Comparative Study of Metagenomics and Metatranscriptomics to Reveal Microbiomes in Overwintering Pepper Fruits

Red pepper (Capsicum annuum, L.), is one of the most important spice plants in Korea. Overwintering pepper fruits are a reservoir of various microbial pepper diseases. Here, we conducted metagenomics (DNA sequencing) and metatranscriptomics (RNA sequencing) using samples collected from three different fields. We compared two different library types and three different analytical methods for the identification of microbiomes in overwintering pepper fruits. Our results demonstrated that DNA sequencing might be useful for the identification of bacteria and DNA viruses such as bacteriophages, while mRNA sequencing might be beneficial for the identification of fungi and RNA viruses. Among three analytical methods, KRAKEN2 with raw data reads (KRAKEN2_R) might be superior for the identification of microbial species to other analytical methods. However, some microbial species with a low number of reads were wrongly assigned at the species level by KRAKEN2_R. Moreover, we found that the databases for bacteria and viruses were better established as compared to the fungal database with limited genome data. In summary, we carefully suggest that different library types and analytical methods with proper databases should be applied for the purpose of microbiome study.

Clinical relevance of Scedosporium spp. and Exophiala dermatitidis in patients with cystic fibrosis: A nationwide study

An increased prevalence of various filamentous fungi in sputum samples of patients with cystic fibrosis (CF) has been reported. The clinical significance, however, is mostly unclear. The aim of this study was to investigate the clinical relevance of Scedosporium spp. and Exophiala dermatitidis from sputum samples of patients with CF in the Netherlands. In this cross-sectional study, all CF patients of the Dutch national CF registry who were treated at five of the seven recognized CF centers during a 3-year period were included. We linked clinical data of the national CF registry with the national Dutch filamentous fungal database. We investigated the association between clinical characteristics and a positive sputum sample for Scedosporium spp. and E. dermatitidis, using logistic regression. Positive cultures for fungi were obtained from 3787 sputum samples from 699 of the 1312 patients with CF. Scedosporium spp. was associated with severe genotype, CF-related diabetes, several microorganisms, and inhaled antibiotics. E. dermatitidis was associated with older age, female sex, and Aspergillus spp. CF patients with and without Scedosporium spp. or E. dermatitidis seemed comparable in body mass index and lung function. This study suggests that Scedosporium spp. and E. dermatitidis are probably no major pathogens in CF patients in the Netherlands. Greater understanding of epidemiologic trends, risk factors, and pathogenicity of filamentous fungi in the respiratory tracts of patients with CF is needed.

Metagenomic datasets of cerebrospinal fluid from a small cohort of MS/non-MS patients do not show DNA from the fungal genus Trichosporon

In [2] Alonso et al used nested PCR assays together with next-generation sequencing to find Trichosporon species in the nervous tissue of 10 patients with MS. We deemed it possible that the fungus would be present in cerebrospinal fluid (CSF) samples. Whole metagenomic shotgun (WMGS) sequencing allows detection of any organism in a sample. With Trichosporon any detection would be a true positive because these fungi are not known to be on the skin, or as typical lab contamination. We screened public WMGS datasets of CSF from a cohort of 43 Canadian patients (28 MS, 13 non-MS)[1], using Kraken2[7], the ultrafast kmer-based classifier, using a fungal database augmented with all cleaned available Trichosporon genome assemblies from the NCBI. Blasting the marked reads against an equally augmented blastn database revealed no alignments with an evalue <= 1e-50. In general Kraken2 marked not more than 5 consecutive kmers in any read, which is a clear negative.

Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses

Ghost-tree is a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach uses one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families) as a “foundation” phylogeny. A second, more rapidly evolving genetic marker is then used to build “extension” phylogenies for more closely related organisms (e.g., fungal species or strains) that are then grafted on to the foundation tree by mapping taxonomic names. We apply ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. The result is a phylogenetic tree, compatible with the commonly used UNITE fungal database, that supports phylogenetic diversity analysis (e.g., UniFrac) of fungal communities profiled using ITS markers. Availability: ghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree.

Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses

Ghost-tree is a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach uses one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families) as a “foundation” phylogeny. A second, more rapidly evolving genetic marker is then used to build “extension” phylogenies for more closely related organisms (e.g., fungal species or strains) that are then grafted on to the foundation tree by mapping taxonomic names. We apply ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. The result is a phylogenetic tree, compatible with the commonly used UNITE fungal database, that supports phylogenetic diversity analysis (e.g., UniFrac) of fungal communities profiled using ITS markers. Availability: ghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree.