Fear, J. | bioRxiv Science

bioRxiv2022

A novel mechanistic framework for precise sequence replacement using reverse transcriptase and diverse CRISPR-Cas systems

CRISPR/Cas systems coupled with reverse transcriptase (RT), such as the recently described Prime editing, allow for site-specific replacement of DNA sequences. Despite widespread testing of Prime editing, it is currently only compatible with type II CRISPR/Cas proteins such as Streptococcus pyogenes and Staphylococcus aureus Cas9. Enabling RT compatibility with other CRISPR/Cas domains, such as type V enzymes with orthogonal protospacer adjacent motif specificities and smaller protein size would expand the range of edits that can be made in therapeutic and industrial applications. We achieve this with a novel mode of DNA editing at CRISPR-targeted sites that reverse transcribes the edit into the target strand DNA (e.g., the complement of the PAM-containing strand), rather than the non-target strand DNA, as in Prime editing. We term this technology RNA encoded DNA replacement of alleles with CRISPR (hereafter, REDRAW). We show that REDRAW extends the utility of RT-mediated editing beyond type II to include multiple type V CRISPR domains. REDRAW features a broad (8-10 bases) targeting window, at which all types of substitutions, insertions and deletions are possible. REDRAW combines the advantages of type V CRISPR domains with the extensive range of genetic variation enabled by RT-mediated, templated sequence replacement strategies.

molecular biology↗

bioRxiv2020

Reconstruction of Gene Regulatory Networks by integrating biological model and a recommendation system

Gene Regulatory Networks (GRNs) control many aspects of cellular processes including cell differentiation, maintenance of cell type specific states, signal transduction, and response to stress. Since GRNs provide information that is essential for understanding cell function, the inference of these networks is one of the key challenges in systems biology. Leading algorithms to reconstruct GRN utilize, in addition to gene expression data, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of DNA binding experiments. However, such prior knowledge is typically incomplete hence resulting in missing values. Therefore, the integration of such incomplete prior knowledge with gene expression to elucidate the underlying GRNs remains difficult. To address this challenge we introduce NetREX-CF - Regulatory Network Reconstruction using EXpression and Collaborative Filtering - a GRN reconstruction approach that brings together a modern machine learning strategy (Collaborative Filtering model) and a biologically justified model of gene expression (sparse Network Component Analysis based model). The Collaborative Filtering (CF) model is able to overcome the incompleteness of the prior knowledge and make edge recommends for building the GRN. Complementing CF, the sparse Network Component Analysis (NCA) model can use gene expression data to validate the recommended edges. Here we combine these two approaches using a novel data integration method and show that the new approach outperforms the currently leading GRN reconstruction methods. Furthermore, our mathematical formalization of the model has lead to a complex optimization problem of a type that has not been attempted before. Specifically, the formulation contains{ell} 0 norm that can not be separated from other variables. To fill this gap, we introduce here a new method Generalized PALM (GPALM) that allows us to solve a broad class of non-convex optimization problems and prove its convergence.

bioinformatics↗