Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Dt, 0.56 × 10 -3 ) ( Fig. 1d and Supplementary Fig. 3). This shows that a large amount is associated with the development of the long fiber trait in cultivated cotton (Fig. 3b). 217Domestication has led to the transformation of cotton fiber from brown to white. 218To understand this phenomenon, we examined two homoeologous gene pairs only 219 subjected to domestication selection in the Dt, 4-COUMARATE:COA LIGASE (4CL) 220 and CHALCONE SYNTHASE (CHS), which encode enzymes involved in the 221 phenylpropanoid metabolic pathway ( Fig. 3c and Supplementary Fig. 6 Fig. 3c). These SNPs display reductions in nucleotide diversity that occurred 225 during domestication (Fig. 3c). Interestingly, we found that the two SNPs in the Fig. 8) 42 . We identified a total of 188,360 DNase I-hypersensitive 248 sites (DHSs) in cotton leaves and fibers, of which ca. 47% are common to both tissues 249 (Fig. 4a). DHSs were preferentially identified in chromosomal arms and 250 approximately half were detected in promoter and intergenic regions ( Fig. 4b and 251 Supplementary Fig. 9). We found DHSs are hypo-methylated, consistent with 252 previous studies 42 (Fig. 4c) H3K4me1 and inactive H3K9me2 (Fig. 4d). Intergenic DHSs were also found to 255 exhibit an enrichment of H3K4me3 and H3K27me3, but depletion of H3K9me2 and 256 no enrichment of H3K4me1 (Fig. 4e). As predicted, the patterns of chromatin 257 modification marks in cotton are different between genic and TE regions 258 ( Supplementary Fig. 10). In addition, genes with promoter DHSs are generally 259 expressed at a higher level in both tissues than those without promoter DHSs (Fig. 4f), 260 and tissue-specific promoter DHSs corresponded to higher levels of gene expression 261 ( Fig. 4g) Hi-C analysis was carried out using the TM-1 accession to characterize global 296 chromatin interactions. We generated 1.1 billion Hi-C paired-end reads, of which ca. possible Hi-C bias, HindIII fragments of less than 2 kb were merged to obtain 299 305,682 chromosomal anchor regions (Fig. 5a). On the basis of a high-quality 300 genome assembly of TM-1 (Supplementary Fig. 11), we used the Hi-C data to 301 characterize the cotton chromatin interactome (Supplementary Fig. 12) and ( Fig. 5b), but many topologically associated domain-like (TAD-like) regions were 305 identified (Fig. 5c, Supplementary Fig. 13 and Supplementary are less frequent at regions marked by H3K9me2 (Fig. 5d). (Fig. 5g). 320We...

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SARS-CoV-2 is the cause of the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. Using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as of the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

Background: The gut microbiota is increasingly recognized as playing an important role in the development of obesity, but the influence of gender remains elusive. Using a large cohort of Chinese adults, our study aimed to identify differences in gut microbiota as a function of body mass index (BMI) and investigate gender specific features within these differences.Methods: Five hundred fifty-one participants were categorized as underweight, normal, overweight, or obese, based on their BMI. Fecal microbiome composition was profiled via 16S rRNA gene sequencing. Generalized linear model (GLM), BugBase, PICRUSt, and SPIEC-EASI were employed to assess the variabilities in richness, diversity, structure, organism-level microbiome phenotypes, molecular functions, and ecological networks of the bacterial community that associated with BMI and sex.Results: The bacterial community of the underweight group exhibited significantly higher alpha diversity than other BMI groups. When stratified by gender, the pattern of alpha diversity across BMI was maintained in females, but no significant difference in alpha diversity was detected among the BMI groups of males. An enrichment of Fusobacteria was observed in the fecal microbiota of obese males, while obese females demonstrated an increased relative abundance of Actinobacteria. Analysis of microbial community-level phenotypes revealed that underweight males tend to have more anaerobic and less facultatively anaerobic bacteria, indicating a reduced resistance to oxidative stress. Functionally, butyrate-acetoacetate CoA-transferase was enriched in obese individuals, which might favor energy accumulation. PhoH-like ATPase was found to be increased in male obese subjects, indicating a propensity to harvest energy. The microbial ecological network of the obese group contained more antagonistic microbial interactions as well as high-degree nodes.Conclusion: Using a large Chinese cohort, we demonstrated BMI-associated differences in gut microbiota composition, functions, and ecological networks, which were influenced by gender. Results in this area have shown variability across several independent studies, suggesting that further investigation is needed to understand the role of the microbiota in modulating host energy harvest and storage, and the impact of sex on these functions.

