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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

DataSheet_1_On-farm harvest timing effects on alfalfa weevil across the Intermountain West region of the United States.pdf

Alfalfa (Medicago sativa L.) is an economically important commodity in the Intermountain Western United States. A major concern for alfalfa producers in this region is the alfalfa weevil (Hypera postica Gyllenhal). Insecticide resistance development coupled with regulatory changes in pesticide use has resulted in renewed interest by producers in non-chemical control methods such as cultural control. One such cultural control method is early harvest, which consists of producers timing their harvests early in the season to decrease alfalfa weevil damage. This method is thought to be effective by exposing weevil larvae to adverse conditions before significant damage occurs. Still, early harvest can be difficult to employ because recommendations are often vague. To better understand how early harvest impacts both alfalfa weevils and their natural enemies and how producers are using this method across the Intermountain Western United States, we conducted a study in alfalfa production fields in Colorado, Montana, and Wyoming over three growing seasons. We determined that the timing of the initial alfalfa harvest spanned more than 1 month across fields, and alfalfa plant stage at harvest ranged from late vegetative to early bloom. Harvest was more impactful on reducing alfalfa weevil densities the earlier it was implemented. Removing windrows in a timely manner is likely useful to further decrease alfalfa weevil densities. Harvest timing was not associated with parasitism rates of alfalfa weevil, but higher parasitism rates were associated with lower post-harvest alfalfa weevil densities. This work has increased our understanding of early harvest in an on-farm setting and to improve recommendations for producers across the Intermountain Western United States.</p

GC-MS Metabolomics to Evaluate the Composition of Plant Cuticular Waxes for Four Triticum aestivum Cultivars

Wheat (Triticum aestivum L.) is an important food crop, and biotic and abiotic stresses significantly impact grain yield. Wheat leaf and stem surface waxes are associated with traits of biological importance, including stress resistance. Past studies have characterized the composition of wheat cuticular waxes, however protocols can be relatively low-throughput and narrow in the range of metabolites detected. Here, gas chromatography-mass spectrometry (GC-MS) metabolomics methods were utilized to provide a comprehensive characterization of the chemical composition of cuticular waxes in wheat leaves and stems. Further, waxes from four wheat cultivars were assayed to evaluate the potential for GC-MS metabolomics to describe wax composition attributed to differences in wheat genotype. A total of 263 putative compounds were detected and included 58 wax compounds that can be classified (e.g., alkanes and fatty acids). Many of the detected wax metabolites have known associations to important biological functions. Principal component analysis and ANOVA were used to evaluate metabolite distribution, which was attributed to both tissue type (leaf, stem) and cultivar differences. Leaves contained more primary alcohols than stems such as 6-methylheptacosan-1-ol and octacosan-1-ol. The metabolite data were validated using scanning electron microscopy of epicuticular wax crystals which detected wax tubules and platelets. Conan was the only cultivar to display alcohol-associated platelet-shaped crystals on its abaxial leaf surface. Taken together, application of GC-MS metabolomics enabled the characterization of cuticular wax content in wheat tissues and provided relative quantitative comparisons among sample types, thus contributing to the understanding of wax composition associated with important phenotypic traits in a major crop

Phylogeography of the Wheat Stem Sawfly, Cephus cinctus Norton (Hymenoptera: Cephidae): Implications for Pest Management.

The wheat stem sawfly, Cephus cinctus Norton (Hymenoptera: Cephidae), is a key pest of wheat in the northern Great Plains of North America, and damage resulting from this species has recently expanded southward. Current pest management practices are inadequate and uncertainty regarding geographic origin, as well as limited data on population structure and dynamics across North America impede progress towards more informed management. We examined the genetic divergence between samples collected in North America and northeastern Asia, the assumed native range of C. cinctus using two mitochondrial regions (COI and 16S). Subsequently, we characterized the structure of genetic diversity in the main wheat producing areas in North America using a combination of mtDNA marker and microsatellites in samples collected both in wheat fields and in grasses in wildlands. The strong genetic divergence observed between North American samples and Asian congeners, in particular the synonimized C. hyalinatus, did not support the hypothesis of a recent American colonization by C. cinctus. Furthermore, the relatively high genetic diversity both with mtDNA and microsatellite markers offered additional evidence in favor of the native American origin of this pest. The genetic diversity of North American populations is structured into three genetic clusters and these are highly correlated with geography. Regarding the recent southern outbreaks in North America, the results tend to exclude the hypothesis of recent movement of damaging wheat stem sawfly populations from the northern area. The shift in host plant use by local populations appears to be the most likely scenario. Finally, the significance of these findings is discussed in the context of pest management

Indices of genetic diversity per clusters identified in SAMOVA, Tajima’s <i>D</i> and Fu’s <i>Fs</i> statistics.

<p>Indices of genetic diversity per clusters identified in SAMOVA, Tajima’s <i>D</i> and Fu’s <i>Fs</i> statistics.</p

Phylogenetic relationships and genetic clustering <i>Cephus cinctus</i> across North America using a mitochondrial marker (<i>COI</i>).

<p>A. <i>COI</i> mitochondrial network of the North American <i>Cephus cinctus</i>. Each circle corresponds to one haplotype; circle size gives the proportion of individuals belonging to the haplotype. The color inside each circle represents the host and indicates the proportion of individuals sampled in the different hosts. Each link between circles indicates one mutational event. Black circles represent missing intermediate haplotypes. B. Geographic locations of <i>Cephus cinctus</i> samples used in the mtDNA phylogeography and distribution of <i>COI</i> haplogroups. Pie chart sizes are proportional to sample size and each haplogroup is colored according to the results of Bayesian tree and haplotype network. Dotted white lines represent the three groups identified by the SAMOVA.</p

Bayesian phylogenetic tree from analysis of the combined mtDNA dataset.

<p>Posterior probabilities associated with major nodes are indicated in bold. Branch lengths represent expected substitutions per site. The scale bar indicates the expected number of substitutions per site. Results of the PTP analysis are provided using colored branches. Monophyletic groups in red indicate a single putative species as well as terminal branches in black. Names of terminals indicate codes of the samples and the GenBank number follows the names when sequences were obtained from Genbank. Names in bold after a | symbol are taxonomic or geographic identifiers of the putative species.</p

Genetic clustering of <i>Cephus cinctus</i> across North America using 5 microsatellites.

<p>A. Neighbor-joining tree based on the chord distances of Cavalli-Sforza & Edwards (1967) computed between the sampled populations. B. Above. Graphical representation of population genetic structure estimated by the Bayesian clustering approach implemented in Structure software. Provinces / states are indicated above the plots whereas sampling localities below. Each individual is represented by a vertical line and the proportion of each color corresponds to the percentage of coancestry in each genetic cluster. B. Below. Map of collections in the North America. Each location is represented by a pie chart showing ancestry (<i>Q</i>) among three genetic clusters as determined by analysis with Structure. Pie chart sizes are proportional to sample size. C. Principal coordinate analysis (PCoA) plot based on the genetic distance among the different populations. Each population is colored according to the Structure clustering (<i>K</i> = 3).</p