Abstract P501: Comprehensive Analysis Of Alternative Polyadenylation In Human Left Ventricle Failure By Using The Novel Algorithm: Polya-miner

Background: Alternative polyadenylation (APA) is an emerging post-transcriptional mechanism for gene regulation that generates distinct isoforms of mRNA with different 3′ untranslated regions (3’UTR) lengths. APA plays an important role in different biological processes and dysregulation of APA leads to many human diseases. However, the functional consequences of APA events in the left ventricle (LV) failure in humans remain unexplored. Objective: To identify whether the 3′UTR length is modulated by APA in the LV failure in humans compared to healthy LV. Methods and Results: We used Poly(A)-ClickSeq RNA sequencing and PolyA-miner algorithm to measure the global patterns of APA in healthy and failing human LV specimens. We determined shortening versus lengthening of 3′UTRs based on the PolyA index, a metric unit that determines the length of 3′UTR. Based on these scores, we identified 129 genes with a significant shift of cleavage site usage in failing LV compared to healthy LV specimens. By examining polyadenylation events in these hearts, we identified disease-specific APA signatures in many genes. In addition, differential APA events in LV failure regulate many pathways important for the progression of LV failure. Finally, the regulator proteins of APA including cleavage and polyadenylation specificity factor (CPSF) 6 and 7, cleavage factor Im (CFIm) 25 and 59 have been regulated in LV failure compared to healthy LV specimens. Conclusions: Our results provide genome-wide, polyadenylation maps of the human heart and show that APA of mRNA is dynamic in the progression of LV failure in humans. Demonstrating that APA mediated 3’UTR length regulation provides the additional layer of gene expressions in LV failure.

Identification of Cell-Free DNA Methylation Patterns Unique to the Human Left Ventricle as a Potential Indicator of Acute Cellular Rejection

Increased levels of donor-derived cell-free DNA (dd-cfDNA) in recipient plasma have been associated with rejection after transplantation. DNA sequence differences have been used to distinguish between donor and recipient but epigenetic differences could also potentially identify dd-cfDNA. This pilot study aimed to identify ventricle-specific differentially methylated regions of DNA (DMRs) that could be detected in cfDNA. We identified 24 ventricle-specific DMRs and chose two for further study, one on chromosome 9 and one on chromosome 12. The specificity of both DMRs for the left ventricle was confirmed using genomic DNA from multiple human tissues. Serial matched samples of myocardium (n=33) and plasma (n=24) were collected from stable adult heart transplant recipients undergoing routine endomyocardial biopsy for rejection surveillance. Plasma DMR levels increased with biopsy-proven rejection grade for individual patients. Mean cellular apoptosis in biopsy samples increased significantly with rejection severity (2.4%, 4.4% and 10.0% for ACR 0R, 1R and 2R, respectively) but did not show a consistent relationship with DMR levels. We identified multiple DNA methylation patterns unique to the human ventricle and conclude that epigenetic differences in cfDNA populations represent a promising alternative strategy for the non-invasive detection of rejection.

Novel Signature Genes for Human Left Ventricle Cardiomyopathies Identified by Weighted Co-expression Network Analysis (WGCNA)

BackgroundCardiomyopathy, a heart disease that arises from different etiologies, brings a huge healthcare burden to the global society. Identification of biomarkers can be very useful for early diagnosis of cardiomyopathy, interruption of the disease procession to heart failure, and decrement of the mortality. MethodsClinical cases of cardiomyopathy were screened out of independently investigations from the genomic database. Exploration of its whole transcription disorder pattern by WGCNA (Weighted Gene Co-expression Network Analysis) to discover the signature genes for different subtypes of cardiomyopathy. The hub genes and key pathways, which are correlated to cardiomyopathy traits, were identified through co-expression and protein-protein interaction (PPI) networks enrichment analysis. Discovered hub genes were blast through the Cardiovascular Disease Portal to verify functions related to human cardiomyopathies.ResultsThree common axes of signature genes were discovered for five subtypes of cardiomyopathy: 1) Four genes (MDM4, CFLAR, RPS6KB1, PKD1L2) were common for ischemic and ischemic cardiomyopathy subgroups; 2) Subtypes of cardiomyopathy (ischemic, post. partum, familiar and idiopathic) shared eight genes (MAPK1, MAPK11, MAPK14, LMNA, RAC1, PECAM1, XIAP, CREB1); 3) TFAM and RHEB were the common signature genes for subtypes of cardiomyopathy (viral, post. partum, familiar, and idiopathic). Major pathways enriched were including MAPK signaling pathway, the pathway of protein processing in endoplasmic reticulum, and pathway of regulatory actin cytoskeleton. Aberrant in these pathways caused disorders metabolic process and cellular malfunctions that generally contributes to cardiac dysregulation and functional relapse into cardiomyopathies.ConclusionThis study identified the key signaling pathways, functions and biological process related to cardiomyopathies and will give a light to better understand the molecular mechanism of processes of cardiomyopathies and figure out the rational clinical interference way to cure the patients. Therein, these novel signature genes may work as potential promising biomarkers for cardiomyopathy diagnosis, and will benefit for the better clinical diagnostics and outcome for patients with cardiomyopathies.

