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Neff SL, Doing G, Reiter T, Hampton TH, Greene CS, Hogan DA. Pseudomonas aeruginosa transcriptome analysis of metal restriction in ex vivo cystic fibrosis sputum. Microbiol Spectr 2024; 12:e0315723. [PMID: 38385740 PMCID: PMC10986534 DOI: 10.1128/spectrum.03157-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Chronic Pseudomonas aeruginosa lung infections are a feature of cystic fibrosis (CF) that many patients experience even with the advent of highly effective modulator therapies. Identifying factors that impact P. aeruginosa in the CF lung could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa studies using laboratory models or RNA isolated from sputum, we analyzed transcripts of strain PAO1 after incubation in sputum from different CF donors prior to RNA extraction. We compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in synthetic cystic fibrosis sputum medium to determine key genes, which are among the most differentially expressed or most highly expressed. Using the key genes, gene sets with correlated expression were determined using the gene expression analysis tool eADAGE. Gene sets were used to analyze the activity of specific pathways in P. aeruginosa grown in sputum from different individuals. Gene sets that we found to be more active in sputum showed similar activation in published data that included P. aeruginosa RNA isolated from sputum relative to corresponding in vitro reference cultures. In the ex vivo samples, P. aeruginosa had increased levels of genes related to zinc and iron acquisition which were suppressed by metal amendment of sputum. We also found a significant correlation between expression of the H1-type VI secretion system and CFTR corrector use by the sputum donor. An ex vivo sputum model or synthetic sputum medium formulation that imposes metal restriction may enhance future CF-related studies.IMPORTANCEIdentifying the gene expression programs used by Pseudomonas aeruginosa to colonize the lungs of people with cystic fibrosis (CF) will illuminate new therapeutic strategies. To capture these transcriptional programs, we cultured the common P. aeruginosa laboratory strain PAO1 in expectorated sputum from CF patient donors. Through bioinformatic analysis, we defined sets of genes that are more transcriptionally active in real CF sputum compared to a synthetic cystic fibrosis sputum medium. Many of the most differentially active gene sets contained genes related to metal acquisition, suggesting that these gene sets play an active role in scavenging for metals in the CF lung environment which may be inadequately represented in some models. Future studies of P. aeruginosa transcript abundance in CF may benefit from the use of an expectorated sputum model or media supplemented with factors that induce metal restriction.
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Affiliation(s)
- Samuel L. Neff
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Georgia Doing
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Taylor Reiter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Thomas H. Hampton
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Casey S. Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Deborah A. Hogan
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
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Neff SL, Hampton TH, Koeppen K, Sarkar S, Latario CJ, Ross BD, Stanton BA. Rocket-miR, a translational launchpad for miRNA-based antimicrobial drug development. mSystems 2023; 8:e0065323. [PMID: 37975659 PMCID: PMC10734502 DOI: 10.1128/msystems.00653-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/06/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Antimicrobial-resistant infections contribute to millions of deaths worldwide every year. In particular, the group of bacteria collectively known as ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter sp.) pathogens are of considerable medical concern due to their virulence and exceptional ability to develop antibiotic resistance. New kinds of antimicrobial therapies are urgently needed to treat patients for whom existing antibiotics are ineffective. The Rocket-miR application predicts targets of human miRNAs in bacterial and fungal pathogens, rapidly identifying candidate miRNA-based antimicrobials. The application's target audience are microbiologists that have the laboratory resources to test the application's predictions. The Rocket-miR application currently supports 24 recognized human pathogens that are relevant to numerous diseases including cystic fibrosis, chronic obstructive pulmonary disease (COPD), urinary tract infections, and pneumonia. Furthermore, the application code was designed to be easily extendible to other human pathogens that commonly cause hospital-acquired infections.
