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The immunostimulatory properties conferred by vaccine adjuvants require Caspase-1 for processing of IL-1β and IL-18. Caspase-1 is activated in response to a breach of the cytosolic compartment by microbes and the process is initiated by intracellular pattern recognition receptors within inflammasomes. Listeria monocytogenes is detected in the cytosol by the NLRC4, NLRP3 and AIM2 inflammasomes. NLRC4 is activated by flagellin, and L. monocytogenes evades this detector by repressing flagellin expression. We generated an L. monocytogenes strain that was forced to express flagellin in the host cell cytosol. This strain hyperactivated Caspase-1 and was preferentially cleared via NLRC4 detection in an IL-1β/IL-18 independent manner. We also created a strain of L. monocytogenes with forced expression of another NLRC4 agonist, PrgJ from the Type III secretion system of S. typhimurium. Forced expression of flagellin or PrgJ resulted in attenuation, yet both strains conferred protective immunity in mice against lethal challenge with L. monocytogenes. This work is the first demonstration of specific targeting of the Caspase-1 activation pathway to generate a safe and potent L. monocytogenes based vaccine. Moreover, the attenuated strains with embedded flagellin or PrgJ adjuvants, represent attractive vectors for vaccines aimed at eliciting T cell responses.

Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ∼1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain—a common problem in laboratory animal and human studies.

Peroxisomes are intracellular organelles that house a number of diverse metabolic processes, notably those required for β-oxidation of fatty acids. Peroxisomes biogenesis can be induced by the presence of peroxisome proliferators, including fatty acids, which activate complex cellular programs that underlie the induction process. Here, we used multi-parameter quantitative phenotype analyses of an arrayed mutant collection of yeast cells induced to proliferate peroxisomes, to establish a comprehensive inventory of genes required for peroxisome induction and function. The assays employed include growth in the presence of fatty acids, and confocal imaging and flow cytometry through the induction process. In addition to the classical phenotypes associated with loss of peroxisomal functions, these studies identified 169 genes required for robust signaling, transcription, normal peroxisomal development and morphologies, and transmission of peroxisomes to daughter cells. These gene products are localized throughout the cell, and many have indirect connections to peroxisome function. By integration with extant data sets, we present a total of 211 genes linked to peroxisome biogenesis and highlight the complex networks through which information flows during peroxisome biogenesis and function.