Manganese and fungal pathogens: Metabolism and potential association with virulence

Kinskovski, Uriel Perin; Staats, Charley Christian

doi:10.1016/j.fbr.2022.06.003

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Nitrogen and phosphorous availability are well‐known factors structuring microbial communities (Fierer & Jackson, 2006), but less is known about manganese and boron. Manganese is a micronutrient and plays a role in redox equilibrium and as a co‐factor in enzymes and transcription factors (Kinskovski & Staats, 2022). On the other hand, boron is thought to be a nonessential element for fungi, but it can be toxic thus is often used as an antifungal agent (Estevez‐Fregoso et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Predicting soil fungal communities from chemical and physical properties

Bodenhausen,

Hess,

Valzano

et al. 2023

J of Sust Agri & Env

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IntroductionBiogeography describes spatial patterns of diversity and explains why organisms occur in given conditions. While it is well established that the diversity of soil microbes is largely controlled by edaphic environmental variables, microbiome community prediction from soil properties has received less attention. In this study, we specifically investigated whether it is possible to predict the composition of soil fungal communities based on physicochemical soil data using multivariate ordination.Materials and MethodsWe sampled soil from 59 arable fields in Switzerland and assembled paired data of physicochemical soil properties as well as profiles of soil fungal communities. Fungal communities were characterized using long‐read sequencing of the entire ribosomal internal transcribed spacer. We used redundancy analysis to combine the physical and chemical soil measurements with the fungal community data.ResultsWe identified a reduced set of 10 soil properties that explained fungal community composition. Soil properties with the strongest impact on the fungal community included pH, potassium and sand content. Finally, we evaluated the model for its suitability for prediction using leave‐one‐out validation. The prediction of community composition was successful for most soils, and only 3/59 soils could not be well predicted (Pearson correlation coefficients between observed and predicted communities of <0.5). Further, we successfully validated our prediction approach with a publicly available data set. With both data sets, prediction was less successful for soils characterized by very unique properties or diverging fungal communities, while it was successful for soils with similar characteristics and microbiome.ConclusionsReliable prediction of microbial communities from chemical soil properties could bypass the complex and laborious sequencing‐based generation of microbiota data, thereby making soil microbiome information available for agricultural purposes such as pathogen monitoring, field inoculation or yield projections.

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Section: Discussionmentioning

confidence: 99%

Predicting soil fungal communities from chemical and physical properties

Bodenhausen,

Hess,

Valzano

et al. 2023

J of Sust Agri & Env

View full text Add to dashboard Cite

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“…One of the major obstacles to the discovery of new drugs is the lack of general knowledge about the metabolism of organisms, especially during infection. This is exactly the case for C. neoformans, for which many studies focus on the potential antigen (Oliveira et al, 2021;Becerra-Álvarez et al, 2022;Nelson et al, 2022) and its functions and pathogenesis mechanisms (Sabiiti and May., 2012;Buchanan., 1998;Rhome et al, 2011;Waterman et al, 2012;Price et al, 2011;Mota Fernandes and Del Poeta., 2020;Gray et al, 2012;Anderson et al, 2014;Huang et al, 2016), and very few studies are carried out on the organism's primary metabolism (Shea et al, 2006;Garcia-Santamarina et al, 2018;Berguson et al, 2022;Jezewski et al, 2022;Kinskovski and Staats, 2022;Ma et al, 2022). Even with genomic information, using genome-scale data to understand and interpret the function of genes in terms of the general biology of the pathogen remains challenging for systems-level understanding.…”

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confidence: 99%

Comprehensive genome-scale metabolic model of the human pathogen Cryptococcus neoformans: A platform for understanding pathogen metabolism and identifying new drug targets

Tezcan

Demirtas²,

Çakar³

et al. 2023

Front. Bioinform.

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Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods.Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data.Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism.

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Manganese and fungal pathogens: Metabolism and potential association with virulence

Cited by 2 publications

References 44 publications

Predicting soil fungal communities from chemical and physical properties

Predicting soil fungal communities from chemical and physical properties

Comprehensive genome-scale metabolic model of the human pathogen Cryptococcus neoformans: A platform for understanding pathogen metabolism and identifying new drug targets

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