International Journal of Genomics最新文献

Sorghum (Sorghum bicolor (L.) Moench) is an important food crop in semi-arid tropics. The crop grain yield ranges from 0.5 t/ha to 0.8 t/ha compared to potential yields of 10 t/ha. The African stem borer Busseola fusca Fuller (Noctuidae) and the spotted stem borer Chilo partellus Swinhoe (Crambidae), are among the most economically important insect pests of sorghum. The two borers can cause 15% - 80% grain yield loss in sorghum. Mapping of QTLs associated with resistance traits to the two stem borers is important towards marker-assisted breeding. The objective of this study was to map QTLs associated with resistance traits to B. fusca and C. partellus in sorghum. 243 F_9:10 sorghum RILs derived from ICSV 745 (S) and PB 15520-1 (R) were selected for the study with 4,955 SNP markers. The RILs were evaluated in three sites. Data was collected on leaf feeding, deadheart, exit holes, stem tunnels, leaf toughness, seedling vigour, bloom waxiness, and leaf glossiness. ANOVA for all the traits was done using Genstat statistical software. Insect damage traits and morphological traits were correlated using Pearson's correlation coefficients. Genetic mapping was done using JoinMap 4 software, while QTL analysis was done using PLABQTL software. A likelihood odds ratio (LOD) score of 3.0 was used to declare linkage. Joint analyses across borer species and sites revealed 4 QTLs controlling deadheart formation; 6 controlling leaf feeding damage; 5 controlling exit holes and stem tunneling damages; 2 controlling bloom waxiness, leaf glossiness, and seedling vigour; 4 conditioning trichome density; and 6 conditioning leaf toughness. Joint analyses for B. fusca and C. partellus further revealed that marker CS132-2 colocalised for leaf toughness and stem tunneling traits on QTLs 1 and 2, respectively; thus, the two traits can be improved using the same linked marker. This study recommended further studies to identify gene(s) underlying the mapped QTLs.

Objective: Testicular germ cell tumors (TGCT) are a serious malignant tumor with low early diagnosis rates and high mortality.

Methods: To investigate novel biomarkers to predict the diagnosis and prognosis of this cancer, bioinformatics analysis was used as an accurate, efficient, and economical method.

Results: Our study detected 39 upregulated and 589 downregulated differentially expressed genes (DEGs) using the GEO and TCGA databases. To identify the function of DEGs, GO functional analysis, three pathway analysis (KEGG, REACTOME, and PANTHER), and protein-protein interaction network were performed using the KOBAS website, as well as the String database. After a series of analyses in GEPIA and TIMER, including differential expression, we found one candidate gene related to the prognosis and diagnosis of TGCT. LAPTM5 was also associated with CD8+ T cell and PDCD1 expression, which suggests that it may affect immune infiltration.

Conclusions: LAPTM5 was identified as a hub gene, which could be used as a potential biomarker for TGCT diagnosis and prognosis.

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the k-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach.