Computational Metabolomics Operations at BioCyc.org

Co-exposure to polyethylene fiber and Salmonella enterica serovar Typhimurium alters microbiome and metabolome of in vitro chicken cecal mesocosms

Humans and animals encounter a summation of exposures during their lifetime (the exposome). In recent years, the scope of the exposome has begun to include microplastics. Microplastics (MPs) have increasingly been found in locations, including in animal gastrointestinal tracts, where there could be an interaction with Salmonella enterica serovar Typhimurium, one of the commonly isolated serovars from processed chicken. However, there is limited knowledge on how gut microbiomes are affected by microplastics and if an effect would be exacerbated by the presence of a pathogen. In this study, we aimed to determine if acute exposure to microplastics in vitro altered the gut microbiome membership and activity. The microbiota response to a 24 h co-exposure to Salmonella enterica serovar Typhimurium and/or low-density polyethylene (PE) microplastics in an in vitro broiler cecal model was determined using 16S rRNA amplicon sequencing (Illumina) and untargeted metabolomics. Community sequencing results indicated that PE fiber with and without S. Typhimurium yielded a lower Firmicutes/Bacteroides ratio compared with other treatment groups, which is associated with poor gut health, and overall had greater changes to the cecal microbial community composition. However, changes in the total metabolome were primarily driven by the presence of S. Typhimurium. Additionally, the co-exposure to PE fiber and S. Typhimurium caused greater cecal microbial community and metabolome changes than either exposure alone. Our results indicate that polymer shape is an important factor in effects resulting from exposure. It also demonstrates that microplastic-pathogen interactions cause metabolic alterations to the chicken cecal microbiome in an in vitro chicken cecal mesocosm.

IMPORTANCE

Researching the exposome, a summation of exposure to one's lifespan, will aid in determining the environmental factors that contribute to disease states. There is an emerging concern that microplastic-pathogen interactions in the gastrointestinal tract of broiler chickens may lead to an increase in Salmonella infection across flocks and eventually increased incidence of human salmonellosis cases. In this research article, we elucidated the effects of acute co-exposure to polyethylene microplastics and Salmonella enterica serovar Typhimurium on the ceca microbial community in vitro. Salmonella presence caused strong shifts in the cecal metabolome but not the microbiome. The inverse was true for polyethylene fiber. Polyethylene powder had almost no effect. The co-exposure had worse effects than either alone. This demonstrates that exposure effects to the gut microbial community are contaminant-specific. When combined, the interactions between exposures exacerbate changes to the gut environment, necessitating future experiments studying low-dose chronic exposure effects with in vivo model systems.

The Omics Dashboard for Interactive Exploration of Metabolomics and Multi-Omics Data

The Omics Dashboard is a software tool for interactive exploration and analysis of metabolomics, transcriptomics, proteomics, and multi-omics datasets. Organized as a hierarchy of cellular systems, the Dashboard at its highest level contains graphical panels for the full range of cellular systems, including biosynthesis, energy metabolism, and response to stimulus. Thus, the Dashboard top level surveys the state of the cell across a broad range of key systems in a single screen. Each Dashboard panel contains a series of X-Y plots depicting the aggregated omics data values relevant to different subsystems of that panel, e.g., subsystems within the biosynthesis panel include amino acid biosynthesis, carbohydrate biosynthesis and cofactor biosynthesis. Users can interactively drill down to focus in on successively lower-level subsystems of interest. In this article, we present for the first time the metabolomics analysis capabilities of the Omics Dashboard, along with significant new extensions to better accommodate metabolomics datasets, enable analysis and visualization of multi-omics datasets, and provide new data-filtering options.

Interrogating nitritation at a molecular level: Understanding the potential influence of Nitrobacter spp

Water resource recovery facilities (WRRFs) increasingly must maximize nitrogen and phosphorus removal, but concurrently face challenges to reduce their energy usage and environmental footprint. In particular, biological nutrient removal (BNR), which targets removal of phosphorus and nitrogen, exhibits a large energy demand. However, a BNR process achieving partial oxidation of NH₃ to NO₂ (nitritation) could reduce energy demands, with secondary environmental emission benefits. Research was conducted on bench-scale systems performing nitritation and nitrification to better understand how mixed microbial consortia, cultured on real wastewater, can sustain nitritation. BNR configurations achieved nitrite accumulation ratios of 64-82%, with excellent overall effluent quality. Applying phylogenetic, transcriptomic, and metabolomic methods, coupled with process monitoring, results indicate that partial nitritation may be induced through a combination of: (1) Employing ammonia-based aeration control, with an ammonia setpoint of 2, 3 mgN/L; (2) Maintaining an aerobic period DO of 1.0-2.0 mg/L; and (3) Operating BNR post-anoxically, integrated within enhanced biological phosphorus removal (EBPR). Significant nitritation was achieved despite the presence Nitrobacter spp., but nitrite oxidoreductase must be functionally impaired or structurally incomplete. Overall, this research demonstrated the value of interrogating a mixed microbial consortia at a macro and molecular level to explore unique metabolic responses such as nitritation.

Rapid and Automatic Annotation of Multiple On-Tissue Chemical Modifications in Mass Spectrometry Imaging with Metaspace

On-tissue chemical derivatization is a valuable tool for expanding compound coverage in untargeted metabolomic studies with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Applying multiple derivatization agents in parallel increases metabolite coverage even further but results in large and more complex datasets that can be challenging to analyze. In this work, we present a pipeline to provide rigorous annotations for on-tissue derivatized MSI data using Metaspace. To test and validate the pipeline, maize roots were used as a model system to obtain MSI datasets after chemical derivatization with four different reagents, Girard's T and P for carbonyl groups, coniferyl aldehyde for primary amines, and 2-picolylamine for carboxylic acids. Using this pipeline helped us annotate 631 unique metabolites from the CornCyc/BraChem database compared to 256 in the underivatized dataset, yet, at the same time, shortening the processing time compared to manual processing and providing robust and systematic scoring and annotation. We have also developed a method to remove false derivatized annotations, which can clean 5-25% of false derivatized annotations from the derivatized data, depending on the reagent. Taken together, our pipeline facilitates the use of broadly targeted spatial metabolomics using multiple derivatization reagents.

Recent advances in plastic degradation - From microbial consortia-based methods to data sciences and computational biology driven approaches

The conventional methods of plastic waste management such as mechanical and chemical recycling, landfill complemented by incineration and pyrosis have limited scope. Thus, microbiological-based approaches by the application of microbial consortia or cocultures are appropriate, cost-effective, and eco-friendly to manage plastic wastes. Screening of novel plastic degrading microorganisms, the formulation of microbial consortia, and utilisation of their enzymes probably play a role in plastic waste management. The by-products of microbial degradation of plastic waste can be used as bio-energy sources, that aids in the development of cost-effective bio-digesters. The recent advancements in computational biology and bioinformatics play a vital role in understanding the molecular basis of enzymatic degradation of plastic polymers by microorganisms. Understanding the three-dimensional structures of plastic degrading enzymes and their metabolic pathways play a vital role in studying the microbial degradation of plastics. The present review highlights the scope of various microorganisms and their enzymes in plastic degradation. The review emphasizes the applications of co-cultures or microbial consortia-based approaches for the enhanced degradation of plastic polymers and the production of value-added end products that can be used as the prototypes of bioenergy sources. The review also provides a comprehensive outlook on the applications of data sciences, computational biology, and bioinformatics resources, and web-based tools towards the study of microbial degradation of plastic polymers.

'Candidatus Gromoviella agglomerans', a novel intracellular Holosporaceae parasite of the ciliate Paramecium showing marked genome reduction

Holosporales are an alphaproteobacterial lineage encompassing bacteria obligatorily associated with multiple diverse eukaryotes. For most representatives, little is known on the interactions with their hosts. In this study, we characterized a novel Holosporales symbiont of the ciliate Paramecium polycaryum. This bacterium inhabits the host cytoplasm, frequently forming quite large aggregates. Possibly due to such aggregates, host cells sometimes displayed lethal division defects. The symbiont was also able to experimentally stably infect another Paramecium polycaryum strain. The bacterium is phylogenetically related with symbionts of other ciliates and diplonemids, forming a putatively fast-evolving clade within the family Holosporaceae. Similarly to many close relatives, it presents a very small genome (<600 kbp), and, accordingly, a limited predicted metabolism, implying a heavy dependence on Paramecium, thanks also to some specialized membrane transporters. Characterized features, including the presence of specific secretion systems, are overall suggestive of a mild parasitic effect on the host. From an evolutionary perspective, a potential ancestral trend towards pronounced genome reduction and possibly linked to parasitism could be inferred, at least among fast-evolving Holosporaceae, with some lineage-specific traits. Interestingly, similar convergent features could be observed in other host-associated lineages, in particular Rickettsiales among Alphaproteobacteria.

