Improving sudden cardiac death risk stratification in hypertrophic cardiomyopathy using established clinical variables and genetic information.

BACKGROUND AND AIMS: The cardiac societies of Europe and the United States have established different risk models for preventing sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). The aim of this study is to validate current SCD risk prediction methods in a German HCM cohort and to improve them by the addition of genotype information. METHODS: HCM patients without prior SCD or equivalent arrhythmic events ≥ 18 years of age were enrolled in an expert cardiomyopathy center in Germany. The primary endpoint was defined as SCD/-equivalent within 5 years of baseline evaluation. 5-year SCD-risk estimates and recommendations for ICD implantations, as defined by the ESC and AHA/ACC guidelines, were analyzed. Multivariate cox proportional hazards analyses were integrated with genetic findings as additive SCD risk. RESULTS: 283 patients were included and followed for in median 5.77 years (2.92; 8.85). A disease-causing variant was found in 138 (49%) patients. 14 (5%) patients reached the SCD endpoint (5-year incidence 4.9%). Kaplan-Meier survival analysis shows significantly lower overall SCD event-free survival for patients with an identified disease-causing variant (p < 0.05). The ESC HCM Risk-SCD model showed an area-under-the-curve (AUC) of 0.74 (95% CI 0.68-0.79; p < 0.0001) with a sensitivity of 0.29 (95% CI 0.08-0.58) and specificity of 0.83 (95% CI 0.78-0.88) for a risk estimate ≥ 6%/5-years. By comparison, the AHA/ACC HCM SCD risk stratification model showed an AUC of 0.70 (95% CI 0.65-0.76; p = 0.003) with a sensitivity of 0.93 (95% CI, 0.66-0.998) and specificity of 0.28 (95% CI 0.23-0.34) at the respective cut-off. The modified SCD Risk Score with genetic information yielded an AUC of 0.76 (95% CI 0.71-0.81; p < 0.0001) with a sensitivity of 0.86 (95% CI 0.57-0.98) and specificity of 0.69 (95% CI 0.63-0.74). The number-needed-to-treat (NNT) to prevent 1 SCD event by prophylactic ICD-implantation is 13 for the ESC model, 28 for AHA/ACC and 9 for the modified Genotype-model. CONCLUSION: This study confirms the performance of current risk models in clinical decision making. The integration of genetic findings into current SCD risk stratification methods seem feasible and can add in decision making, especially in borderline risk-groups. A subgroup of patients with high SCD risk remains unidentified by current risk scores.

NIMA-related kinase 9 regulates the phosphorylation of the essential myosin light chain in the heart.

To adapt to changing hemodynamic demands, regulatory mechanisms modulate actin-myosin-kinetics by calcium-dependent and -independent mechanisms. We investigate the posttranslational modification of human essential myosin light chain (ELC) and identify NIMA-related kinase 9 (NEK9) to interact with ELC. NEK9 is highly expressed in the heart and the interaction with ELC is calcium-dependent. Silencing of NEK9 results in blunting of calcium-dependent ELC-phosphorylation. CRISPR/Cas9-mediated disruption of NEK9 leads to cardiomyopathy in zebrafish. Binding to ELC is mediated via the protein kinase domain of NEK9. A causal relationship between NEK9 activity and ELC-phosphorylation is demonstrated by genetic sensitizing in-vivo. Finally, we observe significantly upregulated ELC-phosphorylation in dilated cardiomyopathy patients and provide a unique map of human ELC-phosphorylation-sites. In summary, NEK9-mediated ELC-phosphorylation is a calcium-dependent regulatory system mediating cardiac contraction and inotropy.

Functional Therapeutic Target Validation Using Pediatric Zebrafish Xenograft Models.

The survival rate among children with relapsed tumors remains poor, due to tumor heterogeneity, lack of directly actionable tumor drivers and multidrug resistance. Novel personalized medicine approaches tailored to each tumor are urgently needed to improve cancer treatment. Current pediatric precision oncology platforms, such as the INFORM (INdividualized Therapy FOr Relapsed Malignancies in Childhood) study, reveal that molecular profiling of tumor tissue identifies targets associated with clinical benefit in a subgroup of patients only and should be complemented with functional drug testing. In such an approach, patient-derived tumor cells are exposed to a library of approved oncological drugs in a physiological setting, e.g., in the form of animal avatars injected with patient tumor cells. We used molecularly fully characterized tumor samples from the INFORM study to compare drug screen results of individual patient-derived cell models in functional assays: (i) patient-derived spheroid cultures within a few days after tumor dissociation; (ii) tumor cells reisolated from the corresponding mouse PDX; (iii) corresponding long-term organoid-like cultures and (iv) drug evaluation with the corresponding zebrafish PDX (zPDX) model. Each model had its advantage and complemented the others for drug hit and drug combination selection. Our results provide evidence that in vivo zPDX drug screening is a promising add-on to current functional drug screening in precision medicine platforms.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system.

BACKGROUND: The development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy. METHODS: The concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up. RESULTS: The safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%). CONCLUSION: PROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient's acceptance of data sharing can be increased by patient-centric decision-making.

Combining APR-246 and HDAC-Inhibitors: A Novel Targeted Treatment Option for Neuroblastoma.

APR-246 (Eprenetapopt/PRIMA-1Met) is a very potent anti-cancer drug in clinical trials and was initially developed as a p53 refolding agent. As an alternative mode of action, the elevation of reactive oxygen species (ROS) has been proposed. Through an in silico analysis, we investigated the responses of approximately 800 cancer cell lines (50 entities; Cancer Therapeutics Response Portal, CTRP) to APR-246 treatment. In particular, neuroblastoma, lymphoma and acute lymphocytic leukemia cells were highly responsive. With gene expression data from the Cancer Cell Line Encyclopedia (CCLE; n = 883) and patient samples (n = 1643) from the INFORM registry study, we confirmed that these entities express low levels of SLC7A11, a previously described predictive biomarker for APR-246 responsiveness. Combining the CTRP drug response data with the respective CCLE gene expression profiles, we defined a novel gene signature, predicting the effectiveness of APR-246 treatment with a sensitivity of 90% and a specificity of 94%. We confirmed the predicted APR-246 sensitivity in 8/10 cell lines and in ex vivo cultures of patient samples. Moreover, the combination of ROS detoxification-impeding APR-246 with approved HDAC-inhibitors, known to elevate ROS, substantially increased APR-246 sensitivity in cell cultures and in vivo in two zebrafish neuroblastoma xenograft models. These data provide evidence that APR-246, in combination with HDAC-inhibitors, displays a novel potent targeted treatment option for neuroblastoma patients.

Broad-Spectrum HDAC Inhibitors Promote Autophagy through FOXO Transcription Factors in Neuroblastoma.

Depending on context and tumor stage, deregulation of autophagy can either suppress tumorigenesis or promote chemoresistance and tumor survival. Histone deacetylases (HDACs) can modulate autophagy; however, the exact mechanisms are not fully understood. Here, we analyze the effects of the broad-spectrum HDAC inhibitors (HDACi) panobinostat and vorinostat on the transcriptional regulation of autophagy with respect to autophagy transcription factor activity (Transcription factor EB-TFEB, forkhead boxO-FOXO) and autophagic flux in neuroblastoma cells. In combination with the late-stage autophagic flux inhibitor bafilomycin A1, HDACis increase the number of autophagic vesicles, indicating an increase in autophagic flux. Both HDACi induce nuclear translocation of the transcription factors FOXO1 and FOXO3a, but not TFEB and promote the expression of pro-autophagic FOXO1/3a target genes. Moreover, FOXO1/3a knockdown experiments impaired HDACi treatment mediated expression of autophagy related genes. Combination of panobinostat with the lysosomal inhibitor chloroquine, which blocks autophagic flux, enhances neuroblastoma cell death in culture and hampers tumor growth in vivo in a neuroblastoma zebrafish xenograft model. In conclusion, our results indicate that pan-HDACi treatment induces autophagy in neuroblastoma at a transcriptional level. Combining HDACis with autophagy modulating drugs suppresses tumor growth of high-risk neuroblastoma cells. These experimental data provide novel insights for optimization of treatment strategies in neuroblastoma.

Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy.

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10-6). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.

microRNA neural networks improve diagnosis of acute coronary syndrome (ACS).

BACKGROUND: Cardiac troponins are the preferred biomarkers of acute myocardial infarction. Despite superior sensitivity, serial testing of Troponins to identify patients suffering acute coronary syndromes is still required in many cases to overcome limited specificity. Moreover, unstable angina pectoris relies on reported symptoms in the troponin-negative group. In this study, we investigated genome-wide miRNA levels in a prospective cohort of patients with clinically suspected ACS and determined their diagnostic value by applying an in silico neural network. METHODS: PAXgene blood and serum samples were drawn and hsTnT was measured in patients at initial presentation to our Chest-Pain Unit. After clinical and diagnostic workup, patients were adjudicated by senior cardiologists in duty to their final diagnosis: STEMI, NSTEMI, unstable angina pectoris and non-ACS patients. ACS patients and a cohort of healthy controls underwent deep transcriptome sequencing. Machine learning was implemented to construct diagnostic miRNA classifiers. RESULTS: We developed a neural network model which incorporates 34 validated ACS miRNAs, showing excellent classification results. By further developing additional machine learning models and selecting the best miRNAs, we achieved an accuracy of 0.96 (95% CI 0.96-0.97), sensitivity of 0.95, specificity of 0.96 and AUC of 0.99. The one-point hsTnT value reached an accuracy of 0.89, sensitivity of 0.82, specificity of 0.96, and AUC of 0.96. CONCLUSIONS: Here we show the concept of neural network based biomarkers for ACS. This approach also opens the possibility to include multi-modal data points to further increase precision and perform classification of other ACS differential diagnoses.

Marathon-Induced Cardiac Strain as Model for the Evaluation of Diagnostic microRNAs for Acute Myocardial Infarction.

BACKGROUND: The current gold standard biomarker for myocardial infarction (MI), cardiac troponin (cTn), is recognized for its high sensitivity and organ specificity; however, it lacks diagnostic specificity. Numerous studies have introduced circulating microRNAs as potential biomarkers for MI. This study investigates the MI-specificity of these serum microRNAs by investigating myocardial stress/injury due to strenuous exercise. METHODS: MicroRNA biomarkers were retrieved by comprehensive review of 109 publications on diagnostic serum microRNAs for MI. MicroRNA levels were first measured by next-generation sequencing in pooled sera from runners (n = 46) before and after conducting a full competitive marathon. Hereafter, reverse transcription quantitative real-time PCR (qPCR) of 10 selected serum microRNAs in 210 marathon runners was performed (>10,000 qPCR measurements). RESULTS: 27 potential diagnostic microRNA for MI were retrieved by the literature review. Eight microRNAs (miR-1-3p, miR-21-5p, miR-26a-5p, miR-122-5p, miR-133a-3p, miR-142-5p, miR-191-5p, miR-486-3p) showed positive correlations with cTnT in marathon runners, whereas two miRNAs (miR-134-5p and miR-499a-5p) showed no correlations. Upregulation of miR-133a-3p (p = 0.03) and miR-142-5p (p = 0.01) went along with elevated cTnT after marathon. CONCLUSION: Some MI-associated microRNAs (e.g., miR-133a-3p and miR-142-5p) have similar kinetics under strenuous exercise and MI as compared to cTnT, which suggests that their diagnostic specificity could be limited. In contrast, several MI-associated microRNAs (miR-26a-5p, miR-134-5p, miR-191-5p) showed different release behavior; hence, combining cTnT with these microRNAs within a multi-marker strategy may add diagnostic accuracy in MI.

Mutation of the Na+/K+-ATPase Atp1a1a.1 causes QT interval prolongation and bradycardia in zebrafish.

The genetic underpinnings that orchestrate the vertebrate heart rate are not fully understood yet, but of high clinical importance, since diseases of cardiac impulse formation and propagation are common and severe human arrhythmias. To identify novel regulators of the vertebrate heart rate, we deciphered the pathogenesis of the bradycardia in the homozygous zebrafish mutant hiphop (hip) and identified a missense-mutation (N851K) in Na+/K+-ATPase α1-subunit (atp1a1a.1). N851K affects zebrafish Na+/K+-ATPase ion transport capacity, as revealed by in vitro pump current measurements. Inhibition of the Na+/K+-ATPase in vivo indicates that hip rather acts as a hypomorph than being a null allele. Consequently, reduced Na+/K+-ATPase function leads to prolonged QT interval and refractoriness in the hip mutant heart, as shown by electrocardiogram and in vivo electrical stimulation experiments. We here demonstrate for the first time that Na+/K+-ATPase plays an essential role in heart rate regulation by prolonging myocardial repolarization.

Genomic structural variations lead to dysregulation of important coding and non-coding RNA species in dilated cardiomyopathy.

The transcriptome needs to be tightly regulated by mechanisms that include transcription factors, enhancers, and repressors as well as non-coding RNAs. Besides this dynamic regulation, a large part of phenotypic variability of eukaryotes is expressed through changes in gene transcription caused by genetic variation. In this study, we evaluate genome-wide structural genomic variants (SVs) and their association with gene expression in the human heart. We detected 3,898 individual SVs affecting all classes of gene transcripts (e.g., mRNA, miRNA, lncRNA) and regulatory genomic regions (e.g., enhancer or TFBS). In a cohort of patients (n = 50) with dilated cardiomyopathy (DCM), 80,635 non-protein-coding elements of the genome are deleted or duplicated by SVs, containing 3,758 long non-coding RNAs and 1,756 protein-coding transcripts. 65.3% of the SV-eQTLs do not harbor a significant SNV-eQTL, and for the regions with both classes of association, we find similar effect sizes. In case of deleted protein-coding exons, we find downregulation of the associated transcripts, duplication events, however, do not show significant changes over all events. In summary, we are first to describe the genomic variability associated with SVs in heart failure due to DCM and dissect their impact on the transcriptome. Overall, SVs explain up to 7.5% of the variation of cardiac gene expression, underlining the importance to study human myocardial gene expression in the context of the individual genome. This has immediate implications for studies on basic mechanisms of cardiac maladaptation, biomarkers, and (gene) therapeutic studies alike.

Clinical genetics and outcome of left ventricular non-compaction cardiomyopathy.

AIMS: In this study, we aimed to clinically and genetically characterize LVNC patients and investigate the prevalence of variants in known and novel LVNC disease genes. INTRODUCTION: Left ventricular non-compaction cardiomyopathy (LVNC) is an increasingly recognized cause of heart failure, arrhythmia, thromboembolism, and sudden cardiac death. We sought here to dissect its genetic causes, phenotypic presentation and outcome. METHODS AND RESULTS: In our registry with follow-up of in the median 61 months, we analysed 95 LVNC patients (68 unrelated index patients and 27 affected relatives; definite familial LVNC = 23.5%) by cardiac phenotyping, molecular biomarkers and exome sequencing. Cardiovascular events were significantly more frequent in LVNC patients compared with an age-matched group of patients with non-ischaemic dilated cardiomyopathy (hazard ratio = 2.481, P = 0.002). Stringent genetic classification according to ACMG guidelines revealed that TTN, LMNA, and MYBPC3 are the most prevalent disease genes (13 patients are carrying a pathogenic truncating TTN variant, odds ratio = 40.7, Confidence interval = 21.6-76.6, P < 0.0001, percent spliced in 76-100%). We also identified novel candidate genes for LVNC. For RBM20, we were able to perform detailed familial, molecular and functional studies. We show that the novel variant p.R634L in the RS domain of RBM20 co-segregates with LVNC, leading to titin mis-splicing as revealed by RNA sequencing of heart tissue in mutation carriers, protein analysis, and functional splice-reporter assays. CONCLUSION: Our data demonstrate that the clinical course of symptomatic LVNC can be severe. The identified pathogenic variants and distribution of disease genes-a titin-related pathomechanism is found in every fourth patient-should be considered in genetic counselling of patients. Pathogenic variants in the nuclear proteins Lamin A/C and RBM20 were associated with worse outcome.

Epigenome-Wide Association Study Identifies Cardiac Gene Patterning and a Novel Class of Biomarkers for Heart Failure.

BACKGROUND: Biochemical DNA modification resembles a crucial regulatory layer among genetic information, environmental factors, and the transcriptome. To identify epigenetic susceptibility regions and novel biomarkers linked to myocardial dysfunction and heart failure, we performed the first multi-omics study in myocardial tissue and blood of patients with dilated cardiomyopathy and controls. METHODS: Infinium human methylation 450 was used for high-density epigenome-wide mapping of DNA methylation in left-ventricular biopsies and whole peripheral blood of living probands. RNA deep sequencing was performed on the same samples in parallel. Whole-genome sequencing of all patients allowed exclusion of promiscuous genotype-induced methylation calls. RESULTS: In the screening stage, we detected 59 epigenetic loci that are significantly associated with dilated cardiomyopathy (false discovery corrected P≤0.05), with 3 of them reaching epigenome-wide significance at P≤5×10-8. Twenty-seven (46%) of these loci could be replicated in independent cohorts, underlining the role of epigenetic regulation of key cardiac transcription regulators. Using a staged multi-omics study design, we link a subset of 517 epigenetic loci with dilated cardiomyopathy and cardiac gene expression. Furthermore, we identified distinct epigenetic methylation patterns that are conserved across tissues, rendering these CpGs novel epigenetic biomarkers for heart failure. CONCLUSIONS: The present study provides to our knowledge the first epigenome-wide association study in living patients with heart failure using a multi-omics approach.

Genotype-phenotype associations in dilated cardiomyopathy: meta-analysis on more than 8000 individuals.

AIMS: Routine genetic testing in Dilated Cardiomyopathy (DCM) has recently become reality using Next-Generation Sequencing. Several studies have explored the relationship between genotypes and clinical phenotypes to support risk estimation and therapeutic decisions, however, most studies are small or restricted to a few genes. This study provides to our knowledge the first systematic meta-analysis on genotype-phenotype associations in DCM. METHODS AND RESULTS: We retrieved PubMed/Medline literature on genotype-phenotype associations in patients with DCM and mutations in LMNA, PLN, RBM20, MYBPC3, MYH7, TNNT2 and TNNI3. We summarized and extensively reviewed all studies that passed selection criteria and performed a meta-analysis on key phenotypic parameters. Together, 48 studies with 8097 patients were included. Furthermore, we reviewed recent studies investigating genotype-phenotype associations in DCM patients with TTN mutations. The average frequency of mutations in the investigated genes was between 1 and 5 %. The mean age of DCM onset was the beginning of the fifth decade for all genes. Heart transplantation (HTx) rate was highest in LMNA mutation carriers (27 %), while RBM20 mutation carriers were transplanted at a markedly younger age (mean 28.5 years). While 73 % of DCM patients with LMNA mutations showed cardiac conduction diseases, low voltage was the reported ECG hallmark in PLN mutation carriers. The frequency of ventricular arrhythmia in DCM patients with LMNA (50 %) and PLN (43 %) mutations was significantly higher. The penetrance of DCM phenotype in subjects with TTN truncating variants increased with age and reached 100 % by age of 70. CONCLUSION: A pooled analysis of available genotype-phenotype data shows a higher prevalence of sudden cardiac death (SCD), cardiac transplantation, or ventricular arrhythmias in LMNA and PLN mutation carriers compared to sarcomeric gene mutations. This study will further support the clinical interpretation of genetic findings.

The Role of Quality Control in Targeted Next-generation Sequencing Library Preparation.

Next-generation sequencing (NGS) is getting routinely used in the diagnosis of hereditary diseases, such as human cardiomyopathies. Hence, it is of utter importance to secure high quality sequencing data, enabling the identification of disease-relevant mutations or the conclusion of negative test results. During the process of sample preparation, each protocol for target enrichment library preparation has its own requirements for quality control (QC); however, there is little evidence on the actual impact of these guidelines on resulting data quality. In this study, we analyzed the impact of QC during the diverse library preparation steps of Agilent SureSelect XT target enrichment and Illumina sequencing. We quantified the parameters for a cohort of around 600 samples, which include starting amount of DNA, amount of sheared DNA, smallest and largest fragment size of the starting DNA; amount of DNA after the pre-PCR, and smallest and largest fragment size of the resulting DNA; as well as the amount of the final library, the corresponding smallest and largest fragment size, and the number of detected variants. Intriguingly, there is a high tolerance for variations in all QC steps, meaning that within the boundaries proposed in the current study, a considerable variance at each step of QC can be well tolerated without compromising NGS quality.

Bias in High-Throughput Analysis of miRNAs and Implications for Biomarker Studies.

A certain degree of bias in high-throughput molecular technologies including microarrays and next-generation sequencing (NGS) is known. To quantify the actual impact of the biomarker discovery platform on miRNA profiles, we first performed a meta-analysis: raw data of 1 539 microarrays and 705 NGS blood-borne miRNomes were statistically evaluated, suggesting a substantial influence of the technology on biomarker profiles. We observed highly significant dependency of the miRNA nucleotide composition on the expression level. Higher expression in NGS was discovered for uracil-rich miRNAs (p = 7 × 10(-37)), while high expression in microarrays was found predominantly for guanine-rich miRNAs (p = 3 × 10(-33)). To verify the findings, 10 identical replicates of one individual were measured using NGS and microarrays (2 525 miRNAs from miRBase version 21). Overall, we calculated a correlation coefficient of 0.414 for both technologies. Detailed analysis however revealed that the correlation was observed only for miRNAs in the early miRBase versions (<8). The majority of miRNAs (2 013 from miRBase version 8 onward) was not correlated between microarray and NGS. Specifically, we observed 67 miRNAs with a median read count above 10 in NGS, while they were not detected in any of the 10 replicated array experiments. In contrast, 234 miRNAs were discovered in all 10 replicated array measurements but were not found in any of the NGS experiments of the same individual. While the first group had average guanine content of 22%, the latter group consisted of 41% of this nucleotide. Selected concordant and discordant miRNAs were tested in quantitative real-time-polymerase chain reaction (RT-qPCR) experiments again of the same individual, providing further evidence for the substantial bias depending on the base composition. As a consequence, biomarkers that have been discovered by specific high-throughout technologies have to be carefully considered. Especially for validation of the platform, the selection of reasonable candidates is essential.

Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart.

BACKGROUND: Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. METHODS AND RESULTS: State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. CONCLUSION: This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.

miFRame: analysis and visualization of miRNA sequencing data in neurological disorders.

BACKGROUND: While in the past decades nucleic acid analysis has been predominantly carried out using quantitative low- and high-throughput approaches such as qRT-PCR and microarray technology, next-generation sequencing (NGS) with its single base resolution is now frequently applied in DNA and RNA testing. Especially for small non-coding RNAs such as microRNAs there is a need for analysis and visualization tools that facilitate interpretation of the results also for clinicians. METHODS: We developed miFRame, which supports the analysis of human small RNA NGS data. Our tool carries out different data analyses for known as well as predicted novel mature microRNAs from known precursors and presents the results in a well interpretable manner. Analyses include among others expression analysis of precursors and mature miRNAs, detection of novel precursors and detection of potential iso-microRNAs. Aggregation of results from different users moreover allows for evaluation whether remarkable results, such as novel mature miRNAs, are indeed specific for the respective experimental set-up or are frequently detected across a broad range of experiments. RESULTS: We demonstrate the capabilities of miFRame, which is freely available at http://www.ccb.uni-saarland.de/miframe on two studies, circulating biomarker screening for Multiple Sclerosis (cohort includes clinically isolated syndrome, relapse remitting MS, matched controls) as well as Alzheimer Disease (cohort includes Alzheimer Disease, Mild Cognitive Impairment, matched controls). Here, our tool allowed for an improved biomarker discovery by identifying likely false positive marker candidates.

RNA splicing regulated by RBFOX1 is essential for cardiac function in zebrafish.

Alternative splicing is one of the major mechanisms through which the proteomic and functional diversity of eukaryotes is achieved. However, the complex nature of the splicing machinery, its associated splicing regulators and the functional implications of alternatively spliced transcripts are only poorly understood. Here, we investigated the functional role of the splicing regulator rbfox1 in vivo using the zebrafish as a model system. We found that loss of rbfox1 led to progressive cardiac contractile dysfunction and heart failure. By using deep-transcriptome sequencing and quantitative real-time PCR, we show that depletion of rbfox1 in zebrafish results in an altered isoform expression of several crucial target genes, such as actn3a and hug. This study underlines that tightly regulated splicing is necessary for unconstrained cardiac function and renders the splicing regulator rbfox1 an interesting target for investigation in human heart failure and cardiomyopathy.