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.</p

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

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

Abstract 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

peer reviewedComprehensive 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