Finishing the euchromatic sequence of the human genome

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

Ring-polymer molecular dynamics: Rate coefficient calculations for energetically symmetric (near thermoneutral) insertion reactions (X + H[subscript 2]) → HX + H(X = C([superscript 1]D), S([superscript 1]D))

Following our previous study of prototypical insertion reactions of energetically asymmetric type with the RPMD (Ring-Polymer Molecular Dynamics) method [Y. Li, Y. Suleimanov, and H. Guo, J. Phys. Chem. Lett. 5, 700 (2014)], we extend it to two other prototypical insertion reactions with much less exothermicity (near thermoneutral), namely, X + H[subscript 2] → HX + H where X = C([superscript 1] D), S([superscript 1] D), in order to assess the accuracy of this method for calculating thermal rate coefficients for this class of reactions. For both chemical reactions, RPMD displays remarkable accuracy and agreement with the previous quantum dynamic results that make it encouraging for the future application of the RPMD to other barrier-less, complex-forming reactions involving polyatomic reactants with any exothermicity.United States. Dept. of Energy. Office of Basic Energy Sciences (Massachusetts Institute of Technology. Energy Frontier Research Center for Excitonics. Combustion Energy Frontier Research Center. Award DE-SC0001198)MIT Energy Initiativ

<i>MsHDZ23,</i> a Novel <i>Miscanthus HD-ZIP</i> Transcription Factor, Participates in Tolerance to Multiple Abiotic Stresses

The homeodomain-leucine zipper (HD-ZIP) transcription factors, representing one of the largest plant-specific superfamilies, play important roles in the response to various abiotic stresses. However, the functional roles of HD-ZIPs in abiotic stress tolerance and the underlying mechanisms remain relatively limited in Miscanthus sinensis. In this study, we isolated an HD-ZIP TF gene, MsHDZ23, from Miscanthus and ectopically expressed it in Arabidopsis. Transcriptome and promoter analyses revealed that MsHDZ23 responded to salt, alkali, and drought treatments. The overexpression (OE) of MsHDZ23 in Arabidopsis conferred higher tolerance to salt and alkali stresses compared to wild-type (WT) plants. Moreover, MsHDZ23 was able to restore the hb7 mutant, the ortholog of MsHDZ23 in Arabidopsis, to the WT phenotype. Furthermore, MsHDZ23-OE lines exhibited significantly enhanced drought stress tolerance, as evidenced by higher survival rates and lower water loss rates compared to WT. The improved drought tolerance may be attributed to the significantly smaller stomatal aperture in MsHDZ23-OE lines compared to WT. Furthermore, the accumulation of the malondialdehyde (MDA) under abiotic stresses was significantly decreased, accompanied by dramatically enhanced activities in several antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in the transgenic plants. Collectively, these results demonstrate that MsHDZ23 functions as a multifunctional transcription factor in enhancing plant resistance to abiotic stresses

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine