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Multi-omic analysis in injured humans: Patterns align with outcomes and treatment responses.

ABSTRACT: Trauma is a leading cause of death and morbidity worldwide. Here, we present the analysis of a longitudinal multi-omic dataset comprising clinical, cytokine, endotheliopathy biomarker, lipidome, metabolome, and proteome data from severely injured humans. A "systemic storm" pattern with release of 1,061 markers, together with a pattern suggestive of the "massive consumption" of 892 constitutive circulating markers, is identified in the acute phase post-trauma. Data integration reveals two human injury response endotypes, which align with clinical trajectory. Prehospital thawed plasma rescues only endotype 2 patients with traumatic brain injury (30-day mortality: 30.3 versus 75.0%; p = 0.0015). Ubiquitin carboxy-terminal hydrolase L1 (UCHL1) was identified as the most predictive circulating biomarker to identify endotype 2-traumatic brain injury (TBI) patients. These response patterns refine the paradigm for human injury, while the datasets provide a resource for the study of critical illness, trauma, and human stress responses.

SUBMITTER: Wu J

PROVIDER: S-EPMC8715070 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundInflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi-omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.ResultsWe first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi-omic features including both gene expression and DNA methylation. We introduce a phylogenetic-based multi-omic method to study the relationships between multi-omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre-estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes.ConclusionsApplying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF- β gene family, the IL-17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.

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Multi-omic analysis in injured humans: Patterns align with outcomes and treatment responses.

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