Prevention and Control of Pathogens Based on Big-Data Mining and Visualization Analysis.

Morbidity and mortality caused by infectious diseases rank first among all human illnesses. Many pathogenic mechanisms remain unclear, while misuse of antibiotics has led to the emergence of drug-resistant strains. Infectious diseases spread rapidly and pathogens mutate quickly, posing new threats to human health. However, with the increasing use of high-throughput screening of pathogen genomes, research based on big data mining and visualization analysis has gradually become a hot topic for studies of infectious disease prevention and control. In this paper, the framework was performed on four infectious pathogens (Fusobacterium, Streptococcus, Neisseria, and Streptococcus salivarius) through five functions: 1) genome annotation, 2) phylogeny analysis based on core genome, 3) analysis of structure differences between genomes, 4) prediction of virulence genes/factors with their pathogenic mechanisms, and 5) prediction of resistance genes/factors with their signaling pathways. The experiments were carried out from three angles: phylogeny (macro perspective), structure differences of genomes (micro perspective), and virulence and drug-resistance characteristics (prediction perspective). Therefore, the framework can not only provide evidence to support the rapid identification of new or unknown pathogens and thus plays a role in the prevention and control of infectious diseases, but also help to recommend the most appropriate strains for clinical and scientific research. This paper presented a new genome information visualization analysis process framework based on big data mining technology with the accommodation of the depth and breadth of pathogens in molecular level research.

Identification of hormone binding proteins based on machine learning methods.

The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequisite for understanding its biological functions and molecular mechanisms. Since experimental methods are still labor intensive and cost ineffective to identify HBP, it's necessary to develop computational methods to accurately and efficiently identify HBP. In this paper, a machine learning-based method was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method. In the 5-fold cross-validation test, the proposed method yielded an overall accuracy of 97.15%. For the convenience of scientific community, a user-friendly webserver called HBPred2.0 was built, which could be freely accessed at http://lin-group.cn/server/HBPred2.0/.

Dual modes of antitumor action of an amphiphilic peptide A(9)K.

Following our recent report of attractive antibacterial properties of a designed amphiphilic peptide, A(9)K, we have investigated its antitumor activities by examining the modes of its action against different mammalian cell types. The peptide strongly inhibited the growth of cancerous HeLa cells and human promyelocytic leukemia HL60 cells whilst remaining benign to the host cells, including Cos 7 cells, mouse fibroblast NIH3T3 cells and human red blood cells. Images from SEM and fluorescence microscopy showed that A(9)K penetrated HeLa cell membranes and disrupted membrane structures, a feature broadly similar to that observed from its bactericidal actions. Further interactions of A(9)K with inner cellular membranes caused mitochondrial dysfunction associated with the F-actin reorganization and the decreased transcription of bcl-2 and c-myc genes, resulting in HeLa cell apoptosis in a mitochondria-induced apoptosis pathway. Thus A(9)K has high selectivity against cancerous cells and kills them by dual modes of action: membrane disruption and cell apoptosis. In addition, the peptide does not induce non-specific immunological effects and is not degraded by proteases. These features are crucial for developing their applications in future research.

Engagement of PSGL-1 upregulates CSF-1 transcription via a mechanism that may involve Syk.

PSGL-1, the optimal selectin ligand demonstrated by in vivo studies to date, is an essential adhesive molecule mediating the rolling of leukocytes on the endothelial cells and the recruitment of leukocytes to the inflamed tissue. Recent studies demonstrated, in addition to its direct role in capture of leukocytes from the bloodstream, PSGL-1 also functions as a signal-transducing receptor and initiates a series of intracellular signal events during the activation of leukocytes. Our present work showed antibody engagement of PSGL-1 upregulated the transcriptional activity of CSF-1 promotor and increased the endogenous expression of CSF-1 mRNA in Jurkat cell. Overexpression of wild-typed non-receptor tyrosine kinase Syk, but not kinase dead mutant of Syk, promoted the upregulation of the transcriptional activity of CSF-1 promoter caused by antibody engagement of PSGL-1. Additionally, special inhibitor of Syk Piceatannol suppressed the increase of CSF-1 mRNA caused by the engagement of PSGL-1. The results suggest that signal transducted by PSGL-1 upregulate the transcriptional activity of CSF-1, and non-receptor tyrosine kinase Syk participates in this pathway.

[Genetic mapping of resistant gene to southern corn rust and the tagging analysis on different genetic background].

Southern corn rust (SCR) is a destructive disease in maize. The inbred line Qi319 is highly resistant to southern corn rust. SSR technique was employed to preliminary mapping of the resistance gene. Bulked segregant analysis revealed that two primers, phi 118 and phi 041, amplified polymorphic bands. SSR analysis on populations indicated the two primers were linked to the rust resistance gene, which was mapped on the short arm of chromosome 10. In addition, comparative analysis of the amplification bands among different populations revealed that the amplification products with the same primer in different populations were dissimilar. This result indicates that the genetic background may affect results of gene mapping and tagging. So, it is important to select suitable population to performing molecular marker analysis and gene mapping.

[Inheritance of resistance of southern corn rust in the inbred line Qi319].

Southern corn rust (SCR) is a destructive disease in maize. The inbred line Qi319 is highly resistant to southern corn rust. The inheritance of resistance to southern rust in Qi319 was investigated. Five F1 hybrids were derived from Qi319 crossed with five susceptible inbred lines respectively. The F2 generations were produced by F1 self-pollinated and BC1 F1 generations were abtained by backcrossing F1 with the susceptible parents. Inoculation of the P1, P2, F1 s and 10 individuals of F2, BC1 F1 were completed with the southern corn rust pathogen and showed that all of the 10 F1 s were resistant, the all 5 F2 populations segregated in a ratio of 3R:1S, and all of the 5 BC1 F1 populations segregated in a ratio of 1R:1S. Therefore, it is considered that Qi319 carries one dominant gene for resistance to southern corn rust.