Glycogen storage diseases (GSDs) are the heterogeneous group of disorders caused by mutations in at least 30 different genes. Different types of GSDs, especially liver GSDs, take overlapping symptoms and can be clinically indistinguishable. This survey evaluated the use of whole-exome sequencing (WES) for the genetic analysis of the liver GSD-suspected patients in three unrelated families. An in-house filtering pipeline was used to assess rare pathogenic variants in GSD-associated genes, autosomal recessive/mendelian disorder genes (carrier status for genetic counseling subjects), and the ACMG's list of 59 actionable genes. For the interpretation of the causative variants and the incidental/secondary findings, ACMG guidelines were applied. Additionally, we have explored PharmGKB class IA/IB pharmacogenetic variants.  https://www.selleckchem.com/CDK.html  The segregation analysis was performed using Sanger sequencing for the novel causative variants. Bioinformatics analysis of the exome data in three individuals revealed three novel homozygous causative variants in the GSD-associated genes. The first variant, c.298_307delATGATCAACC in PYGL gene has related to HERS disease (GSD VI). Both variants of c.1043dupT and c.613-1G > C in SLC2A2 gene have been associated with Fanconi-Bickel syndrome (GSDXI). Eight pathogenic/likely pathogenic medical actionable findings in Mendelian disease genes and 10 pharmacogenetic variants with underlying drug response phenotypes have been identified. No known/expected pathogenic variants were detected in the ACMG's list of 59 actionable genes. The logical filtering steps can help in finding other medical actionable secondary/incidental findings as well as effectively identifying the causative variants in heterogeneous conditions such as GSDs. Three novel variants related to GSD genes recognized in liver GSD-suspected patients with early infantile and childhood-age onset.Vasculature plays critical roles in the pathogenesis and neurological repair of traumatic brain injury (TBI). However, how vascular endothelial cells respond to TBI at the molecular level has not been systematically reviewed. Here, by integrating three transcriptome datasets including whole cortex of mouse brain, FACS-sorted mouse brain endothelial cells, and single cell sequencing of mouse brain hippocampus, we revealed the key molecular alteration of endothelial cells characterized by increased Myc targets and Epithelial-Mesenchymal Transition signatures. In addition, immunofluorescence staining of patients' samples confirmed that IGFBP7 was up-regulated in vasculature in response to TBI. TGFβ1, mainly derived from microglia and endothelial cells, sufficiently induces IGFBP7 expression in cultured endothelial cells, and is significantly upregulated in response to TBI. Our results identified IGFBP7 as a potential biomarker of vasculature in response to TBI, and indicate that TGFβ signaling may contribute to the upregulation of IGFBP7 in the vasculature.UDP-glucose dehydrogenase (UGD; EC1.1.1.22) is a NAD+-dependent enzyme that catalyzes the two-fold oxidation of UDP-glucose (UDP-Glc) to produce UDP-glucuronic acid and plays an important role in plant cell wall synthesis. A total of 42 UGD genes from four Gossypium genomes including G. hirsutum, G. arboretum, G. barbadense, and G. raimondii were identified and found that the UGD gene family has conservative evolution patterns in gene structure and protein domain. The growth of fibers can be effectively promoted after adding the UDP-Glc to the medium, and the GhUGD gene expression enhanced. In addition, the transgenic Arabidopsis lines over-expressing GH_D12G1806 had longer root lengths and higher gene expression level than the wild-type plants of Columbia-0. These results indicated that UGD may play important roles in cotton fiber development and has a guiding significance for dissecting fiber development mechanism.
  Gastric cancer (GC) is one of the most common malignancies in the world, and the fourth most frequent malignancy worldwide. YTHDF2 (YTH domain family 2, YTHDF2) binds to mRNA containing m6A, thereby regulating the localization and stability of the bound mRNA. YTHDF2 was shown to be associated with some cancer patient prognosis. However, the effect of YTHDF2 on gastric cancer and the molecular mechanism of this effect have not been documented.
 
  To conduct this research, YTHDF2 expression levels in public databases and gastric cancer patient samples were analyzed. The effects of YTHDF2 on the growth of gastric cancer cells were detected 
  and 
  RNA-seq was used to analyze the signal pathways regulated by YTHDF2, and experiments were carried out for verification.
 
  In our study, we found that YTHDF2 has lower expression in GC tissues and GC cells, and inhibits the growth of GC cells. In addition, the analysis of clinical data found that the expression level of YTHDF2 is closely related to the stage of GC and the survival of patients with GC. RNA sequencing results showed that overexpression of YTHDF2 significantly reduced protein expression in the FOXC2 (Forkhead box protein C2, FOXC2) signaling pathway. Finally, we found that knockout of FOXC2 reversed the inhibitory effect of YTHDF2 on GC cells.
 
  In summary, YTHDF2 inhibits the growth of GC cells by negatively regulating FOXC2 and may serve as a prognostic marker in GC.
 In summary, YTHDF2 inhibits the growth of GC cells by negatively regulating FOXC2 and may serve as a prognostic marker in GC.SARS-CoV-2 has caused a worldwide pandemic. Existing research on coronavirus mutations is based on small data sets, and multiple sequence alignment using a global-scale data set has yet to be conducted. Statistical analysis of integral mutations and global spread are necessary and could help improve primer design for nucleic acid diagnosis and vaccine development. Here, we optimized multiple sequence alignment using a conserved sequence search algorithm to align 24,768 sequences from the GISAID data set. A phylogenetic tree was constructed using the maximum likelihood (ML) method. Coronavirus subtypes were analyzed via t-SNE clustering. We performed haplotype network analysis and t-SNE clustering to analyze the coronavirus origin and spread. Overall, we identified 33 sense, 17 nonsense, 79 amino acid loss, and 4 amino acid insertion mutations in full-length open reading frames. Phylogenetic trees were successfully constructed and samples clustered into subtypes. The COVID-19 pandemic differed among countries and continents.