The MACADAM database: a MetAboliC pAthways DAtabase for Microbial taxonomic groups for mining potential metabolic capacities of archaeal and bacterial taxonomic groups

Plant-nanoparticles enhance anti-PD-L1 efficacy by shaping human commensal microbiota metabolites

Diet has emerged as a key impact factor for gut microbiota function. However, the complexity of dietary components makes it difficult to predict specific outcomes. Here we investigate the impact of plant-derived nanoparticles (PNP) on gut microbiota and metabolites in context of cancer immunotherapy with the humanized gnotobiotic mouse model. Specifically, we show that ginger-derived exosome-like nanoparticle (GELN) preferentially taken up by Lachnospiraceae and Lactobacillaceae mediated by digalactosyldiacylglycerol (DGDG) and glycine, respectively. We further demonstrate that GELN aly-miR159a-3p enhances anti-PD-L1 therapy in melanoma by inhibiting the expression of recipient bacterial phospholipase C (PLC) and increases the accumulation of docosahexaenoic acid (DHA). An increased level of circulating DHA inhibits PD-L1 expression in tumor cells by binding the PD-L1 promoter and subsequently prevents c-myc-initiated transcription of PD-L1. Colonization of germ-free male mice with gut bacteria from anti-PD-L1 non-responding patients supplemented with DHA enhances the efficacy of anti-PD-L1 therapy compared to controls. Our findings reveal a previously unknown mechanistic impact of PNP on human tumor immunotherapy by modulating gut bacterial metabolic pathways.

Understanding LC/MS-Based Metabolomics: A Detailed Reference for Natural Product Analysis

Liquid chromatography, when used in conjunction with mass spectrometry (LC/MS), is a powerful tool for conducting accurate and reproducible investigations of numerous metabolites in natural products (NPs). LC/MS has gained prominence in metabolomic research due to its high throughput, the availability of multiple ionization techniques and its ability to provide comprehensive metabolite coverage. This unique method can significantly influence various scientific domains. This review offers a comprehensive overview of the current state of LC/MS-based metabolomics in the investigation of NPs. This review provides a thorough overview of the state of the art in LC/MS-based metabolomics for the investigation of NPs. It covers the principles of LC/MS, various aspects of LC/MS-based metabolomics such as sample preparation, LC modes, method development, ionization techniques and data pre-processing. Moreover, it presents the applications of LC/MS-based metabolomics in numerous fields of NPs research such as including biomarker discovery, the agricultural research, food analysis, the study of marine NPs and microbiological research. Additionally, this review discusses the challenges and limitations of LC/MS-based metabolomics, as well as emerging trends and developments in this field.

ReptTraits: a comprehensive dataset of ecological traits in reptiles

Trait datasets are increasingly being used in studies investigating eco-evolutionary theory and global conservation initiatives. Reptiles are emerging as a key group for studying these questions because their traits are crucial for understanding the ability of animals to cope with environmental changes and their contributions to ecosystem processes. We collected data from earlier databases, and the primary literature to create an up-to-date dataset of reptilian traits, encompassing 40 traits from 12060 species of reptiles (Archelosauria: Crocodylia and Testudines, Rhynchocephalia, and Squamata: Amphisbaenia, Sauria, and Serpentes). The data were gathered from 1288 sources published between 1820 and 2023. The dataset includes morphological, physiological, behavioral, and life history traits, as well as information on the availability of genetic data, IUCN Red List assessments, and population trends.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Functional comparison of metabolic networks across species

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

CDEMI: characterizing differences in microbial composition and function in microbiome data

