Genome characteristics reveal the impact of lichenization on lichen-forming fungus Endocarpon pusillum Hedwig (Verrucariales, Ascomycota).

BACKGROUND: Lichen is a classic mutualistic organism and the lichenization is one of the fungal symbioses. The lichen-forming fungus Endocarpon pusillum is living in symbiosis with the green alga Diplosphaera chodatii Bialsuknia as a lichen in the arid regions. RESULTS: 454 and Illumina technologies were used to sequence the genome of E. pusillum. A total of 9,285 genes were annotated in the 37.5 Mb genome of E. pusillum. Analyses of the genes provided direct molecular evidence for certain natural characteristics, such as homothallic reproduction and drought-tolerance. Comparative genomics analysis indicated that the expansion and contraction of some protein families in the E. pusillum genome reflect the specific relationship with its photosynthetic partner (D. chodatii). Co-culture experiments using the lichen-forming fungus E. pusillum and its algal partner allowed the functional identification of genes involved in the nitrogen and carbon transfer between both symbionts, and three lectins without signal peptide domains were found to be essential for the symbiotic recognition in the lichen; interestingly, the ratio of the biomass of both lichen-forming fungus and its photosynthetic partner and their contact time were found to be important for the interaction between these two symbionts. CONCLUSIONS: The present study lays a genomic analysis of the lichen-forming fungus E. pusillum for demonstrating its general biological features and the traits of the interaction between this fungus and its photosynthetic partner D. chodatii, and will provide research basis for investigating the nature of its drought resistance and symbiosis.

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.