The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

Acute gut microbiome changes after traumatic brain injury are associated with chronic deficits in decision-making and impulsivity in male rats

The mechanisms underlying chronic psychiatric-like impairments after traumatic brain injury (TBI) are currently unknown. The goal of the present study was to assess the role of diet and the gut microbiome in psychiatric symptoms after TBI. Rats were randomly assigned to receive a high-fat diet (HFD) or calorie-matched low-fat diet (LFD). After 2 weeks of free access, rats began training on the rodent gambling task (RGT), a measure of risky decision-making and motor impulsivity. After training, rats received a bilateral frontal TBI or a sham procedure and continued postinjury testing for 10 weeks. Fecal samples were collected before injury and 3-, 30-, and 60 days postinjury to evaluate the gut microbiome. HFD altered the microbiome, but ultimately had low-magnitude effects on behavior and did not modify functional outcomes after TBI. Injury-induced functional deficits were far more robust; TBI substantially decreased optimal choice and increased suboptimal choice and motor impulsivity on the RGT. TBI also affected the microbiome, and a model comparison approach revealed that bacterial diversity measured 3 days postinjury was predictive of chronic psychiatric-like deficits on the RGT. A functional metagenomic analysis identified changes to dopamine and serotonin synthesis pathways as a potential candidate mechanism. Thus, the gut may be a potential acute treatment target for psychiatric symptoms after TBI, as well as a biomarker for injury and deficit severity. However, further research will be needed to confirm and extend these findings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Detailed analysis of metabolism reveals growth-rate-promoting interactions between Anaerostipes caccae and Bacteroides spp

INTRODUCTION

Human gut microbiota species which are next-generation probiotics (NGPs) candidates are of high interest as they have shown the potential to treat intestinal inflammation and other diseases. Unfortunately, these species are often not robust enough for large-scale cultivation, especially in maintaining diversity in co-culture production.

OBJECTIVES

In this study, we describe interactions between human gut microbiota species in the cultivation process with unique substrates. We also demonstrated that it is possible to change the species ratio in co-culture by changing the ratio of carbon sources.

METHODS

We screened 25 different bacterial species based on their metabolic capabilities. After evaluating unique substrate possibilities, we chose Anaerostipes caccae (A. caccae), Bacteroides thetaiotaomicron (B. thetaiotaomicron), and Bacteroides vulgatus (B. vulgatus) as subjects for further study. D-sorbitol, D-xylose, and D-galacturonic acid were selected as substrates for A. caccae, B. thetaiotaomicron, and B. vulgatus respectively. All three species were cultivated as both monocultures and in co-cultures in serial batch fermentations in an isothermal microcalorimeter.

RESULTS

Positive interactions were detected between the species in both co-cultures (A. caccae + B. thetaiotaomicron; A. caccae + B. vulgatus) resulting in higher heat production compared to the sum of the monocultures. The same positive cross-feeding interactions took place in larger-scale cultivation experiments. We confirmed acetate and lactate cross-feeding between A. caccae and B. thetaiotaomicron with flux balance analysis (FBA).

CONCLUSION

Changing the ratio of the selected carbon sources in the medium changed the species ratio accordingly. Such robustness is the basis for developing more efficient industrial co-culture processes including the production of NGPs.

Bacterial lifestyle switch in response to algal metabolites

Unicellular algae, termed phytoplankton, greatly impact the marine environment by serving as the basis of marine food webs and by playing central roles in the biogeochemical cycling of elements. The interactions between phytoplankton and heterotrophic bacteria affect the fitness of both partners. It is becoming increasingly recognized that metabolic exchange determines the nature of such interactions, but the underlying molecular mechanisms remain underexplored. Here, we investigated the molecular and metabolic basis for the bacterial lifestyle switch, from coexistence to pathogenicity, in Sulfitobacter D7 during its interaction with Emiliania huxleyi, a cosmopolitan bloom-forming phytoplankter. To unravel the bacterial lifestyle switch, we analyzed bacterial transcriptomes in response to exudates derived from algae in exponential growth and stationary phase, which supported the Sulfitobacter D7 coexistence and pathogenicity lifestyles, respectively. In pathogenic mode, Sulfitobacter D7 upregulated flagellar motility and diverse transport systems, presumably to maximize assimilation of E. huxleyi-derived metabolites released by algal cells upon cell death. Algal dimethylsulfoniopropionate (DMSP) was a pivotal signaling molecule that mediated the transition between the lifestyles, supporting our previous findings. However, the coexisting and pathogenic lifestyles were evident only in the presence of additional algal metabolites. Specifically, we discovered that algae-produced benzoate promoted the growth of Sulfitobacter D7 and hindered the DMSP-induced lifestyle switch to pathogenicity, demonstrating that benzoate is important for maintaining the coexistence of algae and bacteria. We propose that bacteria can sense the physiological state of the algal host through changes in the metabolic composition, which will determine the bacterial lifestyle during interaction.

Biofilms on Indwelling Artificial Urinary Sphincter Devices Harbor Complex Microbe-Metabolite Interaction Networks and Reconstitute Differentially In Vitro by Material Type

The artificial urinary sphincter (AUS) is an effective treatment option for incontinence due to intrinsic sphincteric deficiency in the context of neurogenic lower urinary tract dysfunction, or stress urinary incontinence following radical prostatectomy. A subset of AUS devices develops infection and requires explant. We sought to characterize biofilm composition of the AUS device to inform prevention and treatment strategies. Indwelling AUS devices were swabbed for biofilm at surgical removal or revision. Samples and controls were subjected to next-generation sequencing and metabolomics. Biofilm formation of microbial strains isolated from AUS devices was reconstituted in a bioreactor mimicking subcutaneous tissue with a medical device present. Mean patient age was 73 (SD 10.2). All eighteen artificial urinary sphincter devices harbored microbial biofilms. Central genera in the overall microbe−metabolite interaction network were Staphylococcus (2620 metabolites), Escherichia/Shigella (2101), and Methylobacterium-Methylorubrum (674). An rpoB mutation associated with rifampin resistance was detected in 8 of 15 (53%) biofilms. Staphylococcus warneri formed greater biofilm on polyurethane than on any other material type (p < 0.01). The results of this investigation, wherein we comprehensively characterized the composition of AUS device biofilms, provide the framework for future identification and rational development of inhibitors and preventive strategies against device-associated infection.

Community Structure and Predicted Functions of Actively Growing Bacteria Responsive to Released Coral Mucus in Surrounding Seawater

A direct relationship exists between diverse corals and fish farming in Keten Bay, Amami-Oshima, Japan. The release of coral mucus has a significant impact on the microbial activity of surrounding seawater. To obtain a more detailed understanding of biogeochemical cycles in this environment, the effects of coral mucus on the community structure and function of bacteria in surrounding seawater need to be elucidated. We herein used a bromodeoxyuridine approach to investigate the structures and functions of bacterial communities growing close to mucus derived from two different Acropora corals, AC1 and AC2. The alpha diversities of actively growing bacteria (AGB) were lower in mucus-containing seawater than in control seawater and their community structures significantly differed, suggesting that the growth of specific bacteria was modulated by coral mucus. Rhodobacteraceae and Cryomorphaceae species were the most dominant AGB in response to the mucus of Acropora AC1 and AC2, respectively. In contrast, the growth of Actinomarinaceae, Alteromonadaceae, Flavobacteriaceae, and SAR86 clade bacteria was inhibited by coral mucus. The results of a Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) ana-lysis suggested that the predicted functions of AGB in mucus-containing seawater differed from those in seawater. These functions were related to the biosynthesis and degradation of the constituents of coral mucus, such as polysaccharides, sugar acids, and aromatic compounds. The present study demonstrated that complex bacterial community structures and functions may be shaped by coral mucus, suggesting that corals foster diverse bacterial communities that enhance the ecological resilience of this fish farming area.

