Identification of a single nucleotide polymorphism associated with adiposity following transcriptional profiling of gene expression in the anterior pituitary gland

Although the anterior pituitary secretes three hormones that affect metabolism and body fat stores, a comprehensive analysis of pituitary gene expression associated with body fat has not been performed. This research used cDNA microarrays to investigate pituitary gene expression in two chicken lines that were selected for low and high body fat (Lean and Fat). RNA was extracted from pituitaries at 1, 3, 5, and 7 weeks of age. 386 genes that showed significant differences in expression levels by line or in the line-by-age interaction were analyzed further. Differentially expressed genes between lines are potential candidates as genetic markers for high and low potential for body fat accumulation. One such candidate, the lysophosphatidic acid (LPA) receptor-1 (LPAR1), was identified as a potential marker, being differentially expressed between the 2 lines at the early ages. Genomic DNA from the Fat and Lean F0 generation was sequenced upstream of the LPAR1 coding region. A SNP consisting of a T to C transversion that introduces a GATA-1 transcription factor binding site was identified in the Lean line (Fisher's Exact Test, p ≤ 0.001). The fattest and leanest animals of both sexes in the back-crossed F2 generation (n=48 each) were genotyped by allele-specific PCR, and an association was present between the genotype and phenotype (generalized linear model, p ≤ 0.05). Expression of GATA transcription factors in mice inhibits differentiation of preadipocytes into mature adipocytes. LPAR1 also inhibits differentiation of preadipocytes in mice, and LPAR1 knock-out mice become significantly fatter than wild-type mice. A SNP that introduces a GATA site in the promoter of LPAR1 could up-regulate its expression in the Lean line, and increased LPA signaling could then inhibit preadipocyte differentiation. Conversely, loss of the GATA binding site could explain decreased levels of LPAR1 expression and attenuated inhibition of adipocyte maturation in the Fat line

Introduction

Introduction / Cathy Callaway -- Linguistics and lentil soup / Pamela A. Draper -- Eugene Lane and Ellis Library / Michael Muchow -- Gene Lane : Commitment to scholarship, teaching, and community / Robert A. Seelinger, Jr. -- Tabula Gratulatoria

PubRunner: a light-weight framework for updating text mining results

Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications

Comparative Analysis of Cervical Spine Management in a Subset of Severe Traumatic Brain Injury Cases Using Computer Simulation

BACKGROUND: No randomized control trial to date has studied the use of cervical spine management strategies in cases of severe traumatic brain injury (TBI) at risk for cervical spine instability solely due to damaged ligaments. A computer algorithm is used to decide between four cervical spine management strategies. A model assumption is that the emergency room evaluation shows no spinal deficit and a computerized tomogram of the cervical spine excludes the possibility of fracture of cervical vertebrae. The study's goal is to determine cervical spine management strategies that maximize brain injury functional survival while minimizing quadriplegia. METHODS/FINDINGS: The severity of TBI is categorized as unstable, high risk and stable based on intracranial hypertension, hypoxemia, hypotension, early ventilator associated pneumonia, admission Glasgow Coma Scale (GCS) and age. Complications resulting from cervical spine management are simulated using three decision trees. Each case starts with an amount of primary and secondary brain injury and ends as a functional survivor, severely brain injured, quadriplegic or dead. Cervical spine instability is studied with one-way and two-way sensitivity analyses providing rankings of cervical spine management strategies for probabilities of management complications based on QALYs. Early collar removal received more QALYs than the alternative strategies in most arrangements of these comparisons. A limitation of the model is the absence of testing against an independent data set. CONCLUSIONS: When clinical logic and components of cervical spine management are systematically altered, changes that improve health outcomes are identified. In the absence of controlled clinical studies, the results of this comparative computer assessment show that early collar removal is preferred over a wide range of realistic inputs for this subset of traumatic brain injury. Future research is needed on identifying factors in projecting awakening from coma and the role of delirium in these cases

NCBI’s virus discovery codeathon: building “FIVE” —the Federated Index of Viral Experiments API index

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus–host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE