Computational Approaches to Study Microbes and Microbiomes

Greene, Casey S.; Foster, James A.; Hogan, Deborah A.; Bromberg, Yana

doi:10.1142/9789814749411_0051

Cited by 8 publications

(7 citation statements)

References 66 publications

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“…Existing public gene expression data compendia for more than one hundred organisms are of sufficient size to support eADAGE models (Greene et al, 2016). Cross-compendium analyses provide the opportunity to use existing data to identify regulatory patterns that are evident across multiple experiments, datasets, and labs.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

et al. 2017

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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.

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Section: Discussionmentioning

confidence: 99%

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

et al. 2017

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“…Furthermore, pathway definition relies on expert-contributed annotations, yet ∼38% (2,162 of 5,704) of genes for PAO1 reference strain (pseudomonas.com) lack description. Recent methods are using unsupervised machine learning to leverage large amounts of transcriptomic data and automatically identify sets of genes with correlated expression across large compendia of samples, agnostic of gene annotations and previously characterized pathways [56, 57, 59–62]. With over 2,000 transcriptional profiles of P. aeruginosa in the public sphere, such an approach has been successfully implemented to make expression-based gene sets which can be used as data-driven analytical tools that can bolster transcriptional analyses [53, 58, 63, 64].…”

Section: Introductionmentioning

confidence: 99%

Conditional antagonism in co-cultures ofPseudomonas aeruginosaandCandida albicans: an intersection of ethanol and phosphate signaling distilled from dual-seq transcriptomics

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Koeppen

Occhipinti

et al. 2020

Preprint

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1Pseudomonas aeruginosa and Candida albicans are opportunistic pathogens 2 whose interactions involve the secreted products ethanol and phenazines. Here we 3 describe the focal role of ethanol in mixed-species co-cultures by dual RNA-seq analyses. 4 P. aeruginosa and C. albicans transcriptomes were assessed after growth in mono-5 culture or co-culture with either ethanol-producing C. albicans or a C. albicans mutant 6 lacking the primary ethanol dehydrogenase, Adh1. Analyses using KEGG-pathways and 7 the previously published eADAGE method revealed several P. aeruginosa responses to 8 C. albicans-produced ethanol including the induction of a non-canonical low phosphate 9 response mediated by PhoB. C. albicans wild-type, but not C. albicans adh1∆/∆, induces 10 P. aeruginosa production of 5-methyl-phenazine-1-carboxylic acid (5-MPCA), which 11 forms a red derivative within fungal cells. We first demonstrate that PhoB is required for 12 this interaction and that PhoB hyperactivity, via deletion of pstB, leads to increased 13 production of 5-MPCA even when phosphate concentrations are high, but only in the 14 presence of ethanol. Second, we show that ethanol is only sufficient to promote 5-MPCA 15 production at permissive phosphate concentrations. The intersection of ethanol and 16 phosphate in co-culture is mirrored in C. albicans; the adh1∆/∆ mutant had increased 17 expression of genes regulated by Pho4, the C. albicans transcription factor that responds 18 to low phosphate which we confirmed by showing the adh1∆/∆ strain had elevated Pho4-19 dependent phosphatase activity. The dual-dependence on ethanol and phosphate 20 concentrations for anti-fungal production highlights how environmental factors modulate 21 microbial interactions and dictate antagonisms such as those between P. aeruginosa and 22 C. albicans. 23 24Author Summary 25 26 Pseudomonas aeruginosa and Candida albicans are opportunistic pathogens that are 27 frequently isolated from co-infections. Using a Dual-Seq approach in combination with 28 genetics approaches, we found that ethanol produced by C. albicans stimulates the PhoB 29 regulon in P. aeruginosa asynchronously with activation of the Pho4 regulon in C. 30 albicans. In doing so, we demonstrate that eADAGE-based analysis can improve the 31 understanding of the P. aeruginosa response to ethanol-producing C. albicans as 32 measured by transcriptomics: we identify a subset of PhoB-regulated genes as 33 differentially expressed in response to ethanol. We validate our result by showing that 34PhoB is necessary for multiple roles in co-culture including the competition for phosphate 35 and the production of 5-methyl-phenazine-1-carboxylic acid, and that the P. aeruginosa 36 response to C. albicans-produced ethanol depends on phosphate availability. The 37 conditional stimulation of virulence production in response to sub-inhibitory 38 concentrations of ethanol only under phosphate limitation highlights the importance of 39 considering nutrient concentrations in the analysis of co-culture interactions....

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“…In contrast to the paucity of carefully curated gene sets specific to these non-traditional models, the amount of genome-wide gene expression data has grown rapidly, especially for microbes which have a relatively small transcriptome and are inexpensive to assay [8]. For single-cell organisms, a complete compendium of public data ideally captures expression under numerous conditions.…”

Section: Introductionmentioning

confidence: 99%

ADAGE signature analysis: differential expression analysis with data-defined gene sets

Tan

Huyck

et al. 2017

Preprint

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Background: Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. Conclusions: We designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

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Computational Approaches to Study Microbes and Microbiomes

Cited by 8 publications

References 66 publications

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks

Conditional antagonism in co-cultures ofPseudomonas aeruginosaandCandida albicans: an intersection of ethanol and phosphate signaling distilled from dual-seq transcriptomics

ADAGE signature analysis: differential expression analysis with data-defined gene sets

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