Dincer, C. | Search bioRxiv

bioRxiv2019

CRISPR/Cas13a powered electrochemical microfluidic biosensor for nucleic acid amplification-free miRNA diagnostics

Non-coding small RNAs, such as microRNAs, are becoming the biomarkers of choice for multiple diseases in clinical diagnostics. A dysregulation of these microRNAs can be associated to many different diseases, such as cancer, dementia or cardiovascular conditions. The key for an effective treatment is an accurate initial diagnosis at an early stage, improving the patients survival chances. Here, we introduce a CRISPR/Cas13a powered microfluidic, integrated electrochemical biosensor for the on-site detection of microRNAs. Through this unique combination, the quantification of the potential tumor markers microRNA miR-19b and miR-20a has been realized without any nucleic acid amplification. With a readout time of 9 minutes and an overall process time of less than 4 hours, a limit of detection of 10 pM was achieved, using a measuring volume of less than 0.6 {micro}l. Furthermore, we demonstrate the feasibility of our versatile sensor platform to detect miR-19b in serum samples of children, suffering from brain cancer. The validation of our results with a standard qRT-PCR method shows the ability of our system to be a low-cost and target amplification-free tool for nucleic acid based diagnostics.

molecular biology↗

bioRxiv2019

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.\n\nAuthor SummaryGlioblastoma (GBM) is the most aggressive brain tumor type with a 15 months of survival on average. The mutation distribution of the GBM patients is very heterogeneous and standard treatments fail to consider the inter-tumor heterogeneity. In our study, we follow a systems level approach that integrates mutation profiles with protein-protein interaction networks. We hypothesized that although the mutations are heterogeneous, the mutations that are close in 3D of the same protein may affect the protein function similarly and this information can be used to get meaningful relation between disease state and mutations. Therefore, we spatially grouped these mutations as \"patches\" and reconstructed patient specific protein interaction networks. When we cluster these networks based on their pathway similarities, we found four patient groups in GBM. Then, the comparison of groups revealed overrepresentation of 3D patches. This finding can be used for patient specific therapy.

systems biology↗