Evidence map›Paper›PMID 25814982›Full record

ArticleFrontiers in microbiology2015

Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study.

Andreas Dix, Kerstin Hünniger, Michael Weber, Reinhard Guthke, Oliver Kurzai, Jörg Linde

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 48 citations in OpenAlex.

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  12. Fungal biotechnology: From yesterday to tomorrow.Frontiers in fungal biology · 2023
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  18. ACOD1 in immunometabolism and disease.Cellular & molecular immunology · 2020
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Andreas DixSystems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Kerstin HünnigerSeptomics Research Centre, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Michael WeberSeptomics Research Centre, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Reinhard GuthkeSystems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Oliver KurzaiSeptomics Research Centre, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Jörg LindeSystems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

decision tree based methodsfeature selectionfungal pathogensimmune responsemicroarraysystems biology

Identifiers

PMID25814982
PMCPMC4356159
OpenAlexW2004104098

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.