Machine learning-based classification and diagnosis of clinical cardiomyopathies

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies. RNA-Seq data of human left ventricle tissues were collected from 41 DCM patients, 47 ICM patients, and 49 nonfailure controls (NF) and tested using five ML algorithms: support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet), and random forest (RF). Initial ML classifications achieved ~93% accuracy (svmRadial) for NF vs. DCM, ~82% accuracy (RF) for NF vs. ICM, and ~80% accuracy (ENet and svmRadial) for DCM vs. ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for retraining ML models. Impressively, the retrained models achieved ~90% accuracy (RF) for NF vs. DCM, ~90% accuracy (pcaNNet) for NF vs. ICM, and ~85% accuracy (pcaNNet and RF) for DCM vs. ICM. Pathway analyses further confirmed the involvement of those selected HCGs in cardiac dysfunctions such as cardiomyopathies, cardiac hypertrophies, and fibrosis. Overall, our study demonstrates the promising potential of using artificial intelligence via ML modeling as a novel approach to achieve a greater level of precision in diagnosing different types of cardiomyopathies.

Identification of Upstream Transcriptional Regulators of Ischemic Cardiomyopathy Using Cardiac RNA-Seq Meta-Analysis

Ischemic cardiomyopathy (ICM), characterized by pre-existing myocardial infarction or severe coronary artery disease, is the major cause of heart failure (HF). Identification of novel transcriptional regulators in ischemic HF can provide important biomarkers for developing new diagnostic and therapeutic strategies. In this study, we used four RNA-seq datasets from four different studies, including 41 ICM and 42 non-failing control (NF) samples of human left ventricle tissues, to perform the first RNA-seq meta-analysis in the field of clinical ICM, in order to identify important transcriptional regulators and their targeted genes involved in ICM. Our meta-analysis identified 911 differentially expressed genes (DEGs) with 582 downregulated and 329 upregulated. Interestingly, 54 new DEGs were detected only by meta-analysis but not in individual datasets. Upstream regulator analysis through Ingenuity Pathway Analysis (IPA) identified three key transcriptional regulators. TBX5 was identified as the only inhibited regulator (z-score = −2.89). F2R and SFRP4 were identified as the activated regulators (z-scores = 2.56 and 2.00, respectively). Multiple downstream genes regulated by TBX5, F2R, and SFRP4 were involved in ICM-related diseases such as HF and arrhythmia. Overall, our study is the first to perform an RNA-seq meta-analysis for clinical ICM and provides robust candidate genes, including three key transcriptional regulators, for future diagnostic and therapeutic applications in ischemic heart failure.

Drift of Scroll Waves in a Mathematical Model of a Heterogeneous Human Heart Left Ventricle

Rotating spiral waves of electrical excitation underlie many dangerous cardiac arrhythmias. The heterogeneity of myocardium is one of the factors that affects the dynamics of such waves. In this paper, we present results of our simulations for scroll wave dynamics in a heterogeneous model of the human left ventricle with analytical anatomically based representation of the geometry and anisotropy. We used a set of 18 coupled differential equations developed by ten Tusscher and Panfilov (TP06 model) which describes human ventricular cells based on their measured biophysical properties. We found that apicobasal heterogeneity dramatically changes the scroll wave dynamics. In the homogeneous model, the scroll wave annihilates at the base, but the moderate heterogeneity causes the wave to move to the apex and then continuously rotates around it. The rotation speed increased with the degree of the heterogeneity. However, for large heterogeneity, we observed formation of additional wavebreaks and the onset of complex spatio-temporal patterns. Transmural heterogeneity did not change the dynamics and decreased the lifetime of the scroll wave with an increase in heterogeneity. Results of our numerical experiments show that the apex may be a preferable location of the scroll wave, which may be important for development of clinical interventions.