COL4A3 expression in asthmatic epithelium depends on intronic methylation and ZNF263 binding

Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)

BACKGROUND

Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES).

RESULTS

First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen's Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems.

CONCLUSIONS

Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.

The role of DNA methylation in personalized medicine for immune-related diseases

Epigenetics functions as a bridge between host genetic & environmental factors, aiding in human health and diseases. Many immune-related diseases, including infectious and allergic diseases, have been linked to epigenetic mechanisms, particularly DNA methylation. In this review, we summarized an updated overview of DNA methylation and its importance in personalized medicine, and demonstrated that DNA methylation has excellent potential for disease prevention, diagnosis, and treatment in a personalized manner. The future implications and limitations of the DNA methylation study have also been well-discussed.

Highway to heal: Influence of altered extracellular matrix on infiltrating immune cells during acute and chronic lung diseases

Environmental insults including respiratory infections, in combination with genetic predisposition, may lead to lung diseases such as chronic obstructive pulmonary disease, lung fibrosis, asthma, and acute respiratory distress syndrome. Common characteristics of these diseases are infiltration and activation of inflammatory cells and abnormal extracellular matrix (ECM) turnover, leading to tissue damage and impairments in lung function. The ECM provides three-dimensional (3D) architectural support to the lung and crucial biochemical and biophysical cues to the cells, directing cellular processes. As immune cells travel to reach any site of injury, they encounter the composition and various mechanical features of the ECM. Emerging evidence demonstrates the crucial role played by the local environment in recruiting immune cells and their function in lung diseases. Moreover, recent developments in the field have elucidated considerable differences in responses of immune cells in two-dimensional versus 3D modeling systems. Examining the effect of individual parameters of the ECM to study their effect independently and collectively in a 3D microenvironment will help in better understanding disease pathobiology. In this article, we discuss the importance of investigating cellular migration and recent advances in this field. Moreover, we summarize changes in the ECM in lung diseases and the potential impacts on infiltrating immune cell migration in these diseases. There has been compelling progress in this field that encourages further developments, such as advanced in vitro 3D modeling using native ECM-based models, patient-derived materials, and bioprinting. We conclude with an overview of these state-of-the-art methodologies, followed by a discussion on developing novel and innovative models and the practical challenges envisaged in implementing and utilizing these systems.