Novel diagnostic potential of miR-1 in patients with acute heart failure

Protein features fusion using attributed network embedding for predicting protein-protein interaction

BACKGROUND

Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations.

METHODS

We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs' prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature.

RESULTS

When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively.

CONCLUSION

Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.

Evaluation of New Cardiac Damage Biomarkers in Polytrauma: GDF-15, HFABP and uPAR for Predicting Patient Outcomes

Background: Polytrauma is one of the leading mortality factors in younger patients, and in particular, the presence of cardiac damage correlates with a poor prognosis. Currently, troponin T is the gold standard, although troponin is limited as a biomarker. Therefore, there is a need for new biomarkers of cardiac damage early after trauma. Methods: Polytraumatized patients (ISS ≥ 16) were divided into two groups: those with cardiac damage (troponin T > 50 pg/mL, n = 37) and those without cardiac damage (troponin T < 12 pg/mL, n = 32) on admission to the hospital. Patients' plasma was collected in the emergency room 24 h after trauma, and plasma from healthy volunteers (n = 10) was sampled. The plasma was analyzed for the expression of HFABP, GDF-15 and uPAR proteins, as well as miR-21, miR-29, miR-34, miR-122, miR-125b, miR-133, miR-194, miR-204, and miR-155. Results were correlated with patients' outcomes. Results: HFABP, uPAR, and GDF-15 were increased in polytraumatized patients with cardiac damage (p < 0.001) with a need for catecholamines. HFABP was increased in non-survivors. Analysis of systemic miRNA concentrations showed a significant increase in miR-133 (p < 0.01) and miR-21 (p < 0.05) in patients with cardiac damage. Conclusion: All tested plasma proteins, miR-133, and miR-21 were found to reflect the cardiac damage in polytrauma patients. GDF-15 and HFABP were shown to strongly correlate with patients' outcomes.

Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network

miRNAs are important regulators for many crucial biological processes. Many recent studies have shown that miRNAs are closely related to various human diseases and can be potential biomarkers or therapeutic targets for some diseases, such as cancers. Therefore, accurately predicting miRNA-disease associations is of great importance for understanding and curing diseases. However, how to efficiently utilize the characteristics of miRNAs and diseases and the information on known miRNA-disease associations for prediction is still not fully explored. In this study, we propose a novel computational method for predicting miRNA-disease associations. The proposed method combines the graph convolutional network and the hypergraph convolutional network. The graph convolutional network is utilized to extract the information from miRNA-similarity data as well as disease-similarity data. Based on the representations of miRNAs and diseases learned by the graph convolutional network, we further use the hypergraph convolutional network to capture the complex high-order interactions in the known miRNA-disease associations. We conduct comprehensive experiments with different datasets and predictive tasks. The results show that the proposed method consistently outperforms several other state-of-the-art methods. We also discuss the influence of hyper-parameters and model structures on the performance of our method. Some case studies also demonstrate that the predictive results of the method can be verified by independent experiments.

Anti-inflammatory, Anti-fibrotic and Pro-cardiomyogenic Effects of Genetically Engineered Extracellular Vesicles Enriched in miR-1 and miR-199a on Human Cardiac Fibroblasts

RATIONALE

Emerging evidence indicates that stem cell (SC)- derived extracellular vesicles (EVs) carrying bioactive miRNAs are able to repair damaged or infarcted myocardium and ameliorate adverse remodeling. Fibroblasts represent a major cell population responsible for scar formation in the damaged heart. However, the effects of EVs on cardiac fibroblast (CFs) biology and function has not been investigated.

OBJECTIVE

To analyze the biological impact of stem cell-derived EVs (SC-EVs) enriched in miR-1 and miR-199a on CFs and to elucidate the underlying molecular mechanisms.

METHODS AND RESULTS

Genetically engineered human induced pluripotent stem cells (hiPS) and umbilical cord-derived mesenchymal stem cells (UC-MSCs) expressing miR-1 or miR-199a were used to produce miR-EVs. Cells and EVs were thoughtfully analyzed for miRNA expression using RT-qPCR method. Both hiPS-miRs-EVs and UC-MSC-miRs-EVs effectively transferred miRNAs to recipient CFs, however, hiPS-miRs-EVs triggered cardiomyogenic gene expression in CFs more efficiently than UC-MSC-miRs-EVs. Importantly, hiPS-miR-1-EVs exhibited cytoprotective effects on CFs by reducing apoptosis, decreasing levels of pro-inflammatory cytokines (CCL2, IL-1β, IL-8) and downregulating the expression of a pro-fibrotic gene - α-smooth muscle actin (α-SMA). Notably, we identified a novel role of miR-199a-3p delivered by hiPS-EVs to CFs, in triggering the expression of cardiomyogenic genes (NKX2.5, TNTC, MEF2C) and ion channels involved in cardiomyocyte contractility (HCN2, SCN5A, KCNJ2, KCND3). By targeting SERPINE2, miR-199a-3p may reduce pro-fibrotic properties of CFs, whereas miR-199a-5p targeted BCAM and TSPAN6, which may be implicated in downregulation of inflammation.

CONCLUSIONS

hiPS-EVs carrying miR-1 and miR-199a attenuate apoptosis and pro-fibrotic and pro-inflammatory activities of CFs, and increase cardiomyogenic gene expression. These finding serve as rationale for targeting fibroblasts with novel EV-based miRNA therapies to improve heart repair after myocardial injury.

GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm

BACKGROUND

The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments.

METHODS

In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions.

RESULTS

To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method.

CONCLUSION

GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.