Colemanweinreich1869

In summary, we provide further evidence that SMS and AGS exhibit the same disease spectrum following a gain-of-function mutation in the IFIH1 gene. Our data highlight the genetic heterogeneity of these conditions and expand our knowledge of differential phenotypes created by IFIH1 gain-of-function mutation.As an important tool for systematic analysis, genome-scale metabolic network (GSMN) model has been widely used in various organisms. However, there are few reports on the GSMNs of aquatic crustaceans. Litopenaeus vannamei is the largest and most productive shrimp species. Feed improvement is one of the important methods to improve the yield of L. vannamei and control water pollution caused by the inadequate absorption of feed. In this work, the first L. vannamei GSMN named iGH3005 was reconstructed and applied to the optimization of feed. iGH3005 was reconstructed based on the genomic data. P7C3 The model includes 2,292 reactions and 3,005 genes. iGH3005 was used to analyze the nutritional requirements of five different L. vannamei commercial varieties and the genes influencing the metabolism of the nutrients. Based on the simulation, we found that tyrosine-protein kinase src64b like may catalyze different reactions in different commercial varieties. The preference of carbohydrate utilization is different in various commercial varieties, which may due to the different expressions of some genes. In addition, this investigation suggests that a rational and targeted modification in the macronutrient content of shrimp feed would lead to an increase in growth and feed conversion rate. The feed for different commercial varieties should be adjusted accordingly, and possible adjustment schemes were provided. The results of this work provided important information for physiological research and optimization of the components in feed of L. vannamei.The TIFY gene family, a key plant-specific transcription factor (TF) family, is involved in diverse biological processes including plant defense and growth regulation. Despite TIFY proteins being reported in some plant species, a genome-wide comparative and comprehensive analysis of TIFY genes in plant species can reveal more details. In the current study, the members of the TIFY gene family were significantly increased by the identification of 18 and six new members using maize and tomato reference genomes, respectively. Thus, a genome-wide comparative analysis of the TIFY gene family between 48 tomato (Solanum lycopersicum, a dicot plant) genes and 26 maize (Zea mays, a monocot plant) genes was performed in terms of sequence structure, phylogenetics, expression, regulatory systems, and protein interaction. The identified TIFYs were clustered into four subfamilies, namely, TIFY-S, JAZ, ZML, and PPD. The PPD subfamily was only detected in tomato. Within the context of the biological process, TIFY family genesthat TIFY genes play a critical role in cell reproduction, plant growth, and responses to stress conditions, and the conserved regulatory mechanisms may control their expression.Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.

Growing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.

Stromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.

Low stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (

= 0.0046). Three hundred and seven stromal score-related differenel based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

The well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected "driver" genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes.