Effects of vitamin A deficiency in the postnatal mouse heart: role of hepatic retinoid stores.

To determine whether hepatic depletion of vitamin A (VA) stores has an effect on the postnatal heart, studies were carried out with mice lacking liver retinyl ester stores fed either a VA-sufficient (LRVAS) or VA-deficient (LRVAD) diet (to deplete circulating retinol and extrahepatic stores of retinyl esters). There were no observable differences in the weights or gross morphology of hearts from LRVAS or LRVAD mice relative to sex-matched, age-matched, and genetically matched wild-type (WT) controls fed the VAS diet (WTVAS), but changes in the transcription of functionally relevant genes were consistent with a state of VAD in LRVAS and LRVAD ventricles. In silico analysis revealed that 58/67 differentially expressed transcripts identified in a microarray screen are products of genes that have DNA retinoic acid response elements. Flow cytometric analysis revealed a significant and cell-specific increase in the number of proliferating Sca-1 cardiac progenitor cells in LRVAS animals relative to WTVAS controls. Before myocardial infarction, LRVAS and WTVAS mice had similar cardiac systolic function and structure, as measured by echocardiography, but, unexpectedly, repeat echocardiography demonstrated that LRVAS mice had less adverse remodeling by 1 wk after myocardial infarction. Overall, the results demonstrate that the adult heart is responsive to retinoids, and, most notably, reducing hepatic VA stores (while maintaining circulating levels of VA) impacts ventricular gene expression profiles, progenitor cell numbers, and response to injury.

MDI Biological Laboratory Arsenic Summit: Approaches to Limiting Human Exposure to Arsenic.

This report is the outcome of the meeting "Environmental and Human Health Consequences of Arsenic" held at the MDI Biological Laboratory in Salisbury Cove, Maine, August 13-15, 2014. Human exposure to arsenic represents a significant health problem worldwide that requires immediate attention according to the World Health Organization (WHO). One billion people are exposed to arsenic in food, and more than 200 million people ingest arsenic via drinking water at concentrations greater than international standards. Although the US Environmental Protection Agency (EPA) has set a limit of 10 µg/L in public water supplies and the WHO has recommended an upper limit of 10 µg/L, recent studies indicate that these limits are not protective enough. In addition, there are currently few standards for arsenic in food. Those who participated in the Summit support citizens, scientists, policymakers, industry, and educators at the local, state, national, and international levels to (1) establish science-based evidence for setting standards at the local, state, national, and global levels for arsenic in water and food; (2) work with government agencies to set regulations for arsenic in water and food, to establish and strengthen non-regulatory programs, and to strengthen collaboration among government agencies, NGOs, academia, the private sector, industry, and others; (3) develop novel and cost-effective technologies for identification and reduction of exposure to arsenic in water; (4) develop novel and cost-effective approaches to reduce arsenic exposure in juice, rice, and other relevant foods; and (5) develop an Arsenic Education Plan to guide the development of science curricula as well as community outreach and education programs that serve to inform students and consumers about arsenic exposure and engage them in well water testing and development of remediation strategies.

Common features of microRNA target prediction tools.

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

No-boundary thinking in bioinformatics research.

Currently there are definitions from many agencies and research societies defining "bioinformatics" as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT).

An Exploration Into Improving DNA Motif Inference by Looking for Highly Conserved Core Regions.

Although most verified functional elements in noncoding DNA contain a highly conserved core region, this concept is not generally incorporated into de novo motif inference systems. In this work, we explore the utility of adding the notion of conserved core regions into a comparative genomics approach for the search for putative functional elements in noncoding DNA. By modifying the scoring function for GAMI, Genetic Algorithms for Motif Inference, we investigate tradeoffs between the strength of conservation of the full motif vs. the strength of conservation of a core region. This work illustrates that incorporating information about the structure of transcription factor binding sites can be helpful in identifying biologically functional elements.

Bioinformatic prediction of arthropod/nematode-like peptides in non-arthropod, non-nematode members of the Ecdysozoa.

The Onychophora, Priapulida and Tardigrada, along with the Arthropoda, Nematoda and several other small phyla, form the superphylum Ecdysozoa. Numerous peptidomic studies have been undertaken for both the arthropods and nematodes, resulting in the identification of many peptides from each group. In contrast, little is known about the peptides used as paracrines/hormones by species from the other ecdysozoan taxa. Here, transcriptome mining and bioinformatic peptide prediction were used to identify peptides in members of the Onychophora, Priapulida and Tardigrada, the only non-arthropod, non-nematode members of the Ecdysozoa for which there are publicly accessible expressed sequence tags (ESTs). The extant ESTs for each phylum were queried using 106 arthropod/nematode peptide precursors. Transcripts encoding calcitonin-like diuretic hormone and pigment-dispersing hormone (PDH) were identified for the onychophoran Peripatopsis sedgwicki, with transcripts encoding C-type allatostatin (C-AST) and FMRFamide-like peptide identified for the priapulid Priapulus caudatus. For the Tardigrada, transcripts encoding members of the A-type allatostatin, C-AST, insect kinin, orcokinin, PDH and tachykinin-related peptide families were identified, all but one from Hypsibius dujardini (the exception being a Milnesium tardigradum orcokinin-encoding transcript). The proteins deduced from these ESTs resulted in the prediction of 48 novel peptides, six onychophoran, eight priapulid and 34 tardigrade, which are the first described from these phyla.

An evaluation of information content as a metric for the inference of putative conserved noncoding regions in DNA sequences using a genetic algorithms approach.

In previous work, we presented GAMI, an approach to motif inference that uses a genetic algorithms search. GAMI is designed specifically to find putative conserved regulatory motifs in noncoding regions of divergent species, and is designed to allow for analysis of long nucleotide sequences. In this work, we compare GAMI's performance when run with its original fitness function (a simple count of the number of matches) and when run with information content, as well as several variations on these metrics. Results indicate that information content does not identify highly conserved regions, and thus is not the appropriate metric for this task, while variations on information content as well as the original metric succeed in identifying putative conserved regions.

Estimating patient's length of stay in the Emergency Department with an artificial neural network.

Predicting a patient's expected length of stay for an Emergency Department encounter is valuable to anticipate impending operational bottlenecks that may lead to diversion. We developed and validated an artificial neural network using data from >16,000 patients using clinical and operational parameters that are commonly available early during an encounter. Performance on the training set predicted length of stay within an average of 2 hours (sigmae2<500), but declined to an average of 7.5 hours (sigmae2<6000) in the validation set. Chief complaint specific trials using the most frequent chief complaints, however, predicted within an average of 3.5 hours (sigmae2 <145), with similar validation.