A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia

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Abstract

Pneumonia stands as the primary cause of death among children under five, yet current diagnosis methods often result in inadequate or unnecessary treatments. Our research seeks to address this gap by identifying host transcriptomic biomarkers in the blood of children with definitive viral and bacterial pneumonia. We performed RNA sequencing on 192 prospectively collected whole blood samples, including 38 controls and 154 pneumonia cases, uncovering a 5-transcript signature (genes FAM20A, BAG3, TDRD9, MXRA7, and KLF14) that effectively distinguishes bacterial from viral pneumonia (area under the curve (AUC): 0.95 [0.88–1.00]). Initial validation using combined definitive and probable cases yielded an AUC of 0.87 [0.77–0.97], while full validation in a new prospective cohort of 32 patients achieved an AUC of 0.92 [0.83–1.00]. This robust signature holds significant potential to enhance diagnostics accuracy for pediatric pneumonia, reducing diagnostic delays and unnecessary treatments and potentially transforming clinical practice.

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Viz-Lasheras, S., Gómez-Carballa, A., Pardo-Seco, J., Bello, X., Rivero-Calle, I., Dacosta, A. I., Kaforou, M., Habgood-Coote, D., Cunnington, A. J., Emonts, M., Herberg, J. A., Wright, V. J., Carrol, E. D., Paulus, S. C., Zenz, W., Kohlfürst, D. S., Van der Flier, M., de Groot, R., Schlapbach, L. J., … Salas, A. (2025). A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia. iScience, 28(2), Article 111747. https://doi.org/10.1016/j.isci.2025.111747

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The authors would like to express their appreciation to the study investigators of GENDRES network (www.gendres.org) (Annex), as well as the nursery and laboratory service at the Hospital Clínico Universitario de Santiago de Com- postela, for their invaluable dedication and support. This research project was made possible through the access granted by the Galician Supercomputing Center (CESGA) to its supercomputing infrastructure. The supercomputer FinisTerrae III and its permanent data storage system have been funded by the Spanish Ministry of Science and Innovation, the Galician Government, and the European Regional Development Fund (ERDF). This work was supported by the European Seventh Framework Programme for Research and Technolog- ical Development (FP7) under the EUCLIDS project (grant agreement number 279185) and the European Union’s Horizon 2020 research and innovation program under grant agreement nos. 668303 (PERFORM) and 848196 (DIAMONDS). This study also received support by (1) ISCIII: TRINEO: PI22/ 00162, DIAVIR: DTS19/00049, Resvi-Omics: PI19/01039 (to A.S.), ReSVinext: PI16/01569, Enterogen: PI19/01090, OMI-COVI-VAC: PI22/00406 (to F.M.-T.), and cofinanciados FEDER; (2) GAIN: IN607B 2020/08 and IN607A 2023/02 (to A.S.) and GEN-COVID (IN845D 2020/23 [to F.M.-T.], IIN607A2021/05 [to F.M.-T.], and IN677D 2024/06 [to A.G.-C.]; (3) ACIS: BI-BACVIR (PRIS-3, to A.S.) and CovidPhy (SA 304 C, to A.S.); (4) Spanish Ministry of Science and Innovation (MCIN)/Spanish Research Agency (AEI) (PID2022-142156OB-I00, to A.G.-C.); and (5) consorcio Centro de Investigacion Biomedica en Red de Enfermedades Respiratorias (CB21/06/00103, to A.S. and F.M.-T.). A.G.-C. is supported by the Miguel Servet programme (CP23/00080) contract, funded by the Instituto de Salud Carlos III (ISCIII) and co-funded by the European Union. L.J.S. was supported by the NOMIS Foundation. The funders were not involved in the study design; collection, analysis, and interpretation of data; the writing of this article; or the decision to submit it for publication.

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@2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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