The Simplified Human Intestinal Microbiota (SIHUMIx) Shows High Structural and Functional Resistance against Changing Transit Times in In Vitro Bioreactors

Chemical mixture effects on the simplified human intestinal microbiota: Assessing xenobiotics at environmentally realistic concentrations

The microbial community present in our intestines is pivotal for converting indigestible substances into vital nutrients and signaling molecules such as short-chain fatty acids (SCFAs). These compounds have considerable influence over our immune system and the development of diverse human diseases. However, ingested environmental contaminants, known as xenobiotics, can upset the delicate balance of the microbial gut community and enzymatic processes, consequently affecting the host organism. In our study, we employed an in vitro bioreactor model system based on the simplified human microbiome model (SIHUMIx) to investigate the direct effects of specific xenobiotics, such as perfluorooctanoic acid (PFOA), perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA) or bisphenol S (BPS) and bisphenol F (BPF), either individually or in combination, on the microbiota. We observed increased SCFA production, particularly acetate and butyrate, with PFAS exposure. Metaproteomics revealed pathway alterations across treatments, including changes in vitamin synthesis and fatty acid metabolism with BPX. This study underscores the necessity of assessing the combined effects of xenobiotics to better safeguard public health. It emphasizes the significance of considering adverse effects on the microbiome in the risk assessment of environmental chemicals.

PETR: A novel peristaltic mixed tubular bioreactor simulating human colonic conditions

A novel bioreactor simulating human colonic conditions for in vitro cultivation of intestinal microbiota is presented. The PEristaltic mixed Tubular bioReactor (PETR) is modular designed and periodically kneaded to simulate intestinal peristalsis. The reactor is introduced, characterized from a bioprocess engineer's perspective and discussed in its ability to mimic colon conditions. PETR provides physiological temperature and appropriate anaerobic conditions, simulates intestinal peristalsis, and has a mean residence time of 32.8 ± 0.8 h comparable to the adult human colon. The single-tube design enables a time-constant and longitudinally progressive pH gradient from 5.5 to 7.0. Using a dialysis liquid containing high molecular weight polyethylene glycol, the integrated dialysis system efficiently absorbs short chain fatty acids (up to 60%) and water (on average 850 mL d-1 ). Cultivation of a typical gut bacterium (Bifidobacterium animalis) was performed to demonstrate the applicability for controlled microbiota cultivation. PETR is unique in combining simulation of the entire colon, peristaltic mixing, dialytic water and metabolite absorption, and a progressive pH gradient in a single-tube design. PETR is a further step to precise replication of colonic conditions in vitro for reliable and reproducible microbiota research, such as studying the effect of food compounds, prebiotics or probiotics, or the development and treatment of infections with enteric pathogens, but also for further medical applications such as drug delivery studies or to study the effect of drugs on and their degradation by the microbiota.

High-throughput screening of the effects of 90 xenobiotics on the simplified human gut microbiota model (SIHUMIx): a metaproteomic and metabolomic study

The human gut microbiota is a complex microbial community with critical functions for the host, including the transformation of various chemicals. While effects on microorganisms has been evaluated using single-species models, their functional effects within more complex microbial communities remain unclear. In this study, we investigated the response of a simplified human gut microbiota model (SIHUMIx) cultivated in an in vitro bioreactor system in combination with 96 deep-well plates after exposure to 90 different xenobiotics, comprising 54 plant protection products and 36 food additives and dyes, at environmentally relevant concentrations. We employed metaproteomics and metabolomics to evaluate changes in bacterial abundances, the production of Short Chain Fatty Acids (SCFAs), and the regulation of metabolic pathways. Our findings unveiled significant changes induced by 23 out of 54 plant protection products and 28 out of 36 food additives across all three categories assessed. Notable highlights include azoxystrobin, fluroxypyr, and ethoxyquin causing a substantial reduction (log2FC < -0.5) in the concentrations of the primary SCFAs: acetate, butyrate, and propionate. Several food additives had significant effects on the relative abundances of bacterial species; for example, acid orange 7 and saccharin led to a 75% decrease in Clostridium butyricum, with saccharin causing an additional 2.5-fold increase in E. coli compared to the control. Furthermore, both groups exhibited up- and down-regulation of various pathways, including those related to the metabolism of amino acids such as histidine, valine, leucine, and isoleucine, as well as bacterial secretion systems and energy pathways like starch, sucrose, butanoate, and pyruvate metabolism. This research introduces an efficient in vitro technique that enables high-throughput screening of the structure and function of a simplified and well-defined human gut microbiota model against 90 chemicals using metaproteomics and metabolomics. We believe this approach will be instrumental in characterizing chemical-microbiota interactions especially important for regulatory chemical risk assessments.

Synthetic Communities of Gut Microbes for Basic Research and Translational Approaches in Animal Health and Nutrition

Microbes and animals have a symbiotic relationship that greatly influences nutrient uptake and animal health. This relationship can be studied using selections of microbes termed synthetic communities, or SynComs. SynComs are used in many different animal hosts, including agricultural animals, to investigate microbial interactions with nutrients and how these affect animal health. The most common host focuses for SynComs are currently mouse and human, from basic mechanistic research through to translational disease models and live biotherapeutic products (LBPs) as treatments. We discuss SynComs used in basic research models and findings that relate to human and animal health and nutrition. Translational use cases of SynComs are discussed, followed by LBPs, especially within the context of agriculture. SynComs still face challenges, such as standardization for reproducibility and contamination risks. However, the future of SynComs is hopeful, especially in the areas of genome-guided SynCom design and custom SynCom-based treatments.

DSS treatment does not affect murine colonic microbiota in absence of the host

The prevalence of inflammatory bowel disease (IBD) is rising globally; however, its etiology is still not fully understood. Patient genetics, immune system, and intestinal microbiota are considered critical factors contributing to IBD. Preclinical animal models are crucial to better understand the importance of individual contributing factors. Among these, the dextran sodium sulfate (DSS) colitis model is the most widely used. DSS treatment induces gut inflammation and dysbiosis. However, its exact mode of action remains unclear. To determine whether DSS treatment induces pathogenic changes in the microbiota, we investigated the microbiota-modulating effects of DSS on murine microbiota in vitro. For this purpose, we cultured murine microbiota from the colon in six replicate continuous bioreactors. Three bioreactors were supplemented with 1% DSS and compared with the remaining PBS-treated control bioreactors by means of microbiota taxonomy and functionality. Using metaproteomics, we did not identify significant changes in microbial taxonomy, either at the phylum or genus levels. No differences in the metabolic pathways were observed. Furthermore, the global metabolome and targeted short-chain fatty acid (SCFA) quantification did not reveal any DSS-related changes. DSS had negligible effects on microbial functionality and taxonomy in vitro in the absence of the host environment. Our results underline that the DSS colitis mouse model is a suitable model to study host-microbiota interactions, which may help to understand how intestinal inflammation modulates the microbiota at the taxonomic and functional levels.

Culturing the unculturables: strategies, challenges, and opportunities for gut microbiome study

Metagenome sequencing techniques revolutionized the field of gut microbiome study. However, it is equipped with experimental and computational biases, which affect the downstream analysis results. Also, live microbial strains are needed for a better understanding of host-microbial crosstalks and for designing next-generation treatment therapies based on probiotic strains and postbiotic molecules. Conventional culturing methodologies are insufficient to get the dark gut matter on the plate; therefore, there is an urgent need to propose novel culturing methods that can fill the limitations of metagenomics. The current work aims to provide a consolidated evaluation of the available methods for host-microbe interaction with an emphasis on in vitro culturing of gut microbes using organoids, gut on a chip, and gut bioreactor. Further, the knowledge of microbial crosstalk in the gut helps us to identify core microbiota, and key metabolites that will aid in designing culturing media and co-culturing systems for gut microbiome study. After the deeper mining of the current culturing methods, we recommend that 3D-printed intestinal cells in a multistage continuous flow reactor equipped with an extended organoid system might be a good practical choice for gut microbiota-based studies.

