An Invitation to the Marriage of Metagenomics and Metabolomics

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Microbiome-metabolite linkages drive greenhouse gas dynamics over a permafrost thaw gradient

Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This contradicts common assumptions in trait-based microbial models and highlights the limitations of measuring microbial community-level data alone. Furthermore, feature-scale analysis revealed connections between microbial taxa, metabolites and observed CO2 and CH4 porewater variations. Our study showcases insights gained by using feature-level data and microorganism-metabolite interactions to better understand metabolic processes that drive greenhouse gas emissions during ecosystem changes.

Metabolic interactions of host-gut microbiota: New possibilities for the precise diagnosis and therapeutic discovery of gastrointestinal cancer in the future-A review

Gastrointestinal (GI) cancer continues to pose a significant global health challenge. Recent advances in our understanding of the complex relationship between the host and gut microbiota have shed light on the critical role of metabolic interactions in the pathogenesis and progression of GI cancer. In this study, we examined how microbiota interact with the host to influence signalling pathways that impact the formation of GI tumours. Additionally, we investigated the potential therapeutic approach of manipulating GI microbiota for use in clinical settings. Revealing the complex molecular exchanges between the host and gut microbiota facilitates a deeper understanding of the underlying mechanisms that drive cancer development. Metabolic interactions hold promise for the identification of microbial signatures or metabolic pathways associated with specific stages of cancer. Hence, this study provides potential strategies for the diagnosis, treatment and management of GI cancers to improve patient outcomes.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

IMPORTANCE

We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

From nets to networks: tools for deciphering phytoplankton metabolic interactions within communities and their global significance

Our oceans are populated with a wide diversity of planktonic organisms that form complex dynamic communities at the base of marine trophic networks. Within such communities are phytoplankton, unicellular photosynthetic taxa that provide an estimated half of global primary production and support biogeochemical cycles, along with other essential ecosystem services. One of the major challenges for microbial ecologists has been to try to make sense of this complexity. While phytoplankton distributions can be well explained by abiotic factors such as temperature and nutrient availability, there is increasing evidence that their ecological roles are tightly linked to their metabolic interactions with other plankton members through complex mechanisms (e.g. competition and symbiosis). Therefore, unravelling phytoplankton metabolic interactions is the key for inferring their dependency on, or antagonism with, other taxa and better integrating them into the context of carbon and nutrient fluxes in marine trophic networks. In this review, we attempt to summarize the current knowledge brought by ecophysiology, organismal imaging, in silico predictions and co-occurrence networks using 'omics data, highlighting successful combinations of approaches that may be helpful for future investigations of phytoplankton metabolic interactions within their complex communities.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Emerging tools and best practices for studying gut microbial community metabolism

The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.

Investigating gut microbiota-blood and urine metabolite correlations in early sepsis-induced acute kidney injury: insights from targeted KEGG analyses

Background

The interplay between gut microbiota and metabolites in the early stages of sepsis-induced acute kidney injury (SA-AKI) is not yet clearly understood. This study explores the characteristics and interactions of gut microbiota, and blood and urinary metabolites in patients with SA-AKI.

Methods

Utilizing a prospective observational approach, we conducted comparative analyses of gut microbiota and metabolites via metabolomics and metagenomics in individuals diagnosed with SA-AKI compared to those without AKI (NCT06197828). Pearson correlations were used to identify associations between microbiota, metabolites, and clinical indicators. The Comprehensive Antibiotic Resistance Database was employed to detect antibiotic resistance genes (ARGs), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways informed on metabolic processes and microbial resistance patterns.

Results

Our study included analysis of four patients with SA-AKI and five without AKI. Significant disparities in bacterial composition were observed, illustrated by diversity indices (Shannon index: 2.0 ± 0.4 vs. 1.4 ± 0.6, P = 0.230; Simpson index: 0.8 ± 0.1 vs. 0.6 ± 0.2, P = 0.494) between the SA-AKI group and the non-AKI group. N6, N6, N6-Trimethyl-L-lysine was detected in both blood and urine metabolites, and also showed significant correlations with specific gut microbiota (Campylobacter hominis and Bacteroides caccae, R > 0, P < 0.05). Both blood and urine metabolites were enriched in the lysine degradation pathway. We also identified the citrate cycle (TCA cycle) as a KEGG pathway enriched in sets of differentially expressed ARGs in the gut microbiota, which exhibits an association with lysine degradation.

Conclusions

Significant differences in gut microbiota and metabolites were observed between the SA-AKI and non-AKI groups, uncovering potential biomarkers and metabolic changes linked to SA-AKI. The lysine degradation pathway may serve as a crucial link connecting gut microbiota and metabolites.

Chronic exposure to tris(1,3-dichloro-2-propyl) phosphate: Effects on intestinal microbiota and serum metabolism in rats

Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) is a widely used organophosphate ester that can adversely affect animal or human health. The intestinal microbiota is critical to human health. High-dose exposure to TDCIPP can markedly affect the intestinal ecosystem of mice, but the effects of long-term exposure to lower concentrations of TDCIPP on the intestinal flora and body metabolism remain unclear. In this study, TDCIPP was administered to Sprague-Dawley rats by gavage at a dose of 13.3 mg/kg bw/day for 90 days. TDCIPP increased the relative weight of the kidneys (P = 0.017), but had no effect on the relative weight of the heart, liver, spleen, lungs, testes, and ovaries (P > 0.05). 16 S rRNA gene sequencing revealed that long-term TDCIPP exposure affected the diversity, relative abundance, and functions of rat gut microbes. The serum metabolomics of the rats showed that TDCIPP can disrupt the serum metabolic profiles, result in the up-regulation of 26 metabolites and down-regulation of 3 metabolites, and affect multiple metabolic pathways in rat sera. In addition, the disturbed genera and metabolites were correlated. The functions of some disturbed gut microbes were consistent with the affected metabolic pathways in the sera, and these metabolic pathways were all associated with kidney disease, suggesting that TDCIPP may cause kidney injury in rats by affecting the intestinal flora and serum metabolism.

High microbiome and metabolome diversification in coexisting sponges with different bio-ecological traits

Marine Porifera host diverse microbial communities, which influence host metabolism and fitness. However, functional relationships between sponge microbiomes and metabolic signatures are poorly understood. We integrate microbiome characterization, metabolomics and microbial predicted functions of four coexisting Mediterranean sponges -Petrosia ficiformis, Chondrosia reniformis, Crambe crambe and Chondrilla nucula. Microscopy observations reveal anatomical differences in microbial densities. Microbiomes exhibit strong species-specific trends. C. crambe shares many rare amplicon sequence variants (ASV) with the surrounding seawater. This suggests important inputs of microbial diversity acquired by selective horizontal acquisition. Phylum Cyanobacteria is mainly represented in C. nucula and C. crambe. According to putative functions, the microbiome of P. ficiformis and C. reniformis are functionally heterotrophic, while C. crambe and C. nucula are autotrophic. The four species display distinct metabolic profiles at single compound level. However, at molecular class level they share a "core metabolome". Concurrently, we find global microbiome-metabolome association when considering all four sponge species. Within each species still, sets of microbe/metabolites are identified driving multi-omics congruence. Our findings suggest that diverse microbial players and metabolic profiles may promote niche diversification, but also, analogous phenotypic patterns of "symbiont evolutionary convergence" in sponge assemblages where holobionts co-exist in the same area.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Mathematical modeling of fluid dynamics in in vitro gut fermentation systems: A new tool to improve the interpretation of microbial metabolism

In vitro systems are widely employed to assess the impact of dietary compounds on the gut microbiota and their conversion into beneficial bacterial metabolites. However, the complex fluid dynamics and multi-segmented nature of these systems can complicate the comprehensive analysis of dietary compound fate, potentially confounding physical dilution or washout with microbial catabolism. In this study, we developed fluid dynamics models based on sets of ordinary differential equations to simulate the behavior of an inert compound within two commonly used in vitro systems: the continuous two-stage PolyFermS system and the semi-continuous multi-segmented SHIME® system as well as into various declinations of those systems. The models were validated by investigating the fate of blue dextran, demonstrating excellent agreement between experimental and modeling data (with r2 values ranging from 0.996 to 0.86 for different approaches). As a proof of concept for the utility of fluid dynamics models in in vitro system, we applied generated models to interpret metabolomic data of procyanidin A2 (ProA2) generated from the addition of proanthocyanidin (PAC)-rich cranberry extract to both the PolyFermS and SHIME® systems. The results suggested ProA2 degradation by the gut microbiota when compared to the modeling of an inert compound. Models of fluid dynamics developed in this study provide a foundation for comprehensive analysis of gut metabolic data in commonly utilized in vitro PolyFermS and SHIME® bioreactor systems and can enable a more accurate understanding of the contribution of bacterial metabolism to the variability in the concentration of target metabolites.

Adverse impacts of environmentally relevant PFOS alternatives on mice pancreatic tissues

Perfluorooctane sulfonate (PFOS) alternatives are chemicals that are used to make a range of products. Researchers have found that PFOS alternatives are probably no less toxic than PFOS, which has aroused concern. It has also revealed that the pancreas may be harmed by exposure to PFOS alternatives. However, there is insufficient evidence to demonstrate the toxicity mechanisms of PFOS alternatives. This study demonstrates the adverse effects of three PFOS alternatives on the pancreatic health of mice. After subchronic exposure to PFOS alternatives at environmentally relevant concentrations (800 μg/L perfluorohexanesulfonate, 800 μg/L perfluorobutanesulfonate, and 3 μg/L sodium ρ-perfluorous nonenoxybenzene sulfonate) via drinking water for 6 weeks, toxicity mechanisms were elucidated by examining histopathology, immunity, endoplasmic reticulum stress, 16S rRNA, and short-chain fatty acid targeted metabolomics. Sodium ρ-perfluorous nonenoxybenzene sulfonate significantly increased levels of TNF-α, IL-6, p-PERK, and ATF-4 and decreased the abundance of Akkermansia muciniphila and Lactobacillus reuteri. In addition, the three PFOS alternatives changed the composition of the gut microbiota in mice. Short-chain fatty acids, which are metabolites of the gut microbiota, also significantly decreased. Correlation analysis demonstrates that the alteration of gut microbes is related to the adverse effects on the mice pancreas. Results suggest that the murine pancreas may be toxic endpoints of PFOS alternatives. This study alerts the threats to human health and accelerates the toxicology research of an increasing number of emerging PFOS alternatives.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Optimizing the Gut Microbiota for Individualized Performance Development in Elite Athletes

The human gut microbiota can be compared to a fingerprint due to its uniqueness, hosting trillions of living organisms. Taking a sport-centric perspective, the gut microbiota might represent a physiological system that relates to health aspects as well as individualized performance in athletes. The athletes' physiology has adapted to their exceptional lifestyle over the years, including the diversity and taxonomy of the microbiota. The gut microbiota is influenced by several physiological parameters and requires a highly individual and complex approach to unravel the linkage between performance and the microbial community. This approach has been taken in this review, highlighting the functions that the microbial community performs in sports, naming gut-centered targets, and aiming for both a healthy and sustainable athlete and performance development. With this article, we try to consider whether initiating a microbiota analysis is practicable and could add value in elite sport, and what possibilities it holds when influenced through a variety of interventions. The aim is to support enabling a well-rounded and sustainable athlete and establish a new methodology in elite sport.

Exploring the humification process of municipal sludge in hyperthermophilic composting through metagenomic and untargeted metabolomic

Hyperthermophilic composting (HC) has been widely recognized for the advantage of high treatment efficiency for organic wastes. However, the humification process is still unclear. In this study, the humification process of HC was investigated, compared to conventional composting (CK). The results showed that the highest composting temperature, organic matter degradation rate, and humification index in HC were 92.62 °C, 23.98%, and 1.59, while those in CK were 70.23 °C, 14.49 %, and 1.04, indicating HC accelerated humification process. Moreover, the results of metagenomic and untargeted metabolomic showed that the genes and metabolisms related to carbohydrate, lipid, amino acid, fatty acid, and nucleotide were more abundant in HC. Consequently, the metabolic pathways regarding organic matter degradation and microbial reproduction were enhanced in the high temperature stage of HC, further accelerating the humification reaction in the low temperature stage. This work contributes to the comprehension of the humification mechanism in HC.

Comprehensive Analysis of Microbiome, Metabolome, and Transcriptome Revealed the Mechanisms of Intestinal Injury in Rainbow Trout under Heat Stress

Global warming is one of the most common environmental challenges faced by cold-water fish farming. Intestinal barrier function, gut microbiota, and gut microbial metabolites are significantly altered under heat stress, posing serious obstacles to the healthy artificial culture of rainbow trout. However, the molecular mechanisms underlying intestinal injury in rainbow trout under heat stress remain unclear. In the present study, the optimal growth temperature for rainbow trout (16 °C) was used for the control group, and the maximum temperature tolerated by rainbow trout (24 °C) was used for the heat stress group, which was subjected to heat stress for 21 days. The mechanism of intestinal injury in rainbow trout under heat stress was explored by combining animal histology, 16S rRNA gene amplicon sequencing, ultra-high performance liquid chromatography-mass spectrometry, and transcriptome sequencing. The results showed that the antioxidant capacity of rainbow trout was enhanced under heat stress, the levels of stress-related hormones were significantly increased, and the relative expression of genes related to heat stress proteins was significantly increased, indicating that the heat stress model of rainbow trout was successfully established. Secondly, the intestinal tract of rainbow trout showed inflammatory pathological characteristics under heat stress, with increased permeability, activation of the inflammatory factor signaling pathway, and increased relative expression of inflammatory factor genes, suggesting that the intestinal barrier function was impaired. Thirdly, heat stress caused an imbalance of intestinal commensal microbiota and changes in intestinal metabolites in rainbow trout, which participated in the stress response mainly by affecting lipid metabolism and amino acid metabolism. Finally, heat stress promoted intestinal injury in rainbow trout by activating the peroxisome proliferator-activated receptor-α signaling pathway. These results not only expand the understanding of fish stress physiology and regulation mechanisms, but also provide a scientific basis for healthy artificial culture and the reduction of rainbow trout production costs.

An untargeted exometabolomics approach to characterize dissolved organic matter in groundwater of the Samail Ophiolite

The process of serpentinization supports life on Earth and gives rise to the habitability of other worlds in our Solar System. While numerous studies have provided clues to the survival strategies of microbial communities in serpentinizing environments on the modern Earth, characterizing microbial activity in such environments remains challenging due to low biomass and extreme conditions. Here, we used an untargeted metabolomics approach to characterize dissolved organic matter in groundwater in the Samail Ophiolite, the largest and best characterized example of actively serpentinizing uplifted ocean crust and mantle. We found that dissolved organic matter composition is strongly correlated with both fluid type and microbial community composition, and that the fluids that were most influenced by serpentinization contained the greatest number of unique compounds, none of which could be identified using the current metabolite databases. Using metabolomics in conjunction with metagenomic data, we detected numerous products and intermediates of microbial metabolic processes and identified potential biosignatures of microbial activity, including pigments, porphyrins, quinones, fatty acids, and metabolites involved in methanogenesis. Metabolomics techniques like the ones used in this study may be used to further our understanding of life in serpentinizing environments, and aid in the identification of biosignatures that can be used to search for life in serpentinizing systems on other worlds.

Thermophilic Carboxylesterases from Hydrothermal Vents of the Volcanic Island of Ischia Active on Synthetic and Biobased Polymers and Mycotoxins

Hydrothermal vents are geographically widespread and host microorganisms with robust enzymes useful in various industrial applications. We examined microbial communities and carboxylesterases of two terrestrial hydrothermal vents of the volcanic island of Ischia (Italy) predominantly composed of Firmicutes, Proteobacteria, and Bacteroidota. High-temperature enrichment cultures with the polyester plastics polyhydroxybutyrate and polylactic acid (PLA) resulted in an increase of Thermus and Geobacillus species and to some extent Fontimonas and Schleiferia species. The screening at 37 to 70°C of metagenomic fosmid libraries from above enrichment cultures identified three hydrolases (IS10, IS11, and IS12), all derived from yet-uncultured Chloroflexota and showing low sequence identity (33 to 56%) to characterized enzymes. Enzymes expressed in Escherichia coli exhibited maximal esterase activity at 70 to 90°C, with IS11 showing the highest thermostability (90% activity after 20-min incubation at 80°C). IS10 and IS12 were highly substrate promiscuous and hydrolyzed all 51 monoester substrates tested. Enzymes were active with PLA, polyethylene terephthalate model substrate, and mycotoxin T-2 (IS12). IS10 and IS12 had a classical α/β-hydrolase core domain with a serine hydrolase catalytic triad (Ser155, His280, and Asp250) in their hydrophobic active sites. The crystal structure of IS11 resolved at 2.92 Å revealed the presence of a N-terminal β-lactamase-like domain and C-terminal lipocalin domain. The catalytic cleft of IS11 included catalytic Ser68, Lys71, Tyr160, and Asn162, whereas the lipocalin domain enclosed the catalytic cleft like a lid and contributed to substrate binding. Our study identified novel thermotolerant carboxylesterases with a broad substrate range, including polyesters and mycotoxins, for potential applications in biotechnology. IMPORTANCE High-temperature-active microbial enzymes are important biocatalysts for many industrial applications, including recycling of synthetic and biobased polyesters increasingly used in textiles, fibers, coatings and adhesives. Here, we identified three novel thermotolerant carboxylesterases (IS10, IS11, and IS12) from high-temperature enrichment cultures from Ischia hydrothermal vents and incubated with biobased polymers. The identified metagenomic enzymes originated from uncultured Chloroflexota and showed low sequence similarity to known carboxylesterases. Active sites of IS10 and IS12 had the largest effective volumes among the characterized prokaryotic carboxylesterases and exhibited high substrate promiscuity, including hydrolysis of polyesters and mycotoxin T-2 (IS12). Though less promiscuous than IS10 and IS12, IS11 had a higher thermostability with a high temperature optimum (80 to 90°C) for activity and hydrolyzed polyesters, and its crystal structure revealed an unusual lipocalin domain likely involved in substrate binding. The polyesterase activity of these enzymes makes them attractive candidates for further optimization and potential application in plastics recycling.

Evaluating a potential model to analyze the function of the gut microbiota of the giant panda

To contribute to the conservation of endangered animals, the utilization of model systems is critical to analyze the function of their gut microbiota. In this study, the results of a fecal microbial transplantation (FMT) experiment with germ-free (GF) mice receiving giant panda or horse fecal microbiota showed a clear clustering by donor microbial communities in GF mice, which was consistent with the results of blood metabolites from these mice. At the genus level, FMT re-established approximately 9% of the giant panda donor microbiota in GF mice compared to about 32% for the horse donor microbiota. In line with this, the difference between the panda donor microbiota and panda-mice microbiota on whole-community level was significantly larger than that between the horse donor microbiota and the horse-mice microbiota. These results were consistent with source tracking analysis that found a significantly higher retention rate of the horse donor microbiota (30.9%) than the giant panda donor microbiota (4.0%) in GF mice where the microbiota remained stable after FMT. Further analyzes indicated that the possible reason for the low retention rate of the panda donor microbiota in GF mice was a low relative abundance of Clostridiaceae in the panda donor microbiota. Our results indicate that the donor microbiota has a large effect on GF mice microbiota after FMT.

Progress and opportunities in microbial community metabolomics

The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.

Effects of Milk Replacer-Based Lactobacillus on Growth and Gut Development of Yaks' Calves: a Gut Microbiome and Metabolic Study

The gut microbiota and its metabolic activities are crucial for maintaining host homoeostasis and health, of which the role of probiotics has indeed been emphasized. The current study delves into the performance of probiotics as a beneficial managemental strategy, which further highlights their impact on growth performance, serologic investigation, gut microbiota, and metabolic profiling in yaks' calves. A field experiment was employed consisting of 2 by 3 factorial controls, including two development stages, namely, 21 and 42 days (about one and a half month), with three different feeding treatments. Results showed a positive impact of probiotic supplements on growth performance by approximately 3.16 kg (P < 0.01) compared with the blank control. Moreover, they had the potential to improve serum antioxidants and biochemical properties. We found that microorganisms that threaten health were enriched in the gut of the blank control with the depletion of beneficial bacteria, although all yaks were healthy. Additionally, the gut was colonized by a microbial succession that assembled into a more mature microbiome, driven by the probiotics strategy. The gut metabolic profiling was also changed significantly after the probiotic strategy, i.e., the concentrations of metabolites and the metabolic pattern, including enrichments in protein digestion and absorption, vitamin digestion and absorption, and biosynthesis of secondary metabolites. In summary, probiotics promoted gut microbiota/metabolites, developing precise interventions and achieving physiological benefits based on intestinal microecology. Hence, it is important to understand probiotic dietary changes to the gut microbiome, metabolome, and the host phenotype. IMPORTANCE The host microbiome is a composite of the trillion microorganisms colonizing host bodies. It can be impacted by various factors, including diet, environmental conditions, and physical activities. The yaks' calves have a pre-existing imbalance in the intestinal microbiota with an inadequate feeding strategy, resulting in poor growth performance, diarrhea, and other intestinal diseases. Hence, targeting gut microbiota might provide a new effective feeding strategy for enhancing performance and maintaining a healthy intestinal environment. Based on the current findings, milk replacer-based Lactobacillus feeding may improve growth performance and health in yaks' calves.

Metabolomics in Team-Sport Athletes: Current Knowledge, Challenges, and Future Perspectives

Metabolomics is a promising tool for studying exercise physiology and exercise-associated metabolism. It has recently been defined with the term “sportomics” due to metabolomics’ capability to characterize several metabolites in several biological samples simultaneously. This narrative review on exercise metabolomics provides an initial and brief overview of the different metabolomics technologies, sample collection, and further processing steps employed for sport. It also discusses the data analysis and its biological interpretation. Thus, we do not cover sample collection, preparation, and analysis paragraphs in detail here but outline a general outlook to help the reader to understand the metabolomics studies conducted in team-sports athletes, alongside endeavoring to recognize existing or emergent trends and deal with upcoming directions in the field of exercise metabolomics in a team-sports setting.

Targeting the gut to prevent and counteract metabolic disorders and pathologies during aging

Impairment of gut function is one of the explanatory mechanisms of health status decline in elderly population. These impairments involve a decline in gut digestive physiology, metabolism and immune status, and associated to that, changes in composition and function of the microbiota it harbors. Continuous deteriorations are generally associated with the development of systemic dysregulations and ultimately pathologies that can worsen the initial health status of individuals. All these alterations observed at the gut level can then constitute a wide range of potential targets for development of nutritional strategies that can impact gut tissue or associated microbiota pattern. This can be key, in a preventive manner, to limit gut functionality decline, or in a curative way to help maintaining optimum nutrients bioavailability in a context on increased requirements, as frequently observed in pathological situations. The aim of this review is to give an overview on the alterations that can occur in the gut during aging and lead to the development of altered function in other tissues and organs, ultimately leading to the development of pathologies. Subsequently is discussed how nutritional strategies that target gut tissue and gut microbiota can help to avoid or delay the occurrence of aging-related pathologies.

Current State and Challenges of the Global Outcomes of Dental Caries Research in the Meta-Omics Era

Despite significant healthcare advances in the 21st century, the exact etiology of dental caries remains unsolved. The past two decades have witnessed a tremendous growth in our understanding of dental caries amid the advent of revolutionary omics technologies. Accordingly, a consensus has been reached that dental caries is a community-scale metabolic disorder, and its etiology is beyond a single causative organism. This conclusion was based on a variety of microbiome studies following the flow of information along the central dogma of biology from genomic data to the end products of metabolism. These studies were facilitated by the unprecedented growth of the next- generation sequencing tools and omics techniques, such as metagenomics and metatranscriptomics, to estimate the community composition of oral microbiome and its functional potential. Furthermore, the rapidly evolving proteomics and metabolomics platforms, including nuclear magnetic resonance spectroscopy and/or mass spectrometry coupled with chromatography, have enabled precise quantification of the translational outcomes. Although the majority supports 'conserved functional changes' as indicators of dysbiosis, it remains unclear how caries dynamics impact the microbiota functions and vice versa, over the course of disease onset and progression. What compounds the situation is the host-microbiota crosstalk. Genome-wide association studies have been undertaken to elucidate the interaction of host genetic variation with the microbiome. However, these studies are challenged by the complex interaction of host genetics and environmental factors. All these complementary approaches need to be orchestrated to capture the key players in this multifactorial disease. Herein, we critically review the milestones in caries research focusing on the state-of-art singular and integrative omics studies, supplemented with a bibliographic network analysis to address the oral microbiome, the host factors, and their interactions. Additionally, we highlight gaps in the dental literature and shed light on critical future research questions and study designs that could unravel the complexities of dental caries, the most globally widespread disease.

