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Pahikkala, T.

Publications and source records attributed to Pahikkala, T..

2 recordsLinked to original sources
bioRxiv2018

Molecular atlas of postnatal mouse heart development

RationaleMammals lose the ability to regenerate their hearts within one week after birth. During this regenerative window, cardiac energy metabolism shifts from glycolysis to fatty acid oxidation, and recent evidence suggests that metabolism may participate in controlling cardiomyocyte cell cycle. However, the molecular mechanisms mediating the loss of postnatal cardiac regeneration are not fully understood.\n\nObjectiveThis study aims at providing an integrated resource of mRNA, protein and metabolite changes in the neonatal heart to identify metabolism-related mechanisms associated with the postnatal loss of regenerative capacity.\n\nMethods and ResultsMouse ventricular tissue samples taken on postnatal days 1, 4, 9 and 23 (P01, P04, P09 and P23, respectively) were analyzed with RNA sequencing (RNAseq) and global proteomics and metabolomics. Differential expression was observed for 8547 mRNAs and for 1199 of the 2285 quantified proteins. Furthermore, 151 metabolites with significant changes were identified. Gene ontology analysis, KEGG pathway analysis and fuzzy c-means clustering were used to identify biological processes and metabolic pathways either up- or downregulated on all three levels. Among these were branched chain amino acid degradation (upregulated at P23) and production of free saturated and monounsaturated medium- to long-chain fatty acids (upregulated at P04 and P09; downregulated at P23). Moreover, the HMG-CoA synthase (HMGCS)-mediated mevalonate pathway and ketogenesis were transiently activated. Pharmacological inhibition of HMGCS in primary neonatal rat ventricular cardiomyocytes reduced the percentage of BrdU+ cardiomyocytes, providing evidence that the mevalonate and ketogenesis routes may participate in regulating cardiomyocyte cell cycle.\n\nConclusionsThis is the first systems-level resource combining data from genome-wide transcriptomics with global quantitative proteomics and untargeted metabolomics analyses of the mouse heart throughout the early postnatal period. This integrated multi-level data of molecular changes associated with the loss of cardiac regeneration may open up new possibilities for the development of regenerative therapies.

systems biology↗
bioRxiv2018

Algebraic Shortcuts for Leave-One-Out Cross-Validation in Supervised Network Inference

MotivationSupervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using the model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.\n\nResultsWe present a series of leave-one-out cross-validation shortcuts to rapidly estimate the performance of state-of-the-art kernel-based network inference techniques.\n\nAvailabilityThe machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package.

systems biology↗