Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders

Understanding disease-associated metabolic changes in human colon epithelial cells using i ColonEpithelium metabolic reconstruction

The colon epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colon epithelium, our present study focuses on the generation of the first cell-type specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. The iColonEpithelium is also capable of performing metabolic tasks specific to the cell type. A unique transport reaction compartment has been included to allow simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We integrated single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples with the iColonEpithelium metabolic network to predict metabolic signatures of colonocytes between CD and UC compared to healthy samples. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and our network has the potential to be integrated with gut microbiome models to explore the metabolic interactions between host and gut microbiota under various conditions.

Polysaccharides of Floccularia luteovirens regulate intestinal immune response, and oxidative stress activity through MAPK/Nrf2/Keap1 signaling pathway in immunosuppressive mice

This study explores the novel immunomodulatory effects of polysaccharides from the rare Floccularia luteovirens, a fungus with significant potential yet unexplored bioactive components, traditionally used in Tibetan medicine. This study employs a wide array of analytical techniques, including HPGPC, HPLC, western blotting, ELISA, and 16S rRNA gene sequencing, to comprehensively investigate FLP1's effects. The main structure of FLP1 was characterized by IF-TR and NMR spectrometry. The structural backbone of FLP1 was →3,6)-β-D-Glcp-(1 → and →2,3)-α-D-Manp-(1→. After immunosuppressed mice treated with FLP1, the findings demonstrated that FLP1 stimulated the production of secretory sIgA and secretion of cytokines (IL-4, TNF-α, and IFN-γ) in the intestine of Cy-treated mice, resulting in the activation of the MAPK pathway. Additionally, FLP1 protected oxidative stress by triggering Nrf2/Keap1 pathways and antioxidation enzymes (SOD, MDA, T-AOC, CAT, and GSH-Px). It also enhanced the intestinal barrier function by regulating the villous height ratio and expression of tight-junction protein. Furthermore, FLP1 remarkably reversed the gut microbiota dysbiosis in immunosuppressed mice by increasing the abundance of Oscilliospiraceae, and Lachnospiraceae, and altered the fecal metabolites by increasing LysoPE (0:0/18:0); 0:0/16:0; 18:1(11Z)/0:0, LysoPG (16:0/0:0), LysoPG 18:1 (2n) PE (14:0/20:1), echinenone, 2-(2-Nitroimidazol-1-yl)-N-(2,2,3,3,3-pentafluoropropyl) acetamide, and suberic acid which is closely related to the immunity function. These results suggested that FLP1 may regulate the intestinal immune response by modulating the gut microbiota and fecal metabolites in immunosuppressed mice thereby activating the immune system.

Targeted and untargeted serum NMR metabolomics to reveal initial kidney disease in diabetes mellitus

Serum 1H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed.

Changes in the urinary metabolome accompanied alterations in body mass and composition in women with overweight - impact of high versus low protein breakfast

INTRODUCTION

Understanding why subjects with overweight and with obesity vary in their response to dietary interventions is of major interest for developing personalized strategies for body mass regulation.

OBJECTIVES

The aim of this study was to investigate the relationship between changes in the urine metabolome and body mass during a breakfast meal intervention. Furthermore, we aimed to elucidate if the baseline urine metabolome could predict the response to the two types of breakfast meals (high versus low protein) during the intervention.

METHODS

A total of 75 young, women with overweight were randomly allocated to one of two intervention groups: (1) High-protein (HP) or (2) low-protein (LP) breakfast as part of their habitual diet during a 12-week intervention. Beside the breakfast meal, participants were instructed to eat their habitual diet and maintain their habitual physical activity level. Nuclear magnetic resonance-based metabolomics was conducted on urine samples collected at baseline (wk 0), mid-intervention (wk 6), and at endpoint (wk 12). At baseline and endpoint, body mass was measured and DXA was used to measure lean body mass and fat mass.

RESULTS

The baseline urine metabolite profile showed a slightly higher correlation (R2 = 0.56) to body mass in comparison with lean body mass (R2 = 0.51) and fat mass (R2 = 0.53). Baseline 24-h urinary excretion of trigonelline (p = 0.04), N, N-dimethylglycine (p = 0.02), and trimethylamine (p = 0.03) were significantly higher in individuals who responded with a reduction in body mass to the HP breakfast.

CONCLUSIONS

Differences in the urine metabolome were seen for women that obtained a body weight loss in the response to the HP breakfast intervention and women who did not obtain a body weight loss, indicating that the urine metabolome contains information about the metabolic phenotype that influences the responsiveness to dietary interventions.

NMR-Based Metabolomics of Blood Serum in Predicting Response to Induction Chemotherapy in Head and Neck Cancer-A Preliminary Approach

The role of induction chemotherapy (iCHT) in locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is still to be established due to high toxicity and variable response rates. The aim of this retrospective study is to use NMR-based serum metabolomics to predict the response rates to iCHT from the pretreatment samples. The studied group consisted of 46 LA-HNSCC patients treated with iCHT. The response to the treatment was evaluated by the clinical, fiberoptic, and radiological examinations made before and after iCHT. The proton nuclear magnetic resonance (1H NMR) serum spectra of the samples collected before iCHT were acquired with a 400 MHz spectrometer and were analyzed using multivariate and univariate statistical methods. A significant multivariate model was obtained only for the male patients. The treatment-responsive men with >75% primary tumor regression after iCHT showed pretreatment elevated levels of isoleucine, alanine, glycine, tyrosine, N-acetylcysteine, and the lipid compounds, as well as decreased levels of acetate, glutamate, formate, and ketone bodies compared to those who did not respond (regression of the primary tumor <75%). The results indicate that the nutritional status, capacity of the immune system, and the efficiency of metabolism related to protein synthesis may be prognostic factors for the response to induction chemotherapy in male HNSCC patients. However, larger studies are required that would validate the findings and could contribute to the development of more personalized treatment protocols for HNSCC patients.

GEM-based computational modeling for exploring metabolic interactions in a microbial community

Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.

Engineering Gut Symbionts: A Way to Promote Bee Growth?

Bees play a crucial role as pollinators, contributing significantly to ecosystems. However, the honeybee population faces challenges such as global warming, pesticide use, and pathogenic microorganisms. Promoting bee growth using several approaches is therefore crucial for maintaining their roles. To this end, the bacterial microbiota is well-known for its native role in supporting bee growth in several respects. Maximizing the capabilities of these microorganisms holds the theoretical potential to promote the growth of bees. Recent advancements have made it feasible to achieve this enhancement through the application of genetic engineering. In this review, we present the roles of gut symbionts in promoting bee growth and collectively summarize the engineering approaches that would be needed for future applications. Particularly, as the engineering of bee gut symbionts has not been advanced, the dominant gut symbiotic bacteria Snodgrassella alvi and Gilliamella apicola are the main focus of the paper, along with other dominant species. Moreover, we propose engineering strategies that will allow for the improvement in bee growth with listed gene targets for modification to further encourage the use of engineered gut symbionts to promote bee growth.

Gut microbiota, blood metabolites, and left ventricular diastolic dysfunction in US Hispanics/Latinos

BACKGROUND

Left ventricular diastolic dysfunction (LVDD) is an important precursor of heart failure (HF), but little is known about its relationship with gut dysbiosis and microbial-related metabolites. By leveraging the multi-omics data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a study with population at high burden of LVDD, we aimed to characterize gut microbiota associated with LVDD and identify metabolite signatures of gut dysbiosis and incident LVDD.

RESULTS

We included up to 1996 Hispanic/Latino adults (mean age: 59.4 years; 67.1% female) with comprehensive echocardiography assessments, gut microbiome, and blood metabolome data. LVDD was defined through a composite criterion involving tissue Doppler assessment and left atrial volume index measurements. Among 1996 participants, 916 (45.9%) had prevalent LVDD, and 212 out of 594 participants without LVDD at baseline developed incident LVDD over a median 4.3 years of follow-up. Using multivariable-adjusted analysis of compositions of microbiomes (ANCOM-II) method, we identified 7 out of 512 dominant gut bacterial species (prevalence > 20%) associated with prevalent LVDD (FDR-q < 0.1), with inverse associations being found for Intestinimonas_massiliensis, Clostridium_phoceensis, and Bacteroide_coprocola and positive associations for Gardnerella_vaginali, Acidaminococcus_fermentans, Pseudomonas_aeruginosa, and Necropsobacter_massiliensis. Using multivariable adjusted linear regression, 220 out of 669 circulating metabolites with detection rate > 75% were associated with the identified LVDD-related bacterial species (FDR-q < 0.1), with the majority being linked to Intestinimonas_massiliensis, Clostridium_phoceensis, and Acidaminococcus_fermentans. Furthermore, 46 of these bacteria-associated metabolites, mostly glycerophospholipids, secondary bile acids, and amino acids, were associated with prevalent LVDD (FDR-q < 0.1), 21 of which were associated with incident LVDD (relative risk ranging from 0.81 [p = 0.001, for guanidinoacetate] to 1.25 [p = 9 × 10-5, for 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4)]). The inclusion of these 21 bacterial-related metabolites significantly improved the prediction of incident LVDD compared with a traditional risk factor model (the area under the receiver operating characteristic curve [AUC] = 0.73 vs 0.70, p = 0.001). Metabolite-based proxy association analyses revealed the inverse associations of Intestinimonas_massilliensis and Clostridium_phoceensis and the positive association of Acidaminococcus_fermentans with incident LVDD.