Long-Read Metagenomics and CAZyme Discovery

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems restricts access to potentially novel bacteria and beneficial CAZymes. While commonplace molecular-based culture-independent methods such as metagenomics enable researchers to study microbial communities directly from environmental samples, recent progress in long-read sequencing technologies are advancing the field. We outline key methodological stages that are required as well as describe specific protocols that are currently used for long-read metagenomic projects dedicated to CAZyme discovery.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Growth of Porphyromonas gingivalis on human serum albumin triggers programmed cell death

Aims

Gingival crevicular fluid (GCF) constitutes the primary growth substrate for Porphyromonas gingivalis in vivo. The goal of this work was to evaluate the growth of different strains of P. gingivalis on human serum albumin (HSA), a major constituent of GCF.

Methods

Growth of five different strains of P. gingivalis in the HSA medium was examined and, surprisingly, three of the strains underwent autolysis within 24 h. Comparative transcriptomic analysis was used to identify genes involved in autolysis.

Results

Two highly related reference strains (W50 and W83) differed dramatically in their survival when grown on HSA. Strain W83 grew fast and lysed within 24 h, while W50 survived for an additional 20 h. Differential gene expression analysis led us to a gene cluster containing enzymes involved in arginine metabolism and a gene predicted to be lytic murein transglycosylase, which are known to play a role in autolysis. Deletion of this gene (PG0139) resulted in a mutant that did not lyse, and complementation restored the HSA lysis phenotype, indicating that this enzyme plays a central role in the autolysis of P. gingivalis.

Conclusions

P. gingivalis undergoes autolysis when provided with HSA as a substrate for growth.

Multiomics Characterization of the Canada Goose Fecal Microbiome Reveals Selective Efficacy of Simulated Metagenomes

16S rRNA amplicon sequences are predominantly used to identify the taxonomic composition of a microbiome, but they can also be used to generate simulated metagenomes to circumvent costly empirical shotgun sequencing. The effectiveness of using "simulated metagenomes" (shotgun metagenomes simulated from 16S rRNA amplicons using a database of full genomes closely related to the amplicons) in nonmodel systems is poorly known. We sought to determine the accuracy of simulated metagenomes in a nonmodel organism, the Canada goose (Branta canadensis), by comparing metagenomes and metatranscriptomes to simulated metagenomes derived from 16S amplicon sequencing. We found significant differences between the metagenomes, metatranscriptomes, and simulated metagenomes when comparing enzymes, KEGG orthologies (KO), and metabolic pathways. The simulated metagenomes accurately identified the majority (>70%) of the total enzymes, KOs, and pathways. The simulated metagenomes accurately identified the majority of the short-chain fatty acid metabolic pathways crucial to folivores. When narrowed in scope to specific genes of interest, the simulated metagenomes overestimated the number of antimicrobial resistance genes and underestimated the number of genes related to the breakdown of plant matter. Our results suggest that simulated metagenomes should not be used in lieu of empirical sequencing when studying the functional potential of a nonmodel organism's microbiome. Regarding the function of the Canada goose microbiome, we found unexpected amounts of fermentation pathways, and we found that a few taxa are responsible for large portions of the functional potential of the microbiome. IMPORTANCE The taxonomic composition of a microbiome is predominately identified using amplicon sequencing of 16S rRNA genes, but as a single marker, it cannot identify functions (genes). Metagenome and metatranscriptome sequencing can determine microbiome function but can be cost prohibitive. Therefore, computational methods have been developed to generate simulated metagenomes derived from 16S rRNA sequences and databases of full-length genomes. Simulated metagenomes can be an effective alternative to empirical sequencing, but accuracy depends on the genomic database used and whether the database contains organisms closely related to the 16S sequences. These tools are effective in well-studied systems, but the accuracy of these predictions in a nonmodel system is less known. Using a nonmodel bird species, we characterized the function of the microbiome and compared the accuracy of 16S-derived simulated metagenomes to sequenced metagenomes. We found that the simulated metagenomes reflect most but not all functions of empirical metagenome sequencing.

The Sugar Metabolic Model of Aspergillus niger Can Only Be Reliably Transferred to Fungi of Its Phylum

Fungi play a critical role in the global carbon cycle by degrading plant polysaccharides to small sugars and metabolizing them as carbon and energy sources. We mapped the well-established sugar metabolic network of Aspergillus niger to five taxonomically distant species (Aspergillus nidulans, Penicillium subrubescens, Trichoderma reesei, Phanerochaete chrysosporium and Dichomitus squalens) using an orthology-based approach. The diversity of sugar metabolism correlates well with the taxonomic distance of the fungi. The pathways are highly conserved between the three studied Eurotiomycetes (A. niger, A. nidulans, P. subrubescens). A higher level of diversity was observed between the T. reesei and A. niger, and even more so for the two Basidiomycetes. These results were confirmed by integrative analysis of transcriptome, proteome and metabolome, as well as growth profiles of the fungi growing on the corresponding sugars. In conclusion, the establishment of sugar pathway models in different fungi revealed the diversity of fungal sugar conversion and provided a valuable resource for the community, which would facilitate rational metabolic engineering of these fungi as microbial cell factories.

Tackling the Catch-22 Situation of Optimizing a Sensor and a Transporter System in a Whole-Cell Microbial Biosensor Design for an Anthropogenic Small Molecule

Whole-cell biosensors provide a convenient detection tool for the high-throughput screening of genetically engineered biocatalytic activity. But establishing a biosensor for an anthropogenic molecule requires both a custom transporter and a transcription factor. This results in an unavoidable "Catch-22" situation in which transporter activity cannot be easily confirmed without a biosensor and a biosensor cannot be established without a functional transporter in a host organism. We overcame this type of circular problem while developing an adipic acid (ADA) sensor. First, leveraging an established cis,cis-muconic acid (ccMA) sensor, an annotated ccMA transporter MucK, which is expected to be broadly responsive to dicarboxylates, was stably expressed in the genome of Pseudomonas putida to function as a transporter for ADA, and then a PcaR transcription factor (endogenous to the strain and naturally induced by β-ketoadipic acid, BKA) was diversified and selected to detect the ADA molecule. While MucK expression is otherwise very unstable in P. putida under strong promoter expression, our optimized mucK expression was functional for over 70 generations without loss of function, and we selected an ADA sensor that showed a specificity switch of over 35-fold from BKA at low concentrations (typically <0.1 mM of inducers). Our ADA and BKA biosensors show high sensitivity (low detection concentration <10 μM) and dynamic range (∼50-fold) in an industrially relevant organism and will open new avenues for high throughput discovery and optimization of enzymes and metabolic pathways for the biomanufacture of these molecules. In particular, the novel ADA sensor will aid the discovery and evolution of efficient biocatalysts for the biological recycling of ADA from the degradation of nylon-6,6 waste.

Systematic evaluation of genome-wide metabolic landscapes in lactic acid bacteria reveals diet- and strain-specific probiotic idiosyncrasies

Lactic acid bacteria (LAB) are well known to elicit health benefits in humans, but their functional metabolic landscapes remain unexplored. Here, we analyze differences in growth, intestinal persistence, and postbiotic biosynthesis of six representative LAB and their interactions with 15 gut bacteria under 11 dietary regimes by combining multi-omics and in silico modeling. We confirmed predictions on short-term persistence of LAB and their interactions with commensals using cecal microbiome abundance and spent-medium experiments. Our analyses indicate that probiotic attributes are both diet and species specific and cannot be solely explained using genomics. For example, although both Lacticaseibacillus casei and Lactiplantibacillus plantarum encode similarly sized genomes with diverse capabilities, L. casei exhibits a more desirable phenotype. In addition, "high-fat/low-carb" diets more likely lead to detrimental outcomes for most LAB. Collectively, our results highlight that probiotics are not "one size fits all" health supplements and lay the foundation for personalized probiotic design.

Taxonomic and predicted functional signatures reveal linkages between the rumen microbiota and feed efficiency in dairy cattle raised in tropical areas

Ruminants digest plant biomass more efficiently than monogastric animals due to their symbiotic relationship with a complex microbiota residing in the rumen environment. What remains unclear is the relationship between the rumen microbial taxonomic and functional composition and feed efficiency (FE), especially in crossbred dairy cattle (Holstein x Gyr) raised under tropical conditions. In this study, we selected twenty-two F1 Holstein x Gyr heifers and grouped them according to their residual feed intake (RFI) ranking, high efficiency (HE) (n = 11) and low efficiency (LE) (n = 11), to investigate the effect of FE on the rumen microbial taxa and their functions. Rumen fluids were collected using a stomach tube apparatus and analyzed using amplicon sequencing targeting the 16S (bacteria and archaea) and 18S (protozoa) rRNA genes. Alpha-diversity and beta-diversity analysis revealed no significant difference in the rumen microbiota between the HE and LE animals. Multivariate analysis (sPLS-DA) showed a clear separation of two clusters in bacterial taxonomic profiles related to each FE group, but in archaeal and protozoal profiles, the clusters overlapped. The sPLS-DA also revealed a clear separation in functional profiles for bacteria, archaea, and protozoa between the HE and LE animals. Microbial taxa were differently related to HE (e.g., Howardella and Shuttleworthia) and LE animals (e.g., Eremoplastron and Methanobrevibacter), and predicted functions were significatively different for each FE group (e.g., K03395-signaling and cellular process was strongly related to HE animals, and K13643-genetic information processing was related to LE animals). This study demonstrates that differences in the rumen microbiome relative to FE ranking are not directly observed from diversity indices (Faith's Phylogenetic Diversity, Pielou's Evenness, Shannon's diversity, weighted UniFrac distance, Jaccard index, and Bray-Curtis dissimilarity), but from targeted identification of specific taxa and microbial functions characterizing each FE group. These results shed light on the role of rumen microbial taxonomic and functional profiles in crossbred Holstein × Gyr dairy cattle raised in tropical conditions, creating the possibility of using the microbial signature of the HE group as a biological tool for the development of biomarkers that improve FE in ruminants.

IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database

Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.

Single sample pathway analysis in metabolomics: performance evaluation and application

BACKGROUND

Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures.

RESULTS

While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/ ), providing implementations of all the methods benchmarked in this study.

CONCLUSION

This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.

Intraintestinal Analysis of the Functional Activity of Microbiomes and Its Application to the Common Marmoset Intestine

The intestinal microbiome is closely related to host health, and metatranscriptomic analysis can be used to assess the functional activity of microbiomes by quantifying microbial gene expression levels, helping elucidate the interactions between the microbiome and the environment. However, the functional changes in the microbiome along the host intestinal tract remain unknown, and previous analytical methods have limitations, such as potentially overlooking unknown genes due to dependence on existing databases. The objective of this study is to develop a computational pipeline combined with next-generation sequencing for spatial covariation analysis of the functional activity of microbiomes at multiple intestinal sites (biogeographic locations) within the same individual. This method reconstructs a reference metagenomic sequence across multiple intestinal sites and integrates the metagenome and metatranscriptome, allowing the gene expression levels of the microbiome, including unknown bacterial genes, to be compared among multiple sites. When this method was applied to metatranscriptomic analysis in the intestinal tract of common marmosets, a New World monkey, the reconstructed metagenome covered most of the expressed genes and revealed that the differences in microbial gene expression among the cecum, transverse colon, and feces were more dynamic and sensitive to environmental shifts than the abundances of the genes. In addition, metatranscriptomic profiling at three intestinal sites of the same individual enabled covariation analysis incorporating spatial relevance, accurately predicting the function of a total of 10,856 unknown genes. Our findings demonstrate that our proposed analytical method captures functional changes in microbiomes at the gene resolution level. IMPORTANCE We developed an analysis method that integrates metagenomes and metatranscriptomes from multiple intestinal sites to elucidate how microbial function varies along the intestinal tract. This method enables spatial covariation analysis of the functional activity of microbiomes and accurate identification of gene expression changes among intestinal sites, including changes in the expression of unknown bacterial genes. Moreover, we applied this method to the investigation of the common marmoset intestine, which is anatomically and pharmacologically similar to that of humans. Our findings indicate the expression pattern of the microbiome varies in response to changes in the internal environment along the intestinal tract, and this microbial change may affect the intestinal environment.

Genomic insights into the physiology of Quinella, an iconic uncultured rumen bacterium

Quinella is a genus of iconic rumen bacteria first reported in 1913. There are no cultures of these bacteria, and information on their physiology is scarce and contradictory. Increased abundance of Quinella was previously found in the rumens of some sheep that emit low amounts of methane (CH₄) relative to their feed intake, but whether Quinella contributes to low CH₄ emissions is not known. Here, we concentrate Quinella cells from sheep rumen contents, extract and sequence DNA, and reconstruct Quinella genomes that are >90% complete with as little as 0.20% contamination. Bioinformatic analyses of the encoded proteins indicate that lactate and propionate formation are major fermentation pathways. The presence of a gene encoding a potential uptake hydrogenase suggests that Quinella might be able to use free hydrogen (H₂). None of the inferred metabolic pathways is predicted to produce H₂, a major precursor of CH₄, which is consistent with the lower CH₄ emissions from those sheep with high abundances of this bacterium.

Isolation of Pseudomonas aromaticivorans sp. nov from a hydrocarbon-contaminated groundwater capable of degrading benzene-, toluene-, m- and p-xylene under microaerobic conditions

