The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes.

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

A global reconnaissance of particulates and metals/metalloids in untreated drinking water sources.

Metal and metalloid contamination in drinking water sources is a global concern, particularly in developing countries. This study used hollow membrane water filters and metal-capturing polyurethane foams to sample 71 drinking water sources in 22 different countries. Field sampling was performed with sampling kits prepared in the lab at Hope College in Holland, MI, USA. Filters and foams were sent back to the lab after sampling, and subsequent analysis of flushates and rinsates allowed the estimation of suspended solids and metal and other analayte concentrations in source waters. Estimated particulate concentrations were 0-92 mg/L, and consisted of quartz, feldspar, and clay, with some samples containing metal oxides or sulfide phases. As and Cu were the only analytes which occurred above the World Health Organization (WHO) guidelines of 10 µg/L and 2000 µg/L, respectively, with As exceeding the guideline in 45% of the sources and Cu in 3%. Except for one value of ~ 285 µg/L, As concentrations were 45-200 µg/L (river), 65-179 µg/L (well), and 112-178 µg/L (tap). Other metals (Ce, Fe, Mg, Mn, Zn) with no WHO guideline were also detected, with Mn the most common. This study demonstrated that filters and foams can be used for reconnaissance characterization of untreated drinking water. However, estimated metal and other analyte concentrations could only be reported as minimum values due to potential incomplete retrieval of foam-bound analytes. A qualitative reporting methodology was used to report analytes as "present" if the concentration was below the WHO guideline, and "present-recommend retesting" if the concentration was quantifiable and above the WHO guideline.

Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria.

BACKGROUND: Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group. RESULTS: A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported. CONCLUSIONS: The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov).

Predicting variable gene content in Escherichia coli using conserved genes.

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

Diversity and versatility of the Thermotoga maritima sugar kinome.

Sugar phosphorylation is an indispensable committed step in a large variety of sugar catabolic pathways, which are major suppliers of carbon and energy in heterotrophic species. Specialized sugar kinases that are indispensable for most of these pathways can be utilized as signature enzymes for the reconstruction of carbohydrate utilization machinery from microbial genomic and metagenomic data. Sugar kinases occur in several structurally distinct families with various partially overlapping as well as yet unknown substrate specificities that often cannot be accurately assigned by homology-based techniques. A subsystems-based metabolic reconstruction combined with the analysis of genome context and followed by experimental testing of predicted gene functions is a powerful approach of functional gene annotation. Here we applied this integrated approach for functional mapping of all sugar kinases constituting an extensive and diverse sugar kinome in the thermophilic bacterium Thermotoga maritima. Substrate preferences of 14 kinases mainly from the FGGY and PfkB families were inferred by bioinformatics analysis and biochemically characterized by screening with a panel of 45 different carbohydrates. Most of the analyzed enzymes displayed narrow substrate preferences corresponding to their predicted physiological roles in their respective catabolic pathways. The observed consistency supports the choice of kinases as signature enzymes for genomics-based identification and reconstruction of sugar utilization pathways. Use of the integrated genomic and experimental approach greatly speeds up the identification of the biochemical function of unknown proteins and improves the quality of reconstructed pathways.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data.

BACKGROUND: Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. RESULTS: We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. CONCLUSIONS: Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

Connecting genotype to phenotype in the era of high-throughput sequencing.

BACKGROUND: The development of next generation sequencing technology is rapidly changing the face of the genome annotation and analysis field. One of the primary uses for genome sequence data is to improve our understanding and prediction of phenotypes for microbes and microbial communities, but the technologies for predicting phenotypes must keep pace with the new sequences emerging. SCOPE OF REVIEW: This review presents an integrated view of the methods and technologies used in the inference of phenotypes for microbes and microbial communities based on genomic and metagenomic data. Given the breadth of this topic, we place special focus on the resources available within the SEED Project. We discuss the two steps involved in connecting genotype to phenotype: sequence annotation, and phenotype inference, and we highlight the challenges in each of these steps when dealing with both single genome and metagenome data. MAJOR CONCLUSIONS: This integrated view of the genotype-to-phenotype problem highlights the importance of a controlled ontology in the annotation of genomic data, as this benefits subsequent phenotype inference and metagenome annotation. We also note the importance of expanding the set of reference genomes to improve the annotation of all sequence data, and we highlight metagenome assembly as a potential new source for complete genomes. Finally, we find that phenotype inference, particularly from metabolic models, generates predictions that can be validated and reconciled to improve annotations. GENERAL SIGNIFICANCE: This review presents the first look at the challenges and opportunities associated with the inference of phenotype from genotype during the next generation sequencing revolution. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.

Inference of the transcriptional regulatory network in Staphylococcus aureus by integration of experimental and genomics-based evidence.

Transcriptional regulatory networks are fine-tuned systems that help microorganisms respond to changes in the environment and cell physiological state. We applied the comparative genomics approach implemented in the RegPredict Web server combined with SEED subsystem analysis and available information on known regulatory interactions for regulatory network reconstruction for the human pathogen Staphylococcus aureus and six related species from the family Staphylococcaceae. The resulting reference set of 46 transcription factor regulons contains more than 1,900 binding sites and 2,800 target genes involved in the central metabolism of carbohydrates, amino acids, and fatty acids; respiration; the stress response; metal homeostasis; drug and metal resistance; and virulence. The inferred regulatory network in S. aureus includes ∼320 regulatory interactions between 46 transcription factors and ∼550 candidate target genes comprising 20% of its genome. We predicted ∼170 novel interactions and 24 novel regulons for the control of the central metabolic pathways in S. aureus. The reconstructed regulons are largely variable in the Staphylococcaceae: only 20% of S. aureus regulatory interactions are conserved across all studied genomes. We used a large-scale gene expression data set for S. aureus to assess relationships between the inferred regulons and gene expression patterns. The predicted reference set of regulons is captured within the Staphylococcus collection in the RegPrecise database (http://regprecise.lbl.gov).

Providing a Safe, In-Person, Residential College Experience During the COVID-19 Pandemic.

Due to the COVID-19 pandemic, higher education institutions were forced to make difficult decisions regarding the 2020-2021 academic year. Many institutions decided to have courses in an online remote format, others decided to attempt an in-person experience, while still others took a hybrid approach. Hope College (Holland, MI) decided that an in-person semester would be safer and more equitable for students. To achieve this at a residential college required broad collaboration across multiple stakeholders. Here, we share lessons learned and detail Hope College's model, including wastewater surveillance, comprehensive testing, contact tracing, and isolation procedures that allowed us to deliver on our commitment of an in-person, residential college experience.

Diarrhea prevalence in a randomized, controlled prospective trial of point-of-use water filters in homes and schools in the Dominican Republic.

BACKGROUND: Lack of sustainable access to clean drinking water continues to be an issue of paramount global importance, leading to millions of preventable deaths annually. Best practices for providing sustainable access to clean drinking water, however, remain unclear. Widespread installation of low-cost, in-home, point of use water filtration systems is a promising strategy. METHODS: We conducted a prospective, randomized, controlled trial whereby 16 villages were selected and randomly assigned to one of four treatment arms based on the installation location of Sawyer® PointONE™ filters (filter in both home and school; filter in home only; filter in school only; control group). Water samples and self-reported information on diarrhea were collected at multiple times throughout the study. RESULTS: Self-reported household prevalence of diarrhea decreased from 25.6 to 9.76% from installation to follow-up (at least 7 days, and up to 200 days post-filter installation). These declines were also observed in diarrhea with economic or educational consequences (diarrhea which led to medical treatment and/or missing school or work) with baseline prevalence of 9.64% declining to 1.57%. Decreases in diarrhea prevalence were observed across age groups. There was no evidence of a loss of efficacy of filters up to 200 days post-filter installation. Installation of filters in schools was not associated with decreases in diarrhea prevalence in school-aged children or family members. Unfiltered water samples both at schools and homes contained potential waterborne bacterial pathogens, dissolved heavy metals and metals associated with particulates. All dissolved metals were detected at levels below World Health Organization action guidelines. CONCLUSIONS: This controlled trial provides strong evidence of the effectiveness of point-of-use, hollow fiber membrane filters at reducing diarrhea from bacterial sources up to 200 days post-installation when installed in homes. No statistically significant reduction in diarrhea was found when filters were installed in schools. Further research is needed in order to explore filter efficacy and utilization after 200 days post-installation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03972618 . Registered 3 June 2019-retrospectively registered.

Evaluating the efficacy of point-of-use water filtration units in Fiji.

BACKGROUND: To develop and evaluate a strategy for reducing the prevalence and impact of waterborne disease, a water quality intervention was developed for Fiji by Give Clean Water, Inc. in partnership with the Fiji Ministry of Health. Residents were provided and trained on how to use a Sawyer® PointONE™ filter, while also being taught proper handwashing techniques. At the time of the filter installation, all households were surveyed inquiring about the prior 2- to 4-week period. Households were measured a second time between 19 and 225 days later (mean = 66 days). RESULTS: To date, five economic and health outcomes have been tracked on 503 households to evaluate the efficacy of the intervention. When comparing baseline to follow-up among the 503 households, the 2-week diarrhea prevalence decreased in households from 17.5% at baseline to 1.8% at follow-up. Also, the 2-week prevalence of severe diarrhea decreased per household from 9.7% at baseline to 0.6% at follow-up. Finally, monthly diarrhea-related medical costs reduced by an average of Fijian (FJ) $3.54 per person, and monthly water expenses reduced by FJ $0.63 per person. All estimated values are obtained from general linear and logistic mixed-effect models, which adjusted for location, season, time to follow-up, household size, water source, and respondent changing. Changes in economic and health outcomes from installation to follow-up were statistically significant (p < 0.05) in all cases, in both unadjusted and adjusted models. CONCLUSIONS: The installation of water filters shows promise for the reduction of diarrhea prevalence in Fiji, as well as the reduction of diarrhea-related medical costs and water expenses. Future work entails evaluation in other countries and contexts, long-term health monitoring, and comparison to alternative water quality interventions.

