Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.

Transcriptional Circuits Regulating Developmental Processes in Candida albicans.

Candida albicans is a commensal member of the human microbiota that colonizes multiple niches in the body including the skin, oral cavity, and gastrointestinal and genitourinary tracts of healthy individuals. It is also the most common human fungal pathogen isolated from patients in clinical settings. C. albicans can cause a number of superficial and invasive infections, especially in immunocompromised individuals. The ability of C. albicans to succeed as both a commensal and a pathogen, and to thrive in a wide range of environmental niches within the host, requires sophisticated transcriptional regulatory programs that can integrate and respond to host specific environmental signals. Identifying and characterizing the transcriptional regulatory networks that control important developmental processes in C. albicans will shed new light on the strategies used by C. albicans to colonize and infect its host. Here, we discuss the transcriptional regulatory circuits controlling three major developmental processes in C. albicans: biofilm formation, the white-opaque phenotypic switch, and the commensal-pathogen transition. Each of these three circuits are tightly knit and, through our analyses, we show that they are integrated together by extensive regulatory crosstalk between the core regulators that comprise each circuit.

An Efficient, Rapid, and Recyclable System for CRISPR-Mediated Genome Editing in Candida albicans.

Candida albicans is the most common fungal pathogen of humans. Historically, molecular genetic analysis of this important pathogen has been hampered by the lack of stable plasmids or meiotic cell division, limited selectable markers, and inefficient methods for generating gene knockouts. The recent development of clustered regularly interspaced short palindromic repeat(s) (CRISPR)-based tools for use with C. albicans has opened the door to more efficient genome editing; however, previously reported systems have specific limitations. We report the development of an optimized CRISPR-based genome editing system for use with C. albicans. Our system is highly efficient, does not require molecular cloning, does not leave permanent markers in the genome, and supports rapid, precise genome editing in C. albicans. We also demonstrate the utility of our system for generating two independent homozygous gene knockouts in a single transformation and present a method for generating homozygous wild-type gene addbacks at the native locus. Furthermore, each step of our protocol is compatible with high-throughput strain engineering approaches, thus opening the door to the generation of a complete C. albicans gene knockout library. IMPORTANCECandida albicans is the major fungal pathogen of humans and is the subject of intense biomedical and discovery research. Until recently, the pace of research in this field has been hampered by the lack of efficient methods for genome editing. We report the development of a highly efficient and flexible genome editing system for use with C. albicans. This system improves upon previously published C. albicans CRISPR systems and enables rapid, precise genome editing without the use of permanent markers. This new tool kit promises to expedite the pace of research on this important fungal pathogen.