Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR.

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

A cross-study analysis of drug response prediction in cancer cell lines.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

Facilitating Lung Collapse for Thoracoscopic Surgery Utilizing Endobronchial Airway Occlusion Preceded by Pleurotomy and One-minute Suspension of Two-lung Ventilation.

OBJECTIVES: To assess when and whether clamping the double-lumen endobronchial tube (DLT) limb of the non-ventilated lung is more conducive to a rapid and effective lung deflation than simply allowing the open limb of the DLT to communicate with the atmosphere. DESIGN: This was a single-center, single-blind, randomized, controlled trial. SETTING: The trial was performed in a single institutional setting. PARTICIPANTS: The participants were 60 patients undergoing elective video-assisted thoracoscopic surgery. INTERVENTIONS: Patients were randomized to the open-clamp airway technique (OCAT group) or control group. Patients in the control group had one-lung ventilation initiated upon being placed in the lateral decubitus position. The OCAT group had two-lung ventilation maintained until the pleural cavity was opened with the introduction of a planned thoracoscopic access port to allow the operated lung to fall away from the chest wall. Thereafter, ventilation was suspended (temporarily ceased) for 1 minute before the DLT lumen of the isolated lung was clamped. The primary outcome of the trial was the time to complete lung collapse scored as determined from video clips taken during surgery. The secondary outcomes were (1) lung collapse score at 30 minutes after pleural incision, (2) surgeon satisfaction with surgery, and (3) intraoperative hypoxemia. MEASUREMENTS AND MAIN RESULTS: The median time to reach complete lung collapse in the OCAT group was 10 minutes (odds ratio 10.0, 95% CI 6.3-13.7), which was much shorter than that of the control group (25 minutes [odds ratio 25.0, 95% CI 13.6-36.4]). The difference in complete lung collapse at 30 minutes between the 2 groups was significant (p < 0.001). The surgeon's satisfaction with surgery was higher in the OCAT group than in the control group (8.5 ± 0.2 vs 6.8 ± 0.2; p < 0.001). There was no difference regarding intraoperative hypoxemia. CONCLUSIONS: Suspending ventilation of both DLT limbs for 1 minute after pleural cavity opening and then clamping the DLT lumen of the isolated lung resulted in a more rapid deflation of the surgical lung. This open-clamp airway technique is an effective technique for rapid surgical lung collapse during thoracoscopic surgery.

Dexmedetomidine preconditioning ameliorates lung injury induced by pulmonary ischemia/reperfusion by upregulating promoter histone H3K4me3 modification of KGF-2.

Keratinocyte growth factor (KGF)-2 has been highlighted to play a significant role in maintaining the endothelial barrier integrity in lung injury induced by ischemia-reperfusion (I/R). However, the underlying mechanism remains largely unknown. The aims of this study were to determine whether dexmedetomidine preconditioning (DexP) modulates pulmonary I/R-induced lung injury through the alteration in KGF-2 expression. In our I/R-modeled mice, DexP significantly inhibited pathological injury, inflammatory response, and inflammatory cell infiltration, while promoted endothelial barrier integrity and KGF-2 promoter activity in lung tissues. Bioinformatics prediction and ChIP-seq revealed that I/R significantly diminished the level of H3K4me3 modification in the KGF-2 promoter, which was significantly reversed by DexP. Moreover, DexP inhibited the expression of histone demethylase JMJD3, which in turn promoted the expression of KGF-2. In addition, overexpression of JMJD3 weakened the protective effect of DexP on lung injury in mice with I/R. Collectively, the present results demonstrated that DexP ameliorates endothelial barrier dysfunction via the JMJD3/KGF-2 axis.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities.

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Learning curves for drug response prediction in cancer cell lines.

BACKGROUND: Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. METHODS: We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. RESULTS: The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. CONCLUSIONS: A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

Identifying genomic islands with deep neural networks.

BACKGROUND: Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. RESULTS: We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. CONCLUSIONS: We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

PATRIC as a unique resource for studying antimicrobial resistance.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Evaluation of Recombinant CYP3A4 Variants on the Metabolism of Oxycodone In Vitro.

Cytochrome P450 3A4 is a highly polymorphic enzyme and metabolizes approximately 40%-60% of therapeutic drugs. Its genetic polymorphism may significantly affect the expression and function of CYP3A4 resulting in alterations of the pharmacokinetics and pharmacodynamics of the CYP3A4-mediated drugs. The purpose of this study was to evaluate the catalytic activities of 30 CYP3A4 nonsynonymous variants and wild type toward oxycodone in vitro. CYP3A4 proteins were incubated with oxycodone for 30 min at 37 °C and the reaction was terminated by cooling to -80 °C immediately. Ultraperformance liquid chromatography tandem mass-spectrometry was used to analyze noroxycodone, and kinetic parameters Km, Vmax, and intrinsic clearance (Vmax/Km) of noroxycodone were also determined. Compared with CYP3A4.1, 24 CYP3A4 variants (CYP3A4.2-.5, -.7-.16, -.18 and -.19, -.23 and -.24, -.28 and -.29, and -.31-.34) exhibited significantly decreased relative clearance values (from 4.82% ± 0.31% to 80.98% ± 5.08%), whereas CYP3A4.6, -.17, -.20, -.21, -.26, and -.30 displayed no detectable enzyme activity. As the first study of these alleles for oxycodone metabolism in vitro, results of this study may provide insight into establishing the genotype-phenotype relationship for oxycodone and serve as a reference for clinical administrators and advance the provision of personalized precision medicine.

Apelin-13 Reverses Bupivacaine-Induced Cardiotoxicity via the Adenosine Monophosphate-Activated Protein Kinase Pathway.

BACKGROUND: Cardiotoxicity can be induced by the commonly used amide local anesthetic, bupivacaine. Bupivacaine can inhibit protein kinase B (AKT) phosphorylation and activated adenosine monophosphate-activated protein kinase alpha (AMPKα). It can decouple mitochondrial oxidative phosphorylation and enhance reactive oxygen species (ROS) production. Apelin enhances the phosphatidylinositol 3-kinase (PI3K)/AKT and AMPK/acetyl-CoA carboxylase (ACC) pathways, promotes the complete fatty acid oxidation in the heart, and reduces the release of ROS. In this study, we examined whether exogenous (Pyr1) apelin-13 could reverse bupivacaine-induced cardiotoxicity. METHODS: We used the bupivacaine-induced inhibition model in adult male Sprague Dawley (SD) rats (n = 48) and H9c2 cardiomyocyte cell cultures to explore the role of apelin-13 in the reversal of bupivacaine cardiotoxicity, and its possible mechanism of action. AMPKα, ACC, carnitine palmitoyl transferase (CPT), PI3K, AKT, superoxide dismutase 1 (SOD1), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (p47-phox) were quantified. Changes in mitochondrial ultrastructure were examined, and mitochondrial DNA, cell viability, ROS release, oxygen consumption rate (OCR) were determined. RESULTS: Apelin-13 reduced bupivacaine-induced mitochondrial DNA lesions in SD rats (P < .001), while increasing the expression of AMPKα (P = .007) and PI3K (P = .002). Furthermore, apelin-13 blocked bupivacaine-induced depolarization of the mitochondrial membrane potential (P = .019) and the bupivacaine-induced increases in ROS (P = .001). Also, the AMPK pathway was activated by bupivacaine as well as apelin-13 (P = .002) in H9c2 cardiomyocytes. Additionally, the reduction in the PI3K expression by bupivacaine was mitigated by apelin-13 in H9c2 cardiomyocytes (P = .001). While the aforementioned changes induced by bupivacaine were not abated by apelin-13 after pretreatment with AMPK inhibitor compound C; the bupivacaine-induced changes were still mitigated by apelin-13, even when pretreated with PI3K inhibitor-LY294002. CONCLUSIONS: Apelin-13 treatment reduced bupivacaine-induced oxidative stress, attenuated mitochondrial morphological changes and mitochondrial DNA damage, enhanced mitochondrial energy metabolism, and ultimately reversed bupivacaine-induced cardiotoxicity. Our results suggest a role for the AMPK in apelin-13 reversal of bupivacaine-induced cardiotoxicity.

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center.

The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by 'virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research.

BACKGROUND: Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines. RESULTS: This paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks. CONCLUSIONS: Initial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.

Predicting tumor cell line response to drug pairs with deep learning.

BACKGROUND: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. RESULTS: We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. CONCLUSIONS: We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.

Levosimendan is superior to epinephrine on coronary flow for lipid-base resuscitation of bupivacaine-induced asystole in the isolated rat heart.

BACKGROUND: Successful resuscitation from asystole induced by bupivacaine requires the reestablishment of a sufficient coronary flow (CF) quickly. This study was designed to test whether levosimendan was superior to epinephrine in the reestablishment of crucial coronary flows after bupivacaine-induced asystole. METHODS: The isolated, perfused, nonrecirculating, Langendorff rat heart preparation was used. Bupivacaine 100 µmol/L was perfused into rat hearts to induce asystole, and then for 3 min thereafter. Three experimental groups were assessed after asystole with infusions as follow: (1) a mixture of 2% lipid emulsion and 40 µmol/L bupivacaine (control group), (2) a mixture of 0.15 µg/mL epinephrine combined with 2% lipid emulsion and 40 µmol/L bupivacaine (epinephrine group), and (3) a mixture of 5 µmol/L levosimendan combined with a 2% lipid emulsion and 40 µmol/L bupivacaine mixture (levosimendan group). Coronary flow (CF), the time to recovery (Trecovery), the number of ventricular arrhythmias, and cardiac function parameters were recorded for 40 min after heartbeat recovery. RESULTS: All hearts in the control, epinephrine and levosimendan groups had heartbeat recovery. The rank order of the mean CF from highest to lowest was the levosimendan group > the epinepgrine group > the control group (P < 0.05). The rank order of Trecovery from shortest to longest was the levosimendan group < the epinephrine group < the control group (P < 0.01). During the recovery phase, isolated rat hearts developed more ventricular arrhythmias in the epinephrine group than in the levosimendan group (P = 0.01). CONCLUSION: Levosimendan is superior to epinephrine in producing higher CFs and faster recovery when reversing bupivacaine-induced asystole in the isolated rat hearts.

