The MetaCyc database of metabolic pathways and enzymes

Gut Microbiome Composition Is Associated with a Pathologic Response After Preoperative Chemoradiation in Patients with Rectal Cancer

PURPOSE

There are ongoing investigations to find promising biomarkers for predicting a complete response (CR) after concurrent chemoradiation (CCRT) in rectal cancer. We aimed to find the predictive value in the gut microbiome in terms of response after preoperative CCRT.

METHODS AND MATERIALS

We collected a total of 45 fecal samples from patients with rectal cancer before CCRT. Tumor response after CCRT was assessed according to the American Joint Committee on Cancer tumor regression grading system. Analysis of linear discriminant analysis effect size and MetaCyc pathway abundance predictions were performed to compare composition and metabolic function of microbiome between patients with and without CR. We also established a Bayesian network model to identify microbial networks and species to be related with CCRT response.

RESULTS

Seven patients (15.6%) demonstrated pathologically CR, and 38 patients (84.4%) showed non-CR after preoperative CCRT. Between CR and non-CR patients, there was a significant difference in terms of β-diversity (P = .028), but no difference in α-diversity was found. Bacteroidales (Bacteroidaceae, Rikenellaceae, Bacteroides) were relatively more abundant in patients with non-CR than those with CR. Pathways related to anabolic function predominated in CR patients. According to Bayesian network analysis, Duodenibacillus massiliensis was linked with the improved CR rate.

CONCLUSIONS

From the fecal microbiome using samples obtained before preoperative CCRT, differences in microbial community composition and functions were observed between patients with and without CR in rectal cancer. However, the finding that a specific taxon may be linked with the improved therapeutic response should be verified in a prospective setting.

Linoleic acid induces metabolic stress in the intestinal microorganism Bifidobacterium breve DSM 20213

Despite clinical and research interest in the health implications of the conjugation of linoleic acid (LA) by bifidobacteria, the detailed metabolic pathway and physiological reasons underlying the process remain unclear. This research aimed to investigate, at the molecular level, how LA affects the metabolism of Bifidobacterium breve DSM 20213 as a model for the well-known LA conjugation phenotype of this species. The mechanisms involved and the meaning of the metabolic changes caused by LA to B. breve DSM 20213 are unclear due to the lack of comprehensive information regarding the responses of B. breve DSM 20213 under different environmental conditions. Therefore, for the first time, an untargeted metabolomics-based approach was used to depict the main changes in the metabolic profiles of B. breve DSM 20213. Both supervised and unsupervised statistical methods applied to the untargeted metabolomic data allowed confirming the metabolic changes of B. breve DSM 20213 when exposed to LA. In particular, alterations to the amino-acid, carbohydrate and fatty-acid biosynthetic pathways were observed at the stationary phase of growth curve. Among others, significant up-regulation trends were detected for aromatic (such as tyrosine and tryptophan) and sulfur amino acids (i.e., methionine and cysteine). Besides confirming the conjugation of LA, metabolomics suggested a metabolic reprogramming during the whole growth curve and an imbalance in redox status following LA exposure. Such redox stress resulted in the down-accumulation of peroxide scavengers such as low-molecular-weight thiols (glutathione- and mycothiol-related compounds) and ascorbate precursors, together with the up-accumulation of oxidized (hydroxy- and epoxy-derivatives) forms of fatty acids. Consistently, growth was reduced and the levels of the oxidative stress marker malondialdehyde were higher in LA-exposed B. breve DSM 20213 than in the control.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.

Association Between Sulfur-Metabolizing Bacterial Communities in Stool and Risk of Distal Colorectal Cancer in Men

BACKGROUND & AIMS

Sulfur-metabolizing microbes, which convert dietary sources of sulfur into genotoxic hydrogen sulfide (H2S), have been associated with development of colorectal cancer (CRC). We identified a dietary pattern associated with sulfur-metabolizing bacteria in stool and then investigated its association with risk of incident CRC using data from a large prospective study of men.

METHODS

We collected data from 51,529 men enrolled in the Health Professionals Follow-up Study since 1986 to determine the association between sulfur-metabolizing bacteria in stool and risk of CRC over 26 years of follow-up. First, in a subcohort of 307 healthy men, we profiled serial stool metagenomes and metatranscriptomes and assessed diet using semiquantitative food frequency questionnaires to identify food groups associated with 43 bacterial species involved in sulfur metabolism. We used these data to develop a sulfur microbial dietary score. We then used Cox proportional hazards modeling to evaluate adherence to this pattern among eligible individuals (n = 48,246) from 1986 through 2012 with risk for incident CRC.

RESULTS

Foods associated with higher sulfur microbial diet scores included increased consumption of processed meats and low-calorie drinks and lower consumption of vegetables and legumes. Increased sulfur microbial diet scores were associated with risk of distal colon and rectal cancers, after adjusting for other risk factors (multivariable relative risk, highest vs lowest quartile, 1.43; 95% confidence interval 1.14-1.81; P-trend = .002). In contrast, sulfur microbial diet scores were not associated with risk of proximal colon cancer (multivariable relative risk 0.86; 95% CI 0.65-1.14; P-trend = .31).

CONCLUSIONS

In an analysis of participants in the Health Professionals Follow-up Study, we found that long-term adherence to a dietary pattern associated with sulfur-metabolizing bacteria in stool was associated with an increased risk of distal CRC. Further studies are needed to determine how sulfur-metabolizing bacteria might contribute to CRC pathogenesis.

Bioengineering tools for the production of pharmaceuticals: current perspective and future outlook

The bioengineering tools have significant advantages through less time-consuming and utilized as a promising stage for the production of pharmaceutical bioproducts under the single platform. This review highlighted the advantages and current improvement in the plant, animal and microbial bioengineering tools and outlines feasible approaches by biological and process’s bioengineering levels for advancing the economic feasibility of pharmaceutical’s production. The critical analysis results revealed that system biology and synthetic biology along with advanced bioengineering tools like transcriptome, proteome, metabolome and nano bioengineering tools have shown a promising impact on the development of pharmaceutical’s bioproducts. Tools to overcome and resolve the accompanying encounters of pharmaceutical’s production that include nano bioengineering tools are also discussed. As a summary and prospect, it also gives new insight into the challenges and possible breakthrough of the development of pharmaceutical’s bioproducts through bioengineering tools.

An atlas of human metabolism

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

MEMO: A Method for Computing Metabolic Modules for Cell-Free Production Systems

Cell-free bioproduction systems represent a promising alternative to classical microbial fermentation processes to synthesize value-added products from biological feedstocks. An essential step for establishing cell-free production systems is the identification of suitable metabolic modules with defined properties. Here we present MEMO, a novel computational approach to find smallest metabolic modules with specified stoichiometric and thermodynamic constraints supporting the design of cell-free systems in various regards. In particular, one key challenge for a sustained operation of cell-free systems is the regeneration of utilized cofactors (such as ATP and NAD(P)H). Given a production pathway with certain cofactor requirements, MEMO can be used to compute smallest regeneration modules that recover these cofactors with required stoichiometries. MEMO incorporates the stoichiometric and thermodynamic constraints in a single mixed-integer linear program, which can then be solved to find smallest suitable modules from a given reaction database. We illustrate the applicability of MEMO by calculating regeneration modules for the recently published synthetic CETCH cycle for in vitro carbon dioxide fixation. We demonstrate that MEMO is very flexible in taking into account the diverse constraints of the CETCH cycle (e.g., regeneration of 1 ATP, 4 NADPH and of 1 acetyl-group without net production of CO₂ and with permitted side production of malate) and is able to determine multiple solutions in reasonable time in two large reaction databases (MetaCyc and BiGG). The most promising regeneration modules found utilize glycerol as substrate and require only 8 enzymatic steps. It is also shown that some of these modules are robust against spontaneous loss of cofactors (e.g., oxidation of NAD(P)H or hydrolysis of ATP). Furthermore, we demonstrate that MEMO can also find cell-free production systems with integrated product synthesis and cofactor regeneration. Overall, MEMO provides a powerful method for finding metabolic modules and for designing cell-free production systems as one particular application.

The microbiome and resistome of chimpanzees, gorillas, and humans across host lifestyle and geography

The gut microbiome can vary across differences in host lifestyle, geography, and host species. By comparing closely related host species across varying lifestyles and geography, we can evaluate the relative contributions of these factors in structuring the composition and functions of the microbiome. Here we show that the gut microbial taxa, microbial gene family composition, and resistomes of great apes and humans are more related by host lifestyle than geography. We show that captive chimpanzees and gorillas are enriched for microbial genera commonly found in non-Westernized humans. Captive ape microbiomes also had up to ~34-fold higher abundance and up to ~5-fold higher richness of all antibiotic resistance genes compared with wild apes. Through functional metagenomics, we identified a number of novel antibiotic resistance genes, including a gene conferring resistance to colistin, an antibiotic of last resort. Finally, by comparing our study cohorts to human and ape gut microbiomes from a diverse range of environments and lifestyles, we find that the influence of host lifestyle is robust to various geographic locations.

Disinfection exhibits systematic impacts on the drinking water microbiome

Limiting microbial growth during drinking water distribution is achieved either by maintaining a disinfectant residual or through nutrient limitation without using a disinfectant. The impact of these contrasting approaches on the drinking water microbiome is not systematically understood. We use genome-resolved metagenomics to compare the structure, metabolic traits, and population genomes of drinking water microbiome samples from bulk drinking water across multiple full-scale disinfected and non-disinfected drinking water systems. Microbial communities cluster at the structural- and functional potential-level based on the presence/absence of a disinfectant residual. Disinfectant residual alone explained 17 and 6.5% of the variance in structure and functional potential of the drinking water microbiome, respectively, despite including multiple drinking water systems with variable source waters and source water communities and treatment strategies. The drinking water microbiome is structurally and functionally less diverse and variable across disinfected compared to non-disinfected systems. While bacteria were the most abundant domain, archaea and eukaryota were more abundant in non-disinfected and disinfected systems, respectively. Community-level differences in functional potential were driven by enrichment of genes associated with carbon and nitrogen fixation in non-disinfected systems and γ-aminobutyrate metabolism in disinfected systems likely associated with the recycling of amino acids. Genome-level analyses for a subset of phylogenetically-related microorganisms suggests that disinfection selects for microorganisms capable of using fatty acids, presumably from microbial decay products, via the glyoxylate cycle. Overall, we find that disinfection exhibits systematic selective pressures on the drinking water microbiome and may select for microorganisms able to utilize microbial decay products originating from disinfection-inactivated microorganisms. Video abstract.

Uncovering the Hidden Antibiotic Potential of Cannabis

The spread of antimicrobial resistance continues to be a priority health concern worldwide, necessitating the exploration of alternative therapies. Cannabis sativa has long been known to contain antibacterial cannabinoids, but their potential to address antibiotic resistance has only been superficially investigated. Here, we show that cannabinoids exhibit antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA), inhibit its ability to form biofilms, and eradicate preformed biofilms and stationary phase cells persistent to antibiotics. We show that the mechanism of action of cannabigerol is through targeting the cytoplasmic membrane of Gram-positive bacteria and demonstrate in vivo efficacy of cannabigerol in a murine systemic infection model caused by MRSA. We also show that cannabinoids are effective against Gram-negative organisms whose outer membrane is permeabilized, where cannabigerol acts on the inner membrane. Finally, we demonstrate that cannabinoids work in combination with polymyxin B against multidrug resistant Gram-negative pathogens, revealing the broad-spectrum therapeutic potential for cannabinoids.

Correlation-based network analysis combined with machine learning techniques highlight the role of the GABA shunt in Brachypodium sylvaticum freezing tolerance

Perennial grasses will account for approximately 16 billion gallons of renewable fuels by the year 2022, contributing significantly to carbon and nitrogen sequestration. However, perennial grasses productivity can be limited by severe freezing conditions in some geographical areas, although these risks could decrease with the advance of climate warming, the possibility of unpredictable early cold events cannot be discarded. We conducted a study on the model perennial grass Brachypodium sylvaticum to investigate the molecular mechanisms that contribute to cold and freezing adaption. The study was performed on two different B. sylvaticum accessions, Ain1 and Osl1, typical to warm and cold climates, respectively. Both accessions were grown under controlled conditions with subsequent cold acclimation followed by freezing stress. For each treatment a set of morphological parameters, transcription, metabolite, and lipid profiles were measured. State-of-the-art algorithms were employed to analyze cross-component relationships. Phenotypic analysis revealed higher adaption of Osl1 to freezing stress. Our analysis highlighted the differential regulation of the TCA cycle and the GABA shunt between Ain1 and Osl1. Osl1 adapted to freezing stress by repressing the GABA shunt activity, avoiding the detrimental reduction in fatty acid biosynthesis and the concomitant detrimental effects on membrane integrity.

Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models

MOTIVATION

The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.

RESULTS

Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements.

AVAILABILITY AND IMPLEMENTATION

The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

BACKGROUND

Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.

RESULTS

In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.

CONCLUSIONS

The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

Microbial community analysis using high-throughput sequencing technology: a beginner's guide for microbiologists

Microbial communities present in diverse environments from deep seas to human body niches play significant roles in the complex ecosystem and human health. Characterizing their structural and functional diversities is indispensable, and many approaches, such as microscopic observation, DNA fingerprinting, and PCR-based marker gene analysis, have been successfully applied to identify microorganisms. Since the revolutionary improvement of DNA sequencing technologies, direct and high-throughput analysis of genomic DNA from a whole environmental community without prior cultivation has become the mainstream approach, overcoming the constraints of the classical approaches. Here, we first briefly review the history of environmental DNA analysis applications with a focus on profiling the taxonomic composition and functional potentials of microbial communities. To this end, we aim to introduce the shotgun metagenomic sequencing (SMS) approach, which is used for the untargeted ("shotgun") sequencing of all ("meta") microbial genomes ("genomic") present in a sample. SMS data analyses are performed in silico using various software programs; however, in silico analysis is typically regarded as a burden on wet-lab experimental microbiologists. Therefore, in this review, we present microbiologists who are unfamiliar with in silico analyses with a basic and practical SMS data analysis protocol. This protocol covers all the bioinformatics processes of the SMS analysis in terms of data preprocessing, taxonomic profiling, functional annotation, and visualization.

Acute Sleep-Wake Cycle Shift Results in Community Alteration of Human Gut Microbiome

Circadian rhythm misalignment due to social jet lag, shift work, early morning starts, and delayed bedtimes is becoming common in our modern society. Disturbances of sleep and the underlying circadian rhythms are related to multiple human diseases, such as obesity, diabetes, cardiovascular disorders, and cognitive impairments. Given the crucial role of microbiota in the same pathologies as are caused by sleep disturbance, how the gut microbiota is affected by sleep is of increasing interest. The results of this study indicate that the acute circadian rhythm disturbance caused by sleep-wake shifts affect the human gut microbiota, especially the functional profiles of gut microbes and interactions among them. Further experiments with a longer-time-scale intervention and larger sample size are needed to assess the effects of chronic circadian rhythm disruption on the gut microbiome and to guide possible microbial therapies for clinical intervention in the related diseases.

Disturbances of sleep and the underlying circadian rhythm are related to many human diseases, such as obesity, diabetes, cardiovascular disorders, and cognitive impairments. Dysbiosis of the gut microbiome has also been reported to be associated with the pathologies of these diseases. Therefore, we proposed that disturbed sleep may regulate gut microbiota homeostasis. In this study, we mimicked the sleep-wake cycle shift, one typical type of circadian rhythm disturbances in young people, in recruited subjects. We used 16S rRNA gene amplicon sequencing to define microbial taxa from their fecal samples. Although the relative abundances of the microbes were not significantly altered, the functional-profile analysis of gut microbiota revealed functions enriched during the sleep-wake cycle shift. In addition, the microbial networks were quite distinct among baseline, shift, and recovery stages. These results suggest that an acute sleep-wake cycle shift may exert a limited influence on the gut microbiome, mainly including the functional profiles of the microbes and the microbial relationships within the microbial community.

IMPORTANCE Circadian rhythm misalignment due to social jet lag, shift work, early morning starts, and delayed bedtimes is becoming common in our modern society. Disturbances of sleep and the underlying circadian rhythms are related to multiple human diseases, such as obesity, diabetes, cardiovascular disorders, and cognitive impairments. Given the crucial role of microbiota in the same pathologies as are caused by sleep disturbance, how the gut microbiota is affected by sleep is of increasing interest. The results of this study indicate that the acute circadian rhythm disturbance caused by sleep-wake shifts affect the human gut microbiota, especially the functional profiles of gut microbes and interactions among them. Further experiments with a longer-time-scale intervention and larger sample size are needed to assess the effects of chronic circadian rhythm disruption on the gut microbiome and to guide possible microbial therapies for clinical intervention in the related diseases.

Active sulfur cycling in the terrestrial deep subsurface

The deep terrestrial subsurface remains an environment where there is limited understanding of the extant microbial metabolisms. At Olkiluoto, Finland, a deep geological repository is under construction for the final storage of spent nuclear fuel. It is therefore critical to evaluate the potential impact microbial metabolism, including sulfide generation, could have upon the safety of the repository. We investigated a deep groundwater where sulfate is present, but groundwater geochemistry suggests limited microbial sulfate-reducing activity. Examination of the microbial community at the genome-level revealed microorganisms with the metabolic capacity for both oxidative and reductive sulfur transformations. Deltaproteobacteria are shown to have the genetic capacity for sulfate reduction and possibly sulfur disproportionation, while Rhizobiaceae, Rhodocyclaceae, Sideroxydans, and Sulfurimonas oxidize reduced sulfur compounds. Further examination of the proteome confirmed an active sulfur cycle, serving for microbial energy generation and growth. Our results reveal that this sulfide-poor groundwater harbors an active microbial community of sulfate-reducing and sulfide-oxidizing bacteria, together mediating a sulfur cycle that remained undetected by geochemical monitoring alone. The ability of sulfide-oxidizing bacteria to limit the accumulation of sulfide was further demonstrated in groundwater incubations and highlights a potential sink for sulfide that could be beneficial for geological repository safety.

Phosphine as a Biosignature Gas in Exoplanet Atmospheres

A long-term goal of exoplanet studies is the identification and detection of biosignature gases. Beyond the most discussed biosignature gas O₂, only a handful of gases have been considered in detail. In this study, we evaluate phosphine (PH₃). On Earth, PH₃ is associated with anaerobic ecosystems, and as such, it is a potential biosignature gas in anoxic exoplanets. We simulate the atmospheres of habitable terrestrial planets with CO₂- and H₂-dominated atmospheres and find that PH₃ can accumulate to detectable concentrations on planets with surface production fluxes of 10¹⁰ to 10¹⁴ cm^-2 s^-1 (corresponding to surface concentrations of 10s of ppb to 100s of ppm), depending on atmospheric composition and ultraviolet (UV) irradiation. While high, the surface flux values are comparable to the global terrestrial production rate of methane or CH₄ (10¹¹ cm^-2 s^-1) and below the maximum local terrestrial PH₃ production rate (10¹⁴ cm^-2 s^-1). As with other gases, PH₃ can more readily accumulate on low-UV planets, for example, planets orbiting quiet M dwarfs or with a photochemically generated UV shield. PH₃ has three strong spectral features such that in any atmosphere scenario one of the three will be unique compared with other dominant spectroscopic molecules. Phosphine's weakness as a biosignature gas is its high reactivity, requiring high outgassing rates for detectability. We calculate that tens of hours of JWST (James Webb Space Telescope) time are required for a potential detection of PH₃. Yet, because PH₃ is spectrally active in the same wavelength regions as other atmospherically important molecules (such as H₂O and CH₄), searches for PH₃ can be carried out at no additional observational cost to searches for other molecular species relevant to characterizing exoplanet habitability. Phosphine is a promising biosignature gas, as it has no known abiotic false positives on terrestrial planets from any source that could generate the high fluxes required for detection.

Sugar-Phosphate Metabolism Regulates Stationary-Phase Entry and Stalk Elongation in Caulobacter crescentus

Bacteria have a variety of mechanisms for adapting to environmental perturbations. Changes in oxygen availability result in a switch between aerobic and anaerobic respiration, whereas iron limitation may lead to siderophore secretion. In addition to metabolic adaptations, many organisms respond by altering their cell shape. Caulobacter crescentus, when grown under phosphate-limiting conditions, dramatically elongates its polar stalk appendage. The stalk is hypothesized to facilitate phosphate uptake; however, the mechanistic details of stalk synthesis are not well characterized. We used a chemical mutagenesis approach to isolate and characterize stalk-deficient mutants, one of which had two mutations in the phosphomannose isomerase gene (manA) that were necessary and sufficient to inhibit stalk elongation. Transcription of the pho regulon was unaffected in the manA mutant; therefore, ManA plays a unique regulatory role in stalk synthesis. The mutant ManA had reduced enzymatic activity, resulting in a 5-fold increase in the intracellular fructose 6-phosphate/mannose 6-phosphate ratio. This metabolic imbalance impaired the synthesis of cellular envelope components derived from mannose 6-phosphate, namely, lipopolysaccharide O-antigen and exopolysaccharide. Furthermore, the manA mutations prevented C. crescentus cells from efficiently entering stationary phase. Deletion of the stationary-phase response regulator gene spdR inhibited stalk elongation in wild-type cells, while overproduction of the alarmone ppGpp, which triggers growth arrest and stationary-phase entry, increased stalk length in the manA mutant strain. These results demonstrate that sugar-phosphate metabolism regulates stalk elongation independently of phosphate starvation.IMPORTANCE Metabolic control of bacterial cell shape is an important mechanism for adapting to environmental perturbations. Caulobacter crescentus dramatically elongates its polar stalk appendage in response to phosphate starvation. To investigate the mechanism of this morphological adaptation, we isolated stalk-deficient mutants, one of which had mutations in the phosphomannose isomerase gene (manA) that blocked stalk elongation, despite normal activation of the phosphate starvation response. The mutant ManA resulted in an imbalance in sugar-phosphate concentrations, which had effects on the synthesis of cellular envelope components and entry into stationary phase. Due to the interconnectivity of metabolic pathways, our findings may suggest more generally that the modulation of bacterial cell shape involves the regulation of growth phase and the synthesis of cellular building blocks.

Diversity and metabolism of Woeseiales bacteria, global members of marine sediment communities

Surveys of 16S rRNA gene sequences derived from marine sediments have indicated that a widely distributed group of Gammaproteobacteria, named “JTB255-Marine Benthic Group” (now the candidate order Woeseiales), accounts for 1–22% of the retrieved sequences. Despite their ubiquity in seafloor communities, little is known about their distribution and specific ecological niches in the deep sea, which constitutes the largest biome globally. Here, we characterized the phylogeny, environmental distribution patterns, abundance, and metabolic potential of Woeseiales bacteria with a focus on representatives from the deep sea. From a phylogenetic analysis of publicly available 16S rRNA gene sequences (≥1400 bp, n = 994), we identified lineages of Woeseiales with greater prevalence in the deep sea than in coastal environments, a pattern corroborated by the distribution of 16S oligotypes recovered from 28 globally distributed sediment samples. Cell counts revealed that Woeseiales bacteria accounted for 5 ± 2% of all microbial cells in deep-sea surface sediments at 23 globally distributed sites. Comparative analyses of a genome, metagenome bins, and single-cell genomes suggested that members of the corresponding clades are likely to grow on proteinaceous matter, potentially derived from detrital cell membranes, cell walls, and other organic remnants in marine sediments.

Reinforcement Learning for Bioretrosynthesis

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

The MetaCyc database of metabolic pathways and enzymes - a 2019 update

MetaCyc (MetaCyc.org) is a comprehensive reference database of metabolic pathways and enzymes from all domains of life. It contains 2749 pathways derived from more than 60 000 publications, making it the largest curated collection of metabolic pathways. The data in MetaCyc are evidence-based and richly curated, resulting in an encyclopedic reference tool for metabolism. MetaCyc is also used as a knowledge base for generating thousands of organism-specific Pathway/Genome Databases (PGDBs), which are available in BioCyc.org and other genomic portals. This article provides an update on the developments in MetaCyc during September 2017 to August 2019, up to version 23.1. Some of the topics that received intensive curation during this period include cobamides biosynthesis, sterol metabolism, fatty acid biosynthesis, lipid metabolism, carotenoid metabolism, protein glycosylation, antibiotics and cytotoxins biosynthesis, siderophore biosynthesis, bioluminescence, vitamin K metabolism, brominated compound metabolism, plant secondary metabolism and human metabolism. Other additions include modifications to the GlycanBuilder software that enable displaying glycans using symbolic representation, improved graphics and fonts for web displays, improvements in the PathoLogic component of Pathway Tools, and the optional addition of regulatory information to pathway diagrams.

Translational Regulation of Metabolic Dynamics during Effector-Triggered Immunity

Recent studies have shown that global translational reprogramming is an early activation event in pattern-triggered immunity, when plants recognize microbe-associated molecular patterns. However, it is not fully known whether translational regulation also occurs in subsequent immune responses, such as effector-triggered immunity (ETI). In this study, we performed genome-wide ribosome profiling in Arabidopsis upon RPS2-mediated ETI activation and discovered that specific groups of genes were translationally regulated, mostly in coordination with transcription. These genes encode enzymes involved in aromatic amino acid, phenylpropanoid, camalexin, and sphingolipid metabolism. The functional significance of these components in ETI was confirmed by genetic and biochemical analyses. Our findings provide new insights into diverse translational regulation of plant immune responses and demonstrate that translational coordination of metabolic gene expression is an important strategy for ETI.

Novel syntrophic bacteria in full-scale anaerobic digesters revealed by genome-centric metatranscriptomics

Short-chain fatty acid (SCFA) degradation is an important process in methanogenic ecosystems, and is usually catalyzed by SCFA-oxidizing bacteria in syntrophy with methanogens. Current knowledge of this functional guild is mainly based on isolates or enrichment cultures, but these may not reflect the true diversity and in situ activities of the syntrophs predominating in full-scale systems. Here we obtained 182 medium to high quality metagenome-assembled genomes (MAGs) from the microbiome of two full-scale anaerobic digesters. The transcriptomic response of individual MAG was studied after stimulation with low concentrations of acetate, propionate, or butyrate, separately. The most pronounced response to butyrate was observed for two MAGs of the recently described genus Candidatus Phosphitivorax (phylum Desulfobacterota), expressing a butyrate beta-oxidation pathway. For propionate, the largest response was observed for an MAG of a novel genus in the family Pelotomaculaceae, transcribing a methylmalonyl-CoA pathway. All three species were common in anaerobic digesters at Danish wastewater treatment plants as shown by amplicon analysis, and this is the first time their syntrophic features involved in SCFA oxidation were revealed with transcriptomic evidence. Further, they also possessed unique genomic features undescribed in well-characterized syntrophs, including the metabolic pathways for phosphite oxidation, nitrite and sulfate reduction.

Comparison of Independent Evolutionary Origins Reveals Both Convergence and Divergence in the Metabolic Mechanisms of Symbiosis

Through the merger of previously independent lineages, symbiosis promotes the acquisition of new traits and exploitation of inaccessible ecological niches [1, 2], driving evolutionary innovation and important ecosystem functions [3-6]. The transient nature of establishment makes study of symbiotic origins difficult, but experimental comparison of independent origins could reveal the degree of convergence in the underpinning mechanisms [7, 8]. We compared the metabolic mechanisms of two independent origins of Paramecium bursaria-Chlorella photosymbiosis [9-11] using a reciprocal metabolomic pulse-chase method. This showed convergent patterns of nutrient exchange and utilization for host-derived nitrogen in the Chlorella genotypes [12, 13] and symbiont-derived carbon in the P. bursaria genotypes [14, 15]. Consistent with a convergent primary nutrient exchange, partner-switched host-symbiont pairings were functional. Direct competition of hosts containing native or recombined symbionts against isogenic symbiont-free hosts showed that the fitness benefits of symbiosis for hosts increased with irradiance but varied by genotype. Global metabolism varied more between the Chlorella than the P. bursaria genotypes and suggested divergent mechanisms of light management. Specifically, the algal symbiont genotypes either produced photo-protective carotenoid pigments at high irradiance or more chlorophyll, resulting in corresponding differences in photosynthetic efficiency and non-photochemical quenching among host-symbiont pairings. These data suggest that the multiple origins of P. bursaria-Chlorella symbiosis use a convergent nutrient exchange, whereas other photosynthetic traits linked to functioning of photosymbiosis have diverged. Although convergence enables partner switching among diverse strains, phenotypic mismatches resulting from divergence of secondary symbiotic traits could mediate host-symbiont specificity in nature.

Mathematical Modeling of Plant Metabolism in a Changing Temperature Regime

Changes in environmental temperature regimes significantly affect plant growth, development and reproduction. Within a multigenic process termed acclimation, many plant species of the temperate region are able to adjust their metabolism to low and high temperature. Temperature-induced metabolic reprogramming is a nonlinear process affecting numerous enzyme kinetic reactions and pathways. The analysis of metabolic reprogramming during temperature acclimation is essentially supported by mathematical modeling which enables the study of nonlinear enzyme kinetics in context of metabolic networks and pathway regulation. This chapter introduces mathematical modeling of plant metabolism during a dynamic environmental temperature regime. A focus is laid on kinetic modeling and thermodynamic constraints.

Microbial energy management-A product of three broad tradeoffs

Wherever thermodynamics allows, microbial life has evolved to transform and harness energy. Microbial life thus abounds in the most unexpected places, enabled by profound metabolic diversity. Within this diversity, energy is transformed primarily through variations on a few core mechanisms. Energy is further managed by the physiological processes of cell growth and maintenance that use energy. Some aspects of microbial physiology are streamlined for energetic efficiency while other aspects seem suboptimal or even wasteful. We propose that the energy that a microbe harnesses and devotes to growth and maintenance is a product of three broad tradeoffs: (i) economic, trading enzyme synthesis or operational cost for functional benefit, (ii) environmental, trading optimization for a single environment for adaptability to multiple environments, and (iii) thermodynamic, trading energetic yield for forward metabolic flux. Consideration of these tradeoffs allows one to reconcile features of microbial physiology that seem to opposingly promote either energetic efficiency or waste.

Genome Sequences of 72 Bacterial Strains Isolated from Ectocarpus subulatus: A Resource for Algal Microbiology

Brown algae are important primary producers and ecosystem engineers in the ocean, and Ectocarpus has been established as a laboratory model for this lineage. Like most multicellular organisms, Ectocarpus is associated with a community of microorganisms, a partnership frequently referred to as holobiont due to the tight interconnections between the components. Although genomic resources for the algal host are well established, its associated microbiome is poorly characterized from a genomic point of view, limiting the possibilities of using these types of data to study host-microbe interactions. To address this gap in knowledge, we present the annotated draft genome sequences of seventy-two cultivable Ectocarpus-associated bacteria. A screening of gene clusters related to the production of secondary metabolites revealed terpene, bacteriocin, NRPS, PKS-t3, siderophore, PKS-t1, and homoserine lactone clusters to be abundant among the sequenced genomes. These compounds may be used by the bacteria to communicate with the host and other microbes. Moreover, detoxification and provision of vitamin B pathways have been observed in most sequenced genomes, highlighting potential contributions of the bacterial metabolism toward host fitness and survival. The genomes sequenced in this study form a valuable resource for comparative genomic analyses and evolutionary surveys of alga-associated bacteria. They help establish Ectocarpus as a model for brown algal holobionts and will enable the research community to produce testable hypotheses about the molecular interactions within this complex system.

Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is one of the most popular technologies in metabolomics. The large-scale and unambiguous identification of metabolite structures remains a challenging task in LC-MS based metabolomics. Tandem mass spectral databases provide experimental and in silico MS/MS spectra to facilitate the identification of both known and unknown metabolites, which has become a gold standard method in metabolomics. In addition, metabolite knowledge databases offer valuable biological and pathway information of metabolites. In this chapter, we have briefly reviewed the most common and important tandem mass spectral and metabolite databases, and illustrated how they could be used for metabolite identification.

Compartment and hub definitions tune metabolic networks for metabolomic interpretations

BACKGROUND

Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments.

RESULTS

We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications.

CONCLUSIONS

Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

Rhodosporidium toruloides is a red, basidiomycetes yeast that can accumulate a large amount of lipids and produce carotenoids. To better assess this non-model yeast's metabolic capabilities, we reconstructed a genome-scale model of R. toruloides IFO0880's metabolic network (iRhto1108) accounting for 2204 reactions, 1985 metabolites and 1108 genes. In this work, we integrated and supplemented the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organism's metabolic capabilities. Predictions of genotype-phenotype relations were improved through manual curation of gene-protein-reaction rules for 543 reactions leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloides's utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the model's fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for triacylglycerol overproduction. Suggested interventions recapitulated many of the previous successful implementations of genetic modifications and put forth a few new ones.

Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1

A proteome-wide protein-protein interaction (PPI) network of Methanobrevibacter ruminantium M1 (MRU), a predominant rumen methanogen, was constructed from its metabolic genes using a gene neighborhood algorithm and then compared with closely related rumen methanogens Using proteome-wide PPI approach, we constructed network encompassed 2194 edges and 637 nodes interacting with 634 genes. Network quality and robustness of functional modules were assessed with gene ontology terms. A structure-function-metabolism mapping for each protein has been carried out with efforts to extract experimental PPI concomitant information from the literature. The results of our study revealed that some topological properties of its network were robust for sharing homologous protein interactions across heterotrophic and hydrogenotrophic methanogens. MRU proteome has shown to establish many PPI sub-networks for associated metabolic subsystems required to survive in the rumen environment. MRU genome found to share interacting proteins from its PPI network involved in specific metabolic subsystems distinct to heterotrophic and hydrogenotrophic methanogens. Across these proteomes, the interacting proteins from differential PPI networks were shared in common for the biosynthesis of amino acids, nucleosides, and nucleotides and energy metabolism in which more fractions of protein pairs shared with Methanosarcina acetivorans. Our comparative study expedites our knowledge to understand a complex proteome network associated with typical metabolic subsystems of MRU and to improve its genome-scale reconstruction in the future.

Carprofen-induced depletion of proton motive force reverses TetK-mediated doxycycline resistance in methicillin-resistant Staphylococcus pseudintermedius

We previously showed that doxycycline (DOX) and carprofen (CPF), a veterinary non-steroidal anti-inflammatory drug, have synergistic antimicrobial activity against methicillin-resistant Staphylococcus pseudintermedius (MRSP) carrying the tetracycline resistance determinant TetK. To elucidate the molecular mechanism of this synergy, we investigated the effects of the two drugs, individually and in combination, using a comprehensive approach including RNA sequencing, two-dimensional differential in-gel electrophoresis, macromolecule biosynthesis assays and fluorescence spectroscopy. Exposure of TetK-positive MRSP to CPF alone resulted in upregulation of pathways that generate ATP and NADH, and promote the proton gradient. We showed that CPF is a proton carrier that dissipates the electrochemical potential of the membrane. In the presence of both CPF and DOX, the energy compensation strategy was attenuated by downregulation of all the processes involved, such as citric acid cycle, oxidative phosphorylation and ATP-providing arginine deiminase pathway. Furthermore, protein biosynthesis inhibition increased from 20% under DOX exposure alone to 75% upon simultaneous exposure to CPF. We conclude that synergistic interaction of the drugs restores DOX susceptibility in MRSP by compromising proton-motive-force-dependent TetK-mediated efflux of the antibiotic. MRSP is unable to counterbalance CPF-mediated PMF depletion by cellular metabolic adaptations, resulting in intracellular accumulation of DOX and inhibition of protein biosynthesis.

Optimizing cultivation of Cordyceps militaris for fast growth and cordycepin overproduction using rational design of synthetic media

Cordyceps militaris is an entomopathogenic fungus which is often used in Asia as a traditional medicine developed from age-old wisdom. Presently, cordycepin from C. militaris is a great interest in medicinal applications. However, cellular growth of C. militaris and the association with cordycepin production remain poorly understood. To explore the metabolism of C. militaris as potential cell factories in medical and biotechnology applications, this study developed a high-quality genome-scale metabolic model of C. militaris, iNR1329, based on its genomic content and physiological data. The model included a total of 1329 genes, 1821 biochemical reactions, and 1171 metabolites among 4 different cellular compartments. Its in silico growth simulation results agreed well with experimental data on different carbon sources. iNR1329 was further used for optimizing the growth and cordycepin overproduction using a novel approach, POPCORN, for rational design of synthetic media. In addition to the high-quality GEM iNR1329, the presented POPCORN approach was successfully used to rationally design an optimal synthetic medium with C:N ratio of 8:1 for enhancing 3.5-fold increase in cordycepin production. This study thus provides a novel insight into C. militaris physiology and highlights a potential GEM-driven method for synthetic media design and metabolic engineering application. The iNR1329 and the POPCORN approach are available at the GitHub repository: https://github.com/sysbiomics/Cordyceps_militaris-GEM.

