Emerging mould infections: Get prepared to meet unexpected fungi in your patient

Dellière, Sarah; Rivero-Menéndez, Olga; Gautier, Claudie; García-Hermoso, Dea; Alastruey‐Izquierdo, Ana; Alanio, Alexandre

doi:10.1093/mmy/myz039

Cited by 12 publications

(12 citation statements)

References 54 publications

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“…Further, we searched the Taxonomy database [Schoch et al, 2020] using the query '"host human"[Properties] AND Fungi [orgn]', obtaining a list of species with a putative human host and their TaxIDs. We then used a comprehensive selection of literature reviews and comparative studies [Taylor et al, 2001, Chowdhary et al, 2014, Bombassaro et al, 2020, Brunner-Mendoza et al, 2019, Franzen and Müller, 2001, Colombo et al, 2011, Han and Weiss, 2017, Jančič et al, 2015, Kjaerbølling et al, 2018, Köhler et al, 2015, Kwon-Chung et al, 2017, Paulussen et al, 2017, Prakash et al, 2017, Dellière et al, 2020, Teixeira et al, 2017, Seyedmousavi et al, 2018 to collect additional evidence and extend the collected list of human pathogens. We manually extracted the species names and searched the NCBI Taxonomy (including synonyms) to link them to their names as reported in Taxonomy and the respective species-level TaxIDs.…”

Section: Fundingmentioning

confidence: 99%

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

et al. 2022

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Background Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone. Results We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats. Conclusions The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task. Availability and implementation The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Fundingmentioning

confidence: 99%

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

et al. 2022

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“…Further, we searched the Taxonomy database [33] using the query '"host human"[Properties] AND Fungi [orgn]', obtaining a list of species with a putative human host and their TaxIDs. We then used a comprehensive selection of literature reviews and comparative studies [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76] to collect additional evidence and extend the collected list of human pathogens. We manually extracted the species names and searched the NCBI Taxonomy (including synonyms) to link them to their names as reported in Taxonomy and the respective species-level TaxIDs.…”

Section: Ftp Pathmentioning

confidence: 99%

“…Further, we searched the Taxonomy database [Schoch et al, 2020] using the query ‘“host human”[Properties] AND Fungi [orgn]’, obtaining a list of species with a putative human host and their TaxIDs. We then used a comprehensive selection of literature reviews and comparative studies [Taylor et al, 2001, Chowdhary et al, 2014, Bombassaro et al, 2020, Brunner-Mendoza et al, 2019, Franzen and Müller, 2001, Colombo et al, 2011, Han and Weiss, 2017, Hu et al, 2015, Jančič et al, 2015, Kjærbølling et al, 2018, Köhler et al, 2015, Kwon-Chung et al, 2017, Paulussen et al, 2017, Prakash et al, 2017, Dellière et al, 2020, Teixeira et al, 2017, Seyedmousavi et al, 2018] to collect additional evidence and extend the collected list of human pathogens. We manually extracted the species names and searched the NCBI Taxonomy (including synonyms) to link them to their names as reported in Taxonomy and the respective species-level TaxIDs.…”

Section: Supplementary Informationmentioning

confidence: 99%

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

Bartoszewicz

Nasri

Nowicka

et al. 2021

Preprint

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BackgroundEmerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant, curated data remains comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.ResultsWe present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that the resulting database can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and show that the human pathogens are separable from non-human pathogens. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.ConclusionsThe presented data collection enables accurate detection of novel pathogens from sequencing data. It is also a comprehensive resource that can find use beyond this particular task. This can include possible applications in proteomics and genomics, employing both machine learning and direct sequence comparison.AvailabilityThe database and models are hosted at https://zenodo.org/record/5711852 and https://zenodo.org/record/5711877. Source code is available at https://gitlab.com/dacs-hpi/deepac.

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“…Antifungal becomes more complex in modern AML therapy as with any hematological malignancy. On the one hand, this is due to emerging fungal pathogens with resistance to antifungal agents used for prophylaxis [90,91] and breakthrough fungal infections [92]. On the other hand, novel drugs for AML treatment comprising increased interaction potential with triazoles will be more frequently administered.…”

Section: Future Developmentsmentioning

confidence: 99%

Antifungal prophylaxis and novel drugs in acute myeloid leukemia: the midostaurin and posaconazole dilemma

et al. 2020

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With the advent of new targeted drugs in hematology and oncology patient prognosis is improved. Combination with antifungal prophylaxis challenges clinicians due to pharmacological profiles prone to drug–drug interactions (DDI). Midostaurin is a novel agent for FLT3-TKD/-ITDmut-acute myeloid leukemia (AML) and metabolized via cytochrome P450 3A4 (CYP3A4). Posaconazole is a standard of care antifungal agent used for prophylaxis during induction treatment of AML and a strong CYP3A4 inhibitor. Concomitant administration of both drugs leads to elevated midostaurin exposure. Both drugs improve overall survival at low numbers needed to treat. The impact of CYP3A4-related DDI remains to be determined. Severe adverse events have been observed; however, it remains unclear if they can be directly linked to DDI. The lack of prospective clinical studies assessing incidence of invasive fungal infections and clinical impact of DDI contributes to neglecting live-saving antifungal prophylaxis. Management strategies to combine both drugs have been proposed, but evidence on which approach to use is scarce. In this review, we discuss several approaches in the specific clinical setting of concomitant administration of midostaurin and posaconazole and give examples from everyday clinical practice. Therapeutic drug monitoring will become increasingly important to individualize and personalize antineoplastic concomitant and antifungal treatment in the context of DDI. Pharmaceutical companies addressing the issue in clinical trials may take a pioneer role in this field. Other recently developed and approved drugs for the treatment of AML likely inhere potential of DDI marking a foreseeable issue in future treatment of this life-threatening disease.

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Emerging mould infections: Get prepared to meet unexpected fungi in your patient

Cited by 12 publications

References 54 publications

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

Antifungal prophylaxis and novel drugs in acute myeloid leukemia: the midostaurin and posaconazole dilemma

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