Model-guided dynamic control of essential metabolic nodes boosts acetyl-coenzyme A-dependent bioproduction in rewired Pseudomonas putida

Pseudomonas putida is evolutionarily endowed with features relevant for bioproduction, especially under harsh operating conditions. The rich metabolic versatility of this species, however, comes at the price of limited formation of acetyl-coenzyme A (CoA) from sugar substrates. Since acetyl-CoA is a key metabolic precursor for a number of added-value products, in this work we deployed an in silico-guided rewiring program of central carbon metabolism for upgrading P. putida as a host for acetyl-CoA-dependent bioproduction. An updated kinetic model, integrating fluxomics and metabolomics datasets in addition to manually-curated information of enzyme mechanisms, identified targets that would lead to increased acetyl-CoA levels. Based on these predictions, a set of plasmids based on clustered regularly interspaced short palindromic repeats (CRISPR) and dead CRISPR-associated protein 9 (dCas9) was constructed to silence genes by CRISPR interference (CRISPRi). Dynamic reduction of gene expression of two key targets (gltA, encoding citrate synthase, and the essential accA gene, encoding subunit A of the acetyl-CoA carboxylase complex) mediated an 8-fold increase in the acetyl-CoA content of rewired P. putida. Poly(3-hydroxybutyrate) (PHB) was adopted as a proxy of acetyl-CoA availability, and two synthetic pathways were engineered for biopolymer accumulation. By including cell morphology as an extra target for the CRISPRi approach, fully rewired P. putida strains programmed for PHB accumulation had a 5-fold increase in PHB titers in bioreactor cultures using glucose. Thus, the strategy described herein allowed for rationally redirecting metabolic fluxes in P. putida from central metabolism towards product biosynthesis-especially relevant when deletion of essential pathways is not an option.

Prediction of Selected Biosynthetic Pathways for the Lipopolysaccharide Components in Porphyromonas gingivalis

Porphyromonas gingivalis is an oral human pathogen. The bacterium destroys dental tissue and is a serious health problem worldwide. Experimental data and bioinformatic analysis revealed that the pathogen produces three types of lipopolysaccharides (LPS): normal (O-type), anionic (A-type), and capsular (K-type). The enzymes involved in the production of all three types of lipopolysaccharide have been largely identified for the first two and partially for the third type. In the current work, we use bioinformatics tools to predict biosynthetic pathways for the production of the normal (O-type) lipopolysaccharide in the W50 strain Porphyromonas gingivalis and compare the pathway with other putative pathways in fully sequenced and completed genomes of other pathogenic strains. Selected enzymes from the pathway have been modeled and putative structures are presented. The pathway for the A-type antigen could not be predicted at this time due to two mutually exclusive structures proposed in the literature. The pathway for K-type antigen biosynthesis could not be predicted either due to the lack of structural data for the antigen. However, pathways for the synthesis of lipid A, its core components, and the O-type antigen ligase reaction have been proposed based on a combination of experimental data and bioinformatic analyses. The predicted pathways are compared with known pathways in other systems and discussed. It is the first report in the literature showing, in detail, predicted pathways for the synthesis of selected LPS components for the model W50 strain of P. gingivalis.

Characterization of a novel Pantoea symbiont allows inference of a pattern of convergent genome reduction in bacteria associated with Pentatomidae

Phytophagous stink bugs typically harbor nutritional symbiotic bacteria in their midgut, to integrate their unbalanced diet. In the Pentatomidae, most symbionts are affiliated to the genus Pantoea, and are polyphyletic. This suggests a scenario of an ancestral establishment of symbiosis, followed by multiple symbiont replacement events by akin environmental bacteria in different host lineages. In this study, a novel Pantoeaspecies ('CandidatusPantoea persica') was characterized from the gut of the pentatomid Acrosternum arabicum, and shown to be highly abundant in a specific portion of the gut and necessary for the host development. The genome of the symbiont (2.9 Mb), while presenting putative host-supportive metabolic pathways, including those for amino acids and vitamin synthesis, showed a high level of pseudogenization, indicating ongoing genome reduction. Comparative analyses with other free-living and symbiotic Pantoea highlighted a convergent pattern of genome reduction in symbionts of pentatomids, putatively following the typical phases modelized in obligate nutritional symbionts of insects. Additionally, this system has distinctive traits, as hosts are closely related, and symbionts originated multiple independent times from closely related free-living bacteria, displaying convergent and independent conspicuous genome reduction. Due to such peculiarities, this may become an ideal model to study genome evolutionary processes in insect symbionts.

Transcriptomic Behavior of Salmonella enterica Newport in Response to Oxidative Sanitizers

ABSTRACT

Agricultural water is a known source of contamination to fresh produce and can contain foodborne pathogens including Salmonella enterica, pathogenic Escherichia coli, Listeria monocytogenes, and Campylobacter jejuni. To mitigate such risks, antimicrobial agents such as hypochlorites and peroxyacetic acid (PAA) can be applied to in-line irrigation systems as well as to water used in postharvest washing. Although these compounds are effective and widely used, some pathogenic bacteria adapt to survive exposure. RNA sequencing was used to analyze the Salmonella Newport transcriptome after exposure to sodium hypochlorite (NaOCl) and PAA in a simulated agricultural water system. Overall cellular adaptive response was determined quantitatively as a function of overall gene expression of the >4,000 genes in the Salmonella Newport genome. Differentially expressed genes ranged from 11 due to 10-ppm NaOCl treatment, 316 due to 20-ppm NaOCl treatment, 1,719 due to 10-ppm PAA treatment, and 2,010 due to 20-ppm PAA treatment compared with that of the controls (water only). Differentially expressed transcripts included cellular functions such as biosynthesis, degradation, energy generation, and nonmetabolically linked functions. Oxidative exposure upregulated genes associated with key virulence, attachment, and gene transfer. Amino acid biosynthesis was upregulated due to NaOCl exposure but primarily downregulated when Salmonella Newport was exposed to PAA. Slight upregulation occurred in nucleoside and nucleotide biosynthesis, a known DNA repair mechanism seen during exposure to sanitizers. Our results indicate that Salmonella Newport reacts differently when exposed to NaOCl versus PAA, despite oxidative activity being the primary modes of antimicrobial action of both compounds.

HIGHLIGHTS

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.

Using Pathway Covering to Explore Connections among Metabolites

Interpreting changes in metabolite abundance in response to experimental treatments or disease states remains a major challenge in metabolomics. Pathway Covering is a new algorithm that takes a list of metabolites (compounds) and determines a minimum-cost set of metabolic pathways in an organism that includes (covers) all the metabolites in the list. We used five functions for assigning costs to pathways, including assigning a constant for all pathways, which yields a solution with the smallest pathway count; two methods that penalize large pathways; one that prefers pathways based on the pathway's assigned function, and one that loosely corresponds to metabolic flux. The pathway covering set computed by the algorithm can be displayed as a multi-pathway diagram ("pathway collage") that highlights the covered metabolites. We investigated the pathway covering algorithm by using several datasets from the Metabolomics Workbench. The algorithm is best applied to a list of metabolites with significant statistics and fold-changes with a specified direction of change for each metabolite. The pathway covering algorithm is now available within the Pathway Tools software and BioCyc website.

The Genome Sequence of "Candidatus Fokinia solitaria": Insights on Reductive Evolution in Rickettsiales

"Candidatus Fokinia solitaria" is an obligate intracellular endosymbiont of a unicellular eukaryote, a ciliate of the genus Paramecium. Here, we present the genome sequence of this bacterium and subsequent analysis. Phylogenomic analysis confirmed the previously reported positioning of the symbiont within the "Candidatus Midichloriaceae" family (order Rickettsiales), as well as its high sequence divergence from other members of the family, indicative of fast sequence evolution. Consistently with this high evolutionary rate, a comparative genomic analysis revealed that the genome of this symbiont is the smallest of the Rickettsiales to date. The reduced genome does not present flagellar genes, nor the pathway for the biosynthesis of lipopolysaccharides (present in all the other so far sequenced members of the family "Candidatus Midichloriaceae") or genes for the Krebs cycle (present, although not always complete, in Rickettsiales). These results indicate an evolutionary trend toward a stronger dependence on the host, in comparison with other members of the family. Two alternative scenarios are compatible with our results; "Candidatus Fokinia solitaria" could be either a recently evolved, vertically transmitted mutualist, or a parasite with a high host-specificity.

