Living in mangroves: a syntrophic scenario unveiling a resourceful microbiome.

BACKGROUND: Mangroves are complex and dynamic coastal ecosystems under frequent fluctuations in physicochemical conditions related to the tidal regime. The frequent variation in organic matter concentration, nutrients, and oxygen availability, among other factors, drives the microbial community composition, favoring syntrophic populations harboring a rich and diverse, stress-driven metabolism. Mangroves are known for their carbon sequestration capability, and their complex and integrated metabolic activity is essential to global biogeochemical cycling. Here, we present a metabolic reconstruction based on the genomic functional capability and flux profile between sympatric MAGs co-assembled from a tropical restored mangrove. RESULTS: Eleven MAGs were assigned to six Bacteria phyla, all distantly related to the available reference genomes. The metabolic reconstruction showed several potential coupling points and shortcuts between complementary routes and predicted syntrophic interactions. Two metabolic scenarios were drawn: a heterotrophic scenario with plenty of carbon sources and an autotrophic scenario with limited carbon sources or under inhibitory conditions. The sulfur cycle was dominant over methane and the major pathways identified were acetate oxidation coupled to sulfate reduction, heterotrophic acetogenesis coupled to carbohydrate catabolism, ethanol production and carbon fixation. Interestingly, several gene sets and metabolic routes similar to those described for wastewater and organic effluent treatment processes were identified. CONCLUSION: The mangrove microbial community metabolic reconstruction reflected the flexibility required to survive in fluctuating environments as the microhabitats created by the tidal regime in mangrove sediments. The metabolic components related to wastewater and organic effluent treatment processes identified strongly suggest that mangrove microbial communities could represent a resourceful microbial model for biotechnological applications that occur naturally in the environment.

Genome-Scale Metabolic Reconstruction, Non-Targeted LC-QTOF-MS Based Metabolomics Data, and Evaluation of Anticancer Activity of Cannabis sativa Leaf Extracts.

Over the past decades, Colombia has suffered complex social problems related to illicit crops, including forced displacement, violence, and environmental damage, among other consequences for vulnerable populations. Considerable effort has been made in the regulation of illicit crops, predominantly Cannabis sativa, leading to advances such as the legalization of medical cannabis and its derivatives, the improvement of crops, and leaving an open window to the development of scientific knowledge to explore alternative uses. It is estimated that C. sativa can produce approximately 750 specialized secondary metabolites. Some of the most relevant due to their anticancer properties, besides cannabinoids, are monoterpenes, sesquiterpenoids, triterpenoids, essential oils, flavonoids, and phenolic compounds. However, despite the increase in scientific research on the subject, it is necessary to study the primary and secondary metabolism of the plant and to identify key pathways that explore its great metabolic potential. For this purpose, a genome-scale metabolic reconstruction of C. sativa is described and contextualized using LC-QTOF-MS metabolic data obtained from the leaf extract from plants grown in the region of Pesca-Boyaca, Colombia under greenhouse conditions at the Clever Leaves facility. A compartmentalized model with 2101 reactions and 1314 metabolites highlights pathways associated with fatty acid biosynthesis, steroids, and amino acids, along with the metabolism of purine, pyrimidine, glucose, starch, and sucrose. Key metabolites were identified through metabolomic data, such as neurine, cannabisativine, cannflavin A, palmitoleic acid, cannabinoids, geranylhydroquinone, and steroids. They were analyzed and integrated into the reconstruction, and their potential applications are discussed. Cytotoxicity assays revealed high anticancer activity against gastric adenocarcinoma (AGS), melanoma cells (A375), and lung carcinoma cells (A549), combined with negligible impact against healthy human skin cells.

The Space-Exposed Kombucha Microbial Community Member Komagataeibacter oboediens Showed Only Minor Changes in Its Genome After Reactivation on Earth.

Komagataeibacter is the dominant taxon and cellulose-producing bacteria in the Kombucha Microbial Community (KMC). This is the first study to isolate the K. oboediens genome from a reactivated space-exposed KMC sample and comprehensively characterize it. The space-exposed genome was compared with the Earth-based reference genome to understand the genome stability of K. oboediens under extraterrestrial conditions during a long time. Our results suggest that the genomes of K. oboediens IMBG180 (ground sample) and K. oboediens IMBG185 (space-exposed) are remarkably similar in topology, genomic islands, transposases, prion-like proteins, and number of plasmids and CRISPR-Cas cassettes. Nonetheless, there was a difference in the length of plasmids and the location of cas genes. A small difference was observed in the number of protein coding genes. Despite these differences, they do not affect any genetic metabolic profile of the cellulose synthesis, nitrogen-fixation, hopanoid lipids biosynthesis, and stress-related pathways. Minor changes are only observed in central carbohydrate and energy metabolism pathways gene numbers or sequence completeness. Altogether, these findings suggest that K. oboediens maintains its genome stability and functionality in KMC exposed to the space environment most probably due to the protective role of the KMC biofilm. Furthermore, due to its unaffected metabolic pathways, this bacterial species may also retain some promising potential for space applications.

Dual RNA Sequencing of Mycobacterium tuberculosis-Infected Human Splenic Macrophages Reveals a Strain-Dependent Host-Pathogen Response to Infection.

Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb), leading to pulmonary and extrapulmonary TB, whereby Mtb is disseminated to many other organs and tissues. Dissemination occurs early during the disease, and bacteria can be found first in the lymph nodes adjacent to the lungs and then later in the extrapulmonary organs, including the spleen. The early global gene expression response of human tissue macrophages and intracellular clinical isolates of Mtb has been poorly studied. Using dual RNA-seq, we have explored the mRNA profiles of two closely related clinical strains of the Latin American and Mediterranean (LAM) family of Mtb in infected human splenic macrophages (hSMs). This work shows that these pathogens mediate a distinct host response despite their genetic similarity. Using a genome-scale host-pathogen metabolic reconstruction to analyze the data further, we highlight that the infecting Mtb strain also determines the metabolic response of both the host and pathogen. Thus, macrophage ontogeny and the genetic-derived program of Mtb direct the host-pathogen interaction.

Novel Symbiotic Genome-Scale Model Reveals Wolbachia's Arboviral Pathogen Blocking Mechanism in Aedes aegypti.

Wolbachia are endosymbiont bacteria known to infect arthropods causing different effects, such as cytoplasmic incompatibility and pathogen blocking in Aedes aegypti. Although several Wolbachia strains have been studied, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their obligate endosymbiont nature and its pathogen blocking ability. Motivated by the potential applications on disease control, we developed a genome-scale model of two Wolbachia strains: wMel and the strongest Dengue blocking strain known to date: wMelPop. The obtained metabolic reconstructions exhibit an energy metabolism relying mainly on amino acids and lipid transport to support cell growth that is consistent with altered lipid and cholesterol metabolism in Wolbachia-infected mosquitoes. The obtained metabolic reconstruction was then coupled with a reconstructed mosquito model to retrieve a symbiotic genome-scale model accounting for 1,636 genes and 6,408 reactions of the Aedes aegypti-Wolbachia interaction system. Simulation of an arboviral infection in the obtained novel symbiotic model represents a metabolic scenario characterized by pathogen blocking in higher titer Wolbachia strains, showing that pathogen blocking by Wolbachia infection is consistent with competition for lipid and amino acid resources between arbovirus and this endosymbiotic bacteria. IMPORTANCE Arboviral diseases such as Zika and Dengue have been on the rise mainly due to climate change, and the development of new treatments and strategies to limit their spreading is needed. The use of Wolbachia as an approach for disease control has motivated new research related to the characterization of the mechanisms that underlie its pathogen-blocking properties. In this work, we propose a new approach for studying the metabolic interactions between Aedes aegypti and Wolbachia using genome-scale models, finding that pathogen blocking is mainly influenced by competition for the resources required for Wolbachia and viral replication.

From Genome to Drugs: New Approaches in Antimicrobial Discovery.

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.

A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism.

BACKGROUND: Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown. RESULTS: To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum. CONCLUSIONS: In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.

