Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data.

The kingdom Fungi is highly diverse in morphology and ecosystem function. Yet fungi are challenging to characterize as they can be difficult to culture and morphologically indistinct. Overall, their description and analysis lag far behind other microbes such as bacteria. Classification of species via high-throughput sequencing is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. With the rapid development of sequencing technologies, however, standardized procedures for taxonomic assignment of long sequence reads have not yet been well established. Focusing on nanopore sequencing technology, we compared classification and community composition analysis pipelines using shotgun and amplicon sequencing data generated from mock communities comprising 43 fungal species. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungal-specific database. During the assessment of classification algorithms, we found that applying cutoffs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without major data loss. We also generated draft genome assemblies for three fungal species from nanopore data which were absent from genome databases. Our study improves sequence-based classification and estimation of relative sequence abundance using real fungal community data and provides a practical guide for the design of metagenomics analyses focusing on fungi. IMPORTANCE Our study is unique in that it provides an in-depth comparative study of a real-life complex fungal community analyzed with multiple long- and short-read sequencing approaches. These technologies and their application are currently of great interest to diverse biologists as they seek to characterize the community compositions of microbiomes. Although great progress has been made on bacterial community compositions, microbial eukaryotes such as fungi clearly lag behind. Our study provides a detailed breakdown of strategies to improve species identification with immediate relevance to real-world studies. We find that real-life data sets do not always behave as expected, distinct from reports based on simulated data sets.

Long-Reads-Based Metagenomics in Clinical Diagnosis With a Special Focus on Fungal Infections.

Identification of the causative infectious agent is essential in the management of infectious diseases, with the ideal diagnostic method being rapid, accurate, and informative, while remaining cost-effective. Traditional diagnostic techniques rely on culturing and cell propagation to isolate and identify the causative pathogen. These techniques are limited by the ability and the time required to grow or propagate an agent in vitro and the facts that identification based on morphological traits are non-specific, insensitive, and reliant on technical expertise. The evolution of next-generation sequencing has revolutionized genomic studies to generate more data at a cheaper cost. These are divided into short- and long-read sequencing technologies, depending on the length of reads generated during sequencing runs. Long-read sequencing also called third-generation sequencing emerged commercially through the instruments released by Pacific Biosciences and Oxford Nanopore Technologies, although relying on different sequencing chemistries, with the first one being more accurate both platforms can generate ultra-long sequence reads. Long-read sequencing is capable of entirely spanning previously established genomic identification regions or potentially small whole genomes, drastically improving the accuracy of the identification of pathogens directly from clinical samples. Long-read sequencing may also provide additional important clinical information, such as antimicrobial resistance profiles and epidemiological data from a single sequencing run. While initial applications of long-read sequencing in clinical diagnosis showed that it could be a promising diagnostic technique, it also has highlighted the need for further optimization. In this review, we show the potential long-read sequencing has in clinical diagnosis of fungal infections and discuss the pros and cons of its implementation.

Long-read sequencing based clinical metagenomics for the detection and confirmation of Pneumocystis jirovecii directly from clinical specimens: A paradigm shift in mycological diagnostics.

The advent of next generation sequencing technologies has enabled the characterization of the genetic content of entire communities of organisms, including those in clinical specimens, without prior culturing. The MinION from Oxford Nanopore Technologies offers real-time, direct sequencing of long DNA fragments directly from clinical samples. The aim of this study was to assess the ability of unbiased, genome-wide, long-read, shotgun sequencing using MinION to identify Pneumocystis jirovecii directly from respiratory tract specimens and to characterize the associated mycobiome. Pneumocystis pneumonia (PCP) is a life-threatening fungal disease caused by P. jirovecii. Currently, the diagnosis of PCP relies on direct microscopic or real-time quantitative polymerase chain reaction (PCR) examination of respiratory tract specimens, as P. jirovecii cannot be cultured readily in vitro. P. jirovecii DNA was detected in bronchoalveolar lavage (BAL) and induced sputum (IS) samples from three patients with confirmed PCP. Other fungi present in the associated mycobiome included known human pathogens (Aspergillus, Cryptococcus, Pichia) as well as commensal species (Candida, Malassezia, Bipolaris). We have established optimized sample preparation conditions for the generation of high-quality data, curated databases, and data analysis tools, which are key to the application of long-read MinION sequencing leading to a fundamental new approach in fungal diagnostics.

Dual DNA Barcoding for the Molecular Identification of the Agents of Invasive Fungal Infections.

Invasive fungal infections, such as aspergillosis, candidiasis, and cryptococcosis, have significantly increased among immunocompromised people. To tackle these infections the first and most decisive step is the accurate identification of the causal pathogen. Routine identification of invasive fungal infections has progressed away from culture-dependent methods toward molecular techniques, including DNA barcoding, a highly efficient and widely used diagnostic technique. Fungal DNA barcoding previously relied on a single barcoding region, the internal transcribed spacer (ITS) region. However, this allowed only for 75% of all fungi to be correctly identified. As such, the translational elongation factor 1α (TEF1α) was recently introduced as the secondary barcode region to close the gap. Both loci together form the dual fungal DNA barcoding scheme. As a result, the ISHAM Barcoding Database has been expanded to include sequences for both barcoding regions to enable practical implementation of the dual barcoding scheme into clinical practice. The present study investigates the impact of the secondary barcode on the identification of clinically important fungal taxa, that have been demonstrated to cause severe invasive disease. Analysis of the barcoding regions was performed using barcoding gap analysis based on the genetic distances generated with the Kimura 2-parameter model. The secondary barcode demonstrated an improvement in identification for all taxa that were unidentifiable with the primary barcode, and when combined with the primary barcode ensured accurate identification for all taxa analyzed, making DNA barcoding an important, efficient and reliable addition to the diagnostic toolset of invasive fungal infections.

Database establishment for the secondary fungal DNA barcode translational elongation factor 1α (TEF1α) 1.

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.