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Furthermore, the plants harboring homozygous in-frame mutations exhibited similar phenotypes compared to the plants harboring homozygous knockout mutations. This result indicates that the amino acids lost in the in-frame mutant are essential for proper gene function. In-frame edits for this gene may need to target less essential and/or evolutionarily conserved domains in order to generate novel seed composition phenotypes.In plant and animal breeding, genomic prediction models are established to select new lines based on genomic data, without the need for laborious phenotyping. Prediction models can be trained on recent or historic phenotypic data and increasingly available genotypic data. This enables the adoption of genomic selection also in under-used legume crops such as common bean. Beans are an important staple food in the tropics and mainly grown by smallholders under limiting environmental conditions such as drought or low soil fertility. Therefore, genotype-by-environment interactions (G × E) are an important consideration when developing new bean varieties. However, G × E are often not considered in genomic prediction models nor are these models implemented in current bean breeding programs. Here we show the prediction abilities of four agronomic traits in common bean under various environmental stresses based on twelve field trials. The dataset includes 481 elite breeding lines characterized by 5,820 SNP markers. Prediction abilities over all twelve trials ranged between 0.6 and 0.8 for yield and days to maturity, respectively, predicting new lines into new seasons. In all four evaluated traits, the prediction abilities reached about 50-80% of the maximum accuracies given by phenotypic correlations and heritability. Predictions under drought and low phosphorus stress were up to 10 and 20% improved when G × E were included in the model, respectively. Our results demonstrate the potential of genomic selection to increase the genetic gain in common bean breeding. Prediction abilities improved when more phenotypic data was available and G × E could be accounted for. Furthermore, the developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.Understanding the genetic diversity and population structure of germplasms is essential when selecting parents for crop breeding. The genomic changes that occurred during the domestication and improvement of Upland cotton (Gossypium hirsutum) remains poorly understood. Besides, the available genetic resources from cotton cultivars are limited. By applying restriction site-associated DNA marker sequencing (RAD-seq) technology to 582 tetraploid cotton accessions, we confirmed distinct genomic regions on chromosomes A06 and A08 in Upland cotton cultivar subgroups. Based on the pedigree, reported QTLs, introgression analyses, and genome-wide association study (GWAS), we suggest that these divergent regions might have resulted from the introgression of exotic lineages of G. hirsutum landraces and their wild relatives. These regions were the typical genomic signatures that might be responsible for maturity and fiber quality on chromosome A06 and chromosome A08, respectively. Moreover, these genomic regions are located in the putative pericentromeric regions, implying that their application will be challenging. In the study, based on high-density SNP markers, we reported two genomic signatures on chromosomes A06 and A08, which might originate from the introgression events in the Upland cotton population. Our study provides new insights for understanding the impact of historic introgressions on population divergence and important agronomic traits of modern Upland cotton cultivars.Age-dependent neurodegenerative disorders are a set of diseases that affect millions of individuals worldwide. Apart from a small subset that are the result of well-defined inherited autosomal dominant gene mutations (e.g., those encoding the β-amyloid precursor protein and presenilins), our understanding of the genetic network that underscores their pathology, remains scarce. Genome-wide association studies (GWAS) especially in Alzheimer's disease patients and research in Parkinson's disease have implicated inflammation and the innate immune response as risk factors. However, even if GWAS etiology points toward innate immunity, untangling cause, and consequence is a challenging task. Specifically, it is not clear whether predisposition to de-regulated immunity causes an inadequate response to protein aggregation (such as amyloid or α-synuclein) or is the direct cause of this aggregation. Given the evolutionary conservation of the innate immune response in Drosophila and humans, unraveling whether hyperactive immune response in glia have a protective or pathological role in the brain could be a potential strategy in combating age-related neurological diseases.Long non-coding RNAs are essential regulators of the inflammatory response, especially for transcriptional regulation of inflammatory genes. It has been reported that the expression of transmembrane channel-like 3 (TMC3)-AS1 is increased following lipopolysaccharide stimulation. However, the potential function of TMC3-AS1 in immunity is largely unknown. Herein, we report a specific role for TMC3-AS1 in the regulation of inflammatory gene expression. TMC3-AS1 negatively regulates the expression of interleukin 10 (IL-10) in macrophage and intestinal epithelial cell lines. Mechanistically, TMC3-AS1 may interact with p65 in the nucleus, preventing p65 from binding to the κB consensus site within IL-10 promoter. These findings suggest that TMC3-AS1 may function as an important regulator in the innate immune response.Type 2 diabetes (T2D) is a metabolic disease characterized by increased inflammation, NOD-like receptors (NLRs) activation and gut dysbiosis. Our research group has recently reported that intestinal Th17 response limits gut dysbiosis and LPS translocation to visceral adipose tissue (VAT), protecting against metabolic syndrome. [https://www.selleckchem.com/products/CP-690550.html Tofacitinib concentration] However, whether NOD2 receptor contributes intestinal Th17 immunity, modulates dysbiosis-driven metabolic tissue inflammation, and obesity-induced T2D remain poorly understood. In this context, we observed that mice lacking NOD2 fed a high-fat diet (HFD) display severe obesity, exhibit greater adiposity, and more hepatic steatosis compared to HFD-fed wild-type (WT) mice. In addition, they develop increased hyperglycemia, worsening of glucose intolerance, and insulin resistance. Notably, the deficiency of NOD2 causes a deviation from M2 macrophage and regulatory T cells (Treg) to M1 macrophage and mast cells into VAT compared to WT mice fed HFD. An imbalance was also observed in Th17/Th1 cell populations, with reduced IL-17 and IL-22 gene expression in the mesenteric lymph nodes (MLNs) and ileum, respectively, of NOD2-deficient mice fed HFD.