Redoxhigh phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma

Metabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

FAAH Pro129Thr Variant Is Associated with Increased Cholesterol Levels in Normal-Weight Metabolically Unhealthy Subjects

Introduction: The endocannabinoid system (ECS) plays an integral role in maintaining metabolic homeostasis, where an hyperactivation has been related with serum lipid alterations. The biological effects of ECS are limited by the activation of the endocannabinoid-degrading enzyme fatty acid amide hydrolase (FAAH) and by polyunsaturated fatty acid (PUFA) intake as precursors. The FAAH Pro129Thr variant has been associated with obesity in some populations. However, the association with metabolic phenotypes in the Mexican population has not been studied. This study aimed to analyze the association of the FAAH Pro129Thr variant with serum lipids and diet in Mexican adults with different metabolic phenotypes. Methods: This is a cross-sectional study with 306 subjects between 18 and 65 years of age. They were classified with normal weight (NW) or excess weight (EW) according to their body mass index (BMI). The EW group included individuals with overweight or obesity (BMI 25-39.9 kg/m2). The individuals were classified into two metabolic phenotypes, metabolically healthy and metabolically unhealthy (MUH), using the homeostatic model assessment of insulin resistance and the National Cholesterol Education Program-adenosine triphosphate III cutoff points for blood pressure, triglycerides, high-density lipoprotein cholesterol, and fasting glucose. Subjects with ≥2 of 5 altered parameters were classified as MUH. The FAAH Pro129Thr variant was determined by allelic discrimination with TaqMan® probes. Results: The total cholesterol and very low-density lipoprotein cholesterol levels were associated with the FAAH Pro129Thr variant in NW-MUH subjects. Moreover, a lower PUFA intake was found in EW-MUH subjects with the FAAH variant. Conclusions: FAAH Pro129Thr variant has an important role in lipid metabolism, especially in NW-MUH subjects. By contrast, a low dietary intake of endocannabinoid PUFA precursors may partly counteract the development of the altered lipid profile associated with overweight/obesity.

Cyclocarya paliurus leaves extracts alleviate metabolic phenotypes in Chinese T2DM patients by modulating gut microbiota and metabolites: a clinical randomized controlled trial

Objective

We aimed to investigate the effect of Cyclocarya paliurus leaves extracts (CP) on glucose and blood lipid metabolism and its relationship with intestinal flora in type 2 diabetes mellitus (T2DM) patients.

Methods

In this open-label, 84-day randomized controlled trial, a total of 38 T2DM patients were randomly assigned to the CP group or the Glipizide group (G group) in a 2:1 ratio. T2DM-associated metabolic phenotypes, gut microbiota and metabolites including short-chain fatty acids (SCFAs) and bile acids (BAs) were detected.

Results

At the end of intervention, CP, like Glipizide, significantly improved HbA1c level and other glucose metabolism parameters (fasting plasma glucose (FBG), 2-hour post-meal blood glucose (2hPBG), the area under curve of oral glucose tolerance test glucose (OGTT glucose AUC)). Moreover, CP also resulted in the significant improvement in the levels of blood lipid and blood pressure. Notably, the improvement in blood lipid(triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c)) and blood pressure (diastolic blood pressure (DBP)) was significantly greater in the CP group compared with the G group. Furthermore, the liver and kidney function parameters did not significantly change in both CP group and the G group over the 84-day period. Additionally, the enrichment of potentially beneficial bacteria (Faecalibacterium and Akkermansia), SCFAs and unconjugated BAs and the depletion of potential pathogenic bacteria (Prevotella_9) and conjugated BAs were observed in the CP group, while the abundances of the gut microbial were kept stable in the G group after intervention.

Conclusion

CP displays a more beneficial effect in the alleviation of T2DM-associated metabolic phenotypes than glipizide by regulating gut microbiota and metabolites in T2DM patients, with no significant effects on liver and kidney function.

Effects of a ketogenic diet on reproductive and metabolic phenotypes in mice with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is one of the most common female reproductive and metabolic disorders. The ketogenic diet (KD) is a diet high in fat and low in carbohydrate. The beneficial effects of KD intervention have been demonstrated in obese women with PCOS. The underlying mechanisms, however, remain unknown. The aim of the present study was to investigate the effects of a ketogenic diet on both reproductive and metabolic phenotypes of dehydroepiandrosterone (DHEA)-induced PCOS mice. Female C57BL/6 mice were divided into three groups, designated Control, DHEA, and DHEA+KD groups. Mice of both Control and DHEA groups were fed the control diet, whereas DHEA+KD mice were fed a ketogenic diet with 89%(kcal) fat for 3 weeks or 1 week after PCOS mouse model was completed. At the end of the experiment, both reproductive and metabolic characteristics were assessed. Our data show that KD treatment significantly increased blood ketone levels, reduced body weight and random and fasting blood glucose levels in DHEA+KD mice compared with DHEA mice. Glucose tolerance, however, was impaired in DHEA+KD mice. Ovarian functions were improved in some DHEAmice after KD feeding, especially in mice treated with KD for 3 weeks. In addition, inflammation and cell apoptosis were inhibited in the ovaries of DHEA+KD mice. Results from in vitro experiments showed that the main ketone body β-hydroxybutyrate reduced inflammation and cell apoptosis in DHEA-treated KGN cells. These findings support the therapeutic effects of KD and reveal a possible mechanism by which KD improves ovarian functions in PCOS mice.

Dietary determinants of healthy/unhealthy metabolic phenotype in individuals with normal weight or overweight/obesity: a systematic review

Objectives: Nutritional factors are amongst the major determinants in the onset and development of obesity and metabolic complications. Nevertheless, the dietary determinants of metabolic health are not completely elucidated. The aim of this systematic review is to investigate nutritional and dietary factors that may contribute to metabolic heterogeneity in individuals with obesity or normal weight. Methods: A literature search was performed in PubMed, Scopus, EMBASE, and google scholar databases until August 2021, to locate studies that examined metabolic health and its association with intakes of specific foods or food groups, nutrient intakes or status, as well as adherence to certain dietary patterns. Two researchers had independently screened titles and abstracts, examined full-text studies, conducted data extraction, and evaluated their quality using the Newcastle-Ottawa Scale. Results: Twenty-seven studies, with a total of 39518 subjects, were included. Of these studies, 11 articles evaluated the association between different dietary patterns and metabolic phenotypes, while 15 had investigated the association of single food/nutrients intakes or nutrient status with metabolic phenotype, and one paper evaluated the association of dietary inflammatory index with metabolic health. The findings of these studies propose that healthy dietary patterns such as the Mediterranean pattern, Dietary Approaches to Stop Hypertension, and population-derived patterns (such as the "Healthy" and "Fruit and vegetable" patterns) were associated with higher odds of the metabolically healthy phenotype. Higher intakes of fruits, vegetables, dairy products, coffee/tea, vitamin D, magnesium, and flavonoids, were suggested to lower the risk of developing metabolically unhealthy phenotype, while, higher consumption of saturated fat, carbohydrate and sugar-sweetened beverages, fast foods, organ meats, and a pro-inflammatory diet increased the risk. Conclusion: Results from published studies, which were mostly cross-sectional, suggest that higher adherence to unhealthier dietary patterns, characterized by the consumption of refined and processed foods, was associated with a lower likelihood of having a healthy metabolic phenotype, while the opposite was observed for healthier dietary patterns. Findings may be used in developing nutritional strategies aimed at improving metabolic health in the population.

Multiple Metabolic Phenotypes as Screening Criteria Are Correlated With the Plant Growth-Promoting Ability of Rhizobacterial Isolates