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Affiliation(s)
- Samuel L. Neff
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Thomas H. Hampton
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Katja Koeppen
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Sharanya Sarkar
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Casey J. Latario
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Benjamin D. Ross
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Bruce A. Stanton
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
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Neff SL, Doing G, Reiter T, Hampton TH, Greene CS, Hogan DA. Analysis of Pseudomonas aeruginosa transcription in an ex vivo cystic fibrosis sputum model identifies metal restriction as a gene expression stimulus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554169. [PMID: 37662412 PMCID: PMC10473638 DOI: 10.1101/2023.08.21.554169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Chronic Pseudomonas aeruginosa lung infections are a distinctive feature of cystic fibrosis (CF) pathology, that challenge adults with CF even with the advent of highly effective modulator therapies. Characterizing P. aeruginosa transcription in the CF lung and identifying factors that drive gene expression could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa gene expression studies in laboratory culture models designed to model the CF lung environment, we employed an ex vivo sputum model in which laboratory strain PAO1 was incubated in sputum from different CF donors. As part of the analysis, we compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in artificial sputum medium (ASM). Analyses focused on genes that were differentially expressed between sputum and ASM and genes that were most highly expressed in sputum. We present a new approach that used sets of genes with correlated expression, identified by the gene expression analysis tool eADAGE, to analyze the differential activity of pathways in P. aeruginosa grown in CF sputum from different individuals. A key characteristic of P. aeruginosa grown in expectorated CF sputum was related to zinc and iron acquisition, but this signal varied by donor sputum. In addition, a significant correlation between P. aeruginosa expression of the H1-type VI secretion system and corrector use by the sputum donor was observed. These methods may be broadly useful in looking for variable signals across clinical samples.
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Affiliation(s)
- Samuel L. Neff
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Georgia Doing
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Taylor Reiter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Thomas H. Hampton
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Casey S. Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Deborah A. Hogan
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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Doing G, Lee AJ, Neff SL, Reiter T, Holt JD, Stanton BA, Greene CS, Hogan DA. Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia. mSystems 2023; 8:e0034122. [PMID: 36541761 PMCID: PMC9948711 DOI: 10.1128/msystems.00341-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/09/2022] [Indexed: 12/24/2022] Open
Abstract
Thousands of Pseudomonas aeruginosa RNA sequencing (RNA-seq) gene expression profiles are publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In this work, the transcriptional profiles from hundreds of studies performed by over 75 research groups were reanalyzed in aggregate to create a powerful tool for hypothesis generation and testing. Raw sequence data were uniformly processed using the Salmon pseudoaligner, and this read mapping method was validated by comparison to a direct alignment method. We developed filtering criteria to exclude samples with aberrant levels of housekeeping gene expression or an unexpected number of genes with no reported values and normalized the filtered compendia using the ratio-of-medians method. The filtering and normalization steps greatly improved gene expression correlations for genes within the same operon or regulon across the 2,333 samples. Since the RNA-seq data were generated using diverse strains, we report the effects of mapping samples to noncognate reference genomes by separately analyzing all samples mapped to cDNA reference genomes for strains PAO1 and PA14, two divergent strains that were used to generate most of the samples. Finally, we developed an algorithm to incorporate new data as they are deposited into the SRA. Our processing and quality control methods provide a scalable framework for taking advantage of the troves of biological information hibernating in the depths of microbial gene expression data and yield useful tools for P. aeruginosa RNA-seq data to be leveraged for diverse research goals. IMPORTANCE Pseudomonas aeruginosa is a causative agent of a wide range of infections, including chronic infections associated with cystic fibrosis. These P. aeruginosa infections are difficult to treat and often have negative outcomes. To aid in the study of this problematic pathogen, we mapped, filtered for quality, and normalized thousands of P. aeruginosa RNA-seq gene expression profiles that were publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The resulting compendia facilitate analyses across experiments, strains, and conditions. Ultimately, the workflow that we present could be applied to analyses of other microbial species.
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Affiliation(s)
- Georgia Doing
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Alexandra J. Lee
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samuel L. Neff
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Taylor Reiter
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Denver, Colorado, USA
| | - Jacob D. Holt
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Bruce A. Stanton
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Casey S. Greene
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Denver, Colorado, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Deborah A. Hogan
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
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Lee AJ, Reiter T, Doing G, Oh J, Hogan DA, Greene CS. Using genome-wide expression compendia to study microorganisms. Comput Struct Biotechnol J 2022; 20:4315-4324. [PMID: 36016717 PMCID: PMC9396250 DOI: 10.1016/j.csbj.2022.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/07/2022] [Accepted: 08/07/2022] [Indexed: 11/30/2022] Open
Abstract
A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.
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Affiliation(s)
- Alexandra J. Lee
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA
| | - Taylor Reiter
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Denver, CO, USA
| | - Georgia Doing
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Julia Oh
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Deborah A. Hogan
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth, Hanover, NH, USA
| | - Casey S. Greene
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Denver, CO, USA
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