Proteomic Analysis, Immune Dysregulation, and Pathway Interconnections with Obesity

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.

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

Epidemiological and associative research from humans and animals identifies correlations between the environment and health impacts. The environment—health inter-relationship is effected through an individual’s underlying genetic variation and mediated by mechanisms that include the changes to gene regulation that are associated with the diversity of phenotypes we exhibit. However, the causal relationships have yet to be established, in part because the associations are reduced to individual interactions and the combinatorial effects are rarely studied. This problem is exacerbated by the fact that our genomes are highly dynamic; they integrate information across multiple levels (from linear sequence, to structural organisation, to temporal variation) each of which is open to and responds to environmental influence. To unravel the complexities of the genomic basis of human disease, and in particular non-communicable diseases that are also influenced by the environment (e.g., obesity, type II diabetes, cancer, multiple sclerosis, some neurodegenerative diseases, inflammatory bowel disease, rheumatoid arthritis) it is imperative that we fully integrate multiple layers of genomic data. Here we review current progress in integrated genomic data analysis, and discuss cases where data integration would lead to significant advances in our ability to predict how the environment may impact on our health. We also outline limitations which should form the basis of future research questions. In so doing, this review will lay the foundations for future research into the impact of the environment on our health.

Early triggers of moderately high-fat diet-induced kidney damage

Most of the obesity murine models inducing renal injury use calorie-enriched foods, where fat represents 60% of the total caloric supply, however, this strategy doubles the standard proportion of fat ingestion in obese patients. Therefore, it is crucial to study the impact of a high-fat intake on kidney physiology that resembles common obesity in humans to understand the trigger mechanisms of the long-term consequences of overweight and obesity. In this study, we analyzed whether chronic feeding with a moderately high fat diet (MHFD) representing 45% of total calories, may induce kidney function and structural injury compared to C57BL/6 mice fed a control diet. After 14 weeks, MHFD induced significant mice obesity. At the functional level, obese mice showed signs of kidney injury characterized by increased albuminuria/creatinine ratio and higher excretion of urinary biomarkers of kidney damage. While, at the structural level, glomerular hypertrophy was observed. Although, we did not detect renal fibrosis, the obese mice exhibited a significant elevation of Tgfb1 mRNA levels. Kidney damage caused by the exposure to MHFD was associated with greater oxidative stress, renal inflammation, higher endoplasmic reticulum (ER)-stress, and disruption of mitochondrial dynamics. In summary, our data demonstrate that obesity induced by a milder fat content diet is enough to establish renal injury, where oxidative stress, inflammation, ER-stress, and mitochondrial damage take relevance, pointing out the importance of opportune interventions to avoid the long-term consequences associated with obesity and metabolic syndrome.

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.

Proteomic Analysis of Plasma Reveals Fat Mass Influences Cancer-Related Pathways in Healthy Humans Fed Controlled Diets Differing in Glycemic Load

Increased adiposity and diets high in glycemic load (GL) are associated with increased risk of many chronic diseases including cancer. Using plasma from 80 healthy individuals [40 men/40 women, 29 with DXA-derived low fat mass (FM) and 51 with high FM] in a randomized cross-over-controlled feeding trial and arrays populated with 3,504 antibodies, we measured plasma proteins collected at baseline and end of each of two 28-day controlled diets: a low GL diet high in whole grains, legumes, fruits, and vegetables (WG) and a high GL diet high in refined grains and added sugars (RG). Following univariate testing for proteins differing by diet, we evaluated pathway-level involvement. Among all 80 participants, 172 proteins were identified as differing between diets. Stratifying participants by high and low FM identified 221 and 266 proteins, respectively, as differing between diets (unadjusted P < 0.05). These candidate proteins were tested for overrepresentation in Reactome pathways, corresponding to 142 (of 291) pathways in the high-FM group and 72 (of 274) pathways in the low-FM group. We observed that the cancer-related pathways, DNA Repair, DNA Replication, and Cell Cycle, were overrepresented in the high-FM participants while pathways involved in post-translational protein modification were overrepresented in participants with either FM. Although high-GL diets are associated with increased risk of some cancers, our study further suggests that biology associated with consumption of GL diets is variable depending on an individual's adiposity and dietary recommendations related to cancer prevention be made with the additional consideration of an individual's FM.

Plasma Proteomics Analysis Reveals Dysregulation of Complement Proteins and Inflammation in Acquired Obesity-A Study on Rare BMI-Discordant Monozygotic Twin Pairs

PURPOSE

The purpose of this study is to elucidate the effect of excess body weight and liver fat on the plasma proteome without interference from genetic variation.

EXPERIMENTAL DESIGN

The effect of excess body weight is assessed in young, healthy monozygotic twins from pairs discordant for body mass index (intrapair difference (Δ) in BMI > 3 kg m^-2 , n = 26) with untargeted LC-MS proteomics quantification. The effect of liver fat is interrogated via subgroup analysis of the BMI-discordant twin cohort: liver fat discordant pairs (Δliver fat > 2%, n = 12) and liver fat concordant pairs (Δliver fat < 2%, n = 14), measured by magnetic resonance spectroscopy.

RESULTS

Seventy-five proteins are differentially expressed, with significant enrichment for complement and inflammatory response pathways in the heavier co-twins. The complement dysregulation is found in obesity in both the liver fat subgroups. The complement and inflammatory proteins are significantly associated with adiposity measures, insulin resistance and impaired lipids.

CONCLUSIONS AND CLINICAL RELEVANCE

The early pathophysiological mechanisms in obesity are incompletely understood. It is shown that aberrant complement regulation in plasma is present in very early stages of clinically healthy obese persons, independently of liver fat and in the absence of genetic variation that typically confounds human studies.

Exploratory plasma proteomic analysis in a randomized crossover trial of aspirin among healthy men and women

Long-term use of aspirin is associated with lower risk of colorectal cancer and other cancers; however, the mechanism of chemopreventive effect of aspirin is not fully understood. Animal studies suggest that COX-2, NFκB signaling and Wnt/β-catenin pathways may play a role, but no clinical trials have systematically evaluated the biological response to aspirin in healthy humans. Using a high-density antibody array, we assessed the difference in plasma protein levels after 60 days of regular dose aspirin (325 mg/day) compared to placebo in a randomized double-blinded crossover trial of 44 healthy non-smoking men and women, aged 21-45 years. The plasma proteome was analyzed on an antibody microarray with ~3,300 full-length antibodies, printed in triplicate. Moderated paired t-tests were performed on individual antibodies, and gene-set analyses were performed based on KEGG and GO pathways. Among the 3,000 antibodies analyzed, statistically significant differences in plasma protein levels were observed for nine antibodies after adjusting for false discoveries (FDR adjusted p-value<0.1). The most significant protein was succinate dehydrogenase subunit C (SDHC), a key enzyme complex of the mitochondrial tricarboxylic acid (TCA) cycle. The other statistically significant proteins (NR2F1, MSI1, MYH1, FOXO1, KHDRBS3, NFKBIE, LYZ and IKZF1) are involved in multiple pathways, including DNA base-pair repair, inflammation and oncogenic pathways. None of the 258 KEGG and 1,139 GO pathways was found to be statistically significant after FDR adjustment. This study suggests several chemopreventive mechanisms of aspirin in humans, which have previously been reported to play a role in anti- or pro-carcinogenesis in cell systems; however, larger, confirmatory studies are needed.