Mammalian gut metabolomes mirror microbiome composition and host phylogeny

In the past decade, studies on the mammalian gut microbiome have revealed that different animal species have distinct gut microbial compositions. The functional ramifications of this variation in microbial composition remain unclear: do these taxonomic differences indicate microbial adaptations to host-specific functionality, or are these diverse microbial communities essentially functionally redundant, as has been indicated by previous metagenomics studies? Here, we examine the metabolic content of mammalian gut microbiomes as a direct window into ecosystem function, using an untargeted metabolomics platform to analyze 101 fecal samples from a range of 25 exotic mammalian species in collaboration with a zoological center. We find that mammalian metabolomes are chemically diverse and strongly linked to microbiome composition, and that metabolome composition is further correlated to the phylogeny of the mammalian host. Specific metabolites enriched in different animal species included modified and degraded host and dietary compounds such as bile acids and triterpenoids, as well as fermentation products such as lactate and short-chain fatty acids. Our results suggest that differences in microbial taxonomic composition are indeed translated to host-specific metabolism, indicating that taxonomically distant microbiomes are more functionally diverse than redundant.

Association of Gut Microbiota With Metabolism in Rainbow Trout Under Acute Heat Stress

Global warming is one of the most common environmental challenges faced by cold-water fish farming. Heat stress seriously affects the feeding, growth, immunity, and disease resistance of fish. These changes are closely related to the destruction of intestinal barrier function, the change of intestinal microbiota, and metabolic dysfunction. However, the causal relationship between the phenotypic effects of heat stress as well as intestinal and metabolic functions of fish is unknown. In the current study, the optimal growth temperature (16°C) of rainbow trout was used as the control group, while the fish treated at 22.5°C, 23.5°C, and 24.5°C for 24 h, respectively, were the treatment groups. The 16S rRNA gene sequencing analysis showed that with the increase in temperature, the relative abundance and diversity of intestinal microbiota decreased significantly, while the number of Mycoplasma, Firmicutes, and Tenericutes increased significantly. Non-targeted metabolomics analysis by liquid chromatography-mass spectrometry analysis and correlation analysis showed that the changes of metabolites related to amino acids, vitamins, and short-chain fatty acids in serum of rainbow trout under acute heat stress were strongly correlated with the decrease of relative abundance of various intestinal microbiota, especially Morganella, Enterobacter, Lactobacillus, Lawsonia, and Cloacibacterium. In addition, we also found that acute heat stress seriously affected the intestinal structure and barrier function, and also caused the pathological damage of epithelial cells. These results indicate that the gut microbiome of acute heat-stressed rainbow trout could mediate metabolite transfer through the gut barrier by affecting its integrity. Significant changes in gut morphology, permeability, antioxidant capacity, and pro-inflammatory cytokine levels were observed. Therefore, it is necessary to explore the changes of intestinal microbiota under heat stress to help understand the regulatory mechanism of heat stress and protect the intestinal health of rainbow trout from the negative effects of rising water temperature.

Construction of synthetic microbial consortia for 2-keto-L-gulonic acid biosynthesis

Currently, the establishment of synthetic microbial consortia with rational strategies has gained extensive attention, becoming one of the important frontiers of synthetic biology. Systems biology can offer insights into the design and construction of synthetic microbial consortia. Taking the high-efficiency production of 2-keto-l-gulonic acid (2-KLG) as an example, we constructed a synthetic microbial consortium “Saccharomyces cerevisiae-Ketogulonigenium vulgare” based on systems biology analysis. In the consortium, K. vulgare was the 2-KLG producing strain, and S. cerevisiae acted as the helper strain. Comparative transcriptomic analysis was performed on an engineered S. cerevisiae (VTC2) and a wild-type S. cerevisiae BY4741. The results showed that the up-regulated genes in VTC2, compared with BY4741, were mainly involved in glycolysis, TCA cycle, purine metabolism, and biosynthesis of amino acids, B vitamins, and antioxidant proteases, all of which play important roles in promoting the growth of K. vulgare. Furthermore, Vitamin C produced by VTC2 could further relieve the oxidative stress in the environment to increase the production of 2-KLG. Therefore, VTC2 would be of great advantage in working with K. vulgare. Thus, the synthetic microbial consortium "VTC2-K. vulgare" was constructed based on transcriptomics analyses, and the accumulation of 2-KLG was increased by 1.49-fold compared with that of mono-cultured K. vulgare, reaching 13.2 ± 0.52 g/L. In addition, the increased production of 2-KLG was accompanied by the up-regulated activities of superoxide dismutase and catalase in the medium and the up-regulated oxidative stress-related genes (sod, cat and gpd) in K. vulgare. The results indicated that the oxidative stress in the synthetic microbial consortium was efficiently reduced. Thus, systems analysis confirmed a favorable symbiotic relationship between microorganisms, providing guidance for further engineering synthetic consortia.

"Driver-passenger" bacteria and their metabolites in the pathogenesis of colorectal cancer

Colorectal cancer (CRC) is a significant public health problem accounting for about 10% of all new cancer cases globally. Though genetic and epigenetic factors influence CRC, the gut microbiota acts as a significant component of the disease's etiology. Further research is still needed to clarify the specific roles and identify more bacteria related to CRC development. This review aims to provide an overview of the "driver-passenger" model of CRC. The colonization and active invasion of the "driver(s)" bacteria cause damages allowing other commensals, known as "passengers," or their by-products, i.e., metabolites, to pass through the epithelium . This review will not only focus on the species of bacteria implicated in this model but also on their biological functions implicated in the occurrence of CRC, such as forming biofilms, mucus, penetration and production of enterotoxins and genotoxins.