Efficient screening method is the prerequisite for getting plant growth-promoting (PGP) rhizobacteria (PGPR) which may play an important role in sustainable agriculture from the natural environment. Many current traditional preliminary screening criteria based on knowledge of PGP mechanisms do not always work well due to complex plant-microbe interactions and may lead to the low screening efficiency. More new screening criteria should be evaluated to establish a more effective screening system. However, the studies focused on this issue were not enough, and few new screening criteria had been proposed. The aim of this study was to analyze the correlation between the metabolic phenotypes of rhizobacterial isolates and their PGP ability. The feasibility of using these phenotypes as preliminary screening criteria for PGPR was also evaluated. Twenty-one rhizobacterial isolates were screened for their PGP ability, traditional PGP traits, and multiple metabolic phenotypes that are not directly related to PGP mechanisms, but are possibly related to rhizosphere colonization. Correlations between the PGP traits or metabolic phenotypes and increases in plant agronomic parameters were analyzed to find the indicators that are most closely related to PGP ability. The utilization of 11 nutrient substrates commonly found in root exudates, such as D-salicin, β-methyl-D-glucoside, and D-cellobiose, was significantly positively correlated with the PGP ability of the rhizobacterial isolates. The utilization of one amino acid and two organic acids, namely L-aspartic acid, α-keto-glutaric acid, and formic acid, was negatively correlated with PGP ability. There were no significant correlations between four PGP traits tested in this study and the PGP ability. The ability of rhizobacterial isolates to metabolize nutrient substrates that are identical or similar to root exudate components may act as better criteria than PGP traits for the primary screening of PGPR, because rhizosphere colonization is a prerequisite for PGPR to affect plants.

Metabolic Health, Obesity, and Renal Function: 2013-2018 National Health and Nutrition Examination Surveys

Rising rates of metabolic syndrome, obesity, and mortality from chronic kidney disease (CKD) have prompted further investigation into the association between metabolic phenotypes and CKD. Purpose: To report the frequency of strictly defined metabolic phenotypes, renal function within each phenotype, and individual risk factors associated with reduced renal function. We utilized the 2013-2018 National Health and Nutrition Examination Surveys (NHANES) and complex survey sample weighting techniques to represent 220 million non-institutionalized U.S. civilians. Metabolic health was defined as having zero of the risk factors defined by the National Cholesterol Education Program with the exception of obesity, which was defined as BMI ≥ 30 kg/m2 in non-Asians and BMI ≥ 25 kg/m2 in Asians. The metabolically healthy normal (MUN) phenotype comprised the highest proportion of the population (38.40%), whereas the metabolically healthy obese (MHO) was the smallest (5.59%). Compared to the MHN reference group, renal function was lowest in the strictly defined MUN (B = -9.60, p < 0.001) and highest in the MHO (B = 2.50, p > 0.05), and this persisted when an increased number of risk factors were used to define metabolic syndrome. Systolic blood pressure had the strongest correlation with overall eGFR (r = -0.25, p < 0.001), and individuals with low HDL had higher renal function compared to the overall sample. The MUN phenotype had the greatest association with poor renal function. While the MHO had higher renal function, this may be due to a transient state caused by renal hyperfiltration. Further research should be done to investigate the association between dyslipidemia and CKD.

Circulating chemerin level is associated with metabolic, biochemical and haematological parameters-A population-based study

OBJECTIVE

This study aims to analyse the association of chemerin levels with several metabolic, biochemical and haematological parameters in a large Taiwanese population with relative healthy status.

DESIGN

Cross-sectional study.

METHODS

Data of 4101 healthy participants without history of hypertension, diabetes, dyslipidaemia and renal insufficiency from Taiwan Biobank were analysed. The demographic, biochemical and haematologic parameters were retrieved from the database. Chemerin levels were measured using commercially available enzyme-linked immunosorbent assay. Univariate and multivariate analysis was performed to test the independent correlates of chemerin.

RESULTS

In the univariate analysis, circulating chemerin levels were positively associated with body mass index (BMI), waist circumference, waist-to-hip ratio (WHR), systolic (SBP) and diastolic blood pressure (DBP), haemoglobin A1C (HbA1C), total cholesterol, triglyceride, low-density lipoprotein cholesterol (LDL-C), creatinine, uric acid, alanine aminotransferase (ALT), gamma-glutamyl transferase (γ-GT), leucocyte and platelet counts both in men and women and negatively associated with high-density lipoprotein cholesterol (HDL-C), estimated glomerular filtration rate (eGFR) and total bilirubin. In the multivariate analysis, BMI, HbA1C, triglyceride, uric acid, γ-GT and platelet counts predicted chemerin levels independently both in men and in women with positive correlation, while eGFR, total bilirubin and HDL-C predicted circulating chemerin levels independently with negative correlation.

CONCLUSIONS

Chemerin level is independently associated with multiple metabolic, biochemical and haematological parameters. This study provides further evidence on the molecular basis linking obesity with several human diseases.

Binary Metabolic Phenotypes and Phenotype Diversity Metrics for the Functional Characterization of Microbial Communities

The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence (“1”) or absence (“0”) of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn’s disease cohorts.

Metabolic Phenotypes and Chronic Kidney Disease: A Cross-Sectional Assessment of Patients from a Large Federally Qualified Health Center

The purpose of this study is to determine if renal function varies by metabolic phenotype. A total of 9599 patients from a large Federally Qualified Health Center (FQHC) were included in the analysis. Metabolic health was classified as the absence of metabolic abnormalities defined by the National Cholesterol Education Program Adult Treatment Panel III criteria, excluding waist circumference. Obesity was defined as body mass index >30 kg/m² and renal health as an estimated glomerular filtration rate (eGFR) >60 mL/min/1.73 m². Linear and logistic regressions were used to analyze the data. The metabolically healthy overweight (MHO) phenotype had the highest eGFR (104.86 ± 28.76 mL/min/1.72 m²) and lowest unadjusted odds of chronic kidney disease (CKD) (OR = 0.46, 95%CI = 0.168, 1.267, p = 0.133), while the metabolically unhealthy normal weight (MUN) phenotype demonstrated the lowest eGFR (91.34 ± 33.28 mL/min/1.72 m²) and the highest unadjusted odds of CKD (OR = 3.63, p < 0.0001). After controlling for age, sex, and smoking status, the metabolically unhealthy obese (MUO) (OR = 1.80, 95%CI = 1.08, 3.00, p = 0.024) was the only phenotype with significantly higher odds of CKD as compared to the reference. We demonstrate that the metabolically unhealthy phenotypes have the highest odds of CKD compared to metabolically healthy individuals.

Metabolic Phenotypes as Potential Biomarkers for Linking Gut Microbiome With Inflammatory Bowel Diseases

The gut microbiome is of utmost importance to human health. While a healthy microbiome can be represented by a variety of structures, its functional capacity appears to be more important. Gene content of the community can be assessed by “shotgun” metagenomics, but this approach is still too expensive. High-throughput amplicon-based surveys are a method of choice for large-scale surveys of links between microbiome, diseases, and diet, but the algorithms for predicting functional composition need to be improved to achieve good precision. Here we show how feature engineering based on microbial phenotypes, an advanced method for functional prediction from 16S rRNA sequencing data, improves identification of alterations of the gut microbiome linked to the disease. We processed a large collection of published gut microbial datasets of inflammatory bowel disease (IBD) patients to derive their community phenotype indices (CPI)—high-precision semiquantitative profiles aggregating metabolic potential of the community members based on genome-wide metabolic reconstructions. The list of selected metabolic functions included metabolism of short-chain fatty acids, vitamins, and carbohydrates. The machine-learning approach based on microbial phenotypes allows us to distinguish the microbiome profiles of healthy controls from patients with Crohn's disease and from ones with ulcerative colitis. The classifiers were comparable in quality to conventional taxonomy-based classifiers but provided new findings giving insights into possible mechanisms of pathogenesis. Feature-wise partial dependence plot (PDP) analysis of contribution to the classification result revealed a diversity of patterns. These observations suggest a constructive basis for defining functional homeostasis of the healthy human gut microbiome. The developed features are promising interpretable candidate biomarkers for assessing microbiome contribution to disease risk for the purposes of personalized medicine and clinical trials.

