Multiomics, virtual reality and artificial intelligence in heart failure.

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Lay abstract Multiomics is the integration of multiple sources of health information, for example, genomic, metabolite, etc. This delivers more insight than targeted single investigations and provides an ability to perceive subtle individual differences between people. In this study we applied multiomics to patients with heart failure (HF) using DNA sequencing, metabolomics and machine learning applied to ECG echocardiography. We demonstrated significant differences between subsets of patients with HF using these methods. We also showed that machine learning has significant diagnostic potential in identifying HF patients more efficiently than manual or conventional techniques.

Probing the hras-1Y i-motif with small molecules.

Non-B DNA structures represent intriguing and challenging targets for small molecules. For example, the promoter of the HRAS oncogene contains multiple G-quadruplex and i-motif structures, atypical globular folds that serve as molecular switches for gene expression. Of the two, i-motif structures are far less studied. Here, we report the first example of small organic compounds that directly interact with the hras-1Y i-motif. We use a small molecule microarray screen to identify drug-like small molecules that bind to the hras-1Y i-motif but not to several other DNA or RNA secondary structures. Two different lead compounds, 1 and 2, were discovered to have 7.4 ± 5.3 µM and 5.9 ± 3.7 µM binding affinity by surface plasmon resonance and similar affinity by fluorescence titration. A structure-activity relationship (SAR) was developed and two improved analogues of 2 demonstrated submicromolar binding affinities. Both compounds display pH-dependent binding, indicating that they interact with the DNA only when the i-motif is properly folded. Chemical shift perturbation shows that 1 alters the structure of the i-motif, while 2 has no effect on the i-motif conformation, indicating different modes of interaction.