From Omics Data to Biological Context: The CURIE AI En-gine. Illustrated with the Transcriptomic Response to Bacte-rial Pneumonia
Abstract
High-throughput sequencing necessitates robust computational tools to contextualize complex transcriptomic data. In this study, we analyzed RNA-Seq data from peripheral blood of bacterial pneumonia patients and healthy controls (GEO: GSE161731) to investigate systemic molecular responses to infection. Differential gene expression analysis identified significantly upregulated genes in pneumonia. We used GANGO for Gene Ontology (GO) term enrichment and BioFunctional for hierarchical exploration, providing an initial overview of affected biological processes. To deepen interpretation, we leveraged CURIE (www.curieai.tech), an AI platform that cross-references GO terms with scientific literature. CURIE generates narrative explanations, linking molecular mechanisms, experimental evidence, and publications to each biological process, moving beyond simple enrichment. Our combined analysis revealed a coordinated upregulation of pathways involved in DNA repair and replication, transcriptional activation, RNA processing, translation, ribosome biogenesis, mitochondrial respiration, and cofactor binding. By integrating these findings within a broad, literature-supported framework, we highlighted how multiple cellular compartments and processes collaborate to mount an effective response to infection-induced stress. This study underscores the potential of combining classical statistical enrichment with AI-based interpretation for a holistic understanding of host-pathogen interactions at the transcriptomic level.
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BibTeX
@misc{castellani2026from,
title = {{From Omics Data to Biological Context: The CURIE AI En-gine. Illustrated with the Transcriptomic Response to Bacte-rial Pneumonia}},
author = {G. Castellani and A. Rodriguez-Mena and X. Tarrago-Claramunt and V. Madarnas-Gomez and Y. Torres-Favier and J. Mendez-Viera and T. MONLEON-GETINO},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.19.26365885},
url = {https://www.medrxiv.org/content/10.64898/2026.10.19.26365885v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - From Omics Data to Biological Context: The CURIE AI En-gine. Illustrated with the Transcriptomic Response to Bacte-rial Pneumonia
AU - G. Castellani
AU - A. Rodriguez-Mena
AU - X. Tarrago-Claramunt
AU - V. Madarnas-Gomez
AU - Y. Torres-Favier
AU - J. Mendez-Viera
AU - T. MONLEON-GETINO
PY - 2026
DA - 2026/10/19
PB - medRxiv
DO - 10.64898/2026.10.19.26365885
UR - https://www.medrxiv.org/content/10.64898/2026.10.19.26365885v1
AB - High-throughput sequencing necessitates robust computational tools to contextualize complex transcriptomic data. In this study, we analyzed RNA-Seq data from peripheral blood of bacterial pneumonia patients and healthy controls (GEO: GSE161731) to investigate systemic molecular responses to infection. Differential gene expression analysis identified significantly upregulated genes in pneumonia. We used GANGO for Gene Ontology (GO) term enrichment and BioFunctional for hierarchical exploration, providing an initial overview of affected biological processes. To deepen interpretation, we leveraged CURIE (www.curieai.tech), an AI platform that cross-references GO terms with scientific literature. CURIE generates narrative explanations, linking molecular mechanisms, experimental evidence, and publications to each biological process, moving beyond simple enrichment. Our combined analysis revealed a coordinated upregulation of pathways involved in DNA repair and replication, transcriptional activation, RNA processing, translation, ribosome biogenesis, mitochondrial respiration, and cofactor binding. By integrating these findings within a broad, literature-supported framework, we highlighted how multiple cellular compartments and processes collaborate to mount an effective response to infection-induced stress. This study underscores the potential of combining classical statistical enrichment with AI-based interpretation for a holistic understanding of host-pathogen interactions at the transcriptomic level.
ER -