTIVE

Gastric bypass surgery results in long-term weight loss. Small studies have examined protein changes during rapid weight loss (up to 1 or 2 years post surgery). This study tested whether short-term changes were maintained after 12 years.

METHODS

A 12-year follow-up, protein-wide association study of 1,297 SomaLogic aptamer-based plasma proteins compared short- (2-year) and long-term (12-year) protein changes in 234 individuals who had gastric bypass surgery with 144 nonintervened individuals with severe obesity.

RESULTS

There were 51 replicated 12-year protein changes that differed between the surgery and nonsurgery groups. Adjusting for change in BMI, only 12 proteins remained significant, suggesting that BMI change was the primary reason for most protein changes and not non-BMI-related surgical effects. Protein changes were related to BMI changes during both weight-loss and weight-regain periods. The significant proteins were associated primarily with lipid, uric acid, or resting energy expenditure clinical variables and metabolic pathways. Eight protein changes were associated with 12-year diabetes remission, including apolipoprotein M, sex hormone binding globulin, and adiponectin (p < 3.5 × 10^-5 ).

CONCLUSIONS

This study showed that most short-term postsurgical changes in proteins were maintained at 12 years. Systemic protection pathways, including inflammation, complement, lipid, and adipocyte pathways, were related to the long-term benefits of gastric bypass surgery.

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.

Underestimation of Heritability across the Molecular Layers of the Gene Expression Process

We investigated the extent of the heritability underestimation for molecules from an infinitesimal model in mixed model analysis. To this end, we estimated the heritability of transcription, ribosome occupancy, and translation in lymphoblastoid cell lines from Yoruba individuals. Upon considering all genome-wide nucleotide variants, a considerable underestimation in heritability was observed for mRNA transcription (−0.52), ribosome occupancy (−0.48), and protein abundance (−0.47). We employed a mixed model with an optimal number of nucleotide variants, which maximized heritability, and identified two novel expression quantitative trait loci (eQTLs; p < 1.0 × 10−5): rs11016815 on chromosome 10 that influences the transcription of SCP2, a trans-eGene on chromosome 1—whose expression increases in response to MGMT downregulation-induced apoptosis, the cis-eGene of rs11016815—and rs1041872 on chromosome 11 that influences the ribosome occupancy of CCDC25 on chromosome 8 and whose cis-eGene encodes ZNF215, a transcription factor that potentially regulates the translation speed of CCDC25. Our results suggest that an optimal number of nucleotide variants should be used in a mixed model analysis to accurately estimate heritability and identify eQTLs. Moreover, a heterogeneous covariance structure based on gene identity and the molecular layers of the gene expression process should be constructed to better explain polygenic effects and reduce errors in identifying eQTLs.

Whole-genome re-sequencing association study on yearling wool traits in Chinese fine-wool sheep

To investigate single nucleotide polymorphism (SNP) loci associated with yearling wool traits of fine-wool sheep for optimizing marker-assisted selection and dissection of the genetic architecture of wool traits, we conducted a genome-wide association study (GWAS) based on the fixed and random model circulating probability unification (FarmCPU) for yearling staple length (YSL), yearling mean fiber diameter (YFD), yearling greasy fleece weight (YGFW), and yearling clean fleece rate (YCFR) by using the whole-genome re-sequenced data (totaling 577 sheep) from the following four fine-wool sheep breeds in China: Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Qinghai fine-wool sheep (QHS), and Aohan fine-wool sheep (AHS). A total of 16 SNPs were detected above the genome-wise significant threshold (P = 5.45E-09), and 79 SNPs were located above the suggestive significance threshold (P = 5.00E-07) from the GWAS results. For YFD and YGFW traits, 7 and 9 SNPs reached the genome-wise significance thresholds, whereas 10 and 12 SNPs reached the suggestive significance threshold, respectively. For YSL and YCFR traits, none of the SNPs reached the genome-wise significance thresholds, whereas 57 SNPs exceeded the suggestive significance threshold. We recorded 14 genes located at the region of ±50-kb near the genome-wise significant SNPs and 59 genes located at the region of ±50-kb near the suggestive significant SNPs. Meanwhile, we used the Average Information Restricted Maximum likelihood algorithm (AI-REML) in the “HIBLUP” package to estimate the heritability and variance components of the four desired yearling wool traits. The estimated heritability values (h²) of YSL, YFD, YGFW, and YCFR were 0.6208, 0.7460, 0.6758, and 0.5559, respectively. We noted that the genetic parameters in this study can be used for fine-wool sheep breeding. The newly detected significant SNPs and the newly identified candidate genes in this study would enhance our understanding of yearling wool formation, and significant SNPs can be applied to genome selection in fine-wool sheep breeding.

Anti-Adipogenic Effect of Alchemilla monticola is Mediated Via PI3K/AKT Signaling Inhibition in Human Adipocytes

Obesity is a persistent and continuously expanding social health concern. Excessive fat mass accumulation is associated with increased risk of chronic diseases including diabetes, atherosclerosis, non-alcoholic steatohepatitis, reproductive dysfunctions and certain types of cancer. Alchemilla monticola Opiz. is a perennial plant of the Rosaceae family traditionally used to treat inflammatory conditions and as a component of weight loss herbal mixtures. In the search for bioactive leads with potential anti-adipogenic effect from A. monticola extract (ALM), we have employed nuclear magnetic resonance (NMR) based metabolomics to obtain data for the phytochemical profile of the extract. Further, molecular docking simulation was performed against key adipogenic targets for selected pure compounds, present in the ALM extract. Evaluation of the biological activity was done in human adipocytes exposed to ALM (5, 10 and 25 μg/ml), pure astragalin (AST) or quercitrin (QUE) both at the concentrations of 5, 10 and 25 μM. Investigation of the molecular pathways involved was performed through real-time quantitative PCR and Western blot analyses. According to the docking predictions strong putative affinity was revealed for both AST and QUE towards peroxisome proliferator-activated receptor gamma (PPARγ) and phosphoinositide 3-kinase (PI3K). Assessment of the intracellular lipid accumulation revealed anti-adipogenic activity of ALM. Correspondingly, the expression of the adipogenic genes CCAAT/enhancer-binding protein alpha (CEBPA) and PPARG was downregulated upon ALM and AST treatment. The Western blotting results exposed protein kinase B (AKT), PI3K and PPARγ as targets for the inhibitory effect of ALM and AST on adipogenesis. Collectively, we provide a broader insight of the phytochemical composition of A. monticola. Additionally, we demonstrate the anti-adipogenic effect of ALM and its active compound AST in human adipocytes. Furthermore, PI3K/AKT signaling pathway is identified to mediate the ALM anti-adipogenic action. Hence, the ALM extract and its secondary metabolite AST are worth further exploration as potentially active agents in obesity management.

Leptin and Obesity: Role and Clinical Implication

The peptide hormone leptin regulates food intake, body mass, and reproductive function and plays a role in fetal growth, proinflammatory immune responses, angiogenesis and lipolysis. Leptin is a product of the obese (ob) gene and, following synthesis and secretion from fat cells in white adipose tissue, binds to and activates its cognate receptor, the leptin receptor (LEP-R). LEP-R distribution facilitates leptin's pleiotropic effects, playing a crucial role in regulating body mass via a negative feedback mechanism between adipose tissue and the hypothalamus. Leptin resistance is characterized by reduced satiety, over-consumption of nutrients, and increased total body mass. Often this leads to obesity, which reduces the effectiveness of using exogenous leptin as a therapeutic agent. Thus, combining leptin therapies with leptin sensitizers may help overcome such resistance and, consequently, obesity. This review examines recent data obtained from human and animal studies related to leptin, its role in obesity, and its usefulness in obesity treatment.

CONQUER: an interactive toolbox to understand functional consequences of GWAS hits

Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.

Integration of Proteomics and Other Omics Data

In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

FAM46A expression is elevated in glioblastoma and predicts poor prognosis of patients

OBJECTIVE

To study the expression of FAM46A in glioblastoma (GBM) and analyze its significance in predicting the prognosis of patients.

MATERIALS AND METHODS

mRNA expression and clinical data of patients with GBM were retrieved from ONCOMINE databases and The Cancer Genome Atlas (TCGA) database. Immunohistochemistry was performed in a tissue microarray including 110 GBM cases and 12 normal controls to determine the expression of FAM46A protein. Then, Kaplan-Meier curve and Cox regression model were used to investigate the relationship between FAM46A expression and clinical outcome. Coexpressed genes of FAM46A were analyzed by Linked Omics, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

RESULTS

Upregulated expression of FAM46A was found in both TCGA and our cohort. High FAM46A expression was associated with the poor prognosis of patients with GBM and could be identified as an independent risk factor for overall survival (HR = 1.652, p = 0.022). Further bioinformatics analysis revealed that FAM46A might be involved in cell motility and endoplasmic reticulum proteostasis and stress to promote GBM progression.

CONCLUSION

Our findings suggest that increased expression of FAM46A in GBM is a novel biomarker for predicting poor outcome of patients and that targeting FAM46A may serve as a potential therapy for this disease.

Genetics meets proteomics: perspectives for large population-based studies

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.

Functions and mechanisms of RNA tailing by metazoan terminal nucleotidyltransferases

Termini often determine the fate of RNA molecules. In recent years, 3' ends of almost all classes of RNA species have been shown to acquire nontemplated nucleotides that are added by terminal nucleotidyltransferases (TENTs). The best-described role of 3' tailing is the bulk polyadenylation of messenger RNAs in the cell nucleus that is catalyzed by canonical poly(A) polymerases (PAPs). However, many other enzymes that add adenosines, uridines, or even more complex combinations of nucleotides have recently been described. This review focuses on metazoan TENTs, which are either noncanonical PAPs or terminal uridylyltransferases with varying processivity. These enzymes regulate RNA stability and RNA functions and are crucial in early development, gamete production, and somatic tissues. TENTs regulate gene expression at the posttranscriptional level, participate in the maturation of many transcripts, and protect cells against viral invasion and the transposition of repetitive sequences. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Processing > 3' End Processing RNA Turnover and Surveillance > Regulation of RNA Stability.

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

BACKGROUND

The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life.

METHODS

We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed.

RESULTS

The analyses showed that each individual has a unique protein profile, and we report on the intra-individual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 × 10-11) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections.

CONCLUSIONS

We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.

Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics

Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.

A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses.

Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637.

Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.

Overexpression of FAM46A, a Non-canonical Poly(A) Polymerase, Promotes Hemin-Induced Hemoglobinization in K562 Cells

FAM46A belongs to the FAM46 subfamily of the nucleotidyltransferase-fold superfamily and is predicted to be a non-canonical poly(A) polymerase. FAM46A has been linked to several human disorders including retinitis pigmentosa, bone abnormality, cancer, and obesity. However, its molecular and functional characteristics are largely unknown. We herein report that FAM46A is expressed in cells of the hematopoietic system and plays a role in hemin-induced hemoglobinization. FAM46A is a nucleocytoplasmic shuttle protein modified by Tyr-phosphorylation only in the cytosol, where it is closely associated with ER. On the other hand, it is located proximal to the chromatin regions of active transcription in the nucleus. FAM46A is a cell cycle-dependent poly-ubiquitinated short-lived protein degraded mostly by proteasome and its overexpression inhibits cell growth and promotes hemin-induced hemoglobinization in K562 cell. Site-directed mutagenesis experiments confirm the non-canonical poly(A) polymerase activity of FAM46A is essential for enhanced hemin-induced hemoglobinization. In summary, FAM46A is a novel poly(A) polymerase that functions as a critical intracellular modulator of hemoglobinization.

Proteomic and Metabolomic Correlates of Healthy Dietary Patterns: The Framingham Heart Study

Data on proteomic and metabolomic signatures of healthy dietary patterns are limited. We evaluated the cross-sectional association of serum proteomic and metabolomic markers with three dietary patterns: the Alternative Healthy Eating Index (AHEI), the Dietary Approaches to Stop Hypertension (DASH) diet; and a Mediterranean-style (MDS) diet. We examined participants from the Framingham Offspring Study (mean age; 55 years; 52% women) who had complete proteomic (n = 1713) and metabolomic (n = 2284) data; using food frequency questionnaires to derive dietary pattern indices. Proteins and metabolites were quantified using the SomaScan platform and liquid chromatography/tandem mass spectrometry; respectively. We used multivariable-adjusted linear regression models to relate each dietary pattern index (independent variables) to each proteomic and metabolomic marker (dependent variables). Of the 1373 proteins; 103 were associated with at least one dietary pattern (48 with AHEI; 83 with DASH; and 8 with MDS; all false discovery rate [FDR] ≤ 0.05). We identified unique associations between dietary patterns and proteins (17 with AHEI; 52 with DASH; and 3 with MDS; all FDR ≤ 0.05). Significant proteins enriched biological pathways involved in cellular metabolism/proliferation and immune response/inflammation. Of the 216 metabolites; 65 were associated with at least one dietary pattern (38 with AHEI; 43 with DASH; and 50 with MDS; all FDR ≤ 0.05). All three dietary patterns were associated with a common signature of 24 metabolites (63% lipids). Proteins and metabolites associated with dietary patterns may help characterize intermediate phenotypes that provide insights into the molecular mechanisms mediating diet-related disease. Our findings warrant replication in independent populations.

Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention

Inflammatory and cardiovascular biomarkers have been associated with obesity, but little is known about how they change upon dietary intervention and concomitant weight loss. Further, protein biomarkers might be useful for predicting weight loss in overweight and obese individuals. We performed secondary analyses in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized intervention trial that included healthy 609 adults (18–50 years old) with BMI 28–40 kg/m², to evaluate associations between circulating protein biomarkers and BMI at baseline, during a weight loss diet intervention, and to assess predictive potential of baseline blood proteins on weight loss. We analyzed 263 plasma proteins at baseline and 6 months into the intervention using the Olink Proteomics CVD II, CVD III and Inflammation arrays. BMI was assessed at baseline, after 3 and 6 months of dietary intervention. At baseline, 102 of the examined inflammatory and cardiovascular biomarkers were associated with BMI (>90% with successful replication in 1,584 overweight/obese individuals from a community-based cohort study) and 130 tracked with weight loss shedding light into the pathophysiology of obesity. However, out of 263 proteins analyzed at baseline, only fibroblast growth factor 21 (FGF-21) predicted weight loss, and none helped individualize dietary assignment.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

BAG6 contributes to glucose uptake by supporting the cell surface translocation of the glucose transporter GLUT4

Defective translocation of glucose transporter 4 (GLUT4) to the cell surface is a key feature of insulin resistance in type 2 diabetes. Therefore, elucidating the mechanism of GLUT4 translocation is of primary importance. The mammalian Bag6/Bat3 gene has been suggested to be linked with potential obesity- and diabetes-associated loci, while its function in the control of glucose incorporation into the cytoplasm has not been investigated. In this study, we established a series of cell lines that stably expressed GLUT4 with three tandem repeats of the antigenic peptide inserted into its 1st extracellular loop. With these cell lines, we found that the depletion of endogenous BAG6 downregulated the cell surface expression of GLUT4, concomitant with the reduced incorporation of a glucose analog into the cells. Defective intracellular translocation of GLUT4 in BAG6-depleted cells is similar to the case observed for the depletion of Rab8a, an essential regulator of insulin-stimulated GLUT4 translocation. In addition, we observed that the assembly of syntaxin 6 into the endoplasmic reticulum membrane was slightly disturbed under BAG6 depletion. Given that Rab8a and syntaxin 6 are critical for GLUT4 translocation, we suggest that BAG6 may play multiple roles in the trafficking of glucose transporters to the cell surface.

This article has an associated First Person interview with the first author of the paper.

Summary: BAG6 is critical for the insulin-stimulated translocation of GLUT4 from its peri-nuclear storage compartments to the cell surface.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Undulating changes in human plasma proteome profiles across the lifespan

Aging is a predominant risk factor for numerous chronic diseases that limit healthspan₁. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues_2–10, which supports a hypothesis that age-related molecular changes in blood could provide novel insights into age-related disease biology. We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18–95 years old) and developed a novel bioinformatics approach, which uncovered marked non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh, and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits. This new approach to the study of aging led to the identification of unexpected signatures and pathways, which might offer potential targets for age-related diseases.

Apolipoprotein M: a novel adipokine decreasing with obesity and upregulated by calorie restriction

BACKGROUND

The adipose tissue (AT) is a secretory organ producing a wide variety of factors that participate in the genesis of metabolic disorders linked to excess fat mass. Weight loss improves obesity-related disorders.

OBJECTIVES

Transcriptomic studies on human AT, and a combination of analyses of transcriptome and proteome profiling of conditioned media from adipocytes and stromal cells isolated from human AT, have led to the identification of apolipoprotein M (apoM) as a putative adipokine. We aimed to validate apoM as novel adipokine, investigate the relation of AT APOM expression with metabolic syndrome and insulin sensitivity, and study the regulation of its expression in AT and secretion during calorie restriction-induced weight loss.

METHODS

We examined APOM mRNA level and secretion in AT from 485 individuals enrolled in 5 independent clinical trials, and in vitro in human multipotent adipose-derived stem cell adipocytes. APOM expression and secretion were measured during dieting.

RESULTS

APOM was expressed in human subcutaneous and visceral AT, mainly by adipocytes. ApoM was released into circulation from AT, and plasma apoM concentrations correlate with AT APOM mRNA levels. In AT, APOM expression inversely correlated with adipocyte size, was lower in obese compared to lean individuals, and reduced in subjects with metabolic syndrome and type 2 diabetes. Regardless of fat depot, there was a positive relation between AT APOM expression and systemic insulin sensitivity, independently of fat mass and plasma HDL cholesterol. In human multipotent adipose-derived stem cell adipocytes, APOM expression was enhanced by insulin-sensitizing peroxisome proliferator-activated receptor agonists and inhibited by tumor necrosis factor α, a cytokine that causes insulin resistance. In obese individuals, calorie restriction increased AT APOM expression and secretion.

CONCLUSIONS

ApoM is a novel adipokine, the expression of which is a hallmark of healthy AT and is upregulated by calorie restriction. AT apoM deserves further investigation as a potential biomarker of risk for diabetes and cardiovascular diseases.

Plasma Protein and MicroRNA Biomarkers of Insulin Resistance: A Network-Based Integrative -Omics Analysis

Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma.

Salivary α-amylase copy number is not associated with weight trajectories and glycemic improvements following clinical weight loss: results from a 2-phase dietary intervention study

BACKGROUND

Several studies recently reported contradicting results regarding the link between amylase 1 (AMY1) copy numbers (CNs), obesity, and type 2 diabetes.

OBJECTIVE

The aim of this study was to assess the impact of AMY1 CN on anthropometrics and glycemic outcomes in obese individuals following a 2-phase dietary weight loss intervention.

METHODS

Using the paralog ratio test, AMY1 CNs were accurately measured in 761 obese individuals from the DiOGenes study. Subjects first underwent an 8-wk low-calorie diet (LCD, at 800 kcal/d) and then were randomly assigned to a 6-mo weight maintenance dietary (WMD) intervention with arms having different glycemic loads.

RESULTS

At baseline, a modest association between AMY1 CN and BMI (P = 0.04) was observed. AMY1 CN was not associated with baseline glycemic variables. In addition, AMY1 CN was not associated with anthropometric or glycemic outcomes following either LCD or WMD. Interaction analyses between AMY1 CN and nutrient intake did not reveal any significant association with clinical parameters (at baseline and following LCD or WMD) or when testing gene × WMD interactions during the WMD phase.

CONCLUSION

In the absence of association with weight trajectories or glycemic improvements, the AMY1 CN cannot be considered as an important biomarker for response to a clinical weight loss and weight maintenance programs in overweight/obese subjects. This trial was registered at www.clinicaltrials.gov as NCT00390637.

Analysis of circulating angiopoietin-like protein 3 and genetic variants in lipid metabolism and liver health: the DiOGenes study

Background

Angiopoietin-like protein 3 (ANGPTL3), a liver-derived protein, plays an important role in the lipid and lipoprotein metabolism. Using data available from the DiOGenes study, we assessed the link with clinical improvements (weight, plasma lipid, and insulin levels) and changes in liver markers, alanine aminotransferase, aspartate aminotransferase (AST), adiponectin, fetuin A and B, and cytokeratin 18 (CK-18), upon low-calorie diet (LCD) intervention. We also examined the role of genetic variation in determining the level of circulating ANGPTL3 and the relation between the identified genetic markers and markers of hepatic steatosis.

Methods

DiOGenes is a multicenter, controlled dietary intervention where obese participants followed an 8-week LCD (800 kcal/day, using a meal replacement product). Plasma ANGPTL3 and liver markers were measured using the SomaLogic (Boulder, CO) platform. Protein quantitative trait locus (pQTL) analyses assessed the link between more than four million common variants and the level of circulating ANGPTL3 at baseline and changes in levels during the LCD intervention.

Results

Changes in ANGPTL3 during weight loss showed only marginal association with changes in triglycerides (nominal p = 0.02) and insulin (p = 0.04); these results did not remain significant after correcting for multiple testing. However, significant association (after multiple-testing correction) were observed between changes in ANGPTL3 and AST during weight loss (p = 0.004) and between ANGPTL3 and CK-18 (baseline p = 1.03 × 10⁻⁷, during weight loss p = 1.47 × 10⁻¹³). Our pQTL study identified two loci significantly associated with changes in ANGPTL3. One of these loci (the APOA4-APOA5-ZNF259-BUD13 gene cluster) also displayed significant association with changes in CK-18 levels during weight loss (p = 0.007).

Conclusion

We clarify the link between circulating levels of ANGPTL3 and specific markers of liver function. We demonstrate that changes in ANGPLT3 and CK-18 during LCD are under genetic control from trans-acting variants. Our results suggest an extended function of ANGPTL3 in the inflammatory state of liver steatosis and toward liver metabolic processes.

Electronic supplementary material

The online version of this article (10.1186/s12263-018-0597-3) contains supplementary material, which is available to authorized users.