Editorial: Recent Advances on Myocardium Physiology

From amoeboid myosin to unique targeted medicines for a genetic cardiac disease

The importance of fundamental basic research in the quest for much needed clinical treatments is a story that constantly must be retold. Funding of basic science in the USA by the National Institutes of Health and other agencies is provided under the assumption that fundamental research eventually will lead to improvements in healthcare worldwide. Understanding how basic research is connected to clinical developments is important, but just part of the story. Many basic science discoveries never see the light of day in a clinical setting because academic scientists are not interested in or do not have the inclination and/or support for entering the world of biotechnology. Even if the interest and inclination are there, often the unknowns about how to enter that world inhibit taking the initial step. Young investigators often ask me how I incorporated biotech opportunities into my otherwise purely academic research endeavors. Here I tell the story of the foundational basic science and early events of my career that led to forming the biotech companies responsible for the development of unique cardiac drugs, including mavacamten, a first in class human β-cardiac myosin inhibitor that is changing the lives of hypertrophic cardiomyopathy patients.

Proteomic and phosphoproteomic profiling in heart failure with preserved ejection fraction (HFpEF)

Although the prevalence of heart failure with preserved ejection fraction (HFpEF) is increasing, evidence-based therapies for HFpEF remain limited, likely due to an incomplete understanding of this disease. This study sought to identify the cardiac-specific features of protein and phosphoprotein changes in a murine model of HFpEF using mass spectrometry. HFpEF mice demonstrated moderate hypertension, left ventricle (LV) hypertrophy, lung congestion and diastolic dysfunction. Proteomics analysis of the LV tissue showed that 897 proteins were differentially expressed between HFpEF and Sham mice. We observed abundant changes in sarcomeric proteins, mitochondrial-related proteins, and NAD-dependent protein deacetylase sirtuin-3 (SIRT3). Upregulated pathways by GSEA analysis were related to immune modulation and muscle contraction, while downregulated pathways were predominantly related to mitochondrial metabolism. Western blot analysis validated SIRT3 downregulated cardiac expression in HFpEF vs. Sham (0.8 ± 0.0 vs. 1.0 ± 0.0; P < 0.001). Phosphoproteomics analysis showed that 72 phosphosites were differentially regulated between HFpEF and Sham LV. Aberrant phosphorylation patterns mostly occurred in sarcomere proteins and nuclear-localized proteins associated with contractile dysfunction and cardiac hypertrophy. Seven aberrant phosphosites were observed at the z-disk binding region of titin. Additional agarose gel analysis showed that while total titin cardiac expression remained unaltered, its stiffer N2B isoform was significantly increased in HFpEF vs. Sham (0.144 ± 0.01 vs. 0.127 ± 0.01; P < 0.05). In summary, this study demonstrates marked changes in proteins related to mitochondrial metabolism and the cardiac contractile apparatus in HFpEF. We propose that SIRT3 may play a role in perpetuating these changes and may be a target for drug development in HFpEF.

Deep Learning-Based Text Emotion Analysis for Legal Anomie

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.