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Fungal biomarker discovery by integration of classifiers
João Pedro Saraiva1,2, Marcus Oswald1,2, Antje Biering1,2
1Network Modelling, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute (HKI), Beutenbergstraße 11a, Jena, Germany.
Background:
The human immune system is responsible for protecting the host from infection. However, in immunocompromised individuals the risk of infection increases substantially with possible drastic consequences. In extreme, systemic infection can lead to sepsis which is responsible for innumerous deaths worldwide. Amongst its causes are infections by bacteria and fungi. To increase survival, it is mandatory to identify the type of infection rapidly. Discriminating between fungal and bacterial pathogens is key to determine if antifungals or antibiotics should be administered, respectively. For this, in situ experiments have been performed to determine regulation mechanisms of the human immune system to identify biomarkers. However, these studies led to heterogeneous results either due different laboratory settings, pathogen strains, cell types and tissues, as well as the time of sample extraction, to name a few.
Methods:
To generate a gene signature capable of discriminating between fungal and bacterial infected samples, we employed Mixed Integer Linear Programming (MILP) based classifiers on several datasets comprised of the above mentioned pathogens.
Results:
When combining the classifiers by a joint optimization we could increase the consistency of the biomarker gene list independently of the experimental setup. An increase in pairwise overlap (the number of genes that overlap in each cross-validation) of 43% was obtained by this approach when compared to that of single classifiers. The refined gene list was composed of 19 genes and ranked according to consistency in expression (up- or down-regulated) and most of them were linked either directly or indirectly to the ERK-MAPK signalling pathway, which has been shown to play a key role in the immune response to infection. Testing of the identified 12 genes on an unseen dataset yielded an average accuracy of 83%.
Conclusions:
In conclusion, our method allowed the combination of independent classifiers and increased consistency and reliability of the generated gene signatures.
Insights
This study developed a gene signature to differentiate fungal and bacterial infections, improving diagnostic accuracy. The method combines classifiers for reliable identification of infection types, crucial for timely treatment.
Area of Science:
- Immunology
- Computational Biology
- Infectious Diseases
Background:
- The human immune system protects against infection, but immunocompromised individuals face increased risks.
- Systemic infections like sepsis, caused by bacteria and fungi, lead to significant mortality.
- Rapidly discriminating between fungal and bacterial pathogens is critical for appropriate treatment with antifungals or antibiotics.
Purpose of the Study:
- To develop a reliable gene signature for distinguishing between fungal and bacterial infections.
- To overcome limitations of previous studies yielding heterogeneous results due to experimental variations.
Main Methods:
- Employed Mixed Integer Linear Programming (MILP) based classifiers on multiple datasets.
- Combined independent classifiers through joint optimization to enhance biomarker gene list consistency.
Main Results:
- Achieved a 43% increase in pairwise overlap for biomarker gene lists compared to single classifiers.
- Identified a refined 19-gene signature, with most genes linked to the ERK-MAPK signaling pathway.
- Validated the signature on an unseen dataset, achieving an average accuracy of 83%.
Conclusions:
- The developed method effectively combines independent classifiers to improve gene signature consistency and reliability.
- This approach offers a more robust tool for differentiating between fungal and bacterial infections.
