Effect of diet and genotype on the miRNome of mice with altered lipoprotein metabolism.

The molecular mechanism by which lipid/lipoprotein biosynthesis is regulated in mammals involves a very large number of genes that are subject to multiple levels of regulation. miRNAs are recognized contributors to lipid homeostasis at the post-transcriptional level, although the elucidation of their role is made difficult by the multiplicity of their targets and the ability of more miRNAs to affect the same mRNAs. In this study, an evaluation of how miRNA expression varies in organs playing a key role in lipid/lipoprotein metabolism was conducted in control mice and in two mouse models carrying genetic ablations which differently affect low-density lipoprotein metabolism. Mice were fed a lipid-poor standard diet and a diet enriched in cholesterol and saturated fat. The results obtained showed that there are no miRNAs whose expression constantly vary with dietary or genetic changes. Furthermore, it appears that diet, more than genotype, impacts on organ-specific miRNA expression profiles.

Parkinson's Disease Phenotypes in Patient Neuronal Cultures and Brain Organoids Improved by 2-Hydroxypropyl-ß-Cyclodextrin Treatment.

BACKGROUND: The etiology of Parkinson's disease (PD) is only partially understood despite the fact that environmental causes, risk factors, and specific gene mutations are contributors to the disease. Biallelic mutations in the phosphatase and tensin homolog (PTEN)-induced putative kinase 1 (PINK1) gene involved in mitochondrial homeostasis, vesicle trafficking, and autophagy are sufficient to cause PD. OBJECTIVES: We sought to evaluate the difference between controls' and PINK1 patients' derived neurons in their transition from neuroepithelial stem cells to neurons, allowing us to identify potential pathways to target with repurposed compounds. METHODS: Using two-dimensional and three-dimensional models of patients' derived neurons we recapitulated PD-related phenotypes. We introduced the usage of midbrain organoids for testing compounds. Using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), we corrected the point mutations of three patients' derived cells. We evaluated the effect of the selected compound in a mouse model. RESULTS: PD patient-derived cells presented differences in their energetic profile, imbalanced proliferation, apoptosis, mitophagy, and a reduced differentiation efficiency to tyrosine hydroxylase positive (TH+) neurons compared to controls' cells. Correction of a patient's point mutation ameliorated the metabolic properties and neuronal firing rates as well as reversing the differentiation phenotype, and reducing the increased astrocytic levels. Treatment with 2-hydroxypropyl-ß-cyclodextrin increased the autophagy and mitophagy capacity of neurons concomitant with an improved dopaminergic differentiation of patient-specific neurons in midbrain organoids and ameliorated neurotoxicity in a mouse model. CONCLUSION: We show that treatment with a repurposed compound is sufficient for restoring the impaired dopaminergic differentiation of PD patient-derived cells. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Global network of computational biology communities: ISCB's Regional Student Groups breaking barriers.

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: "Nurture the new generation of computational biologists".

An integrative method to predict signalling perturbations for cellular transitions.

Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.

SeesawPred: A Web Application for Predicting Cell-fate Determinants in Cell Differentiation.

Cellular differentiation is a complex process where a less specialized cell evolves into a more specialized cell. Despite the increasing research effort, identification of cell-fate determinants (transcription factors (TFs) determining cell fates during differentiation) still remains a challenge, especially when closely related cell types from a common progenitor are considered. Here, we develop SeesawPred, a web application that, based on a gene regulatory network (GRN) model of cell differentiation, can computationally predict cell-fate determinants from transcriptomics data. Unlike previous approaches, it allows the user to upload gene expression data and does not rely on pre-compiled reference data sets, enabling its application to novel differentiation systems. SeesawPred correctly predicted known cell-fate determinants on various cell differentiation examples in both mouse and human, and also performed better compared to state-of-the-art methods. The application is freely available for academic, non-profit use at http://seesaw.lcsb.uni.lu.