Biochemical and Structural Studies on PrfA, the Transcriptional Regulator of Virulence in Listeria monocytogenes

Abstract

Listeria monocytogenes is a gram-positive soil saprophytic bacterium that is capable of causing fatal infection in humans. The main virulence regulator PrfA, a member of the Crp/FNR family of transcriptional regulators, activates the expression of essential proteins required for host cell invasion and cell-to-cell spread. The mechanism of PrfA activation and the identity of its small molecule coactivator have remained a mystery for more than 20 years, but it is hypothesized that PrfA shares mechanistic similarity to the E. coli cAMP binding protein, Crp. Crp activates gene expression by binding cAMP, increasing the DNA binding affinity of the protein and causing a significant DNA bend that facilitates RNA polymerase binding and downstream gene activation. Our data suggests PrfA activates virulence protein expression through a mechanism distinct from the canonical Crp activation mechanism that involves a combination of cysteine residue reduction and glutathione (GSH) binding.

Listeria lacking glutathione synthase (ΔgshF) is avirulent in mice; however virulence is rescued when the bacterium expresses the constitutively active PrfA mutant G145S. Interestingly, Listeria expressing a PrfA mutant in which its four cysteines are mutated to alanine (Quad PrfA), demonstrate a 30-fold decrease in virulence. The Quad and ΔgshF double mutant strains are avirulent. DNA-binding affinity, measured through fluorescence polarization assays, indicate reduction of the cysteine side chains is sufficient to allow PrfA to binds its physiological promoters Phly and PactA with low nanomolar affinity. Oxidized PrfA binds the promoters poorly.

Unexpectedly, Quad also binds promoter DNA with nanomolar affinity, suggesting that the cysteines play a role in transcription efficiency in addition to DNA binding. Both PrfA and Quad bind GSH at physiologically relevant and comparable affinities, however GSH did not affect DNA binding in either case. Thermal denaturation assays suggest that Quad and wild-type PrfA differ structurally upon binding GSH, which supports the in vivo difference in infection between the regulator and its mutant.

Structures of PrfA in complex with cognate DNA, determined through X-ray crystallography, further support the disparity between PrfA and Crp activation mechanisms as two structures of reduced PrfA bound to Phly (PrfA-Phly30 and PrfA-Phly24) suggest the DNA adopts a less bent DNA conformation when compared to Crp-cAMP- DNA. The structure of Quad-Phly30 confirms the DNA-binding data as the protein-DNA complex adopts the same overall conformation as PrfA-Phly.

From these results, we hypothesize a two-step activation mechanism wherein PrfA, oxidized upon cell entry and unable to bind DNA, is reduced upon its intracellular release and binds DNA, causing a slight bend in the promoter and small increase in transcription of PrfA-regulated genes. The structures of PrfA-Phly30 and PrfA-Phly24 likely visualize this intermediate complex. Increasing concentrations of GSH shift the protein to a (PrfA-GSH)-DNA complex which is fully active transcriptionally and is hypothesized to resemble closely the transcriptionally active structure of the cAMP-(Crp)-DNA complex. Thermal denaturation results suggest Quad PrfA is deficient in this second step, which explains the decrease in virulence and implicates the cysteine residues as critical for transcription efficiency. Further structural and biochemical studies are on-going to clarify this mechanism of activation.

Targeted Gene Repression Technologies for Regenerative Medicine, Genomics, and Gene Therapy

Gene regulation is a complex and tightly controlled process that defines cell function in physiological and abnormal states. Programmable gene repression technologies enable loss-of-function studies for dissecting gene regulation mechanisms and represent an exciting avenue for gene therapy. Established and recently developed methods now exist to modulate gene sequence, epigenetic marks, transcriptional activity, and post-transcriptional processes, providing unprecedented genetic control over cell phenotype. Our objective was to apply and develop targeted repression technologies for regenerative medicine, genomics, and gene therapy applications. We used RNA interference to control cell cycle regulation in myogenic differentiation and enhance the proliferative capacity of tissue engineered cartilage constructs. These studies demonstrate how modulation of a single gene can be used to guide cell differentiation for regenerative medicine strategies. RNA-guided gene regulation with the CRISPR/Cas9 system has rapidly expanded the targeted repression repertoire from silencing single protein-coding genes to modulation of genes, promoters, and other distal regulatory elements. In order to facilitate its adaptation for basic research and translational applications, we demonstrated the high degree of specificity for gene targeting, gene silencing, and chromatin modification possible with Cas9 repressors. The specificity and effectiveness of RNA-guided transcriptional repressors for silencing endogenous genes are promising characteristics for mechanistic studies of gene regulation and cell phenotype. Furthermore, our results support the use of Cas9-based repressors as a platform for novel gene therapy strategies. We developed an in vivo AAV-based gene repression system for silencing endogenous genes in a mouse model. Together, these studies demonstrate the utility of gene repression tools for guiding cell phenotype and the potential of the RNA-guided CRISPR/Cas9 platform for applications such as causal studies of gene regulatory mechanisms and gene therapy.

Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi

© Medina et al.Although cell cycle control is an ancient, conserved, and essential process, some core animal and fungal cell cycle regulators share no more sequence identity than non-homologous proteins. Here, we show that evolution along the fungal lineage was punctuated by the early acquisition and entrainment of the SBF transcription factor through horizontal gene transfer. Cell cycle evolution in the fungal ancestor then proceeded through a hybrid network containing both SBF and its ancestral animal counterpart E2F, which is still maintained in many basal fungi. We hypothesize that a virally-derived SBF may have initially hijacked cell cycle control by activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F, much like extant viral oncogenes. Consistent with this hypothesis, we show that SBF can regulate promoters with E2F binding sites in budding yeast.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.