Advancing human gut microbiota research by considering gut transit time

Accumulating evidence indicates that gut transit time is a key factor in shaping the gut microbiota composition and activity, which are linked to human health. Both population-wide and small-scale studies have identified transit time as a top covariate contributing to the large interindividual variation in the faecal microbiota composition. Despite this, transit time is still rarely being considered in the field of the human gut microbiome. Here, we review the latest research describing how and why whole gut and segmental transit times vary substantially between and within individuals, and how variations in gut transit time impact the gut microbiota composition, diversity and metabolism. Furthermore, we discuss the mechanisms by which the gut microbiota may causally affect gut motility. We argue that by taking into account the interindividual and intraindividual differences in gut transit time, we can advance our understanding of diet-microbiota interactions and disease-related microbiome signatures, since these may often be confounded by transient or persistent alterations in transit time. Altogether, a better understanding of the complex, bidirectional interactions between the gut microbiota and transit time is required to better understand gut microbiome variations in health and disease.

Designs and methodologies to recreate in vitro human gut microbiota models

The human gut microbiota is widely considered to be a metabolic organ hidden within our bodies, playing a crucial role in the host’s physiology. Several factors affect its composition, so a wide variety of microbes residing in the gut are present in the world population. Individual excessive imbalances in microbial composition are often associated with human disorders and pathologies, and new investigative strategies to gain insight into these pathologies and define pharmaceutical therapies for their treatment are needed. In vitro models of the human gut microbiota are commonly used to study microbial fermentation patterns, community composition, and host-microbe interactions. Bioreactors and microfluidic devices have been designed to culture microorganisms from the human gut microbiota in a dynamic environment in the presence or absence of eukaryotic cells to interact with. In this review, we will describe the overall elements required to create a functioning, reproducible, and accurate in vitro culture of the human gut microbiota. In addition, we will analyze some of the devices currently used to study fermentation processes and relationships between the human gut microbiota and host eukaryotic cells.

Graphic abstract

MetaLab-MAG: A Metaproteomic Data Analysis Platform for Genome-Level Characterization of Microbiomes from the Metagenome-Assembled Genomes Database

The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.

Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties

Microbe–microbe interactions in the human gut are influenced by host-derived glycans and diet. The high complexity of the gut microbiome poses a major challenge for unraveling the metabolic interactions and trophic roles of key microbes. Synthetic minimal microbiomes provide a pragmatic approach to investigate their ecology including metabolic interactions. Here, we rationally designed a synthetic microbiome termed Mucin and Diet based Minimal Microbiome (MDb-MM) by taking into account known physiological features of 16 key bacteria. We combined 16S rRNA gene-based composition analysis, metabolite measurements and metatranscriptomics to investigate community dynamics, stability, inter-species metabolic interactions and their trophic roles. The 16 species co-existed in the in vitro gut ecosystems containing a mixture of complex substrates representing dietary fibers and mucin. The triplicate MDb-MM’s followed the Taylor’s power law and exhibited strikingly similar ecological and metabolic patterns. The MDb-MM exhibited resistance and resilience to temporal perturbations as evidenced by the abundance and metabolic end products. Microbe-specific temporal dynamics in transcriptional niche overlap and trophic interaction network explained the observed co-existence in a competitive minimal microbiome. Overall, the present study provides crucial insights into the co-existence, metabolic niches and trophic roles of key intestinal microbes in a highly dynamic and competitive in vitro ecosystem.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide–spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Pout2Prot: An Efficient Tool to Create Protein (Sub)groups from Percolator Output Files

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can be present not only in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

MAIT cell activation is reduced by direct and microbiota-mediated exposure to bisphenols

Oral uptake is the primary route of human bisphenol exposure, resulting in an exposure of the intestinal microbiota and intestine-associated immune cells. Therefore, we compared the impact of bisphenol A (BPA), bisphenol F (BPF) and bisphenol S (BPS) on (i) intestinal microbiota, (ii) microbiota-mediated immunomodulatory effects and (iii) direct effects on mucosal-associated invariant T (MAIT) cells in vitro. We acutely exposed human fecal microbiota, Bacteroides thetaiotaomicron and Escherichia coli to BPA and its analogues BPF and BPS referring to the European tolerable daily intake (TDI), i.e. 2.3 µg/mL, 28.3 µg/mL and 354.0 µg/mL. Growth and viability of E. coli was most susceptible to BPF, whereas B.thetaiotaomicron and fecal microbiota were affected by BPA > BPF > BPS. At 354.0 µg/mL bisphenols altered microbial diversity in compound-specific manner and modulated microbial metabolism, with BPA already acting on metabolism at 28.3 µg/mL. Microbiota-mediated effects on MAIT cells were observed for the individual bacteria at 354.0 µg/mL only. However, BPA and BPF directly modulated MAIT cell responses at low concentrations, whereby bisphenols at concentrations equivalent for the current TDI had no modulatory effects for microbiota or for MAIT cells. Our findings indicate that acute bisphenol exposure may alter microbial metabolism and impact directly on immune cells.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Development and Validation of a LC-MS/MS Technique for the Analysis of Short Chain Fatty Acids in Tissues and Biological Fluids without Derivatisation Using Isotope Labelled Internal Standards

