A framework for inferring and analyzing pharmacotherapy treatment patterns.

BACKGROUND: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. METHODS: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. RESULTS: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. CONCLUSIONS: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.

Metabolic environments and genomic features associated with pathogenic and mutualistic interactions between bacteria and plants.

Genomic characteristics discriminating parasitic and mutualistic relationship of bacterial symbionts with plants are poorly understood. This study comparatively analyzed the genomes of 54 mutualists and pathogens to discover genomic markers associated with the different phenotypes. Using metabolic network models, we predict external environments associated with free-living and symbiotic lifestyles and quantify dependences of symbionts on the host in terms of the consumed metabolites. We show that specific differences between the phenotypes are pronounced at the levels of metabolic enzymes, especially carbohydrate active, and protein functions. Overall, biosynthetic functions are enriched and more diverse in plant mutualists whereas processes and functions involved in degradation and host invasion are enriched and more diverse in pathogens. A distinctive characteristic of plant pathogens is a putative novel secretion system with a circadian rhythm regulator. A specific marker of plant mutualists is the co-residence of genes encoding nitrogenase and ribulose bisphosphate carboxylase/oxygenase (RuBisCO). We predict that RuBisCO is likely used in a putative metabolic pathway to supplement carbon obtained heterotrophically with low-cost assimilation of carbon from CO2. We validate results of the comparative analysis by predicting correct phenotype, pathogenic or mutualistic, for 20 symbionts in an independent set of 30 pathogens, mutualists, and commensals.

Analyzing large biological datasets with association networks.

Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.

BESC knowledgebase public portal.

UNLABELLED: The BioEnergy Science Center (BESC) is undertaking large experimental campaigns to understand the biosynthesis and biodegradation of biomass and to develop biofuel solutions. BESC is generating large volumes of diverse data, including genome sequences, omics data and assay results. The purpose of the BESC Knowledgebase is to serve as a centralized repository for experimentally generated data and to provide an integrated, interactive and user-friendly analysis framework. The Portal makes available tools for visualization, integration and analysis of data either produced by BESC or obtained from external resources. AVAILABILITY: http://besckb.ornl.gov.

A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry.

BACKGROUND: High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms. RESULTS: In this study, a new de novo sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of Rhodopseudomonas palustris. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of de novo sequenced spectra and the sequencing accuracy. CONCLUSIONS: Here, we improved de novo sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at http://compbio.ornl.gov/Vonode.

CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database.

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

Conserved synteny at the protein family level reveals genes underlying Shewanella species' cold tolerance and predicts their novel phenotypes.

Bacteria of the genus Shewanella can thrive in different environments and demonstrate significant variability in their metabolic and ecophysiological capabilities including cold and salt tolerance. Genomic characteristics underlying this variability across species are largely unknown. In this study, we address the problem by a comparison of the physiological, metabolic, and genomic characteristics of 19 sequenced Shewanella species. We have employed two novel approaches based on association of a phenotypic trait with the number of the trait-specific protein families (Pfam domains) and on the conservation of synteny (order in the genome) of the trait-related genes. Our first approach is top-down and involves experimental evaluation and quantification of the species' cold tolerance followed by identification of the correlated Pfam domains and genes with a conserved synteny. The second, a bottom-up approach, predicts novel phenotypes of the species by calculating profiles of each Pfam domain among their genomes and following pair-wise correlation of the profiles and their network clustering. Using the first approach, we find a link between cold and salt tolerance of the species and the presence in the genome of a Na(+)/H(+) antiporter gene cluster. Other cold-tolerance-related genes include peptidases, chemotaxis sensory transducer proteins, a cysteine exporter, and helicases. Using the bottom-up approach, we found several novel phenotypes in the newly sequenced Shewanella species, including degradation of aromatic compounds by an aerobic hybrid pathway in Shewanella woodyi, degradation of ethanolamine by Shewanella benthica, and propanediol degradation by Shewanella putrefaciens CN32 and Shewanella sp. W3-18-1.

Application Health Monitoring for Extreme-scale Resiliency using Cooperative Fault Management.

Resiliency is and will be a critical factor in determining scientific productivity on current and exascale supercomputers, and beyond. Applications oblivious to and incapable of handling transient soft and hard errors could waste supercomputing resources or, worse, yield misleading scientific insights. We introduce a novel application-driven silent error detection and recovery strategy based on application health monitoring. Our methodology uses application output that follows known patterns as indicators of an application's health, and knowledge that violation of these patterns could be indication of faults. Information from system monitors that report hardware and software health status is used to corroborate faults. Collectively, this information is used by a fault coordinator agent to take preventive and corrective measures by applying computational steering to an application between checkpoints. This cooperative fault management system uses the Fault Tolerance Backplane as a communication channel. The benefits of this framework are demonstrated with two real application case studies, molecular dynamics and quantum chemistry simulations, on scalable clusters with simulated memory and I/O corruptions. The developed approach is general and can be easily applied to other applications.

Oxidative stress increases the risk of pancreatic ß cell damage in chronic renal hypertensive rats.

Hypertension often occurs in conjunction with insulin resistance. The purpose of this study was to evaluate whether sustained renal hypertension increases the risk of diabetes mellitus in rats, and to define the underlying mechanisms. Two-kidney, one-clip hypertensive (2K1C) rats received captopril (50 mg/kg/day), α-lipoic acid (100 mg/kg/day), or vehicle treatment for 3 months after surgery. Blood pressure was measured by tail cuff plethysmography. Oral glucose tolerance test (OGTT), immunohistochemistry, and western blotting were performed. In addition, insulin secretion from islet cells was measured. OGTT yielded abnormal results, and the number of islet cells and the size of pancreatic ß/α cells were decreased in 2K1C rats. Basal insulin levels were also reduced in the plasma. Insulin secretion from pancreatic islet cells in response to high glucose was also attenuated in 2K1C rats compared with sham rats. The levels of oxidative stress markers, including 8-hydroxydeoxyguanosine and NADPH oxidase-4, were increased in pancreatic tissue and pancreatic islets in 2K1C rats. The abnormalities observed in 2K1C rats were improved by captopril or α-lipoic acid treatment. These findings indicate that sustained renal hypertension may lead to pancreatic dysfunction, increasing oxidative stress in pancreatic islets.

Shewanella knowledgebase: integration of the experimental data and computational predictions suggests a biological role for transcription of intergenic regions.

Shewanellae are facultative gamma-proteobacteria whose remarkable respiratory versatility has resulted in interest in their utility for bioremediation of heavy metals and radionuclides and for energy generation in microbial fuel cells. Extensive experimental efforts over the last several years and the availability of 21 sequenced Shewanella genomes made it possible to collect and integrate a wealth of information on the genus into one public resource providing new avenues for making biological discoveries and for developing a system level understanding of the cellular processes. The Shewanella knowledgebase was established in 2005 to provide a framework for integrated genome-based studies on Shewanella ecophysiology. The present version of the knowledgebase provides access to a diverse set of experimental and genomic data along with tools for curation of genome annotations and visualization and integration of genomic data with experimental data. As a demonstration of the utility of this resource, we examined a single microarray data set from Shewanella oneidensis MR-1 for new insights into regulatory processes. The integrated analysis of the data predicted a new type of bacterial transcriptional regulation involving co-transcription of the intergenic region with the downstream gene and suggested a biological role for co-transcription that likely prevents the binding of a regulator of the upstream gene to the regulator binding site located in the intergenic region. Database URL: http://shewanella-knowledgebase.org:8080/Shewanella/ or http://spruce.ornl.gov:8080/Shewanella/

Increased expression and activity of 11beta-HSD-1 in diabetic islets and prevention with troglitazone.

To determine if increased local production of glucocorticoids by the pancreatic islets might play a role in the spontaneous noninsulin-dependent diabetes mellitus of obesity, we compared islet 11beta-HSD-1 mRNA and activity in islets of obese prediabetic and diabetic Zucker Diabetic Fatty (ZDF) (fa/fa) rats and lean wild-type (+/+) controls. In diabetic rat islets, both mRNA and enzymatic activity of the enzyme were increased in proportion to the hyperglycemia. Troglitazone (TGZ) treatment, beginning at 6 weeks of age, prevented the hyperglycemia, the hyperlipidemia, and the increase in 11beta-HSD-1. To determine if the metabolic abnormalities had caused the 11beta-HSD-1 increase, prediabetic islets were cultured in high or low glucose or in 2:1 oleate:palmitate for 3 days. Neither nutrient enhanced the expression of 11beta-HSD-1. We conclude that 11beta-HSD-1 expression and activity are increased in islets of diabetic, but not prediabetic ZDF rats, and that TGZ prevents both the increase in 11beta-HSD-1 and the diabetes.