Targeted sequencing analysis pipeline for species identification of human pathogenic fungi using long-read nanopore sequencing

Nattapong Langsiri; Navaporn Worasilchai; Laszlo Irinyi; Piroon Jenjaroenpun; Thidathip Wongsurawat; Janet Jennifer Luangsa-Ard; Wieland Meyer; Ariya Chindamporn

doi:10.1186/s43008-023-00125-6

Targeted sequencing analysis pipeline for species identification of human pathogenic fungi using long-read nanopore sequencing

IMA Fungus. 2023 Sep 6;14(1):18. doi: 10.1186/s43008-023-00125-6.

Authors

Nattapong Langsiri¹, Navaporn Worasilchai^{2

3}, Laszlo Irinyi^{4

5

6}, Piroon Jenjaroenpun^{7

8}, Thidathip Wongsurawat^{7

8}, Janet Jennifer Luangsa-Ard⁹, Wieland Meyer^{4

5

6

10}, Ariya Chindamporn^{11

12}

Affiliations

¹ Medical Microbiology, Interdisciplinary Program, Graduated School, Chulalongkorn University, Bangkok, Thailand.
² Department of Transfusion Medicine and Clinical Microbiology, Faculty of Allied Health Science, Chulalongkorn University, Bangkok, Thailand.
³ Immunomodulation of Natural Products Research Group, Chulalongkorn University, Bangkok, Thailand.
⁴ Westmead Clinical School, Sydney Medical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
⁵ Sydney Institute for Infectious Diseases, The University of Sydney, Sydney, NSW, Australia.
⁶ Curtin Medical School, Curtin University, Perth, Bentley, WA, Australia.
⁷ Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
⁸ Division of Medical Bioinformatics, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
⁹ National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 111 Thailand Science Park, Phahonyothin Road, Klong Nueng, Klong Luang, Pathum Thani, Thailand.
¹⁰ Westerdijk Fungal Biodiversity Institute, KNAW, Utrecht, The Netherlands.
¹¹ Medical Microbiology, Interdisciplinary Program, Graduated School, Chulalongkorn University, Bangkok, Thailand. drariya@gmail.com.
¹² Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand. drariya@gmail.com.

Abstract

Among molecular-based techniques for fungal identification, Sanger sequencing of the primary universal fungal DNA barcode, the internal transcribed spacer (ITS) region (ITS1, 5.8S, ITS2), is commonly used in clinical routine laboratories due to its simplicity, universality, efficacy, and affordability for fungal species identification. However, Sanger sequencing fails to identify mixed ITS sequences in the case of mixed infections. To overcome this limitation, different high-throughput sequencing technologies have been explored. The nanopore-based technology is now one of the most promising long-read sequencing technologies on the market as it has the potential to sequence the full-length ITS region in a single read. In this study, we established a workflow for species identification using the sequences of the entire ITS region generated by nanopore sequencing of both pure yeast isolates and mocked mixed species reads generated with different scenarios. The species used in this study included Candida albicans (n = 2), Candida tropicalis (n = 1), Nakaseomyces glabratus (formerly Candida glabrata) (n = 1), Trichosporon asahii (n = 2), Pichia kudriavzevii (formerly Candida krusei) (n = 1), and Cryptococcus neoformans (n = 1). Comparing various methods to generate the consensus sequence for fungal species identification, the results from this study indicate that read clustering using a modified version of the NanoCLUST pipeline is more sensitive than Canu or VSEARCH, as it classified species accurately with a lower abundance cluster of reads (3% abundance compared to 10% with VSEARCH). The modified NanoCLUST also reduced the number of classified clusters compared to VSEARCH, making the subsequent BLAST+ analysis faster. Subsampling of the datasets, which reduces the size of the datasets by approximately tenfold, did not significantly affect the identification results in terms of the identified species name, percent identity, query coverage, percentage of reads in the classified cluster, and the number of clusters. The ability of the method to distinguish mixed species within sub-populations of large datasets has the potential to aid computer analysis by reducing the required processing power. The herein presented new sequence analysis pipeline will facilitate better interpretation of fungal sequence data for species identification.

Keywords: Fungal identification; Internal transcribed spacer (ITS); Nanopore technology; Targeted long-read sequencing.

Abstract

Grants and funding