Mutation in an Unannotated Protein Confers Carbapenem Resistance in Mycobacterium tuberculosis.

ß-Lactams are the most widely used antibacterials. Among ß-lactams, carbapenems are considered the last line of defense against recalcitrant infections. As recent developments have prompted consideration of carbapenems for treatment of drug-resistant tuberculosis, it is only a matter of time before Mycobacterium tuberculosis strains resistant to these drugs will emerge. In the present study, we investigated the genetic basis that confers such resistance. To our surprise, instead of mutations in the known ß-lactam targets, a single nucleotide polymorphism in the Rv2421c-Rv2422 intergenic region was common among M. tuberculosis mutants selected with meropenem or biapenem. We present data supporting the hypothesis that this locus harbors a previously unidentified gene that encodes a protein. This protein binds to ß-lactams, slowly hydrolyzes the chromogenic ß-lactam nitrocefin, and is inhibited by select penicillins and carbapenems and the ß-lactamase inhibitor clavulanate. The mutation results in a W62R substitution that reduces the protein's nitrocefin-hydrolyzing activity and binding affinities for carbapenems.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.

Lipid emulsion mitigates impaired pulmonary function induced by limb I/R in rats through attenuation of local cellular injury and the subsequent systemic inflammatory response/inflammation.

BACKGROUND: Limb ischemia/reperfusion causes inflammation and elicits oxidative stress that may lead to local tissue damage and remote organ such as lung injury. This study investigates pulmonary function after limb ischemia/reperfusion and the protective effect of a lipid emulsion (Intralipid). METHODS: Twenty-four rats were divided into three groups: sham operation group (group S), ischemia/reperfusion group (group IR), and lipid emulsion treatment group (group LE). limb ischemia/reperfusion was induced through occlusion of the infrarenal abdominal aorta for 3 h. The microvascular clamp was removed carefully and reperfusion was provided for 3 h. RESULTS: The mean arterial pressure in group LE was higher than group IR during the reperfusion period (P = 0.024). The heart rate of both group LE and IR are significantly higher than group S during the ischemia period(P < 0.001, P < 0.001, respectively). The arterial oxygen pressure of group LE was significantly higher than group IR (P = 0.003), the arterial carbon dioxide pressure of group LE were lower than that of group IR (P = 0.005). The concentration of plasma interleukin-6, tumor necrosis factor-α and malondialdehyde in group LE were significantly lower than group IR (P < 0.001, P = 0.009 and 0.029, respectively). The plasma superoxide dismutase activity in group LE was significantly higher than group IR (P = 0.029). The myeloperoxidase activity in lung tissues of group LE was significantly less than group IR (P = 0.046). Both muscle and lung in group IR were damaged seriously, whereas lipid emulsion (Intralipid) effectively reversed the damage. In summary, Intralipid administration resulted in several beneficial effects as compared to group IR, such as the pulmonary gas exchange and inflammatory. CONCLUSIONS: The ischemic/reperfusion injury of limb muscles with resultant inflammatory damage to lung tissue can be mitigated by administration of a lipid emulsion (Intralipid, 20%, 5 ml/kg). The mechanisms attenuating such a physiological may be attributed to reduction of the degree of limb injury through a decrease in the release of local inflammatory mediators, a reduction of lipid peroxidation, and a blunting of the subsequent remote inflammatory response.

Modeling central metabolism and energy biosynthesis across microbial life.

BACKGROUND: Automatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles. RESULTS: To overcome this challenge, we developed methods and tools ( http://coremodels.mcs.anl.gov ) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis. CONCLUSIONS: We predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.

Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models.

Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High-throughput annotation and metabolic modeling of these genomes is now a reality. The high-throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represent the next frontier in microbial bioinformatics. The fruition of this next frontier will depend on the integration of numerous data sources relating to mechanisms, components and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here, we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data and binding site locations and we explore how these data are being used for the reconstruction of new regulatory networks. From template network-based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. We also explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST).

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.