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

Acute and Delayed Effects of Stress Eliciting Post-Traumatic Stress-Like Disorder Differentially Alters Fecal Microbiota Composition in a Male Mouse Model

The association between the shift in fecal resident microbiome and social conflicts with long-term consequences on psychological plasticity, such as the development of post-traumatic stress disorder (PTSD), is yet to be comprehended. We developed an aggressor-exposed (Agg-E) social stress (SS) mouse model to mimic warzone-like conflicts, where random life-threatening interactions took place between naïve intruder mice and aggressive resident mice. Gradually these Agg-E mice developed distinct characteristics simulating PTSD-like aspects, whereas the control mice not exposed to Agg-E SS demonstrated distinct phenotypes. To further investigate the role of Agg-E SS on the resident microbiome, 16S rRNA gene sequencing was assayed using fecal samples collected at pre-, during, and post-SS time points. A time agonist shift in the fecal microbial composition of Agg-E mice in contrast to its controls suggested a persistent impact of Agg-E SS on resident microbiota. At the taxonomic level, Agg-E SS caused a significant shift in the time-resolved ratios of Firmicutes and Bacteroidetes abundance. Furthermore, Agg-E SS caused diverging shifts in the relative abundances of Verrucomicrobia and Actinobacteria. An in silico estimation of genomic potential identified a potentially perturbed cluster of bioenergetic networks, which became increasingly enriched with time since the termination of Agg-E SS. Supported by a growing number of studies, our results indicated the roles of the microbiome in a wide range of phenotypes that could mimic the comorbidities of PTSD, which would be directly influenced by energy deficiency. Together, the present work suggested the fecal microbiome as a potential tool to manage long-term effects of social conflicts, including the management of PTSD.

Integrated Metabolomics and Lipidomics Reveal High Accumulation of Glycerophospholipids in Human Astrocytes under the Lipotoxic Effect of Palmitic Acid and Tibolone Protection

Lipotoxicity is a metabolic condition resulting from the accumulation of free fatty acids in non-adipose tissues which involves a series of pathological responses triggered after chronic exposure to high levels of fatty acids, severely detrimental to cellular homeostasis and viability. In brain, lipotoxicity affects both neurons and other cell types, notably astrocytes, leading to neurodegenerative processes, such as Alzheimer (AD) and Parkinson diseases (PD). In this study, we performed for the first time, a whole lipidomic characterization of Normal Human Astrocytes cultures exposed to toxic concentrations of palmitic acid and the protective compound tibolone, to establish and identify the set of potential metabolites that are modulated under these experimental treatments. The study covered 3843 features involved in the exo- and endo-metabolome extracts obtained from astrocytes with the mentioned treatments. Through multivariate statistical analysis such as PCA (principal component analysis), partial least squares (PLS-DA), clustering analysis, and machine learning enrichment analysis, it was possible to determine the specific metabolites that were affected by palmitic acid insult, such as phosphoethanolamines, phosphoserines phosphocholines and glycerophosphocholines, with their respective metabolic pathways impact. Moreover, our results suggest the importance of tibolone in the generation of neuroprotective metabolites by astrocytes and may be relevant to the development of neurodegenerative processes.

The airway microbiota of non-small cell lung cancer patients and its relationship to tumor stage and EGFR gene mutation

BACKGROUND

Accumulating studies have suggested the airway microbiota in lung cancer patients is significantly different from that of healthy controls. However, little is known about the relationship between airway microbiota and important clinical parameters of lung cancer. In this study, we aimed to explore the association between sputum microbiota and lung cancer stage, lymph node metastasis, intrathoracic metastasis, and epidermal growth factor receptor (EGFR) gene mutation.

METHODS

The microbiota of sputum samples from 85 newly-diagnosed NSCLC patients were sequenced via 16S rRNA sequencing of the V3-V4 region. Sequencing reads were filtered using QIIME2 and clustered against UPARSE.

RESULTS

Alpha- and β-diversity was significantly different between patients in stages I to II (early stage, ES) and patients in stages III to IV (advanced stage, AS). Linear discriminant analysis Effect Size (LEfSe) identified that genera Granulicatella and Actinobacillus were significantly enriched in ES, and the genus Actinomyces was significantly enriched in AS. PICRUSt2 identified that the NAD salvage pathway was significantly enriched in AS, which was positively associated with Granulicatella. Patients with intrathoracic metastasis were associated with increased genus Peptostreptococcus and incomplete reductive TCA cycle, which was associated with increased Peptostreptococcus. Genera Parvimonas, Pseudomona and L-valine biosynthesis were positively associated with lymph node metastasis. L-valine biosynthesis was related with increased Pseudomona. Finally, the genus Parvimonas was significantly enriched in adenocarcinoma patients with EGFR mutation.

CONCLUSION

The taxonomy structure differed between different lung cancer stages. The tumor stage, intrathoracic metastasis, lymph node metastasis, and EGFR mutation were associated with alteration of specific airway genera and metabolic function of sputum microbiota.

Effect of Magnesium Supplementation on Inflammatory Parameters: A Meta-Analysis of Randomized Controlled Trials

Magnesium (Mg) may have several beneficial effects on human health outcomes. One hypothesized mechanism eliciting such effects is the action of Mg on serum inflammatory parameters. However, studies on this topic to date have several important limitations. Therefore, the present systematic review and meta-analysis aimed to summarize the current state of the art of all randomized control trials (RCTs) investigating the effects of Mg supplementation versus placebo on serum parameters of inflammation. We searched several databases until 23 November 2021 for RCTs. Eligible studies were RCTs investigating the effect of oral Mg supplementation vs. placebo and having serum inflammatory markers as an outcome. Among 2484 papers initially screened, 17 randomized controlled trials (889 participants; mean age: 46 years; females: 62.5%) were included. Generally, a low risk of bias was present. In meta-analysis, Mg supplementation significantly decreased serum C reactive protein (CRP) and increased nitric oxide (NO) levels. In descriptive findings, Mg supplementation significantly reduced plasma fibrinogen, tartrate-resistant acid phosphatase type 5, tumor necrosis factor-ligand superfamily member 13B, ST2 protein, and IL-1. In conclusion, Mg supplementation may significantly reduce different human inflammatory markers, in particular serum CRP and NO levels.

A Genome-Scale Metabolic Model for the Human Pathogen Candida Parapsilosis and Early Identification of Putative Novel Antifungal Drug Targets

Candida parapsilosis is an emerging human pathogen whose incidence is rising worldwide, while an increasing number of clinical isolates display resistance to first-line antifungals, demanding alternative therapeutics. Genome-Scale Metabolic Models (GSMMs) have emerged as a powerful in silico tool for understanding pathogenesis due to their systems view of metabolism, but also to their drug target predictive capacity. This study presents the construction of the first validated GSMM for C. parapsilosis—iDC1003—comprising 1003 genes, 1804 reactions, and 1278 metabolites across four compartments and an intercompartment. In silico growth parameters, as well as predicted utilisation of several metabolites as sole carbon or nitrogen sources, were experimentally validated. Finally, iDC1003 was exploited as a platform for predicting 147 essential enzymes in mimicked host conditions, in which 56 are also predicted to be essential in C. albicans and C. glabrata. These promising drug targets include, besides those already used as targets for clinical antifungals, several others that seem to be entirely new and worthy of further scrutiny. The obtained results strengthen the notion that GSMMs are promising platforms for drug target discovery and guide the design of novel antifungal therapies.

A Graph-based Molecular Communications Model Analysis of the Human Gut Bacteriome

Alterations in the human Gut Bacteriome (GB) can be associated with human health issues, such as type-2 diabetes and obesity. Both external and internal factors can drive changes in the composition and in interactions of the human GB, impacting negatively on the host cells. This paper focuses on the human GB metabolism and proposes a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human GB network system. Our results show that regulation of molecular inputs strongly affect bacterial population growth and create an unbalanced network, as shown by shifts in the node weights based on the produced molecular signals. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human GB network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviors of the human GB depending on molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial population growth and exchange of metabolites.

Prolonged Impairment of Short-Chain Fatty Acid and L-Isoleucine Biosynthesis in Gut Microbiome in Patients With COVID-19

BACKGROUND AND AIMS

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with altered gut microbiota composition. Phylogenetic groups of gut bacteria involved in the metabolism of short chain fatty acids (SCFAs) were depleted in SARS-CoV-2-infected patients. We aimed to characterize a functional profile of the gut microbiome in patients with COVID-19 before and after disease resolution.

METHODS

We performed shotgun metagenomic sequencing on fecal samples from 66 antibiotics-naïve patients with COVID-19 and 70 non-COVID-19 controls. Serial fecal samples were collected (at up to 6 times points) during hospitalization and beyond 1 month after discharge. We assessed gut microbial pathways in association with disease severity and blood inflammatory markers. We also determined changes of microbial functions in fecal samples before and after disease resolution and validated these functions using targeted analysis of fecal metabolites.

RESULTS

Compared with non-COVID-19 controls, patients with COVID-19 with severe/critical illness showed significant alterations in gut microbiome functionality (P < .001), characterized by impaired capacity of gut microbiome for SCFA and L-isoleucine biosynthesis and enhanced capacity for urea production. Impaired SCFA and L-isoleucine biosynthesis in gut microbiome persisted beyond 30 days after recovery in patients with COVID-19. Targeted analysis of fecal metabolites showed significantly lower fecal concentrations of SCFAs and L-isoleucine in patients with COVID-19 before and after disease resolution. Lack of SCFA and L-isoleucine biosynthesis significantly correlated with disease severity and increased plasma concentrations of CXCL-10, NT- proB-type natriuretic peptide, and C-reactive protein (all P < .05).

CONCLUSIONS

Gut microbiome of patients with COVID-19 displayed impaired capacity for SCFA and L-isoleucine biosynthesis that persisted even after disease resolution. These 2 microbial functions correlated with host immune response underscoring the importance of gut microbial functions in SARS-CoV-2 infection pathogenesis and outcome.

GAL08, an Uncultivated Group of Acidobacteria, Is a Dominant Bacterial Clade in a Neutral Hot Spring

GAL08 are bacteria belonging to an uncultivated phylogenetic cluster within the phylum Acidobacteria. We detected a natural population of the GAL08 clade in sediment from a pH-neutral hot spring located in British Columbia, Canada. To shed light on the abundance and genomic potential of this clade, we collected and analyzed hot spring sediment samples over a temperature range of 24.2–79.8°C. Illumina sequencing of 16S rRNA gene amplicons and qPCR using a primer set developed specifically to detect the GAL08 16S rRNA gene revealed that absolute and relative abundances of GAL08 peaked at 65°C along three temperature gradients. Analysis of sediment collected over multiple years and locations revealed that the GAL08 group was consistently a dominant clade, comprising up to 29.2% of the microbial community based on relative read abundance and up to 4.7 × 10⁵ 16S rRNA gene copy numbers per gram of sediment based on qPCR. Using a medium quality threshold, 25 single amplified genomes (SAGs) representing these bacteria were generated from samples taken at 65 and 77°C, and seven metagenome-assembled genomes (MAGs) were reconstructed from samples collected at 45–77°C. Based on average nucleotide identity (ANI), these SAGs and MAGs represented three separate species, with an estimated average genome size of 3.17 Mb and GC content of 62.8%. Phylogenetic trees constructed from 16S rRNA gene sequences and a set of 56 concatenated phylogenetic marker genes both placed the three GAL08 bacteria as a distinct subgroup of the phylum Acidobacteria, representing a candidate order (Ca. Frugalibacteriales) within the class Blastocatellia. Metabolic reconstructions from genome data predicted a heterotrophic metabolism, with potential capability for aerobic respiration, as well as incomplete denitrification and fermentation. In laboratory cultivation efforts, GAL08 counts based on qPCR declined rapidly under atmospheric levels of oxygen but increased slightly at 1% (v/v) O₂, suggesting a microaerophilic lifestyle.

Anemochore Seeds Harbor Distinct Fungal and Bacterial Abundance, Composition, and Functional Profiles

Many plants adapted to harsh environments have evolved low seed mass ('light seeds') with specific dispersal strategies, primarily either by wind (anemochory) or water (hydrochory). However, the role of their seed microbiota in their survival, and their seed microbial abundance and structure, remain insufficiently studied. Herein, we studied the light seed microbiome of eight anemochores and two hydrochores (as controls) collected from four provinces in China, using qPCR and metagenomic sequencing targeting both bacteria and fungi. Substantial variations were found for seed endophytic fungi (9.9 × 1010~7.3 × 102 gene copy numbers per seed) and bacteria (1.7 × 1010~8.0 × 106). Seed microbial diversity and structure were mainly driven by the plant genotype (species), with weak influences from their host plant classification level or dispersal mode. Seed microbial composition differences were clear at the microbial phylum level, with dominant proportions (~75%) for Proteobacteria and Ascomycota. The light seeds studied harbored unique microbial signatures, sharing only two Halomonas amplicon sequence variants (ASVs) and two fungal ASVs affiliated to Alternaria and Cladosporium. A genome-level functional profile analysis revealed that seed bacterial microbiota were enriched in amino acid, nucleoside, and nucleotide biosynthesis, while in fungal communities the generation of precursor metabolites and respiration were more highly represented. Together, these novel insights provide a deeper understanding of highly diversified plant-specific light seed microbiota and ecological strategies for plants in harsh environments.

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.

Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711

BACKGROUND

Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source.

RESULTS

While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway.

CONCLUSIONS

Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome

The gut microbiome has been implicated in a variety of physiological states, but controversy over causality remains unresolved. Here, we performed bidirectional Mendelian randomization analyses on 3,432 Chinese individuals with whole-genome, whole-metagenome, anthropometric and blood metabolic trait data. We identified 58 causal relationships between the gut microbiome and blood metabolites, and replicated 43 of them. Increased relative abundances of fecal Oscillibacter and Alistipes were causally linked to decreased triglyceride concentration. Conversely, blood metabolites such as glutamic acid appeared to decrease fecal Oxalobacter, and members of Proteobacteria were influenced by metabolites such as 5-methyltetrahydrofolic acid, alanine, glutamate and selenium. Two-sample Mendelian randomization with data from Biobank Japan partly corroborated results with triglyceride and with uric acid, and also provided causal support for published fecal bacterial markers for cancer and cardiovascular diseases. This study illustrates the value of human genetic information to help prioritize gut microbial features for mechanistic and clinical studies.

Gut Microbiota as a Source of Uremic Toxins

Uremic retention solutes are the compounds that accumulate in the blood when kidney excretory function is impaired. Some of these compounds are toxic at high concentrations and are usually known as “uremic toxins”. The cumulative detrimental effect of uremic toxins results in numerous health problems and eventually mortality during acute or chronic uremia, especially in end-stage renal disease. More than 100 different solutes increase during uremia; however, the exact origin for most of them is still debatable. There are three main sources for such compounds: exogenous ones are consumed with food, whereas endogenous ones are produced by the host metabolism or by symbiotic microbiota metabolism. In this article, we identify uremic retention solutes presumably of gut microbiota origin. We used database analysis to obtain data on the enzymatic reactions in bacteria and human organisms that potentially yield uremic retention solutes and hence to determine what toxins could be synthesized in bacteria residing in the human gut. We selected biochemical pathways resulting in uremic retention solutes synthesis related to specific bacterial strains and revealed links between toxin concentration in uremia and the proportion of different bacteria species which can synthesize the toxin. The detected bacterial species essential for the synthesis of uremic retention solutes were then verified using the Human Microbiome Project database. Moreover, we defined the relative abundance of human toxin-generating enzymes as well as the possibility of the synthesis of a particular toxin by the human metabolism. Our study presents a novel bioinformatics approach for the elucidation of the origin of both uremic retention solutes and uremic toxins and for searching for the most likely human microbiome producers of toxins that can be targeted and used for the therapy of adverse consequences of uremia.

Characterization of the gut microbiota among Veterans with unique military-related exposures and high prevalence of chronic health conditions: A United States-Veteran Microbiome Project (US-VMP) study

The gut microbiome is impacted by environmental exposures and has been implicated in many physical and mental health conditions, including anxiety disorders, affective disorders, and trauma- and stressor-related disorders such as posttraumatic stress disorder (PTSD). United States (US) military Veterans are a unique population in that their military-related exposures can have consequences for both physical and mental health, but the gut microbiome of this population has been understudied. In this publication, we describe exposures, health conditions, and medication use of Veterans in the US Veteran Microbiome Project (US-VMP) and examine the associations between these characteristics and the gut microbiota. This cohort included 331 US Veterans seeking healthcare with the Veterans Health Administration who were 83% male with an average (±SD) age of 47.6 ​± ​13.4 years. The cohort displayed a high prevalence of PTSD (49.8%) and history of traumatic brain injuries (76.1%), and high current use of prescription medications (74.9%) to treat various acute and chronic conditions. We observed significant associations between the gut microbiota composition and gastroenteritis, peripheral vascular disease (PVD), bipolar disorders, symptoms of severe depression based on the Beck Depression Inventory, stimulant and opioid use disorders, beta-blockers, serotonin and norepinephrine reuptake inhibitor antidepressants, diabetes medications, and proton pump inhibitors. Many of the Veteran characteristics examined were associated with altered relative abundances of specific taxa. We found that PVD and cardiovascular disease were associated with lower microbiota diversity in the gut (i.e., α-diversity), while supplemental vitamin use was associated with higher α-diversity. Our study contributes novel insights as to whether the unique exposures of Veterans in this cohort correlate with gut microbiota characteristics and, in line with previous findings with other population-level studies of the microbiome, confirms associations between numerous health conditions and medications with the gut microbiome.

Subgingival Microbiota and Cytokines Profile Changes in Patients with Periodontitis: A Pilot Study Comparing Healthy and Diseased Sites in the Same Oral Cavities

Periodontitis is a common condition characterized by an exacerbated pro-inflammatory response, which leads to tissue destruction and, ultimately, alveolar bone loss. In this pilot study, we assess the microbiota composition and cytokine profile changes in patients with stage III/IV, grade B/C periodontitis, specifically by comparing healthy and diseased sites in the same oral cavity. Overall, we found that microbiota architecture was significantly disrupted between diseased and healthy sites, and that the clustering was driven, in part, by the increased relative abundances of Synergistetes in diseased sites, as well as the increased abundances of Firmicutes in healthy sites. We also observed that diseased sites were enriched in Synergistetes, TM7, SR1, Spirochaetes, Bacteroidetes and Fusobacteria, and depleted in Firmicutes, Proteobacteria, Tenericutes and Actinobacteria compared to healthy sites. We found that Interleukin-1b, Interleukin-4, Interleukin-10, and Interleukin-17A were significantly overexpressed in diseased sites, whereas Interleukin-6 and TNF-alpha do not differ significantly between healthy and diseased sites. Here, we observed concomitant changes in the subgingival plaque microbiota and cytokines profile, suggesting that this combined alteration could contribute to the pathobiology of periodontitis.

HumanNet v3: an improved database of human gene networks for disease research

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

Novel taxa of Acidobacteriota implicated in seafloor sulfur cycling

Acidobacteriota are widespread and often abundant in marine sediments, yet their metabolic and ecological properties are poorly understood. Here, we examined metabolisms and distributions of Acidobacteriota in marine sediments of Svalbard by functional predictions from metagenome-assembled genomes (MAGs), amplicon sequencing of 16S rRNA and dissimilatory sulfite reductase (dsrB) genes and transcripts, and gene expression analyses of tetrathionate-amended microcosms. Acidobacteriota were the second most abundant dsrB-harboring (averaging 13%) phylum after Desulfobacterota in Svalbard sediments, and represented 4% of dsrB transcripts on average. Meta-analysis of dsrAB datasets also showed Acidobacteriota dsrAB sequences are prominent in marine sediments worldwide, averaging 15% of all sequences analysed, and represent most of the previously unclassified dsrAB in marine sediments. We propose two new Acidobacteriota genera, Candidatus Sulfomarinibacter (class Thermoanaerobaculia, "subdivision 23") and Ca. Polarisedimenticola ("subdivision 22"), with distinct genetic properties that may explain their distributions in biogeochemically distinct sediments. Ca. Sulfomarinibacter encode flexible respiratory routes, with potential for oxygen, nitrous oxide, metal-oxide, tetrathionate, sulfur and sulfite/sulfate respiration, and possibly sulfur disproportionation. Potential nutrients and energy include cellulose, proteins, cyanophycin, hydrogen, and acetate. A Ca. Polarisedimenticola MAG encodes various enzymes to degrade proteins, and to reduce oxygen, nitrate, sulfur/polysulfide and metal-oxides. 16S rRNA gene and transcript profiling of Svalbard sediments showed Ca. Sulfomarinibacter members were relatively abundant and transcriptionally active in sulfidic fjord sediments, while Ca. Polarisedimenticola members were more relatively abundant in metal-rich fjord sediments. Overall, we reveal various physiological features of uncultured marine Acidobacteriota that indicate fundamental roles in seafloor biogeochemical cycling.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

PlantPathMarks (PPMdb): an interactive hub for pathways-based markers in plant genomes

Over the past decade, the problem of finding an efficient gene-targeting marker set or signature for plant trait characterization has remained challenging. Many databases focusing on pathway mining have been released with one major deficiency, as they lack to develop marker sets that target only genes controlling a specific pathway or certain biological process. Herein, we present the PlantPathMarks database (PPMdb) as a comprehensive, web-based, user-friendly, and interactive hub for pathway-based markers in plant genomes. Based on our newly developed pathway gene set mining approach, two novel pathway-based marker systems called pathway gene-targeted markers (PGTMs) and pathway microsatellite-targeted markers (PMTMs) were developed as a novel class of annotation-based markers. In the PPMdb database, 2,690,742 pathway-based markers reflecting 9,894 marker panels were developed across 82 plant genomes. The markers include 691,555 PGTMs and 1,999,187 PMTMs. Across these genomes, 165,378 enzyme-coding genes were mapped against 126 KEGG reference pathway maps. PPMdb is furnished with three interactive visualization tools (Map Browse, JBrowse and Species Comparison) to visualize, map, and compare the developed markers over their KEGG reference pathway maps. All the stored marker panels can be freely downloaded. PPMdb promises to create a radical shift in the paradigm of the area of molecular marker research. The use of PPMdb as a mega-tool represents an impediment for non-bioinformatician plant scientists and breeders. PPMdb is freely available at http://ppmdb.easyomics.org.

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

Ornithine Transcarbamylase - From Structure to Metabolism: An Update

Ornithine transcarbamylase (OTC; EC 2.1.3.3) is a ubiquitous enzyme found in almost all organisms, including vertebrates, microorganisms, and plants. Anabolic, mostly trimeric OTCs catalyze the production of L-citrulline from L-ornithine which is a part of the urea cycle. In eukaryotes, such OTC localizes to the mitochondrial matrix, partially bound to the mitochondrial inner membrane and part of channeling multi-enzyme assemblies. In mammals, mainly two organs express OTC: the liver, where it is an integral part of the urea cycle, and the intestine, where it synthesizes citrulline for export and plays a major role in amino acid homeostasis, particularly of L-glutamine and L-arginine. Here, we give an overview on OTC genes and proteins, their tissue distribution, regulation, and physiological function, emphasizing the importance of OTC and urea cycle enzymes for metabolic regulation in human health and disease. Finally, we summarize the current knowledge of OTC deficiency, a rare X-linked human genetic disorder, and its emerging role in various chronic pathologies.

