The MetaCyc database of metabolic pathways and enzymes

Capturing the microbial volatilome: an oft overlooked 'ome'

Among the diverse metabolites produced by microbial communities, some are volatile. Volatile organic compounds (VOCs) are vigorously cycled by microbes as metabolic substrates and products and as signaling molecules. Yet, current microbial metabolomic studies predominantly focus on nonvolatile metabolites and overlook VOCs, which therefore represent a missing component of the metabolome. Advances in VOC detection now allow simultaneous observation of the numerous VOCs constituting the 'volatilome' of microbial systems. We present a roadmap for integrating and advancing VOC and other 'omics approaches and highlight the potential for realtime VOC measurements to help overcome limitations in discrete 'omics sampling. Including volatile metabolites in metabolomics, both conceptually and in practice, will build a more comprehensive understanding of microbial processes across ecological communities.

Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711

BACKGROUND

Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source.

RESULTS

While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway.

CONCLUSIONS

Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases

Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions. Plant Reactome knowledgebase, a conceptual plant pathway network, is built by biocuration and integrating (bio)chemical entities, gene products, and macromolecular interactions. It provides manually curated pathways for the reference species Oryza sativa (rice) and gene orthology-based projections that extend pathway knowledge to 106 plant species. Currently, it hosts 320 reference pathways for plant metabolism, hormone signaling, transport, genetic regulation, plant organ development and differentiation, and biotic and abiotic stress responses. In addition to the pathway browsing and search functions, the Plant Reactome provides the analysis tools for pathway comparison between reference and projected species, pathway enrichment in gene expression data, and overlay of gene-gene interaction data on pathways. PubChem, a popular reference database of (bio)chemical entities, provides information on small molecules and other types of chemical entities, such as siRNAs, miRNAs, lipids, carbohydrates, and chemically modified nucleotides. The data in PubChem is collected from hundreds of data sources, including Plant Reactome. This chapter provides a brief overview of the Plant Reactome and the PubChem knowledgebases, their association to other public resources providing accessory information, and how users can readily access the contents.

Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome

The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual's genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics, respectively. Moreover, a diet quality score derived from metabolite levels was significantly associated with diet quality, as assessed by a detailed food frequency questionnaire. Through Mendelian randomization and mediation analyses, we revealed putative causal relationships between diet, the gut microbiome and metabolites. For example, Mendelian randomization analyses support a potential causal effect of Eubacterium rectale in decreasing plasma levels of hydrogen sulfite-a toxin that affects cardiovascular function. Lastly, based on analysis of the plasma metabolome of 311 individuals at two time points separated by 4 years, we observed a positive correlation between the stability of metabolite levels and the amount of variance in the levels of that metabolite that could be explained in our analysis. Altogether, characterization of factors that explain inter-individual variation in the plasma metabolome can help design approaches for modulating diet or the gut microbiome to shape a healthy metabolome.

Metagenomic and metabolic analyses of poly-extreme microbiome from an active crater volcano lake

El Chichón volcano is one of the most active volcanoes in Mexico. Previous studies have described its poly-extreme conditions and its bacterial composition, although the functional features of the complete microbiome have not been characterized yet. By using metabarcoding analysis, metagenomics, metabolomics and enzymology techniques, the microbiome of the crater lake was characterized in this study. New information is provided on the taxonomic and functional diversity of the representative Archaea phyla, Crenarchaeota and Euryarchaeota, as well as those that are representative of Bacteria, Thermotogales and Aquificae. With culture of microbial consortia and with the genetic information collected from the natural environment sampling, metabolic interactions were identified between prokaryotes, which can withstand multiple extreme conditions. The existence of a close relationship between the biogeochemical cycles of carbon and sulfur in an active volcano has been proposed, while the relationship in the energy metabolism of thermoacidophilic bacteria and archaea in this multi-extreme environment was biochemically revealed for the first time. These findings contribute towards understanding microbial metabolism under extreme conditions, and provide potential knowledge pertaining to "microbial dark matter", which can be applied to biotechnological processes and evolutionary studies.

Current understanding and perspectives in anaerobic digestion based on genome-resolved metagenomic approaches

Anaerobic digestion (AD) is a technique that can be used to treat high concentrations of various organic wastes using a consortium of functionally diverse microorganisms under anaerobic conditions. Methane gas, a beneficial by-product of the AD process, is a renewable energy source that can replace fossil fuels following purification. However, detailed functional roles and metabolic interactions between microbial populations involved in organic waste removal and methanogenesis are yet to be known. Recent metagenomic approaches based on advanced high-throughput sequencing techniques have enabled the exploration of holistic microbial taxonomy and functionality of complex microbial populations involved in the AD process. Gene-centric and genome-centric analyses based on metagenome-assembled genomes are a platform that can be used to study the composition of microbial communities and their roles during AD. This review looks at how these up-to-date metagenomic analyses can be applied to promote our understanding and improved the development of the AD process.

The gut microbiome and microbial metabolites in acute myocardial infarction

Emerging evidence has highlighted the role of the gut microbiome in human health. However, the integrative role of the gut microbiome and microbial metabolites in acute myocardial infarction (AMI) remains unclear. The current study profiled the microbial community through 16S rRNA gene sequencing and shotgun metagenomic sequencing, and measured fecal short-chain fatty acids and circulating choline pathway metabolites among 117 new-onset AMI cases and 78 controls. Significant microbial alternations were observed in AMI patients compared with controls (P = 0.001). The abundances of nine species (e.g., Streptococcus salivarius, Klebsiella pneumoniae) were positively associated, and one species (Roseburia hominis) was inversely associated with AMI status and severity. A gut microbial score at disease onset was associated with the risk of major adverse cardiovascular events in 3.2 years (hazard ratio [95% CI]: 2.01 [1.04-4.24]) in AMI patients. The molar proportions of fecal acetate and butyrate were higher, and the circulating levels of choline and carnitine were lower in AMI patients than in controls. In addition, disease classifiers showed that AMI cases and controls had a more distinct pattern in taxonomical composition than in pathways or metabolites. Our findings suggested that microbial composition and functional potentials were associated with AMI status and severity.

Characterization of the Gastrointestinal and Reproductive Tract Microbiota in Fertile and Infertile Pakistani Couples

Simple Summary

We describe microbial taxa associated with the gastrointestinal and reproductive tracts of married Pakistani couples. We highlight differences in microbial composition and diversity that are associated with fertile and infertile couples and provide a baseline for future in-depth studies to target the association of the human microbiome with infertility.

