Objectives/Goals: Ventilator-associated pneumonia (VAP) is an infection caused by bacteria, viruses, or fungi during mechanical ventilation. We analyzed a cohort of COVID-19 patients admitted to the intensive care unit with respiratory failure with different VAP outcomes. We hypothesize that the multiomics data can help predict VAP development within this cohort. Methods/Study Population: We recruited participants from a cohort on a NYU IRB protocol (i22–00616), who had COVID19 respiratory failure, admitted to ICU, and required invasive mechanical ventilation (n = 245). We collected and analyzed research specimens (bronchoalveolar lavage [BAL, n = 213], tracheal aspirates [n = 246], background [n = 18]) and clinical cultures (sputum and BAL) for 245 participants. A panel of experts adjudicated VAP within the cohort, resulting in 92 VAP diagnoses. We annotated metatranscriptome (Illumina NovaSeq) using a Kraken/Bracken database, and KEGG for functional annotation of transcriptome data (Illumina HiSeq). We used edgeR (v.4.0.16) to analyze differential expression of metatranscriptome and transcriptome data. Results/Anticipated Results: We diagnosed VAP in n = 92 (38%) participants. We found significant differences in days of overall hospital stay (p Discussion/Significance of Impact: VAP is a serious complication of mechanical ventilation, and oral commensals alter the lung microbiome and host immunity. We identified a transcriptome-metatranscriptome signature that identifies those at VAP risk. VAP was associate with both pro- and anti-inflammatory gene expression resulting in increased risk for lower airway infection.