Transcriptional Activity of Predominant Streptococcus Species at Multiple Oral Sites Associate With Periodontal Status

Background

Streptococcus species are predominant members of the oral microbiota in both health and diseased conditions. The purpose of the present study was to explore if different ecological characteristics, such as oxygen availability and presence of periodontitis, associates with transcriptional activity of predominant members of genus Streptococcus. We tested the hypothesis that genetically closely related Streptococcus species express different transcriptional activities in samples collected from environments with critically different ecological conditions determined by site and inflammatory status.

Methods

Metagenomic and metatranscriptomic data was retrieved from 66 oral samples, subgingival plaque (n=22), tongue scrapings (n=22) and stimulated saliva (n=22) collected from patients with periodontitis (n=11) and orally healthy individuals (n=11). Species-specific transcriptional activity was computed as Log2(RNA/DNA), and transcriptional activity of predominant Streptococcus species was compared between multiple samples collected from different sites in the same individual, and between individuals with different oral health status.

Results

The predominant Streptococcus species were identified with a site-specific colonization pattern of the tongue and the subgingival plaque. A total of 11, 4 and 2 pathways expressed by S. parasanguinis, S. infantis and S. salivarius, respectively, were recorded with significantly higher transcriptional activity in saliva than in tongue biofilm in healthy individuals. In addition, 18 pathways, including pathways involved in synthesis of peptidoglycan, amino acid biosynthesis, glycolysis and purine nucleotide biosynthesis expressed by S. parasanguinis and 3 pathways expressed by S. salivarius were identified with significantly less transcriptional activity in patients with periodontitis.

Conclusion

Data from the present study significantly demonstrates the association of site-specific ecological conditions and presence of periodontitis with transcriptional activity of the predominant Streptococcus species of the oral microbiota. In particular, pathways expressed by S. parasanguinis being involved in peptidoglycan, amino acid biosynthesis, glycolysis, and purine nucleotide biosynthesis were identified to be significantly associated with oral site and/or inflammation status.

An orthogonal metabolic framework for one-carbon utilization

Metabolic engineering often entails concurrent engineering of substrate utilization, central metabolism and product synthesis pathways, inevitably creating interdependency with native metabolism. Here we report an alternative approach using synthetic pathways for C1 bioconversion that generate multicarbon products directly from C1 units and hence are orthogonal to the host metabolic network. The engineered pathways are based on formyl-CoA elongation (FORCE) reactions catalysed by the enzyme 2-hydroxyacyl-CoA lyase. We use thermodynamic and stoichiometric analyses to evaluate FORCE pathway variants, including aldose elongation, α-reduction and aldehyde elongation. Promising variants were prototyped in vitro and in vivo using the non-methylotrophic bacterium Escherichia coli. We demonstrate the conversion of formate, formaldehyde and methanol into various products including glycolate, ethylene glycol, ethanol and glycerate. FORCE pathways also have the potential to be integrated with the host metabolism for synthetic methylotrophy by the production of native growth substrates as demonstrated in a two-strain co-culture system.

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus' pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https://www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool, findCPcli, that implements the computational method is available at https://github.com/findCP/findCPcli.

Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single- and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes.

Periodontitis associates with species-specific gene expression of the oral microbiota

The purpose of the present investigation was to characterize species-specific bacterial activity of the oral microbiota in periodontitis. We tested the hypotheses that chronic inflammation, i.e., periodontitis, associates with bacterial gene expression of the oral microbiota. Oral microbial samples were collected from three oral sites—subgingival plaque, tongue, and saliva from patients with periodontitis and healthy controls. Paired metagenomics and metatranscriptomics were used to perform concomitant characterization of taxonomic composition and to determine species-specific bacterial activity as expressed by the ratio of specific messenger RNA reads to their corresponding genomic DNA reads. Here, we show the association of periodontitis with bacterial gene expression of the oral microbiota. While oral site was the main determinant of taxonomic composition as well as bacterial gene expression, periodontitis was significantly associated with a reduction of carbohydrate metabolism of the oral microbiota at three oral sites (subgingival plaque, tongue, and saliva). Data from the present study revealed the association of periodontitis with bacterial gene expression of the oral microbiota. Conditions of periodontitis was associated with bacterial activity of local subgingival plaque, but also on tongue and the salivary microbiota. Collectively, data suggest that periodontitis associates with impaired carbohydrate metabolism of the oral microbiota. Future longitudinal and interventional studies are warranted to evaluate the potential pathogenic role of impaired bacterial carbohydrate metabolism not only in periodontitis but also in other diseases with low-grade inflammation, such as type 2 diabetes mellitus.

Gene-gene relationships in an Escherichia coli accessory genome are linked to function and mobility

The pangenome contains all genes encoded by a species, with the core genome present in all strains and the accessory genome in only a subset. Coincident gene relationships are expected within the accessory genome, where the presence or absence of one gene is influenced by the presence or absence of another. Here, we analysed the accessory genome of an Escherichia coli pangenome consisting of 400 genomes from 20 sequence types to identify genes that display significant co-occurrence or avoidance patterns with one another. We present a complex network of genes that are either found together or that avoid one another more often than would be expected by chance, and show that these relationships vary by lineage. We demonstrate that genes co-occur by function, and that several highly connected gene relationships are linked to mobile genetic elements. We find that genes are more likely to co-occur with, rather than avoid, another gene in the accessory genome. This work furthers our understanding of the dynamic nature of prokaryote pangenomes and implicates both function and mobility as drivers of gene relationships.

Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome

Delivery mode and perinatal antibiotics influence gut microbiome composition in children. Most microbiome studies have used the sequencing of the bacterial 16S marker gene but have not reported the metabolic function of the gut microbiome, which may mediate biological effects on the host. Here, we used the PICRUSt2 bioinformatics tool to predict the functional profiles of the gut microbiome based on 16S sequencing in two child cohorts. Both Caesarean section and perinatal antibiotics markedly influenced the functional profiles of the gut microbiome at the age of 1 year. In machine learning analysis, bacterial fatty acid, phospholipid, and biotin biosynthesis were the most important pathways that differed according to delivery mode. Proteinogenic amino acid biosynthesis, carbohydrate degradation, pyrimidine deoxyribonucleotide and biotin biosynthesis were the most important pathways differing according to antibiotic exposure. Our study shows that both Caesarean section and perinatal antibiotics markedly influence the predicted metabolic profiles of the gut microbiome at the age of 1 year.

WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization

In our group, we aim to understand metabolism in the nematode Caenorhabditis elegans and its relationships with gene expression, physiology, and the response to therapeutic drugs. Visualization of the metabolic pathways that comprise the metabolic network is extremely useful for interpreting a wide variety of experiments. Detailed annotated metabolic pathway maps for C. elegans are mostly limited to pan-organismal maps, many with incomplete or inaccurate pathway and enzyme annotations. Here, we present WormPaths, which is composed of two parts: (1) the careful manual annotation of metabolic genes into pathways, categories, and levels, and (2) 62 pathway maps that include metabolites, metabolite structures, genes, reactions, and pathway connections between maps. These maps are available on the WormFlux website. We show that WormPaths provides easy-to-navigate maps and that the different levels in WormPaths can be used for metabolic pathway enrichment analysis of transcriptomic data. In the future, we envision further developing these maps to be more interactive, analogous to road maps that are available on mobile devices.

MicFunPred: A conserved approach to predict functional profiles from 16S rRNA gene sequence data

The 16S rRNA gene amplicon sequencing is a popular technique that provides accurate characterization of microbial taxonomic abundances but does not provide any functional information. Several tools are available to predict functional profiles based on 16S rRNA gene sequence data that use different genome databases and approaches. As variable regions of partially-sequenced 16S rRNA gene cannot resolve taxonomy accurately beyond the genus level, these tools may give inflated results. Here, we developed 'MicFunPred', which uses a novel approach to derive imputed metagenomes based on a set of core genes only, thereby minimizing false-positive predictions. On simulated datasets, MicFunPred showed the lowest False Positive Rate (FPR) with mean Spearman's correlation of 0.89 (SD = 0.03), while on seven real datasets the mean correlation was 0.75 (SD = 0.08). MicFunPred was found to be faster with low computational requirements and performed better or comparable when compared with other tools.

Vitamin supplementation as a potential adjunctive therapeutic approach for COVID-19: biological and clinical plausibility

The recent pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus Disease 2019 (COVID-19) has introduced itself into the human population in the 21st century after the coronavirus diseases SARS-CoV and Middle East respiratory syndrome (MERS-CoV). Major investigations are underway worldwide in the search for pharmaceutical interventions for COVID-19 and many agents are administered in off-label routes. Several cases are under study to check or restrict clinical manifestations of COVID-19. According to the fact that the efficacy of some micro-nutrients like vitamins is proven to treat or prevent infectious diseases because of their antimicrobial and immunomodulatory activity, the potential role of vitamins in the COVID-19 treatment or prevention must be considered.

Using Functional Annotations to Study Pairwise Interactions in Urinary Tract Infection Communities

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict putative interactions between pair of organisms with the aim to explain pairwise growth effects. We apply our method to 66 genomes selected from online databases, which belong to 6 genera representing members of UTI communities. This resulted in a selection of metabolic pathways with high correlation for each pairwise combination between a complementarity index and the experimentally derived growth data. Our results indicated that Enteroccus spp. were most complemented in its metabolism by the other members of the UTI community. This suggests that the growth of Enteroccus spp. can potentially be enhanced by complementary metabolites produced by other community members. We tested a few putative predicted interactions by experimental supplementation of the relevant predicted metabolites. As predicted by our method, folic acid supplementation led to the increase in the population density of UTI Enterococcus isolates. Overall, we believe our method is a rapid initial in silico screening for the prediction of metabolic interactions in microbial communities.

Decreased Abundance of Akkermansia muciniphila Leads to the Impairment of Insulin Secretion and Glucose Homeostasis in Lean Type 2 Diabetes

Although obesity occurs in most of the patients with type 2 diabetes (T2D), a fraction of patients with T2D are underweight or have normal weight. Several studies have linked the gut microbiome to obesity and T2D, but the role of gut microbiota in lean individuals with T2D having unique clinical characteristics remains unclear. A metagenomic and targeted metabolomic analysis is conducted in 182 lean and abdominally obese individuals with and without newly diagnosed T2D. The abundance of Akkermansia muciniphila (A. muciniphila) significantly decreases in lean individuals with T2D than without T2D, but not in the comparison of obese individuals with and without T2D. Its abundance correlates inversely with serum 3β-chenodeoxycholic acid (βCDCA) levels and positively with insulin secretion and fibroblast growth factor 15/19 (FGF15/19) concentrations. The supplementation with A. muciniphila is sufficient to protect mice against high sucrose-induced impairment of glucose intolerance by decreasing βCDCA and increasing insulin secretion and FGF15/19. Furthermore, βCDCA inhibits insulin secretion and FGF15/19 expression. These findings suggest that decreased abundance of A. muciniphila is linked to the impairment of insulin secretion and glucose homeostasis in lean T2D, paving the way for new therapeutic options for the prevention or treatment of diabetes.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (J_max) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high J_max was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.

