Parkinson’s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

DNA methylation is one of the most important epigenetic modifications and is closely related with several biological processes such as regulation of gene transcription and the development of non-malignant diseases. The prevailing dogma states that DNA methylation in eukaryotes occurs essentially through 5-methylcytosine (5mC) but recently adenine methylation was also found to be present in eukaryotes. In mouse embryonic stem cells, 6-methyladenine (6mA) was associated with the repression and silencing of genes, particularly in the X-chromosome, known to play an important role in cell fate determination. Here, we have demonstrated that 6mA is a ubiquitous eukaryotic epigenetic modification that is put in place during epigenetically sensitive periods such as embryogenesis and fetal development. In somatic cells there are clear tissue specificity in 6mA levels, with the highest 6mA levels being observed in the brain. In zebrafish, during the first 120 h of embryo development, from a single pluripotent cell to an almost fully formed individual, 6mA levels steadily increase. An identical pattern was observed over embryonic days 7–21 in the mouse. Furthermore, exposure to a neurotoxic environmental pollutant during the same early life period may led to a decrease in the levels of this modification in female rats. The identification of the periods during which 6mA epigenetic marks are put in place increases our understanding of this mammalian epigenetic modification, and raises the possibility that it may be associated with developmental processes.

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.

Development of heart failure (HF) after myocardial infarction (MI) is responsible for premature death. Complex cellular and molecular mechanisms are involved in this process. A number of studies have linked the epitranscriptomic RNA modification N6-methyladenosine (m6A) with HF, but it remains unknown how m6A affects the risk of developing HF after MI. We addressed the regulation of m6A and its demethylase fat mass and obesity-associated (FTO) after MI and their association with HF. Using liquid chromatography coupled to mass spectrometry, we observed an increase of m6A content in the infarcted area of rat hearts subjected to coronary ligation and a decrease in blood. FTO expression measured by quantitative PCR was downregulated in the infarcted hearts. In whole blood samples collected at the time of reperfusion in MI patients, m6A content was lower in patients who developed HF as attested by a 4-month ejection fraction (EF) of ≤40% as compared to patients who did not develop HF (EF > 50%). M6A content was higher in females. These results show that m6A measured in blood is associated with HF development after MI and motivate further investigation of the potential role of m6A as a novel epitranscriptomics biomarker and therapeutic target of HF.

So far, imaging of the adult zebrafish heart and assessment of heart failure in adult zebrafish have been very limited. Here, we describe a new method for in vivo imaging of the hypertrabeculated heart of the adult zebrafish using miniaturized cardiac ultrasound catheters obtained from the cardiac catheterization laboratory. This method allows the observation of the ventricle of zebrafish and the assessment of ventricular diameters during diastole and systole, as well as heart rate and fractional shortening. Significant changes in these parameters were detected through the use of an adult zebrafish heart failure model induced by chronic anemia. This imaging technique opens the door to detailed in vivo analysis of the adult heart failure phenotype in zebrafish.