Microbial communities influence host phenotypes through microbiota-derived metabolites and interactions between exogenous active substances (EASs) and the microbiota. Owing to the high dynamics of microbial community composition and difficulty in microbial functional analysis, the identification of mechanistic links between individual microbes and host phenotypes is complex. Thus, it is important to characterize variations in microbial composition across various conditions (for example, topographical locations, times, physiological and pathological conditions, and populations of different ethnicities) in microbiome studies. However, no web server is currently available to facilitate such characterization. Moreover, accurately annotating the functions of microbes and investigating the possible factors that shape microbial function are critical for discovering links between microbes and host phenotypes. Herein, an online tool, CDEMI, is introduced to discover microbial composition variations across different conditions, and five types of microbe libraries are provided to comprehensively characterize the functionality of microbes from different perspectives. These collective microbe libraries include (1) microbial functional pathways, (2) disease associations with microbes, (3) EASs associations with microbes, (4) bioactive microbial metabolites, and (5) human body habitats. In summary, CDEMI is unique in that it can reveal microbial patterns in distributions/compositions across different conditions and facilitate biological interpretations based on diverse microbe libraries. CDEMI is accessible at http://rdblab.cn/cdemi/.

Cultivar-specific response of rhizosphere bacterial community to uptake of cadmium and mineral elements in rice (Oryza sativa L.)

Toxic metal-contaminated farmland from Cadmium (Cd) can enhance the accumulation of Cd and impair the absorption of mineral elements in brown rice. Although several studies have been conducted on Cd exposure on rice, little has been reported on the relationship between Cd and mineral elements in brown rice and the regulatory mechanism of rhizosphere microorganisms during element uptake. Thus, a field study was undertaken to screen japonica rice cultivars with low Cd and high mineral elements levels, analyze the quantitative relationship between Cd and seven mineral elements, and investigate the cultivar-specific response of rice rhizosphere bacterial communities to differences in Cd and mineral uptake in japonica rice. Results showed that Huaidao-9 and Xudao-7 had low Cd absorption and high amounts of mineral nutrient elements (Fe, Zn, Mg, and Ca, LCHM group), whereas Zhongdao-1 and Xinkedao-31 showed opposite accumulation characteristics (HCLM group). Stepwise regression analysis showed that zinc, iron, and potassium are the key minerals that affect Cd accumulation in japonica rice and zinc was the most important factor, accounting for 68.99 %. The accumulation of Cd and mineral elements is potentially associated with rhizosphere soil bacteria. Taxa enriched in the LCHM rhizosphere (phyla Acidobacteriota and MBNT15) indicated the high nutrient characteristics of the soil and reduced activity of Cd in soil. The HCLM rhizosphere was highly colonized by metal-activating bacteria (Actinobacteria), lignin-degrading bacteria (Actinobacteria and Chlorofexi), and bacteria scavenging nutrients and trace elements (Anaerolinea and Ketobacter). Moreover, the differences in the uptake of Cd and mineral elements affected predicted functions of microbial communities, including sulfur oxidation and sulfur derivative formation, human or plant pathogen, and functions related to the iron oxidation and nitrate reduction. The results indicate a potential association of Cd and mineral elements uptake and accumulation with rhizosphere bacteria in rice, thus providing theoretical basis and a new perspective on the maintenance of rice security and high quality simultaneously.

Comparative in-silico analysis of microbial dysbiosis discern potential metabolic link in neurodegenerative diseases

A healthy gut flora contains a diverse and stable commensal group of microorganisms, whereas, in disease conditions, there is a shift toward pathogenic microbes, termed microbial dysbiosis. Many studies associate microbial dysbiosis with neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Amyotrophic lateral sclerosis (ALS). Although, an overall comparative analysis of microbes and their metabolic involvement in these diseases is still lacking. In this study, we have performed a comparative analysis of microbial composition changes occurring in these four diseases. Our research showed a high resemblance of microbial dysbiosis signatures between AD, PD, and MS. However, ALS appeared dissimilar. The most common population of microbes to show an increase belonged to the phyla, Bacteroidetes, Actinobacteria, Proteobacteria, and Firmicutes. Although, Bacteroidetes and Firmicutes were the only phyla that showed a decrease in their population. The functional analysis of these dysbiotic microbes showed several potential metabolic links which can be involved in the altered microbiome-gut-brain axis in neurodegenerative diseases. For instance, the microbes with elevated populations lack pathways for synthesizing SCFA acetate and butyrate. Also, these microbes have a high capacity for producing L-glutamate, an excitatory neurotransmitter and precursor of GABA. Contrastingly, Tryptophan and histamine have a lower representation in the annotated genome of elevated microbes. Finally, the neuroprotective compound spermidine was less represented in elevated microbes' genomes. Our study provides a comprehensive catalog of potential dysbiotic microbes and their metabolic involvement in neurodegenerative disorders, including AD, PD, MS, and ALS.