This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

The integration of data at different levels of cellular organization is an important goal in computational biology for understanding the way the genotypic variation translates into phenotypic variation. Novel profiling technologies and accurate high-throughput phenotyping now allows genomic, transcriptomic, metabolic and proteomic characterization of a large number of individuals under various environmental conditions. However, the metabolic fluxes remain difficult to measure. In this work, we take advantage of recent advances in genome-scale functional annotation and constraint-based metabolic modeling to provide a mathematical framework that allows to estimate internal cellular fluxes from protein abundances and elucidate the biological mechanisms underlying phenotypic variation. Applied to yeast as a model system, this approach highlights that the negative correlation between production traits such as maximum population size and growth and fermentation traits is explained by a differential usage of energy production pathways. The ability to identify molecular and metabolic bases of the variation of integrated traits through population studies has a broad applicability domain.

The Role of Metabolic Engineering Technologies for the Production of Fatty Acids in Yeast

Simple Summary

Metabolic engineering involves the sustainable production of high-value products. E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques (i.e., CRISPR–Cas and synthetic biology) for developing FA-producing yeast cell factories.

Abstract

Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.

The ecological impacts of multiple environmental stressors on coastal biofilm bacteria

Ecological communities are increasingly exposed to multiple interacting stressors. For example, warming directly affects the physiology of organisms, eutrophication stimulates the base of the food web, and harvesting larger organisms for human consumption dampens top-down control. These stressors often combine in the natural environment with unpredictable results. Bacterial communities in coastal ecosystems underpin marine food webs and provide many important ecosystem services (e.g. nutrient cycling and carbon fixation). Yet, how microbial communities will respond to a changing climate remains uncertain. Thus, we used marine mesocosms to examine the impacts of warming, nutrient enrichment, and altered top-predator population size structure (common shore crab) on coastal microbial biofilm communities in a crossed experimental design. Warming increased bacterial α-diversity (18% increase in species richness and 67% increase in evenness), but this was countered by a decrease in α-diversity with nutrient enrichment (14% and 21% decrease for species richness and evenness, respectively). Thus, we show some effects of these stressors could cancel each other out under climate change scenarios. Warming and top-predator population size structure both affected bacterial biofilm community composition, with warming increasing the abundance of bacteria capable of increased mineralization of dissolved and particulate organic matter, such as Flavobacteriia, Sphingobacteriia, and Cytophagia. However, the community shifts observed with warming depended on top-predator population size structure, with Sphingobacteriia increasing with smaller crabs and Cytophagia increasing with larger crabs. These changes could alter the balance between mineralization and carbon sequestration in coastal ecosystems, leading to a positive feedback loop between warming and CO₂ production. Our results highlight the potential for warming to disrupt microbial communities and biogeochemical cycling in coastal ecosystems, and the importance of studying these effects in combination with other environmental stressors.

Genomic insights into diverse bacterial taxa that degrade extracellular DNA in marine sediments

Extracellular DNA is a major macromolecule in global element cycles, and is a particularly crucial phosphorus, nitrogen and carbon source for microorganisms in the seafloor. Nevertheless, the identities, ecophysiology and genetic features of DNA-foraging microorganisms in marine sediments are largely unknown. Here, we combined microcosm experiments, DNA stable isotope probing (SIP), single-cell SIP using nano-scale secondary isotope mass spectrometry (NanoSIMS) and genome-centric metagenomics to study microbial catabolism of DNA and its subcomponents in marine sediments. ¹³C-DNA added to sediment microcosms was largely degraded within 10 d and mineralized to ¹³CO₂. SIP probing of DNA revealed diverse ‘Candidatus Izemoplasma’, Lutibacter, Shewanella and Fusibacteraceae incorporated DNA-derived ¹³C-carbon. NanoSIMS confirmed incorporation of ¹³C into individual bacterial cells of Fusibacteraceae sorted from microcosms. Genomes of the ¹³C-labelled taxa all encoded enzymatic repertoires for catabolism of DNA or subcomponents of DNA. Comparative genomics indicated that diverse ‘Candidatus Izemoplasmatales’ (former Tenericutes) are exceptional because they encode multiple (up to five) predicted extracellular nucleases and are probably specialized DNA-degraders. Analyses of additional sediment metagenomes revealed extracellular nuclease genes are prevalent among Bacteroidota at diverse sites. Together, our results reveal the identities and functional properties of microorganisms that may contribute to the key ecosystem function of degrading and recycling DNA in the seabed.

Using microcosms, stable isotope probing, genome-resolved metagenomics and NanoSIMS, the authors identify diverse bacterial taxa that can degrade extracellular DNA in marine sediments, including ‘Candidatus Izemoplasma’, which encode numerous extracellular nucleases.

Biological production of H2 , CH4 and CO2 in the deep subsurface of the Iberian Pyrite Belt

Most of the terrestrial deep subsurfaces are oligotrophic environments in which some gases, mainly H₂ , CH₄ and CO₂ , play an important role as energy and/or carbon sources. In this work, we assessed their biotic and abiotic origin in samples from subsurface hard-rock cores of the Iberian Pyrite Belt (IPB) at three different depths (414, 497 and 520 m). One set of samples was sterilized (abiotic control) and all samples were incubated under anaerobic conditions. Our results showed that H₂ , CH₄ and CO₂ remained low and constant in the sterilized controls while their levels were 4, 4.1 and 2.5 times higher respectively, in the unsterilized samples compared to the abiotic controls. The δ¹³ C_CH4 -values measured in the samples (range -31.2 to -43.0 ‰) reveals carbon isotopic signatures that are within the range for biological methane production. Possible microorganisms responsible for the biotic production of the gases were assessed by CARD-FISH. The analysis of sequenced genomes of detected microorganisms within the subsurface of the IPB allowed to identify possible metabolic activities involved in H₂ (Rhodoplanes, Shewanella and Desulfosporosinus), CH₄ (Methanobacteriales) and CO₂ production. The obtained results suggest that part of the H₂ , CH₄ and CO₂ detected in the deep subsurface has a biological origin.

The evolving landscape of untargeted metabolomics

AIMS

Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics.

DATA SYNTHESIS

Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available.

