Caloric dose-responsive genes in blood cells differentiate the metabolic status of obese men

Optimizing metabolic health with digital twins

A hallmark of subclinical metabolic decline is impaired metabolic flexibility, which refers to the ability to switch fuel utilization between glucose and fat according to energy demand and substrate availability. Herein, we propose optimizing metabolic health with digital twins that model an individual's metabolic flexibility profile to gamify the process of health optimization and predict long-term health outcomes. We explore key characteristics of this approach from technological and socioeconomical perspectives, with the objective of reducing the burden from metabolic disorders through driving behavior change and early detection of metabolic decline.

Systematic Review and Quantitative Data Synthesis of Peripheral Blood Mononuclear Cell Transcriptomics Reveals Consensus Gene Expression Changes in Response to a High Fat Meal

SCOPE

Metabolic flexibility is essential for a healthy response to a high fat meal, and is assessed by measuring postprandial changes in blood markers including peripheral blood mononuclear cells (PBMCs; lymphocytes and monocytes). However, there is no clear consensus on postprandial gene expression and protein changes in these cells.

METHOD AND RESULTS

The study systematically reviews the literature reporting transcriptional and proteomic changes in PBMCs after consumption of a high fat meal. After re-analysis of the raw data to ensure equivalence between studies, ≈85 genes are significantly changed (defined as in the same direction in ≥3 studies) with about half involved in four processes: inflammation/oxidative stress, GTP metabolism, apoptosis, and lipid localization/transport. For meals consisting predominantly of unsaturated fatty acids (UFA), notable additional processes are phosphorylation and glucocorticoid response. For saturated fatty acids (SFA), genes related to migration/angiogenesis and platelet aggregation are also changed.

CONCLUSION

Despite differences in study design, common gene changes are identified in PBMCs following a high fat meal. These common genes and processes will facilitate definition of the postprandial transcriptome as part of the overall postcibalome, linking all molecules and processes that change in the blood after a meal.

Molecular effects of the consumption of margarine and butter varying in trans fat composition: a parallel human intervention study

BACKGROUND

Whereas the dietary intake of industrial trans fatty acids (iTFA) has been specifically associated with inflammation, cardiovascular disease, and type 2 diabetes, understanding the impact of dietary fats on human health remains challenging owing to their complex composition and individual effects of their lipid components on metabolism. The aim of this study is to profile the composition of blood, measured by the fatty acid (FAs) profile and untargeted metabolome of serum and the transcriptome of blood cells, in order to identify molecular signatures that discriminate dietary fat intakes.

METHODS

In a parallel study, the molecular effects of consuming dairy fat containing ruminant TFA (rTFA) or margarine containing iTFA were investigated. Healthy volunteers (n = 42; 45-69 y) were randomly assigned to diets containing margarine without TFA as major source of fat (wTFA control group with 0.4 g TFA per 100 g margarine), margarine with iTFA (iTFA group with 4.1 g TFA per 100 g margarine), or butter with rTFA (rTFA group with 6.3 g TFA per 100 g butter) for 4 weeks. The amounts of test products were individually selected so that fat intake contributed to 30-33% of energy requirements and TFA in the rTFA and iTFA groups contributed to up to 2% of energy intake. Changes in fasting blood values of lipid profiles (GC with flame-ionization detection), metabolome profiles (LC-MS, GC-MS), and gene expression (microarray) were measured.

RESULTS

Eighteen FAs, as well as 242 additional features measured by LC-MS (185) and GC-MS (54) showed significantly different responses to the diets (PFDR-adjusted < 0.05), mainly distinguishing butter from the margarine diets while gene expression was not differentially affected. The most abundant TFA in the butter, i.e. TFA containing (E)-octadec-11-enoic acid (C18:1 t11; trans vaccenic acid), and margarines, i.e. TFA containing (E)-octadec-9-enoic acid (C18:1 t9; elaidic acid) were reflected in the significantly different serum levels of TFAs measured after the dietary interventions.

CONCLUSIONS

The untargeted serum metabolome differentiates margarine from butter intake although the identification of the discriminating features remains a bottleneck. The targeted serum FA profile provides detailed information on specific molecules differentiating not only butter from margarine intake but also diets with different content of iTFAs in margarine.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00933322.

Measuring Postprandial Metabolic Flexibility to Assess Metabolic Health and Disease

Metabolic abnormalities substantially increase the risk of noncommunicable diseases, which are among the leading causes of mortality globally. Mitigating and preventing these adverse consequences remains challenging due to a limited understanding of metabolic health. Metabolic flexibility, a key tenet of metabolic health, encompasses the responsiveness of interrelated pathways to maintain energy homeostasis throughout daily physiologic challenges, such as the response to meal challenges. One critical underlying research gap concerns the measurement of postprandial metabolic flexibility, which remains incompletely understood. We concisely review the methodology for assessment of postprandial metabolic flexibility in recent human studies. We identify 3 commonalities of study design, specifically the nature of the challenge, nature of the response measured, and approach to data analysis. Primary interventions were acute short-term nutrition challenges, including single- and multiple-macronutrient tolerance tests. Postmeal challenge responses were measured via laboratory assays and instrumentation, based on a diverse set of metabolic flexibility indicators [e.g., energy expenditure (whole-body indirect calorimetry), glucose and insulin kinetics, metabolomics, transcriptomics]. Common standard approaches have been diabetes-centric with single-macronutrient challenges (oral-glucose-tolerance test) to characterize the postprandial response based on glucose and insulin metabolism; or broad measurements of energy expenditure with calculated macronutrient oxidation via indirect calorimetry. Recent methodological advances have included the use of multiple-macronutrient meal challenges that are more representative of physiologic meals consumed by free-living humans, combinatorial approaches for assays and instruments, evaluation of other metabolic flexibility indicators via precision health, systems biology, and temporal perspectives. Omics studies have identified potential novel indicators of metabolic flexibility, which provide greater granularity to prior evidence from canonical approaches. In summary, recent findings indicate the potential for an expanded understanding of postprandial metabolic flexibility, based on nonclassical measurements and methodology, which could represent novel dynamic indicators of metabolic diseases.

Effect of black ginseng and silkworm supplementation on obesity, the transcriptome, and the gut microbiome of diet-induced overweight dogs

Like humans, weight control in overweight dogs is associated with a longer life expectancy and a healthier life. Dietary supplements are one of the best strategies for controlling obesity and obesity-associated diseases. This study was conducted to assess the potential of black ginseng (BG) and silkworm (SW) as supplements for weight control in diet-induced overweight beagle dogs. To investigate the changes that occur in dogs administered the supplements, different obesity-related parameters, such as body condition score (BCS), blood fatty acid profile, transcriptome, and microbiome, were assessed in high energy diet (HD) and HD with BG + SW supplementation (HDT) groups of test animals. After 12 weeks of BG + SW supplementation, total cholesterol and triglyceride levels were reduced in the HDT group. In the transcriptome analysis, nine genes (NUGGC, EFR3B, RTP4, ACAN, HOXC4, IL17RB, SOX13, SLC18A2, and SOX4) that are known to be associated with obesity were found to be differentially expressed between the ND (normal diet) and HD groups as well as the HD and HDT groups. Significant changes in some taxa were observed between the HD and ND groups. These data suggest that the BG + SW supplement could be developed as dietary interventions against diet-induced obesity, and obesity-related differential genes could be important candidates in the mechanism of the anti-obesity effects of the BG + SW supplement.

Serum Metabolites Responding in a Dose-Dependent Manner to the Intake of a High-Fat Meal in Normal Weight Healthy Men Are Associated with Obesity

Although the composition of the human blood metabolome is influenced both by the health status of the organism and its dietary behavior, the interaction between these two factors has been poorly characterized. This study makes use of a previously published randomized controlled crossover acute intervention to investigate whether the blood metabolome of 15 healthy normal weight (NW) and 17 obese (OB) men having ingested three doses (500, 1000, 1500 kcal) of a high-fat (HF) meal can be used to identify metabolites differentiating these two groups. Among the 1024 features showing a postprandial response, measured between 0 h and 6 h, in the NW group, 135 were dose-dependent. Among these 135 features, 52 had fasting values that were significantly different between NW and OB men, and, strikingly, they were all significantly higher in OB men. A subset of the 52 features was identified as amino acids (e.g., branched-chain amino acids) and amino acid derivatives. As the fasting concentration of most of these metabolites has already been associated with metabolic dysfunction, we propose that challenging normal weight healthy subjects with increasing caloric doses of test meals might allow for the identification of new fasting markers associated with obesity.

Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer

Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.

Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high-calorie diet and high-fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein-protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.

Temporal changes in postprandial blood transcriptomes reveal subject-specific pattern of expression of innate immunity genes after a high-fat meal

White blood cells are among the first responders to dietary components and their metabolites absorbed from the gut. The objective of this study was to determine the whole blood transcriptome response to high-fat challenge meals. A total of 45 fasting and postprandial (3-h and 6-h) whole blood transcriptomes from 5 subjects in a crossover intervention trial of a high-fat meal supplemented with placebo, blueberry powder or docosahexaenoic acid (DHA) were analyzed using RNA sequencing. Select target genes were validated by quantitative reverse-transcription polymerase chain reaction in 180 samples from 20 subjects. The largest contributor to variance was the subject (13,856 genes differentially expressed), followed by the subject on a specific day (2276 genes), followed by the subject's postprandial response (651 genes). After determining the nonsignificance of individual dietary treatments (blueberry, DHA, placebo), treatments were used as replicates to examine postprandial responses to a high-fat meal. The universal postprandial response (95 genes) was associated with lipid utilization, fatty acid beta-oxidation and circadian rhythms. Subject-specific postprandial responses were enriched for genes involved in the innate immune response, particularly those of pattern recognition receptors and their downstream signaling components. Genes involved in innate immune responses are differentially expressed in a subject-specific and time-dependent manner in response to the high-fat meals. These genes can serve as biomarkers to assess individual responsiveness to a high-fat diet in inducing postprandial inflammation. Furthermore, the dynamic temporal change in gene expression in postprandial blood suggests that monitoring these genes at multiple time points is necessary to reveal responders to dietary intervention.

Fasting and time of day independently modulate circadian rhythm relevant gene expression in adipose and skin tissue

BACKGROUND

Intermittent fasting and time-restricted diets are associated with lower risk biomarkers for cardio-metabolic disease. The shared mechanisms underpinning the similar physiological response to these events is not established, but circadian rhythm could be involved. Here we investigated the transcriptional response to fasting in a large cross-sectional study of adipose and skin tissue from healthy volunteers (N = 625) controlling for confounders of circadian rhythm: time of day and season.

RESULTS

We identified 367 genes in adipose and 79 in skin whose expression levels were associated (FDR < 5%) with hours of fasting conditionally independent of time of day and season, with 19 genes common to both tissues. Among these genes, we replicated 38 in human, 157 in non-human studies, and 178 are novel associations. Fasting-responsive genes were enriched for regulation of and response to circadian rhythm. We identified 99 genes in adipose and 54 genes in skin whose expression was associated to time of day; these genes were also enriched for circadian rhythm processes. In genes associated to both exposures the effect of time of day was stronger and in an opposite direction to that of hours fasted. We also investigated the relationship between fasting and genetic regulation of gene expression, including GxE eQTL analysis to identify personal responses to fasting.

CONCLUSION

This study robustly implicates circadian rhythm genes in the response to hours fasting independently of time of day, seasonality, age and BMI. We identified tissue-shared and tissue-specific differences in the transcriptional response to fasting in a large sample of healthy volunteers.

Modulation of the peripheral blood transcriptome by the ingestion of probiotic yoghurt and acidified milk in healthy, young men

The metabolic health benefits of fermented milks have already been investigated using clinical biomarkers but the development of transcriptomic analytics in blood offers an alternative approach that may help to sensitively characterise such effects. We aimed to assess the effects of probiotic yoghurt intake, compared to non-fermented, acidified milk intake, on clinical biomarkers and gene expression in peripheral blood. To this end, a randomised, crossover study was conducted in fourteen healthy, young men to test the two dairy products. For a subset of seven subjects, RNA sequencing was used to measure gene expression in blood collected during postprandial tests and after two weeks daily intake. We found that the postprandial response in insulin was different for probiotic yoghurt as compared to that of acidified milk. Moreover changes in several clinical biomarkers were associated with changes in the expression of genes representing six metabolic genesets. Assessment of the postprandial effects of each dairy product on gene expression by geneset enrichment analysis revealed significant, similar modulation of inflammatory and glycolytic genes after both probiotic yoghurt and acidified milk intake, although distinct kinetic characteristics of the modulation differentiated the dairy products. The aryl hydrocarbon receptor was a major contributor to the down-regulation of the inflammatory genesets and was also positively associated with changes in circulating insulin at 2h after yoghurt intake (p = 0.05). Daily intake of the dairy products showed little effect on the fasting blood transcriptome. Probiotic yoghurt and acidified milk appear to affect similar gene pathways during the postprandial phase but differences in the timing and the extent of this modulation may lead to different physiological consequences. The functional relevance of these differences in gene expression is supported by their associations with circulating biomarkers.