Wendt, C. | bioRxiv Science

bioRxiv2025

Apremilast improves cardiomyocyte cohesion and arrhythmia in different models for arrhythmogenic cardiomyopathy

BackgroundArrhythmogenic cardiomyopathy (ACM) is a genetically inherited desmosome heart disease leading to life-threatening arrhythmias and sudden cardiac death. Currently, ACM treatment paradigms are merely symptom targeting. Recently, apremilast was shown to stabilize keratinocyte adhesion in the desmosomal disease pemphigus vulgaris. Therefore, this study investigated whether apremilast can be a therapeutic option for ACM. MethodsHuman induced pluripotent stem cells from a healthy control (hiPSC) and an ACM index patient (ACM-hiPSC) carrying a heterozygous desmoplakin (DSP) gene mutation (c.2854G>T, p.Glu952Ter), confirmed by whole exome sequencing (WES), were established. Cyclic-AMP ELISA, dissociation assay, immunostaining, and Western blotting analyses were performed in human iPSC-derived cardiomyocytes (hiPSC-CMs), murine HL-1 cardiomyocytes, and cardiac slices derived from wild-type (WT) mice, plakoglobin (PG, Jup) knockout (Jup-/-) (murine ACM model) or PG Serine 665 phosphodeficient (JUP-S665A) mice. Microelectrode array (MEA) analyses in ventricular cardiac slices and Langendorff heart perfusion were performed to analyze heart rate variability and arrhythmia. ResultsACM-hiPSC derived cardiomyocytes (ACM-hiPSC-CMs) revealed a significant loss of cohesion, which was rescued by apremilast. Further, treatment with apremilast strengthened basal cardiomyocyte cohesion in HL-1 cells and WT murine cardiac slices, paralleled by phosphorylation of PG at Serine 665 in human and murine models. In HL-1 cells, apremilast in addition activated ERK1/2, inhibition of which abolished apremilast-enhanced cardiomyocyte cohesion. Further, dissociation assays in slice cultures from JUP-S665A and Jup-/- mice revealed that PG is crucial for apremilast-enhanced cardiomyocyte cohesion. In parallel to enhanced cell adhesion, MEA and Langendorff measurements from WT and Jup-/- mice demonstrated decreased heart rate variability and arrhythmia after apremilast treatment. ConclusionsApremilast improves loss of cardiomyocyte cohesion, enhances localization of DSG2, and reduces arrhythmia in human and murine models of ACM ex vivo and in vitro, providing a novel treatment strategy for ACM by preserving desmosome function. Translational perspectiveThe current therapeutic options for patients with arrhythmogenic cardiomyopathy (ACM) include lifestyle changes, treatment with anti-arrhythmic drugs, catheter ablation, implantable cardiac defibrillators, and ultimately, heart transplantation for patients who are having therapy refractory arrhythmia or developed heart failure. However, lifestyle changes, such as restraining from physical endurance activities and {beta}-blocker therapy, are most used in patients carrying genetic variants coding for proteins of the desmosomal complex. Recent advancements hint that strategies enhancing intracellular cAMP could be beneficial in treating desmosomal diseases and can be effective therapeutics, which would be highly relevant for ACM patients. In this study, we show that apremilast improves loss of cardiomyocyte cohesion, enhances localization of desmosomal proteins, and reduces arrhythmia in both human and murine models of ACM ex vivo and in vitro, providing a novel treatment strategy for ACM by preserving desmosome function.

cell biology↗

bioRxiv2018

The association between weight at birth and breast cancer risk revisited using Mendelian randomisation

Observational studies suggest that higher birth weight (BW) is associated with increased risk of breast cancer in adult life. We conducted a two-sample Mendelian randomisation (MR) study to assess whether this association is causal. Sixty independent single nucleotide polymorphisms (SNPs) known to be associated at P < 5 x 10-8 with BW were used to construct (1) a 41-SNP instrumental variable (IV) for univariable MR after removing SNPs with pleiotropic associations with other breast cancer risk factors and (2) a 49-SNP IV for multivariable MR after filtering SNPs for data availability. BW predicted by the 41-SNP IV was not associated with overall breast cancer risk in inverse-variance weighted (IVW) univariable MR analysis of genetic association data from 122,977 breast cancer cases and 105,974 controls (odds ratio = 0.86 per 500 g higher BW; 95% confidence interval: 0.73--1.01). Sensitivity analyses using four alternative methods and three alternative IVs, including an IV with 59 of the 60 BW-associated SNPs, yielded similar results. Multivariable MR adjusting for the effects of the 49-SNP IV on birth length, adult height, adult body mass index, age at menarche, and age at menopause using IVW and MR-Egger methods provided estimates consistent with univariable analyses. Results were also similar when all analyses were repeated after restricting to estrogen receptor-positive or -negative breast cancer cases. Point estimates of the odds ratios from most analyses performed indicated an inverse relationship between genetically-predicted BW and breast cancer. Thus, there is little evidence from MR to suggest that the previously observed association between higher BW and increased risk of breast cancer in adult life is causal.

epidemiology↗

bioRxiv2017

Normalization of Mass Spectrometry Data (NOMAD)

MotivationiTRAQ reagent-based mass spectrometry (MS) is a commonly used technology for identification and quantification of proteins in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. iTRAQ MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that such normalization does not efficiently remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality.\n\nResultsNOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD efficiently removes bias which allows for valid across MS run comparisons.\n\nAvailabilityThe NOMAD Bioconductor package: www.bioconductor.org\n\nContactola.larsson@ki.se; carl.murie@ki.se

bioinformatics↗