Quantitative analysis of disease-related metabolic dysregulation of human microbiota

The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.

Reproducible and opposing gut microbiome signatures distinguish autoimmune diseases and cancers: a systematic review and meta-analysis

BACKGROUND

The gut microbiome promotes specific immune responses, and in turn, the immune system has a hand in shaping the microbiome. Cancer and autoimmune diseases are two major disease families that result from the contrasting manifestations of immune dysfunction. We hypothesized that the opposing immunological profiles between cancer and autoimmunity yield analogously inverted gut microbiome signatures. To test this, we conducted a systematic review and meta-analysis on gut microbiome signatures and their directionality in cancers and autoimmune conditions.

METHODOLOGY

We searched PubMed, Web of Science, and Embase to identify relevant articles to be included in this study. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements and PRISMA 2009 checklist. Study estimates were pooled by a generic inverse variance random-effects meta-analysis model. The relative abundance of microbiome features was converted to log fold change, and the standard error was calculated from the p-values, sample size, and fold change.

RESULTS

We screened 3874 potentially relevant publications. A total of 82 eligible studies comprising 37 autoimmune and 45 cancer studies with 4208 healthy human controls and 5957 disease cases from 27 countries were included in this study. We identified a set of microbiome features that show consistent, opposite directionality between cancers and autoimmune diseases in multiple studies. Fusobacterium and Peptostreptococcus were the most consistently increased genera among the cancer cases which were found to be associated in a remarkable 13 (+0.5 log fold change in 5 studies) and 11 studies (+3.6 log fold change in 5 studies), respectively. Conversely, Bacteroides was the most prominent genus, which was found to be increased in 12 autoimmune studies (+0.2 log fold change in 6 studies) and decreased in six cancer studies (-0.3 log fold change in 4 studies). Sulfur-metabolism pathways were found to be the most frequent pathways among the member of cancer-increased genus and species.

CONCLUSIONS

The surprising reproducibility of these associations across studies and geographies suggests a shared underlying mechanism shaping the microbiome across cancers and autoimmune diseases. Video Abstract.

Deciphering the potential role of Maca compounds prescription influencing gut microbiota in the management of exercise-induced fatigue by integrative genomic analysis

A growing number of nutraceuticals and cosmeceuticals have been utilized for millennia as anti-fatigue supplements in folk medicine. However, the anti-fatigue mechanism underlying is still far from being clearly explained. The aim of the study is to explore the underlying mechanism of the Maca compound preparation (MCP), a prescription for management of exercise-induced fatigue. In this study, mice weight-loaded swimming test was used to evaluate the anti-fatigue effect of MCP. MCP significantly improved the forelimb grip strength and Rota-rod test in behavioral tests via regulating energy metabolism. 16S rDNA sequencing results showed MCP can regulate the intestinal flora at the genus level by increasing several beneficial bacteria (i.e., Lactobacillus, Akkermansia and etc.), and decreasing the harmful bacteria (i.e., Candidatus_Planktophila and Candidatus_Arthromitus), where notable high relevance was observed between the fatigue-related biomarkers and fecal microbiota. The results of microbial function analysis suggested that MCP might improve exercise-induced fatigue by enhancing energy metabolism, carbohydrate and lipid metabolism and metabolism of terpenoids and polyketides and breakdown of amino acid metabolism. In addition, and H₂O₂-induced oxidative stress model on C2C12 cells was employed to further validate the regulation of MCP on energy metabolisms. MCP pre-treatment significantly reduced intracellular ROS accumulation, and increased glycogen content, ATP generation capacity and mitochondrial membrane potential of skeletal muscle cells, as well as conferred anti-cell necrosis ability. In conclusion, MCP plays a key role in regulating fatigue occurrence in exercising and gut microbiota balance, which may be of particular importance in the case of manual workers or sub-healthy populations.

