ArticleFrontiers in microbiology2015
Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study.
Article in Frontiers in microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 48 citations in OpenAlex.
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- A potential XGBoost Diagnostic Score forFrontiers in immunology · 2025Article
- Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses.Biomedicines · 2024Article
- Identification and validation of diagnostic biomarkers and immune cell abundance characteristics inFrontiers in immunology · 2024Article
- Transcriptional markers classifying Escherichia coli and Staphylococcus aureus induced sepsis in adults: A data-driven approach.PloS one · 2024Article
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- Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections.Journal of translational medicine · 2023Review
- Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity.Nature communications · 2023Article
- Fungal biotechnology: From yesterday to tomorrow.Frontiers in fungal biology · 2023Review
- Mitochondrial ACOD1/IRG1 in infection and sterile inflammation.Journal of intensive medicine · 2022Review
- Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions.Frontiers in genetics · 2022Article
- The network interplay of interferon and Toll-like receptor signaling pathways in the anti-Candida immune response.Scientific reports · 2021Article
- In vitro infection models to study fungal-host interactions.FEMS microbiology reviews · 2021Review
- Survival Strategies of PathogenicmBio · 2020Article
- ACOD1 in immunometabolism and disease.Cellular & molecular immunology · 2020Review
- Two Gene Set Variation Index as Biomarker of Bacterial and Fungal Sepsis.BioMed research international · 2020Article
- Dynamic Interplay of Host and Pathogens in an Avian Whole-Blood Model.Frontiers in immunology · 2020Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to classify if a sample contains a bacterial, fungal, or mock-infection. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional test dataset comprising Cryptococcus neoformans infections. Furthermore, the classifier is robust to Gaussian noise, indicating correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances.
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