Ronald C. Taylor

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics

BackgroundBioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce.DescriptionAn overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date.ConclusionsHadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.

Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE)

One purpose of the biomedical literature is to report results in sufficient detail that the methods of data collection and analysis can be independently replicated and verified. Here we present reporting guidelines for gene expression localization experiments: the minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). MISFISHIE is modeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarray experiments. Both guidelines define what information should be reported without dictating a format for encoding that information. MISFISHIE describes six types of information to be provided for each experiment: experimental design, biomaterials and treatments, reporters, staining, imaging data and image characterizations. This specification has benefited the consortium within which it was developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal.

Reverse Engineering Adverse Outcome Pathways

The toxicological effects of many stressors are mediated through unknown, or incompletely characterized, mechanisms of action. The application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) can be used to overcome these limitations. This approach was used to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows (FHM, Pimephales promelas). Gene expression changes in FHM ovaries in response to seven different chemicals, over different times, doses, and in vivo versus in vitro conditions, were captured in a large data set of 868 arrays. Potential AOPs of the antiandrogen flutamide were examined using two mutual information-based methods to infer gene regulatory networks and potential AOPs. Representative networks from these studies were used to predict network paths from stressor to adverse outcome as candidate AOPs. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment, thus leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biological processes, biomarkers, or alternative endpoints that can be used to monitor an AOP. Finally, the unique challenges facing the application of this approach in ecotoxicology were identified and a road map for the utilization of these tools presented.

The Toxoplasma gondii cyst wall protein CST1 is critical for cyst wall integrity and promotes bradyzoite persistence

Toxoplasma gondii infects up to one third of the worlds population. A key to the success of T. gondii as a parasite is its ability to persist for the life of its host as bradyzoites within tissue cysts. The glycosylated cyst wall is the key structural feature that facilitates persistence and oral transmission of this parasite. Because most of the antibodies and reagents that recognize the cyst wall recognize carbohydrates, identification of the components of the cyst wall has been technically challenging. We have identified CST1 (TGME49_064660) as a 250 kDa SRS (SAG1 related sequence) domain protein with a large mucin-like domain. CST1 is responsible for the Dolichos biflorus Agglutinin (DBA) lectin binding characteristic of T. gondii cysts. Deletion of CST1 results in reduced cyst number and a fragile brain cyst phenotype characterized by a thinning and disruption of the underlying region of the cyst wall. These defects are reversed by complementation of CST1. Additional complementation experiments demonstrate that the CST1-mucin domain is necessary for the formation of a normal cyst wall structure, the ability of the cyst to resist mechanical stress, and binding of DBA to the cyst wall. RNA-seq transcriptome analysis demonstrated dysregulation of bradyzoite genes within the various cst1 mutants. These results indicate that CST1 functions as a key structural component that confers essential sturdiness to the T. gondii tissue cyst critical for persistence of bradyzoite forms.

Evidence supporting dissimilatory and assimilatory lignin degradation in Enterobacter lignolyticus SCF1

Lignocellulosic biofuels are promising as sustainable alternative fuels, but lignin inhibits access of enzymes to cellulose, and by-products of lignin degradation can be toxic to cells. The fast growth, high efficiency and specificity of enzymes employed in the anaerobic litter deconstruction carried out by tropical soil bacteria make these organisms useful templates for improving biofuel production. The facultative anaerobe Enterobacter lignolyticus SCF1 was initially cultivated from Cloud Forest soils in the Luquillo Experimental Forest in Puerto Rico, based on anaerobic growth on lignin as sole carbon source. The source of the isolate was tropical forest soils that decompose litter rapidly with low and fluctuating redox potentials, where bacteria using oxygen-independent enzymes likely play an important role in decomposition. We have used transcriptomics and proteomics to examine the observed increased growth of SCF1 grown on media amended with lignin compared to unamended growth. Proteomics suggested accelerated xylose uptake and metabolism under lignin-amended growth, with up-regulation of proteins involved in lignin degradation via the 4-hydroxyphenylacetate degradation pathway, catalase/peroxidase enzymes, and the glutathione biosynthesis and glutathione S-transferase (GST) proteins. We also observed increased production of NADH-quinone oxidoreductase, other electron transport chain proteins, and ATP synthase and ATP-binding cassette (ABC) transporters. This suggested the use of lignin as terminal electron acceptor. We detected significant lignin degradation over time by absorbance, and also used metabolomics to demonstrate moderately significant decreased xylose concentrations as well as increased metabolic products acetate and formate in stationary phase in lignin-amended compared to unamended growth conditions. Our data show the advantages of a multi-omics approach toward providing insights as to how lignin may be used in nature by microorganisms coping with poor carbon availability.

Data Standards for Omics Data: The Basis of Data Sharing and Reuse

To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data.

SEBINI: Software Environment for BIological Network Inference

UNLABELLED The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. It also allows the analysis within the same framework of experimental high-throughput expression data using the suite of (trained) inference methods; hence SEBINI should be useful to software developers wishing to evaluate, compare, refine or combine inference techniques, and to bioinformaticians analyzing experimental data. SEBINI provides a platform that aids in more accurate reconstruction of biological networks, with less effort, in less time. AVAILABILITY A demonstration website is located at https://www.emsl.pnl.gov/NIT/NIT.html. The Java source code and PostgreSQL database schema are available freely for non-commercial use.

The highly conserved MraZ protein is a transcriptional regulator in Escherichia coli

The mraZ and mraW genes are highly conserved in bacteria, both in sequence and in their position at the head of the division and cell wall (dcw) gene cluster. Located directly upstream of the mraZ gene, the Pmra promoter drives the transcription of mraZ and mraW, as well as many essential cell division and cell wall genes, but no regulator of Pmra has been found to date. Although MraZ has structural similarity to the AbrB transition state regulator and the MazE antitoxin and MraW is known to methylate the 16S rRNA, mraZ and mraW null mutants have no detectable phenotypes. Here we show that overproduction of Escherichia coli MraZ inhibited cell division and was lethal in rich medium at high induction levels and in minimal medium at low induction levels. Co-overproduction of MraW suppressed MraZ toxicity, and loss of MraW enhanced MraZ toxicity, suggesting that MraZ and MraW have antagonistic functions. MraZ-green fluorescent protein localized to the nucleoid, suggesting that it binds DNA. Consistent with this idea, purified MraZ directly bound a region of DNA containing three direct repeats between Pmra and the mraZ gene. Excess MraZ reduced the expression of an mraZ-lacZ reporter, suggesting that MraZ acts as a repressor of Pmra, whereas a DNA-binding mutant form of MraZ failed to repress expression. Transcriptome sequencing (RNA-seq) analysis suggested that MraZ also regulates the expression of genes outside the dcw cluster. In support of this, purified MraZ could directly bind to a putative operator site upstream of mioC, one of the repressed genes identified by RNA-seq.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction

Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016.