A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity

Impact of dietary carbohydrate, fat or protein restriction on the human gut microbiome: a systematic review

Restriction of dietary carbohydrates, fat and/or protein is often used to reduce body weight and/or treat (metabolic) diseases. Since diet is a key modulator of the human gut microbiome, which plays an important role in health and disease, this review aims to provide an overview of current knowledge of the effects of macronutrient-restricted diets on gut microbial composition and metabolites. A structured search strategy was performed in several databases. After screening for inclusion and exclusion criteria, thirty-six articles could be included. Data are included in the results only when supported by at least three independent studies to enhance the reliability of our conclusions. Low-carbohydrate (<30 energy%) diets tended to induce a decrease in the relative abundance of several health-promoting bacteria, including Bifidobacterium, as well as a reduction in short-chain fatty acid (SCFA) levels in faeces. In contrast, low-fat diets (<30 energy%) increased alpha diversity, faecal SCFA levels and abundance of some beneficial bacteria, including Faecalibacterium prausnitzii. There were insufficient data to draw conclusions concerning the effects of low-protein (<10 energy%) diets on gut microbiota. Although the data of included studies unveil possible benefits of low-fat and potential drawbacks of low-carbohydrate diets for human gut microbiota, the diversity in study designs made it difficult to draw firm conclusions. Using a more uniform methodology in design, sample processing and sharing raw sequence data could foster our understanding of the effects of macronutrient restriction on gut microbiota composition and metabolic dynamics relevant to health. This systematic review was registered at https://www.crd.york.ac.uk/prospero as CRD42020156929.

The Beneficial and Adverse Effects of Autophagic Response to Caloric Restriction and Fasting

Each cell is equipped with a conserved housekeeping mechanism, known as autophagy, to recycle exhausted materials and dispose of injured organelles via lysosomal degradation. Autophagy is an early-stage cellular response to stress stimuli in both physiological and pathological situations. It is thought that the promotion of autophagy flux prevents host cells from subsequent injuries by removing damaged organelles and misfolded proteins. As a correlate, the modulation of autophagy is suggested as a therapeutic approach in diverse pathological conditions. Accumulated evidence suggests that intermittent fasting or calorie restriction can lead to the induction of adaptive autophagy and increase longevity of eukaryotic cells. However, prolonged calorie restriction with excessive autophagy response is harmful and can stimulate a type II autophagic cell death. Despite the existence of a close relationship between calorie deprivation and autophagic response in different cell types, the precise molecular mechanisms associated with this phenomenon remain unclear. Here, we aimed to highlight the possible effects of prolonged and short-term calorie restriction on autophagic response and cell homeostasis.

Adiponectin reverses β-Cell damage and impaired insulin secretion induced by obesity

Obesity significantly decreases life expectancy and increases the incidence of age-related dysfunctions, including β-cell dysregulation leading to inadequate insulin secretion. Here, we show that diluted plasma from obese human donors acutely impairs β-cell integrity and insulin secretion relative to plasma from lean subjects. Similar results were observed with diluted sera from obese rats fed ad libitum, when compared to sera from lean, calorically restricted, animals. The damaging effects of obese circulating factors on β-cells occurs in the absence of nutrient overload, and mechanistically involves mitochondrial dysfunction, limiting glucose-supported oxidative phosphorylation and ATP production. We demonstrate that increased levels of adiponectin, as found in lean plasma, are the protective characteristic preserving β-cell function; indeed, sera from adiponectin knockout mice limits β-cell metabolic fluxes relative to controls. Furthermore, oxidative phosphorylation and glucose-sensitive insulin secretion, which are completely abrogated in the absence of this hormone, are restored by the presence of adiponectin alone, surprisingly even in the absence of other serological components, for both the insulin-secreting INS1 cell line and primary islets. The addition of adiponectin to cells treated with plasma from obese donors completely restored β-cell functional integrity, indicating the lack of this hormone was causative of the dysfunction. Overall, our results demonstrate that low circulating adiponectin is a key damaging element for β-cells, and suggest strong therapeutic potential for the modulation of the adiponectin signaling pathway in the prevention of age-related β-cell dysfunction.

Exploitation of a shared genetic signature between obesity and endometrioid endometrial cancer

Aims

The findings in epidemiological studies suggest that endometrioid endometrial cancer (EEC) is associated with obesity. However, evidence from gene expression data for the relationship between the two is still lacking. The purpose of this study was to explore the merits of establishing an obesity-related genes (ORGs) signature in the treatment and the prognostic assessment of EEC.

Methods

Microarray data from GSE112307 were utilized to identify ORGs by using weighted gene co-expression network analysis. Based on the sequencing data from TCGA, we established the prognostic ORGs signature, confirmed its value as an independent risk factor, and constructed a nomogram. We further investigated the association between grouping based on ORGs signature and clinicopathological characteristics, immune infiltration, tumor mutation burden and drug sensitivity.

Results

A total of 10 ORGs were identified as key genes for the construction of the signature. According to the ORGs score computed from the signature, EEC patients were divided into high and low-scoring groups. Overall survival (OS) was shorter in EEC patients in the high-scoring group compared with the low-scoring group (P < 0.001). The results of the Cox regression analysis showed that ORGs score was an independent risk factor for OS in EEC patients (HR = 1.017, 95% confidence interval = 1.011-1.023; P < 0.001). We further revealed significant disparities between scoring groups in terms of clinical characteristics, tumor immune cell infiltration, and tumor mutation burden. Patients in the low-scoring group may be potential beneficiaries of immunotherapy and targeted therapies.

Conclusions

The ORGs signature established in this study has promising prognostic predictive power and may be a useful tool for the selection of EEC patients who benefit from immunotherapy and targeted therapies.

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.

Identification of hub genes and candidate herbal treatment in obesity through integrated bioinformatic analysis and reverse network pharmacology

Obesity is a global epidemic elevating the risk of various metabolic disorders. As there is a lack of effective drugs to treat obesity, we combined bioinformatics and reverse network pharmacology in this study to identify effective herbs to treat obesity. We identified 1011 differentially expressed genes (DEGs) of adipose tissue after weight loss by analyzing five expression profiles (GSE103766, GSE35411, GSE112307, GSE43471, and GSE35710) from the Gene Expression Omnibus (GEO) database. We identified 27 hub genes from the protein-protein interaction (PPI) network by performing MCODE using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hub genes have roles in the extracellular matrix-receptor interaction, cholesterol metabolism, PI3K-Akt signaling pathway, etc. Ten herbs (Aloe, Portulacae Herba, Mori Follum, Silybum Marianum, Phyllanthi Fructus, Pollen Typhae, Ginkgo Semen, Leonuri Herba, Eriobotryae Folium, and Litseae Fructus) targeting the nine hub genes (COL1A1, MMP2, MMP9, SPP1, DNMT3B, MMP7, CETP, COL1A2, and MUC1) using six ingredients were identified as the key herbs. Quercetin and (-)-epigallocatechin-3-gallate were determined to be the key ingredients. Lastly, Ingredients-Targets, Herbs-Ingredients-Targets, and Herbs-Taste-Meridian Tropism networks were constructed using Cytoscape to elucidate this complex relationship. This study could help identify promising therapeutic targets and drugs to treat obesity.

Diet-Induced Host-Microbe Interactions: Personalized Diet Strategies for Improving Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is an idiopathic inflammatory disease. Environmental sanitization, modern lifestyles, advanced medicines, ethnic origins, host genetics and immune systems, mucosal barrier function, and the gut microbiota have been delineated to explain how they cause mucosal inflammation. However, the pathogenesis of IBD and its therapeutic targets remain elusive. Recent studies have highlighted the importance of the human gut microbiota in health and disease, suggesting that the pathogenesis of IBD is highly associated with imbalances of the gut microbiota or alterations of epithelial barrier function in the gastrointestinal (GI) tract. Moreover, diet-induced alterations of the gut microbiota in the GI tract modulate immune responses and perturb metabolic homeostasis. This review summarizes recent findings on IBD and its association with diet-induced changes in the gut microbiota; furthermore, it discusses how diets can modulate host gut microbes and immune systems, potentiating the impact of personalized diets on therapeutic targets for IBD.

Gut microbiota predicts body fat change following a low-energy diet: a PREVIEW intervention study

Background

Low-energy diets (LEDs) comprise commercially formulated food products that provide between 800 and 1200 kcal/day (3.3–5 MJ/day) to aid body weight loss. Recent small-scale studies suggest that LEDs are associated with marked changes in the gut microbiota that may modify the effect of the LED on host metabolism and weight loss. We investigated how the gut microbiota changed during 8 weeks of total meal replacement LED and determined their associations with host response in a sub-analysis of 211 overweight adults with pre-diabetes participating in the large multicentre PREVIEW (PREVention of diabetes through lifestyle intervention and population studies In Europe and around the World) clinical trial.

Methods

Microbial community composition was analysed by Illumina sequencing of the hypervariable V3-V4 regions of the 16S ribosomal RNA (rRNA) gene. Butyrate production capacity was estimated by qPCR targeting the butyryl-CoA:acetate CoA-transferase gene. Bioinformatics and statistical analyses, such as comparison of alpha and beta diversity measures, correlative and differential abundances analysis, were undertaken on the 16S rRNA gene sequences of 211 paired (pre- and post-LED) samples as well as their integration with the clinical, biomedical and dietary datasets for predictive modelling.

Results

The overall composition of the gut microbiota changed markedly and consistently from pre- to post-LED (P = 0.001), along with increased richness and diversity (both P < 0.001). Following the intervention, the relative abundance of several genera previously associated with metabolic improvements (e.g., Akkermansia and Christensenellaceae R-7 group) was significantly increased (P < 0.001), while flagellated Pseudobutyrivibrio, acetogenic Blautia and Bifidobacterium spp. were decreased (all P < 0.001). Butyrate production capacity was reduced (P < 0.001). The changes in microbiota composition and predicted functions were significantly associated with body weight loss (P < 0.05). Baseline gut microbiota features were able to explain ~25% of variation in total body fat change (post–pre-LED).

Conclusions

The gut microbiota and individual taxa were significantly influenced by the LED intervention and correlated with changes in total body fat and body weight in individuals with overweight and pre-diabetes. Despite inter-individual variation, the baseline gut microbiota was a strong predictor of total body fat change during the energy restriction period.

Trial registration

The PREVIEW trial was prospectively registered at ClinicalTrials.gov (NCT01777893) on January 29, 2013.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01053-7.

Impact of Food-Based Weight Loss Interventions on Gut Microbiome in Individuals with Obesity: A Systematic Review

The observation that the gut microbiota is different in healthy weight as compared with the obese state has sparked interest in the possible modulation of the microbiota in response to weight change. This systematic review investigates the effect of food-based weight loss diets on microbiota outcomes (α-diversity, β-diversity, relative bacterial abundance, and faecal short-chain fatty acids, SCFAs) in individuals without medical comorbidities who have successfully lost weight. Nineteen studies were included using the keywords ‘obesity’, ‘weight loss’, ‘microbiota’, and related terms. Across all 28 diet intervention arms, there were minimal changes in α- and β-diversity and faecal SCFA concentrations following weight loss. Changes in relative bacterial abundance at the phylum and genus level were inconsistent across studies. Further research with larger sample sizes, detailed dietary reporting, and consistent microbiota analysis techniques are needed to further our understanding of the effect of diet-induced weight loss on the gut microbiota.

Therapeutic Effects of Modified Tempeh on Glycemic Control and Gut Microbiota Diversity in Diabetic Rats

Background:

The role of the gut microbiota in improving glycemic control in diabetic patients is gaining attention. Tempeh is a fermented soy food from Indonesia that has antidiabetic and antidysbiotic effects. Interestingly, modification of tempeh processing by adding lactic acid bacteria has been reported to enhance the antidiabetic effect of tempeh.

Aim:

To evaluate the effects of modified tempeh on serum glucose, insulin, and gut microbiota diversity of diabetic rats.

Methods:

Modified tempeh was developed by adding lactic acid bacteria from fermented cassava during tempeh processing. Diabetes was induced by injection of streptozotocin nicotinamide. Normal tempeh or modified tempeh was added to the diet and replaced 15% or 30% of casein. Serum glucose and insulin were analyzed before and after 30 days of intervention. At the end of the experiment, the appendix was sampled for gut microbiota analysis.

