YB-1 Is a Novel Target for the Inhibition of α-Adrenergic-Induced Hypertrophy.

Cardiac hypertrophy resulting from sympathetic nervous system activation triggers the development of heart failure. The transcription factor Y-box binding protein 1 (YB-1) can interact with transcription factors involved in cardiac hypertrophy and may thereby interfere with the hypertrophy growth process. Therefore, the question arises as to whether YB-1 influences cardiomyocyte hypertrophy and might thereby influence the development of heart failure. YB-1 expression is downregulated in human heart biopsies of patients with ischemic cardiomyopathy (n = 8), leading to heart failure. To study the impact of reduced YB-1 in cardiac cells, we performed small interfering RNA (siRNA) experiments in H9C2 cells as well as in adult cardiomyocytes (CMs) of rats. The specificity of YB-1 siRNA was analyzed by a miRNA-like off-target prediction assay identifying potential genes. Testing three high-scoring genes by transfecting cardiac cells with YB-1 siRNA did not result in downregulation of these genes in contrast to YB-1, whose downregulation increased hypertrophic growth. Hypertrophic growth was mediated by PI3K under PE stimulation, as well by downregulation with YB-1 siRNA. On the other hand, overexpression of YB-1 in CMs, caused by infection with an adenovirus encoding YB-1 (AdYB-1), prevented hypertrophic growth under α-adrenergic stimulation with phenylephrine (PE), but not under stimulation with growth differentiation factor 15 (GDF15; n = 10-16). An adenovirus encoding the green fluorescent protein (AdGFP) served as the control. YB-1 overexpression enhanced the mRNA expression of the Gq inhibitor regulator of G-protein signaling 2 (RGS2) under PE stimulation (n = 6), potentially explaining its inhibitory effect on PE-induced hypertrophic growth. This study shows that YB-1 protects cardiomyocytes against PE-induced hypertrophic growth. Like in human end-stage heart failure, YB-1 downregulation may cause the heart to lose its protection against hypertrophic stimuli and progress to heart failure. Therefore, the transcription factor YB-1 is a pivotal signaling molecule, providing perspectives for therapeutic approaches.

Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.

BACKGROUND: The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein-protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model. RESULTS: We created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein-protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species. CONCLUSION: We developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred .

Disease- and headache-specific microRNA signatures and their predicted mRNA targets in peripheral blood mononuclear cells in migraineurs: role of inflammatory signalling and oxidative stress.

BACKGROUND: Migraine is a primary headache with genetic susceptibility, but the pathophysiological mechanisms are poorly understood, and it remains an unmet medical need. Earlier we demonstrated significant differences in the transcriptome of migraineurs' PBMCs (peripheral blood mononuclear cells), suggesting the role of neuroinflammation and mitochondrial dysfunctions. Post-transcriptional gene expression is regulated by miRNA (microRNA), a group of short non-coding RNAs that are emerging biomarkers, drug targets, or drugs. MiRNAs are emerging biomarkers and therapeutics; however, little is known about the miRNA transcriptome in migraine, and a systematic comparative analysis has not been performed so far in migraine patients. METHODS: We determined miRNA expression of migraineurs' PBMC during (ictal) and between (interictal) headaches compared to age- and sex-matched healthy volunteers. Small RNA sequencing was performed from the PBMC, and mRNA targets of miRNAs were predicted using a network theoretical approach by miRNAtarget.com™. Predicted miRNA targets were investigated by Gene Ontology enrichment analysis and validated by comparing network metrics to differentially expressed mRNA data. RESULTS: In the interictal PBMC samples 31 miRNAs were differentially expressed (DE) in comparison to healthy controls, including hsa-miR-5189-3p, hsa-miR-96-5p, hsa-miR-3613-5p, hsa-miR-99a-3p, hsa-miR-542-3p. During headache attacks, the top DE miRNAs as compared to the self-control samples in the interictal phase were hsa-miR-3202, hsa-miR-7855-5p, hsa-miR-6770-3p, hsa-miR-1538, and hsa-miR-409-5p. MiRNA-mRNA target prediction and pathway analysis indicated several mRNAs related to immune and inflammatory responses (toll-like receptor and cytokine receptor signalling), neuroinflammation and oxidative stress, also confirmed by mRNA transcriptomics. CONCLUSIONS: We provide here the first evidence for disease- and headache-specific miRNA signatures in the PBMC of migraineurs, which might help to identify novel targets for both prophylaxis and attack therapy.

Ischaemic post-conditioning in rats: Responder and non-responder differ in transcriptome of mitochondrial proteins.