Abstract

The human microbiota is recognized as a vital “virtual” organ of the human body that influences human health, metabolism, and physiology. While the microbiomes of the gut, oral cavity, and skin have been extensively studied in the literature, relatively little work has been done on characterizing the microbiota of the human reproductive tract organs, and specifically on investigating its association to fertility. Here, we implemented a 16S ribosomal RNA (rRNA) amplicon sequencing approach to sequence and characterize the gut and genital tract microbiomes from several married Pakistani couples. The recruited individuals included 31 fertile and 35 infertile individuals, with ages ranging from 19–45 years. We identified several fluctuations in the diversity and composition of the gut and genital microbiota among fertile and infertile samples. For example, measures of α-diversity varied significantly between the genital samples donated by fertile and infertile men and there was overall greater between-sample variability in genital samples regardless of gender. In terms of taxonomic composition, Actinobacteria, Bacteroidetes, and Firmicutes fluctuated significantly between the gut microbiomes of fertile and infertile samples. Finally, biomarker analyses identified features (genera and molecular functions and pathways) that differed significantly between the fertile and infertile samples and in the past have been associated with bacterial vaginosis. However, we emphasize that 16S amplicon data alone has no bearing on individual health and is merely representative of microbial taxonomic differences that could also arise due to multiple other factors. Our findings, however, represent the first effort to characterize the microbiome associated with fertile and infertile couples in Pakistan and will hopefully pave the way for more comprehensive and broad-scale investigations in the future.

Gut Microbiota Composition across Normal Range Prostate-Specific Antigen Levels

Animal studies have shown the interaction between androgens and the gut microbiome directly and indirectly; however, limited evidence from human studies is available. To evaluate the association between prostate-specific antigen (PSA) levels within the normal range, reflective of androgen receptor activity, and the gut microbiota composition, a cross-sectional analysis was performed in 759 Korean men aged between 25 and 78 years with normal PSA levels of ≤4.0 ng/mL. We evaluated the biodiversity of gut microbiota as well as the taxonomic and functional signatures associated with PSA levels using 16S rRNA gene sequencing data. PSA levels within the normal range were categorized into three groups: lowest quartile (G1), interquartile range (G2, reference), and highest quartile (G3). The G3 group had higher microbial richness than the G2 group, although it was dominated by a few bacteria. An increase in Escherichia/Shigella abundance and a reduction in Megamonas abundance in the G3 group were also detected. A U-shaped relationship was observed between the three groups across most analyses, including biodiversity, taxonomic composition, and inferred pathways in the gut microbiota. This study showed different microbiota patterns across PSA levels within the normal range. Further studies are required to elucidate the role of microbiota in regulating PSA levels.

The gut microbiota in pediatric multiple sclerosis and demyelinating syndromes

Objective

To examine the gut microbiota in individuals with and without pediatric‐onset multiple sclerosis (MS).

Methods

We compared stool‐derived microbiota of Canadian Pediatric Demyelinating Disease Network study participants ≤21 years old, with MS (disease‐modifying drug [DMD] exposed and naïve) or monophasic acquired demyelinating syndrome [monoADS] (symptom onset <18 years), and unaffected controls. All were ≥30 days without antibiotics or corticosteroids. V4 region 16S RNA gene‐derived amplicon sequence variants (Illumina MiSeq) were assessed using negative binomial regression and network analyses; rate ratios were age‐ and sex‐adjusted (aRR).

Results

Thirty‐two MS, 41 monoADS (symptom onset [mean] = 14.0 and 6.9 years) and 36 control participants were included; 75%/56%/58% were female, with mean ages at stool sample = 16.5/13.8/15.1 years, respectively. Nine MS cases (28%) were DMD‐naïve. Although microbiota diversity (alpha, beta) did not differ between participants (p > 0.1), taxa‐level and gut community networks did. MS (vs. monoADS) exhibited > fourfold higher relative abundance of the superphylum Patescibacteria (aRR = 4.2;95%CI:1.6–11.2, p = 0.004, Q = 0.01), and lower abundances of short‐chain fatty acid (SCFA)‐producing Lachnospiraceae (Anaerosporobacter) and Ruminococcaceae (p, Q < 0.05). DMD‐naïve MS cases were depleted for Clostridiales vadin‐BB60 (unnamed species) versus either DMD‐exposed, controls (p, Q < 0.01), or monoADS (p = 0.001, Q = 0.06) and exhibited altered community connectedness (p < 10⁻⁹ Kruskal–Wallis), with SCFA‐producing taxa underrepresented. Consistent taxa‐level findings from an independent US Network of Pediatric MS Centers case/control (n = 51/42) cohort included >eightfold higher abundance for Candidatus Stoquefichus and Tyzzerella (aRR = 8.8–12.8, p < 0.05) in MS cases and 72%–80% lower abundance of SCFA‐producing Ruminococcaceae‐NK4A214 (aRR = 0.38–0.2, p ≤ 0.01).

Interpretation

Gut microbiota community structure, function and connectivity, and not just individual taxa, are of likely importance in MS.

Fruit and Vegetable Supplemented Diet Modulates the Pig Transcriptome and Microbiome after a Two-Week Feeding Intervention

A study was conducted to determine the effects of a diet supplemented with fruits and vegetables (FV) on the host whole blood cell (WBC) transcriptome and the composition and function of the intestinal microbiome. Nine six-week-old pigs were fed a pig grower diet alone or supplemented with lyophilized FV equivalent to half the daily recommended amount prescribed for humans by the Dietary Guideline for Americans (DGA) for two weeks. Host transcriptome changes in the WBC were evaluated by RNA sequencing. Isolated DNA from the fecal microbiome was used for 16S rDNA taxonomic analysis and prediction of metabolomic function. Feeding an FV-supplemented diet to pigs induced differential expression of several genes associated with an increase in B-cell development and differentiation and the regulation of cellular movement, inflammatory response, and cell-to-cell signaling. Linear discriminant analysis effect size (LEfSe) in fecal microbiome samples showed differential increases in genera from Lachnospiraceae and Ruminococcaceae families within the order Clostridiales and Erysipelotrichaceae family with a predicted reduction in rgpE-glucosyltransferase protein associated with lipopolysaccharide biosynthesis in pigs fed the FV-supplemented diet. These results suggest that feeding an FV-supplemented diet for two weeks modulated markers of cellular inflammatory and immune function in the WBC transcriptome and the composition of the intestinal microbiome by increasing the abundance of bacterial taxa that have been associated with improved intestinal health.

Metformin action over gut microbiota is related to weight and glycemic control in gestational diabetes mellitus: A randomized trial

BACKGROUND

Metformin, which is known to produce profound changes in gut microbiota, is being increasingly used in gestational diabetes mellitus (GDM). The aim of this study was to elucidate the differences in gut microbiota composition and function in women with GDM treated with metformin compared to those treated with insulin.

METHODS

From May to December 2018, 58 women with GDM were randomized to receive insulin (INS; n = 28) or metformin (MET; n = 30) at the University Hospital Virgen de la Victoria, Málaga, Spain. Basal visits, with at least 1 follow-up visit and prepartum visit, were performed. At the basal and prepartum visits, blood and stool samples were collected. The gut microbiota profile was determined through 16S rRNA analysis.

RESULTS

Compared to INS, women on MET presented a lower mean postprandial glycemia and a lower increase in weight and body mass index (BMI). Firmicutes and Peptostreptococcaceae abundance declined, while Proteobacteria and Enterobacteriaceae abundance increased in the MET group. We found inverse correlations between changes in the abundance of Proteobacteria and mean postprandial glycemia (p = 0.023), as well as between Enterobacteriaceae and a rise in BMI and weight gain (p = 0.031 and p = 0.036, respectively). Regarding the metabolic profile of gut microbiota, predicted metabolic pathways related to propionate degradation and ubiquinol biosynthesis predominated in the MET group.

CONCLUSION

Metformin in GDM affects the composition and metabolic profile of gut microbiota. These changes could mediate, at least in part, its clinical effects. Studies designed to assess how these changes influence metabolic control during and after pregnancy are necessary.

Gut microbiota of patients with type 2 diabetes and gastrointestinal intolerance to metformin differs in composition and functionality from tolerant patients

OBJECTIVE

Metformin modifies the gut microbiome in type 2 diabetes and gastrointestinal tolerance to metformin could be mediated by the gut microbiome.

METHODS

We enrolled 35 patients with type 2 diabetes not receiving treatment with metformin due to suspected gastrointestinal intolerance. Metformin was reintroduced at 425 mg, increasing 425 mg every two weeks until reaching 1700 mg per day. According to the occurrence of metformin-related gastrointestinal symptoms, patients were classified into three groups: early intolerance, non-tolerant, and tolerant. Gut microbiota was profiled with 16 S rRNA. This sequencing aimed to determine the differences in the baseline gut microbiota in all groups and prospectively in the tolerant and non-tolerant groups.

RESULTS

The classification resulted in 15 early intolerant, 10 tolerant, and 10 non-tolerant subjects. Early tolerance was characterized by a higher abundance of Subdoligranulum; while Veillonella and Serratia were higher in the non-tolerant group. The tolerant group showed enrichment of Megamonas, Megamonas rupellensis, and Phascolarctobacterium spp; Ruminococcus gnavus was lower in the longitudinal analysis. At the end point Prevotellaceae, Prevotella stercorea, Megamonas funiformis, Bacteroides xylanisolvens, and Blautia producta had a higher relative abundance in the tolerant group compared to the non-tolerant group. Subdoligranulum, Ruminococcus torques_1, Phascolarctobacterium faecium, and Eubacterium were higher in the non-tolerant group. The PICRUSt analysis showed a lower activity of the amino acid biosynthesis pathways and a higher sugar degradation pathway in the intolerant groups.

CONCLUSIONS

Gut microbiota of subjects with gastrointestinal intolerance depicted taxonomic and functional differences compared to tolerant patients, and this changed differently after metformin administration.

Comparative genomic analysis of hyper-ammonia producing Acetoanaerobium sticklandii DSM 519 with purinolytic Gottschalkia acidurici 9a and pathogenic Peptoclostridium difficile 630

Acetoanaerobium sticklandii DSM519 (CST) is a hype-ammonia producing non-pathogenic anaerobe that can use amino acids as important carbon and energy sources through the Stickland reactions. Biochemical aspects of this organism have been extensively studied, but systematic studies addressing its metabolic discrepancy remain scant. In this perspective, we have intensively analyzed its genomic and metabolic characteristics to comprehend the evolutionary conservation of amino acid catabolism by a comparative genomic approach. The whole-genome data indicated that CST has shown a phylogenomic similarity with hyper-ammonia producing, purinolytic, and proteolytic pathogenic Clostridia. CST has shown to common genomic context sharing across the purinolytic Gottschalkia acidurici 9a and pathogenic Peptoclostridium difficile 630. Genome syntenic analysis described that syntenic orthologs might be originated from the recent ancestor at a slow evolution rate and syntenic-out paralogs evolved from either CDF or CAC via α-event and β-event. Collinearity of either gene orders or gene families was adjusted with syntenic out-paralogs across these genomes. The genome-wide metabolic analysis predicted 11 unique putative metabolic subsystems from the CST genome for amino acid catabolism and hydrogen production. The in silico analysis of our study revealed that a characteristic system for amino acid catabolism-directed biofuel synthesis might have slowly evolved and established as a core genomic content of CST.

Three Distinct Proteases Are Responsible for Overall Cell Surface Proteolysis in Streptococcus thermophilus

The lactic acid bacterium Streptococcus thermophilus was believed to display only two distinct proteases at the cell surface, namely, the cell envelope protease PrtS and the housekeeping protease HtrA. Using peptidomics, we demonstrate here the existence of an additional active cell surface protease, which shares significant homology with the SepM protease of Streptococcus mutans. Although all three proteases-PrtS, HtrA, and SepM-are involved in the turnover of surface proteins, they demonstrate distinct substrate specificities. In particular, SepM cleaves proteins involved in cell wall metabolism and cell elongation, and its inactivation has consequences for cell morphology. When all three proteases are inactivated, the residual cell-surface proteolysis of S. thermophilus is approximately 5% of that of the wild-type strain. IMPORTANCE Streptococcus thermophilus is a lactic acid bacterium used widely as a starter in the dairy industry. Due to its "generally recognized as safe" status and its weak cell surface proteolytic activity, it is also considered a potential bacterial vector for heterologous protein production. Our identification of a new cell surface protease made it possible to construct a mutant strain with a 95% reduction in surface proteolysis, which could be useful in numerous biotechnological applications.

Compositional Changes of the High-Fat Diet-Induced Gut Microbiota upon Consumption of Common Pulses

The gut microbiome is involved in the host's metabolism, development, and immunity, which translates to measurable impacts on disease risk and overall health. Emerging evidence supports pulses, i.e., grain legumes, as underutilized nutrient-dense, culinarily versatile, and sustainable staple foods that promote health benefits through modulating the gut microbiota. Herein, the effects of pulse consumption on microbial composition in the cecal content of mice were assessed. Male mice were fed an obesogenic diet formulation with or without 35% of the protein component comprised by each of four commonly consumed pulses-lentil (Lens culinaris L.), chickpea (Cicer arietinum L.), common bean (Phaseolus vulgaris L.), or dry pea (Pisum sativum L.). Mice consuming pulses had distinct microbial communities from animals on the pulse-free diet, as evidenced by β-diversity ordinations. At the phylum level, animals consuming pulses showed an increase in Bacteroidetes and decreases in Proteobacteria and Firmicutes. Furthermore, α-diversity was significantly higher in pulse-fed animals. An ecosystem of the common bacteria that were enhanced, suppressed, or unaffected by most of the pulses was identified. These compositional changes are accompanied by shifts in predicted metagenome functions and are concurrent with previously reported anti-obesogenic physiologic outcomes, suggestive of microbiota-associated benefits of pulse consumption.

