49 research outputs found

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

    Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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    Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

    Uncovering sensory axonal dysfunction in asymptomatic type 2 diabetic neuropathy.

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    This study investigated sensory and motor nerve excitability properties to elucidate the development of diabetic neuropathy. A total of 109 type 2 diabetes patients were recruited, and 106 were analyzed. According to neuropathy severity, patients were categorized into G0, G1, and G2+3 groups using the total neuropathy score-reduced (TNSr). Patients in the G0 group were asymptomatic and had a TNSr score of 0. Sensory and motor nerve excitability data from diabetic patients were compared with data from 33 healthy controls. Clinical assessment, nerve conduction studies, and sensory and motor nerve excitability testing data were analyzed to determine axonal dysfunction in diabetic neuropathy. In the G0 group, sensory excitability testing revealed increased stimulus for the 50% sensory nerve action potential (P<0.05), shortened strength-duration time constant (P<0.01), increased superexcitability (P<0.01), decreased subexcitability (P<0.05), decreased accommodation to depolarizing current (P<0.01), and a trend of decreased accommodation to hyperpolarizing current in threshold electrotonus. All the changes progressed into G1 (TNSr 1-8) and G2+3 (TNSr 9-24) groups. In contrast, motor excitability only had significantly increased stimulus for the 50% compound motor nerve action potential (P<0.01) in the G0 group. This study revealed that the development of axonal dysfunction in sensory axons occurred prior to and in a different fashion from motor axons. Additionally, sensory nerve excitability tests can detect axonal dysfunction even in asymptomatic patients. These insights further our understanding of diabetic neuropathy and enable the early detection of sensory axonal abnormalities, which may provide a basis for neuroprotective therapeutic approaches

    Differences in biophysical properties of human peripheral nerves

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    Comparison of sensory and motor nerve excitability parameters between groups.

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    <p>Comparison of sensory and motor nerve excitability parameters between groups.</p

    Patient demographics and clinical and electrophysiological profiles.

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    <p>Patient demographics and clinical and electrophysiological profiles.</p

    Progression of diabetic neuropathy from pathophysiologic, symptomatologic, and nerve excitability viewpoints.

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    <p><b>(A)</b> Pathogenesis of diabetic neuropathy typically progresses from metabolic alteration, to ion current defect, and then the development of further structural and functional defects. <b>(B)</b> Both positive and negative clinical signs/symptoms would also progress in extent and severity as diabetic neuropathy worsens. <b>(C)</b> Sensory excitability changes, reflecting sensory axonal dysfunction, could be detected even in asymptomatic patients. Superexcitability, subexcitability, SDTC, and TEd parameter changes progress over the course of diabetic neuropathy, and eventually the peak response decreases, reflecting axonal loss. <b>(D)</b> Motor excitability changes in superexcitability, SDTC, TEh, and TEd parameters could be detected in later stages of diabetic neuropathy compared to sensory axons.</p

    Summary of conventional nerve conduction study results.

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    <p>Summary of conventional nerve conduction study results.</p

    Correlation analysis in patients without clinically relevant neuropathy (n = 78).

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    <p><b>(A)</b> Correlation between sensory superexcitability and HbA1c level. <b>(B)</b> Correlation between motor subexcitability and HbA1c level. <b>(C)</b> Correlation between sensory and motor superexcitability parameters. <b>(D)</b> Correlation between sensory and motor subexcitability parameters.</p
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