Members of the genus Pseudomonas are known to be widespread in hydrocarbon contaminated environments because of their remarkable ability to degrade a variety of petroleum hydrocarbons, including BTEX (benzene, toluene, ethylbenzene and xylene) compounds. During an enrichment investigation which aimed to study microaerobic xylene degradation in a legacy petroleum hydrocarbon-contaminated groundwater, a novel Gram-stain-negative, aerobic, motile and rod-shaped bacterial strain, designated as MAP12T was isolated. It was capable of degrading benzene, toluene, meta- and para- xylene effectively under both aerobic and microaerobic conditions. The 16S rRNA gene sequence analysis revealed that strain MAP12T belongs to the genus Pseudomonas, with the highest 16S rRNA gene similarity to Pseudomonas linyingensis LYBRD3-7 T (98.42%), followed by Pseudomonas sagittaria JCM 18195 T (98.29%) and Pseudomonas alcaliphila JCM 10630 T (98.08%). Phylogenomic tree constructed using a concatenated alignment of 92 core genes indicated that strain MAP12T is distinct from any known Pseudomonas species. The draft genome sequence of strain MAP12T is 4.36 Mb long, and the G+C content of MAP12T genome is 65.8%. Orthologous average nucleotide identity (OrthoANI) and digital DNA-DNA hybridization (dDDH) analyses confirmed that strain MAP12T is distinctly separated from its closest neighbors (OrthoANI < 89 %; dDDH < 36%). Though several members of the genus Pseudomonas are well known for their aerobic BTEX degradation capability, this is the first report of a novel Pseudomonas species capable of degrading xylene under microaerobic conditions. By applying genome-resolved metagenomics, we were able to partially reconstruct the genome of strain MAP12 T from metagenomics sequence data and showed that strain MAP12 T was an abundant member of the xylene-degrading bacterial community under microaerobic conditions. Strain MAP12T contains ubiquinone 9 (Q9) as the major respiratory quinone and diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine as major polar lipids. The major cellular fatty acids of strain MAP12T are summed feature 3 (C16:1ω6c and/or C16:1ω7c), C16:0 and summed feature 8 (C18:1ω6c and/or C18:1ω7c). The results of this polyphasic study support that strain MAP12T represents a novel species of the genus Pseudomonas, hence the name of Pseudomonas aromaticivorans sp. nov. is proposed for this strain considering its aromatic hydrocarbon degradation capability. The type strain is MAP12T (=LMG 32466, =NCAIM B.02668).

Multi-omic analyses identify mucosa bacteria and fecal metabolites associated with weight loss after fecal microbiota transplantation

Fecal microbiota transplantation (FMT) has shown promising results in animal models of obesity, while results in human studies are inconsistent. We aimed to determine factors associated with weight loss after FMT in nine obese subjects using serial multi-omics analysis of the fecal and mucosal microbiome. The mucosal microbiome, fecal microbiome, and fecal metabolome showed individual clustering in each subject after FMT. The colonic microbiome in patients showed more marked variance after FMT compared with the duodenal microbiome, characterized by an increased relative abundance of Bacteroides. Subjects who lost weight after FMT sustained enrichment of Bifidobacterium bifidum and Alistipes onderdonkii in the duodenal, colonic mucosal, and fecal microbiome and increased levels of phosphopantothenate biosynthesis and fecal metabolite eicosapentaenoic acid (EPA), compared with those without weight loss. Fecal levels of amino acid metabolism-associated were positively correlated with the fecal abundance of B. bifidum, and fatty acid metabolism-associated metabolites showed positive correlations with A. onderdonkii. We report for the first time the individualized response of fecal and mucosa microbiome to FMT in obese subjects and highlight that FMT is less capable of shaping the small intestine microbiota. These findings contribute to personalized microbe-based therapies for obesity.

Identification of Novel Genes Associated with Partial Resistance to Aphanomyces Root Rot in Field Pea by BSR-Seq Analysis

Aphanomyces root rot, caused by Aphanomyces euteiches, causes severe yield loss in field pea (Pisum sativum). The identification of a pea germplasm resistant to this disease is an important breeding objective. Polygenetic resistance has been reported in the field pea cultivar '00-2067'. To facilitate marker-assisted selection (MAS), bulked segregant RNA-seq (BSR-seq) analysis was conducted using an F8 RIL population derived from the cross of 'Carman' × '00-2067'. Root rot development was assessed under controlled conditions in replicated experiments. Resistant (R) and susceptible (S) bulks were constructed based on the root rot severity in a greenhouse study. The BSR-seq analysis of the R bulks generated 44,595,510~51,658,688 reads, of which the aligned sequences were linked to 44,757 genes in a reference genome. In total, 2356 differentially expressed genes were identified, of which 44 were used for gene annotation, including defense-related pathways (jasmonate, ethylene and salicylate) and the GO biological process. A total of 344.1 K SNPs were identified between the R and S bulks, of which 395 variants were located in 31 candidate genes. The identification of novel genes associated with partial resistance to Aphanomyces root rot in field pea by BSR-seq may facilitate efforts to improve management of this important disease.

Parturition and postpartum dietary change altered ruminal pH and the predicted functions of rumen bacterial communities but did not alter the bacterial composition in Holstein cows

We investigated the temporal dynamics of ruminal pH and the composition and predicted functions of the rumen bacterial community in Holstein cows during the periparturient period. Eight multiparous Holstein cows (body weight; 707.4 ± 29.9 kg, parity; 3.6 ± 0.6) were used for continuous reticulo-ruminal pH measurement, among which five were also used for bacterial community analysis. Rumen fluid samples were collected at 3 weeks before and 2 and 6 weeks after parturition, and blood samples were collected 3 weeks before and 0, 2, 4, and 6 weeks after parturition. After the parturition, reduction in the 1-h mean reticulo-ruminal pH was associated with a significant (P < 0.05) increase in total volatile fatty acid concentration. However, with the exception of a significant change in an unclassified genus of Prevotellaceae (P < 0.05), we detected no significant changes in the compositions of major bacterial phyla or genera or diversity indices during the periparturient period. On the basis of predicted functional analysis, we identified a total of 53 MetaCyc pathways (45 upregulated), 200 enzyme commissions (184 upregulated), and 714 Kyoto Encyclopedia of Genes and Genomes orthologs (667 upregulated) at 6 weeks postpartum that were significantly (P < 0.05) different to those at 3 weeks prepartum. Among the 14 MetaCyc pathways (P < 0.05) in which pyruvate is an end product, PWY-3661 [log2-fold change (FC) = 5.49, false discovery rate (FDR) corrected P < 0.001] was the most highly upregulated pyruvate-producing pathway. In addition, P381-PWY [adenosylcobalamin biosynthesis II (aerobic); FC = 5.48, FDR corrected P < 0.001] was the second most upregulated adenosylcobalamin (Vitamin B12)-producing pathway. In contrast, PWY-2221 (FC = −4.54, FDR corrected P = 0.003), predominantly found in pectinolytic bacteria, was the most downregulated pathway. Our findings indicate that changes in rumen bacterial community structure are not strictly associated with transitions in parturition or diet; however, we did observe changes in reticulo-ruminal pH and the metabolic adaptation of predicted functional pathways. Consequently, predictive analysis of postpartum functional pathways may enable us to gain insights into the underlying functional adaptations of bacterial communities in Holstein cows during the periparturient period.

Exploring the medicinally important secondary metabolites landscape through the lens of transcriptome data in fenugreek (Trigonella foenum graecum L.)

Fenugreek (Trigonella foenum-graecum L.) is a self-pollinated leguminous crop belonging to the Fabaceae family. It is a multipurpose crop used as herb, spice, vegetable and forage. It is a traditional medicinal plant in India attributed with several nutritional and medicinal properties including antidiabetic and anticancer. We have performed a combined transcriptome assembly from RNA sequencing data derived from leaf, stem and root tissues. Around 209,831 transcripts were deciphered from the assembly of 92% completeness and an N50 of 1382 bases. Whilst secondary metabolites of medicinal value, such as trigonelline, diosgenin, 4-hydroxyisoleucine and quercetin, are distributed in several tissues, we report transcripts that bear sequence signatures of enzymes involved in the biosynthesis of such metabolites and are highly expressed in leaves, stem and roots. One of the antidiabetic alkaloid, trigonelline and its biosynthesising enzyme, is highly abundant in leaves. These findings are of value to nutritional and the pharmaceutical industry.