Plant Metabolic Network 15: A resource of genome-wide metabolism databases for 126 plants and algae

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.

Metabolic Modeling with MetaFlux

The MetaFlux software supports creating, executing, and solving quantitative metabolic flux models using flux balance analysis (FBA). MetaFlux offers four modes of operation: (1) solving mode executes an FBA model for an individual organism or for an organism community, (2) gene knockout mode executes an FBA model with one or many gene knockouts, (3) development mode assists the user in creating and improving FBA models, and (4) flux variability analysis mode generates a report of the robustness of an FBA model. MetaFlux also solves dynamic FBA (dFBA) for both individual organisms and communities of organisms. MetaFlux can be used in two different environments: on your local computer, which requires the installation of the Pathway Tools software, or through the web, which does not require installation of Pathway Tools. On your local computer, MetaFlux offers all four modes of operation, whereas the web environment provides only the solving mode.Several visualization tools are available to analyze model solutions. The Cellular Overview tool graphically shows the reaction fluxes on an organism's metabolic map once a model is solved. The Omics Dashboard provides a hierarchical approach to visualizing reaction fluxes, organized by metabolic subsystems. For a community of organisms, plotting of accumulated biomasses and metabolites can be performed using the Gnuplot tool.In this chapter, we present eight methods using MetaFlux. Five solving mode methods illustrate execution of models for individual organisms and for organism communities. One method illustrates the gene knockout mode. Two methods for the development mode illustrate steps for developing new metabolic models.

Multivariable association discovery in population-scale meta-omics studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

Multivariable association discovery in population-scale meta-omics studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

Unraveling the Microbiome of Necrotizing Enterocolitis: Insights in Novel Microbial and Metabolomic Biomarkers

Necrotizing enterocolitis (NEC) is among the most relevant gastrointestinal diseases affecting mostly prematurely born infants with low birth weight. While intestinal dysbiosis has been proposed as one of the possible factors involved in NEC pathogenesis, the role of the gut microbiota remains poorly understood. In this study, the gut microbiota of preterm infants was explored to highlight differences in the composition between infants affected by NEC and infants prior to NEC development. A large-scale gut microbiome analysis was performed, including 47 shotgun sequencing data sets generated in the framework of this study, along with 124 retrieved from publicly available repositories. Meta-analysis led to the identification of preterm community state types (PT-CSTs), which recur in healthy controls and NEC infants. Such analyses revealed an overgrowth of a range of opportunistic microbial species accompanying the loss of gut microbial biodiversity in NEC subjects. Moreover, longitudinal insights into preterm infants prior to NEC development indicated Clostridium neonatale and Clostridium perfringens species as potential biomarkers for predictive early diagnosis of this disease. Furthermore, functional investigation of the enzymatic reaction profiles associated with pre-NEC condition suggested DL-lactate as a putative metabolic biomarker for early detection of NEC onset.

IMPORTANCE Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease occurring predominantly in premature infants whose etiology is still not fully understood. In this study, the analysis of infant fecal samples through shotgun metagenomics approaches revealed a marked reduction of the intestinal (bio)diversity and an overgrowth of (opportunistic) pathogens associated with the NEC development. In particular, dissection of the infant’s gut microbiome before NEC diagnosis highlighted the potential involvement of Clostridium genus members in the progression of NEC. Remarkably, our analyses highlighted a gastrointestinal DL-lactate accumulation among NEC patients that might represent a novel potential functional biomarker for the early diagnosis of NEC.

Relationship between gut microbiome diversity and hepatitis B viral load in patients with chronic hepatitis B

Background

Hepatitis B virus (HBV) infection is associated with a reduced risk of developing dyslipidemia and non-alcoholic fatty liver diseases. Given that the gut microbiota plays a significant role in cholesterol metabolism, we compared the differences in gut microbial diversity and composition between HBV-infected and uninfected subjects.

Results

A prospective case–control study was designed comprising healthy controls (group A) and HBV-infected individuals (group B) in a 1:1 ratio (57 participants each; total = 114). The patients in group B were divided into two subgroups according to their HBV DNA loads: B1 < 2000 IU/mL (N = 40) and B2 ≥ 2000 IU/mL (N = 17). In a pairwise comparison of HBV-infected individuals and controls, higher alpha diversity was noted in group B, and the difference was significant only in patients in group B1. Alloprevotella and Eubacterium coprostanoligenes were predominant in group B1 compared to the control, whereas the abundance of Bacteroides fragilis and Prevotella 2 was lower.

Conclusions

The gut microbiome in HBV-infected individuals with a low viral load is highly diverse and is dominated by specific taxa involved in fatty acid and lipid metabolism. To our knowledge, this is the first demonstration of a correlation between the presence of certain bacterial taxa and chronic HBV infection depending on the load of HBV DNA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13099-021-00461-1.

Gut Microbiota Metabolism of Bile Acids Could Contribute to the Bariatric Surgery Improvements in Extreme Obesity

Bariatric surgery is the only procedure to obtain and maintain weight loss in the long term, although the mechanisms driving these benefits are not completely understood. In the last years, gut microbiota has emerged as one of the drivers through its metabolites, especially secondary bile acids. In the current study, we have compared the gut microbiota and the bile acid pool, as well as anthropometric and biochemical parameters, of patient with morbid obesity who underwent bariatric surgery by two different techniques, namely Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG). Gut microbiota populations differed after the respective procedures, particularly with respect to the Enterobacteriaceae family. Both techniques resulted in changes in the bile acids pool, but RYGB was the procedure which suffered the greatest changes, with a reduction in most of their levels. Blautia and Veillonella were the two genera that more relationships showed with secondary bile acids, indicating a possible role in their formation and inhibition, respectively. Correlations with the anthropometric and biochemical variables showed that secondary bile acids could have a role in the amelioration of the glucose and HDL-cholesterol levels. Thus, we have observed a possible relationship between the interaction of the bile acids pool metabolized by the gut microbiota in the metabolic improvements obtained by bariatric surgery in the frame of morbid obesity, deserving further investigation in greater cohorts to decipher the role of each bile acid in the homeostasis of the host for their possible use in the development of microbiota-based therapeutics, such as new drugs, postbiotics or probiotics.

BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of these metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles, and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression under different conditions. To illustrate its usefulness, we analyzed 96 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 252 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated with tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. IMPORTANCE Microbes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi)omics data sets that can help in understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics data sets. This report presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. Here, we describe the tool and its functionalities, as well as its validation using a mock community. Finally, using an oral microbiome data set, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.

