Andrews, S. S. | Search bioRxiv

bioRxiv2018

Silencing of ANKRD12 circRNA induces molecular and functional changes associated with invasive phenotypes

Circular RNAs (circRNA) that form through non-canonical backsplicing events of pre-mRNA transcripts are evolutionarily conserved and abundantly expressed across species. However, the functional relevance of circRNAs remains a topic of debate. In this study, we identified and characterized a circular RNA derived from Exon 2 and Exon 8 of the ANKRD12 gene, termed here as circANKRD12. We show that this circRNA is abundantly expressed in breast and ovarian cancers. The circANKRD12 is RNase R resistant and predominantly localized in the cytoplasm in contrast to its source gene mRNA. We confirmed the expression of this circRNA across a variety of cancer cell lines and provide evidence for its functional relevance through downstream regulation of several tumor invasion genes. We show that silencing of circANKRD12 induces a phenotypic change by significantly regulating cell cycle, increasing invasion and migration, altering the metabolism in cancer cells. These results reveal the functional significance of circANKRD12 and provide evidence of a regulatory role for this circRNA in cancer progression.

cancer biology↗

bioRxiv2017

Rule-based modeling using wildcards

Many biological molecules exist in multiple variants, such as proteins with different post-translational modifications, DNAs with different sequences, and phospholipids with different chain lengths. Representing these variants as distinct species, as most biochemical simulators do, leads to the problem that the number of species, and chemical reactions that interconvert them, typically increase combinatorially with the number of ways that the molecules can vary. This can be alleviated by \"rule-based modeling methods,\" in which software generates the chemical reaction network from relatively simple \"rules.\" This article presents a new approach to rule-based modeling. It is based on wildcards that match to species names, much as wildcards can match to file names in computer operating systems. It is much simpler to use than the formal rule-based modeling approaches developed previously but can also lead to unintended consequences if not used carefully. This article demonstrates rule-based modeling with wildcards through examples for: signaling systems, protein complexation, polymerization, nucleic acid sequence copying and mutation, the \"SMILES\" chemical notation, and others. The method is implemented in Smoldyn, a spatial and stochastic biochemical simulator, for both the generate-first and on-the-fly expansion, meaning whether the reaction network is generated before or during the simulation.

systems biology↗