CONCLUSIONS

Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

Comparative Genomics Analysis of Keratin-Degrading Chryseobacterium Species Reveals Their Keratinolytic Potential for Secondary Metabolite Production

A promising keratin-degrading strain from the genus Chryseobacterium (Chryseobacterium sp. KMC2) was investigated using comparative genomic tools against three publicly available reference genomes to reveal the keratinolytic potential for biosynthesis of valuable secondary metabolites. Genomic features and metabolic potential of four species were compared, showing genomic differences but similar functional categories. Eleven different secondary metabolite gene clusters of interest were mined from the four genomes successfully, including five common ones shared across all genomes. Among the common metabolites, we identified gene clusters involved in biosynthesis of flexirubin-type pigment, microviridin, and siderophore, showing remarkable conservation across the four genomes. Unique secondary metabolite gene clusters were also discovered, for example, ladderane from Chryseobacterium sp. KMC2. Additionally, this study provides a more comprehensive understanding of the potential metabolic pathways of keratin utilization in Chryseobacterium sp. KMC2, with the involvement of amino acid metabolism, TCA cycle, glycolysis/gluconeogenesis, propanoate metabolism, and sulfate reduction. This work uncovers the biosynthesis of secondary metabolite gene clusters from four keratinolytic Chryseobacterium species and shades lights on the keratinolytic potential of Chryseobacterium sp. KMC2 from a genome-mining perspective, can provide alternatives to valorize keratinous materials into high-value bioactive natural products.

IDARE2-Simultaneous Visualisation of Multiomics Data in Cytoscape

Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.

Gut Microbial Signature of Hepatocellular Cancer in Men With Cirrhosis

The gut microbiome is altered in cirrhosis. Recent evidence has suggested a key role for the gut microbiota in the progression of cirrhosis and the development of hepatocellular carcinoma (HCC). We studied the differences in the microbial composition in patients with cirrhosis with prior and future HCC in the context of other complications (eg, infections, hepatic encephalopathy). The following 2 cohorts were recruited prospectively: the prior HCC cohort, in which outpatients with HCC within 2 years were age-matched, sex-matched, and Model for End-Stage Liver Disease (MELD) score-matched with those without HCC; and the future HCC cohort, in which patients were followed for 2 years and divided into future HCC versus no HCC after age, sex, and MELD-score matching and other complications were also recorded. Microbiota composition and predicted function were analyzed with ribosomal RNA sequencing and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PiCRUST)and compared between (1) prior HCC versus none and (2) future HCC versus none, and in the future cohort, comparisons were also made between those patients who developed (1) HCC only versus without complications, (2) HCC only versus non-HCC complications only, and (3) HCC + other complications versus non-HCC complications only. A total of 142 men (76 total in the prior cohort [38 with/38 without HCC] and 66 total in the future cohort [33 with/33 without future HCC]) were included. The groups had similar etiology, lactulose/rifaximin/proton pump inhibitor use, diabetes mellitus, and non-HCC complications. Microbial diversity was similar between prior HCC/not or future HCC/not. On DESeq2 higher Clostridium sensu stricto and Anaerotruncus were significantly associated with protection from HCC, whereas the reverse was seen with Raoultella and Haemophilus regardless of prior/future HCC comparisons. Functions focused on urea cycle, bioenergetics, tryptophan, and toluene metabolism were different between the groups. Rothia was specific for other complications. Despite age, sex, and MELD-score matching and accounting for other complications, gut microbiota composition and the predicted function are different in men with cirrhosis with and without prior HCC and can be extended toward future HCC development.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

Acetate as a potential feedstock for the production of value-added chemicals: Metabolism and applications

Acetate is regarded as a promising carbon feedstock in biological production owing to its possible derivation from C₁ gases such as CO, CO₂ and methane. To best use of acetate, comprehensive understanding of acetate metabolisms from genes and enzymes to pathways and regulations is needed. This review aims to provide an overview on the potential of acetate as carbon feedstock for industrial biotechnology. Biochemical, microbial and biotechnological aspects of acetate metabolism are described. Especially, the current state-of-the art in the production of value-added chemicals from acetate is summarized. Challenges and future perspectives are also provided.

Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance

Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the composition of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5–200 μmol photons m^-2 s^-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry.

Natural combinatorial genetics and prolific polyamine production enable siderophore diversification in Serratia plymuthica

BACKGROUND

Iron is essential for bacterial survival. Bacterial siderophores are small molecules with unmatched capacity to scavenge iron from proteins and the extracellular milieu, where it mostly occurs as insoluble Fe3+. Siderophores chelate Fe3+ for uptake into the cell, where it is reduced to soluble Fe2+. Siderophores are key molecules in low soluble iron conditions. The ability of bacteria to synthesize proprietary siderophores may have increased bacterial evolutionary fitness; one way that bacteria diversify siderophore structure is by incorporating different polyamine backbones while maintaining the catechol moieties.

RESULTS

We report that Serratia plymuthica V4 produces a variety of siderophores, which we term the siderome, and which are assembled by the concerted action of enzymes encoded in two independent gene clusters. Besides assembling serratiochelin A and B with diaminopropane, S. plymuthica utilizes putrescine and the same set of enzymes to assemble photobactin, a siderophore found in the bacterium Photorhabdus luminescens. The enzymes encoded by one of the gene clusters can independently assemble enterobactin. A third, independent operon is responsible for biosynthesis of the hydroxamate siderophore aerobactin, initially described in Enterobacter aerogenes. Mutant strains not synthesizing polyamine-siderophores significantly increased enterobactin production levels, though lack of enterobactin did not impact the production of serratiochelins. Knocking out SchF0, an enzyme involved in the assembly of enterobactin alone, significantly reduced bacterial fitness.

CONCLUSIONS

This study shows the natural occurrence of serratiochelins, photobactin, enterobactin, and aerobactin in a single bacterial species and illuminates the interplay between siderophore biosynthetic pathways and polyamine production, indicating routes of molecular diversification. Given its natural yields of diaminopropane (97.75 μmol/g DW) and putrescine (30.83 μmol/g DW), S. plymuthica can be exploited for the industrial production of these compounds.