Ischaemic post-conditioning (IPoC) is a clinical applicable procedure to reduce reperfusion injury. Non-responsiveness to IPoC possibly caused by co-morbidities limits its clinical attractiveness. We analysed differences in the expression of mitochondrial proteins between IPoC responder (IPoC-R) and non-responder (IPoC-NR). Eighty rats were randomly grouped to sham, ischaemia/reperfusion (I/R), IPoC or ischaemic pre-conditioning (IPC, as positive cardioprotective intervention) in vivo. Infarct sizes were quantified by plasma troponin I levels 60 minutes after reperfusion. After 7 days, rats were sacrificed and left ventricular tissue was taken for post hoc analysis. The transcriptome was analysed by qRT-PCR and small RNA sequencing. Key findings were verified by immunoblots. I/R increased plasma troponin I levels compared to Sham. IPC reduced troponin I compared to I/R, whereas IPoC produced either excellent protection (IPoC-R) or no protection (IPoC-NR). Twenty-one miRs were up-regulated by I/R and modified by IPoC. qRT-PCR analysis revealed that IPoC-R differed from other groups by reduced expression of arginase-2 and bax, whereas the mitochondrial uncoupling protein (UCP)-2 was induced in IPC and IPoC-R. IPoC-R and IPoC-NR synergistically increased the expression of non-mitochondrial proteins like VEGF and SERCA2a independent of the infarct size. Cardiac function was more closely linked to differences in mitochondrial proteins than on regulation of calcium-handling proteins. In conclusion, healthy rats could not always be protected by IPoC. IPoC-NR displayed an incomplete responsiveness which is reflected by different changes in the mitochondrial transcriptome compared to IPoC-R. This study underlines the importance of mitochondrial proteins for successful long-term outcome.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Pressure overload-induced systolic heart failure is associated with characteristic myocardial microRNA expression signature and post-transcriptional gene regulation in male rats.

Although systolic function characteristically shows gradual impairment in pressure overload (PO)-evoked left ventricular (LV) hypertrophy (LVH), rapid progression to congestive heart failure (HF) occurs in distinct cases. The molecular mechanisms for the differences in maladaptation are unknown. Here, we examined microRNA (miRNA) expression and miRNA-driven posttranscriptional gene regulation in the two forms of PO-induced LVH (with/without systolic HF). PO was induced by aortic banding (AB) in male Sprague-Dawley rats. Sham-operated animals were controls. The majority of AB animals demonstrated concentric LVH and slightly decreased systolic function (termed as ABLVH). In contrast, in some AB rats severely reduced ejection fraction, LV dilatation and increased lung weight-to-tibial length ratio was noted (referred to as ABHF). Global LV miRNA sequencing revealed fifty differentially regulated miRNAs in ABHF compared to ABLVH. Network theoretical miRNA-target analysis predicted more than three thousand genes with miRNA-driven dysregulation between the two groups. Seventeen genes with high node strength value were selected for target validation, of which five (Fmr1, Zfpm2, Wasl, Ets1, Atg16l1) showed decreased mRNA expression in ABHF by PCR. PO-evoked systolic HF is associated with unique miRNA alterations, which negatively regulate the mRNA expression of Fmr1, Zfmp2, Wasl, Ets1 and Atg16l1.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety.

INTRODUCTION: Signal detection yields confirmed signals in only 2.1%, which imposes a heavy burden on the pharmacovigilance system in the European Union. OBJECTIVES: We aimed to develop a network theoretical metric to increase the confirmed signal ratio of individual case safety report (ICSR) networks. METHODS: ICSRs of five cardiovascular adverse events were requested from EudraVigilance. We developed Vigilace™, a web-based application to build network representation of ICSRs. Three network-based signal scores, which we termed NEWS (normalized edge weight for signals) scores, were calculated by normalizing the weight of each edge in the report-based weighted network by the weight of the same edge in topological weighted networks. Depending on the third node in topological network edges, we defined full-, adverse event-, and drug-type NEWS scores. Area under the receiver operating characteristic curves (AUROC) were analyzed to compare the reporting odds ratio (ROR) and NEWS scores. RESULTS: Overall, 72,475 ICSRs were accessed from EudraVigilance. Drug-type NEWS (NEWSD) score performed better (DeLong test, p-value <0.05) compared with the ROR in case of four adverse events: acute myocardial infarction (AUROC: 0.856 vs. 0.720), arrhythmia (0.657 vs. 0.614), pulmonary hypertension (0.861 vs. 0.720), and QT prolongation (0.830 vs. 0.749). Postural orthostatic tachycardia syndrome was excluded due to the lack of reference data. CONCLUSION: This is the first demonstration that report-based weighting normalized by topological weighting of co-reported drugs, which we termed as NEWSD score, can perform better compared with the ROR. An application was developed for ICSR network analysis that facilitates the calculation of this score.