Metabolomics and Precision Medicine in Trauma: The State of the Field

Trauma is a major problem in the United States. Mortality from trauma is the number one cause of death under the age of 45 in the United States and is the third leading cause of death for all age groups. There are approximately 200,000 deaths per year due to trauma in the United States at a cost of over $671 billion in combined healthcare costs and lost productivity. Unsurprisingly, trauma accounts for approximately 30% of all life-years lost in the United States. Due to immense development of trauma systems, a large majority of trauma patients survive the injury, but then go on to die from complications arising from the injury. These complications are marked by early and significant metabolic changes accompanied by inflammatory responses that lead to progressive organ failure and, ultimately, death. Early resuscitative and surgical interventions followed by close monitoring to identify and rescue treatment failures are key to successful outcomes. Currently, the adequacy of resuscitation is measured using vital signs, noninvasive methods such as bedside echocardiography or stroke volume variation, and other laboratory endpoints of resuscitation, such as lactate and base deficit. However, these methods may be too crude to understand cellular and subcellular changes that may be occurring in trauma patients. Better diagnostic and therapeutic markers are needed to assess the adequacy of interventions and monitor responses at a cellular and subcellular level and inform clinical decision-making before complications are clinically apparent. The developing field of metabolomics holds great promise in the identification and application of biochemical markers toward the clinical decision-making process.

Metabolomics in Plant Priming Research: The Way Forward?

A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.

Current Challenges in Plant Eco-Metabolomics

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria

Bacterial genes that change in expression upon environmental disturbance have commonly been seen as those that must also phenotypically matter. However, several studies suggest that differentially expressed genes are rarely phenotypically important. We demonstrate, for Gram-positive and Gram-negative bacteria, that these seemingly uncoordinated gene sets are involved in responses that can be linked through topological network analysis. However, the level of coordination is stress dependent. While a well-coordinated response is triggered in response to nutrient stress, antibiotics trigger an uncoordinated response in which transcriptionally and phenotypically important genes are neither linked spatially nor in their magnitude. Moreover, a gene expression meta-analysis reveals that genes with large fitness changes during stress have low transcriptional variation across hundreds of other conditions, and vice versa. Our work suggests that cellular responses can be understood through network models that incorporate regulatory and genetic relationships, which could aid drug target predictions and genetic network engineering.

A geographically-diverse collection of 418 human gut microbiome pathway genome databases

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GutCyc, a compendium of environmental pathway genome databases (ePGDBs) constructed from 418 assembled human microbiome datasets using MetaPathways, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the Pathway Tools software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GutCyc provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn's disease, and type 2 diabetes. GutCyc data products are searchable online, or may be downloaded and explored locally using MetaPathways and Pathway Tools.

Pathway Analysis and Omics Data Visualization Using Pathway Genome Databases: FragariaCyc, a Case Study

The species-specific plant Pathway Genome Databases (PGDBs) based on the BioCyc platform provide a conceptual model of the cellular metabolic network of an organism. Such frameworks allow analysis of the genome-scale expression data to understand changes in the overall metabolisms of an organism (or organs, tissues, and cells) in response to various extrinsic (e.g. developmental and differentiation) and/or extrinsic signals (e.g. pathogens and abiotic stresses) from the surrounding environment. Using FragariaCyc, a pathway database for the diploid strawberry Fragaria vesca, we show (1) the basic navigation across a PGDB; (2) a case study of pathway comparison across plant species; and (3) an example of RNA-Seq data analysis using Omics Viewer tool. The protocols described here generally apply to other Pathway Tools-based PGDBs.

DESM: portal for microbial knowledge exploration systems

Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4 538 278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.