Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/ijms22063148
Keywords
RNA; RNAseq; microarrays; transcriptome; transcriptomic biomarkers; RNA signature; multi-cohort; viral infection; bacterial infection; machine learning
Funding
- project: GePEM(Instituto de Salud Carlos III(ISCIII)/FEDER) [PI16/01478]
- project: DIAVIR (Instituto de Salud Carlos III(ISCIII)/FEDER
- Proyecto de Desarrollo Tecnologico en Salud) [DTS19/00049]
- project: Resvi-Omics (Instituto de Salud Carlos III(ISCIII)/FEDER) [PI19/01039]
- project: BI-BACVIR (PRIS-3
- Agencia de Conocimiento en Salud (ACIS)-Servicio Gallego de Salud (SERGAS)-Xunta de Galicia
- Spain)
- project: Programa Traslaciona Covid 19 (ACIS-Servicio Gallego de Salud (SERGAS)-Xunta de Galicia
- Spain)
- project: Axencia Galega de Innovacion (GAIN
- Xunta de Galicia Spain) [IN607B 2020/08]
- project ReSVinext (Instituto de Salud Carlos III(ISCIII)/FEDER) [PI16/01569]
- project Enterogen (Instituto de Salud Carlos III(ISCIII)/FEDER) [PI19/01090]
- project Axencia Galega de Innovacion (GAIN, Xunta de Galicia
- Spain) [IN845D 2020/23]
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Developing a three-gene signature based on host transcriptomics can accurately differentiate viral and bacterial infections, contributing to improved patient management and treatment in the fight against antibiotic resistance.
The fight against the spread of antibiotic resistance is one of the most important challenges facing health systems worldwide. Given the limitations of current diagnostic methods, the development of fast and accurate tests for the diagnosis of viral and bacterial infections would improve patient management and treatment, as well as contribute to reducing antibiotic misuse in clinical settings. In this scenario, analysis of host transcriptomics constitutes a promising target to develop new diagnostic tests based on the host-specific response to infections. We carried out a multi-cohort meta-analysis of blood transcriptomic data available in public databases, including 11 different studies and 1209 samples from virus- (n = 695) and bacteria- (n = 514) infected patients. We applied a Parallel Regularized Regression Model Search (PReMS) on a set of previously reported genes that distinguished viral from bacterial infection to find a minimum gene expression bio-signature. This strategy allowed us to detect three genes, namely BAFT, ISG15 and DNMT1, that clearly differentiate groups of infection with high accuracy (training set: area under the curve (AUC) 0.86 (sensitivity: 0.81; specificity: 0.87); testing set: AUC 0.87 (sensitivity: 0.82; specificity: 0.86)). BAFT and ISG15 are involved in processes related to immune response, while DNMT1 is related to the preservation of methylation patterns, and its expression is modulated by pathogen infections. We successfully tested this three-transcript signature in the 11 independent studies, demonstrating its high performance under different scenarios. The main advantage of this three-gene signature is the low number of genes needed to differentiate both groups of patient categories.
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