Multivariate Analysis in Microbiome Description: Correlation of Human Gut Protein Degraders, Metabolites, and Predicted Metabolic Functions

Tropical postbiotics alleviate the disorders in the gut microbiota and kidney damage induced by ochratoxin A exposure

Ochratoxin A (OTA), commonly found in various foods, significantly impacts the health of humans and animals, especially their kidneys. Our study explores OTA's effects on the gut microbiota and kidney damage while examining how postbiotics offer protection. Using metagenomic sequencing, we observed that OTA increased the potential gut pathogens such as Alistipes, elevating detrimental metabolites and inflammation. Also, OTA inhibited the Nrf2/HO-1 pathway, reducing kidney ROS elimination and leading to cellular ferroptosis and subsequent kidney damage. Postbiotics mitigate OTA's effects by downregulating the abundance of the assimilatory sulfate reduction IV pathway and virulence factors associated with iron uptake and relieving the inhibition of OTA on Nrf2/HO-1, restoring ROS-clearing capabilities and thereby alleviating chronic OTA-induced kidney damage. Understanding the OTA-gut-kidney link provides new approaches for preventing kidney damage, with postbiotics showing promise as a preventive treatment.

Genomic and functional analysis of the mucinolytic species Clostridium celatum, Clostridium tertium, and Paraclostridium bifermentans

Mucins are large glycoproteins whose degradation requires the expression of several glycosil hydrolases to catalyze the cleavage of the oligosaccharide chains and release monosaccharides that can be assimilated. In this study, we present a characterization on the strains Clostridium celatum WC0700, Clostridium tertium WC0709, and Paraclostridium bifermentans WC0705. These three strains were previously isolated from enrichment cultures on mucin of fecal samples from healthy subjects and can use mucin as sole carbon and nitrogen source. Genome analysis and in vitro functional analysis of these strains elucidated their physiological and biochemical features. C. celatum WC0700 harbored the highest number of glycosyl hydrolases specific for mucin degradation, while P. bifermentans WC0705 had the least. These predicted differences were confirmed growing the strains on 5 mucin-decorating monosaccharides (L-fucose, N-Acetylneuraminic acid, galactose, N-acetylgalactosamine, and N-acetylglucosamine) as only source of carbon. Fermenting mucin, they all produced formic, acetic, propionic, butyric, isovaleric, and lactic acids, and ethanol; acetic acid was the main primary metabolite. Further catabolic capabilities were investigated, as well as antibiotic susceptibility, biofilm formation, tolerance to oxygen and temperature. The potential pathogenicity of the strains was evaluated through in silico research of virulence factors. The merge between comparative and functional genomics and biochemical/physiological characterization provided a comprehensive view of these mucin degraders, reassuring on the safety of these species and leaving ample scope for deeper investigations on the relationship with the host and for assessing if some relevant health-promoting effect could be ascribed to these SCFA producing species.

Deep insight into oriented propionate production from food waste: Microbiological interpretation and design practice

Using mixed microbial cultures (MMCs) for oriented volatile fatty acids (VFAs) refining in an open environment is a typical challenge due to the microbial diversiform and the process complexity. Especially for carbohydrate-rich waste (such as food waste), butyrate-type fermentation is usually dominant in a single-stage MMCs anaerobic process, while the production of odd-carbon VFAs (such as propionate) is difficult although it plays a significant role in chemicals industries. In this study, firstly, we gave a new perspective on the rationality of the oriented propionate production using MMCs with lactate as feedstock by conducting in-depth microbial informatics and reaction analysis. Secondly, we verified the feasibility of the "food waste-lactate-propionate" route to reverse the original butyrate-type fermentation situation and explore mechanisms for maintaining stability. In the first stage, a defined lactate fermentation microbiome was used to produce lactate-containing broth (80% of total chemical oxygen demand) at pH=4. In the second stage, an undomesticated undefined anaerobic microbiome was used to drive propionate production (45.26% ± 2.23% of total VFAs) under optimized conditions (C/N = 100:1-200:1 and pH=5.0). The low pH environment in the first stage enhanced the lactic acid bacteria to resist the invasion of non-functional flanking bacteria, making the community stable. In the second stage, the system maintained the propionate-type fermentation due to the absence of the ecological niche of the invasive lactic acid bacteria; The selection of propionate-producing specialists was a necessary but not sufficient condition for propionate-type fermentation. At last, this study proposed an enhanced engineering strategy framework for understanding elaborate MMCs fermentation.

Infant Formula Supplemented with Five Human Milk Oligosaccharides Shifts the Fecal Microbiome of Formula-Fed Infants Closer to That of Breastfed Infants

Breastmilk is the optimal source of infant nutrition, with short-term and long-term health benefits. Some of these benefits are mediated by human milk oligosaccharides (HMOs), a unique group of carbohydrates representing the third most abundant solid component of human milk. We performed the first clinical study on infant formula supplemented with five different HMOs (5HMO-mix), comprising 2'-fucosyllactose, 3-fucosyllactose, lacto-N-tetraose, 3'-sialyllactose and 6'-sialyllactose at a natural total concentration of 5.75 g/L, and here report the analysis of the infant fecal microbiome. We found an increase in the relative abundance of bifidobacteria in the 5HMO-mix cohort compared with the formula-fed control, specifically affecting bifidobacteria that can produce aromatic lactic acids. 5HMO-mix influenced the microbial composition as early as Week 1, and the observed changes persisted to at least Week 16, including a relative decrease in species with opportunistic pathogenic strains down to the level observed in breastfed infants during the first 4 weeks. We further analyzed the functional potential of the microbiome and observed features shared between 5HMO-mix-supplemented and breastfed infants, such as a relative enrichment in mucus and tyrosine degradation, with the latter possibly being linked to the aromatic lactic acids. The 5HMO-mix supplement, therefore, shifts the infant fecal microbiome closer to that of breastfed infants.

Fecal Microbiota and Associated Volatile Organic Compounds Distinguishing No-Adenoma from High-Risk Colon Adenoma Adults

Microbiota and the metabolites they produce within the large intestine interact with the host epithelia under the influence of a range of host-derived metabolic, immune, and homeostatic factors. This complex host-microbe interaction affects intestinal tumorigenesis, but established microbial or metabolite profiles predicting colorectal cancer (CRC) risk are missing. Here, we aimed to identify fecal bacteria, volatile organic compounds (VOC), and their associations that distinguish healthy (non-adenoma, NA) from CRC prone (high-risk adenoma, HRA) individuals. Analyzing fecal samples obtained from 117 participants ≥15 days past routine colonoscopy, we highlight the higher abundance of Proteobacteria and Parabacteroides distasonis, and the lower abundance of Lachnospiraceae species, Roseburia faecis, Blautia luti, Fusicatenibacter saccharivorans, Eubacterium rectale, and Phascolarctobacterium faecium in the samples of HRA individuals. Volatolomic analysis of samples from 28 participants revealed a higher concentration of five compounds in the feces of HRA individuals, isobutyric acid, methyl butyrate, methyl propionate, 2-hexanone, and 2-pentanone. We used binomial logistic regression modeling, revealing 68 and 96 fecal bacteria-VOC associations at the family and genus level, respectively, that distinguish NA from HRA endpoints. For example, isobutyric acid associations with Lachnospiraceae incertae sedis and Bacteroides genera exhibit positive and negative regression lines for NA and HRA endpoints, respectively. However, the same chemical associates with Coprococcus and Colinsella genera exhibit the reverse regression line trends. Thus, fecal microbiota and VOC profiles and their associations in NA versus HRA individuals indicate the significance of multiple levels of analysis towards the identification of testable CRC risk biomarkers.

Profiling of the intestinal community of Clostridia: taxonomy and evolutionary analysis

Aim: Clostridia are relevant commensals of the human gut due to their major presence and correlations to the host. In this study, we investigated intestinal Clostridia of 51 healthy subjects and reconstructed their taxonomy and phylogeny. The relatively small number of intestinal Clostridia allowed a systematic whole genome approach based on average amino acid identity (AAI) and core genome with the aim of revising the current classification into genera and determining evolutionary relationships. Methods: 51 healthy subjects' metagenomes were retrieved from public databases. After the dataset's validation through comparison with Human Microbiome Project (HMP) samples, the metagenomes were profiled using MetaPhlAn3 to identify the population ascribed to the class Clostridia. Intestinal Clostridia genomes were retrieved and subjected to AAI analysis and core genome identification. Phylogeny investigation was conducted with RAxML and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithms, and SplitsTree for split decomposition. Results: 225 out of 406 bacterial taxonomic units were ascribed to Bacillota [Firmicutes], among which 124 were assigned to the class Clostridia. 77 out of the 124 taxonomic units were referred to a species, altogether covering 87.7% of Clostridia abundance. According to the lowest AAI genus boundary set at 55%, 15 putative genera encompassing more than one species (G1 to G15) were identified, while 19 species did not cluster with any other one and each appeared to belong to a diverse genus. Phylogenetic investigations highlighted that most of the species clustered into three main evolutive clades. Conclusion: This study shed light on the species of Clostridia colonizing the gut of healthy adults and pinpointed several gaps in knowledge regarding the taxonomy and the phylogeny of Clostridia.

Integrative analysis of gut microbiota and fecal metabolites in metabolic associated fatty liver disease patients

Objective

Metabolic associated fatty liver disease (MAFLD) affects nearly a quarter of the world’s population. Our study aimed to characterize the gut microbiome and overall changes in the fecal and serum metabolomes in MAFLD patients.

Methods

Thirty-two patients diagnosed with MAFLD and 30 healthy individuals (control group, CG) were included in this study, the basic clinical characteristics and laboratory test results including routine biochemistry, etc. were recorded for all, and their serum and fecal samples were collected. A portion of the fecal samples was subjected to 16S rDNA sequencing, and the other portion of the fecal samples and serum samples were subjected to non-targeted metabolomic detection based on liquid chromatography-mass spectrometry (LC–MS). Statistical analysis of clinical data was performed using SPSS software package version 25.0 (SPSS Inc., Chicago, IL, United States). The analysis of 16S rDNA sequencing results was mainly performed by R software (V. 2.15.3), and the metabolomics data analysis was mainly performed by CD 3.1 software. Two-tailed p value < 0.05 was considered statistically significant.

Results

The 16S sequencing data suggested that the species richness and diversity of MAFLD patients were reduced compared with controls. At the phylum level, the relative abundance of Bacteroidota, Pseudomonadota, and Fusobacteriota increased and Bacillota decreased in MAFLD patients. At the genus level, the relative abundances of Prevotella, Bacteroides, Escherichia-Shigella, etc. increased. 2,770 metabolites were detected in stool samples and 1,245 metabolites were detected in serum samples. The proportion of differential lipid metabolites in serum (49%) was higher than that in feces (21%). There were 22 differential metabolites shared in feces and serum. And the association analysis indicated that LPC 18:0 was positively correlated with Christensenellaceae_R-7_group, Oscillospiraceae_UCG-002; neohesperidin was also positively correlated with Peptoniphilus, Phycicoccus, and Stomatobaculum.

Conclusion

Microbial sequencing data suggested decreased species richness and diversity and altered β-diversity in feces. Metabolomic analysis identified overall changes in fecal and serum metabolites dominated by lipid molecules. And the association analysis with gut microbes provided potentially pivotal gut microbiota-metabolite combinations in MAFLD patients, which might provide new clues for further research on the disease mechanism and the development of new diagnostic markers and treatments.

EasyMap - An Interactive Web Tool for Evaluating and Comparing Associations of Clinical Variables and Microbiome Composition

One of the most common tasks in microbiome studies is comparing microbial profiles across various groups of people (e.g., sick vs. healthy). Routinely, researchers use multivariate linear regression models to address these challenges, such as linear regression packages, MaAsLin2, LEfSe, etc. In many cases, it is unclear which metadata variables should be included in the linear model, as many human-associated variables are correlated with one another. Thus, multiple models are often tested, each including a different set of variables, however the challenge of selecting the metadata variables in the final model remains. Here, we present EasyMap, an interactive online tool allowing for (1) running multiple multivariate linear regression models, on the same features and metadata; (2) visualizing the associations between microbial features and clinical metadata found in each model; and (3) comparing across the various models to identify the critical metadata variables and select the optimal model. EasyMap provides a side-by-side visualization of association results across the various models, each with additional metadata variables, enabling us to evaluate the impact of each metadata variable on the associated feature. EasyMap’s interface enables filtering associations by significance, focusing on specific microbes and finding the robust associations that are found across multiple models. While EasyMap was designed to analyze microbiome data, it can handle any other tabular data with numeric features and metadata variables. EasyMap takes the common task of multivariate linear regression to the next level, with an intuitive and simple user interface, allowing for wide comparisons of multiple models to identify the robust microbial feature associations. EasyMap is available at http://yassour.rcs.huji.ac.il/easymap.

β-Glucuronidase Pattern Predicted From Gut Metagenomes Indicates Potentially Diversified Pharmacomicrobiomics

β-glucuronidases (GUS) of intestinal bacteria remove glucuronic acid from glucoronides, reversing phase II metabolism of the liver and affecting the level of active deconjugated metabolites deriving from drugs or xenobiotics. Two hundred seventy-nine non-redundant GUS sequences are known in the gut microbiota, classified in seven structural categories (NL, L1, L2, mL1, mL2, mL1,2, and NC) with different biocatalytic properties. In the present study, the intestinal metagenome of 60 healthy subjects from five geographically different cohorts was assembled, binned, and mined to determine qualitative and quantitative differences in GUS profile, potentially affecting response to drugs and xenobiotics. Each metagenome harbored 4–70 different GUS, altogether accounting for 218. The amount of intestinal bacteria with at least one GUS gene was highly variable, from 0.7 to 82.2%, 25.7% on average. No significant difference among cohorts could be identified, except for the Ethiopia (ETH) cohort where GUS-encoding bacteria were significantly less abundant. The structural categories were differently distributed among the metagenomes, but without any statistical significance related to the cohorts. GUS profiles were generally dominated by the category NL, followed by mL1, L2, and L1. The GUS categories most involved in the hydrolysis of small molecules, including drugs, are L1 and mL1. Bacteria contributing to these categories belonged to Bacteroides ovatus, Bacteroides dorei, Bacteroides fragilis, Escherichia coli, Eubacterium eligens, Faecalibacterium prausnitzii, Parabacteroides merdae, and Ruminococcus gnavus. Bacteria harboring L1 GUS were generally scarcely abundant (<1.3%), except in three metagenomes, where they reached up to 24.3% for the contribution of E. coli and F. prausnitzii. Bacteria harboring mL1 GUS were significantly more abundant (mean = 4.6%), with Bacteroides representing a major contributor. Albeit mL1 enzymes are less active than L1 ones, Bacteroides likely plays a pivotal role in the deglucuronidation, due to its remarkable abundance in the microbiomes. The observed broad interindividual heterogeneity of GUS profiles, particularly of the L1 and mL1 categories, likely represent a major driver of pharmacomicrobiomics variability, affecting drug response and toxicity. Different geographical origins, genetic, nutritional, and lifestyle features of the hosts seemed not to be relevant in the definition of glucuronidase activity, albeit they influenced the richness of the GUS profile.