Project description:ContextGlucagon is produced and released from the pancreatic alpha-cell to regulate glucose levels during periods of fasting. The main target for glucagon action is the liver, where it activates gluconeogenesis and glycogen breakdown; however, glucagon is postulated to have other roles within the body.ObjectiveWe sought to identify the circulating metabolites that would serve as markers of glucagon action in humans.MethodsIn this study (NCT03139305), we performed a continuous 72-hour glucagon infusion in healthy individuals with overweight/obesity. Participants were randomized to receive glucagon 12.5 ng/kg/min (GCG 12.5), glucagon 25 ng/kg/min (GCG 25), or a placebo control. A comprehensive metabolomics analysis was then performed from plasma isolated at several time points during the infusion to identify markers of glucagon activity.ResultsGlucagon (GCG 12.5 and GCG 25) resulted in significant changes in the plasma metabolome as soon as 4 hours following infusion. Pathways involved in amino acid metabolism were among the most affected. Rapid and sustained reduction of a broad panel of amino acids was observed. Additionally, time-dependent changes in free fatty acids and diacylglycerol and triglyceride species were observed.ConclusionThese results define a distinct signature of glucagon action that is broader than the known changes in glucose levels. In particular, the robust changes in amino acid levels may prove useful to monitor changes induced by glucagon in the context of additional glucagon-like peptide-1 or gastric inhibitory polypeptide treatment, as these agents also elicit changes in glucose levels.
Project description:To characterize the proteomic signature of chronological age, 1,301 proteins were measured in plasma using the SOMAscan assay (SomaLogic, Boulder, CO, USA) in a population of 240 healthy men and women, 22-93 years old, who were disease- and treatment-free and had no physical and cognitive impairment. Using a p ≤ 3.83 × 10-5 significance threshold, 197 proteins were positively associated, and 20 proteins were negatively associated with age. Growth differentiation factor 15 (GDF15) had the strongest, positive association with age (GDF15; 0.018 ± 0.001, p = 7.49 × 10-56 ). In our sample, GDF15 was not associated with other cardiovascular risk factors such as cholesterol or inflammatory markers. The functional pathways enriched in the 217 age-associated proteins included blood coagulation, chemokine and inflammatory pathways, axon guidance, peptidase activity, and apoptosis. Using elastic net regression models, we created a proteomic signature of age based on relative concentrations of 76 proteins that highly correlated with chronological age (r = 0.94). The generalizability of our findings needs replication in an independent cohort.
Project description:Obesity is well recognized as a risk factor for coronary heart disease and mortality. The relationship between abdominal obesity and ischemic stroke remains less clear. Previous publication showed the obesity is an independent, potent risk factor for ischemic stroke in all race-ethnic groups. It is a stronger risk factor than BMI and has a greater effect among younger persons. The goal of this experiment was to compare genome wide enrichment of H3K9ac histone mark profile of white blood cells of healthy controls, patients with obesity and/or stroke in order to understand the histone modifications differences behind the different phenotypes. There were 3 subjects in each group.
Project description:Obesity is commonly associated with metabolic diseases including type 2 diabetes, hypertension, and dyslipidemia. Moreover, individuals with obesity are at increased risk of cardiovascular disease. However, a subgroup of individuals within the obese population presents without concurrent metabolic disorders. Even though this group has a stable metabolic status and does not exhibit overt metabolic disease, this status may be transient; these individuals may have subclinical metabolic derangements. To investigate the latter hypothesis, an analysis of the proteome signature was conducted. Plasma samples from 27 subjects with obesity but without an associated metabolic disorder (obesity only (OBO)) and 15 lean healthy control (LHC) subjects were examined. Fasting samples were subjected to Olink proteomics analysis targeting 184 proteins enriched in cardiometabolic and inflammation pathways. Our results distinctly delineated two groups with distinct plasma protein expression profiles. Specifically, a total of 24 proteins were differentially expressed in individuals with obesity compared to LHC. Among these, 13 proteins were downregulated, whereas 11 proteins were upregulated. The pathways that were upregulated in the OBO group were related to chemoattractant activity, growth factor activity, G protein-coupled receptor binding, chemokine activity, and cytokine activity, whereas the pathways that were downregulated include regulation of T cell differentiation, leukocyte differentiation, reproductive system development, inflammatory response, neutrophil, lymphocyte, monocyte and leukocyte chemotaxis, and neutrophil migration. The study identifies several pathways that are altered in individuals with obesity compared to healthy control subjects. These findings provide valuable insights into the underlying mechanisms, potentially paving the way for the identification of therapeutic targets aimed at improving metabolic health in individuals with obesity.