Uncovering the structure and function of specialist bacterial lineages in environments routinely exposed to explosives

Environmental contamination by hexahydro-1, 3, 5-trinitro-1, 3, 5-triazine (RDX), and Octahydro-1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazocine (HMX), the two most widely used compounds for military operations, is a long-standing problem at the manufacturing and decommissioning plants. Since explosives contamination has previously been shown to favour the growth of specific bacterial communities, the present study attempts to identify the specialist bacterial communities and their potential functional and metabolic roles by using amplicon targeted and whole-metagenome sequencing approaches (WMS) in samples collected from two distinct explosives manufacturing sites. We hypothesize that the community structure and functional attributes of bacterial population are substantially altered by the concentration of explosives and physicochemical conditions. The results highlight the predominance of Planctomycetes in contrast to previous reports from similar habitats. The detailed phylogenetic analysis revealed the presence of OTU's related to bacterial members known for their explosives degradation. Further, the functional and metabolic analyses highlighted the abundance of putative genes and unidentified taxa possibly associated with xenobiotic biodegradation. Our findings suggest that microbial species capable of utilizing explosives as a carbon, energy, or electron source are favoured by certain selective pressures based on the prevailing physicochemical and geographical conditions.

AnimalTraits - a curated animal trait database for body mass, metabolic rate and brain size

Trait databases have become important resources for large-scale comparative studies in ecology and evolution. Here we introduce the AnimalTraits database, a curated database of body mass, metabolic rate and brain size, in standardised units, for terrestrial animals. The database has broad taxonomic breadth, including tetrapods, arthropods, molluscs and annelids from almost 2000 species and 1000 genera. All data recorded in the database are sourced from their original empirical publication, and the original metrics and measurements are included with each record. This allows for subsequent data transformations as required. We have included rich metadata to allow users to filter the dataset. The additional R scripts we provide will assist researchers with aggregating standardised observations into species-level trait values. Our goals are to provide this resource without restrictions, to keep the AnimalTraits database current, and to grow the number of relevant traits in the future.

Measurement(s)	metabolic rate quantification • body mass • brain size
Technology Type(s)	metabolic rate measurement • body mass quantification • brain mass brain volume

Gut bacterial isoamylamine promotes age-related cognitive dysfunction by promoting microglial cell death

The intestinal microbiome releases a plethora of small molecules. Here, we show that the Ruminococcaceae metabolite isoamylamine (IAA) is enriched in aged mice and elderly people, whereas Ruminococcaceae phages, belonging to the Myoviridae family, are reduced. Young mice orally administered IAA show cognitive decline, whereas Myoviridae phage administration reduces IAA levels. Mechanistically, IAA promotes apoptosis of microglial cells by recruiting the transcriptional regulator p53 to the S100A8 promoter region. Specifically, IAA recognizes and binds the S100A8 promoter region to facilitate the unwinding of its self-complementary hairpin structure, thereby subsequently enabling p53 to access the S100A8 promoter and enhance S100A8 expression. Thus, our findings provide evidence that small molecules released from the gut microbiome can directly bind genomic DNA and act as transcriptional coregulators by recruiting transcription factors. These findings further unveil a molecular mechanism that connects gut metabolism to gene expression in the brain with implications for disease development.

A metagenomics study of hexabromocyclododecane degradation with a soil microbial community

Hexabromocyclododecanes (HBCDs) are globally prevalent and persistent organic pollutants (POPs) listed by the Stockholm Convention in 2013. They have been detected in many environmental media from waterbodies to Plantae and even in the human body. Due to their highly bioaccumulative characterization, they pose an urgent public health issue. Here, we demonstrate that the indigenous microbial community in the agricultural soil in Taiwan could decompose HBCDs with no additional carbon source incentive. The degradation kinetics reached 0.173 day^-1 after the first treatment and 0.104 day^-1 after second exposure. With additional C-sources, the rate constants decreased to 0.054-0.097 day^-1. The hydroxylic debromination metabolites and ring cleavage long-chain alkane metabolites were identified to support the potential metabolic pathways utilized by the soil microbial communities. The metagenome established by Nanopore sequencing showed significant compositional alteration in the soil microbial community after the HBCD treatment. After ranking, comparing relative abundances, and performing network analyses, several novel bacterial taxa were identified to contribute to HBCD biotransformation, including Herbaspirillum, Sphingomonas, Brevundimonas, Azospirillum, Caulobacter, and Microvirga, through halogenated / aromatic compound degradation, glutathione-S-transferase, and hydrolase activity. We present a compelling and applicable approach combining metagenomics research, degradation kinetics, and metabolomics strategies, which allowed us to decipher the natural attenuation and remediation mechanisms of HBCDs.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Iron oxide minerals promote simultaneous bio-reduction of Cr(VI) and nitrate: Implications for understanding natural attenuation

Nitrate and Cr(VI) coexist in aquifers, posing a potential threat to ecological environment and public health. Iron oxide minerals (hematite and magnetite) exist ubiquitously in groundwater, which are hot spots for biogeochemical transformation. However, there is still a knowledge gap anout the effect of iron oxide minerals on bioreduction of nitrate and Cr(VI). Here we observed that iron oxide minerals can significantly improve the ability of microorganisms to simultaneously reduce nitrate and Cr(VI), the reduction rates of nitrate and Cr(VI) increased by 7.3 and 8.5 times, respectively. The addition of minerals reinforced biofilm formation and shaped microbial communities with a new dominant strain of Azoarcus. The expression levels of functional genes were also upregulated, including napA, narG, nfsA, yieF, POD, and CAT. Furthermore, nitrate and chromate reductases' activities increased by 11 and 5 folds, respectively. These results demonstrated that iron oxide minerals participated in the bio-transformation of nitrate and Cr(VI) co-contamination, alleviating oxidative stress, shaping the microbial community, and ultimately accelerating bio-transformation. These findings offer a window into the biological transformation of co-contamination in the presence of iron oxide minerals, and insights to reveal strategies for microbial detoxification and to develop promising approaches for dealing with complex pollution conditions.

mbImpute: an accurate and robust imputation method for microbiome data

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data-mbImpute-to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

Molecular Mechanisms of Nanomaterial-Bacterial Interactions Revealed by Omics-The Role of Nanomaterial Effect Level

Nanotechnology is employed across a wide range of antibacterial applications in clinical settings, food, pharmaceutical and textile industries, water treatment and consumer goods. Depending on type and concentration, engineered nanomaterials (ENMs) can also benefit bacteria in myriad contexts including within the human body, in biotechnology, environmental bioremediation, wastewater treatment, and agriculture. However, to realize the full potential of nanotechnology across broad applications, it is necessary to understand conditions and mechanisms of detrimental or beneficial effects of ENMs to bacteria. To study ENM effects, bacterial population growth or viability are commonly assessed. However, such endpoints alone may be insufficiently sensitive to fully probe ENM effects on bacterial physiology. To reveal more thoroughly how bacteria respond to ENMs, molecular-level omics methods such as transcriptomics, proteomics, and metabolomics are required. Because omics methods are increasingly utilized, a body of literature exists from which to synthesize state-of-the-art knowledge. Here we review relevant literature regarding ENM impacts on bacterial cellular pathways obtained by transcriptomic, proteomic, and metabolomic analyses across three growth and viability effect levels: inhibitory, sub-inhibitory or stimulatory. As indicated by our analysis, a wider range of pathways are affected in bacteria at sub-inhibitory vs. inhibitory ENM effect levels, underscoring the importance of ENM exposure concentration in elucidating ENM mechanisms of action and interpreting omics results. In addition, challenges and future research directions of applying omics approaches in studying bacterial-ENM interactions are discussed.

Relative Weight of Organic Waste Origin on Compost and Digestate 16S rRNA Gene Bacterial Profilings and Related Functional Inferences

Even though organic waste (OW) recycling via anaerobic digestion (AD) and composting are increasingly used, little is known about the impact of OW origin (fecal matters and food and vegetable wastes) on the end products' bacterial contents. The hypothesis of a predictable bacterial community structure in the end products according to the OW origin was tested. Nine OW treatment plants were selected to assess the genetic structure of bacterial communities found in raw OW according to their content in agricultural and urban wastes and to estimate their modifications through AD and composting. Two main bacterial community structures among raw OWs were observed and matched a differentiation according to the occurrences of urban chemical pollutants. Composting led to similar 16S rRNA gene OTU profiles whatever the OW origin. With a significant shift of about 140 genera (representing 50% of the bacteria), composting was confirmed to largely shape bacterial communities toward similar structures. The enriched taxa were found to be involved in detoxification and bioremediation activities. This process was found to be highly selective and favorable for bacterial specialists. Digestates showed that OTU profiles differentiated into two groups according to their relative content in agricultural (manure) and urban wastes (mainly activated sludge). About one third of the bacterial taxa was significantly affected by AD. In digestates of urban OW, this sorting led to an enrichment of 32 out of the 50 impacted genera, while for those produced from agricultural or mixed urban/agricultural OW (called central OW), a decay of 54 genera over 60 was observed. Bacteria from activated sludge appeared more fit for AD than those of other origins. Functional inferences showed AD enriched genera from all origins to share similar functional traits, e.g., chemoheterotrophy and fermentation, while being often taxonomically distinct. The main functional traits among the dominant genera in activated sludge supported a role in AD. Raw OW content in activated sludge was found to be a critical factor for predicting digestate bacterial contents. Composting generated highly predictable and specialized community patterns whatever the OW origin. AD and composting bacterial changes were driven by functional traits selected by physicochemical factors such as temperature and chemical pollutants.

Using neural networks to mine text and predict metabolic traits for thousands of microbes

Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. Information is buried in text of books and journals, and investigators have no easy way to extract it out. Here we investigate if neural networks can extract out this information and predict metabolic traits. For proof of concept, we predicted two traits: whether microbes carry one type of metabolism (fermentation) or produce one metabolite (acetate). We collected written descriptions of 7,021 species of bacteria and archaea from Bergey's Manual. We read the descriptions and manually identified (labeled) which species were fermentative or produced acetate. We then trained neural networks to predict these labels. In total, we identified 2,364 species as fermentative, and 1,009 species as also producing acetate. Neural networks could predict which species were fermentative with 97.3% accuracy. Accuracy was even higher (98.6%) when predicting species also producing acetate. Phylogenetic trees of species and their traits confirmed that predictions were accurate. Our approach with neural networks can extract information efficiently and accurately. It paves the way for putting more metabolic traits into databases, providing easy access of information to investigators.

Gut-Brain Axis Cross-Talk and Limbic Disorders as Biological Basis of Secondary TMAU

Background: Trimethylaminuria (TMAU) is a rare metabolic syndrome characterized by the accumulation and the excretion of trimethylamine (TMA), a volatile diet compound produced by gut microbiota. Gut microbiota alterations are mainly involved in the secondary TMAU, whose patients show also different psychiatric conditions. We hypothesized that the biological activity of several molecules acting as intermediate in TMA metabolic reaction might be at the basis of TMAU psychiatric comorbidities. Methods: To corroborate this hypothesis, we performed the analysis of microbiota of both psychiatric suffering secondary TMAU patients and TMAU "mentally ill" controls, comparing the alteration of metabolites produced by their gut bacteria possibly involved in neurotransmission and, in the same time, belonging to biochemical pathways leading to TMA accumulation. Results: Microbiota analyses showed that Clostridiaceae, Lachnospiraceae and Coriobacteriaceae alterations represented the bacterial families with highest variations. This results in an excessive release of serotonin and an hyperactivation of the vagus nerve that might determine the widest spectrum of psychiatric disorders shown by affected patients. These metabolites, as short chain fatty acids, lactate and neurotransmitter precursors, are also related to TMA accumulation. Conclusions: Knowledge of microbiota-gut-brain axis may become a potential new strategy for improving metabolic diseases and to treat linked psychiatric disorders.

