Translational informatics approach for identifying the functional molecular communicators linking coronary artery disease, infection and inflammation

Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease

Background

Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.

Methods

At the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.

Results

863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.

Conclusion

Taken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.

Acute and Subacute Triggers of Cardiovascular Events

Inability to predict short-term cardiovascular (CV) events and take immediate preemptive actions has long been the Achilles heel of cardiology. However, certain triggers of these events have come to light. Although these triggers are nonspecific and are part of normal life, studying their temporal relationship with the onset of CV events provides an opportunity to alert high-risk atherosclerotic patients who may be most vulnerable to such triggers, the "vulnerable patient". Herein, we review the literature and shed light on the epidemiology and underlying pathophysiology of different triggers. We describe that certain adrenergic triggers can precipitate a CV event within minutes or hours; whereas triggers that elicit an immune or inflammatory response such as infections may tip an asymptomatic "vulnerable patient" to become symptomatic days and weeks later. In conclusion, healthcare providers should counsel high-risk CV patients (e.g., in secondary prevention clinics or those with coronary artery Calcium >75th percentile) on the topic, advise them to avoid such triggers, take protective measures once exposed, and seek emergency care immediately after becoming symptomatic after such triggers. Furthermore, clinical trials targeting triggers (prevention or intervention) are needed.

In-Cardiome: integrated knowledgebase for coronary artery disease enabling translational research

Coronary artery disease (CAD) is a leading cause of death worldwide. Prevention, diagnosis and clinical interventions are dependent on the conventional risk factors like hypertension, diabetes and obesity. However, these conventional risk factors do not completely identify high risk individuals. One major hurdle in the improvement of diagnosis and treatment for CAD is the lack of integration of knowledge from different areas of research like molecular, clinical and drug development. In order to provide comprehensive information from hitherto dispersed data, we developed an integrative knowledgebase called “In-Cardiome or Integrated Cardiome” for all the stake holders in healthcare such as scientists, clinicians and pharmaceutical companies. It is created by integrating 16 different data sources, 995 curated genes classified into 12 different functional categories associated with disease, 1204 completed clinical trials, 12 therapy or drug classifications with 62 approved drugs and drug target networks. This knowledgebase gives the most needed opportunity to understand the disease process and therapeutic impact along with gene expression data from both animal models and patients. The data is classified into three different search categories functional groups, risk factors and therapy/drug based classes. One more unique aspect of In-Cardiome is integration of clinical data of 10,217 subject data from our ongoing Indian Atherosclerosis Research Study (IARS) (6357 unaffected and 3860 CAD affected). IARS data showing demographics and associations of individual and combinations of risk factors in Indian population along with molecular information will enable better translational and drug development research.