Gene expression profiling in whole blood identifies distinct biological pathways associated with obesity

Unraveling Adipose Tissue Dysfunction: Molecular Mechanisms, Novel Biomarkers, and Therapeutic Targets for Liver Fat Deposition

Adipose tissue (AT), once considered a mere fat storage organ, is now recognized as a dynamic and complex entity crucial for regulating human physiology, including metabolic processes, energy balance, and immune responses. It comprises mainly two types: white adipose tissue (WAT) for energy storage and brown adipose tissue (BAT) for thermogenesis, with beige adipocytes demonstrating the plasticity of these cells. WAT, beyond lipid storage, is involved in various metabolic activities, notably lipogenesis and lipolysis, critical for maintaining energy homeostasis. It also functions as an endocrine organ, secreting adipokines that influence metabolic, inflammatory, and immune processes. However, dysfunction in WAT, especially related to obesity, leads to metabolic disturbances, including the inability to properly store excess lipids, resulting in ectopic fat deposition in organs like the liver, contributing to non-alcoholic fatty liver disease (NAFLD). This narrative review delves into the multifaceted roles of WAT, its composition, metabolic functions, and the pathophysiology of WAT dysfunction. It also explores diagnostic approaches for adipose-related disorders, emphasizing the importance of accurately assessing AT distribution and understanding the complex relationships between fat compartments and metabolic health. Furthermore, it discusses various therapeutic strategies, including innovative therapeutics like adipose-derived mesenchymal stem cells (ADMSCs)-based treatments and gene therapy, highlighting the potential of precision medicine in targeting obesity and its associated complications.

Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity

INTRODUCTION

Obesity is a multifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not a single country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without a comprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes.

OBJECTIVE

The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how a multi-omics approach can be fundamental for the implementation of precision obesity management.

RESULTS

The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity.

CONCLUSION

Integrating multi-omics approaches will enable a paradigm shift from the one size fits all approach towards precision obesity management, i.e. (1) precision prevention of the onset of obesity, (2) precision medicine and tailored treatment of obesity, and (3) precision risk reduction and prevention of secondary diseases related to obesity.

Obesity Modifies the Proteomic Profile of the Periodontal Ligament

This study aimed to assess the obesity effects on the proteomic profile of the periodontal ligament of rats submitted to obesity induction by a high-fat diet. Eight Holtzman rats were divided into control (n = 3) and obese (n = 5) groups. The maxillae were histologically processed for laser capture microdissection of the periodontal ligament of the first maxillary molars. Peptide mixtures were analyzed by LC-MS/MS. A total of 1379 proteins were identified in all groups. Among them, 335 (24.30%) were exclusively detected in the obese group, while 129 (9.35%) proteins were uniquely found in the control group. Out of the 110 (7.98%) differentially abundant proteins, 10 were more abundant and 100 had decreased abundance in the obese group. A gene ontology analysis showed some proteins related to obesity in the “extracellular exosome” term among differentially identified proteins in the gene ontology cellular component terms Prelp, Sec13, and Sod2. These three proteins were upregulated in the obese group (p < 0.05), as shown by proteomic and immunohistochemistry analyses. In summary, our study presents novel evidence that the proteomic profile of the periodontal ligament is altered in experimental obesity induction, providing a list of differentially abundant proteins associated with obesity, which indicates that the periodontal ligament is responsive to obesity.

Impact of Visceral Obesity on Structural and Functional Alterations of Gut Microbiota in Polycystic Ovary Syndrome (PCOS): A Pilot Study Using Metagenomic Analysis

OBJECTIVE

We aimed to identify structural and functional alterations of gut microbiota associated with visceral obesity in adult women with polycystic ovary syndrome (PCOS).

METHODS

Twenty-seven adults with PCOS underwent stool and fasting blood collection, oral glucose tolerance testing, and visceral fat area (VFA) measurement via dual-bioimpedance technique. Metagenomic analysis was used to analyze gut microbiota.

RESULTS

PCOS patients were divided into three groups: visceral obesity group (PCOS-VO, n=9, age 28.33±5.68 years, BMI 37.06±4.27 kg/m², VFA 128.67±22.45 cm²), non-visceral obesity group (PCOS-NVO, n=10, age 25.40±4.53, BMI 30.74±3.95, VFA 52.00±24.04), normal BMI group (PCOS-NB, n=8, age 27.88±2.53, BMI 21.56±2.20, VFA 27.00±21.18), with no statistical difference in age (P>0.05) and significantly statistical differences in BMI and VFA (P<0.05). The groups showed a significant difference in microbial β-diversity between PCOS-VO and PCOS-NVO (P=0.002) and no difference between PCOS-NVO and PCOS-NB (P=0.177). Bacteroidetes was the phylum with the highest relative abundance among all patients, followed by Firmicutes. Those with visceral obesity had a higher abundance of Prevotella, Megamonas, and Dialister genera, positively correlated with metabolic markers (r>0.4, P<0.05), and lower abundance of Phascolarctobacterium and Neisseria genera, negatively correlated with metabolic markers (r<-0.4, P<0.05). Functional annotation analysis showed significant differences in relative abundance of ribosome pathway, fatty acid biosynthesis pathway, and sphingolipid signaling pathway between groups, affecting lipid homeostasis and visceral fat accumulation.

CONCLUSION

Alteration in β-diversity of gut microbiota exists in PCOS with visceral obesity versus those without visceral obesity and relates to functional differences in ribosomes, fatty acid biosynthesis, and sphingolipid signaling pathways.

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes

BACKGROUND

This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature.

METHODS/RESULTS

We combined classic plasma biochemistry and plasma biomarkers with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (morbidly obese and lipodystrophy) and lean individuals to identify the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells. Our analyses showed that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, 6 months after bariatric surgery, revealed the loss of the abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurred via the establishment of novel gene expression landscapes. NETosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention.

CONCLUSIONS

We showed that the morbidly obese and lipodystrophy groups, despite some differences, shared a common cardiometabolic syndrome signature. We also showed that this could be used to discriminate, amongst the normal population, those individuals with a higher likelihood of presenting with the disease, even when not displaying the classic features.

Depression and Metabolic Syndrome: A Narrative Review

We reviewed the literature to investigate the relationship between depression and metabolic syndrome. Major depressive disorder is characterized by a low mood or a loss of interest for longer than two weeks. Metabolic syndrome describes multiple metabolic risk factors including obesity, insulin resistance, dyslipidemia, and hypertension. We divided our findings into environmental, genetic, epigenetic, and biological pathway links between depression and the different aspects of metabolic syndrome. We found various sources linking obesity and metabolic syndrome genetically, environmentally, biological pathway-wise, and, while not fully explored, epigenetically. Diabetes and depression were also found to be linked environmentally with both conditions increasing the risk of the other. Depression was also shown to be linked to cardiovascular complications as it increased the risk of occurrence of such complications in healthy people. These findings have led us to believe that there is a link between depression and metabolic syndrome on various levels, especially obesity.

School-Based Exercise and Life Style Motivation Intervention (SEAL.MI) on Adolescent's Cardiovascular Risk Factors and Academic Performance: Catch Them Young

There are shreds of evidence of shared biological mechanisms between obesity and hypertension during childhood intoadulthood, and loads of research literature has proven that it will profoundly cost nations' economies and health if neglected. The prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its' complications. The study aims to assess the effect of school-based Exercise and Lifestyle Motivation Intervention (SEAL-MI) on adolescents' cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents-520 and 485 were randomly selected for the control and study groups, respectively.A structured interview questionnaire was used to collect demographic details and data related to dietary habits, physical activity, sleep qualityand academic performance. The study group adolescents were given the SEAL-MI for six months, including a school-based rope exercise for 45 min per day for 5 days a week and a motivation intervention related to dietary habits, physical activity, and sleep. Post tests-1 and 2 were done after 3 and 6 months of intervention.The prevalence of overweight among adolescents was 28.73%, and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). There was a significant weight reduction in post-intervention B.P. (p = 0.000) and improvement in dietary habits, physical activity, sleep (p = 0.000), and academic performance. A significant positive correlation was found between BMI and SBP (p = 0.000) and BMI and academic performance (p = 0.003). The linear regression analyses revealed that the gender (ß: 0.47, 95% CI: 0.39, 0.81), age (ß: 0.39, 95% CI: 0.17, 0.46), family income (ß: 0.2, 95% CI: 0.41, 0.5), residence (ß: 0.19, 95% CI: 0.01, 0.27), and type of family (ß: 0.25, 95% CI: 0.39, 0.02) had the strongest correlation with the BMI of the adolescents. Additionally, Mother's education (ß: 0.35, 95% CI: 0.18, 0.59) had the strongest correlation with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age (ß: -0.36, 95% CI: 1.54, 0.29) and gender (ß: -0.26, 95% CI: 1.34, 0.12) of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity, and quality sleep among adolescents prevent and control childhood CVD risk factors such asoverweight and hypertension. The SEAL-MI may lead to age-appropriate development of adolescents as well as improve their academic performance and quality of life. Giving importance to adolescents from urban habitats, affluent, nuclear families, and catching them young will change the disease burden significantly.

Potential evidence for epigenetic biomarkers of metabolic syndrome in human whole blood in Latinos

Metabolic syndrome (MetS) is highly prevalent worldwide. In the United States, estimates show that more than 30% of the adult population has MetS. MetS consists of multiple phenotypes, including obesity, dyslipidemia, and impaired glucose tolerance. Therefore, identifying the molecular mechanisms to explain this complex disease is critical for diagnosing and treating MetS. We previously showed 70 increased genes and 20 decreased genes in whole blood in MetS participants. The present study aimed to identify blood-based DNA methylation biomarkers in non-MetS versus MetS participants. The present study analyzed whole blood DNA samples from 184 adult participants of Latino descent from the Arizona Insulin Resistance (AIR) registry. We used the National Cholesterol Education Program Adult Treatment Panel III (NCEP: ATP III) criteria to identify non-MetS (n = 110) and MetS (n = 74) participants. We performed whole blood methylation analysis on select genes: ATP Synthase, H+ Transporting mitochondrial F1 Complex, Epsilon Subunit (ATP5E), Cytochrome C Oxidase Subunit VIc (COX6C), and Ribosomal Protein L9 (RPL9). The pyrosequencing analysis was a targeted approach focusing on the promoter region of each gene that specifically captured CpG methylation sites. In MetS participants, we showed decreased methylation in two CpG sites in COX6C and three CpG sites in RPL9, all p < 0.05 using the Mann-Whitney U test. There were no ATP5E CpG sites differently methylated in the MetS participants. Furthermore, while adjusting for age, gender, and smoking status, logistic regression analysis reaffirmed the associations between MetS and mean methylation within COX6C and RPL9 (both p < 0.05). In addition, Spearman's correlation revealed a significant inverse relationship between the previously published gene expression data and methylation data for RPL9 (p < 0.05). In summary, these results highlight potential blood DNA methylation biomarkers for the MetS phenotype. However, future validation studies are warranted to strengthen our findings.

Is platelet responsiveness to clopidogrel attenuated in overweight or obese patients and why? A reverse translational study in mice

BACKGROUND AND PURPOSE

Overweight or obese patients exhibit poorer platelet responses to clopidogrel. However, the mechanisms behind this phenotype remain to be elucidated. Here, we sought to discover whether and why obesity could affect the metabolic activation of and/or platelet response to clopidogrel in obese patients and high-fat diet-induced obese mice.

EXPERIMENTAL APPROACH

A post hoc stratified analysis of an observational clinical study was performed to investigate changes in residual platelet reactivity with increasing body weight in patients taking clopidogrel. Furthermore, high-fat diet-induced obese mice were used to reveal alterations in systemic exposure of clopidogrel thiol active metabolite H4, ADP-induced platelet activation and aggregation, the expression of genes involved in the metabolic activation of clopidogrel, count of circulating reticulated and mature platelets, and proliferation profiles of megakaryocytes in bone marrow. The relevant genes and potential signalling pathways were predicted and enriched according to the GEO datasets available from obese patients.

KEY RESULTS

Obese patients exhibited significantly attenuated antiplatelet effects of clopidogrel. In diet-induced obese mice, systemic exposure of clopidogrel active metabolite H4 was reduced but that of its hydrolytic metabolite was increased due to down-regulation of certain P450s but up-regulation of carboxylesterase-1 in the liver. Moreover, enhanced proliferation of megakaryocytes and elevated platelet count also contributed.

CONCLUSION AND IMPLICATIONS

Obesity attenuated metabolic activation of clopidogrel and increased counts of circulating reticulated and mature platelets, leading to impaired platelet responsiveness to the drug in mice, suggesting that clopidogrel dosage may need to be adjusted adequately in overweight or obese patients.

Cardiorespiratory fitness in children with overweight/obesity: Insights into the molecular mechanisms

OBJECTIVES

High cardiorespiratory fitness (CRF) levels reduce the risk of developing cardiovascular disease (CVD) during adulthood. However, little is known about the molecular mechanisms underlying the health benefits of high CRF levels at the early stage of life. This study aimed to analyze the whole-blood transcriptome profile of fit children with overweight/obesity (OW/OB) compared to unfit children with OW/OB.

DESIGN

27 children with OW/OB (10.14 ± 1.3 years, 59% boys) from the ActiveBrains project were evaluated. VO₂ peak was assessed using a gas analyzer, and participants were categorized into fit or unfit according to the CVD risk-related cut-points. Whole-blood transcriptome profile (RNA sequencing) was analyzed. Differential gene expression analysis was performed using the limma R/Bioconductor software package (analyses adjusted by sex and maturational status), and pathways' enrichment analysis was performed with DAVID. In addition, in silico validation data mining was performed using the PHENOPEDIA database.

RESULTS

256 genes were differentially expressed in fit children with OW/OB compared to unfit children with OW/OB after adjusting by sex and maturational status (FDR < 0.05). Enriched pathway analysis identified gene pathways related to inflammation (eg, dopaminergic and GABAergic synapse pathways). Interestingly, in silico validation data mining detected a set of the differentially expressed genes to be related to CVD, metabolic syndrome, hypertension, inflammation, and asthma.

CONCLUSION

The distinct pattern of whole-blood gene expression in fit children with OW/OB reveals genes and gene pathways that might play a role in reducing CVD risk factors later in life.

Whole blood gene expression signature in patients with obstructive sleep apnea and effect of continuous positive airway pressure treatment

The molecular mechanisms of obstructive sleep apnea (OSA), in particular the gene expression patterns in whole blood of patients with OSA, can shed more light on the underlying pathophysiology of OSA and suggest potential biomarkers. In the current study, we have enrolled thirty patients with untreated moderate-severe OSA together with 20 BMI, age, and sex-matched controls and 15 normal-weight controls. RNA-sequencing of whole blood and home sleep apnea testing were performed in the untreated state and after three and twelve months of continuous positive airway pressure (CPAP) treatment. Analysis of the whole blood transcriptome of the patients with OSA revealed a unique pattern of differential expression with a significant number of downregulated immune-related genes including many heavy and light chain immunoglobulins and interferon-inducible genes. This was confirmed by the gene ontology analysis demonstrating enrichment with the biological processes associated with various immune functions. Expression of these genes was recovered after three months of CPAP treatment. After 12 months of CPAP treatment, the overall gene expression profile returns to the initial, untreated level. In addition, we have confirmed the importance of choosing BMI-matched controls as a reference group as opposed to normal-weight healthy individuals based on the significantly different gene expression signatures between these two groups.

Transcriptomic changes in peripheral blood mononuclear cells with weight loss: systematic literature review and primary data synthesis

Background and objectives

Peripheral blood mononuclear cells (PBMCs) have shown promise as a tissue sensitive to subtle and possibly systemic transcriptomic changes, and as such may be useful in identifying responses to weight loss interventions. The primary aim was to comprehensively evaluate the transcriptomic changes that may occur during weight loss and to determine if there is a consistent response across intervention types in human populations of all ages.

Methods

Included studies were randomised control trials or cohort studies that administered an intervention primarily designed to decrease weight in any overweight or obese human population. A systematic search of the literature was conducted to obtain studies and gene expression databases were interrogated to locate corresponding transcriptomic datasets. Datasets were normalised using the ArrayAnalysis online tool and differential gene expression was determined using the limma package in R. Over-represented pathways were explored using the PathVisio software. Heatmaps and hierarchical clustering were utilised to visualise gene expression.

Results

Seven papers met the inclusion criteria, five of which had raw gene expression data available. Of these, three could be grouped into high responders (HR, ≥ 5% body weight loss) and low responders (LR). No genes were consistently differentially expressed between high and low responders across studies. Adolescents had the largest transcriptomic response to weight loss followed by adults who underwent bariatric surgery. Seven pathways were altered in two out of four studies following the intervention and the pathway ‘cytoplasmic ribosomal proteins’ (WikiPathways: WP477) was altered between HR and LR at baseline in the two datasets with both groups. Pathways related to ‘toll-like receptor signalling’ were altered in HR response to the weight loss intervention in two out of three datasets.

Conclusions

Transcriptomic changes in PBMCs do occur in response to weight change. Transparent and standardised data reporting is needed to realise the potential of transcriptomics for investigating phenotypic features.

Registration number

PROSPERO: CRD42019106582

Supplementary Information

The online version contains supplementary material available at 10.1186/s12263-021-00692-6.

Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs

Background

Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts.

Results

Gene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available. Merging the three datasets allowed considering FCR values (Mean = 2.85; Min = 1.92; Max = 5.00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred’s (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R² = 0.80, RMSE = 8%). The accuracy performance of RF models was slightly lower in classification and markedly lower in regression.

Conclusion

From small subsets of genes expressed in the whole blood, it is possible to predict the binary class and the individual value of feed efficiency. These predictive models offer good perspectives to identify animals with higher feed efficiency in precision farming applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07843-4.

Elevated plasma levels of the appetite-stimulator ACBP/DBI in fasting and obese subjects

Eukaryotic cells release the phylogenetically ancient protein acyl coenzyme A binding protein (ACBP, which in humans is encoded by the gene DBI, diazepam binding inhibitor) upon nutrient deprivation. Accordingly, mice that are starved for one to two days and humans that undergo voluntary fasting for one to three weeks manifest an increase in the plasma concentration of ACBP/DBI. Paradoxically, ACBP/DBI levels also increase in obese mice and humans. Since ACBP/DBI stimulates appetite, this latter finding may explain why obesity constitutes a self-perpetuating state. Here, we present a theoretical framework to embed these findings in the mechanisms of weight control, as well as a bioinformatics analysis showing that, irrespective of the human cell or tissue type, one single isoform of ACBP/DBI (ACBP1) is preponderant (~90% of all DBI transcripts, with the sole exception of the testis, where it is ~70%). Based on our knowledge, we conclude that ACBP1 is subjected to a biphasic transcriptional and post-transcriptional regulation, explaining why obesity and fasting both are associated with increased circulating ACBP1 protein levels.

Factors affecting weight loss variability in obesity

Current obesity treatment strategies include diet, exercise, bariatric surgery, and a limited but growing repertoire of medications. Individual weight loss in response to each of these strategies is highly variable. Here we review research into factors potentially contributing to inter-individual variability in response to treatments for obesity, with a focus on studies in humans. Well-recognized factors associated with weight loss capacity include diet adherence, physical activity, sex, age, and specific medications. However, following control for each of these, differences in weight loss appear to persist in response to behavioral, pharmacological and surgical interventions. Adaptation to energy deficit involves complex feedback mechanisms, and inter-individual differences likely to arise from a host of poorly defined genetic factors, as well as differential responses in neurohormonal mechanisms (including gastrointestinal peptides), metabolic efficiency and capacity of tissues, non-exercise activity thermogenesis, thermogenic response to food, and in gut microbiome. A better understanding of the factors involved in inter-individual variability in response to therapies will guide more personalized approaches to the treatment of obesity.

Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies

BACKGROUND

Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder.

METHODS

We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods.

RESULTS

We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions.

CONCLUSIONS

Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

Methylome-wide association study of central adiposity implicates genes involved in immune and endocrine systems

Aim: We conducted a methylome-wide association study to examine associations between DNA methylation in whole blood and central adiposity and body fat distribution, measured as waist circumference, waist-to-hip ratio and waist-to-height ratio adjusted for body mass index, in 2684 African-American adults in the Atherosclerosis Risk in Communities study. Materials & methods: We validated significantly associated cytosine-phosphate-guanine methylation sites (CpGs) among adults using the Women's Health Initiative and Framingham Heart Study participants (combined n = 5743) and generalized associations in adolescents from The Raine Study (n = 820). Results & conclusion: We identified 11 CpGs that were robustly associated with one or more central adiposity trait in adults and two in adolescents, including CpG site associations near TXNIP, ADCY7, SREBF1 and RAP1GAP2 that had not previously been associated with obesity-related traits.

Omics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention

PURPOSE OF REVIEW

Omics-based technologies were suggested to provide an advanced understanding of obesity etiology and its metabolic consequences. This review highlights the recent developments in "omics"-based research aimed to identify obesity-related biomarkers.

RECENT FINDINGS

Recent advances in obesity and metabolism research increasingly rely on new technologies to identify mechanisms in the development of obesity using various "omics" platforms. Genetic and epigenetic biomarkers that translate into changes in transcriptome, proteome, and metabolome could serve as targets for obesity prevention. Despite a number of promising candidate biomarkers, there is an increased demand for larger prospective cohort studies to validate findings and determine biomarker reproducibility before they can find applications in primary care and public health. "Omics" biomarkers have advanced our knowledge on the etiology of obesity and its links with chronic diseases. They bring substantial promise in identifying effective public health strategies that pave the way towards patient stratification and precision prevention.

