ABSTRACT
Currently, either highly multiplexed genetic manipulations can be delivered to mammalian cells all at once or extensive engineering of gene regulatory sequences can be used to conditionally activate a few manipulations. Here, we provide proof of principle for a new system enabling multiple genetic manipulations to be executed as a preprogrammed cascade of events. The system leverages the programmability of the S. pyogenes Cas9 and is based on flexible arrangements of individual modules of activity. The basic module consists of an inactive single-guide RNA (sgRNA)-like component that is converted to an active state through the effects of another sgRNA. Modules can be arranged to bring about an algorithmic program of sequential genetic manipulations without the need for engineering cell-type-specific promoters or gene regulatory sequences. With the expanding diversity of available tools that use spCas9, this sgRNA-based system provides multiple levels of interfacing with mammalian cell biology.
Subject(s)
CRISPR-Associated Protein 9/genetics , CRISPR-Cas Systems , Clustered Regularly Interspaced Short Palindromic Repeats , Gene Editing/methods , RNA, Guide, Kinetoplastida/genetics , Animals , Base Pairing , Base Sequence , CRISPR-Associated Protein 9/metabolism , Genes, Reporter , Green Fluorescent Proteins/genetics , Green Fluorescent Proteins/metabolism , HEK293 Cells , Humans , Mice , Mouse Embryonic Stem Cells/cytology , Mouse Embryonic Stem Cells/metabolism , Nucleic Acid Conformation , Plasmids/chemistry , Plasmids/metabolism , Promoter Regions, Genetic , RNA, Guide, Kinetoplastida/chemistry , RNA, Guide, Kinetoplastida/metabolism , Streptococcus pyogenes/chemistry , Streptococcus pyogenes/enzymologyABSTRACT
The process of wound healing is governed by complex interactions between proteins and the extracellular matrix, involving a range of signaling pathways. This study aimed to formulate, quantify, and analyze a mathematical model describing interactions among matrix metalloproteinases (MMP-1), their inhibitors (TIMP-1), and extracellular matrix in the healing of a diabetic foot ulcer. De-identified patient data for modeling were taken from Muller et al. (Diabet Med 25(4):419-426, 2008), a research outcome that collected average physiological data for two patient subgroups: "good healers" and "poor healers," where classification was based on rate of ulcer healing. Model parameters for the two patient subgroups were estimated using least squares. The model and parameter values were analyzed by conducting a steady-state analysis and both global and local sensitivity analyses. The global sensitivity analysis was performed using Latin hypercube sampling and partial rank correlation analysis, while local analysis was conducted through a classical sensitivity analysis followed by an SVD-QR subset selection. We developed a "local-to-global" analysis to compare the results of the sensitivity analyses. Our results show that the sensitivities of certain parameters are highly dependent on the size of the parameter space, suggesting that identifying physiological bounds may be critical in defining the sensitivities.