Metagenomic analysis reveals the signature of gut microbiota associated with human chronotypes

Patterns of diurnal activity differ substantially between individuals, with early risers and late sleepers being examples of opposite chronotypes. Growing evidence suggests that the late chronotype significantly impacts the risk of developing mood disorders, obesity, diabetes, and other chronic diseases. Despite the vast potential of utilizing chronotype information for precision medicine, those factors that shape chronotypes remain poorly understood. Here, we assessed whether the various chronotypes are associated with different gut microbiome compositions. Using metagenomic sequencing analysis, we established a distinct signature associated with chronotype based on two bacterial genera, Alistipes (elevated in "larks") and Lachnospira (elevated in "owls"). We identified three metabolic pathways associated with the early chronotype, and linked distinct dietary patterns with different chronotypes. Our work demonstrates an association between the gut microbiome and chronotype and may represent the first step towards developing dietary interventions aimed at ameliorating the deleterious health correlates of the late chronotype.

Multi-omic evaluation of metabolic alterations in multiple sclerosis identifies shifts in aromatic amino acid metabolism

The circulating metabolome provides unique insights into multiple sclerosis (MS) pathophysiology, but existing studies are relatively small or characterized limited metabolites. We test for differences in the metabolome between people with MS (PwMS; n = 637 samples) and healthy controls (HC; n = 317 samples) and assess the association between metabolomic profiles and disability in PwMS. We then assess whether metabolic differences correlate with changes in cellular gene expression using publicly available scRNA-seq data and whether identified metabolites affect human immune cell function. In PwMS, we identify striking abnormalities in aromatic amino acid (AAA) metabolites (p = 2.77E−18) that are also strongly associated with disability (p = 1.01E−4). Analysis of scRNA-seq data demonstrates altered AAA metabolism in CSF and blood-derived monocyte cell populations in PwMS. Treatment with AAA-derived metabolites in vitro alters monocytic endocytosis and pro-inflammatory cytokine production. We identify shifts in AAA metabolism resulting in the reduced production of immunomodulatory metabolites and increased production of metabotoxins in PwMS.

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Significant alterations in the circulating metabolome are noted in multiple sclerosis

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Aromatic amino acid (AAA) metabolite levels are linked to disease severity

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Expression of AAA metabolism genes is altered in MS blood and CSF immune cells

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AAA metabolites alter human monocyte cytokine production and endocytosis

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism

The relationship between the number of available nutrients and community diversity is a central question in ecological research that remains unanswered. Here we studied the assembly of hundreds of soil-derived microbial communities on a wide range of well-defined resource environments, from single carbon sources to combinations of up to 16. We found that, while single resources supported multispecies communities varying from 8 to 40 taxa, mean community richness increased only one-by-one with additional resources. Cross-feeding could reconcile these seemingly contrasting observations, with the metabolic network seeded by the supplied resources explaining the changes in richness due to both the identity and the number of resources, as well as the distribution of taxa across different communities. By using a consumer-resource model incorporating the inferred cross-feeding network, we provide further theoretical support to our observations and a framework to link the type and number of environmental resources to microbial community diversity.

Sugar-Phosphate Toxicities

Accumulation of phosphorylated intermediates during cellular metabolism can have wide-ranging toxic effects on many organisms, including humans and the pathogens that infect them. These toxicities can be induced by feeding an upstream metabolite (a sugar, for instance) while simultaneously blocking the appropriate metabolic pathway with either a mutation or an enzyme inhibitor. Here, we survey the toxicities that can arise in the metabolism of glucose, galactose, fructose, fructose-asparagine, glycerol, trehalose, maltose, mannose, mannitol, arabinose, and rhamnose. Select enzymes in these metabolic pathways may serve as novel therapeutic targets. Some are conserved broadly among prokaryotes and eukaryotes (e.g., glucose and galactose) and are therefore unlikely to be viable drug targets. However, others are found only in bacteria (e.g., fructose-asparagine, rhamnose, and arabinose), and one is found in fungi but not in humans (trehalose). We discuss what is known about the mechanisms of toxicity and how resistance is achieved in order to identify the prospects and challenges associated with targeted exploitation of these pervasive metabolic vulnerabilities.

Biological treatment of coke plant effluents: from a microbiological perspective

During coke production, large volume of effluent is generated, which has a very complex chemical composition and contains several toxic and carcinogenic substances, mainly aromatic compounds, cyanide, thiocyanate and ammonium. The composition of these high-strength effluents is very diverse and depends on the quality of coals used and the operating and technological parameters of coke ovens. In general, after initial physicochemical treatment, biological purification steps are applied in activated sludge bioreactors. This review summarizes the current knowledge on the anaerobic and aerobic transformation processes and describes key microorganisms, such as phenol- and thiocyanate-degrading, floc-forming, nitrifying and denitrifying bacteria, which contribute to the removal of pollutants from coke plant effluents. Providing the theoretical basis for technical issues (in this case the microbiology of coke plant effluent treatment) aids the optimization of existing technologies and the design of new management techniques.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Mussel Mass Mortality and the Microbiome: Evidence for Shifts in the Bacterial Microbiome of a Declining Freshwater Bivalve

Freshwater mussels (Unionida) are suffering mass mortality events worldwide, but the causes remain enigmatic. Here, we describe an analysis of bacterial loads, community structure, and inferred metabolic pathways in the hemolymph of pheasantshells (Actinonaias pectorosa) from the Clinch River, USA, during a multi-year mass mortality event. Bacterial loads were approximately 2 logs higher in moribund mussels (cases) than in apparently healthy mussels (controls). Bacterial communities also differed between cases and controls, with fewer sequence variants (SVs) and higher relative abundances of the proteobacteria Yokenella regensburgei and Aeromonas salmonicida in cases than in controls. Inferred bacterial metabolic pathways demonstrated a predominance of degradation, utilization, and assimilation pathways in cases and a predominance of biosynthesis pathways in controls. Only two SVs correlated with Clinch densovirus 1, a virus previously shown to be strongly associated with mortality in this system: Deinococcota and Actinobacteriota, which were associated with densovirus-positive and densovirus-negative mussels, respectively. Overall, our results suggest that bacterial invasion and shifts in the bacterial microbiome during unionid mass mortality events may result from primary insults such as viral infection or environmental stressors. If so, bacterial communities in mussel hemolymph may be sensitive, if generalized, indicators of declining mussel health.

Rapid compensatory evolution can rescue low fitness symbioses following partner switching

Partner switching plays an important role in the evolution of symbiosis, enabling local adaptation and recovery from the breakdown of symbiosis. Because of intergenomic epistasis, partner-switched symbioses may possess novel combinations of phenotypes but may also exhibit low fitness due to their lack of recent coevolutionary history. Here, we examine the structure and mechanisms of intergenomic epistasis in the Paramecium-Chlorella symbiosis and test whether compensatory evolution can rescue initially low fitness partner-switched symbioses. Using partner-switch experiments coupled with metabolomics, we show evidence for intergenomic epistasis wherein low fitness is associated with elevated symbiont stress responses either in dark or high irradiance environments, potentially owing to mismatched light management traits between the host and symbiont genotypes. Experimental evolution under high light conditions revealed that an initially low fitness partner-switched non-native host-symbiont pairing rapidly adapted, gaining fitness equivalent to the native host-symbiont pairing in less than 50 host generations. Compensatory evolution took two alternative routes: either hosts evolved higher symbiont loads to mitigate for their new algal symbiont's poor performance, or the algal symbionts themselves evolved higher investment in photosynthesis and photoprotective traits to better mitigate light stress. These findings suggest that partner switching combined with rapid compensatory evolution can enable the recovery and local adaptation of symbioses in response to changing environments.

Genome erosion and evidence for an intracellular niche - exploring the biology of mycoplasmas in Atlantic salmon

Mycoplasmas are the smallest autonomously self-replicating life form on the planet. Members of this bacterial genus are known to parasitise a wide array of metazoans including vertebrates. Whilst much research has been significant targeted at parasitic mammalian mycoplasmas, very little is known about their role in other vertebrates. In the current study, we aim to explore the biology of mycoplasmas in Atlantic Salmon, a species of major significance for aquaculture, including cellular niche, genome size structure and gene content. Using fluorescent in-situ hybridisation (FISH), mycoplasmas were targeted in epithelial tissues across the digestive tract (stomach, pyloric caecum and midgut) from different development stages (eggs, parr, subadult) of farmed Atlantic salmon (Salmo salar), and we present evidence for an intracellular niche for some of the microbes visualised. Via shotgun metagenomic sequencing, a nearly complete, albeit small, genome (~0.57 MB) as assembled from a farmed Atlantic salmon subadult. Phylogenetic analysis of the recovered genome revealed taxonomic proximity to other salmon derived mycoplasmas, as well as to the human pathogen Mycoplasma penetrans (~1.36 Mb). We annotated coding sequences and identified riboflavin pathway encoding genes and sugar transporters, the former potentially consistent with micronutrient provisioning in salmonid development. Our study provides insights into mucosal adherence, the cellular niche and gene catalog of Mycoplasma in the gut ecosystem of the Atlantic salmon, suggesting a high dependency of this minimalist bacterium on its host. Further study is required to explore and functional role of Mycoplasma in the nutrition and development of its salmonid host.

Gut microbiome stability and dynamics in healthy donors and patients with non-gastrointestinal cancers

As microbial therapeutics are increasingly being tested in diverse patient populations, it is essential to understand the host and environmental factors influencing the microbiome. Through analysis of 1,359 gut microbiome samples from 946 healthy donors of the Milieu Intérieur cohort, we detail how microbiome composition is associated with host factors, lifestyle parameters, and disease states. Using a genome-based taxonomy, we found biological sex was the strongest driver of community composition. Additionally, bacterial populations shift across decades of life (age 20-69), with Bacteroidota species consistently increased with age while Actinobacteriota species, including Bifidobacterium, decreased. Longitudinal sampling revealed that short-term stability exceeds interindividual differences. By accounting for these factors, we defined global shifts in the microbiomes of patients with non-gastrointestinal tumors compared with healthy donors. Together, these results demonstrated that the microbiome displays predictable variations as a function of sex, age, and disease state. These variations must be considered when designing microbiome-targeted therapies or interpreting differences thought to be linked to pathophysiology or therapeutic response.

Investigating the Chemolithoautotrophic and Formate Metabolism of Nitrospira moscoviensis by Constraint-Based Metabolic Modeling and 13C-Tracer Analysis

Nitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used flux balance analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and [¹³C]bicarbonate and [¹³C]formate tracer experiments coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings corroborate that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO₂ fixation, and we also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO₂ followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.

IMPORTANCENitrospira spp. are globally abundant nitrifying bacteria in soil and aquatic ecosystems and in wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of Nitrospira moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and ¹³C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite-oxidizing bacteria.

Escherichia coli AraJ boosts utilization of arabinose in metabolically engineered cyanobacterium Synechocystis sp. PCC 6803

Lignocellulosic biomass can serve as an inexpensive and renewable source of carbon for the biosynthesis of commercially important compounds. L-arabinose is the second most abundant pentose sugar present in the plant materials. Model cyanobacterium Synechocystis sp. PCC 6803 is incapable of catabolism of L-arabinose as a source of carbon and energy. In this study, all the heterologous genes expressed in Synechocystis were derived from Escherichia coli K-12. Initially we constructed four Synechocystis strains that expressed AraBAD enzymes involved in L-arabinose catabolism, either in combination with or without one of the three arabinose transporters, AraE, AraFGH or AraJ. Among the recombinants, the strain possessing AraJ transporter was observed to be the most efficient in terms of dry biomass production and L-arabinose consumption. Later, an additional strain was generated by the expression of AraJ in the AraE-possessing strain. The resultant strain was shown to be advantageous over its parent. This study demonstrates that AraJ, a protein with hitherto unknown function plays a role in the uptake of L-arabinose to boost its catabolism in the transgenic Synechocystis strains. The work also contributes to the current knowledge regarding metabolic engineering of cyanobacteria for the utilization of pentose sugars.

Maternal cecal microbiota transfer rescues early-life antibiotic-induced enhancement of type 1 diabetes in mice

Early-life antibiotic exposure perturbs the intestinal microbiota and accelerates type 1 diabetes (T1D) development in the NOD mouse model. Here, we found that maternal cecal microbiota transfer (CMT) to NOD mice after early-life antibiotic perturbation largely rescued the induced T1D enhancement. Restoration of the intestinal microbiome was significant and persistent, remediating the antibiotic-depleted diversity, relative abundance of particular taxa, and metabolic pathways. CMT also protected against perturbed metabolites and normalized innate and adaptive immune effectors. CMT restored major patterns of ileal microRNA and histone regulation of gene expression. Further experiments suggest a gut-microbiota-regulated T1D protection mechanism centered on Reg3γ, in an innate intestinal immune network involving CD44, TLR2, and Reg3γ. This regulation affects downstream immunological tone, which may lead to protection against tissue-specific T1D injury.

DTYMK promote hepatocellular carcinoma proliferation by regulating cell cycle

Overexpression of DTYMK is related with tumorigenesis and progression in several human tumors. However, the role of upregulated DTYMK in hepatocellular carcinoma (HCC) patients still remains unclear. In this study, the DTYMK expression in HCC tumors was evaluated in three GEO series (GSE14520, GSE54236, GSE63898), TCGA-LIHC, and ICGC-IRLR-JP cohorts. Survival analysis of DTYMK based on TCGA-LIHC and ICGC-LIRI-JP cohorts was conducted. We found that DTYMK was dramatically upregulated in tumor tissue compared with that in adjacent liver tissue. Kaplan-Meier analysis revealed that high expression of DTYMK in HCC patients' tumor tissue was significantly corresponded to worse overall survival (OS) (P < 0.05). Further analysis showed that overexpressing DTYMK led to poor relapse free survival (RFS) and disease-specific survival (DSS) (all P < 0.05). In conclusion, DTYMK is upregulated in tumors and correlated with poor prognosis in HCC patients. In our report, DTYMK is higher expression in HCC cancer tissue and cell line than tumor adjacent tissue and normal liver cell line. Knocking down DTYMK can inhabit tumor cell proliferation by interfering cell cycle, whereas overexpression of DTYMK can promote tumor cell proliferation. These findings indicate that upregulation of DTYMK enhances tumor growth and proliferation by promoting cell cycle.

Gut Microbiota Dynamics during Chemotherapy in Epithelial Ovarian Cancer Patients Are Related to Therapeutic Outcome

Simple Summary

This pilot study on the trajectory of the gut microbiota (GM) in patients with epithelial ovarian cancer undergoing neoadjuvant and adjuvant chemotherapy highlighted peculiar dynamics associated with the therapeutic outcome. In particular, platinum-resistant patients showed a marked temporal reduction in GM diversity and increased instability with loss of health-associated taxa and increased proportions of lactate-producing microorganisms compared to those sensitive to platinum. These potential GM signatures of therapeutic failure are detectable within the first half of chemotherapy cycles, suggesting that early integrated treatments also aimed at modulating GM could influence therapeutic outcome. Further studies in larger cohorts combining multiple omics and possibly animal models are urgently needed for in-depth mechanistic understanding.

Abstract

Epithelial ovarian cancer (EOC) is one of the most lethal and silent gynecological tumors. Despite appropriate surgery and chemotherapy, relapse occurs in over half of patients with a poor prognosis. Recently, the gut microbiota (GM) was hypothesized to influence the efficacy of anticancer therapies, but no data are available in EOC. Here, by 16S rRNA gene sequencing and inferred metagenomics, we profiled the GM of EOC patients at diagnosis and reconstructed its trajectory along the course of neoadjuvant or adjuvant chemotherapy up to follow-up. Compared to healthy subjects, the GM of EOC patients appeared unbalanced and severely affected by chemotherapy. Strikingly, discriminating patterns were identified in relation to the therapeutic response. Platinum-resistant patients showed a marked temporal reduction in GM diversity and increased instability with loss of health-associated taxa and increased proportions of Coriobacteriaceae and Bifidobacterium. Notably, most of these microorganisms are lactate producers, suggesting increased lactate production as supported by inferred metagenomics. In contrast, the GM of platinum-sensitive patients appeared overall more diverse and stable and enriched in lactate utilizers from the Veillonellaceae family. In conclusion, we identified potential GM signatures of therapeutic outcome in EOC patients, which could open up new opportunities for cancer prognosis and treatment.

Type 1 diabetes in pregnancy is associated with distinct changes in the composition and function of the gut microbiome

BACKGROUND

The gut microbiome changes in response to a range of environmental conditions, life events and disease states. Pregnancy is a natural life event that involves major physiological adaptation yet studies of the microbiome in pregnancy are limited and their findings inconsistent. Pregnancy with type 1 diabetes (T1D) is associated with increased maternal and fetal risks but the gut microbiome in this context has not been characterized. By whole metagenome sequencing (WMS), we defined the taxonomic composition and function of the gut bacterial microbiome across 70 pregnancies, 36 in women with T1D.

RESULTS

Women with and without T1D exhibited compositional and functional changes in the gut microbiome across pregnancy. Profiles in women with T1D were distinct, with an increase in bacteria that produce lipopolysaccharides and a decrease in those that produce short-chain fatty acids, especially in the third trimester. In addition, women with T1D had elevated concentrations of fecal calprotectin, a marker of intestinal inflammation, and serum intestinal fatty acid-binding protein (I-FABP), a marker of intestinal epithelial damage.

CONCLUSIONS

Women with T1D exhibit a shift towards a more pro-inflammatory gut microbiome during pregnancy, associated with evidence of intestinal inflammation. These changes could contribute to the increased risk of pregnancy complications in women with T1D and are potentially modifiable by dietary means. Video abstract.

An extended reconstruction of human gut microbiota metabolism of dietary compounds

Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.

Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome

Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples obtained from 40 subjects were characterized using high throughput microbiome sequence data processed via DADA2 error correction combined with PIME machine-learning de-noising and taxa binning/parsing of prevalent ASVs at the single nucleotide level of resolution. Application of ALDEx2 differential abundance analysis with assessed effect sizes and stringent PICRUSt2 predicted metabolic pathways. This multivariate machine-learning approach significantly differentiated DEPR (n = 20) vs. NODEP (n = 20) (PERMANOVA P < 0.001) based on microbiome taxa clustering and neurocircuit-relevant metabolic pathway network analysis for GABA, butyrate, glutamate, monoamines, monosaturated fatty acids, and inflammasome components. Gut microbiome dysbiosis using ASV prevalence data may offer the diagnostic potential of using human metaorganism biomarkers to identify individuals with a depression phenotype.

Genome-scale insights into the metabolic versatility of Limosilactobacillus reuteri

BACKGROUND

Limosilactobacillus reuteri (earlier known as Lactobacillus reuteri) is a well-studied lactic acid bacterium, with some specific strains used as probiotics, that exists in different hosts such as human, pig, goat, mouse and rat, with multiple body sites such as the gastrointestinal tract, breast milk and mouth. Numerous studies have confirmed the beneficial effects of orally administered specific L. reuteri strains, such as preventing bone loss and promoting regulatory immune system development. L. reuteri ATCC PTA 6475 is a widely used strain that has been applied in the market as a probiotic due to its positive effects on the human host. Its health benefits may be due, in part, to the production of beneficial metabolites. Considering the strain-specific effects and genetic diversity of L. reuteri strains, we were interested to study the metabolic versatility of these strains.

RESULTS

In this study, we aimed to systematically investigate the metabolic features and diversities of L. reuteri strains by using genome-scale metabolic models (GEMs). The GEM of L. reuteri ATCC PTA 6475 was reconstructed with a template-based method and curated manually. The final GEM iHL622 of L. reuteri ATCC PTA 6475 contains 894 reactions and 726 metabolites linked to 622 metabolic genes, which can be used to simulate growth and amino acids utilization. Furthermore, we built GEMs for the other 35 L. reuteri strains from three types of hosts. The comparison of the L. reuteri GEMs identified potential metabolic products linked to the adaptation to the host.

CONCLUSIONS

The GEM of L. reuteri ATCC PTA 6475 can be used to simulate metabolic capabilities and growth. The core and pan model of 35 L. reuteri strains shows metabolic capacity differences both between and within the host groups. The GEMs provide a reliable basis to investigate the metabolism of L. reuteri in detail and their potential benefits on the host.

The transferred translocases: An old wine in a new bottle

The role of translocases was underappreciated and was not included as a separate class in the enzyme commission until August 2018. The recent research interests in proteomics of orphan enzymes, ionomics, and metallomics along with high-throughput sequencing technologies generated overwhelming data and revamped this enzyme into a separate class. This offers a great opportunity to understand the role of new or orphan enzymes in general and specifically translocases. The enzymes belonging to translocases regulate/permeate the transfer of ions or molecules across the membranes. These enzyme entries were previously associated with other enzyme classes, which are now transferred to a new enzyme class 7 (EC 7). The entries that are reclassified are important to extend the enzyme list, and it is the need of the hour. Accordingly, there is an upgradation of entries of this class of enzymes in several databases. This review is a concise compilation of translocases with reference to the number of entries currently available in the databases. This review also focuses on function as well as dysfunction of translocases during normal and disordered states, respectively.

Influence of a cholesterol-lowering strain Lactobacillus plantarum LP3 isolated from traditional fermented yak milk on gut bacterial microbiota and metabolome of rats fed with a high-fat diet

L. plantarum LP3 isolated from traditinal fermented Tibetan yak milk has been identified as a potential probiotic candidate strain with high cholesterol-lowering activity. In this study, thirty Sprague-Dawley (SD) rats were randomly divided into three groups, including normal diet (NC), high-fat diet (HC), and high-fat diet + L. plantarum LP3 (HLp). The effects of L. plantarum LP3 on plasma lipid profile, gut bacterial microbiota, and metabolome induced by high-fat diet in rats were investigated. Results shown that L. plantarum LP3 administration was found to reduce the levels of total cholesterol, triglyceride, and low-density lipoprotein cholesterol (LDL-C) and atherogenic index in the serum of high-fat diet rats. It also controlled the decrease of Bacteroidetes and increase of Firmicutes at the phylum level in gut microbiota induced by high-fat diet in SD rats and increased the diversity and relative abundance of intestinal flora in obese rats. In particular, the LP3 strain controlled the changes induced by the high-fat diet in the abundance of for Lachnospiraceae and Erysipelotrichaceae. We also further observed the beneficial regulatory effects of L. plantarum LP3 on changes in the levels of obesity-related metabolites. The biosynthesis of fatty acids, steroids, and bile acids and metabolism of linoleic acid, linolenic acid, and arachidonic acid were the main metabolic pathways adjusted by L. plantarum LP3 in obese rats, and the metabolic rates were similar to those observed in normal diet rats levels. The findings of this study provided useful information on the mechanism underlying the hypocholesterolemic effects of L. plantarum LP3 in the high-fat induced SD rat model with the perspective of modulation of gut microbiota and metabolites.

Circulating Levels of the Short-Chain Fatty Acid Acetate Mediate the Effect of the Gut Microbiome on Visceral Fat

Background

Acetate is a short-chain fatty acid (SCFA) produced by gut bacteria, which has been implicated in cardio-metabolic health. Here we examine the relationships of circulating acetate levels with gut microbiome composition and diversity and with visceral fat in a large population-based cohort.

Results

Microbiome alpha-diversity was positively correlated with circulating acetate levels (Shannon, Beta [95%CI] = 0.12 [0.06, 0.18], P = 0.002) after adjustment for covariates. Six serum acetate-associated bacterial genera were also identified, including positive correlations with Coprococcus, Barnesiella, Ruminococcus, and Ruminococcaceae NK4A21 and negative correlations were observed with Lachnoclostridium and Bacteroides. We also identified a correlation between visceral fat and serum acetate levels (Beta [95%CI] = −0.07 [−0.11, −0.04], P = 2.8 × 10^–4) and between visceral fat and Lachnoclostridium (Beta [95%CI] = 0.076 [0.042, 0.11], P = 1.44 × 10^–5). Formal mediation analysis revealed that acetate mediates ∼10% of the total effect of Lachnoclostridium on visceral fat. The taxonomic diversity showed that Lachnoclostridium and Coprococcus comprise at least 18 and 9 species, respectively, including novel bacterial species. By predicting the functional capabilities, we found that Coprococcus spp. present pathways involved in acetate production and metabolism of vitamins B, whereas we identified pathways related to the biosynthesis of trimethylamine (TMA) and CDP-diacylglycerol in Lachnoclostridium spp.

Conclusions

Our data indicates that gut microbiota composition and diversity may influence circulating acetate levels and that acetate might exert benefits on certain cardio-metabolic disease risk by decreasing visceral fat. Coprococcus may play an important role in host health by its production of vitamins B and SCFAs, whereas Lachnoclostridium might have an opposing effect by influencing negatively the circulating levels of acetate and being involved in the biosynthesis of detrimental lipid compounds.

OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security

The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.

Anti-Microbiota Vaccines Modulate the Tick Microbiome in a Taxon-Specific Manner

The lack of tools for the precise manipulation of the tick microbiome is currently a major limitation to achieve mechanistic insights into the tick microbiome. Anti-tick microbiota vaccines targeting keystone bacteria of the tick microbiota alter tick feeding, but their impact on the taxonomic and functional profiles of the tick microbiome has not been tested. In this study, we immunized a vertebrate host model (Mus musculus) with live bacteria vaccines targeting keystone (i.e., Escherichia-Shigella) or non-keystone (i.e., Leuconostoc) taxa of tick microbiota and tested the impact of bacterial-specific antibodies (Abs) on the structure and function of tick microbiota. We also investigated the effect of these anti-microbiota vaccines on mice gut microbiota composition. Our results showed that the tick microbiota of ticks fed on Escherichia coli-immunized mice had reduced Escherichia-Shigella abundance and lower species diversity compared to ticks fed on control mice immunized with a mock vaccine. Immunization against keystone bacteria restructured the hierarchy of nodes in co-occurrence networks and reduced the resistance of the bacterial network to taxa removal. High levels of E. coli-specific IgM and IgG were negatively correlated with the abundance of Escherichia-Shigella in tick microbiota. These effects were not observed when Leuconostoc was targeted with vaccination against Leuconostoc mesenteroides. Prediction of functional pathways in the tick microbiome using PICRUSt2 revealed that E. coli vaccination reduced the abundance of lysine degradation pathway in tick microbiome, a result validated by qPCR. In contrast, the gut microbiome of immunized mice showed no significant alterations in the diversity, composition and abundance of bacterial taxa. Our results demonstrated that anti-tick microbiota vaccines are a safe, specific and an easy-to-use tool for manipulation of vector microbiome. These results guide interventions for the control of tick infestations and pathogen infection/transmission.