microRNAs are small non-coding RNAs gaining interest for their potential roles as reliable biomarkers for the diagnosis and therapeutics of numerous pathologies, ranging from cancer to neurodegenerative or psychiatric disorders. Indeed, microRNAs are present in various accessible biofluids, including peripheral blood, and specific dysregulation of their expression may be associated with these different pathological conditions. microRNAs can be isolated from plasma or serum for sequencing with commercial kits. However, these two biofluids might exhibit some differences in their microRNA contents, due notably to the coagulation process occurring during serum collection. It remains unclear from previous studies and commercial recommendations which blood fraction is preferable. Because of the small amount of circulating microRNAs in a given blood volume, this question appears crucial for qualitative and quantitative optimization of microRNA profiling, especially in animal models used for investigating the pathophysiological relevancy of this approach. We therefore evaluated the efficiency of RNA isolation and microRNA levels from plasma and sera isolated from rats and humans, with a widely used extraction kit (QIAGEN miRNeasy), and assessed microRNA quality and quantity with high-throughput sequencing. Fewer reads with length corresponding to non-miRNAs sequences were observed in plasma than in serum, both from rats and humans. Moreover, rat plasma produced twice as many aligned reads compared to sera, as well as more aligned reads corresponding to microRNAs (84.6% against 38.7%), differences that were not find in human samples. Our results, therefore, clearly indicate that plasma should be preferred for miRNA investigations, particularly for translational studies.

Background: Care management of Parkinson disease (PD) patients currently remains symptomatic, especially because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Detecting PD earlier therefore represents a key step for developing therapies able to delay or slow down its progression. Methods: We investigated metabolic markers in three different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 2 cohorts of de novo PD patients (n = 95). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics. Results: Our translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence, and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, non-motor signs and very interestingly, also discriminated PD patients from healthy subjects. Conclusion: From our translational study which included three animal models and two PD patient cohorts, we propose a promising composite biomarker exhibiting a high level of predictivity for PD diagnosis in its early phase, before motor symptoms appearance.