CONCLUSION

In this study of US Hispanics/Latinos, we identified multiple gut bacteria and related metabolites linked to LVDD, suggesting their potential roles in this preclinical HF entity. Video Abstract.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

The interactions and biological pathways among metabolomics products of patients with coronary heart disease

BACKGROUND

Through bioinformatics analysis, this study explores the interactions and biological pathways involving metabolomic products in patients diagnosed with coronary heart disease (CHD).

METHODS

A comprehensive search for relevant studies focusing on metabolomics analysis in CHD patients was conducted across databases including CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Nature, Web of Science, Springer, and Science Direct. Metabolites reported in the literature underwent statistical analysis and summarization, with the identification of differential metabolites. The pathways associated with these metabolites were examined using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Molecular annotation of metabolites and their relationships with enzymes or transporters were elucidated through analysis with the Human Metabolome Database (HMDB). Visual representation of the properties related to these metabolites was achieved using Metabolomics Pathway Analysis (metPA).

RESULTS

A total of 13 literatures satisfying the criteria for enrollment were included. A total of 91 metabolites related to CHD were preliminarily screened, and 87 effective metabolites were obtained after the unrecognized metabolites were excluded. A total of 45 pathways were involved. Through the topology analysis (TPA) of pathways, their influence values were calculated, and 13 major metabolic pathways were selected. The pathways such as Phenylalanine, tyrosine, and tryptophan biosynthesis, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism, and Glycine, serine, and threonine metabolism primarily involved the regulation of processes and metabolites related to inflammation, oxidative stress, one-carbon metabolism, energy metabolism, lipid metabolism, immune regulation, and nitric oxide expression.

CONCLUSION

Multiple pathways, including Phenylalanine, tyrosine, and tryptophan biosynthesis, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism, and Glycine, serine, and threonine metabolism, were involved in the occurrence of CHD. The occurrence of CHD is primarily associated with the regulation of processes and metabolites related to inflammation, oxidative stress, one-carbon metabolism, energy metabolism, lipid metabolism, immune regulation, and nitric oxide expression.

Environmentally relevant concentrations of 2,3,7,8-TCDD induced inhibition of multicellular alternative splicing and transcriptional dysregulation

Alternative splicing (AS), found in approximately 95 % of human genes, significantly amplifies protein diversity and is implicated in disease pathogenesis when dysregulated. However, the precise involvement of AS in the toxic mechanisms induced by TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) remains incompletely elucidated. This study conducted a thorough global AS analysis in six human cell lines following TCDD exposure. Our findings revealed that environmentally relevant concentration (0.1 nM) of TCDD significantly suppressed AS events in all cell types, notably inhibiting diverse splicing events and reducing transcript diversity, potentially attributed to modifications in the splicing patterns of the inhibitory factor family, particularly hnRNP. And we identified 151 genes with substantial AS alterations shared among these cell types, particularly enriched in immune and metabolic pathways. Moreover, TCDD induced cell-specific changes in splicing patterns and transcript levels, with increased sensitivity notably in THP-1 monocyte, potentially linked to aberrant expression of pivotal genes within the spliceosome pathway (DDX5, EFTUD2, PUF60, RBM25, SRSF1, and CRNKL1). This study extends our understanding of disrupted alternative splicing and its relation to the multisystem toxicity of TCDD. It sheds light on how environmental toxins affect post-transcriptional regulatory processes, offering a fresh perspective for toxicology and disease etiology investigations.

Effects of Glucagon-Like Peptide-1 Receptor Agonists on Gut Microbiota in Dehydroepiandrosterone-Induced Polycystic Ovary Syndrome Mice: Compared Evaluation of Liraglutide and Semaglutide Intervention

Purpose

Polycystic ovary syndrome (PCOS) is a frequent cause of infertility in reproductive-age women. Our work aims to evaluate the effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on gut microbiota, with metabolic parameters including body weight and the hormone profile in PCOS.

Patients and Methods

Dehydroepiandrosterone (DHEA)-induced PCOS mice were established and then treated with two GLP-1RAs: liraglutide and novel form semaglutide for four weeks. Changes in body weight and metabolic parameters were measured. Fecal samples were collected and analyzed using metagenomic sequencing.

Results

Liraglutide and semaglutide modulated both alpha and beta diversity of the gut microbiota in PCOS. Liraglutide increased the Bacillota-to-Bacteroidota ratio through up-regulating the abundance of butyrate-producing members of Bacillota like Lachnospiraceae. Moreover, liraglutide showed the ability to reverse the altered microbial composition and the disrupted microbiota functions caused by PCOS. Semaglutide increased the abundance of Helicobacter in PCOS mice (p < 0.01) which was the only bacteria found negatively correlated with body weight. Moreover, pathways involving porphyrin and flavonoids were increased after semaglutide intervention.

Conclusion

Liraglutide and semaglutide improved reproductive and metabolic disorders by modulating the whole structure of gut microbiota in PCOS. The greater efficacy in weight loss compared with liraglutide observed after semaglutide intervention was positively related with Helicobacter. The study may provide new ideas in the treatment and the underlying mechanisms of GLP-1RAs to improve PCOS.

High-throughput transcriptomics of 409 bacteria-drug pairs reveals drivers of gut microbiota perturbation

Many drugs can perturb the gut microbiome, potentially leading to negative health consequences. However, mechanisms of most microorganism-drug responses have not been elucidated at the genetic level. Using high-throughput bacterial transcriptomics, we systematically characterized the gene expression profiles of prevalent human gut bacteria exposed to the most frequently prescribed orally administered pharmaceuticals. Across >400 drug-microorganism pairs, significant and reproducible transcriptional responses were observed, including pathways involved in multidrug resistance, metabolite transport, tartrate metabolism and riboflavin biosynthesis. Importantly, we discovered that statin-mediated upregulation of the AcrAB-TolC efflux pump in Bacteroidales species enhances microbial sensitivity to vitamin A and secondary bile acids. Moreover, gut bacteria carrying acrAB-tolC genes are depleted in patients taking simvastatin, suggesting that drug-efflux interactions generate collateral toxicity that depletes pump-containing microorganisms from patient microbiomes. This study provides a resource to further understand the drivers of drug-mediated microbiota shifts for better informed clinical interventions.

Flux sampling in genome-scale metabolic modeling of microbial communities

BACKGROUND

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions and identify strategies to optimize the community. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model; however, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling can capture additional heterogeneity across a population, especially when cells exhibit sub-maximal growth rates.

RESULTS

In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. With sampling, we find significant differences in the predicted metabolism, including an increase in cooperative interactions and pathway-specific changes in predicted flux.

CONCLUSIONS

Our results suggest the importance of sampling-based approaches to evaluate metabolic interactions. Furthermore, we emphasize the utility of flux sampling in quantitatively studying interactions between cells and organisms.

Longitudinal associations between metabolites and immediate, short- and medium-term exposure to ambient air pollution: Results from the KORA cohort study

BACKGROUND

Short-term exposure to air pollution has been reported to be associated with cardiopulmonary diseases, but the underlying mechanisms remain unclear. This study aimed to investigate changes in serum metabolites associated with immediate, short- and medium-term exposures to ambient air pollution.

METHODS

We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in this analysis. We collected daily averages of fine particles (PM2.5), coarse particles (PMcoarse), nitrogen dioxide (NO2), and ozone (O3) at urban background monitors located in Augsburg, Germany. Covariate-adjusted generalized additive mixed-effects models were used to examine the associations between immediate (2-day average of same day and previous day as individual's blood withdrawal), short- (2-week moving average), and medium-term exposures (8-week moving average) to air pollution and metabolites. We further performed pathway analysis for the metabolites significantly associated with air pollutants in each exposure window.

