Predicting mechanical ventilation effects on six human tissue transcriptomes

Abstract

Background Mechanical ventilation (MV) is a lifesaving therapy used for patients with respiratory failure. Nevertheless, MV is associated with numerous complications and increased mortality. The aim of this study is to define the effects of MV on gene expression of direct and peripheral human tissues. Methods Classification models were applied to Genotype-Tissue Expression Project (GTEx) gene expression data of six representative tissues–liver, adipose, skin, nerve-tibial, muscle and lung, for performance comparison and feature analysis. We utilized 18 prediction models using the Random Forest (RF), XGBoost (eXtreme Gradient Boosting) decision tree and ANN (Artificial Neural Network) methods to classify ventilation and non-ventilation samples and to compare their prediction performance for the six tissues. In the model comparison, the AUC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. We then conducted feature analysis per each tissue to detect MV marker genes followed by pathway enrichment analysis for these genes. Results XGBoost outperformed the other methods and predicted samples had undergone MV with an average accuracy for the six tissues of 0.951 and average AUC of 0.945. The feature analysis detected a combination of MV marker genes per each tested tissue, some common across several tissues. MV marker genes were mainly related to inflammation and fibrosis as well as cell development and movement regulation. The MV marker genes were significantly enriched in inflammatory and viral pathways. Conclusion The XGBoost method demonstrated clear enhanced performance and feature analysis compared to the other models. XGBoost was helpful in detecting the tissue-specific marker genes for identifying transcriptomic changes related to MV. Our results show that MV is associated with reduced development and movement in the tissues and higher inflammation and injury not only in direct tissues such as the lungs but also in peripheral tissues and thus should be carefully considered before being implemented.

Whole-body positive synchronization pattern of metabolic transcriptomes

“…In cohort 1, we identified 40 metabolic co-expression modules out of 609, while in cohort 2, we identified 52 metabolic co-expression modules out of 652. We used the WGCNA algorithm for co-expression network analysis, Singular Value Decomposition (SVD) [22] of the generated co-expression modules for each tissue and plurality votes for metabolic modules annotation, as elaborated in the Methods section.…”

“…The death classification of the samples (DTHHRDY) was along the Hardy Scale [21] We filtered the samples into two cohorts: (1) Cohort 1 - samples of death types 1 and 2 (violent and fast death or fast death due to natural causes), (2) Cohort 2 - donors that were attached to a ventilation machine before death. We split into two cohorts since we observed from our prior work [22] that mechanical ventilation impacts multiple tissues and is related to ischemic time (the time between death and sample extraction).…”

Multi-tissue transcriptional changes and core circadian clock disruption following intensive care

“…These patients are typically at risk for hypoxia or decreased systemic perfusion, often related to trauma, acute neurologic events, or profound illness with systemic inflammatory response (Smith and Nielsen, 1999). Indeed, critical care and illness have been found to influence tissue physiology to an extent that gene expression can predict subjects' pre-mortem critical care status (Somekh et al, 2022). Disease related disruption of the hypothalamic-pituitary-stress axis, along with associated changes in body temperature, autonomic tone, and hormone secretion are well established, and all have been implicated in peripheral circadian entrainment (Nicolaides et al, 2014(Nicolaides et al, , 2017Astiz et al, 2019).…”