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BackgroundDisease severity, illness perceptions, coping strategies, stress, psychological well-being, and quality of life were reported to have close relationships. According to the Common Sense Model, illness perceptions and coping strategies could mediate the relationship between illness stimuli and illness outcomes such as psychological health and quality of life. Stress was also associated with the individual’s disease severity, anxiety, depression, and quality of life.ObjectivesThe study aimed to explore the influencing factors of illness outcomes, and to what extent illness perceptions, coping strategies, and stress mediate the relationship between disease severity and anxiety, and depression and quality of life.MethodsOur study included 159 patients with Crohn’s disease who were attending a tertiary hospital outpatient clinic or who were hospitalized. Disease severity was measured with the Crohn’s Disease Activity Index. Illness perceptions were measured with the Brief Illness Perceptions Questionnaire. Coping strategies were measured with the Carver Brief Coping Questionnaire. Stress was measured with the Perceived Stress Questionnaire. Anxiety and depression were measured with the Hospital Anxiety and Depression Scale. Quality of life was measured with the Inflammatory Bowel Disease Questionnaire.ResultsDisease severity, illness perceptions, maladaptive coping, stress, anxiety, depression and quality of life were significantly correlated with each other among patients with Crohn’s disease. Using structural equation modeling to describe the inner relationship of the aforementioned variables, an excellent-fitted model was drawn. (χ2[10]=13.83, P=0.18, χ2/N=1.38, standardized root mean square residual [SRMR] <0.05, root mean square error of approximation [RMSEA] <0.05, goodness of fit index [GFI] >0.97, comparative fit index [CFI] >0.99). Disease severity had a direct influence on illness perceptions. Illness perceptions had a direct influence on stress. Both illness perceptions and stress had direct influences on anxiety, depression, and quality of life, while maladaptive coping did not directly influence anxiety, depression, or quality of life. Stress had a direct influence on maladaptive coping. Quality of life was also directly influenced by disease severity and anxiety.ConclusionInterrelationships between disease stimuli, disease perceptions and management and disease outcomes could be found in patients with Crohn’s disease. Illness perceptions and stress mediated an individual’s disease severity and anxiety, depression and quality of life, while coping strategy was not an applicable mediator.

Leucine rich repeat containing G protein-coupled receptor 5 (LGR5), an intestinal stem cell marker, is known to exhibit tumor suppressor activity in colon cancer, the mechanism of which is not understood. Here we show that R-spondin 1 (RSPO1)/LGR5 directly activates TGFβ signaling cooperatively with TGFβ type II receptor in colon cancer cells, enhancing TGFβ-mediated growth inhibition and stress-induced apoptosis. Knockdown of LGR5 attenuated downstream TGFβ signaling and increased cell proliferation, survival, and metastasis in an orthotopic model of colon cancer in vivo. Upon RSPO1 stimulation, LGR5 formed complexes with TGFβ receptors. Studies of patient specimens indicate that LGR5 expression was reduced in advanced stages and positively correlated with markers of TGFβ activation in colon cancer. Our study uncovers a novel crosstalk between LGR5 and TGFβ signaling in colon cancer and identifies LGR5 as a new modulator of TGFβ signaling able to suppress colon cancer metastasis.

SUMMARYSARS-CoV-2 is an RNA virus of the Coronaviridae family that is the causal pathogen of the ongoing Coronavirus Disease 2019 pandemic. There are currently no antiviral drugs or vaccines to treat COVID-19, and the failure to identify effective interventions can be blamed on our incomplete understanding of the nature of this virus and its host cell infection process. Here, we experimentally determined structural maps of the SARS-CoV-2 RNA genome in infected human cells and also characterized in vitro refolded RNA structures for SARS-CoV-2 and 6 other coronaviruses. Our in vivo data confirms several structural elements predicted from theoretical analysis and goes much further in revealing many previously unknown structural features that functionally impact viral translation and discontinuous transcription in cells. Importantly, we harnessed our in vivo structure data alongside a deep-learning tool and accurately predicted several dozen functionally related host cell proteins that bind to the SARS-CoV-2 RNA genome, none of which were known previously. Thus, our in vivo structural study lays a foundation for coronavirus RNA biology and indicates promising directions for the rapid development of therapeutics to treat COVID-19.HIGHLIGHTSWe mapped the in vivo structure and built secondary structural models of the SARS-CoV-2 RNA genomeWe discovered functionally impactful structural features in the RNA genomes of multiple coronavirusesWe predicted and validated host cell proteins that bind to the SARS-CoV-2 RNA genome based on our in vivo RNA structural data using a deep-learning tool