The Metabolic Mechanisms of Breast Cancer Metastasis

Breast cancer is one of the most common malignancy among women worldwide. Metastasis is mainly responsible for treatment failure and is the cause of most breast cancer deaths. The role of metabolism in the progression and metastasis of breast cancer is gradually being emphasized. However, the regulatory mechanisms that conduce to cancer metastasis by metabolic reprogramming in breast cancer have not been expounded. Breast cancer cells exhibit different metabolic phenotypes depending on their molecular subtypes and metastatic sites. Both intrinsic factors, such as MYC amplification, PIK3CA, and TP53 mutations, and extrinsic factors, such as hypoxia, oxidative stress, and acidosis, contribute to different metabolic reprogramming phenotypes in metastatic breast cancers. Understanding the metabolic mechanisms underlying breast cancer metastasis will provide important clues to develop novel therapeutic approaches for treatment of metastatic breast cancer.

A Randomized Study on the Bioequivalence of Desloratadine in Healthy Chinese Subjects and the Association of Different Metabolic Phenotypes With UGT2B10 and CYP2C8 Genotypes

BACKGROUND

Desloratadine is a drug with a phenotypic polymorphism in metabolism and has been approved for use in many countries to treat allergic diseases. CYP2C8 and UGT2B10 are metabolic enzymes, which may be involved in the metabolism of desloratadine.

OBJECTIVE

This study aimed to demonstrate bioequivalence between the test product (desloratadine tablet) and the reference product AERIUS (5mg), both orally administered. And the role of UGT2B10 and CYP2C8 genotypes in healthy Chinese subjects with different Desloratadine metabolic phenotypes was examined.

METHODS

It was a randomized, open-label, and four-sequence, single-dose crossover study conducted on 56 healthy Chinese subjects. The pharmacokinetics (PK) and safety of the test and reference Desloratadine products were compared. UGT2B10 and CYP2C8 genotypes were determined by the TaqMan assay using genomic DNA. Multiple linear regression was applied to analyze the correlation between genotypes and the metabolic ratio.

RESULTS

The mean serum concentration-time curves of desloratadine and 3-OH-desloratadine were similar between the test product and the reference product. For the PK similarity comparison, the 90% CIs for the geometric mean ratios of Cmax, AUC0-t, and AUC0-∞ of desloratadine and 3-OH-desloratadine of test and reference product were completely within 80-125%. None of all 56 subjects had serious adverse events. Only 2 subjects were poor-metabolizers in 56 healthy subjects. There was no significant correlation between investigated genotypes of CYP2C8 and UGT2B10 and the metabolic ratio.

CONCLUSION

The test desloratadine tablet was bioequivalent to the reference product. No direct relationship between CYP2C8 and UGT2B10 genotypes and desloratadine metabolic ratio was identified.

Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study

SCOPE

Previous work identified three metabolically homogeneous subgroups of individuals ("metabotypes") using k-means cluster analysis based on fasting serum levels of triacylglycerol, total cholesterol, HDL cholesterol, and glucose. The aim is to reproduce these findings and describe metabotype groups by dietary habits and by incident disease occurrence.

METHODS AND RESULTS

1744 participants from the KORA F4 study and 2221 participants from the KORA FF4 study are assigned to the three metabotype clusters previously identified by minimizing the Euclidean distances. In both KORA studies, the assignment of participants results in three metabolically distinct clusters, with cluster 3 representing the group of participants with the most unfavorable metabolic characteristics. Individuals of cluster 3 are further characterized by the highest incident disease occurrence during follow-up; they also reveal the most unfavorable diet with significantly lowest intakes of vegetables, dairy products, and fibers, and highest intakes of total, red, and processed meat.

CONCLUSION

The three metabotypes originally identified in an Irish population are successfully reproduced. In addition to this validation approach, the observed differences in disease incidence across metabotypes represent an important new finding that strongly supports the metabotyping approach as a tool for risk stratification.

Influence of a dynamic rearing environment on development of metabolic phenotypes in age-0 Lake Sturgeon, Acipenser fulvescens

Environment-phenotype interactions are the most pronounced during early life stages and can strongly influence metabolism and ultimately ecological fitness. In the present study, we examined the effect of temperature [ambient river temperature (ART) vs ART+2°C], dissolved oxygen (DO; 100% vs 80%) and substrate (presence vs absence) on standard metabolic rate, forced maximum metabolic rate and metabolic scope with Fulton's condition factor (K), energy density (ED) and critical thermal maximum (CTmax) in age-0 Lake Sturgeon, Acipenser fulvescens, before and after a simulated overwintering event. We found that all the environmental variables strongly influenced survival, K, ED and CTmax. Fish reared in elevated temperature showed higher mortality and reduced K pre-winter at 127 days post-hatch (dph). Interestingly, we did not find any significant difference in terms of metabolic rate between treatments at both sampling points of pre- and post-winter. Long-term exposure to 80% DO reduced ED in Lake Sturgeon post-winter at 272 dph. Our data suggest that substrate should be removed at the onset of exogenous feeding to enhance the survival rate of age-0 Lake Sturgeon in the first year of life. Effects of early rearing environment during larval development on survival over winter are discussed with respect to successful recruitment of stock enhanced Lake Sturgeon, a species that is at risk throughout its natural range.

Metabolic Determinants of Weight Gain in Humans

One of the fundamental challenges in obesity research is to identify subjects prone to weight gain so that obesity and its comorbidities can be promptly prevented or treated. The principles of thermodynamics as applied to human body energetics demonstrate that susceptibility to weight gain varies among individuals as a result of interindividual differences in energy expenditure and energy intake, two factors that counterbalance one another and determine daily energy balance and, ultimately, body weight change. This review focuses on the variability among individuals in human metabolism that determines weight change. Conflicting results have been reported about the role of interindividual differences in energy metabolism during energy balance in relation to future weight change. However, recent studies have shown that metabolic responses to acute, short-term dietary interventions that create energy imbalance, such as low-protein overfeeding or fasting for 24 hours, may reveal the underlying metabolic phenotype that determines the degree of resistance to diet-induced weight loss or the propensity to spontaneous weight gain over time. Metabolically "thrifty" individuals, characterized by a predilection for saving energy in settings of undernutrition and dietary protein restriction, display a minimal increase in plasma fibroblast growth factor 21 concentrations in response to a low-protein overfeeding diet and tend to gain more weight over time compared with metabolically "spendthrift" individuals. Similarly, interindividual variability in the causal relationship between energy expenditure and energy intake ("energy sensing") and in the metabolic response to cold exposure (e.g., brown adipose tissue activation) seems, to some extent, to be indicative of individual propensity to weight gain. Thus, an increased understanding and the clinical characterization of phenotypic differences in energy metabolism among individuals (metabolic profile) may lead to new strategies to prevent weight gain or improve weight-loss interventions by targeted therapies on the basis of metabolic phenotype and susceptibility to obesity in individual persons.

Polyamine is a critical determinant of Pseudomonas chlororaphis O6 for GacS-dependent bacterial cell growth and biocontrol capacity

The Gac/Rsm network regulates, at the transcriptional level, many beneficial traits in biocontrol-active pseudomonads. In this study, we used Phenotype MicroArrays, followed by specific growth studies and mutational analysis, to understand how catabolism is regulated by this sensor kinase system in the biocontrol isolate Pseudomonas chlororaphis O6. The growth of a gacS mutant was decreased significantly relative to that of the wild-type on ornithine and arginine, and on the precursor of these amino acids, N-acetyl-l-glutamic acid. The gacS mutant also showed reduced production of polyamines. Expression of the genes encoding arginine decarboxylase (speA) and ornithine decarboxylases (speC) was controlled at the transcriptional level by the GacS sensor of P. chlororaphis O6. Polyamine production was reduced in the speC mutant, and was eliminated in the speAspeC mutant. The addition of exogenous polyamines to the speAspeC mutant restored the in vitro growth inhibition of two fungal pathogens, as well as the secretion of three biological control-related factors: pyrrolnitrin, protease and siderophore. These results extend our knowledge of the regulation by the Gac/Rsm network in a biocontrol pseudomonad to include polyamine synthesis. Collectively, our studies demonstrate that bacterial polyamines act as important regulators of bacterial cell growth and biocontrol potential.

The KSR2-rs7973260 Polymorphism is Associated with Metabolic Phenotypes, but Not Psychological Phenotypes, in Chinese Elders

OBJECTIVE

To examine the associations between genetic variants of KSR2 (kinase suppressor of RAS)-rs7973260, RAPGEF6 (guanine nucleotide exchange factor 6)-rs3756290, LOC105377703-rs4481363, and subjective well-being (SWB) and depressive symptoms (DSs) in Chinese elders, which were recently associated in a genome-wide association study conducted in Caucasians. The pleiotropic effects of KSR2-rs7973260 on metabolic phenotypes were also explored.

MATERIALS AND METHODS

We used data from 1788 older individuals aged 70-84 years from the aging arm of the Rugao Longevity and Aging Study, a population-based cohort study conducted in the Jiangsu province of China.

RESULTS

No significant distributions of genotype frequencies were observed between life-satisfied and -unsatisfied groups across those with the three polymorphisms. The level of SWB components (positive affect, negative affect, and affect balance) and DSs did not differ among genotypes of the three variants. However, the presence of GA+AA of KSR2-rs7973260 was significantly higher in the metabolic syndrome (MetS), severe hypertriglyceridemia (HTG), and diabetes groups than in control groups (43.7% vs. 37.6%, 46.4% vs. 37.6%, 45.8% vs. 37.9%, respectively). The A allele of rs7973260 was associated with increased risk of MetS, severe HTG, and diabetes with an odds ratios (95% confidence intervals) of 1.289 (1.002-1.658), 1.438 (1.076-1.921), and 1.384 (1.022-1.875), which remained significant after multiple adjustments.

CONCLUSION

Rs7973260, rs3756290, and rs4481363 were not associated with SWB and DSs in Chinese elders. However, the KSR2-rs7973260 A allele exhibited pleiotropic effects on some metabolic phenotypes in Chinese elders. These effects should be validated in future studies.

Pharmacogenetics and ethnicity: relevance for clinical implementation, clinical trials, pharmacovigilance and drug regulation in Latin America

Congress of Pharmacogenetics and Personalized Medicine. Ethnicity, clinical implementation and regulatory environment (MESTIFAR 2016 Quito) Quito, Ecuador, 19-21 May 2016. The Ibero-American Network of Pharmacogenetics and Pharmacogenomics (RIBEF) was created in 2006 with the main aim of promoting personalized medicine and collaborative pharmacogenetics research in Spanish- and Portuguese-speaking countries in America and the Iberian Peninsula. The final goal of this initiative was the inclusion of Latin American populations that may benefit from the implementation of personalized medicine in drug therapy. Several initiatives have been promoted including the MESTIFAR project, which aimed to analyze the ethnicity, genotype and/or metabolic phenotype in Ibero-American populations. To date, 6060 healthy volunteers have been analyzed; among them, 2571 were admixed, 1824 were Caucasians, 1395 were Native Americans, 174 were Jews and 96 were Afro-descendants. Due to the large genetic variability within Latin Americans, ethnicity may be a relevant factor for the clinical implementation of personalized medicine. Moreover, the present status of clinical implementation and the future perspectives of pharmacogenetics, pharmacovigilance and clinical trials for drug regulation in Latin America compared with the EMA-Pharmacogenomics Working Party and the US FDA initiatives were analyzed.

MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

Population pharmacogenetics of Ibero-Latinoamerican populations (MESTIFAR 2014)

MESTIFAR 2014 28-30 November 2014, Panama City, Panama The CEIBA consortium was created within the Ibero-American network of Pharmacogenetics (RIBEF) to study population pharmacogenetics. The current status of these initiatives and results of the MESTIFAR project were analyzed in Panama, 28-30 November 2014. The MESTIFAR project focused on studying CYPs genetic polymorphisms in populations of different ethnic origin. So far, more than 6000 healthy volunteers have been evaluated, making this one of the largest population pharmacogenomic studies worldwide. Three symposia were organized, 'Pharmacogenetics of indigenous and mestizos populations and its clinical implications', 'Methodological innovation in pharmacogenetics and its application in health', and 'General discussion and concluding remarks', about mechanisms and proposals for training, diffusion of pharmacogenetics for Spanish- and Portuguese-speaking health professionals, and 'bench to bedside' pilot projects.