The gut microbiota is critical to the maintenance of physiological homeostasis and as such is implicated in a range of diseases such as colon cancer, ulcerative colitis, diabetes, cardiovascular diseases, and neurodegenerative diseases. Short chain fatty acids (SCFAs) are key metabolites produced by the gut microbiota from the fermentation of dietary fibre. Here we present a novel, sensitive, and direct LC-MS/MS technique using isotopically labelled internal standards without derivatisation for the analysis of SCFAs in different biological matrices. The technique has significant advantages over the current widely used techniques based on sample derivatization and GC-MS analysis, including fast and simple sample preparation and short LC runtime (10 min). The technique is specific and sensitive for the quantification of acetate, butyrate, isobutyrate, isovalerate, lactate, propionate and valerate. The limits of detection were all 0.001 mM except for acetate which was 0.003 mM. The calibration curves for all the analytes were linear with correlation coefficients r² > 0.998. The intra- and inter-day precisions in three levels of known concentrations were <12% and <20%, respectively. The quantification accuracy ranged from 92% to 120%. The technique reported here offers a valuable analytical tool for use in studies of SCFA production in the gut and their distribution to host tissues.

Polyphenol-Mediated Gut Microbiota Modulation: Toward Prebiotics and Further

The genome of gut microbes encodes a collection of enzymes whose metabolic functions contribute to the bioavailability and bioactivity of unabsorbed (poly)phenols. Datasets from high throughput sequencing, metabolome measurements, and other omics have expanded the understanding of the different modes of actions by which (poly)phenols modulate the microbiome conferring health benefits to the host. Progress have been made to identify direct prebiotic effects of (poly)phenols; albeit up to date, these compounds are not recognized as prebiotics sensu stricto. Interestingly, certain probiotics strains have an enzymatic repertoire, such as tannase, α-L-rhamnosidase, and phenolic acid reductase, involved in the transformation of different (poly)phenols into bioactive phenolic metabolites. In vivo studies have demonstrated that these (poly)phenol-transforming bacteria thrive when provided with phenolic substrates. However, other taxonomically distinct gut symbionts of which a phenolic-metabolizing activity has not been demonstrated are still significantly promoted by (poly)phenols. This is the case of Akkermansia muciniphila, a so-called antiobesity bacterium, which responds positively to (poly)phenols and may be partially responsible for the health benefits formerly attributed to these molecules. We surmise that (poly)phenols broad antimicrobial action free ecological niches occupied by competing bacteria, thereby allowing the bloom of beneficial gut bacteria. This review explores the capacity of (poly)phenols to promote beneficial gut bacteria through their direct and collaborative bacterial utilization and their inhibitory action on potential pathogenic species. We propose the term duplibiotic, to describe an unabsorbed substrate modulating the gut microbiota by both antimicrobial and prebiotic modes of action. (Poly)phenol duplibiotic effect could participate in blunting metabolic disturbance and gut dysbiosis, positioning these compounds as dietary strategies with therapeutic potential.

A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations

Background

Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions.

Results

We observe that number and quality of the peptide-spectrum matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that have previously been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence at the proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins.

Conclusions

The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration of transcriptomics data and other sources of genome-level information.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04159-8.

Intestinal fermentation in vitro models to study food-induced gut microbiota shift: an updated review

In vitro gut fermentation models were firstly introduced in nutrition and applied microbiology research back in the 1990s. These models have improved greatly during time, mainly over the resemblance to the complexity of digestion stages, the replication of experimental conditions, the multitude of ecological parameters to assay. The state of the science is that the most competitive models shall include a complex gut microbiota, small working volumes, distinct interconnected compartments and rigorous bio-chemical and ecological settings, controlled by a computer, as well as a free-hands accessibility, not to contaminate the mock microbiota. These models are a useful tool to study the impact of a given diet compound, e.g. prebiotics, on the human gut microbiota. The principal application is to focus on the shift of the core microbial groups and selected species together with their metabolites, assaying their diversity, richness and abundance in the community over time. Besides, it is possible to study how a compound is digested, which metabolic pathways are triggered, and the type and quantity of microbial metabolites produced. Further prospective should focus on challenges with pathogens as well as on ecology of gut syndromes. In this minireview an updated presentation of the most used intestinal models is presented, basing on their concept, technical features, as well as on research applications.

Metaproteomics-An Advantageous Option in Studies of Host-Microbiota Interaction

Gut microbiome contributes to host health by maintaining homeostasis, increasing digestive efficiency, and facilitating the development of the immune system. Manipulating gut microbiota is being recognized as a therapeutic target to manage various chronic diseases. The therapeutic manipulation of the intestinal microbiome is achieved through diet modification, the administration of prebiotics, probiotics, or antibiotics, and more recently, fecal microbiome transplantation (FMT). In this opinion paper, we give a perspective on the current status of application of multi-omics technologies in the analysis of host-microbiota interactions. The aim of this paper was to highlight the strengths of metaproteomics, which integrates with and often relies on other approaches.

Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome

BACKGROUND

The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities.

RESULTS

We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx.

CONCLUSIONS

We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract. Video abstract.

Effect of a novel animal milk oligosaccharide biosimilar on the gut microbial communities and metabolites of in vitro incubations using feline and canine fecal inocula

Milk oligosaccharides (MO) confer multiple potential physiological benefits, such as the selective growth promotion of beneficial microbiota, inhibition of enteric pathogen growth and adhesion to enterocytes, maturation of the gut mucosal barrier, and modulation of the gastrointestinal immune system. This study was conducted to determine the fermentation potential of GNU100, an animal MO biosimilar, in an in vitro system using healthy canine and feline fecal inocula. Single feline and single canine fecal samples were used to inoculate a batch fermentation system. Tubes containing a blank control (BNC), GNU100 at 0.5% (5 g/L; GNU1), or GNU100 at 1.0% (10 g/L; GNU2) were incubated for 48 h. Gas pressure, pH, lactate, short-chain fatty acids (SCFA; acetate, propionate, and butyrate), and branched-chain fatty acids (BCFA; isobutyrate, isovalerate, and valerate) were measured after 6, 24, and 48 h. Ammonium and microbiota (total bacteria by flow cytometry and Pet-16Seq; Lactobacillus and Bifidobacterium by quantitative polymerase chain reaction ) were measured after 24 and 48 h. Data were analyzed using the Mixed Models procedure of SAS. Substrates were considered to be a fixed effect and replicates considered to be a random effect. Tukey's multiple comparison analysis was used to compare least squares means, with differences considered significant with P < 0.05. In feline and canine incubations, SCFA increases were greater (P < 0.0001) in GNU100 compared with BNC, with acetate making up the largest SCFA proportion (P < 0.0001). GNU100 cultures led to greater increases (P < 0.0001) in lactate and ammonium than BNC in the feline incubations. GNU100 cultures led to greater increases (P < 0.0001) in ammonium than BNC in canine incubations and greater increases (P < 0.0001) in BCFA than BNC in feline incubations. Pet-16Seq microbial profiles from the feline and canine fecal incubations exhibited a modulation after GNU100 fermentation, with a reduction of the genera Escherichia/Shigella and Salmonella. In feline incubations, Bifidobacterium populations had greater increases (P < 0.0001) in GNU100 than BNC. In feline incubations, Lactobacillus populations had greater increases (P = 0.01) in GNU100 than BNC, with GNU1 leading to greater increases (P = 0.02) in Lactobacillus than BNC tubes in canine incubations. Overall, this study demonstrated that GNU100 was fermented in an in vitro fermentation system inoculated with canine and feline microbiota, resulting in the growth of beneficial bacteria and the production of SCFA, BCFA, and ammonium.

The glyphosate formulation Roundup® LB plus influences the global metabolome of pig gut microbiota in vitro

Glyphosate is the world's most widely used herbicide, and its potential side effects on the intestinal microbiota of various animals, from honeybees to livestock and humans, are currently under discussion. Pigs are among the most abundant livestock animals worldwide and an impact of glyphosate on their intestinal microbiota function can have serious consequences on their health, not to mention the economic effects. Recent studies that addressed microbiota-disrupting effects focused on microbial taxonomy but lacked functional information. Therefore, we chose an experimental design with a short incubation time in which effects on the community structure are not expected, but functional effects can be detected. We cultivated intestinal microbiota derived from pig colon in chemostats and investigated the acute effect of 228 mg/d glyphosate acid equivalents from Roundup® LB plus, a frequently applied glyphosate formulation. The applied glyphosate concentration resembles a worst-case scenario for an 8-9 week-old pig and relates to the maximum residue levels of glyphosate on animal fodder. The effects were determined on the functional level by metaproteomics, targeted and untargeted meta-metabolomics, while variations in community structure were analyzed by 16S rRNA gene profiling and on the single cell level by microbiota flow cytometry. Roundup® LB plus did not affect the community taxonomy or the enzymatic repertoire of the cultivated microbiota in general or on the expression of the glyphosate target enzyme 5-enolpyruvylshikimate-3-phosphate synthase in detail. On the functional level, targeted metabolite analysis of short chain fatty acids (SCFAs), free amino acids and bile acids did not reveal significant changes, whereas untargeted meta-metabolomics did identify some effects on the functional level. This multi-omics approach provides evidence for subtle metabolic effects of Roundup® LB plus under the conditions applied.

Environmentally Relevant Concentration of Bisphenol S Shows Slight Effects on SIHUMIx

Bisphenol S (BPS) is an industrial chemical used in the process of polymerization of polycarbonate plastics and epoxy resins and thus can be found in various plastic products and thermal papers. The microbiota disrupting effect of BPS on the community structure of the microbiome has already been reported, but little is known on how BPS affects bacterial activity and function. To analyze these effects, we cultivated the simplified human intestinal microbiota (SIHUMIx) in bioreactors at a concentration of 45 µM BPS. By determining biomass, growth of SIHUMIx was followed but no differences during BPS exposure were observed. To validate if the membrane composition was affected, fatty acid methyl esters (FAMEs) profiles were compared. Changes in the individual membrane fatty acid composition could not been described; however, the saturation level of the membranes slightly increased during BPS exposure. By applying targeted metabolomics to quantify short-chain fatty acids (SCFA), it was shown that the activity of SIHUMIx was unaffected. Metaproteomics revealed temporal effect on the community structure and function, showing that BPS has minor effects on the structure or functionality of SIHUMIx.

Cytometric fingerprints of gut microbiota predict Crohn's disease state

Variations in the gut microbiome have been associated with changes in health state such as Crohn's disease (CD). Most surveys characterize the microbiome through analysis of the 16S rRNA gene. An alternative technology that can be used is flow cytometry. In this report, we reanalyzed a disease cohort that has been characterized by both technologies. Changes in microbial community structure are reflected in both types of data. We demonstrate that cytometric fingerprints can be used as a diagnostic tool in order to classify samples according to CD state. These results highlight the potential of flow cytometry to perform rapid diagnostics of microbiome-associated diseases.

The Activation of Mucosal-Associated Invariant T (MAIT) Cells Is Affected by Microbial Diversity and Riboflavin Utilization in vitro

Recent research has demonstrated that MAIT cells are activated by individual bacterial or yeasts species that possess the riboflavin biosynthesis pathway. However, little is known about the MAIT cell activating potential of microbial communities and the contribution of individual community members. Here, we analyze the MAIT cell activating potential of a human intestinal model community (SIHUMIx) as well as intestinal microbiota after bioreactor cultivation. We determined the contribution of individual SIHUMIx community members to the MAIT cell activating potential and investigated whether microbial stress can influence their MAIT cell activating potential. The MAIT cell activating potential of SIHUMIx was directly related to the relative species abundances in the community. We therefore suggest an additive relationship between the species abundances and their MAIT cell activating potential. In diverse microbial communities, we found that a low MAIT cell activating potential was associated with high microbial diversity and a high level of riboflavin demand and vice versa. We suggest that microbial diversity might affect MAIT cell activation via riboflavin utilization within the community. Microbial acid stress significantly reduced the MAIT cell activating potential of SIHUMIx by impairing riboflavin availability through increasing the riboflavin demand. We show that MAIT cells can perceive microbial stress due to changes in riboflavin utilization and that riboflavin availability might also play a central role for the MAIT cell activating potential of diverse microbiota.

Quantification of glyphosate and aminomethylphosphonic acid from microbiome reactor fluids

RATIONALE

Glyphosate is one of the most widely used herbicides and it is suspected to affect the intestinal microbiota through inhibition of aromatic amino acid synthesis via the shikimate pathway. In vitro microbiome bioreactors are increasingly used as model systems to investigate effects on intestinal microbiota and consequently methods for the quantitation of glyphosate and its degradation product aminomethylphosphonic acid (AMPA) in microbiome model systems are required.

METHODS

An optimized protocol enables the analysis of both glyphosate and AMPA by simple extraction with methanol:acetonitrile:water (2:3:1) without further enrichment steps. Glyphosate and AMPA are separated by liquid chromatography on an amide column and identified and quantified with a targeted tandem mass spectrometry method using a QTRAP 5500 system (AB Sciex).

RESULTS

Our method has a limit of detection (LOD) in extracted water samples of <2 ng/mL for both glyphosate and AMPA. In complex intestinal medium, the LOD is 2 and 5 ng/mL for glyphosate and AMPA, respectively. These LODs allow for measurement at exposure-relevant concentrations. Glyphosate levels in a bioreactor model of porcine colon were determined and consequently it was verified whether AMPA was produced by porcine gut microbiota.

CONCLUSIONS

The method presented here allows quantitation of glyphosate and AMPA in complex bioreactor fluids and thus enables studies of the impact of glyphosate and its metabolism on intestinal microbiota. In addition, the extraction protocol is compatible with an untargeted metabolomics analysis, thus allowing one to look for other perturbations caused by glyphosate in the same sample.

Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics

Introduction: Metaproteomics is an established method to obtain a comprehensive taxonomic and functional view of microbial communities. After more than a decade, we are now able to describe the promise, reality, and perspectives of metaproteomics and provide useful information about the choice of method, applications, and potential improvement strategies.Areas covered: In this article, we will discuss current challenges of species and proteome coverage, and also highlight functional aspects of metaproteomics analysis of microbial communities with different levels of complexity. To do this, we re-analyzed data from microbial communities with low to high complexity (8, 72, 200 and >300 species). High species diversity leads to a reduced number of protein group identifications in a complex community, and thus the number of species resolved is underestimated. Ultimately, low abundance species remain undiscovered in complex communities. However, we observed that the main functional categories were better represented within complex microbiomes when compared to species coverage.Expert opinion: Our findings showed that even with low species coverage, metaproteomics has the potential to reveal habitat-specific functional features. Finally, we exploit this information to highlight future research avenues that are urgently needed to enhance our understanding of taxonomic composition and functions of complex microbiomes.

Bacterial Community Diversity Dynamics Highlight Degrees of Nestedness and Turnover Patterns

Bacterial communities change their structure rapidly due to short generation times of their members. How bacteria assemble to certain structures provides insight into ecological mechanisms that shape a bacterial community. Microbial community flow cytometry was used to create community fingerprints based on subcommunity distributions and to visualize the dynamic variations of 10 independently grown communities under equal conditions. Inventory diversity values were recorded by α- and γ-diversity whereas the degree of subsistence of subcommunities (nestedness) and the degree of gain or loss of subcommunities (turnover) was calculated as multi-sites ß-diversity terms ß_NES and ß_SIM . Numbers of unique subcommunities of pairwise samples were determined by intra- and inter-community ß-diversity values. Although all communities were exposed to niche-differentiating conditions they assembled to disparate structures. In our study, the turnover coefficients were high (> 0.6), while the nestedness coefficients were complementary low in the separate 10 bioreactors. Intra- and inter-community ß-diversity values indicated fast community shifts. Microbial community flow cytometry straightforwardly identifies the dominance and subsistence of subsets of cells in a community or the degree of their replacement. The calculation of either turnover or nestedness patterns might have implications in medical, biotechnological, or environmental research. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.