Comparative Sequence Analysis of TRI1 of Fusarium

Trichothecene mycotoxins are a class of secondary metabolites produced by multiple genera of fungi, including certain plant pathogenic Fusarium species. Functional variation in the TRI1 gene produces a novel Type A trichothecene called NX-2 in strains of F. graminearum. Using a bioinformatics approach, a systematic analysis of 52 translated TRI1 sequences of Fusarium species, including five F. graminearum NX-2 producers and four F. graminearum non-NX-2 producers, was conducted to explain the functional difference of TRI1p of FGNX-2. An assessment of several signature motifs of fungal P450s revealed amino acid substitutions in addition to the post-translational N-X-S/T sequons motif, which is indicative of N-linked glycosylation of this TRI1-encoded protein characteristic of NX-2 producers. There was evidence of selection bias, where TRI1 gene sequences were found to be under positive selection and, therefore, under functional constraints. The cumulative amino acid changes in the TRI1p sequences were reflected in the phylogenetic analyses which revealed species-specific clustering with a distinct separation of FGNX-2 from FG-non-NX-2 producers with high bootstrap support. Together, our findings provide insight into the amino acid sequence features responsible for the functional diversification of this TRI1p.

Genetic variation associated with infection and the environment in the accidental pathogen Burkholderia pseudomallei

The environmental bacterium Burkholderia pseudomallei causes melioidosis, an important endemic human disease in tropical and sub-tropical countries. This bacterium occupies broad ecological niches including soil, contaminated water, single-cell microbes, plants and infection in a range of animal species. Here, we performed genome-wide association studies for genetic determinants of environmental and human adaptation using a combined dataset of 1,010 whole genome sequences of B. pseudomallei from Northeast Thailand and Australia, representing two major disease hotspots. With these data, we identified 47 genes from 26 distinct loci associated with clinical or environmental isolates from Thailand and replicated 12 genes in an independent Australian cohort. We next outlined the selective pressures on the genetic loci (dN/dS) and the frequency at which they had been gained or lost throughout their evolutionary history, reflecting the bacterial adaptability to a wide range of ecological niches. Finally, we highlighted loci likely implicated in human disease.

Putative metabolic pathway for the bioproduction of bikaverin and intermediates thereof in the wild Fusarium oxysporum LCP531 strain

Fungal naphthoquinones, like red bikaverin, are of interest due to their growing applications in designing pharmaceutical products. Though considerable work has been done on the elucidation of bikaverin biosynthesis pathway in Fusarium fujikuroi, very few reports are available regarding its bioproduction in F. oxysporum. We are hereby proposing a putative metabolic pathway for bikaverin bioproduction in a wild F. oxysporum strain by cross-linking the pigment profiles we obtained under two different fermentation conditions with literature. Naphthoquinone pigments were extracted with a pressurized liquid extraction method, and characterized by HPLC–DAD and UHPLC-HRMS. The results led to the conclusions that the F. oxysporum LCP531 strain was able to produce bikaverin and its various intermediates, e.g., pre-bikaverin, oxo-pre-bikaverin, dinor-bikaverin, me-oxo-pre-bikaverin, and nor-bikaverin, in submerged cultures in various proportions. To our knowledge, this is the first report of the isolation of these five bikaverin intermediates from F. oxysporum cultures, providing us with steady clues for confirming a bikaverin metabolic pathway as well as some of its regulatory patterns in the F. oxysporum LCP531 strain, based on the previously reported model in F. fujikuroi. Interestingly, norbikaverin accumulated along with bikaverin in mycelial cells when the strain grew on simple carbon and nitrogen sources and additional cofactors. Along bikaverin production, we were able to describe the excretion of the toxin beauvericin as main extrolite exclusively in liquid medium containing complex nitrogen and carbon sources, as well as the isolation of ergosterol derivate in mycelial extracts, which have potential for pharmaceutical uses. Therefore, culture conditions were also concluded to trigger some specific biosynthetic route favoring various metabolites of interest. Such observation is of great significance for selective production of pigments and/or prevention of occurrence of others (aka mycotoxins).

Genome-Scale Metabolic Networks Shed Light on the Carotenoid Biosynthesis Pathway in the Brown Algae Saccharina japonica and Cladosiphon okamuranus

Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome-Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an average of 2230 metabolites and 3370 reactions. Based on these GSMNs and previously published work, we propose a model for the biosynthetic pathways of the main carotenoids in these two algae. We highlight, on the one hand, the reactions and enzymes that have been preserved through evolution and, on the other hand, the specificities related to brown algae. Our data further indicate that, if abscisic acid is produced by Saccharina japonica, its biosynthesis pathway seems to be different in its final steps from that described in land plants. Thus, our work illustrates the potential of GSMNs reconstructions for formalizing hypotheses that can be further tested using targeted biochemical approaches.

Shedding light on biogas: Phototrophic biofilms in anaerobic digesters hold potential for improved biogas production

Conventional anaerobic digesters intended for the production of biogas usually operate in complete darkness. Therefore, little is known about the effect of light on their microbial communities. In the present work, 16S rRNA gene amplicon Nanopore sequencing and shotgun metagenomic sequencing were used to study the taxonomic and functional structure of the microbial community forming a biofilm on the inner wall of a laboratory-scale transparent anaerobic biodigester illuminated with natural sunlight. The biofilm was composed of microorganisms involved in the four metabolic processes needed for biogas production, and it was surprisingly rich in Rhodopseudomonas faecalis, a versatile bacterium able to carry out photoautotrophic metabolism when grown under anaerobic conditions. The results suggested that this bacterium, which is able to fix carbon dioxide, could be considered for use in transparent biogas fermenters in order to contribute to the production of optimized biogas with a higher CH₄:CO₂ ratio than the biogas produced in regular, opaque digesters. To the best of our knowledge, this is the first study characterising the phototrophic biofilm associated with illuminated bioreactors.

Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model

Background

Kluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus.

Results

Here, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures.

Conclusions

K. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.

Characterization and evolution of gene clusters for terpenoid phytoalexin biosynthesis in tobacco

MAIN CONCLUSION

The study performed genome-wide identification, characterization and evolution analysis of gene clusters for phytoalexin terpenoid biosynthesis in tobacco, and specifically illustrated ones for capsidiol, an efficient defensive specialized metabolite. Terpenoid phytoalexins play an important role in plant self-defense against pest and pathogen attack. Terpenoid biosynthesis involves terpene synthase and cytochrome P450, which always locate and function as cluster(s). In this study, we performed genome-wide investigation of metabolic gene clusters involved in terpenoid production in tobacco (Nicotiana tabacum). Due to the complexity of the tobacco genome, we modified a published prediction pipeline to reduce the influence of the large number of repeats and to improve the annotation of tobacco genes with respect to their metabolic functions. We identified 1181 metabolic gene clusters with 34 of them potentially being involved in terpenoid biosynthesis. Through integration with transcriptome and metabolic pathway annotation analyses, 3 of the 34 terpenoid biosynthesis-related gene clusters were determined to be high-confidence ones, with 2 involved in biosynthesis of capsidiol, a terpenoid recognized as 1 of the effective resistance compounds in the Nicotiana species. The capsidiol-related gene cluster was conserved in N. sylvestris, N. tomentosiformis and N. attenuate. Our findings demonstrate that phytoalexins in tobacco can arise from operon-like gene clusters, a genomic pattern characterized as being beneficial for rapid stress response, gene co-regulation, co-function and co-heredity.

Metabolic Modeling of Streptococcus mutans Reveals Complex Nutrient Requirements of an Oral Pathogen

Streptococcus mutans is a Gram-positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism of S. mutans on a systematic level, we manually constructed a genome-scale metabolic model of the S. mutans type strain UA159. The model, called iSMU, contains 675 reactions involving 429 metabolites and the products of 493 genes. We validated iSMU by comparing simulations with growth experiments in defined medium. The model simulations matched experimental results for 17 of 18 carbon source utilization assays and 47 of 49 nutrient depletion assays. We also simulated the effects of single gene deletions. The model's predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental data sets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines and more complete than other manually curated models. We used iSMU to generate hypotheses about the S. mutans metabolic network. Subsequent genetic experiments confirmed that (i) S. mutans catabolizes sorbitol via a sorbitol-6-phosphate 2-dehydrogenase (SMU_308) and (ii) the Leloir pathway is required for growth on complex carbohydrates such as raffinose. We believe the iSMU model is an important resource for understanding the metabolism of S. mutans and guiding future experiments.IMPORTANCE Tooth decay is the most prevalent chronic disease in the United States. Decay is caused by the bacterium Streptococcus mutans, an oral pathogen that ferments sugars into tooth-destroying lactic acid. We constructed a complete metabolic model of S. mutans to systematically investigate how the bacterium grows. The model provides a valuable resource for understanding and targeting S. mutans' ability to outcompete other species in the oral microbiome.

Metabolomics for Investigating Physiological and Pathophysiological Processes

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.

Cofactor F420-Dependent Enzymes: An Under-Explored Resource for Asymmetric Redox Biocatalysis

The asymmetric reduction of enoates, imines and ketones are among the most important reactions in biocatalysis. These reactions are routinely conducted using enzymes that use nicotinamide cofactors as reductants. The deazaflavin cofactor F420 also has electrochemical properties that make it suitable as an alternative to nicotinamide cofactors for use in asymmetric reduction reactions. However, cofactor F420-dependent enzymes remain under-explored as a resource for biocatalysis. This review considers the cofactor F420-dependent enzyme families with the greatest potential for the discovery of new biocatalysts: the flavin/deazaflavin-dependent oxidoreductases (FDORs) and the luciferase-like hydride transferases (LLHTs). The characterized F420-dependent reductions that have the potential for adaptation for biocatalysis are discussed, and the enzymes best suited for use in the reduction of oxidized cofactor F420 to allow cofactor recycling in situ are considered. Further discussed are the recent advances in the production of cofactor F420 and its functional analog FO-5′-phosphate, which remains an impediment to the adoption of this family of enzymes for industrial biocatalytic processes. Finally, the prospects for the use of this cofactor and dependent enzymes as a resource for industrial biocatalysis are discussed.

Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives

The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.

Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop

In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics.

RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms

Background

The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions.

Results

Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules.

Conclusions

The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3025-9) contains supplementary material, which is available to authorized users.

The functional repertoire contained within the native microbiota of the model nematode Caenorhabditis elegans

The microbiota is generally assumed to have a substantial influence on the biology of multicellular organisms. The exact functional contributions of the microbes are often unclear and cannot be inferred easily from 16S rRNA genotyping, which is commonly used for taxonomic characterization of bacterial associates. In order to bridge this knowledge gap, we here analyzed the metabolic competences of the native microbiota of the model nematode Caenorhabditis elegans. We integrated whole-genome sequences of 77 bacterial microbiota members with metabolic modeling and experimental characterization of bacterial physiology. We found that, as a community, the microbiota can synthesize all essential nutrients for C. elegans. Both metabolic models and experimental analyses revealed that nutrient context can influence how bacteria interact within the microbiota. We identified key bacterial traits that are likely to influence the microbe’s ability to colonize C. elegans (i.e., the ability of bacteria for pyruvate fermentation to acetoin) and affect nematode fitness (i.e., bacterial competence for hydroxyproline degradation). Considering that the microbiota is usually neglected in C. elegans research, the resource presented here will help our understanding of this nematode’s biology in a more natural context. Our integrative approach moreover provides a novel, general framework to characterize microbiota-mediated functions.

The number and type of oxygen-utilizing enzymes indicates aerobic vs. anaerobic phenotype

Oxygen is a major metabolic driving force that enabled the expansion of metabolic networks including new metabolites and new enzymes. It had a dramatic impact on the primary electron transport chain where it serves as terminal electron acceptor, but oxygen is also used by many enzymes as electron acceptor for a variety of reactions. The organismal oxygen phenotype, aerobic vs. anaerobic, should be manifested in its O₂-utilizing enzymes. Traditionally, enzymes involved in primary oxygen metabolism such as cytochrome c, and reactive oxygen species (ROS)-neutralizing enzymes (e.g. catalase), were used as identifiers of oxygen phenotype. However, these enzymes are often found in strict anaerobes. We aimed to identify the O₂-utilizing enzymes that may distinguish between aerobes and anaerobes. To this end, we annotated the O₂-utilizing enzymes across the prokaryotic tree of life. We recovered over 700 enzymes and mapped their presence/absence in 272 representative genomes. As seen before, enzymes mediating primary oxygen metabolism, and ROS neutralizing enzymes, could be found in both aerobes and anaerobes. However, there exists a subset of enzymes, primarily oxidases that catabolyze various substrates, including amino acids and xenobiotics, that are preferentially enriched in aerobes. Overall it appears that the total number of oxygen-utilizing enzymes, and the presence of enzymes involved in 'peripheral', secondary oxygen metabolism, can reliably distinguish aerobes from anaerobes based solely on genome sequences. These criteria can also indicate the oxygen phenotype in metagenomic samples.