Database establishment for the secondary fungal DNA barcodetranslational elongation factor 1α(TEF1α)

Abstract

With new or emerging fungal infections, human and animal fungal pathogens are a growing threat worldwide. Current diagnostic tools are slow, non-specific at the species and subspecies levels, and require specific morphological expertise to accurately identify pathogens from pure cultures. DNA barcodes are easily amplified, universal, short species-specific DNA sequences, which enable rapid identification by comparison with a well-curated reference sequence collection. The primary fungal DNA barcode, ITS region, was introduced in 2012 and is now routinely used in diagnostic laboratories. However, the ITS region only accurately identifies around 75% of all medically relevant fungal species, which has prompted the development of a secondary barcode to increase the resolution power and suitability of DNA barcoding for fungal disease diagnostics. The translational elongation factor 1α (TEF1α) was selected in 2015 as a secondary fungal DNA barcode, but it has not been implemented into practice, due to the absence of a reference database. Here, we have established a quality-controlled reference database for the secondary barcode that together with the ISHAM-ITS database, forms the ISHAM barcode database, available online at http://its.mycologylab.org/ . We encourage the mycology community for active contributions.

Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?

“…freshwater Sordariomycetes , Trichoderma ) including slime molds. For these, secondary barcoding markers, such as the intergenic spacer (IGS), β-tubulin II ( TUB2 ), DNA-directed RNA polymerase II largest ( RPB1 ) and second largest ( RPB2 ) subunits, translational elongation factor 1α ( TEF1 ), DNA topoisomerase I ( TOP1 ), phosphoglycerate kinase ( PGK ), and cytochrome c oxidase subunit I ( COX1 ) and subunit II ( COX2 ), have been proposed (Table 1 ; Geiser et al 2007 ; Gilmore et al 2009 ; Damm et al 2012 ; Maharachchikumbura et al 2012 ; López-Quintero et al 2013 ; Balasundaram et al 2015 ; Choi et al 2015 ; Stielow et al 2015 ; Xu 2016 ; Al-Hatmi et al 2016 ; Irinyi et al 2016 ; Větrovský et al 2016 ; Woudenberg et al 2017 ; Schnittler et al 2017 ; Tekpinar and Kalmer 2019 ; Luo et al 2019 ; Meyer et al 2019 ). Occasional cases in fungal groups where ITS otherwise provides sufficient resolution, such as the subcosmopolitan and threatened macrolichens, Sticta fuliginosa and S. limbata (Magain and Sérusiaux 2015 ; Moncada et al 2020 ), indicate that this problem is not necessarily taxon-specific, but may denote recently or dynamically evolving lineages, which can occur in any group of fungi but is apparently more prevalent in some than in others.…”

“…In recently analyzed barcode datasets (Vu et al 2016 , 2019 ), between 6 and 17% of yeast and filamentous fungal species were shown to be indistinguishable by ITS. Meyer et al ( 2019 ) found that 25% of human/animal pathogenic fungi cannot be identified based on ITS alone. Many plant-parasitic lineages in Dothideomycetes and Sordariomycetes cannot be resolved to species level using ITS (Damm et al 2012 ; Maharachchikumbura et al 2012 ; Hyde et al 2013 ; Manamgoda et al 2014 ; Woudenberg et al 2017 ; Haridas et al 2020 ).…”

A New Age in Molecular Diagnostics for Invasive Fungal Disease: Are We Ready?

“…The development of NGS has enabled the simultaneous sequencing of mixed microbial communities directly from a variety of clinical samples (e.g., blood, sputum, BALF, tissue, and stools) using either Illumina or Oxford Nanopore Technology (Lefterova et al, 2015;Zhou et al, 2016;Hong et al, 2018;Langelier et al, 2018;Blauwkamp et al, 2019;Charalampous et al, 2019;McTaggart et al, 2019). Sequences obtained from clinical samples through NGS can then be identified to the genus and species level by using sequence alignment tools such as BLAST or WIMP (Camacho et al, 2009;Juul et al, 2015) against appropriate publicly available quality-controlled reference sequence databases, e.g., ISHAM barcoding database UNITE, RefSeq, and BOLD (Hebert et al, 2003;Kõljalg et al, 2013;Schoch et al, 2014;Meyer et al, 2019). However, there are currently a number of major limitations in this technology which may lead to inaccurate or insufficient identification of the fungal pathogen, including: (i) pre-PCR biases, such as sample handling, contamination introduced during sample collection, aliquoting, nucleic acid extraction, library preparation, or pooling (Salter et al, 2014;Strong et al, 2014), DNA extraction methods including the choice of storage buffer and extraction kit (Hallmaier-Wacker et al, 2018), the quantity of host DNA, which could be reduced by using various depletion methods (Hasan et al, 2016), and the issues related to the extraction of DNA of adequate quality (high purity, high molecular weight, and high concentration) (Hasan et al, 2016;Hallmaier-Wacker et al, 2018;Sanderson et al, 2018;Nicholls et al, 2019); (ii) PCR biases, including primer mismatches and variable length of the obtained amplicon (Schloss and Westcott, 2011;Boers et al, 2019); (iii) high sequencing error rate of the current NGS technologies, especially long read sequencing (Bakker et al, 2012;Schirmer et al, 2015;Tyler et al, 2018); (iv) the lack of complete and quality-controlled reference sequence databases with correct taxono...…”

Clinical Pathogen Genomics

“… 19 , 27 Although there are unique challenges for WGS-based fungal outbreak investigations, it has been successfully used in this context. 19 , 28 , 29 …”