Critical Roles of ELVOL4 and IL-33 in the Progression of Obesity-Related Cardiomyopathy via Integrated Bioinformatics Analysis

The molecular mechanisms underlying obesity-related cardiomyopathy (ORCM) progression involve multiple signaling pathways, and the pharmacological treatment for ORCM is still limited. Thus, it is necessary to explore new targets and develop novel therapies. Microarray analysis for gene expression profiles using different bioinformatics tools has been an effective strategy for identifying novel targets for various diseases. In this study, we aimed to explore the potential genes related to ORCM using the integrated bioinformatics analysis. The GSE18897 (whole blood expression profiling of obese diet-sensitive, obese diet-resistant, and lean human subjects) and GSE47022 (regular weight C57BL/6 and diet-induced obese C57BL/6 mice) were used for bioinformatics analysis. Weighted gene co-expression network analysis (WGCNA) of GSE18897 was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the co-expression genes. The expression levels of the hub genes were validated in the clinical samples. Yellow co-expression module of WGCNA in GSE18897 was found to be significantly related to the caloric restriction treatment. In addition, GO functional enrichment analysis and KEGG pathway analysis were performed on the co-expression genes in yellow co-expression module, which showed an association with oxygen transport and the porphyrins pathway. Overlap analysis of yellow co-expression module genes from GSE18897 andGSE47022 revealed six upregulated genes, and further experimental validation results showed that elongation of very-long-chain fatty acids protein 4 (ELOVL4), matrix metalloproteinase-8 (MMP-8), and interleukin-33 (IL-33) were upregulated in the peripheral blood from patients with ORCM compared to that in the controls. The bioinformatics analysis revealed that ELOVL4 expression levels are positively correlated with that of IL-33. Collectively, using WGCNA in combination with integrated bioinformatics analysis, the hub genes of ELVOL4 and IL-33 might serve as potential biomarkers for diagnosis and/or therapeutic targets for ORCM. The detailed roles of ELVOL4 and IL-33 in the pathophysiology of ORCM still require further investigation.

Evaluation of Transcriptomic Regulations behind Metabolic Syndrome in Obese and Lean Subjects

Multiple mechanisms have been suggested to confer to the pathophysiology of metabolic syndrome (MetS), however despite great interest from the scientific community, the exact contribution of each of MetS risk factors still remains unclear. The present study aimed to investigate molecular signatures in peripheral blood of individuals affected by MetS and different degrees of obesity. Metabolic health of 1204 individuals from 1000PLUS cohort was assessed, and 32 subjects were recruited to four study groups: MetS lean, MetS obese, “healthy obese”, and healthy lean. Whole-blood transcriptome next generation sequencing with functional data analysis were carried out. MetS obese and MetS lean study participants showed the upregulation of genes involved in inflammation and coagulation processes: granulocyte adhesion and diapedesis (p < 0.0001, p = 0.0063), prothrombin activation pathway (p = 0.0032, p = 0.0091), coagulation system (p = 0.0010, p = 0.0155). The results for “healthy obese” indicate enrichment in molecules associated with protein synthesis (p < 0.0001), mitochondrial dysfunction (p < 0.0001), and oxidative phosphorylation (p < 0.0001). Our results suggest that MetS is related to the state of inflammation and vascular system changes independent of excess body weight. Furthermore, “healthy obese”, despite not fulfilling the criteria for MetS diagnosis, seems to display an intermediate state with a lower degree of metabolic abnormalities, before they proceed to a full blown MetS.

Involvement of inflammatory gene expression pathways in depressed patients with hyperphagia

The pathophysiology of major depressive disorder (MDD) is highly heterogeneous. Previous evidence at the DNA level as well as on the serum protein level suggests that the role of inflammation in MDD pathology is stronger in patients with hyperphagia during an active episode. Which inflammatory pathways differ in MDD patients with hyperphagia inflammatory pathways in terms of gene expression is unknown. We analyzed whole-blood gene expression profiles of 881 current MDD cases and 331 controls from the Netherlands Study of Depression and Anxiety (NESDA). The MDD patients were stratified according to patients with hyperphagia (characterized by increased appetite and/or weight, N = 246) or hypophagia (characterized by decreased appetite and/or weight, N = 342). Using results of differential gene expression analysis between controls and the MDD subgroups, enrichment of curated inflammatory pathways was estimated. The majority of the pathways were significantly (FDR < 0.1) enriched in the expression profiles of MDD cases with hyperphagia, including top pathways related to factors responsible for the onset of inflammatory response ('caspase', 'GATA3', 'NFAT', and 'inflammasomes' pathways). Only two pathways ('adaptive immune system' and 'IL-8- and CXCR2-mediated signaling') were enriched in the MDD with hypophagia subgroup, these were also enriched in the total current MDD group and the group with hyperphagia. This confirms the importance of inflammation in MDD pathology of patients with hyperphagia, and suggests that distinguishing more uniform MDD phenotypes can help in finding their pathophysiological basis.

Comprehensive and Systematic Analysis of Gene Expression Patterns Associated with Body Mass Index

Both genetic and environmental factors are suggested to influence overweight and obesity risks. Although individual loci and genes have been frequently shown to be associated with body mass index (BMI), the overall interaction of these genes and their role in BMI remains underexplored. Data were collected in 90 healthy, predominately Caucasian participants (51% female) with a mean age of 26.00 ± 9.02 years. Whole blood samples were assayed by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. We integrated and analyzed the clinical and microarray gene expression data from those individuals to understand various systematic gene expression patterns underlying BMI. Conventional differential expression analysis identified seven genes RBM20, SEPT12, AX748233, SLC30A3, WTIP, CASP10, and OR12D3 associated with BMI. Weight gene co-expression network analysis among 4,647 expressed genes identified two gene modules associated with BMI. These two modules, with different extents of gene connectivity, are enriched for catabolic and muscle system processes respectively, and tend to be regulated by zinc finger transcription factors. A total of 246 hub genes were converted to non-hub genes, and 286 non-hub genes were converted to hub genes between normal and overweight individuals, revealing the network dynamics underlying BMI. A total of 28 three-way gene interactions were identified, suggesting the existence of high-order gene expression patterns underlying BMI. Our study demonstrated a variety of systematic gene expression patterns associated with BMI and thus provided novel understanding regarding the genetic factors for overweight and obesity risks on system levels.

A Co-expression Analysis of the Placental Transcriptome in Association With Maternal Pre-pregnancy BMI and Newborn Birth Weight

Maternal body mass index (BMI) before pregnancy is known to affect both fetal growth and later-life health of the newborn, yet the implicated molecular mechanisms remain largely unknown. As the master regulator of the fetal environment, the placenta is a valuable resource for the investigation of processes involved in the developmental programming of metabolic health. We conducted a genome-wide placental transcriptome study aiming at the identification of functional pathways representing the molecular link between maternal BMI and fetal growth. We used RNA microarray (Agilent 8 × 60 K), medical records, and questionnaire data from 183 mother-newborn pairs from the ENVIRONAGE birth cohort study (Flanders, Belgium). Using a weighted gene co-expression network analysis, we identified 17 correlated gene modules. Three of these modules were associated with both maternal pre-pregnancy BMI and newborn birth weight. A gene cluster enriched for genes involved in immune response and myeloid cell differentiation was positively associated with maternal BMI and negatively with low birth weight. Two other gene modules, upregulated in association with maternal BMI as well as birth weight, were involved in processes related to organ and tissue development, with blood vessel morphogenesis and extracellular matrix structure as top Gene Ontology terms. In line with this, erythrocyte-, angiogenesis-, and extracellular matrix-related genes were among the identified hub genes. The association between maternal BMI and newborn weight was significantly mediated by gene expression for 5 of the hub genes (FZD4, COL15A1, GPR124, COL6A1, and COL1A1). As some of the identified hub genes have been linked to obesity in adults, our observation in placental tissue suggests that biological processes may be affected from prenatal life onwards, thereby identifying new molecular processes linking maternal BMI and fetal metabolic programming.

Peripheral Blood Mononuclear Cell Metabolism Acutely Adapted to Postprandial Transition and Mainly Reflected Metabolic Adipose Tissue Adaptations to a High-Fat Diet in Minipigs

Although peripheral blood mononuclear cells (PBMCs) are widely used as a valuable tool able to provide biomarkers of health and diseases, little is known about PBMC functional (biochemistry-based) metabolism, particularly following short-term nutritional challenges. In the present study, the metabolic capacity of minipig PBMCs to respond to nutritional challenges was explored at the biochemical and molecular levels. The changes observed in enzyme activities following a control test meal revealed that PBMC metabolism is highly reactive to the arrival of nutrients and hormones in the circulation. The consumption, for the first time, of a high fat⁻high sucrose (HFHS) meal delayed or sharply reduced most of the observed postprandial metabolic features. In a second experiment, minipigs were subjected to two-month HFHS feeding. The time-course follow-up of metabolic changes in PBMCs showed that most of the adaptations to the new diet took place during the first week. By comparing metabolic (biochemical and molecular) PMBC profiles to those of the liver, skeletal muscle, and adipose tissue, we concluded that although PBMCs conserved common features with all of them, their response to the HFHS diet was closely related to that of the adipose tissue. As a whole, our results show that PBMC metabolism, particularly during short-term (postprandial) challenges, could be used to evaluate the whole-body metabolic status of an individual. This could be particularly interesting for early diagnosis of metabolic disease installation, when fasting clinical analyses fail to diagnose the path towards the pathology.

Nonalcoholic fatty liver disease and gastric bypass surgery regulate serum and hepatic levels of pyruvate kinase isoenzyme M2

Treatment of nonalcoholic fatty liver disease (NAFLD) focuses on the underlying metabolic syndrome, and Roux-en-Y gastric bypass surgery (RYGB) remains one of the most effective options. In rodents and human patients, RYGB induces an increase in the gene and protein expression levels of the M2 isoenzyme of pyruvate kinase (PKM2) in the jejunum. Since PKM2 can be secreted in the circulation, our hypothesis was that the circulating levels of PKM2 increase after RYGB. Our data, however, revealed an unexpected finding and a potential new role of PKM2 for the natural history of metabolic syndrome and NAFLD. Contrary to our initial hypothesis, RYGB-treated patients had decreased PKM2 blood levels compared with a well-matched group of patients with severe obesity before RYGB. Interestingly, PKM2 serum concentration correlated with body mass index before but not after the surgery. This prompted us to evaluate other potential mechanisms and sites of PKM2 regulation by the metabolic syndrome and RYGB. We found that in patients with NAFLD and nonalcoholic steatohepatitis (NASH), the liver had increased PKM2 expression levels, and the enzyme appears to be specifically localized in Kupffer cells. The study of murine models of metabolic syndrome and NASH replicated this pattern of expression, further suggesting a metabolic link between hepatic PKM2 and NAFLD. Therefore, we conclude that PKM2 serum and hepatic levels increase in both metabolic syndrome and NAFLD and decrease after RYGB. Thus, PKM2 may represent a new target for monitoring and treatment of NAFLD.

Integrating genetic, transcriptional, and biological information provides insights into obesity

Objective:

Indices of body fat distribution are heritable, but few genetic signals have been reported from genome-wide association studies (GWAS) of computed tomography (CT) imaging measurements of body fat distribution. We aimed to identify genes associated with adiposity traits and the key drivers that are central to adipose regulatory networks.

Subjects:

We analyzed gene transcript expression data in blood from participants in the Framingham Heart Study, a large community-based cohort (n up to 4,303), as well as implemented an integrative analysis of these data and existing biological information.

Results:

Our association analyses identified unique and common gene expression signatures across several adiposity traits, including body mass index, waist-hip ratio, waist circumference, and CT-measured indices, including volume and quality of visceral and subcutaneous adipose tissues. We identified six enriched KEGG pathways and two co-expression modules for further exploration of adipose regulatory networks. The integrative analysis revealed four gene sets (Apoptosis, p53 signaling pathway, Proteasome, Ubiquitin mediated proteolysis) and two co-expression modules with significant genetic variants and 94 key drivers/genes whose local networks were enriched with adiposity-associated genes, suggesting that these enriched pathways or modules have genetic effects on adiposity. Most identified key driver genes are involved in essential biological processes such as controlling cell cycle, DNA repair and degradation of regulatory proteins and are cancer related.

Conclusion:

Our integrative analysis of genetic, transcriptional and biological information provides a list of compelling candidates for further follow-up functional studies to uncover the biological mechanisms underlying obesity. These candidates highlight the value of examining CT-derived and central adiposity traits.

Timing of eating in adults across the weight spectrum: Metabolic factors and potential circadian mechanisms

Timing of eating is recognized as a significant contributor to body weight regulation. Disruption of sleep-wake cycles from a predominantly diurnal (daytime) to a delayed (evening) lifestyle leads to altered circadian rhythms and metabolic dysfunction. This article reviews current evidence for timed and delayed eating in individuals of normal weight and those with overweight or obesity: although some findings indicate a benefit of eating earlier in the daytime on weight and/or metabolic outcomes, results have not been uniformly consistent, and more rigorous and longer-duration studies are needed. We also review potential circadian mechanisms underlying the metabolic- and weight-related changes resulting from timed and delayed eating. Further identification of such mechanisms using deep phenotyping is required to determine targets for medical interventions for obesity and for prevention of metabolic syndrome and diabetes, and to inform clinical guidelines regarding eating schedules for management of weight and metabolic disease.

Neutrophil elastase and myeloperoxidase mRNA expression in overweight and obese subjects

Neutrophil elastase and myeloperoxidase enzymes have been implicated in high-fat diet-induced obesity, insulin resistance (IR) and atherosclerosis in animal models. The aim of the present study was to explore neutrophil elastase and myeloperoxidase mRNA expressions in the peripheral blood leukocytes (PBL) in overweight and obese subjects, and to correlate those mRNA expressions with BMI, IR and cardiovascular biomarkers. In this cross-sectional study, 74 apparently healthy subjects including 22 lean, 27 overweight and 25 obese subjects were recruited. Cardiovascular and metabolic biomarkers were evaluated from fasting blood samples. The mRNA levels of neutrophil elastase and myeloperoxidase genes in the PBL were quantified by real-time PCR. Compared to lean group, the overweight and obese groups showed significant upregulation of both neutrophil elastase (p < 0.001) and myeloperoxidase (p < 0.03) mRNA expressions in the PBL. But no difference was found between overweight and obese groups. The neutrophil elastase and myeloperoxidase mRNA levels showed significant positive correlation with BMI, serum triglyceride, atherogenic index of plasma and 10-year risk of developing cardiovascular disease. But no correlation was found with glucose, insulin or IR. It was concluded that the neutrophil elastase and myeloperoxidase genes are up-regulated in both overweight and obese subjects and are associated with BMI and markers of cardiovascular disease.

A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles

Recent large-scale genome-wide association studies have identified tens of genetic loci robustly associated with Body Mass Index (BMI). Gene expression profiles were also found to be associated with BMI. However, accurate prediction of obesity risk utilizing genetic data remains challenging. In a cohort of 75 individuals, we integrated 27 BMI-associated SNPs and obesity-associated gene expression profiles. Genetic risk score was computed by adding BMI-increasing alleles. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used that excluded some SNPs. Linear regression and support vector machine models were built to predict obesity risk using gene expression profiles and the genetic risk score. An adjusted R2 of 0.556 and accuracy of 76% was achieved for the linear regression and support vector machine models, respectively. In this paper, we report a new mathematical method to predict obesity genetic risk. We constructed obesity prediction models based on genetic information for a small cohort. Our computational framework serves as an example for using genetic information to predict obesity risk for specific cohorts.

Impact of blood collection and processing on peripheral blood gene expression profiling in type 1 diabetes

BACKGROUND

The natural history of type 1 diabetes (T1D) is challenging to investigate, especially as pre-diabetic individuals are difficult to identify. Numerous T1D consortia have been established to collect whole blood for gene expression analysis from individuals with or at risk to develop T1D. However, with no universally accepted protocol for their collection, differences in sample processing may lead to variances in the results. Here, we examined whether the choice of blood collection tube and RNA extraction kit leads to differences in the expression of genes that are changed during the progression of T1D, and if these differences could be minimized by measuring gene expression directly from the lysate of whole blood.

RESULTS

Microarray analysis showed that the expression of 901 genes is highly influenced by sample processing using the PAXgene versus the Tempus system. These included a significant number of lymphocyte-specific genes and genes whose expression has been reported to differ in the peripheral blood of at-risk and T1D patients compared to controls. We showed that artificial changes in gene expression occur when control and T1D samples were processed differently. The sample processing-dependent differences in gene expression were largely due to loss of transcripts during the RNA extraction step using the PAXgene system. The majority of differences were not observed when gene expression was measured in whole blood lysates prepared from blood collected in PAXgene and Tempus tubes.

CONCLUSION

We showed that the gene expression profile of samples processed using the Tempus system is more accurate than that of samples processed using the PAXgene system. Variation in sample processing can result in misleading changes in gene expression. However, these differences can be minimized by measuring gene expression directly in whole blood lysates.

Gene expression profiles in whole blood and associations with metabolic dysregulation in obesity

BACKGROUND

Gene expression data provides one tool to gain further insight into the complex biological interactions linking obesity and metabolic disease. This study examined associations between blood gene expression profiles and metabolic disease in obesity.

METHODS

Whole blood gene expression profiles, performed using the Illumina HT-12v4 Human Expression Beadchip, were compared between (i) individuals with obesity (O) or lean (L) individuals (n=21 each), (ii) individuals with (M) or without (H) Metabolic Syndrome (n=11 each) matched on age and gender. Enrichment of differentially expressed genes (DEG) into biological pathways was assessed using Ingenuity Pathway Analysis. Association between sets of genes from biological pathways considered functionally relevant and Metabolic Syndrome were further assessed using an area under the curve (AUC) and cross-validated classification rate (CR).

RESULTS

For OvL, only 50 genes were significantly differentially expressed based on the selected differential expression threshold (1.2-fold, p<0.05). For MvH, 582 genes were significantly differentially expressed (1.2-fold, p<0.05) and pathway analysis revealed enrichment of DEG into a diverse set of pathways including immune/inflammatory control, insulin signalling and mitochondrial function pathways. Gene sets from the mTOR signalling pathways demonstrated the strongest association with Metabolic Syndrome (p=8.1×10^-8; AUC: 0.909, CR: 72.7%).

CONCLUSIONS

These results support the use of expression profiling in whole blood in the absence of more specific tissue types for investigations of metabolic disease. Using a pathway analysis approach it was possible to identify an enrichment of DEG into biological pathways that could be targeted for in vitro follow-up.

Genomic and transcriptomic predictors of response levels to endurance exercise training

Predicting the responsiveness to regular exercise is a topic of great relevance due to its potential role in personalized exercise medicine applications. The present review focuses on cardiorespiratory fitness (commonly measured by maximal oxygen uptake, V̇O2 max ), a trait with wide-ranging impact on health and performance indicators. Gains in V̇O2 max demonstrate large inter-individual variation even in response to standardized exercise training programmes. The estimated ΔVO2 max heritability of 47% suggests that genomic-based predictors alone are insufficient to account for the total trainability variance. Candidate gene and genome-wide linkage studies have not significantly contributed to our understanding of the molecular basis of trainability. A genome-wide association study suggested that V̇O2 max trainability is influenced by multiple genes of small effects, but these findings still await rigorous replication. Valuable evidence, however, has been obtained by combining skeletal muscle transcript abundance profiles with common DNA variants for the prediction of the V̇O2 max response to exercise training. Although the physiological determinants of V̇O2 max measured at a given time are largely enunciated, what is poorly understood are the details of tissue-specific molecular mechanisms that limit V̇O2 max and related signalling pathways in response to exercise training. Bioinformatics explorations based on thousands of variants have been used to interrogate pathways and systems instead of single variants and genes, and the main findings, along with those from exercise experimental studies, have been summarized here in a working model of V̇O2 max trainability.

CpG Methylation across the adipogenic PPARγ gene and its relationship with birthweight and child BMI at 9 years

Background

To examine methylation of the peroxisome proliferator-activated receptor γ (PPARγ) gene and its relationship with child weight status, at birth and 9 years.

Methods

We measured PPARγ methylation across 23 CpG sites using the Infinium Illumina 450 k array for children from the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort at birth (N = 373) and 9 years (N = 245).

Results

Methylation level correlation patterns across the 23 PPARγ CpG sites were conserved between birth and 9-year ages. We found high inter-CpG correlations between sites 1–3 (methylation block 1) and also between sites 18–23 (methylation block 2) for both time points, although these patterns were less pronounced at 9 years. Additionally, sites 1–3 (north shore) had the highest intra-CpG correlations over time (r = 0.24, 0.42, and 0.3; P = 0.002, P < 0.001, P < 0.001, respectively). PPARγ methylation levels tended to increase with age, and the largest differences were observed for north shore sites (7.4%). Adjusting for sex, both site 1 and site 20 (gene body) methylation at birth was significantly and inversely associated with birth weight (β = −0.13, P = 0.033; β = −0.09, P = 0.025, respectively). Similarly, we found that site 1 and site 20 methylation at 9 years was significantly and inversely associated with 9-year BMI z-score (β = −0.41, P = 0.015; β = −0.23, P = 0.045, respectively).

Conclusion

Our results indicate that PPARγ methylation is highly organized and conserved over time, and highlight the potential functional importance of north shore sites, adding to a better understanding of regional human methylome patterns. Overall, our results suggest that PPARγ methylation may be associated with child body size.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-016-0365-4) contains supplementary material, which is available to authorized users.

Proteomic Analysis, Immune Dysregulation, and Pathway Interconnections with Obesity

Proteomic studies can offer information on hundreds to thousands of proteins and potentially provide researchers with a comprehensive understanding of signaling response during stress and disease. Large data sets, such as those obtained in high-dimensional proteomic studies, can be leveraged for pathway analysis to discover or describe the biological implications of clinical disease states. Obesity is a worldwide epidemic that is considered a risk factor for numerous other diseases. We performed analysis on plasma proteomic data from 3 separate sample sets of postmenopausal women to identify the pathways that are altered in subjects with a high body mass index (BMI) compared to normal BMI. We found many pathways consistently and significantly associated with inflammation dysregulated in plasma from obese/overweight subjects compared to plasma from normal BMI subjects. These pathways indicate alterations of soluble inflammatory regulators, cellular stress, and metabolic dysregulation. Our results highlight the importance of high-dimensional pathway analysis in complex diseases as well as provide information on the interconnections between pathways that are dysregulated with obesity. Specifically, overlap of obesity related pathways with those activated during cancer and infection could help describe why obesity is a risk factor for disease and help devise treatment options that mitigate its effect.

Disease-specific classification using deconvoluted whole blood gene expression

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.

Whole Blood Transcriptomics Is Relevant to Identify Molecular Changes in Response to Genetic Selection for Feed Efficiency and Nutritional Status in the Pig

The molecular mechanisms underlying feed efficiency need to be better understood to improve animal efficiency, a research priority to support a competitive and sustainable livestock production. This study was undertaken to determine whether pig blood transcriptome was affected by differences in feed efficiency and by ingested nutrients. Growing pigs from two lines divergently selected for residual feed intake (RFI) and fed isoproteic and isocaloric diets contrasted in energy source and nutrients were considered. Between 74 and 132 days of age, pigs (n = 12 by diet and by line) received a regular diet rich in cereals and low in fat (LF) or a diet where cereals where partially substituted by lipids and fibers (HF). At the end of the feeding trial, the total number of white blood cells was not affected by the line or by the diet, whereas the red blood cell number was higher (P<0.001) in low RFI than in high RFI pigs. Analysis of the whole blood transcriptome using a porcine microarray reveals a higher number of probes differentially expressed (DE) between RFI lines than between diets (2,154 versus 92 probes DE, P<0.01). This corresponds to 528 overexpressed genes and 477 underexpressed genes in low RFI pigs compared with high RFI pigs, respectively. Overexpressed genes were predominantly associated with translational elongation. Underexpressed genes were mainly involved in the immune response, regulation of inflammatory response, anti-apoptosis process, and cell organization. These findings suggest that selection for RFI has affected the immune status and defense mechanisms of pigs. Genes DE between diets were mainly related to the immune system and lipid metabolism. Altogether, this study demonstrates the usefulness of the blood transcriptome to identify the main biological processes affected by genetic selection and feeding strategies.

Adverse Effects of β-Blocker Therapy on Weight Loss in Response to a Controlled Dietary Regimen

BACKGROUND

The effects of β-blockers on metabolic parameters including weight loss are poorly understood.

METHODS

From a database of 3582 patients who completed The Ottawa Hospital Weight Management Program between 1992 and 2011, a total of 173 patients were receiving β-blockers and were eligible for the study. We determined differences in rate of weight loss in the first 6 weeks of this 900 kcal/d Optifast (Nestlé Health Science, Vevey, Switzerland) meal replacement program for patients treated with β-blockers compared with (1) matched controls and (2) all participants in the program not being treated with β-blockers. Secondary outcomes included changes in waist circumference.

RESULTS

Mean percent weight loss in the β-blocker group was reduced compared with the rest of the group (9.7% vs 10.0%; P = 0.0001) as well as with matched controls (9.7% vs 10.3%; P = 0.004). Results were the same after adjusting for prevalent cardiovascular disease (9.7% vs 10.0%; P = 0.006). Similarly, a smaller decrease in waist circumference at 6 weeks was observed in the β-blocker-treated group compared with the rest of the group (-24.2 vs -26.3 cm; P = 0.002) and with matched controls (-24.2 vs -25.2 cm; P = 0.04) and was not altered by adjustment for cardiovascular disease (-24.2 vs 26.3 cm; P = 0.004).

CONCLUSIONS

In the absence of a clear medical indication, alternatives to β-blockers should be considered for the treatment of hypertension in obese individuals.

Extensive alterations of the whole-blood transcriptome are associated with body mass index: results of an mRNA profiling study involving two large population-based cohorts

Background

Obesity, defined as pathologically increased body mass index (BMI), is strongly related to an increased risk for numerous common cardiovascular and metabolic diseases. It is particularly associated with insulin resistance, hyperglycemia, and systemic oxidative stress and represents the most important risk factor for type 2 diabetes (T2D). However, the pathophysiological mechanisms underlying these associations are still not completely understood. Therefore, in order to identify potentially disease-relevant BMI-associated gene expression signatures, a transcriptome-wide association study (TWAS) on BMI was performed.

Methods

Whole-blood mRNA levels determined by array-based transcriptional profiling were correlated with BMI in two large independent population-based cohort studies (KORA F4 and SHIP-TREND) comprising a total of 1977 individuals.

Results

Extensive alterations of the whole-blood transcriptome were associated with BMI: More than 3500 transcripts exhibited significant positive or negative BMI-correlation. Three major whole-blood gene expression signatures associated with increased BMI were identified. The three signatures suggested: i) a ratio shift from mature erythrocytes towards reticulocytes, ii) decreased expression of several genes essentially involved in the transmission and amplification of the insulin signal, and iii) reduced expression of several key genes involved in the defence against reactive oxygen species (ROS).

Conclusions

Whereas the first signature confirms published results, the other two provide possible mechanistic explanations for well-known epidemiological findings under conditions of increased BMI, namely attenuated insulin signaling and increased oxidative stress. The putatively causative BMI-dependent down-regulation of the expression of numerous genes on the mRNA level represents a novel finding. BMI-associated negative transcriptional regulation of insulin signaling and oxidative stress management provide new insights into the pathogenesis of metabolic syndrome and T2D.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0141-x) contains supplementary material, which is available to authorized users.

Multi-omic signature of body weight change: results from a population-based cohort study

BACKGROUND

Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.

METHODS

We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.

RESULTS

Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.

CONCLUSIONS

Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.

Obesity is associated with more activated neutrophils in African American male youth

BACKGROUND

There is emerging evidence suggesting the role of peripheral blood leukocytes in the pathogenesis of obesity and related diseases. However, few studies have taken a genome-wide approach to investigating gene expression profiles in peripheral leukocytes between obese and lean individuals with the consideration of obesity-related shifts in leukocyte types.

METHOD

We conducted this study in 95 African Americans (AAs) of both genders (age 14-20 years, 46 lean and 49 obese). Complete blood count with differential test (CBC) was performed in whole blood. Genome-wide gene expression analysis was obtained using the Illumina HumanHT-12 V4 Beadchip with RNA extracted from peripheral leukocytes. Out of the 95 participants, 64 had neutrophils stored. The validation study was based on real-time PCR with RNA extracted from purified neutrophils.

RESULTS

CBC test suggested that, in males, obesity was associated with increased neutrophil percentage (P=0.03). Genome-wide gene expression analysis showed that, in males, the majority of the most differentially expressed genes were related to neutrophil activation. Validation of the gene expression levels of ELANE (neutrophil elastase) and MPO (myeloperoxidase) in purified neutrophils demonstrated that the expression of these two genes--important biomarkers of neutrophils activation--were significantly elevated in obese males (P=0.01 and P=0.02, respectively).

CONCLUSION

The identification of increased neutrophil percentage and activation in obese AA males suggests that neutrophils have an essential role in the pathogenesis of obesity-related disease. Further functional and mechanistic studies on neutrophils may contribute to the development of novel intervention strategies reducing the burden associated with obesity-related health problems.

Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures.

METHODS

Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.

RESULTS

The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin.

CONCLUSION

This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.

