Advancing fungal phylogenetics: integrating modern sequencing, dark taxa discovery, and machine learning.

Abstract

The study of fungal genetics has undergone transformative advancements in recent decades, profoundly reshaping our understanding of fungal diversity, evolution, and pathogenesis. This review synthesizes cutting-edge molecular techniques revolutionizing fungal diagnostics, with a focus on DNA fingerprinting, next-generation sequencing (NGS), and third-generation sequencing (TGS), alongside their applications in species identification, phylogenetic reconstruction, and disease management. We critically evaluated the utility of molecular markers such as the Internal Transcribed Spacer (ITS), Large Subunit (LSU), and protein-coding genes (e.g., RPB1, RPB2, TEF1-α), which have emerged as indispensable tools for resolving taxonomic ambiguities and cryptic species complexes. While ITS remains the gold standard for fungal barcoding due to its high interspecific variability, multi-locus strategies integrating loci like β-tubulin and CaM enhance resolution in challenging genera such as Aspergillus, Fusarium, and Penicillium. The review underscores the limitations of traditional morphology-based taxonomy, particularly its inability to address cryptic speciation or non-reproductive fungal phases. Advances in NGS platforms (e.g., Illumina, PacBio, Oxford Nanopore) have overcome these barriers, enabling high-throughput genomic analyses that reveal unprecedented fungal diversity in environmental and clinical samples. TGS technologies, with their long-read capabilities (> 10 kb), now facilitate the assembly of complex genomes, identification of structural variants, and exploration of horizontal gene transfer events, offering new insights into fungal adaptation and pathogenicity. Despite these breakthroughs, challenges persist in resolving intragenomic variation, reconciling gene tree discordance, and standardizing workflows for large-scale fungal population studies. The integration of multi-omics approaches (transcriptomics, proteomics, metabolomics) and machine learning algorithms promises to address these gaps, enabling predictive modeling of antifungal resistance and host-pathogen interactions. Collaborative efforts among mycologists, clinicians, and bioinformaticians are critical to harmonizing data sharing, refining diagnostic pipelines, and translating genomic insights into precision therapies. Fungal-related diseases pose escalating threats to global agriculture, healthcare, and ecosystem stability. Climate change further exacerbates pathogen spread and antifungal resistance, necessitating innovative management strategies. Emerging tools such as CRISPR-based diagnostics, portable sequencers (MinION), and synthetic biology platforms hold promise for real-time pathogen surveillance and engineered biocontrol solutions. By bridging genomic innovation with interdisciplinary collaboration, this review charts a roadmap for advancing fungal diagnostics, enhancing taxonomic clarity, and mitigating the socio-economic impacts of fungal diseases in an era of rapid environmental change.