Result:

Modified tempeh has a significantly higher number of lactic acid bacteria (9.99±0.09 versus 7.74±0.07 log CFU, p < 0.001) compared to normal tempeh. There was a significant difference (p < 0.01) in serum glucose and insulin after treatment. Both tempeh supplements increased the diversity of the gut microbiota. Gut microbiota diversity has a strong negative correlation with delta glucose (r=-0.63, p < 0.001) and delta insulin resistance index (r=-0.54, p=0.003).

Conclusion:

Modified tempeh has potential therapeutic antidiabetic activity, possibly through increased diversity of the gut microbiota.

Multi-Omics Integration and Network Analysis Reveal Potential Hub Genes and Genetic Mechanisms Regulating Bovine Mastitis

Mastitis, inflammation of the mammary gland, is the most prevalent disease in dairy cattle that has a potential impact on profitability and animal welfare. Specifically designed multi-omics studies can be used to prioritize candidate genes and identify biomarkers and the molecular mechanisms underlying mastitis in dairy cattle. Hence, the present study aimed to explore the genetic basis of bovine mastitis by integrating microarray and RNA-Seq data containing healthy and mastitic samples in comparative transcriptome analysis with the results of published genome-wide association studies (GWAS) using a literature mining approach. The integration of different information sources resulted in the identification of 33 common and relevant genes associated with bovine mastitis. Among these, seven genes—CXCR1, HCK, IL1RN, MMP9, S100A9, GRO1, and SOCS3—were identified as the hub genes (highly connected genes) for mastitis susceptibility and resistance, and were subjected to protein-protein interaction (PPI) network and gene regulatory network construction. Gene ontology annotation and enrichment analysis revealed 23, 7, and 4 GO terms related to mastitis in the biological process, molecular function, and cellular component categories, respectively. Moreover, the main metabolic-signalling pathways responsible for the regulation of immune or inflammatory responses were significantly enriched in cytokine–cytokine-receptor interaction, the IL-17 signaling pathway, viral protein interaction with cytokines and cytokine receptors, and the chemokine signaling pathway. Consequently, the identification of these genes, pathways, and their respective functions could contribute to a better understanding of the genetics and mechanisms regulating mastitis and can be considered a starting point for future studies on bovine mastitis.

Role of the Gut Microbiome in Beta Cell and Adipose Tissue Crosstalk: A Review

In the last decades, obesity has reached epidemic proportions worldwide. Obesity is a chronic disease associated with a wide range of comorbidities, including insulin resistance and type 2 diabetes mellitus (T2D), which results in significant burden of disease and major consequences on health care systems. Of note, intricate interactions, including different signaling pathways, are necessary for the establishment and progression of these two closely related conditions. Altered cell-to-cell communication among the different players implicated in this equation leads to the perpetuation of a vicious circle associated with an increased risk for the development of obesity-related complications, such as T2D, which in turn contributes to the development of cardiovascular disease. In this regard, the dialogue between the adipocyte and pancreatic beta cells has been extensively studied, although some connections are yet to be fully elucidated. In this review, we explore the potential pathological mechanisms linking adipocyte dysfunction and pancreatic beta cell impairment/insulin resistance. In addition, we evaluate the role of emerging actors, such as the gut microbiome, in this complex crosstalk.

Bigu-Style Fasting Affects Metabolic Health by Modulating Taurine, Glucose, and Cholesterol Homeostasis in Healthy Young Adults

BACKGROUND

Dynamic orchestration of metabolic pathways during continuous fasting remains unclear.

OBJECTIVE

We investigated the physiological effects of Bigu-style fasting and underlying metabolic reprogramming in healthy adults.

METHODS

We conducted a 5-d Bigu trial in 43 healthy subjects [age 23.2 ± 2.4 y; BMI (in kg/m2) 22.52 ± 1.79]. Physiological indicators and body composition were monitored daily during fasting day 1 (F1D) to F5D and after 10-d refeeding postfasting (R10D) and R30D. Blood samples were collected in the morning. Risk factors associated with inflammation, aging, cardiovascular diseases, malnutrition, and organ dysfunction were evaluated by biochemical measurements. Untargeted plasma metabolomics and gut microbial profiling were performed using plasma and fecal samples. Data were analyzed by repeated measures ANOVA with Greenhouse-Geisser correction. Correlation analyses for metabolite modules and taurine were analyzed by Spearman's rank and Pearson tests, respectively.

RESULTS

Heart rate was accelerated throughout the fasting period. Risk factors associated with inflammation and cardiovascular diseases were significantly lowered during or after Bigu (P < 0.05). Body composition measurement detected an overconsumption of fat starting from F3D till 1 mo after refeeding. Metabolomics unveiled a coupling between gluconeogenesis and cholesterol biosynthesis beyond F3D. Plasma taurine significantly increased at F3D by 31%-46% followed by a reduction to basal level at F5D (P < 0.001), a pattern inversely correlated with changes in glucose and de novo synthesized cholesterol (r = -0.407 and -0.296, respectively; P < 0.001). Gut microbial profiling showed an enrichment of taurine-utilizing bacteria at F5D, which was completely recovered at R10D.

CONCLUSIONS

Our data demonstrate that 5-d Bigu is potentially beneficial to health in young adults. A starvation threshold of 3-d fasting is necessary for maintaining glucose and cholesterol homeostasis via a taurine-microbiota regulatory loop. Our findings provide novel insights into the physiological and metabolic responses of the human body to continuous Bigu-style fasting. This trial was registered at http://www.chictr.org.cn as ChiCTR1900022917.

Omics Multi-Layers Networks Provide Novel Mechanistic and Functional Insights Into Fat Storage and Lipid Metabolism in Poultry

Fatty acid metabolism in poultry has a major impact on production and disease resistance traits. According to the high rate of interactions between lipid metabolism and its regulating properties, a holistic approach is necessary. To study omics multilayers of adipose tissue and identification of genes and miRNAs involved in fat metabolism, storage and endocrine signaling pathways in two groups of broiler chickens with high and low abdominal fat, as well as high-throughput techniques, were used. The gene–miRNA interacting bipartite and metabolic-signaling networks were reconstructed using their interactions. In the analysis of microarray and RNA-Seq data, 1,835 genes were detected by comparing the identified genes with significant expression differences (p.adjust < 0.01, fold change ≥ 2 and ≤ −2). Then, by comparing between different data sets, 34 genes and 19 miRNAs were detected as common and main nodes. A literature mining approach was used, and seven genes were identified and added to the common gene set. Module finding revealed three important and functional modules, which were involved in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, biosynthesis of unsaturated fatty acids, Alzheimer’s disease metabolic pathway, adipocytokine, insulin, PI3K–Akt, mTOR, and AMPK signaling pathway. This approach revealed a new insight to better understand the biological processes associated with adipose tissue.

Nutrition and Rheumatoid Arthritis in the 'Omics' Era

Modern high-throughput 'omics' science tools (including genomics, transcriptomics, proteomics, metabolomics and microbiomics) are currently being applied to nutritional sciences to unravel the fundamental processes of health effects ascribed to particular nutrients in humans and to contribute to more precise nutritional advice. Diet and food components are key environmental factors that interact with the genome, transcriptome, proteome, metabolome and the microbiota, and this life-long interplay defines health and diseases state of the individual. Rheumatoid arthritis (RA) is a chronic autoimmune disease featured by a systemic immune-inflammatory response, in genetically susceptible individuals exposed to environmental triggers, including diet. In recent years increasing evidences suggested that nutritional factors and gut microbiome have a central role in RA risk and progression. The aim of this review is to summarize the main and most recent applications of 'omics' technologies in human nutrition and in RA research, examining the possible influences of some nutrients and nutritional patterns on RA pathogenesis, following a nutrigenomics approach. The opportunities and challenges of novel 'omics technologies' in the exploration of new avenues in RA and nutritional research to prevent and manage RA will be also discussed.

Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies

Scope

Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health.

Methods and results

The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects (n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone.

Conclusion

The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.

An Integrated Analysis of miRNA and Gene Expression Changes in Response to an Obesogenic Diet to Explore the Impact of Transgenerational Supplementation with Omega 3 Fatty Acids

Insulin resistance decreases the ability of insulin to inhibit hepatic gluconeogenesis, a key step in the development of metabolic syndrome. Metabolic alterations, fat accumulation, and fibrosis in the liver are closely related and contribute to the progression of comorbidities, such as hypertension, type 2 diabetes, or cancer. Omega 3 (n-3) polyunsaturated fatty acids, such as eicosapentaenoic acid (EPA), were identified as potent positive regulators of insulin sensitivity in vitro and in animal models. In the current study, we explored the effects of a transgenerational supplementation with EPA in mice exposed to an obesogenic diet on the regulation of microRNAs (miRNAs) and gene expression in the liver using high-throughput techniques. We implemented a comprehensive molecular systems biology approach, combining statistical tools, such as MicroRNA Master Regulator Analysis pipeline and Boolean modeling to integrate these biochemical processes. We demonstrated that EPA mediated molecular adaptations, leading to the inhibition of miR-34a-5p, a negative regulator of Irs2 as a master regulatory event leading to the inhibition of gluconeogenesis by insulin during the fasting-feeding transition. Omics data integration provided greater biological insight and a better understanding of the relationships between biological variables. Such an approach may be useful for deriving innovative data-driven hypotheses and for the discovery of molecular-biochemical mechanistic links.

Still Living Better through Chemistry: An Update on Caloric Restriction and Caloric Restriction Mimetics as Tools to Promote Health and Lifespan

Caloric restriction (CR), the reduction of caloric intake without inducing malnutrition, is the most reproducible method of extending health and lifespan across numerous organisms, including humans. However, with nearly one-third of the world’s population overweight, it is obvious that caloric restriction approaches are difficult for individuals to achieve. Therefore, identifying compounds that mimic CR is desirable to promote longer, healthier lifespans without the rigors of restricting diet. Many compounds, such as rapamycin (and its derivatives), metformin, or other naturally occurring products in our diets (nutraceuticals), induce CR-like states in laboratory models. An alternative to CR is the removal of specific elements (such as individual amino acids) from the diet. Despite our increasing knowledge of the multitude of CR approaches and CR mimetics, the extent to which these strategies overlap mechanistically remains unclear. Here we provide an update of CR and CR mimetic research, summarizing mechanisms by which these strategies influence genome function required to treat age-related pathologies and identify the molecular fountain of youth.

How Far Are We from Prescribing Fasting as Anticancer Medicine?

(1) Background: the present review provides a comprehensive and up-to date overview of the potential exploitation of fasting as an anticancer strategy. The rationale for this concept is that fasting elicits a differential stress response in the setting of unfavorable conditions, empowering the survival of normal cells, while killing cancer cells. (2) Methods: the present narrative review presents the basic aspects of the hormonal, molecular, and cellular response to fasting, focusing on the interrelationship of fasting with oxidative stress. It also presents nonclinical and clinical evidence concerning the implementation of fasting as adjuvant to chemotherapy, highlighting current challenges and future perspectives. (3) Results: there is ample nonclinical evidence indicating that fasting can mitigate the toxicity of chemotherapy and/or increase the efficacy of chemotherapy. The relevant clinical research is encouraging, albeit still in its infancy. The path forward for implementing fasting in oncology is a personalized approach, entailing counteraction of current challenges, including: (i) patient selection; (ii) fasting patterns; (iii) timeline of fasting and refeeding; (iv) validation of biomarkers for assessment of fasting; and (v) establishment of protocols for patients’ monitoring. (4) Conclusion: prescribing fasting as anticancer medicine may not be far away if large randomized clinical trials consolidate its safety and efficacy.

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

Data integration for prediction of weight loss in randomized controlled dietary trials

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Gut microbiota and human NAFLD: disentangling microbial signatures from metabolic disorders

Gut microbiota dysbiosis has been repeatedly observed in obesity and type 2 diabetes mellitus, two metabolic diseases strongly intertwined with non-alcoholic fatty liver disease (NAFLD). Animal studies have demonstrated a potential causal role of gut microbiota in NAFLD. Human studies have started to describe microbiota alterations in NAFLD and have found a few consistent microbiome signatures discriminating healthy individuals from those with NAFLD, non-alcoholic steatohepatitis or cirrhosis. However, patients with NAFLD often present with obesity and/or insulin resistance and type 2 diabetes mellitus, and these metabolic confounding factors for dysbiosis have not always been considered. Patients with different NAFLD severity stages often present with heterogeneous lesions and variable demographic characteristics (including age, sex and ethnicity), which are known to affect the gut microbiome and have been overlooked in most studies. Finally, multiple gut microbiome sequencing tools and NAFLD diagnostic methods have been used across studies that could account for discrepant microbiome signatures. This Review provides a broad insight into microbiome signatures for human NAFLD and explores issues with disentangling these signatures from underlying metabolic disorders. More advanced metagenomics and multi-omics studies using system biology approaches are needed to improve microbiome biomarkers.

Omics Integration for Mitochondria Systems Biology

Significance: Elucidation of the central importance of mitophagy in homeostasis of cells and organisms emphasizes that mitochondrial functions extend far beyond short-term needs for energy production. In mitochondria systems biology, the mitochondrial genome, proteome, and metabolome operate as a functional network in coordination of cell activities. Organization occurs through subnetworks that are interconnected by membrane potential, transport activities, allosteric and cooperative interactions, redox signaling mechanisms, rheostatic control by post-translational modifications, and metal ion homeostasis. These subnetworks enable use of varied energy precursors, defense against environmental stressors, and macromolecular rewiring to titrate energy production, biosynthesis, and detoxification according to cell-specific needs. Rewiring mechanisms, termed mitochondrial reprogramming, enhance fitness to respond to metabolic resources and challenges from the environment. Maladaptive responses can cause cell death. Maladaptive rewiring can cause disease. In cancer, adaptive rewiring can interfere with effective treatment. Recent Advances: Many recent advances have been facilitated by the development of new omics tools, which create opportunities to use data-driven analysis of omics data to address these complex adaptive and maladaptive mechanisms of mitochondrial reprogramming in human disease. Critical Issues: Application of omics integration to model systems reveals a critical role for metal ion homeostasis broadly impacting mitochondrial reprogramming. Importantly, data show that trans-omics associations are more robust and biologically relevant than single omics associations. Future Directions: Application of omics integration to mitophagy research creates new opportunities to link the complex, interactive functions of mitochondrial form and function in mitochondria systems biology.

Anti-aging Effects of Calorie Restriction (CR) and CR Mimetics based on the Senoinflammation Concept

Chronic inflammation, a pervasive feature of the aging process, is defined by a continuous, multifarious, low-grade inflammatory response. It is a sustained and systemic phenomenon that aggravates aging and can lead to age-related chronic diseases. In recent years, our understanding of age-related chronic inflammation has advanced through a large number of investigations on aging and calorie restriction (CR). A broader view of age-related inflammation is the concept of senoinflammation, which has an outlook beyond the traditional view, as proposed in our previous work. In this review, we discuss the effects of CR on multiple phases of proinflammatory networks and inflammatory signaling pathways to elucidate the basic mechanism underlying aging. Based on studies on senoinflammation and CR, we recognized that senescence-associated secretory phenotype (SASP), which mainly comprises cytokines and chemokines, was significantly increased during aging, whereas it was suppressed during CR. Further, we recognized that cellular metabolic pathways were also dysregulated in aging; however, CR mimetics reversed these effects. These results further support and enhance our understanding of the novel concept of senoinflammation, which is related to the metabolic changes that occur in the aging process. Furthermore, a thorough elucidation of the effect of CR on senoinflammation will reveal key insights and allow possible interventions in aging mechanisms, thus contributing to the development of new therapies focused on improving health and longevity.

Exploration of the microbiota and metabolites within body fluids could pinpoint novel disease mechanisms

Thanks to the emergence and recent advances in high-throughput sequencing technologies, it is becoming more evident every day that changes in the microbiome composition are linked to a myriad of health conditions. Despite this, the mechanisms of host-microbiota signalling remain largely unknown. The microbiome has an extensive metabolic activity that leads to the generation of a large number of compounds that are likely to influence host health. Therefore, the microbiome-host cross-talk is in part mediated by microbial-derived metabolites. Unlike metagenomics, which only provides information about microbial genes and thus the microbiome functional potential, metabolic phenotyping is well suited to capture their actual metabolic activity. Here, we provide an overview of these approaches and propose an integration of metagenomics, as a microbiome compositional readout, with faecal and plasma/urine metabolomics, as a functional readout, to unravel novel mechanisms linking the microbiome to host health and disease.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.