DNA methylation of the LY86 gene is associated with obesity, insulin resistance, and inflammation

BACKGROUND

Previous genome-wide association studies (GWAS) have identified a large number of genetic variants for obesity and its related traits, representing a group of potential key genes in the etiology of obesity. Emerging evidence suggests that epigenetics may play an important role in obesity. It has not been explored whether the GWAS-identified loci contribute to obesity through epigenetics (e.g., DNA (deoxyribonucleic acid) methylation) in addition to genetics.

METHOD

A multi-stage cross-sectional study was designed. We did a literature search and identified 117 genes discovered by GWAS for obesity and its related traits. Then we analyzed whether the methylation levels of these genes were also associated with obesity in two genome-wide methylation panels. We examined an initial panel of seven adolescent obese cases and seven age-matched lean controls, followed by a second panel of 48 adolescent obese cases and 48 age- and gender-matched lean controls. The validated CpG sites were further replicated in two independent replication panels of youth (46 vs. 46 and 230 cases vs. 413 controls, respectively) and a general population of youth, including 703 healthy subjects.

RESULTS

One CpG site in the lymphocyte antigen 86 (LY86) gene, which showed higher methylation in the obese in both the initial (p = .009) and second genome-wide DNA methylation panel (p = .008), was further validated in both replication panels (meta p = .00016). Moreover, in the general population of youth, the methylation levels of this region were significantly correlated with adiposity indices (p ≤ .02), insulin resistance (p = .001), and inflammatory markers (p < .001).

CONCLUSION

By focusing on recent GWAS findings in genome-wide methylation profiles, we identified a solid association between LY86 gene DNA methylation and obesity.

Gene expression in thiazide diuretic or statin users in relation to incident type 2 diabetes

Thiazide diuretics and statins are used to improve cardiovascular outcomes, but may also cause type 2 diabetes (T2DM), although mechanisms are unknown. Gene expression studies may facilitate understanding of these associations. Participants from ongoing population-based studies were sampled for these longitudinal studies of peripheral blood microarray gene expression, and followed to incident diabetes. All sampled subjects were statin or thiazide users. Those who developed diabetes during follow-up comprised cases (44 thiazide users; 19 statin users), and were matched to drug-using controls who did not develop diabetes on several factors. Supervised normalization, surrogate variable analyses removed technical bias and confounding. Differentially-expressed genes were those with a false discovery rate Q-value<0.05. Among thiazide users, diabetes cases had significantly different expression of CCL14 (down-regulated 6%, Q-value=0.0257), compared with controls. Among statin users, diabetes cases had marginal but insignificantly different expression of ZNF532 (up-regulated 15%, Q-value=0.0584), CXORF21 (up-regulated 11%, Q-value=0.0584), and ZNHIT3 (up-regulated 19%, Q-value=0.0959), compared with controls. These genes comprise potential targets for future expression or mechanistic research on medication-related diabetes development.

Whole blood gene expression profiles in insulin resistant Latinos with the metabolic syndrome

Although insulin resistance in skeletal muscle is well-characterized, the role of circulating whole blood in the metabolic syndrome phenotype is not well understood. We set out to test the hypothesis that genes involved in inflammation, insulin signaling and mitochondrial function would be altered in expression in the whole blood of individuals with metabolic syndrome. We further wanted to examine whether similar relationships that we have found previously in skeletal muscle exist in peripheral whole blood cells. All subjects (n=184) were Latino descent from the Arizona Insulin Resistance registry. Subjects were classified based on the metabolic syndrome phenotype according to the National Cholesterol Education Program’s Adult Treatment Panel III. Of the 184 Latino subjects in the study, 74 were classified with the metabolic syndrome and 110 were without. Whole blood gene expression profiling was performed using the Agilent 4x44K Whole Human Genome Microarray. Whole blood microarray analysis identified 1,432 probes that were altered in expression ≥1.2 fold and P<0.05 after Benjamini-Hochberg in the metabolic syndrome subjects. KEGG pathway analysis revealed significant enrichment for pathways including ribosome, oxidative phosphorylation and MAPK signaling (all Benjamini-Hochberg P<0.05). Whole blood mRNA expression changes observed in the microarray data were confirmed by quantitative RT-PCR. Transcription factor binding motif enrichment analysis revealed E2F1, ELK1, NF-kappaB, STAT1 and STAT3 significantly enriched after Bonferroni correction (all P<0.05). The results of the present study demonstrate that whole blood is a useful tissue for studying the metabolic syndrome and its underlying insulin resistance although the relationship between blood and skeletal muscle differs.

Integrative pathway analysis of a genome-wide association study of (V)O(2max) response to exercise training

We previously reported the findings from a genome-wide association study of the response of maximal oxygen uptake (Vo2max) to an exercise program. Here we follow up on these results to generate hypotheses on genes, pathways, and systems involved in the ability to respond to exercise training. A systems biology approach can help us better establish a comprehensive physiological description of what underlies Vo2maxtrainability. The primary material for this exploration was the individual single-nucleotide polymorphism (SNP), SNP-gene mapping, and statistical significance levels. We aimed to generate novel hypotheses through analyses that go beyond statistical association of single-locus markers. This was accomplished through three complementary approaches: 1) building de novo evidence of gene candidacy through informatics-driven literature mining; 2) aggregating evidence from statistical associations to link variant enrichment in biological pathways to Vo2max trainability; and 3) predicting possible consequences of variants residing in the pathways of interest. We started with candidate gene prioritization followed by pathway analysis focused on overrepresentation analysis and gene set enrichment analysis. Subsequently, leads were followed using in silico analysis of predicted SNP functions. Pathways related to cellular energetics (pantothenate and CoA biosynthesis; PPAR signaling) and immune functions (complement and coagulation cascades) had the highest levels of SNP burden. In particular, long-chain fatty acid transport and fatty acid oxidation genes and sequence variants were found to influence differences in Vo2max trainability. Together, these methods allow for the hypothesis-driven ranking and prioritization of genes and pathways for future experimental testing and validation.

Redundancy control in pathway databases (ReCiPa): an application for improving gene-set enrichment analysis in Omics studies and "Big data" biology

Abstract Unparalleled technological advances have fueled an explosive growth in the scope and scale of biological data and have propelled life sciences into the realm of "Big Data" that cannot be managed or analyzed by conventional approaches. Big Data in the life sciences are driven primarily via a diverse collection of 'omics'-based technologies, including genomics, proteomics, metabolomics, transcriptomics, metagenomics, and lipidomics. Gene-set enrichment analysis is a powerful approach for interrogating large 'omics' datasets, leading to the identification of biological mechanisms associated with observed outcomes. While several factors influence the results from such analysis, the impact from the contents of pathway databases is often under-appreciated. Pathway databases often contain variously named pathways that overlap with one another to varying degrees. Ignoring such redundancies during pathway analysis can lead to the designation of several pathways as being significant due to high content-similarity, rather than truly independent biological mechanisms. Statistically, such dependencies also result in correlated p values and overdispersion, leading to biased results. We investigated the level of redundancies in multiple pathway databases and observed large discrepancies in the nature and extent of pathway overlap. This prompted us to develop the application, ReCiPa (Redundancy Control in Pathway Databases), to control redundancies in pathway databases based on user-defined thresholds. Analysis of genomic and genetic datasets, using ReCiPa-generated overlap-controlled versions of KEGG and Reactome pathways, led to a reduction in redundancy among the top-scoring gene-sets and allowed for the inclusion of additional gene-sets representing possibly novel biological mechanisms. Using obesity as an example, bioinformatic analysis further demonstrated that gene-sets identified from overlap-controlled pathway databases show stronger evidence of prior association to obesity compared to pathways identified from the original databases.