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Hypertrophic cardiomyopathy (HCM) caused by autosomal-dominant mutations in genes coding for structural sarcomeric proteins, is the most common inherited heart disease. HCM is associated with myocardial hypertrophy, fibrosis and ventricular dysfunction. Hypoxia-inducible transcription factor-1α (Hif-1α) is the central master regulators of cellular hypoxia response and associated with HCM. Yet its exact role remains to be elucidated. Therefore, the effect of a cardiomyocyte-specific Hif-1a knockout (cHif1aKO) was studied in an established α-MHC 719/+ HCM mouse model that exhibits the classical features of human HCM. The results show that Hif-1α protein and HIF targets were upregulated in left ventricular tissue of α-MHC 719/+ mice. Cardiomyocyte-specific abolishment of Hif-1a blunted the disease phenotype, as evidenced by decreased left ventricular wall thickness, reduced myocardial fibrosis, disordered SRX/DRX state and ROS production. cHif1aKO induced normalization of pro-hypertrophic and pro-fibrotic left ventricular remodeling signaling evidenced on whole transcriptome and proteomics analysis in α-MHC 719/+ mice. Proteomics of serum samples from patients with early onset HCM revealed significant modulation of HIF. These results demonstrate that HIF signaling is involved in mouse and human HCM pathogenesis. Cardiomyocyte-specific knockout of Hif-1a attenuates disease phenotype in the mouse model. Targeting Hif-1α might serve as a therapeutic option to mitigate HCM disease progression.

mRNA-based vaccines have become a major focus in the pharmaceutical industry. The coding sequence as well as the Untranslated Regions (UTRs) of an mRNA can strongly influence translation efficiency, stability, degradation, and other factors that collectively determine a vaccine's effectiveness. However, optimizing mRNA sequences for those properties remains a complex challenge. Existing deep learning models often focus solely on coding region optimization, overlooking the UTRs. We present Helix-mRNA, a structured state-space-based and attention hybrid model to address these challenges. In addition to a first pre-training, a second pre-training stage allows us to specialise the model with high-quality data. We employ single nucleotide tokenization of mRNA sequences with codon separation, ensuring prior biological and structural information from the original mRNA sequence is not lost. Our model, Helix-mRNA, outperforms existing methods in analysing both UTRs and coding region properties. It can process sequences 6x longer than current approaches while using only 10% of the parameters of existing foundation models. Its predictive capabilities extend to all mRNA regions. We open-source the model (https://github.com/helicalAI/helical) and model weights (https://huggingface.co/helical-ai/helix-mRNA).