OMICS and Other Advanced Technologies in Mycological Applications

Abstract

Fungi play many roles in different ecosystems. The precise identification of fungi is important in different aspects. Historically, they were identified based on morphological characteristics, but technological advancements such as polymerase chain reaction (PCR) and DNA sequencing now enable more accurate identification and taxonomy, and higher-level classifications. However, some species, referred to as "dark taxa", lack distinct physical features that makes their identification challenging. High-throughput sequencing and metagenomics of environmental samples provide a solution to identifying new lineages of fungi. This paper discusses different approaches to taxonomy, including PCR amplification and sequencing of rDNA, multi-loci phylogenetic analyses, and the importance of various omics (large-scale molecular) techniques for understanding fungal applications. The use of proteomics, transcriptomics, metatranscriptomics, metabolomics, and interactomics provides a comprehensive understanding of fungi. These advanced technologies are critical for expanding the knowledge of the Kingdom of Fungi, including its impact on food safety and security, edible mushrooms foodomics, fungal secondary metabolites, mycotoxin-producing fungi, and biomedical and therapeutic applications, including antifungal drugs and drug resistance, and fungal omics data for novel drug development. The paper also highlights the importance of exploring fungi from extreme environments and understudied areas to identify novel lineages in the fungal dark taxa.

Keywords: dark taxa; fungal metabolites; fungal taxonomy; high-throughput sequencing; multiomics approaches; phylogenetic analysis.

Figure 1

Outline of omics tools and applications in fungal omics to ensure food safety and security.

Figure 2

Targets of current antifungal drugs (boxed) and generic drug names (highlighted in red). Drug classes are underlined.

Figure 3

The monomeric structures of E. coli ClpB (depicted in light brown, PDB 1QVR) and S. cerevisiae Hsp104 (predicted by Alphafold as AF-P31539-F1, shown in light blue). Molecular graphics and analyses were performed with UCSF Chimera, by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco.