Högskolan i Skövde

Background

Alternative splicing of Titin (TTN) I-band exons produce protein isoforms with variable size and elasticity, but the mechanisms whereby TTN splice factors regulate exon usage and thereby determining cardiomyocyte passive stiffness and diastolic function, is not well understood. Non-coding RNA transcripts from the antisense strand of protein-coding genes have been shown to regulate alternative splicing of the sense gene. The TTN gene locus harbours >80 natural antisense transcripts (NATs) with unknown function in the human heart. The aim of this study was to determine if TTN antisense transcripts play a role in alternative splicing of TTN.

Methods and Results

RNA-sequencing and RNA in situ hybridization (ISH) of cardiac tissue from heart failure patients (HF), unused donor hearts and human iPS-derived cardiomyocytes (iPS-CMs) were used to determine the expression and localization of TTN NATs. Live cell imaging was used to analyze the effect of NATs on sarcomere properties. RNA ISH, immunofluorescence was performed in iPS-CMs to study the interaction between NATs, TTN mRNA and splice factor protein RBM20.

We found that TTN-AS1-276 was the predominant TTN NAT in the human heart and that it was upregulated in HF. Knock down of TTN-AS1-276 in human iPS-CMs resulted in decreased interaction between the splicing factor RBM20 and TTN pre-mRNA, decreased TTN I-band exon skipping, and markedly lower expression of the less compliant TTN isoform N2B. The effect on TTN exon usage was independent of sense-antisense exon overlap and polymerase II elongation rate. Furthermore, knockdown resulted in longer sarcomeres with preserved alignment, improved fractional shortening and relaxation times.

Conclusions

We demonstrate a role for TTN-AS1-276 in facilitating alternative splicing of TTN and regulating sarcomere properties. This transcript could constitute a target for improving cardiac passive stiffness and diastolic function in conditions such as heart failure with preserved ejection fraction.

Making breast cancer prognosis from gene expression profiles of the primary tumor has become a promising application of deep learning. Yet, to be relevant to real world applications in the clinic and for knowledge discovery, these models must be robust to common distribution shifts. In this study, we evaluate recently proposed methods for improving domain and subgroup shifts. We test the in-distribution and out-of-distribution generalization of multiple episode learning, stochastic weight averaging, group distributionally robust optimization, and a subsampling scheme on one training and four external breast cancer prognosis datasets. The evaluation found that the methods can, to various degrees, improve generalization across domains, although there remain, partially high, generalization gaps. Additionally, in-distribution and out-of-distribution generalization differs between clinical subtypes of breast cancer. Thus, we conclude that further research into methods specifically addressing challenges in breast cancer prognosis from gene expression data are warranted. 

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) constitute an appealing tool for drug discovery, disease modeling, and cardiotoxicity screening. However, their physiological immaturity, resembling CMs in the late fetal stage, limits their utility. Herein, we have developed a novel, scalable cell culture medium designed to enhance the maturation of hPSC-CMs. This medium facilitates a metabolic shift towards fatty acid utilization and augments mitochondrial function by targeting Acetyl-CoA carboxylase 2 (ACC2) with a specific small molecule inhibitor. Our findings demonstrate that this maturation protocol significantly advances the metabolic, structural, molecular and functional maturity of hPSC-CMs at various stages of differentiation. Furthermore, it enables the creation of cardiac microtissues with superior structural integrity and contractile properties. Notably, hPSC-CMs cultured in this optimized maturation medium display increased accuracy in modeling a hypertrophic cardiac phenotype following acute endothelin-1 induction and show a strong correlation between in vitro and in vivo target engagement in drug screening efforts. This approach holds promise for improving the utility and translatability of hPSC-CMs in cardiac disease modeling and drug discovery. 

Modern methods of molecular diagnostics and therapy have revolutionized the field of medicine in recent years by providing more precise and effective tools for detecting and treating diseases. This progress includes a growing exploration of the body's secreted vesicles, known as extracellular vesicles (EVs), for both diagnostic and therapeutic purposes. EVs are a heterogeneous population of lipid bilayer vesicles secreted by almost every cell type studied so far. They are detected in body fluids and conditioned culture media from living cells. EVs play a crucial role in communication between cells and organs, both locally and over long distances. They are recognized for their ability to transport endogenous RNA and proteins between cells, including messenger RNA (mRNA), microRNA (miRNA), misfolded neurodegenerative proteins, and several other biomolecules. This review explores the dual utilization of EVs, serving not only for diagnostic purposes but also as a platform for delivering therapeutic molecules to cells and tissues. Through an exploration of their composition, biogenesis, and selective cargo packaging, we elucidate the intricate mechanisms behind RNA transport between cells via EVs, highlighting their potential use for both diagnostic and therapeutic applications. Finally, it addresses challenges and outlines prospective directions for the clinical utilization of EVs.

Small extracellular vesicles (sEV) derived from various cell sources have been demonstrated to enhance cardiac function in preclinical models of myocardial infarction (MI). The aim of this study was to compare different sources of sEV for cardiac repair and determine the most effective one, which nowadays remains limited. We comprehensively assessed the efficacy of sEV obtained from human primary bone marrow mesenchymal stromal cells (BM-MSC), human immortalized MSC (hTERT-MSC), human embryonic stem cells (ESC), ESC-derived cardiac progenitor cells (CPC), human ESC-derived cardiomyocytes (CM), and human primary ventricular cardiac fibroblasts (VCF), in in vitro models of cardiac repair. ESC-derived sEV (ESC-sEV) exhibited the best pro-angiogenic and anti-fibrotic effects in vitro. Then, we evaluated the functionality of the sEV with the most promising performances in vitro, in a murine model of MI-reperfusion injury (IRI) and analysed their RNA and protein compositions. In vivo, ESC-sEV provided the most favourable outcome after MI by reducing adverse cardiac remodelling through down-regulating fibrosis and increasing angiogenesis. Furthermore, transcriptomic, and proteomic characterizations of sEV derived from hTERT-MSC, ESC, and CPC revealed factors in ESC-sEV that potentially drove the observed functions. In conclusion, ESC-sEV holds great promise as a cell-free treatment for promoting cardiac repair following MI. 

The human transcriptome is a highly complex system and is often the focus of research, especially when it fails to function properly, causing disease. Indeed, the amount of publicly available transcriptomic data has grown considerably with the advent of high-throughput techniques. Such special cases are often hard to fully dissect, since studies will be confined to limited data samples and multiple biases. An ideal approach would utilize all available data to learn the fundamentals of the human gene expression system and use these insights in the examination of the more limited sample sets relating to specific diseases. This study shows how a neural network model can be created and used to extract relevant disease genes when applied to limited disease datasets and to suggest relevant pharmaceutical compounds. Thus, it presents a step toward a future where artificial intelligence can advance the analysis of human high-throughput data.

Vascular diseases are the largest cause of death globally and impose a major global burden on healthcare. The gold standard for treating vascular diseases is the transplantation of autologous veins, if applicable. Alternative treatments still suffer from shortcomings, including low patency, lack of growth potential, the need for repeated intervention, and a substantial risk of developing infections. The use of a vascular ECM scaffold reconditioned with the patient's own cells has shown successful results in preclinical and clinical studies. In this study, we have compared the proteomes of personalized tissue-engineered veins of humans and pigs. By applying tandem mass tag (TMT) labeling LC/MS-MS, we have investigated the proteome of decellularized (DC) veins from humans and pigs and reconditioned (RC) DC veins produced through perfusion with the patient's whole blood in STEEN solution, applying the same technology as used in the preclinical studies. The results revealed high similarity between the proteomes of human and pig DC and RC veins, including the ECM texture after decellularization and reconditioning. In addition, functional enrichment analysis showed similarities in signaling pathways and biological processes involved in the immune system response. Furthermore, the classification of proteins involved in immune response activity that were detected in human and pig RC veins revealed proteins that evoke immunogenic responses, which may lead to graft rejection, thrombosis, and inflammation. However, the results from this study imply the initiation of wound healing rather than an immunogenic response, as both systems share the same processes, and no immunogenic response was reported in the preclinical and clinical studies. Finally, our study assessed the application of STEEN solution in tissue engineering and identified proteins that may be useful for the prediction of successful transplantations. 

Background: Diabetic foot ulcers (DFU) pose a significant health risk in diabetic patients, with insufficient revascularization during wound healing being the primary cause. This study aimed to assess microvessel sprouting and wound healing capabilities using vascular endothelial growth factor (VEGF-A) and a modified fibroblast growth factor (FGF1). Methods: An ex vivo aortic ring rodent model and an in vivo wound healing model in diabetic mice were employed to evaluate the microvessel sprouting and wound healing capabilities of VEGF-A and a modified FGF1 both as monotherapies and in combination. Results: The combination of VEGF-A and FGF1 demonstrated increased vascular sprouting in the ex vivo mouse aortic ring model, and topical administration of a combination of VEGF-A and FGF1 mRNAs formulated in lipid nanoparticles (LNPs) in mouse skin wounds promoted faster wound closure and increased neovascularization seven days post-surgical wound creation. RNA-sequencing analysis of skin samples at day three post-wound creation revealed a strong transcriptional response of the wound healing process, with the combined treatment showing significant enrichment of genes linked to skin growth. Conclusion: f-LNPs encapsulating VEGF-A and FGF1 mRNAs present a promising approach to improving the scarring process in DFU.

Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

Stem cell niches have been thoroughly investigated in tissue with high regenerative capacity but not in tissues where cell turnover is slow, such as the human heart. The left AtrioVentricular junction (AVj), the base of the mitral valve, has previously been proposed as a niche region for cardiac progenitors in the adult human heart. In the present study, we explore the right side of the human heart, the base of the tricuspid valve, to investigate the potential of this region as a progenitor niche. Paired biopsies from explanted human hearts were collected from multi-organ donors (N = 12). The lateral side of the AVj, right atria (RA), and right ventricle (RV) were compared for the expression of stem cell niche-related biomarkers using RNA sequencing. Gene expression data indicated upregulation of genes related to embryonic development and extracellular matrix (ECM) composition in the proposed niche region, that is, the AVj. In addition, immunohistochemistry showed high expression of the fetal cardiac markers MDR1, SSEA4, and WT1 within the same region. Nuclear expression of HIF1α was detected suggesting hypoxia. Rare cells were found with the co-staining of the proliferation marker PCNA and Ki67 with cardiomyocyte nuclei marker PCM1 and cardiac Troponin T (cTnT), indicating proliferation of small cardiomyocytes. WT1+/cTnT+ and SSEA4+/cTnT+ cells were also found, suggesting cardiomyocyte-specific progenitors. The expression of the stem cell markers gradually decreased with distance from the tricuspid valve. No expression of these markers was observed in the RV tissue. In summary, the base of the tricuspid valve is an ECM-rich region containing cells with expression of several stem cell niche-associated markers. Co-expression of stem cell markers with cTnT indicates cardiomyocyte-specific progenitors. We previously reported similar data from the base of the mitral valve and thus propose that human adult cardiomyocyte progenitors reside around both atrioventricular valves.