Allogenic stem cell transplant-associated acute graft versus host disease: a computational drug discovery text mining approach using oral and gut microbiome signatures

PURPOSE

Acute graft versus host disease (aGVHD) is a major cause of non-relapse morbidity and mortality post-allogenic hematopoietic stem cell transplant (HSCT). Using conventional literature search and computational approaches, our objective was to identify oral and gut bacterial species associated with aGVHD, potentially affecting drug treatment via lipopolysaccharide (LPS) pathways.

METHODS

Medline, PubMed, PubMed Central, and Google Scholar were searched using MeSH terms. The top 100 hits per database were curated, and 25 research articles were selected to examine oral and gut microbiomes associated with health, HSCT, and aGVHD. Literature search validation, aGVHD drug targets, and microbial metabolic pathway identification were completed using BioReader, MACADAM, KEGG, and STRING programs.

RESULTS

Our review determined that (1) oral genera Rothia, Solobacterium, and Veillonella were identified in HSCT patients' stool and associated with aGVHD; (2) shifts in gut enterococci profiles were determined in HSCT-associated aGVHD; (3) gut microbiome dysbiosis prior or during HSCT and lower Shannon diversity index at time of HSCT were also associated with increased risk of aGVHD and transplant related death; and (4) Coriobacteriaceae family was negatively correlated with gut aGVHD, whereas Eubacterium limosum was associated with decreased risk of chronic GVHD relapse. Additionally, we identified molecular pathways related to TLR4/ LPS, including candidate aGVHD drug targets, impacted by oral and gut bacterial taxa.

CONCLUSION

Reduced microbial diversity reflects higher severity and mortality rate in HSCT patients with aGVHD. Multi-omics approaches to decipher oral and gut microbiome associations will be critical for developing aGVHD preventive therapies.

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

A synthesis of bacterial and archaeal phenotypic trait data

A synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. Trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

Diversity and Co-occurrence Pattern Analysis of Cecal Microbiota Establishment at the Onset of Solid Feeding in Young Rabbits

This study aimed to evaluate how the feeding strategy of rabbit kits at the onset of solid feed intake could affect ecological diversity and co-occurrence patterns of the cecal bacterial community. From birth to 18 days of age kits were exclusively milk-fed, and between 18 and 35 days the young rabbits also had access to solid feed. After weaning at (35 days), young rabbits were exclusively fed solid feed. Three experimental feeds were used: a high concentrate diet [H: 10.16 MJ digestible energy (DE)/kg and 15.3% crude protein (CP)], a low concentrate diet (L: 9.33 MJ DE/kg and 14.7% CP) and a reproductive female diet (R: 10.57 MJ DE/kg and 17.3% CP). The rabbit kits (n = 357) were divided into three groups, differing by the diet received during two periods: from 18 to 28 and from 28 to 49 days of age. In the groups LL and HH, rabbit kits were fed L or H diets, respectively, during both periods. Kits in the group RL received feeds R and L from 18 to 28 and 28 to 49 days of age, respectively. Cecal bacterial communities of 10 rabbits per group were carried out at 18, 28, 35, 43 and 49 days of age by MiSeq Illumina sequencing 16S rRNA encoding genes. Between 18 and 28 days of age, solid feed intake was higher in the group RL compared to the other two groups (+24%; P < 0.01). Overall, 13.4% of the OTUs detected were present in the cecal ecosystem from 18 to 49 days old, whereas 17.4% were acquired with the onset of solid feeding and kept from 28 days on. Exclusive milk consumption constrains the bacterial community toward a similar structure but high phylogenetic beta-diversity. Introduction of solid feed induced a sharp change of microbial community structure and decreased phylogenetic diversity. A strong relationship in bacterial community network occurred only from 43 days on. Our feeding strategy at the onset of solid feed ingestion exhibited only a moderate effect on the microbial community structure (P = 0.072), although the LL group seemed to reach faster maturity compared to the two other groups.