Examining the Interaction of the Gut Microbiome with Host Metabolism and Cardiometabolic Health in Metabolic Syndrome

(1) Background: The microbiota-host cross-talk has been previously investigated, while its role in health is not yet clear. This study aimed to unravel the network of microbial-host interactions and correlate it with cardiometabolic risk factors. (2) Methods: A total of 47 adults with overweight/obesity and metabolic syndrome from the METADIET study were included in this cross-sectional analysis. Microbiota composition (151 genera) was assessed by 16S rRNA sequencing, fecal (m = 203) and plasma (m = 373) metabolites were profiled. An unsupervised sparse generalized canonical correlation analysis was used to construct a network of microbiota-metabolite interactions. A multi-omics score was derived for each cluster of the network and associated with cardiometabolic risk factors. (3) Results: Five multi-omics clusters were identified. Thirty-one fecal metabolites formed these clusters and were correlated with plasma sphingomyelins, lysophospholipids and medium to long-chain acylcarnitines. Seven genera from Ruminococcaceae and a member from the Desulfovibrionaceae family were correlated with fecal and plasma metabolites. Positive correlations were found between the multi-omics scores from two clusters with cholesterol and triglycerides levels. (4) Conclusions: We identified a correlated network between specific microbial genera and fecal/plasma metabolites in an adult population with metabolic syndrome, suggesting an interplay between gut microbiota and host lipid metabolism on cardiometabolic health.

Associations between the gut microbiome and metabolome in early life

BACKGROUND

The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.

RESULTS

Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.

CONCLUSIONS

Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Dietary fibre to reduce colon cancer risk in Alaska Native people: the Alaska FIRST randomised clinical trial protocol

INTRODUCTION

Diet, shown to impact colorectal cancer (CRC) risk, is a modifiable environmental factor. Fibre foods fermented by gut microbiota produce metabolites that not only provide food for the colonic epithelium but also exert regulatory effects on colonic mucosal inflammation and proliferation. We describe methods used in a double-blinded, randomised, controlled trial with Alaska Native (AN) people to determine if dietary fibre supplementation can substantially reduce CRC risk among people with the highest reported CRC incidence worldwide.

METHODS AND ANALYSES

Eligible patients undergoing routine screening colonoscopy consent to baseline assessments and specimen/data collection (blood, urine, stool, saliva, breath and colon mucosal biopsies) at the time of colonoscopy. Following an 8-week stabilisation period to re-establish normal gut microbiota post colonoscopy, study personnel randomise participants to either a high fibre supplement (resistant starch, n=30) or placebo (digestible starch, n=30) condition, repeating stool sample collection. During the 28-day supplement trial, each participant consumes their usual diet plus their supplement under direct observation. On day 29, participants undergo a flexible sigmoidoscopy to obtain mucosal biopsy samples to measure the effect of the supplement on inflammatory and proliferative biomarkers of cancer risk, with follow-up assessments and data/specimen collection similar to baseline. Secondary outcome measures include the impact of a high fibre supplement on the oral and colonic microbiome and biofluid metabolome.

ETHICS AND DISSEMINATION

Approvals were obtained from the Alaska Area and University of Pittsburgh Institutional Review Boards and Alaska Native Tribal Health Consortium and Southcentral Foundation research review bodies. A data safety monitoring board, material transfer agreements and weekly study team meetings provide regular oversight throughout the study. Study findings will first be shared with AN tribal leaders, health administrators, providers and community members. Peer-reviewed journal articles and conference presentations will be forthcoming once approved by tribal review bodies.

TRIAL REGISTRATION NUMBER

NCT03028831.

Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions.

IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

Gut microbiota, NLR proteins, and intestinal homeostasis

The gastrointestinal tract harbors a highly complex microbial community, which is referred to as gut microbiota. With increasing evidence suggesting that the imbalance of gut microbiota plays a significant role in the pathogenesis of multiple diseases, interactions between the host immune system and the gut microbiota are now attracting emerging interest. Nucleotide-binding and leucine-rich repeat–containing receptors (NLRs) encompass a large number of innate immune sensors and receptors, which mediate the activation of Caspase-1 and the subsequent release of mature interleukin-1β and interleukin-18. Several family members have been found to restrain rather than activate inflammatory cytokines and immune signaling. NLR family members are central regulators of pathogen recognition, host immunity, and inflammation with utmost importance in human diseases. In this review, we focus on the potential roles played by NLRs in controlling and shaping the microbiota community and discuss how the functional axes interconnecting gut microbiota with NLRs impact the modulation of colitis, inflammatory bowel diseases, and colorectal cancer.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework

The Human Microbiome Project (HMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the 'microbiome') in human health and disease. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). Conversely, metatranscriptomics, the study of a microbial community's RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome.  In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking.  In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

Relationship between gut microbiota and host-metabolism: Emphasis on hormones related to reproductive function

It has been well recognized that interactions between the gut microbiota and host-metabolism have a proven effect on health. The gut lumen is known for harboring different bacterial communities. Microbial by-products and structural components, which are derived through the gut microbiota, generate a signaling response to maintain homeostasis. Gut microbiota is not only involved in metabolic disorders, but also participates in the regulation of reproductive hormonal function. Bacterial phyla, which are localized in the gut, allow for the metabolization of steroid hormones through the stimulation of different enzymes. Reproductive hormones such as progesterone, estrogen and testosterone play a pivotal role in the successful completion of reproductive events. Disruption in this mechanism may lead to reproductive disorders. Environmental bacteria can affect the metabolism, and degrade steroid hormones and their relevant compounds. This behavior of the bacteria can safely be implemented to eliminate steroidal compounds from a polluted environment. In this review, we summarize the metabolism of steroid hormones on the regulation of gut microbiota and vice-versa, and also examined the significant influence this process has on various events of reproductive function. Altogether, the evidence suggests that steroid hormones and gut microbiota exert a central role in the modification of host bacterial action and impact the reproductive efficiency of animals and humans.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Metaproteomics reveals the structural and functional diversity of Dermatocarpon miniatum (L.) W. Mann. Microbiota

Metaproteomics is a strategy to understand the taxonomy, functionality and metabolic pathways of the microbial communities. The relationship among the symbiotic microbiota in the entire lichen thallus, Dermatocarpon miniatum, was evaluated using the metaproteomic approach. Proteomic profiling using one-dimensional SDS-PAGE followed by LC-MS/MS analysis resulted in a total of 138 identified proteins via Mascot search against UniRef100 and Swiss-Prot databases. In addition to the fungal and algal partners, D. miniatum proteome encompasses proteins from prokaryotes, which is a multifarious community mainly dominated by cyanobacteria and proteobacteria. While proteins assigned to fungus were the most abundant (55 %), followed by protists (16 %), bacterial (13 %), plant (11 %), and viral (1 %) origin, whereas 4 % remained undefined. Various proteins were assigned to the different lichen symbionts by using Gene Ontology (GO) terms, e.g. fungal proteins involved in the oxidation-reduction process, protein folding and glycolytic process, while protists and bacterial proteins were involved in photosynthetic electron transport in photosystem II (PS II), ATP synthesis coupled proton transport, and carbon fixation. The presence of bacterial communities extended the traditional concept of fungal-algal lichen symbiotic interaction.

Characteristics of the Jejunal Microbiota in 35-Day-Old Saba and Landrace Piglets

The balanced microbiological system is a significant hallmark of piglet health. One of the crucial factors affecting intestinal microbiota is the host’s genetics. This study explored the difference in the diversity of jejunal microbiota between Saba (SB) and Landrace (LA) piglets. Nine Saba and nine Landrace piglets were fed with sow’s milk until day 35. Jejunal contents were harvested for 16S rRNA sequencing. The birth weight, body weight, and average daily gain of Saba piglets were lower than those of Landrace piglets (p < 0.01). Firmicutes were the main phylum in Saba and Landrace piglets, and the Saba piglets had a higher (p < 0.05) abundance of Bacteroidetes compared with Landrace piglets. The two most abundant genera were Lactobacilli and Clostridium XI in the jejunum of Landrace and Saba piglets. Compared with Landrace piglets, the Saba piglets had significantly lower (p < 0.05) abundance of Veillonella, Streptococcus, and Saccharibacteria genera incertae sedis. The functional prediction showed that “d-glutamine and d-glutamate metabolism” and “one carbon pool by folate” pathways were enriched in Saba piglets, while “limonene and pinene degradation”, “tryptophan metabolism”, and “sulfur relay system” pathways were enriched in Landrace piglets. In summary, the growth performance was higher for Landrace piglets compared with Saba piglets due to their genetic characteristics. The rich diversity and fewer infection-associated taxa were observed in Saba piglets, partially accounting for their higher adaptability to environmental perturbations than Landrace piglets. Furthermore, different pig breeds may regulate their health through different metabolic pathways.

FoodOmics as a new frontier to reveal microbial community and metabolic processes occurring on table olives fermentation

Table olives are considered the most widespread fermented food in the Mediterranean area and their consumption is expanding all over the world. This fermented vegetable can be considered as a natural functional food thanks to their high nutritional value and high content of bioactive compounds that contribute to the health and well-being of consumers. The presence of bioactive compounds is strongly influenced by a complex microbial consortium, traditionally exploited through culture-dependent approaches. Recently, the rapid spread of omics technologies has represented an important challenge to better understand the function, the adaptation and the exploitation of microbial diversity in different complex ecosystems, such as table olives. This review provides an overview of the potentiality of omics technologies to in depth investigate the microbial composition and the metabolic processes that drive the table olives fermentation, affecting both sensorial profile and safety properties of the final product. Finally, the review points out the role of omics approaches to raise at higher sophisticated level the investigations on microbial, gene, protein, and metabolite, with huge potential for the integration of table olives composition with functional assessments.

The Microbiome in Immuno-oncology

The field of cancer therapy has been revolutionized through the use of immunotherapy, and treatment with these therapies now spans from early to late stage, and even into prevention. However, there are still a significant proportion of patients who do not derive long-term benefit from monotherapy and even combined therapy regimens, and novel approaches are needed to enhance therapeutic responses. Additionally, ideal biomarkers of response to immunotherapy are lacking and are critically needed. An emerging area of interest in immuno-oncology (IO) is the microbiome, which refers to the collection of microbes (and their genomes) that inhabit an individual and live in symbiosis. There is now evidence that these microbes (particularly those within the gut) impact host physiology and can impact responses to immunotherapy. The field of microbiome research in immuno-oncology is quickly emerging, with the potential use of the microbiome (in the gut as well as in the tumor) as a biomarker for response to IO as well as a therapeutic target. Notably, the microbiome may even have a role in toxicity to therapy. The state of the science in microbiome and IO are discussed and caveats and future directions are outlined to provide insights as we move forward as a field.

Respiratory and Gut Microbiota in Commercial Turkey Flocks with Disparate Weight Gain Trajectories Display Differential Compositional Dynamics

Communities of gut bacteria (microbiota) are known to play roles in resistance to pathogen infection and optimal weight gain in turkey flocks. However, knowledge of turkey respiratory microbiota and its link to gut microbiota is lacking. This study presents a 16S rRNA gene-based census of the turkey respiratory microbiota (nasal cavity and trachea) alongside gut microbiota (cecum and ileum) in two identical commercial Hybrid Converter turkey flocks raised in parallel under typical field commercial conditions. The flocks were housed in adjacent barns during the brood stage and in geographically separated farms during the grow-out stage. Several bacterial taxa, primarily Staphylococcus, that were acquired in the respiratory tract at the beginning of the brood stage persisted throughout the flock cycle. Late-emerging predominant taxa in the respiratory tract included Deinococcus and Corynebacterium Tracheal and nasal microbiota of turkeys were identifiably distinct from one another and from gut microbiota. Nevertheless, gut and respiratory microbiota changed in parallel over time and appeared to share many taxa. During the brood stage, the two flocks generally acquired similar gut and respiratory microbiota, and their average body weights were comparable. However, there were qualitative and quantitative differences in microbial profiles and body weight gain trajectories after the flocks were transferred to geographically separated grow-out farms. Lower weight gain corresponded to the emergence of Deinococcus and Ornithobacterium in the respiratory tract and Fusobacterium and Parasutterella in gut. This study provides an overview of turkey microbiota under field conditions and suggests several hypotheses concerning the respiratory microbiome.IMPORTANCE Turkey meat is an important source of animal protein, and the industry around its production contributes significantly to the agricultural economy. The microorganisms present in the gut of turkeys are known to impact bird health and flock performance. However, the respiratory microbiota in turkeys is entirely unexplored. This study has elucidated the microbiota of respiratory tracts of turkeys from two commercial flocks raised in parallel throughout a normal flock cycle. Further, the study suggests that bacteria originating in the gut or in poultry house environments influence respiratory communities; consequently, they induce poor performance, either directly or indirectly. Future attempts to develop microbiome-based interventions for turkey health should delimit the contributions of respiratory microbiota and aim to limit disturbances to those communities.

