Armand Valsesia

Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry.

The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study DiOGenes. Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500u2009kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects

Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear.Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD.Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers.Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m2) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64). However, clinical changes during the LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]). Adding baseline expression to this model improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delongs P = 0.012). Similar analyses were performed to classify subjects with good glycemic improvements. Baseline- and LCD-based clinical models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86). The addition of expression changes during the LCD improved the performance substantially (AUC: 0.80; 95% CI: 0.69, 0.92; P = 0.058).Conclusions: This study investigated AT transcriptome alterations after an LCD in a large cohort of obese, nondiabetic patients. Gene expression combined with clinical variables enabled us to distinguish weight and glycemic responders from nonresponders. These potential biomarkers may help clinicians understand intersubject variability and better predict the success of dietary interventions. This trial was registered at clinicaltrials.gov as NCT00390637.

Variation in extracellular matrix genes is associated with weight regain after weight loss in a sex-specific manner

The extracellular matrix (ECM) of adipocytes is important for body weight regulation. Here, we investigated whether genetic variation in ECM-related genes is associated with weight regain among participants of the European DiOGenes study. Overweight and obese subjects (nxa0=xa0469, 310 females, 159 males) were on an 8-week low-calorie diet with a 6-month follow-up. Body weight was measured before and after the diet, and after follow-up. Weight maintenance scores (WMS, regained weight as percentage of lost weight) were calculated based on the weight data. Genotype data were retrieved for 2903 SNPs corresponding to 124 ECM-related genes. Regression analyses provided us with six significant SNPs associated with the WMS in males: 3 SNPs in the POSTN gene and a SNP in the LAMB1, COL23A1, and FBLN5 genes. For females, 1 SNP was found in the FN1 gene. The risk of weight regain was increased by: the C/C genotype for POSTN in a co-dominant model (OR 8.25, 95xa0% CI 2.85–23.88) and the T/C–C/C genotype in a dominant model (OR 4.88, 95xa0% CI 2.35–10.16); the A/A genotype for LAMB1 both in a co-dominant model (OR 18.43, 95xa0% CI 2.35–144.63) and in a recessive model (OR 16.36, 95xa0% CI 2.14–124.9); the G/A genotype for COL23A1 in a co-dominant model (OR 3.94, 95xa0% CI 1.28–12.10), or the A-allele in a dominant model (OR 2.86, 95xa0% CI 1.10–7.49); the A/A genotype for FBLN5 in a co-dominant model (OR 13.00, 95xa0% CI 1.61–104.81); and the A/A genotype for FN1 in a recessive model (OR 2.81, 95xa0% CI 1.40–5.63). Concluding, variants of ECM genes are associated with weight regain after weight loss in a sex-specific manner.

Distinct lipid profiles predict improved glycemic control in obese, nondiabetic patients after a low-caloric diet intervention: the Diet, Obesity and Genes randomized trial

BACKGROUNDnAn aim of weight loss is to reduce the risk of type 2 diabetes (T2D) in obese subjects. However, the relation with long-term glycemic improvement remains unknown.nnnOBJECTIVEnWe evaluated the changes in lipid composition during weight loss and their association with long-term glycemic improvement.nnnDESIGNnWe investigated the plasma lipidome of 383 obese, nondiabetic patients within a randomized, controlled dietary intervention in 8 European countries at baseline, after an 8-wk low-caloric diet (LCD) (800-1000 kcal/d), and after 6 mo of weight maintenance.nnnRESULTSnAfter weight loss, a lipid signature identified 2 groups of patients who were comparable at baseline but who differed in their capacities to lose weight and improve glycemic control. Six months after the LCD, one group had significant glycemic improvement [homeostasis model assessment of insulin resistance (HOMA-IR) mean change: -0.92; 95% CI: -1.17, -0.67)]. The other group showed no improvement in glycemic control (HOMA-IR mean change: -0.26; 95% CI: -0.64, 0.13). These differences were sustained for ≥1 y after the LCD. The same conclusions were obtained with other endpoints (Matsuda index and fasting insulin and glucose concentrations). Significant differences between the 2 groups were shown in leptin gene expression in adipose tissue biopsies. Significant differences were also observed in weight-related endpoints (body mass index, weight, and fat mass). The lipid signature allowed prediction of which subjects would be considered to be insulin resistant after 6 mo of weight maintenance [validations receiver operating characteristic (ROC) area under the curve (AUC): 71%; 95% CI: 62%, 81%]. This model outperformed a clinical data-only model (validations ROC AUC: 61%; 95% CI: 50%, 71%; P = 0.01).nnnCONCLUSIONSnIn this study, we report a lipid signature of LCD success (for weight and glycemic outcome) in obese, nondiabetic patients. Lipid changes during an 8-wk LCD allowed us to predict insulin-resistant patients after 6 mo of weight maintenance. The determination of the lipid composition during an LCD enables the identification of nonresponders and may help clinicians manage metabolic outcomes with further intervention, thereby improving the long-term outcome and preventing T2D. This trial was registered at clinicaltrials.gov as NCT00390637.

