Fungi's Swiss Army Knife: Pleiotropic Effect of Melanin in Fungal Pathogenesis during Cattle Mycosis.

Fungal threats to public health, food security, and biodiversity have escalated, with a significant rise in mycosis cases globally. Around 300 million people suffer from severe fungal diseases annually, while one-third of food crops are decimated by fungi. Vertebrate, including livestock, are also affected. Our limited understanding of fungal virulence mechanisms hampers our ability to prevent and treat cattle mycoses. Here we aim to bridge knowledge gaps in fungal virulence factors and the role of melanin in evading bovine immune responses. We investigate mycosis in bovines employing a PRISMA-based methodology, bioinformatics, and data mining techniques. Our analysis identified 107 fungal species causing mycoses, primarily within the Ascomycota division. Candida, Aspergillus, Malassezia, and Trichophyton were the most prevalent genera. Of these pathogens, 25% produce melanin. Further research is required to explore the involvement of melanin and develop intervention strategies. While the literature on melanin-mediated fungal evasion mechanisms in cattle is lacking, we successfully evaluated the transferability of immunological mechanisms from other model mammals through homology. Bioinformatics enables knowledge transfer and enhances our understanding of mycosis in cattle. This synthesis fills critical information gaps and paves the way for proposing biotechnological strategies to mitigate the impact of mycoses in cattle.

The effect of orthology and coregulation on detecting regulatory motifs.

BACKGROUND: Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. METHODOLOGY: We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. RESULTS AND CONCLUSIONS: Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/~kmarchal/Supplementary_Storms_Valerie_PlosONE.