Summary Cross experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships.

The genome encodes more than 50 proteins predicted to be involved in c-di-GMP signaling. Here, we demonstrated that, tested across 188 nutrients, these enzymes and effectors appeared capable of impacting biofilm formation. Transcriptional analysis of network members across ∼50 nutrient conditions indicates that altered gene expression can explain a subset of but not all biofilm formation responses to the nutrients. Additional organization of the network is likely achieved through physical interaction, as determined via probing ∼2,000 interactions by bacterial two-hybrid assays. Our analysis revealed a multimodal regulatory strategy using combinations of ligand-mediated signals, protein-protein interaction, and/or transcriptional regulation to fine-tune c-di-GMP-mediated responses. These results create a profile of a large c-di-GMP network that is used to make important cellular decisions, opening the door to future model building and the ability to engineer this complex circuitry in other bacteria. Cyclic diguanylate (c-di-GMP) is a key signaling molecule regulating bacterial biofilm formation, and many microbes have up to dozens of proteins that make, break, or bind this dinucleotide. A major open issue in the field is how signaling specificity is conferred in the unpartitioned space of a bacterial cell. Here, we took a systems approach, using mutational analysis, transcriptional studies, and bacterial two-hybrid analysis to interrogate this network. We found that a majority of enzymes are capable of impacting biofilm formation in a context-dependent manner, and we revealed examples of two or more modes of regulation (i.e., transcriptional control with protein-protein interaction) being utilized to generate an observable impact on biofilm formation.

Pseudomonas aeruginosa and Candida albicans are opportunistic pathogens whose interactions involve the secreted products ethanol and phenazines. Here, we describe the role of ethanol in mixed-species co-cultures by dual-seq analyses. P. aeruginosa and C. albicans transcriptomes were assessed after growth in mono-culture or co-culture with either ethanol-producing C. albicans or a C. albicans mutant lacking the primary ethanol dehydrogenase, Adh1. Analysis of the RNA-Seq data using KEGG pathway enrichment and eADAGE methods revealed several P. aeruginosa responses to C. albicans-produced ethanol including the induction of a non-canonical low-phosphate response regulated by PhoB. C. albicans wild type, but not C. albicans adh1Δ/Δ, induces P. aeruginosa production of 5-methyl-phenazine-1-carboxylic acid (5-MPCA), which forms a red derivative within fungal cells and exhibits antifungal activity. Here, we show that C. albicans adh1Δ/Δ no longer activates P. aeruginosa PhoB and PhoB-regulated phosphatase activity, that exogenous ethanol complements this defect, and that ethanol is sufficient to activate PhoB in single-species P. aeruginosa cultures at permissive phosphate levels. The intersection of ethanol and phosphate in co-culture is inversely reflected in C. albicans; C. albicans adh1Δ/Δ had increased expression of genes regulated by Pho4, the C. albicans transcription factor that responds to low phosphate, and Pho4-dependent phosphatase activity. Together, these results show that C. albicans-produced ethanol stimulates P. aeruginosa PhoB activity and 5-MPCA-mediated antagonism, and that both responses are dependent on local phosphate concentrations. Further, our data suggest that phosphate scavenging by one species improves phosphate access for the other, thus highlighting the complex dynamics at play in microbial communities.

Motivation In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or "batch effects," may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined. Objective We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments. Method We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability. Results The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal. Conclusion When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.

Pseudomonas aeruginosa frequently resides among ethanol-producing microbes, making its response to the microbially produced concentrations of ethanol relevant to understanding its biology. Our transcriptome analysis found that genes involved in trehalose metabolism were induced by low concentrations of ethanol, and biochemical assays showed that levels of intracellular trehalose increased significantly upon growth with ethanol. The increase in trehalose was dependent on the TreYZ pathway but not other trehalose-metabolic enzymes (TreS or TreA). The sigma factor AlgU (AlgT), a homolog of RpoE in other species, was required for increased expression of the treZ gene and trehalose levels, but induction was not controlled by the well-characterized proteolysis of its anti-sigma factor, MucA. Growth with ethanol led to increased SpoT-dependent (p)ppGpp accumulation, which stimulates AlgU-dependent transcription of treZ and other AlgU-regulated genes through DksA, a (p)ppGpp and RNA polymerase binding protein. Ethanol stimulation of trehalose also required acylhomoserine lactone (AHL)-mediated quorum sensing (QS), as induction was not observed in a ΔlasR ΔrhlR strain. A network analysis using a model, eADAGE, built from publicly available P. aeruginosa transcriptome data sets (J. Tan, G. Doing, K. A. Lewis, C. E. Price, et al., Cell Syst 5:63–71, 2017, https://doi.org/10.1016/j.cels.2017.06.003) provided strong support for our model in which treZ and coregulated genes are controlled by both AlgU- and AHL-mediated QS. Consistent with (p)ppGpp- and AHL-mediated quorum-sensing regulation, ethanol, even when added at the time of culture inoculation, stimulated treZ transcript levels and trehalose production in cells from post-exponential-phase cultures but not in cells from exponential-phase cultures. These data highlight the integration of growth and cell density cues in the P. aeruginosa transcriptional response to ethanol. IMPORTANCE Pseudomonas aeruginosa is often found with bacteria and fungi that produce fermentation products, including ethanol. At concentrations similar to those produced by environmental microbes, we found that ethanol stimulated expression of trehalose-biosynthetic genes and cellular levels of trehalose, a disaccharide that protects against environmental stresses. The induction of trehalose by ethanol required the alternative sigma factor AlgU through DksA- and SpoT-dependent (p)ppGpp. Trehalose accumulation also required AHL quorum sensing and occurred only in post-exponential-phase cultures. This work highlights how cells integrate cell density and growth cues in their responses to products made by other microbes and reveals a new role for (p)ppGpp in the regulation of AlgU activity.