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

Thousands of genetic variants have been associated with complex traits through genome-wide association studies. However, the functional variants or mechanistic consequences remain elusive. Intermediate traits such as gene expression or protein levels are good proxies of the metabolic state of an organism. Proteome analysis especially can provide new insights into the molecular mechanisms of complex traits like obesity. The role of genetic variation in determining protein level variation has not been assessed in obesity. To address this, we design a large-scale protein quantitative trait locus (pQTL) analysis based on a set of 1129 proteins from 494 obese subjects before and after a weight loss intervention. This reveals 55 BMI-associated cis-pQTLs and trans-pQTLs at baseline and 3 trans-pQTLs after the intervention. We provide evidence for distinct genetic mechanisms regulating BMI-associated proteins before and after weight loss. Finally, by functional analysis, we identify and validate FAM46A as a trans regulator for leptin.Although many genetic variants are known for obesity, their function remains largely unknown. Here, in a weight-loss intervention cohort, the authors identify protein quantitative trait loci associated with BMI at baseline and after weight loss and find FAM46A to be a regulator of leptin in adipocytes.

Molecular Biomarkers for Weight Control in Obese Individuals Subjected to a Multiphase Dietary Intervention

ContextnAlthough calorie restriction has proven beneficial for weight loss, long-term weight control is variable between individuals.nnnObjectivenTo identify biomarkers of successful weight control during a dietary intervention (DI).nnnDesign, Setting, and ParticipantsnAdipose tissue (AT) transcriptomes were compared between 21 obese individuals who either maintained weight loss or regained weight during the DI. Results were validated on 310 individuals from the same study using quantitative reverse transcription polymerase chain reaction and protein levels of potential circulating biomarkers measured by enzyme-linked immunosorbent assay.nnnInterventionnIndividuals underwent 8 weeks of low-calorie diet, then 6 months of ad libitum diet.nnnOutcome MeasurenWeight changes at the end of the DI.nnnResultsnWe evaluated six genes that had altered expression during DI, encode secreted proteins, and have not previously been implicated in weight control (EGFL6, FSTL3, CRYAB, TNMD, SPARC, IGFBP3), as well as genes for which baseline expression differed between those with good and poor weight control (ASPN, USP53). Changes in plasma concentrations of EGFL6, FSTL3, and CRYAB mirrored AT messenger RNA expression; all decreased during DI in individuals with good weight control. ASPN and USP53 had higher baseline expression in individuals who went on to have good weight control. Expression quantitative trait loci analysis found polymorphisms associated with expression levels of USP53 in AT. A regulatory network was identified in which transforming growth factor β1 (TGF-β1) was responsible for downregulation of certain genes during DI in good controllers. Interestingly, ASPN is a TGF-β1 inhibitor.nnnConclusionsnWe found circulating biomarkers associated with weight control that could influence weight management strategies and genes that may be prognostic for successful weight control.

Network Analysis of Metabolite GWAS Hits: Implication of CPS1 and the Urea Cycle in Weight Maintenance

Background and Scope Weight loss success is dependent on the ability to refrain from regaining the lost weight in time. This feature was shown to be largely variable among individuals, and these differences, with their underlying molecular processes, are diverse and not completely elucidated. Altered plasma metabolites concentration could partly explain weight loss maintenance mechanisms. In the present work, a systems biology approach has been applied to investigate the potential mechanisms involved in weight loss maintenance within the Diogenes weight-loss intervention study. Methods and Results A genome wide association study identified SNPs associated with plasma glycine levels within the CPS1 (Carbamoyl-Phosphate Synthase 1) gene (rs10206976, p-value = 4.709e-11 and rs12613336, p-value = 1.368e-08). Furthermore, gene expression in the adipose tissue showed that CPS1 expression levels were associated with successful weight maintenance and with several SNPs within CPS1 (cis-eQTL). In order to contextualize these results, a gene-metabolite interaction network of CPS1 and glycine has been built and analyzed, showing functional enrichment in genes involved in lipid metabolism and one carbon pool by folate pathways. Conclusions CPS1 is the rate-limiting enzyme for the urea cycle, catalyzing carbamoyl phosphate from ammonia and bicarbonate in the mitochondria. Glycine and CPS1 are connected through the one-carbon pool by the folate pathway and the urea cycle. Furthermore, glycine could be linked to metabolic health and insulin sensitivity through the betaine osmolyte. These considerations, and the results from the present study, highlight a possible role of CPS1 and related pathways in weight loss maintenance, suggesting that it might be partly genetically determined in humans.

Sexual dimorphism, age and fat mass are key phenotypic drivers of proteomic signatures

Validated protein biomarkers are needed for assessing health trajectories, predicting and subclassifying disease, and optimizing diagnostic and therapeutic clinical decision-making. The sensitivity, specificity, accuracy, and precision of single or combinations of protein biomarkers may be altered by differences in physiological states limiting the ability to translate research results to clinically useful diagnostic tests. Aptamer based affinity assays were used to test whether low abundant serum proteins differed based on age, sex, and fat mass in a healthy population of 94 males and 102 females from the MECHE cohort. The findings were replicated in 217 healthy male and 377 healthy female participants in the DiOGenes consortium. Of the 1129 proteins in the panel, 141, 51, and 112 proteins (adjusted p < 0.1) were identified in the MECHE cohort and significantly replicated in DiOGenes for sexual dimorphism, age, and fat mass, respectively. Pathway analysis classified a subset of proteins from the 3 phenotypes to the complement and coagulation cascades pathways and to immune and coagulation processes. These results demonstrated that specific proteins were statistically associated with dichotomous (male vs female) and continuous phenotypes (age, fat mass), which may influence the identification and use of biomarkers of clinical utility for health diagnosis and therapeutic strategies.

Plasma metabolites and lipids predict insulin sensitivity improvement in obese, nondiabetic individuals after a 2-phase dietary intervention

ABSTRACT Background Weight loss in obese individuals aims to reduce the risk of type 2 diabetes by improving glycemic control. Yet, significant intersubject variability is observed and the outcomes remain poorly predictable. Objective The aim of the study was to predict whether an individual will show improvements in insulin sensitivity above or below the median population change at 6 mo after a low-calorie-diet (LCD) intervention. Design With the use of plasma lipidomics and metabolomics for 433 subjects from the Diet, Obesity, and Genes (DiOGenes) Study, we attempted to predict good or poor Matsuda index improvements 6 mo after an 8-wk LCD intervention (800 kcal/d). Three independent analysis groups were defined: “training” (n = 119) for model construction, “testing” (n = 162) for model comparison, and “validation” (n = 152) to validate the final model. Results Initial modeling with baseline clinical variables (body mass index, Matsuda index, total lipid concentrations, sex, age) showed limited performance [area under the curve (AUC) on the “testing dataset” = 0.69; 95% CI: 0.61, 0.77]. Significantly better performance was achieved with an omics model based on 27 variables (AUC = 0.77; 95% CI: 0.70, 0.85; P = 0.0297). This model could be greatly simplified while keeping the same performance. The simplified model relied on baseline Matsuda index, proline, and phosphatidylcholine 0-34:1. It successfully replicated on the validation set (AUC = 0.75; 95% CI: 0.67, 0.83) with the following characteristics: specificity = 0.73, sensitivity = 0.68, negative predictive value = 0.60, and positive predictive value = 0.80. Marginally lower performance was obtained when replacing the Matsuda index with homeostasis model assessment of insulin resistance (AUC = 0.72; 95% CI: 0.64, 0.80; P = 0.08). Conclusions Our study proposes a model to predict insulin sensitivity improvements, 6 mo after LCD completion in a large population of overweight or obese nondiabetic subjects. It relies on baseline information from 3 variables, accessible from blood samples. This model may help clinicians assessing the large variability in dietary interventions and predict outcomes before an intervention. This trial was registered at www.clinicaltrials.gov as NCT00390637.