The athletic gut microbiota

The microorganisms in the gastrointestinal tract play a significant role in nutrient uptake, vitamin synthesis, energy harvest, inflammatory modulation, and host immune response, collectively contributing to human health. Important factors such as age, birth method, antibiotic use, and diet have been established as formative factors that shape the gut microbiota. Yet, less described is the role that exercise plays, particularly how associated factors and stressors, such as sport/exercise-specific diet, environment, and their interactions, may influence the gut microbiota. In particular, high-level athletes offer remarkable physiology and metabolism (including muscular strength/power, aerobic capacity, energy expenditure, and heat production) compared to sedentary individuals, and provide unique insight in gut microbiota research. In addition, the gut microbiota with its ability to harvest energy, modulate the immune system, and influence gastrointestinal health, likely plays an important role in athlete health, wellbeing, and sports performance. Therefore, understanding the mechanisms in which the gut microbiota could play in the role of influencing athletic performance is of considerable interest to athletes who work to improve their results in competition as well as reduce recovery time during training. Ultimately this research is expected to extend beyond athletics as understanding optimal fitness has applications for overall health and wellness in larger communities. Therefore, the purpose of this narrative review is to summarize current knowledge of the athletic gut microbiota and the factors that shape it. Exercise, associated dietary factors, and the athletic classification promote a more “health-associated” gut microbiota. Such features include a higher abundance of health-promoting bacterial species, increased microbial diversity, functional metabolic capacity, and microbial-associated metabolites, stimulation of bacterial abundance that can modulate mucosal immunity, and improved gastrointestinal barrier function.

Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data

Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems.

Bioregional Alterations in Gut Microbiome Contribute to the Plasma Metabolomic Changes in Pigs Fed with Inulin

Inulin (INU) is a non-digestible carbohydrate, known for its beneficial properties in metabolic disorders. However, whether and how gut microbiota in its regulation contributes to host metabolism has yet to be investigated. We conduct this study to examine the possible associations between the gut microbiota and circulating gut microbiota-host co-metabolites induced by inulin interventions. Plasma and intestinal site samples were collected from the pigs that have consumed inulin diet for 60 days. High-throughput sequencing was adopted for microbial composition, and the GC-TOF-MS-based metabolomics were used to characterize featured plasma metabolites upon inulin intervention. Integrated multi-omics analyses were carried out to establish microbiota-host interaction. Inulin consumption decreased the total cholesterol (p = 0.04) and glucose (p = 0.03) level in serum. Greater β-diversity was observed in the cecum and colon of inulin-fed versus that of control-fed pigs (p < 0.05). No differences were observed in the ileum. In the cecum, 18 genera were altered by inulin, followed by 17 in the colon and 6 in the ileum. Inulin increased propionate, and isobutyrate concentrations but decreased the ratio of acetate to propionate in the cecum, and increased total short fatty acids, valerate, and isobutyrate concentrations in the colon. Metabolomic analysis reveals that indole-3-propionic acid (IPA) was significantly higher, and the branched-chain amino acids (BCAA), L-valine, L-isoleucine, and L-leucine are significantly lower in the inulin groups. Mantel test and integrative analysis revealed associations between plasma metabolites (e.g., IPA, BCAA, L-tryptophan) and inulin-responsive cecal microbial genera. These results indicate that the inulin has regional effects on the intestine microbiome in pigs, with the most pronounced effects occurring in the cecum. Moreover, cecum microbiota plays a pivotal role in the modulation of circulating host metabolites upon inulin intervention.

The era of 'omics' technologies in the study of microbiologically influenced corrosion

Efforts to elucidate the relationships between microorganisms and metal corrosion were mainly directed to understanding the formation of biofilm structures grown on corroded surfaces. The emergence of high throughput DNA sequencing techniques has helped in the description of microbial species involved directly and indirectly in the corrosion processes of alloys. Coupled with sequencing from environmental samples, other methodologies such as metatranscriptome, metaproteomics and metabolomics have allowed a new horizon to be opened on the understanding of the role of corrosive microbial biofilm. Several groups of bacteria and archaea were identified, showing the dominance of Proteobacteria in several samples analyzed and members of groups that previously received less attention, such as Firmicutes and Bacteroidetes. Our research also shows that metagenomic studies describe the presence of various Archaea domain thermophilic and methanogenic groups associated with metal corrosion. Thus, opening the prospect of describing new microbial groups as possible participants in this current global concern.

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

Seven-day Green Tea Supplementation Revamps Gut Microbiome and Caecum/Skin Metabolome in Mice from Stress

Green tea supplementation has beneficial health effects. However, its underlying mechanisms, such as effects on modulating the intestinal microbiome and endogenous metabolome, particularly following short-term supplementation, are largely unclear. We conducted an integrative metabolomics study to evaluate the effects of short-term (7-day) supplementation of green tea extract (GTE) or its components, epigallocatechin gallate, caffeine, and theanine, on the caecum microbiota and caecum/skin metabolome in mice. Further, we established an integrative metabolome-microbiome model for correlating gut and skin findings. The effects of short-term supplementation with dietary compounds were evaluated with respect to UV stress response, with GTE showing the most remarkable effects. Biplot analysis revealed that Bifidobacteria and Lactobacillus spp. were considerably influenced by short-term GTE supplementation, while Clostridium butyricum was significantly increased by UV stress without supplementation. GTE supplementation helped the skin metabolome defend against UV stress. Interestingly, a significant positive correlation was observed between caecum bacteria (Bifidobacteria, Lactobacillus spp.) and metabolites including skin barrier function-related skin metabolites, caecal fatty acids, and caecal amino acids. Overall, 7-day GTE supplementation was sufficient to alter the gut microbiota and endogenous caecum/skin metabolome, with positive effects on UV stress response, providing insight into the mechanism of the prebiotic effects of GTE supplementation.