Project description:The molecular genetic analysis of obesity has led to the identification of a limited number of confirmed major genes. While such major genes have a clear influence on the development of the phenotype, the underlying mutations are however (extremely) infrequent and thus of minor clinical importance only. The genetic predisposition to obesity must thus be polygenic; a number of such variants should be found in most obese subjects; however, these variants predisposing to obesity are also found in normal weight and even lean individuals. Therefore, a polygene can only be identified and validated by statistical analyses: the appropriate gene variant (allele) occurs more frequently in obese than in non-obese subjects. Each single polygene makes only a small contribution to the development of obesity. The 103Ile allele of the Val103Ile single nucleotide polymorphism (SNP) of the melanocortin-4 receptor gene (MC4R) was the first confirmed polygenetic variant with an influence on the body mass index (BMI); the more common Val103 allele is more frequent in obese individuals. As determined in a recent, large-scaled meta-analysis the effect size of this allele on mean BMI was approximately -0.5 kg/m(2). The first genome-wide association study (GWA) for obesity, based on approximately 100,000 SNPs analyzed in families of the Framingham study, revealed that a SNP in the proximity of the insulin-induced gene 2 (INSIG2) was associated with obesity. The positive result was replicated in independent samples; however, some other study groups detected no association. Currently, a meta-analysis is ongoing; its result will contribute to the evaluation of the importance of the INSIG2 polymorphism in body weight regulation. SNP alleles in intron 1 of the fat mass and obesity associated gene (FTO) confer the most relevant polygenic effect on obesity. In the first GWA for extreme early onset obesity we substantiated that variation in FTO strongly contributes to early onset obesity.
Project description:Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a 'high impact' SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.
Project description:Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to weight loss interventions. However, the functional determinants underlying this phenomenon remain unclear. We report a weight loss response analysis on a cohort of 105 individuals selected from a larger population enrolled in a commercial wellness program, which included healthy lifestyle coaching. Each individual in the cohort had baseline blood metabolomics, blood proteomics, clinical labs, dietary questionnaires, stool 16S rRNA gene sequencing data, and follow-up data on weight change. We generated additional targeted proteomics data on obesity-associated proteins in blood before and after intervention, along with baseline stool metagenomic data, for a subset of 25 individuals who showed the most extreme weight change phenotypes. We built regression models to identify baseline blood, stool, and dietary features associated with weight loss, independent of age, sex, and baseline body mass index (BMI). Many features were independently associated with baseline BMI, but few were independently associated with weight loss. Baseline diet was not associated with weight loss, and only one blood analyte was associated with changes in weight. However, 31 baseline stool metagenomic functional features, including complex polysaccharide and protein degradation genes, stress-response genes, respiration-related genes, and cell wall synthesis genes, along with gut bacterial replication rates, were associated with weight loss responses after controlling for age, sex, and baseline BMI. Together, these results provide a set of compelling hypotheses for how commensal gut microbiota influence weight loss outcomes in humans. IMPORTANCE Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to dietary interventions, but the exact functional determinants underlying this phenomenon remain unclear. In this study, we set out to better understand interactions between baseline BMI, metabolic health, diet, gut microbiome functional profiles, and subsequent weight changes in a human cohort that underwent a healthy lifestyle intervention. Overall, our results suggest that the microbiota may influence host weight loss responses through variable bacterial growth rates, dietary energy harvest efficiency, and immunomodulation.
Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:Obesity is thought to contribute to worse disease outcome in breast cancer as a result of increased levels of adipocyte-secreted endocrine factors, insulin, and insulin-like growth factors (IGFs) that accelerate tumor cell proliferation and impair treatment response. We examined the effects of patient obesity on primary breast tumor gene expression, by profiling transcription of a set of tumors for which the patients’ body mass index (BMI) was ascertained. Sample profiles were stratified according to patients’ obesity phenotype defined as normal (BMI <25), overweight (BMI 25-29.9), or obese (BMI>30). Widespread alterations in gene expression were evident in breast tumors from obese patients as compared to tumors from other patients, allowing us to define an obesity-associated cancer transcriptional signature of 662 genes. Keywords: two group comparison Primary breast tumor specimens were obtained from patients. Study volunteers completed questionnaires used to define historically normal (BMI<=24.9), overweight (BMI 25-29.9), or obese (BMI>=30) patient categories according to established WHO criteria.
Project description:Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants.