Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis
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 man...
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| Format: | Article |
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MDPI AG
2021-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/6/3148 |
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| author | Alberto Gómez-Carballa Ruth Barral-Arca Miriam Cebey-López Xabier Bello Jacobo Pardo-Seco Federico Martinón-Torres Antonio Salas |
| author_facet | Alberto Gómez-Carballa Ruth Barral-Arca Miriam Cebey-López Xabier Bello Jacobo Pardo-Seco Federico Martinón-Torres Antonio Salas |
| author_sort | Alberto Gómez-Carballa |
| collection | DOAJ |
| description | 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- (<i>n</i> = 695) and bacteria- (<i>n</i> = 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 <i>BAFT</i>, <i>ISG15</i> and <i>DNMT1</i>, 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)). <i>BAFT</i> and <i>ISG15</i> are involved in processes related to immune response, while <i>DNMT1</i> 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. |
| first_indexed | 2024-03-10T13:04:33Z |
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| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T13:04:33Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-1e5d467fe00d47858ee899dc4a2cca612023-11-21T11:12:24ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-03-01226314810.3390/ijms22063148Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort AnalysisAlberto Gómez-Carballa0Ruth Barral-Arca1Miriam Cebey-López2Xabier Bello3Jacobo Pardo-Seco4Federico Martinón-Torres5Antonio Salas6GenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, SpainGenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, SpainGenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, SpainGenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, SpainGenPoB Research Group, Instituto de Investigación Sanitaria (IDIS), Hospital Clínico Universitario de Santiago (SERGAS), 15706 Galicia, SpainGenetics, Vaccines and Infections Research Group (GENVIP), Instituto de Investigación Sanitaria de Santiago de Compostela, 15706 Galicia, SpainTranslational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, 15706 Galicia, SpainThe 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- (<i>n</i> = 695) and bacteria- (<i>n</i> = 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 <i>BAFT</i>, <i>ISG15</i> and <i>DNMT1</i>, 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)). <i>BAFT</i> and <i>ISG15</i> are involved in processes related to immune response, while <i>DNMT1</i> 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.https://www.mdpi.com/1422-0067/22/6/3148RNARNAseqmicroarraystranscriptometranscriptomic biomarkersRNA signature |
| spellingShingle | Alberto Gómez-Carballa Ruth Barral-Arca Miriam Cebey-López Xabier Bello Jacobo Pardo-Seco Federico Martinón-Torres Antonio Salas Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis International Journal of Molecular Sciences RNA RNAseq microarrays transcriptome transcriptomic biomarkers RNA signature |
| title | Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis |
| title_full | Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis |
| title_fullStr | Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis |
| title_full_unstemmed | Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis |
| title_short | Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis |
| title_sort | identification of a minimal 3 transcript signature to differentiate viral from bacterial infection from best genome wide host rna biomarkers a multi cohort analysis |
| topic | RNA RNAseq microarrays transcriptome transcriptomic biomarkers RNA signature |
| url | https://www.mdpi.com/1422-0067/22/6/3148 |
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