Fermentation of Foc TR4-infected bananas and Trichoderma spp.

Fusarium wilt (also known as Panama disease) is one of the most destructive banana diseases, and greatly hampers the global production of bananas. Consequently, it has been very detrimental to the Chinese banana industry. An infected plant is one of the major causes of the spread of Fusarium wilt to nearby regions. It is essential to develop an efficient and environmentally sustainable disease control method to restrict the spread of Fusarium wilt. We isolated Trichoderma spp from the rhizosphere soil, roots, and pseudostems of banana plants that showed Fusarium wilt symptoms in the infected areas. Their cellulase activities were measured by endoglucanase activity, ß-glucosidase activity, and filter paper activity assays. Safety analyses of the Trichoderma isolates were conducted by inoculating them into banana plantlets. The antagonistic effects of the Trichoderma spp on the Fusarium pathogen Foc tropical Race 4 (Foc TR4) were tested by the dual culture technique. Four isolates that had high cellulase activity, no observable pathogenicity to banana plants, and high antagonistic capability were identified. The isolates were used to biodegrade diseased banana plants infected with GFP-tagged Foc TR4, and the compost was tested for biological control of the infectious agent; the results showed that the fermentation suppressed the incidence of wilt and killed the pathogen. This study indicates that Trichoderma isolates have the potential to eliminate the transmission of Foc TR4, and may be developed into an environmentally sustainable treatment for controlling Fusarium wilt in banana plants.

Bioinformatics analysis with graph-based clustering to detect gastric cancer-related pathways.

Despite a dramatic reduction in incidence and mortality rates, gastric cancer still remains one of the most common malignant tumors worldwide, especially in China. We sought to identify a set of discriminating genes that could be used for characterization and prediction of response to gastric cancer. Using bioinformatics analysis, two gastric cancer datasets, GSE19826 and GSE2685, were merged to find novel target genes and domains to explain pathogenesis; we selected differentially expressed genes in these two datasets and analyzed their correlation in order to construct a network. This network was examined to find graph clusters and related significant pathways. We found that ALDH2 and CCNB1 were associated with gastric cancer. We also mined for the underlying molecular mechanisms involving these differently expressed genes. We found that ECM-receptor interaction, focal adhesion, and cell cycle were among the significantly associated pathways. We were able to detect genes and pathways that were not considered in previous research on gastric cancer, indicating that this approach could be an improvement on the investigative mechanisms for finding genetic associations with disease.