The impact of PrsA over-expression on the Bacillus subtilis transcriptome during fed-batch fermentation of alpha-amylase production

The production of the alpha-amylase (AMY) enzyme in Bacillus subtilis at a high rate leads to the accumulation of unfolded AMY, which causes secretion stress. The over-expression of the PrsA chaperone aids enzyme folding and reduces stress. To identify affected pathways and potential mechanisms involved in the reduced growth, we analyzed the transcriptomic differences during fed-batch fermentation between a PrsA over-expressing strain and control in a time-series RNA-seq experiment. We observe transcription in 542 unannotated regions, of which 234 had significant changes in expression levels between the samples. Moreover, 1,791 protein-coding sequences, 80 non-coding genes, and 20 riboswitches overlapping UTR regions of coding genes had significant changes in expression. We identified putatively regulated biological processes via gene-set over-representation analysis of the differentially expressed genes; overall, the analysis suggests that the PrsA over-expression affects ATP biosynthesis activity, amino acid metabolism, and cell wall stability. The investigation of the protein interaction network points to a potential impact on cell motility signaling. We discuss the impact of these highlighted mechanisms for reducing secretion stress or detrimental aspects of PrsA over-expression during AMY production.

Reconsidering the in vivo functions of Clostridial Stickland amino acid fermentations

Stickland amino acid fermentations occur primarily among species of Clostridia. An ancient form of metabolism, Stickland fermentations use amino acids as electron acceptors in the absence of stronger oxidizing agents and provide metabolic capabilities to support growth when other fermentable substrates, such as carbohydrates, are lacking. The reactions were originally described as paired fermentations of amino acid electron donors, such as the branched-chain amino acids, with recipients that include proline and glycine. We present a redox-focused view of Stickland metabolism following electron flow through metabolically diverse oxidative reactions and the defined-substrate reductase systems, including for proline and glycine, and the role of dual redox pathways for substrates such as leucine and ornithine. Genetic studies and Environment and Gene Regulatory Interaction Network (EGRIN) models for the pathogen Clostridioides difficile have improved our understanding of the regulation and metabolic recruitment of these systems, and their functions in modulating inter-species interactions within host-pathogen-commensal systems and uses in industrial and environmental applications.

Genomic insights into versatile lifestyle of three new bacterial candidate phyla

Metagenomic explorations of the Earth's biosphere enable the discovery of previously unknown bacterial lineages of phylogenetic and ecological significance. Here, we retrieved 11 metagenomic-assembled genomes (MAGs) affiliated to three new monophyletic bacterial lineages from the seawater of the Yap Trench. Phylogenomic analysis revealed that each lineage is a new bacterial candidate phylum, subsequently named Candidatus Qinglongiota, Candidatus Heilongiota, and Candidatus Canglongiota. Metabolic reconstruction of genomes from the three phyla suggested that they adopt a versatile lifestyle, with the potential to utilize various types of sugars, proteins, and/or short-chain fatty acids through anaerobic pathways. This was further confirmed by a global distribution map of the three phyla, indicating a preference for oxygen-limited or particle-attached niches, such as anoxic sedimentary environments. Of note, Candidatus Canglongiota genomes harbor genes for the complete Wood- Ljungdahl pathway and sulfate reduction that are similar to those identified in some sulfate-reducing bacteria. Evolutionary analysis indicated that gene gain and loss events, and horizontal gene transfer (HGT) play important roles in shaping the genomic and metabolic features of the three new phyla. This study presents the genomic insight into the ecology, metabolism, and evolution of three new phyla, which broadens the phylum-level diversity within the domain Bacteria.

Sex-specific relationships of the infant microbiome and early-childhood behavioral outcomes

BACKGROUND

A link between the gut microbiome and behavior is hypothesized, but most previous studies are cross-sectional or in animal models. The modifying role of host sex is poorly characterized. We aimed to identify sex-specific prospective associations between the early-life gut microbiome and preschool-age neurobehavior.

METHODS

In a prospective cohort, gut microbiome diversity and taxa were estimated with 16S rRNA sequencing at 6 weeks, 1 year, and 2 years. Species and gene pathways were inferred from metagenomic sequencing at 6 weeks and 1 year. When subjects were 3 years old, parents completed the Behavioral Assessment System for Children, second edition (BASC-2). A total of 260 children contributed 523 16S rRNA and 234 metagenomics samples to analysis. Models adjusted for sociodemographic characteristics.

RESULTS

Higher diversity at 6 weeks was associated with better internalizing problems among boys, but not girls [βBoys = -1.86 points/SD Shannon diversity; 95% CI (-3.29, -0.42), pBoys = 0.01, βGirls = 0.22 (-1.43, 1.87), pGirls = 0.8, pinteraction = 0.06]. Among other taxa-specific associations, Bifidobacterium at 6 weeks was associated with Adaptive Skills scores in a sex-specific manner. We observed relationships between functional features and BASC-2 scores, including vitamin B6 biosynthesis pathways and better Depression scores.

CONCLUSIONS

This study advances our understanding of microbe-host interactions with implications for childhood behavioral health.

IMPACT

This is one of the first studies to examine the early-life microbiome and neurobehavior, and the first to examine prospective sex-specific associations. Infant and early-childhood microbiomes relate to neurobehavior including anxiety, depression, hyperactivity, and social behaviors in a time- and sex-specific manner. Our findings suggest future studies should evaluate whether host sex impacts the relationship between the gut microbiome and behavioral health outcomes.

In Utero Exposure to Caffeine and Acetaminophen, the Gut Microbiome, and Neurodevelopmental Outcomes: A Prospective Birth Cohort Study

Pregnant individuals are exposed to acetaminophen and caffeine, but it is unknown how these exposures interact with the developing gut microbiome. We aimed to determine whether acetaminophen and/or caffeine relate to the childhood gut microbiome and whether features of the gut microbiome alter the relationship between acetaminophen/caffeine and neurodevelopment. Forty-nine and 85 participants provided meconium and stool samples at 6-7, respectively, for exposure and microbiome assessment. Fecal acetaminophen and caffeine concentrations were quantified, and fecal DNA underwent metagenomic sequencing. Caregivers and study staff assessed the participants' motor and cognitive development using standardized scales. Prenatal exposures had stronger associations with the childhood microbiome than concurrent exposures. Prenatal acetaminophen exposure was associated with a trend of lower gut bacterial diversity in childhood [β = -0.17 Shannon Index, 95% CI: (-0.31, -0.04)] and was marginally associated with differences in the relative abundances of features of the gut microbiome at the phylum (Firmicutes, Actinobacteria) and gene pathway levels. Among the participants with a higher relative abundance of Proteobacteria, prenatal exposure to acetaminophen and caffeine was associated with lower scores on WISC-IV subscales. Acetaminophen during bacterial colonization of the naïve gut is associated with lasting alterations in childhood microbiome composition. Future studies may inform our understanding of downstream health effects.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

Organic Connection of Holobiont Components and the Essential Roles of Core Microbes in the Holobiont Formation of Feral Brassica napus

Brassica napus (Rapeseed) is an econfomically important oil-producing crop. The microbial interactions in the plant holobiont are fundamental to the understanding of plant growth and health. To investigate the microbial dynamics in the holobiont of feral B. napus, a total of 215 holobiont samples, comprised of bulk soil, primary root, lateral root, dead leaf, caulosphere, basal leaf, apical leaf, carposphere, and anthosphere, were collected from five different grassland sites in South Korea. The soil properties differed in different sampling sites, but prokaryotic communities were segregated according to plant holobiont components. The structures of the site-specific SparCC networks were similar across the regions. Recurrent patterns were found in the plant holobionts in the recurrent network. Ralstonia sp., Massilia sp., and Rhizobium clusters were observed consistently and were identified as core taxa in the phyllosphere, dead leaf microbiome, and rhizosphere, respectively. Arthropod-related microbes, such as Wolbachia sp., Gilliamella sp., and Corynebacteriales amplicon sequence variants, were found in the anthosphere. PICRUSt2 analysis revealed that microbes also possessed specific functions related to holobiont components, such as functions related to degradation pathways in the dead leaf microbiome. Structural equation modeling analysis showed the organic connections among holobiont components and the essential roles of the core microbes in the holobiont formations in natural ecosystem. Microbes coexisting in a specific plant showed relatively stable community structures, even though the regions and soil characteristics were different. Microbes in each plant component were organically connected to form their own plant holobiont. In addition, plant-related microbes, especially core microbes in each holobiont, showed recurrent interaction patterns that are essential to an understanding of the survival and coexistence of plant microbes in natural ecosystems.

Oligofructose restores postprandial short-chain fatty acid levels during high-fat feeding

OBJECTIVE

Obesity is associated with consumption of a Western diet low in dietary fiber, while prebiotics reduce body weight. Fiber induces short-chain fatty acid (SCFA) production, and SCFA administration is beneficial to host metabolic homeostasis. However, the role of endogenous SCFA signaling in the development of obesity is contentious. Therefore, the primary objective of this study is to evaluate the postprandial time course of SCFA production and uptake in healthy (chow-fed), Western diet-fed (high-fat diet [HFD]) obese, and oligofructose-treated HFD-fed (HFD + OFS) rats.

METHODS

Male Sprague-Dawley rats were maintained on chow or HFD for 5 weeks, with or without supplementation of 10% OFS for 3 weeks. SCFAs were measured in the ileum, cecum, colon, portal vein, and vena cava at 0, 2, 4, 6, and 8 hours postprandially.

RESULTS

Postprandial cecal and portal vein SCFAs were decreased in obese rats compared with lean chow controls, whereas no differences were observed in fasting SCFA concentrations. OFS supplementation increased SCFA levels in the cecum and portal vein during obesity. Butyrate levels were positively associated with portal glucagon-like peptide 1 and adiposity and with Roseburia relative abundance.

CONCLUSIONS

The current study demonstrates that obesity is associated with reduced SCFA production, and that OFS supplementation increases SCFA levels. Additionally, postprandial butyrate production appears to be beneficial to host energy homeostasis.

Computational design and engineering of an Escherichia coli strain producing the nonstandard amino acid para-aminophenylalanine

Introducing heterologous pathways into host cells constitutes a promising strategy for synthesizing nonstandard amino acids (nsAAs) to enable the production of proteins with expanded chemistries. However, this strategy has proven challenging, as the expression of heterologous pathways can disrupt cellular homeostasis of the host cell. Here, we sought to optimize the heterologous production of the nsAA para-aminophenylalanine (pAF) in Escherichia coli. First, we incorporated a heterologous pAF biosynthesis pathway into a genome-scale model of E. coli metabolism and computationally identified metabolic interventions in the host’s native metabolism to improve pAF production. Next, we explored different approaches of imposing these flux interventions experimentally and found that the upregulation of flux in the chorismate biosynthesis pathway through the elimination of feedback inhibition mechanisms could significantly raise pAF titers (∼20-fold) while maintaining a reasonable pAF production-growth rate trade-off. Overall, this study provides a promising strategy for the biosynthesis of nsAAs in engineered cells.

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Sought to optimize para-aminophenylalanine (pAF) production and growth in E. coli

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Identified interventions in the host native metabolism using genome-scale models

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Constructed multiple mutant strains involving gene knockouts and/or overexpressions

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Flux modification in chorismate biosynthesis pathway significantly raised pAF titer

l-Ornithine-N5-monooxygenase (PvdA) Substrate Analogue Inhibitors for Pseudomonas aeruginosa Infections Treatment: Drug Repurposing Computational Studies

Pseudomonas aeruginosa is an opportunistic pathogen that can cause acute and severe infections. Increasing resistance to antibiotics has given rise to the urgent need for an alternative antimicrobial agent. A promising strategy is the inhibition of iron sequestration in the bacteria. The current work aimed to screen for inhibitors of pyoverdine-mediated iron sequestration in P. aeruginosa. As a drug target, we choose l-ornithine-N5-monooxygenase (PvdA), an enzyme involved in the biosynthesis of pyoverdine that catalyzes the FAD-dependent hydroxylation of the side chain amine of ornithine. As drug repurposing is a fast and cost-efficient way of discovering new applications for known drugs, the approach may help to solve emerging clinical problems. In this study, we use data about molecules from drug banks for screening. A total of 15 drugs that are similar in structure to l-ornithine, the substrate of PvdA, and 30 drugs that are sub-structures of l-ornithine were virtually docked against PvdA. N-2-succinyl ornithine and cilazapril were found to be the top binders with a binding energy of -12.8 and -9.1 kcal mol^-1, respectively. As the drug-likeness and ADME properties of the drugs were also found to be promising, molecular dynamics studies were performed to further confirm the stability of the complexes. The results of this in silico study indicate that N-2-succinyl ornithine could potentially be explored as a drug for the treatment of P. aeruginosa infections.

Genomic analysis provides novel insights into diversification and taxonomy of Allorhizobium vitis (i.e. Agrobacterium vitis)

BACKGROUND

Allorhizobium vitis (formerly named Agrobacterium vitis or Agrobacterium biovar 3) is the primary causative agent of crown gall disease of grapevine worldwide. We obtained and analyzed whole-genome sequences of diverse All. vitis strains to get insights into their diversification and taxonomy.

RESULTS

Pairwise genome comparisons and phylogenomic analysis of various All. vitis strains clearly indicated that All. vitis is not a single species, but represents a species complex composed of several genomic species. Thus, we emended the description of All. vitis, which now refers to a restricted group of strains within the All. vitis species complex (i.e. All. vitis sensu stricto) and proposed a description of a novel species, All. ampelinum sp. nov. The type strain of All. vitis sensu stricto remains the current type strain of All. vitis, K309T. The type strain of All. ampelinum sp. nov. is S4T. We also identified sets of gene clusters specific to the All. vitis species complex, All. vitis sensu stricto and All. ampelinum, respectively, for which we predicted the biological function and infer the role in ecological diversification of these clades, including some we could experimentally validate. All. vitis species complex-specific genes confer tolerance to different stresses, including exposure to aromatic compounds. Similarly, All. vitis sensu stricto-specific genes confer the ability to degrade 4-hydroxyphenylacetate and a putative compound related to gentisic acid. All. ampelinum-specific genes have putative functions related to polyamine metabolism and nickel assimilation. Congruently with the genome-based classification, All. vitis sensu stricto and All. ampelinum were clearly delineated by MALDI-TOF MS analysis. Moreover, our genome-based analysis indicated that Allorhizobium is clearly separated from other genera of the family Rhizobiaceae.

CONCLUSIONS

Comparative genomics and phylogenomic analysis provided novel insights into the diversification and taxonomy of Allorhizobium vitis species complex, supporting our redefinition of All. vitis sensu stricto and description of All. ampelinum. Our pan-genome analyses suggest that these species have differentiated ecologies, each relying on specialized nutrient consumption or toxic compound degradation to adapt to their respective niche.

Escherichia/Shigella, SCFAs, and Metabolic Pathways-The Triad That Orchestrates Intestinal Dysbiosis in Patients with Decompensated Alcoholic Cirrhosis from Western Mexico

Gut microbiota undergoes profound alterations in alcohol cirrhosis. Microbiota-derived products, e.g., short chain fatty acids (SCFA), regulate the homeostasis of the gut-liver axis. The objective was to evaluate the composition and functions of the intestinal microbiota in patients with alcohol-decompensated cirrhosis. Fecal samples of 18 patients and 18 healthy controls (HC) were obtained. Microbial composition was characterized by 16S rRNA amplicon sequencing, SCFA quantification was performed by gas chromatography (GC), and metagenomic predictive profiles were analyzed by PICRUSt2. Gut microbiota in the cirrhosis group revealed a significant increase in the pathogenic/pathobionts genera Escherichia/Shigella and Prevotella, a decrease in beneficial bacteria, such as Blautia, Faecalibacterium, and a decreased α-diversity (p < 0.001) compared to HC. Fecal SCFA concentrations were significantly reduced in the cirrhosis group (p < 0.001). PICRUSt2 analysis indicated a decrease in acetyl-CoA fermentation to butyrate, as well as an increase in pathways related to antibiotics resistance, and aromatic amino acid biosynthesis. These metabolic pathways have been poorly described in the progression of alcohol-related decompensated cirrhosis. The gut microbiota of these patients possesses a pathogenic/inflammatory environment; therefore, future strategies to balance intestinal dysbiosis should be implemented. These findings are described for the first time in the population of western Mexico.

Hurricane Harvey Impacts on Water Quality and Microbial Communities in Houston, TX Waterbodies

Extreme weather events can temporarily alter the structure of coastal systems and generate floodwaters that are contaminated with fecal indicator bacteria (FIB); however, every coastal system is unique, so identification of trends and commonalities in these episodic events is challenging. To improve our understanding of the resilience of coastal systems to the disturbance of extreme weather events, we monitored water quality, FIB at three stations within Clear Lake, an estuary between Houston and Galveston, and three stations in bayous that feed into the estuary. Water samples were collected immediately before and after Hurricane Harvey (HH) and then throughout the fall of 2017. FIB levels were monitored by culturing E. coli and Enterococci. Microbial community structure was profiled by high throughput sequencing of PCR-amplified 16S rRNA gene fragments. Water quality and FIB data were also compared to historical data for these water body segments. Before HH, salinity within Clear Lake ranged from 9 to 11 practical salinity units (PSU). Immediately after the storm, salinity dropped to < 1 PSU and then gradually increased to historical levels over 2 months. Dissolved inorganic nutrient levels were also relatively low immediately after HH and returned, within a couple of months, to historical levels. FIB levels were elevated immediately after the storm; however, after 1 week, E. coli levels had decreased to what would be acceptable levels for freshwater. Enterococci levels collected several weeks after the storm were within the range of historical levels. Microbial community structure shifted from a system dominated by Cyanobacteria sp. before HH to a system dominated by Proteobacteria and Bacteroidetes immediately after. Several sequences observed only in floodwater showed similarity to sequences previously reported for samples collected following Hurricane Irene. These changes in beta diversity corresponded to salinity and nitrate/nitrite concentrations. Differential abundance analysis of metabolic pathways, predicted from 16S sequences, suggested that pathways associated with virulence and antibiotic resistance were elevated in floodwater. Overall, these results suggest that floodwater generated from these extreme events may have high levels of fecal contamination, antibiotic resistant bacteria and bacteria rarely observed in other systems.

Child type 1 diabetes associated with mother vaginal bacteriome and mycobiome

Mother vaginal microbes contribute to microbiome of vaginally delivered neonates. Child microbiome can be associated with autoimmune diseases, such as type 1 diabetes (T1D). We collected vaginal DNA samples from 25 mothers with a vaginally delivered child diagnosed with T1D and samples from 24 control mothers who had vaginally delivered a healthy child and analyzed bacteriome and mycobiome of the samples. The total DNA of the samples was extracted, and ribosomal DNA regions (16S for bacteria, ITS2 for fungi) were amplified, followed by next-generation sequencing and machine learning. We found that alpha-diversity of bacteriome was increased (P < 0.002), whereas alpha-diversity of mycobiome was decreased (P < 0.001) in mothers with a diabetic child compared to the control mothers. Beta-diversity analysis suggested differences in mycobiomes between the mother groups (P = 0.001). Random forest models were able to effectively predict diabetes and control status of unknown samples (bacteria: 0.86 AUC, fungi: 0.96 AUC). Our data indicate several fungal genera and bacterial metabolic pathways of mother vaginal microbiome to be associated with child T1D. We suggest that early onset of T1D in a child has a relationship with altered mother vaginal microbiome and that both bacteriome and mycobiome contribute to this shift.

Metagenomic assessment of gut microbial communities and risk of severe COVID-19

The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure. We profiled 127 hospitalized patients with COVID-19 (n=79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites. 48 species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or “long COVID”, suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with predicting whether stool was obtained from patients with severe vs. moderate COVID-19. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease. Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to validate these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

Longitudinal Evaluation of Gut Bacteriomes and Viromes after Fecal Microbiota Transplantation for Eradication of Carbapenem-Resistant Enterobacteriaceae

Understanding the role of fecal microbiota transplantation (FMT) in the decolonization of multidrug-resistant organisms (MDRO) is critical. Specifically, little is known about virome changes in MDRO-infected subjects treated with FMT. Using shotgun metagenomic sequencing, we characterized longitudinal dynamics of the gut virome and bacteriome in three recipients who successfully decolonized carbapenem-resistant Enterobacteriaceae (CRE), including Klebsiella spp. and Escherichia coli, after FMT. We observed large shifts of the fecal bacterial microbiota resembling a donor-like community after transfer of a fecal microbiota dominated by the genus Ruminococcus. We found a substantial expansion of Klebsiella phages after FMT with a concordant decrease of Klebsiella spp. and striking increase of Escherichia phages in CRE E. coli carriers after FMT. We also observed the CRE elimination and similar evolution of Klebsiella phage in mice, which may play a role in the collapse of the Klebsiella population after FMT. In summary, our pilot study documented bacteriome and virome alterations after FMT which mediate many of the effects of FMT on the gut microbiome community.

IMPORTANCE Fecal microbiota transplantation (FMT) is an effective treatment for multidrug-resistant organisms; however, introducing a complex mixture of microbes also has unknown consequences for landscape features of gut microbiome. We sought to understand bacteriome and virome alterations in patients undergoing FMT to treat infection with carbapenem-resistant Enterobacteriaceae. This finding indicates that transkingdom interactions between the virome and bacteriome communities may have evolved in part to support efficient FMT for treating CRE.

Ustilago maydis Metabolic Characterization and Growth Quantification with a Genome-Scale Metabolic Model

Ustilago maydis is an important plant pathogen that causes corn smut disease and serves as an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism’s environmental adaptation and a full use of its metabolic potential. Here, we report the first genome-scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, and the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% annotated genes (6909) in the sequenced genome. Substrate utilization was corrected by BIOLOG phenotype arrays, and exponential batch cultivations were used to test growth predictions. The growth data revealed a decrease in glucose uptake rate with rising glucose concentration. A pangenome of four different U. maydis strains highlighted missing metabolic pathways in iUma22. The new model allows for studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.

Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.

The WalRK Two-Component System Is Essential for Proper Cell Envelope Biogenesis in Clostridioides difficile

The WalR-WalK two-component regulatory system (TCS) is found in all Firmicutes, in which it regulates the expression of multiple genes required for remodeling the cell envelope during growth and division. Unlike most TCSs, WalRK is essential for viability, so it has attracted interest as a potential antibiotic target. In this study, we used overexpression of WalR and CRISPR interference to investigate the Wal system of Clostridioides difficile, a major cause of hospital-associated diarrhea in high-income countries. We confirmed that the wal operon is essential and identified morphological defects and cell lysis as the major terminal phenotypes of altered wal expression. We also used transcriptome sequencing (RNA-seq) to identify over 150 genes whose expression changes in response to WalR levels. This gene set is enriched in cell envelope genes and includes genes encoding several predicted PG hydrolases and proteins that could regulate PG hydrolase activity. A distinct feature of the C. difficile cell envelope is the presence of an S-layer, and we found that WalR affects expression of several genes which encode S-layer proteins. An unexpected finding was that some Wal-associated phenotypic defects were inverted in comparison to what has been reported for other Firmicutes. For example, downregulation of Wal signaling caused C. difficile cells to become longer rather than shorter, as in Bacillus subtilis. Likewise, downregulation of Wal rendered C. difficile more sensitive to vancomycin, whereas reduced Wal activity is linked to increased vancomycin resistance in Staphylococcus aureus. IMPORTANCE The WalRK two-component system (TCS) is essential for coordinating synthesis and turnover of peptidoglycan in Firmicutes. We investigated the WalRK TCS in Clostridioides difficile, an important bacterial pathogen with an atypical cell envelope. We confirmed that WalRK is essential and regulates cell envelope biogenesis, although several of the phenotypic changes we observed were opposite to what has been reported for other Firmicutes. We also identified over 150 genes whose expression is controlled either directly or indirectly by WalR. Overall, our findings provide a foundation for future investigations of an important regulatory system and potential antibiotic target in C. difficile.

Survival strategies of an anoxic microbial ecosystem in Lake Untersee, a potential analog for Enceladus

Lake Untersee located in Eastern Antarctica, is a perennially ice-covered lake. At the bottom of its southern basin lies 20 m of anoxic, methane rich, stratified water, making it a good analog for Enceladus, a moon of Saturn. Here we present the first metagenomic study of this basin and detail the community composition and functional potential of the microbial communities at 92 m, 99 m depths and within the anoxic sediment. A diverse and well-populated microbial community was found, presenting the potential for Enceladus to have a diverse and abundant community. We also explored methanogenesis, sulfur metabolism, and nitrogen metabolism, given the potential presence of these compounds on Enceladus. We found an abundance of these pathways offering a variety of metabolic strategies. Additionally, the extreme conditions of the anoxic basin make it optimal for testing spaceflight technology and life detection methods for future Enceladus exploration.

Gut microbes shape microglia and cognitive function during malnutrition

Fecal-oral contamination promotes malnutrition pathology. Lasting consequences of early life malnutrition include cognitive impairment, but the underlying pathology and influence of gut microbes remain largely unknown. Here, we utilize an established murine model combining malnutrition and iterative exposure to fecal commensals (MAL-BG). The MAL-BG model was analyzed in comparison to malnourished (MAL mice) and healthy (CON mice) controls. Malnourished mice display poor spatial memory and learning plasticity, as well as altered microglia, non-neuronal CNS cells that regulate neuroimmune responses and brain plasticity. Chronic fecal-oral exposures shaped microglial morphology and transcriptional profile, promoting phagocytic features in MAL-BG mice. Unexpectedly, these changes occurred independently from significant cytokine-induced inflammation or blood-brain barrier (BBB) disruption, key gut-brain pathways. Metabolomic profiling of the MAL-BG cortex revealed altered polyunsaturated fatty acid (PUFA) profiles and systemic lipoxidative stress. In contrast, supplementation with an ω3 PUFA/antioxidant-associated diet (PAO) mitigated cognitive deficits within the MAL-BG model. These findings provide valued insight into the malnourished gut microbiota-brain axis, highlighting PUFA metabolism as a potential therapeutic target.

Dysbiosis of the Female Murine Gut Microbiome Exacerbates Neutrophil-Mediated Vascular Allograft Injury by Affecting Immunoregulation by Acetate

BACKGROUND

The gut microbiota affects immune responses that cause organ transplant rejection, but the mechanisms by which this occurs remain poorly understood.

METHODS

We have examined, in a murine model, how disruption of the gut microbiota with antibiotics early in life alters this microbial community later in life to affect immune responses that injure vascular allografts.

RESULTS

Analysis of 16S rRNA and whole genome sequencing of the gut microbiota demonstrated that early life disruption of this microbial community with antibiotics caused a reduction in taxa and enzymatic genes involved in the synthesis of acetate, an immunoregulatory metabolite in mice and humans. When allograft vascular injury was examined, early life disruption of the gut microbiota increased neutrophil accumulation and related medial injury of transplanted arteries. Normalizing the gut microbiota by co-housing and oral administration of acetate prevented neutrophil-mediated vascular allograft injury.

CONCLUSIONS

Dysbiosis of the gut microbiome that reduces its production of the immunoregulatory metabolite acetate exacerbates neutrophil-mediated allograft vascular injury.

Exploring the Effects of Six Weeks of Resistance Training on the Fecal Microbiome of Older Adult Males: Secondary Analysis of a Peanut Protein Supplemented Randomized Controlled Trial

The bacteria inhabiting the gastrointestinal tract contribute to numerous host functions and can be altered by lifestyle factors. We aimed to determine whether a 6-week training intervention altered fecal microbiome diversity and/or function in older males. Fecal samples were collected prior to and following a 6-week twice-weekly supervised resistance training intervention in 14 older Caucasian males (65 ± 10 years, 28.5 ± 3.2 kg/m2) with minimal prior training experience. Participants were randomized to receive a daily defatted peanut powder supplement providing 30 g protein (n = 8) or no supplement (n = 6) during the intervention. Bacterial DNA was isolated from pre-and post-training fecal samples, and taxa were identified using sequencing to amplify the variable region 4 (V4) of the 16S ribosomal RNA gene. Training significantly increased whole-body and lower-body lean mass (determined by dual energy X-ray absorptiometry) as well as leg extensor strength (p < 0.05) with no differences between intervention groups. Overall composition of the microbiome and a priori selected taxa were not significantly altered with training. However, MetaCYC pathway analysis indicated that metabolic capacity of the microbiome to produce mucin increased (p = 0.047); the tight junction protein, zonulin, was measured in serum and non-significantly decreased after training (p = 0.062). Our data suggest that resistance training may improve intestinal barrier integrity in older Caucasian males; further investigation is warranted.

Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation

Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using de novo binning strategies, but these genomes, not yet fully characterized, are not used in classic approaches, whereas machine and deep learning methods can use them as models. In this article, we attempted to review the different methods and their efficiency to improve the annotation of metagenomic sequences. Deep learning models have reached the performance of the widely used k-mer alignment-based tools, with better accuracy in certain cases; however, they still must demonstrate their robustness across the variety of environmental samples and across the rapid expansion of accessible genomes in databases.