Gene set analyses for interpreting microarray experiments on prokaryotic organisms.

BACKGROUND: Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes. RESULTS: We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR. CONCLUSION: MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

The RAST Server: rapid annotations using subsystems technology.

BACKGROUND: The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. DESCRIPTION: We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12-24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. CONCLUSION: By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.

IDENTIFICATION AND ANALYSIS OF BACTERIAL GENOMIC METABOLIC SIGNATURES.

With continued rapid growth in the number and quality of fully sequenced and accurately annotated bacterial genomes, we have unprecedented opportunities to understand metabolic diversity. We selected 101 diverse and representative completely sequenced bacteria and implemented a manual curation effort to identify 846 unique metabolic variants present in these bacteria. The presence or absence of these variants act as a metabolic signature for each of the bacteria, which can then be used to understand similarities and differences between and across bacterial groups. We propose a novel and robust method of summarizing metabolic diversity using metabolic signatures and use this method to generate a metabolic tree, clustering metabolically similar organisms. Resulting analysis of the metabolic tree confirms strong associations with well-established biological results along with direct insight into particular metabolic variants which are most predictive of metabolic diversity. The positive results of this manual curation effort and novel method development suggest that future work is needed to further expand the set of bacteria to which this approach is applied and use the resulting tree to test broad questions about metabolic diversity and complexity across the bacterial tree of life.

Whole-Genome Shotgun Sequences of Salmonella enterica Serovar Typhimurium Lilleengen Type Strains LT1, LT18, LT19, LT20, LT21, and LT22.

The Lilleengen type (LT) collection of Salmonella enterica serovar Typhimurium strains has served the scientific community as a group of model organisms for basic genetic and biochemical pathway research. Here, we report the whole-genome shotgun sequences of Salmonella enterica serovar Typhimurium strains LT1, LT18, LT19, LT20, LT21, and LT22.

Genome Sequences of Mycobacteriophages Amgine, Amohnition, Bella96, Cain, DarthP, Hammy, Krueger, LastHope, Peanam, PhelpsODU, Phrank, SirPhilip, Slimphazie, and Unicorn.

We report the genome sequences of 14 cluster K mycobacteriophages isolated using Mycobacterium smegmatis mc²155 as host. Four are closely related to subcluster K1 phages, and 10 are members of subcluster K6. The phage genomes span considerable sequence diversity, including multiple types of integrases and integration sites.

Characterization of Gut Microbiome Dynamics in Developing Pekin Ducks and Impact of Management System.

Little to no research has been conducted on the gut microbiome of the Pekin duck, yet over 24.5 million ducks are raised for human consumption each year in the United States alone. Knowledge of the microbiome could lead to an understanding of the effects of growing conditions such as the use of prebiotics, probiotics, and enzymes in feeding practices, the use of antibiotics, and the sources of pathogenic bacteria in diseased ducks. In order to characterize changes in the caecal microbiome that occur as ducks develop through a typical industry grow-out period, a 16S rRNA community analysis of caecal contents collected over a 6-week period was conducted using a next generation sequencing approach. Transitions in the composition of the caecal microbiome occurred throughout the lifespan, with a large shift during days 4 through 10 posthatch. Two major phyla of bacteria were found to be present within the caeca of aviary raised ducks, with the relative abundance of each phylum varying by age of the duck. Proteobacteria is dominant for the first 3 days of age, and Firmicutes increases and dominates beginning at day 4. Barn raised ducks contained a significant population of Bacteroidetes in addition to Proteobacteria and Firmicutes at later developmental time points, though this phylum was absent in aviary raised ducks. Genera containing pathogens of anseriformes most often found in industry settings were either absent or found as normal parts of the caecal microbial populations. The high level differences in phylum abundance highlight the importance of well-designed sampling strategies for microbiome based studies. Results showed clear distinctions between Pekin Duck caecal contents and those of Broiler Chickens and Turkey in a qualitative comparison. These data provide a reference point for studies of the Pekin Duck through industry grow-out ages, provide a foundation for understanding the types of bacteria that promote health, and may lead to improved methods to increase yields and decrease instances of disease in agricultural production processes.

A Bayesian Framework for the Classification of Microbial Gene Activity States.

Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences between genes, and failing to provide statistically meaningful confidence estimates. We propose a flexible Bayesian approach to classifying gene activity states based on a Gaussian mixture model. The model integrates genome-wide transcriptomics data from multiple conditions and information about gene co-regulation to provide activity state confidence estimates for each gene in each condition. We compare the performance of our novel method to existing methods on both simulated data and real data from 907 E. coli gene expression arrays, as well as a comparison with experimentally measured flux values in 29 conditions, demonstrating that our method provides more consistent and accurate results than existing methods across a variety of metrics.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.