Genome-Wide Analysis of Secondary Metabolite Gene Clusters in Ophiostoma ulmi and Ophiostoma novo-ulmi Reveals a Fujikurin-Like Gene Cluster with a Putative Role in Infection.

The emergence of new microbial pathogens can result in destructive outbreaks, since their hosts have limited resistance and pathogens may be excessively aggressive. Described as the major ecological incident of the twentieth century, Dutch elm disease, caused by ascomycete fungi from the Ophiostoma genus, has caused a significant decline in elm tree populations (Ulmus sp.) in North America and Europe. Genome sequencing of the two main causative agents of Dutch elm disease (Ophiostoma ulmi and Ophiostoma novo-ulmi), along with closely related species with different lifestyles, allows for unique comparisons to be made to identify how pathogens and virulence determinants have emerged. Among several established virulence determinants, secondary metabolites (SMs) have been suggested to play significant roles during phytopathogen infection. Interestingly, the secondary metabolism of Dutch elm pathogens remains almost unexplored, and little is known about how SM biosynthetic genes are organized in these species. To better understand the metabolic potential of O. ulmi and O. novo-ulmi, we performed a deep survey and description of SM biosynthetic gene clusters (BGCs) in these species and assessed their conservation among eight species from the Ophiostomataceae family. Among 19 identified BGCs, a fujikurin-like gene cluster (OpPKS8) was unique to Dutch elm pathogens. Phylogenetic analysis revealed that orthologs for this gene cluster are widespread among phytopathogens and plant-associated fungi, suggesting that OpPKS8 may have been horizontally acquired by the Ophiostoma genus. Moreover, the detailed identification of several BGCs paves the way for future in-depth research and supports the potential impact of secondary metabolism on Ophiostoma genus' lifestyle.

Preimplantation development regulatory pathway construction through a text-mining approach.

BACKGROUND: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. RESULTS: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. CONCLUSIONS: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as "seeds" for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.