RESULTS

Of 9,620 observations from 4,261 study participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed quality control, multiple significant associations between metabolites and air pollutants with several exposure windows were identified at a Bonferroni corrected p-value threshold (p < 3.9 × 10-5). We found the highest number of associations for NO2, particularly at the medium-term exposure windows. Among the identified metabolic pathways based on the metabolites significantly associated with air pollutants, the glycerophospholipid metabolism was the most robust pathway in different air pollutants exposures.

CONCLUSIONS

Our study suggested that short- and medium-term exposure to air pollution might induce alterations of serum metabolites, particularly in metabolites involved in metabolic pathways related to inflammatory response and oxidative stress.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities

Microbial communities play a crucial role in ecosystem function through metabolic interactions. Genome-scale modeling is a promising method to understand these interactions. Flux balance analysis (FBA) is most often used to predict the flux through all reactions in a genome-scale model. However, the fluxes predicted by FBA depend on a user-defined cellular objective. Flux sampling is an alternative to FBA, as it provides the range of fluxes possible within a microbial community. Furthermore, flux sampling may capture additional heterogeneity across cells, especially when cells exhibit sub-maximal growth rates. In this study, we simulate the metabolism of microbial communities and compare the metabolic characteristics found with FBA and flux sampling. We find significant differences in the predicted metabolism with sampling, including increased cooperative interactions and pathway-specific changes in predicted flux. Our results suggest the importance of sampling-based and objective function-independent approaches to evaluate metabolic interactions and emphasize their utility in quantitatively studying interactions between cells and organisms.

Alterations in glycine metabolism in obesity and chronic metabolic diseases - an update on new advances

PURPOSE OF REVIEW

The metabolic signature associated with obesity is characterized by a decrease in plasma glycine concentration, a feature closely associated with insulin resistance and highly predictive of the risk of developing chronic metabolic diseases. This review presents recent advances in understanding the causes of decreased glycine availability and in targeting strategies to replenish the glycine pool and especially to improve insulin resistance.

RECENT RESULTS

Recent literature has made progress in understanding host and gut microbiota mechanisms in determining circulating glycine levels. It has also explored new clinical pathways to restore circulating glycine levels and insulin resistance in obesity-related metabolic diseases.

SUMMARY

Recent findings suggest that glycine metabolism must now be considered in close interaction with branched-chain amino acid (BCAA) metabolism. Thus, strategies that decrease BCAAs seem to be the best to restore glycine. Furthermore, recent literature has confirmed that lifestyle strategies aimed at inducing weight loss are effective in replenishing the glycine pool. It also confirms that correcting the dysbiosis of the gut microbiota associated with obesity may be a valuable means of achieving this goal. However, it remains unclear whether dietary glycine is an effective strategy for correcting cardiometabolic disorders in obesity.

Fucoidan from Laminaria japonica Ameliorates Type 2 Diabetes Mellitus in Association with Modulation of Gut Microbiota and Metabolites in Streptozocin-Treated Mice

Chronic diseases have been a leading cause of death worldwide, and polysaccharide supplementation is an effective therapeutic strategy for chronic diseases without adverse effects. In this study, the beneficial effect of Laminaria japonica fucoidan (LJF) on type 2 diabetes mellitus (T2DM) was evaluated in streptozocin-treated mice. LJF ameliorated the symptoms of T2DM in a dose-dependent manner, involving reduction in weight loss, water intake, triglyceride, blood glucose, cholesterol and free fatty acids, and increases in high-density lipoprotein cholesterol, catalase, glucagon-like peptide-1, and superoxide dismutase. In addition, LJF regulated the balance between insulin resistance and insulin sensitivity, reduced islet necrosis and β-cell damage, and inhibited fat accumulation in T2DM mice. The protective effect of LJF on T2DM can be associated with modulation of the gut microbiota and metabolites, e.g., increases in Lactobacillus and Allobaculum. Untargeted and targeted metabolomics analysis showed that the microbiota metabolite profile was changed with LJF-induced microbiota alterations, mainly involving amino acids, glutathione, and glyoxylate and dicarboxylate metabolism pathways. This study indicates that LJF can be used as a prebiotic agent for the prevention and treatment of diabetes and microbiota-related diseases.

Metabolic responses in the cortex and hippocampus induced by Il-15rα mutation

Metabolomics showed distinct metabolic phenotypes of the different brain regions related to the IL-15 system, enhancing our understanding of the IL-15 system and its interactions with neuropsychiatric disorders.