l e t t e r sHow an insect evolves to become a successful herbivore is of profound biological and practical importance. Herbivores are often adapted to feed on a specific group of evolutionarily and biochemically related host plants 1 , but the genetic and molecular bases for adaptation to plant defense compounds remain poorly understood 2 . We report the first whole-genome sequence of a basal lepidopteran species, Plutella xylostella, which contains 18,071 protein-coding and 1,412 unique genes with an expansion of gene families associated with perception and the detoxification of plant defense compounds. A recent expansion of retrotransposons near detoxification-related genes and a wider system used in the metabolism of plant defense compounds are shown to also be involved in the development of insecticide resistance. This work shows the genetic and molecular bases for the evolutionary success of this worldwide herbivore and offers wider insights into insect adaptation to plant feeding, as well as opening avenues for more sustainable pest management.The global pest P. xylostella (Lepidoptera: Yponomeutidae) is thought to have coevolved with the crucifer plant family 3 ( Supplementary Fig. 1) and has become the most destructive pest of economically important food crops, including rapeseed, cauliflower and cabbage 4 . Recently, the total cost of damage and management worldwide was estimated at $4-5 billion per annum 5,6 . This insect is the first species to have evolved resistance to dichlorodiphenyltrichloroethane (DDT) in the 1950s 7 and to Bacillus thuringiensis (Bt) toxins in the 1990s 8 and has developed resistance to all classes of insecticide, making it increasingly difficult to control 9,10 . P. xylostella provides an exceptional system for understanding the genetic and molecular bases of how insect herbivores cope with the broad range of plant defenses and chemicals encountered in the environment (Supplementary Fig. 2).We used a P. xylostella strain (Fuzhou-S) collected from a field in Fuzhou in southeastern China (26.08 °N, 119.28 °E) for sequencing ( Supplementary Fig. 1). Whole-genome shotgun-based Illumina sequencing of single individuals (Supplementary Table 1), even after ten generations of laboratory inbreeding, resulted in a poor initial assembly (N50 = 2.4 kb, representing the size above which 50% of the total length of the sequences is included), owing to high levels of heterozygosity ( Supplementary Figs. 3 and 4 and Supplementary Table 2). Subsequently, we sequenced 100,800 fosmid clones (comprising ~10× the genome length) to a depth of 200× ( Supplementary Fig. 5 and Supplementary Tables 3-5), assembling the resulting sequence data into 1,819 scaffolds, with an N50 of 737 kb, spanning ~394 Mb of the genome sequence (version 1; Supplementary Fig. 6 and Supplementary Table 6). The assembly covered 85.5% of a set of protein-coding ESTs (Supplementary Tables 7 and 8) generated by transcriptome sequencing 11 . Alignment of a subject scaffold against a 126-kb BAC (GenBank GU058050) from an altern...

Background Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. Methods In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. Results The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846–.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790–.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. Conclusions Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

Many long noncoding RNAs (lncRNAs) are unstable and rapidly degraded in the nucleus by the nuclear exosome. An exosome adaptor complex called NEXT (nuclear exosome targeting) functions to facilitate turnover of some of these lncRNAs. Here we show that knockdown of one NEXT subunit, Mtr4, but neither of the other two subunits, resulted in accumulation of two types of lncRNAs: prematurely terminated RNAs (ptRNAs) and upstream antisense RNAs (uaRNAs). This suggested a NEXT-independent Mtr4 function, and, consistent with this, we isolated a distinct complex containing Mtr4 and the zinc finger protein ZFC3H1. Strikingly, knockdown of either protein not only increased pt/uaRNA levels but also led to their accumulation in the cytoplasm. Furthermore, all pt/uaRNAs examined associated with active ribosomes, but, paradoxically, this correlated with a global reduction in heavy polysomes and overall repression of translation. Our findings highlight a critical role for Mtr4/ZFC3H1 in nuclear surveillance of naturally unstable lncRNAs to prevent their accumulation, transport to the cytoplasm, and resultant disruption of protein synthesis.

Older age; higher LDH, CRP, RDW, DBIL, BUN; lower ALB on admission correlated with higher odds of severe COVID-19. An effective prognostic nomogram composed of 7 features could allow early identification of patients at risk of exacerbation to severe COVID-19. Abstract BackgroundDue to no reliable risk stratification tool for severe corona virus disease 2019 patients at admission, we aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. MethodsIn this retrospective three-centers study, 372 non-severe COVID-19 patients during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. Based on baseline data of the two groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluate its performance. ResultsThe train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We found that old age, and higher serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin, are associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the train cohort (AUC 0.912 [95% CI 0.846-0.978], sensitivity 85.71%, specificity 87.58%); in validation cohort (0.853 [0.790-0.916], 77.5%, 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that nomogram conferred high clinical net benefit. ConclusionOur nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19, which will enable better centralized management and early treatment of severe patients.