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12.1 Introduction
Fungal siderophores are synthesized by fungus with low molecular weight and iron
chelation properties. Most of them are hydroxamate siderophores (Renshaw et al.
2002) that possess beneficial or harmful effects. While the research work on bacterial siderophores are plenty, only fewer information is known about fungal siderophores. A comprehensive information on fungal siderophores based on literature
resources in PubMed may benefit the microbiologists. This became the focus of the
book volume “Fungal siderophores: From Mineral-Microbe Interactions to Antipathogenicity”. The chapters are written by experts in the field of microbiology,
pharmacology, pharmacognosy, biotechnology and bioinformatics. However, the
main challenge reported by experts is finding the research articles related to fungal
siderophores with a focus on their area of expertise. This motivated us to summarize
the contents on fungal siderophores hidden within ~30 million PubMed articles.
Manual processing of PubMed articles to retrieve the ones related to fungal siderophores is not an effective approach. A Boolean query search (“fungal siderophore” OR “fungal siderophores”) within the PubMed database retrieved only 51
articles. It is possible that we are missing many articles that include other representations for fungal siderophores. An automated approach to retrieve relevant articles
from the PubMed database is mandatory for research focusing on fungal siderophores. Performing new research without the knowledge of existing information is
a waste of time and hinders the progress in the field of microbiology. Alternatively,
text mining is an automated approach meant for the retrieval and extraction of information from unstructured text (Raja et al. 2020). When the approach is applied to
biomedical literature such as PubMed articles, it is known as “Biomedical Text
Mining”. When applied on patient’s electronic health records and clinical notes, the
approach is known as “Clinical Text Mining” (Raja et al. 2017).
Fungal siderophores are mainly involved in iron uptake, transport and storage of
iron. It is crucial for various conditions such as iron starvation, antioxidative
defence, microbial competition and virulence in humans. Recent studies identified
the importance of siderophores and their biosynthetic pathways in the treatment and
diagnosis of various fungal infections. Hence, gene and protein targets associated
with fungal siderophore biosynthetic pathways are found to be the key components
involved in the treatment of fungal diseases. For various fungal infections, we identified gene and protein targets, and constructed the network using Cytoscape.
Despite the fact that there are a few studies investigating fungal siderophores of
humans, the information remains scattered in the literature. Here, we have populated and integrated all the biological components to identify the therapeutic targets
for fungal diseases using text mining of human fungal siderophore data. Text mining and data mining combined with network-based bioinformatics approaches
(Prabahar and Natarajan 2017a, b, c) could unravel the mystery of hidden knowledge embedded in the literature resources and determine the novel targets involved
in therapy.
A. Prabahar et al.
12.1 Introduction
Fungal siderophores are synthesized by fungus with low molecular weight and iron
chelation properties. Most of them are hydroxamate siderophores (Renshaw et al.
2002) that possess beneficial or harmful effects. While the research work on bacterial siderophores are plenty, only fewer information is known about fungal siderophores. A comprehensive information on fungal siderophores based on literature
resources in PubMed may benefit the microbiologists. This became the focus of the
book volume “Fungal siderophores: From Mineral-Microbe Interactions to Antipathogenicity”. The chapters are written by experts in the field of microbiology,
pharmacology, pharmacognosy, biotechnology and bioinformatics. However, the
main challenge reported by experts is finding the research articles related to fungal
siderophores with a focus on their area of expertise. This motivated us to summarize
the contents on fungal siderophores hidden within ~30 million PubMed articles.
Manual processing of PubMed articles to retrieve the ones related to fungal siderophores is not an effective approach. A Boolean query search (“fungal siderophore” OR “fungal siderophores”) within the PubMed database retrieved only 51
articles. It is possible that we are missing many articles that include other representations for fungal siderophores. An automated approach to retrieve relevant articles
from the PubMed database is mandatory for research focusing on fungal siderophores. Performing new research without the knowledge of existing information is
a waste of time and hinders the progress in the field of microbiology. Alternatively,
text mining is an automated approach meant for the retrieval and extraction of information from unstructured text (Raja et al. 2020). When the approach is applied to
biomedical literature such as PubMed articles, it is known as “Biomedical Text
Mining”. When applied on patient’s electronic health records and clinical notes, the
approach is known as “Clinical Text Mining” (Raja et al. 2017).
Fungal siderophores are mainly involved in iron uptake, transport and storage of
iron. It is crucial for various conditions such as iron starvation, antioxidative
defence, microbial competition and virulence in humans. Recent studies identified
the importance of siderophores and their biosynthetic pathways in the treatment and
diagnosis of various fungal infections. Hence, gene and protein targets associated
with fungal siderophore biosynthetic pathways are found to be the key components
involved in the treatment of fungal diseases. For various fungal infections, we identified gene and protein targets, and constructed the network using Cytoscape.
Despite the fact that there are a few studies investigating fungal siderophores of
humans, the information remains scattered in the literature. Here, we have populated and integrated all the biological components to identify the therapeutic targets
for fungal diseases using text mining of human fungal siderophore data. Text mining and data mining combined with network-based bioinformatics approaches
(Prabahar and Natarajan 2017a, b, c) could unravel the mystery of hidden knowledge embedded in the literature resources and determine the novel targets involved
in therapy.
A. Prabahar et al.