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Background: With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database., Results: We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is > 7× faster than MetaMaps and GeNet and > 2× faster than MEGAN-LR and MMseqs2., Conclusion: This proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further., (© 2024. The Author(s).)

Background: Non-ribosomal peptide synthetase-like (NRPS-like) enzymes are highly enriched in fungal genomes and can be discriminated into reducing and non-reducing enzymes. Non-reducing NRPS-like enzymes possess a C-terminal thioesterase domain that catalyses the condensation of two identical aromatic α-keto acids under the formation of enzyme-specific substrate-interconnecting core structures such as terphenylquinones, furanones, butyrolactones or dioxolanones. Ascocoryne sarcoides produces large quantities of ascocorynin, which structurally resembles a terphenylquinone produced from the condensation of p-hydroxyphenylpyruvate and phenylpyruvate. Since the parallel use of two different substrates by a non-reducing NRPS-like enzyme appeared as highly unusual, we investigated the biosynthesis of ascocorynin in A. sarcoides., Results: Here, we searched the genome of A. sarcoides for genes coding for non-reducing NRPS-like enzymes. A single candidate gene was identified that was termed acyN. Heterologous gene expression confirmed that AcyN is involved in ascocorynin production but only produces the non-hydroxylated precursor polyporic acid. Although acyN is embedded in an ascocorynin biosynthesis gene cluster, a gene encoding a monooxygenase required for the hydroxylation of polyporic acid was not present. Expression analyses of all monooxygenase-encoding genes from A. sarcoides identified a single candidate that showed the same expression pattern as acyN. Accordingly, heterologous co-expression of acyN and the monooxygenase gene resulted in the production of ascocorynin. Structural modelling of the monooxygenase suggests that the hydrophobic substrate polyporic acid enters the monooxygenase from a membrane facing entry site and is converted into the more hydrophilic product ascocorynin, which prevents its re-entry for a second round of hydroxylation., Conclusion: This study characterises the first naturally occurring polyporic acid synthetase from an ascomycete. It confirms the high substrate and product specificity of this non-reducing NRPS-like enzyme and highlights the requirement of a monooxygenase to produce the terphenylquinone ascocorynin., (© 2022. The Author(s).)

Background: Paracoccidioides spp is a fungi genus and the agent of paracoccidioidomycosis. The strategies of infection used by these pathogens involve the expression of proteins related to adaptation to the host, particularly regarding the uptake of micronutrients. This study analyzed the adhesion of Paracoccidioides lutzii during conditions of copper (Cu) and iron (Fe) deprivation, while also evaluating the proteins expressed in conditions of Cu depletion in the presence of four extracellular matrix (ECM) components (laminin, fibronectin and types I and IV collagen)., Results: We cultured the P. lutzii in a chemically defined media without Cu and Fe. The fungus was then placed in contact with different ECM components and adhesion was evaluated. A significant increase in binding to all ECM components was observed when the fungus was cultured without Cu; which might be related to some adhesins expression. A proteomic assay was developed and revealed 39 proteins expressed that are involved in processes such as virulence, protein synthesis, metabolism, energy, transcription, transport, stress response and the cell cycle when the fungus was interacting with the ECM components. The up-regulated expression of two important adhesins, enolase and 14-3-3, was observed at the fungal cell wall during the interaction with the ECM components, indicating the role of these proteins in the Paracoccidioides-host interaction., Conclusions: This study is important for determining prospective proteins that may be involved in the interaction of Paracoccidioides with a host. Understanding the adaptive response to different growth conditions, elucidating the processes of adhesion and cell invasion, and identifying the proteins that are differentially expressed during the fungus-host interaction may help elucidate mechanisms used for survival and growth of Paracoccidioides in various human tissues.