A Genome-Scale Metabolic Reconstruction of Phytophthora infestans With the Integration of Transcriptional Data Reveals the Key Metabolic Patterns Involved in the Interaction of Its Host.

Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data (microarrays, RNA-seq) was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen.

An enhanced genome-scale metabolic reconstruction of Streptomyces clavuligerus identifies novel strain improvement strategies.

In this work, we expanded and updated a genome-scale metabolic model of Streptomyces clavuligerus. The model includes 1021 genes and 1494 biochemical reactions; genome-reaction information was curated and new features related to clavam metabolism and to the biomass synthesis equation were incorporated. The model was validated using experimental data from the literature and simulations were performed to predict cellular growth and clavulanic acid biosynthesis. Flux balance analysis (FBA) showed that limiting concentrations of phosphate and an excess of ammonia accumulation are unfavorable for growth and clavulanic acid biosynthesis. The evaluation of different objective functions for FBA showed that maximization of ATP yields the best predictions for cellular behavior in continuous cultures, while the maximization of growth rate provides better predictions for batch cultures. Through gene essentiality analysis, 130 essential genes were found using a limited in silico media, while 100 essential genes were identified in amino acid-supplemented media. Finally, a strain design was carried out to identify candidate genes to be overexpressed or knocked out so as to maximize antibiotic biosynthesis. Interestingly, potential metabolic engineering targets, identified in this study, have not been tested experimentally.

Lipid Metabolic Versatility in Malassezia spp. Yeasts Studied through Metabolic Modeling.

Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia.

Genome-scale metabolic reconstruction for the insidious bacterium in aquaculture Piscirickettsia salmonis.

Piscirickettsia salmonis is a fish bacterium that causes the disease piscirickettsiosis in salmonids. This pathology is partially controlled by vaccines. The lack of knowledge has hindered its culture on laboratory and industrial scale. The study describes the metabolic phenotype of P. salmonis in culture. This study presents the first genome-scale model (iPF215) of the LF-89 strain of P. salmonis, describing the central metabolic pathway, biosynthesis and molecule degradation and transport mechanisms. The model was adjusted with experiment data, allowing the identification of the capacities that were not predicted by the automatic annotation of the genome sequences. The iPF215 model is comprised of 417 metabolites, 445 reactions and 215 genes, was used to reproduce the growth of P. salmonis (µmax 0.052±0.005h-1). The metabolic reconstruction of the P. salmonis LF-89 strain obtained in this research provides a baseline that describes the metabolic capacities of the bacterium and is the basis for developing improvements to its cultivation for vaccine formulation.

Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation.

The first manually curated genome-scale metabolic model for Salinispora tropica strain CNB-440 was constructed. The reconstruction enables characterization of the metabolic capabilities for understanding and modeling the cellular physiology of this actinobacterium. The iCC908 model was based on physiological and biochemical information of primary and specialised metabolism pathways. The reconstructed stoichiometric matrix consists of 1169 biochemical conversions, 204 transport reactions and 1317 metabolites. A total of 908 structural open reading frames (ORFs) were included in the reconstructed network. The number of gene functions included in the reconstructed network corresponds to 20% of all characterized ORFs in the S. tropica genome. The genome-scale metabolic model was used to study strain-specific capabilities in defined minimal media. iCC908 was used to analyze growth capabilities in 41 different minimal growth-supporting environments. These nutrient sources were evaluated experimentally to assess the accuracy of in silico growth simulations. The model predicted no auxotrophies for essential amino acids, which was corroborated experimentally. The strain is able to use 21 different carbon sources, 8 nitrogen sources and 4 sulfur sources from the nutrient sources tested. Experimental observation suggests that the cells may be able to store sulfur. False predictions provided opportunities to gain new insights into the physiology of this species, and to gap fill the missing knowledge. The incorporation of modifications led to increased accuracy in predicting the outcome of growth/no growth experiments from 76 to 93%. iCC908 can thus be used to define the metabolic capabilities of S. tropica and guide and enhance the production of specialised metabolites.