Essential O2-responsive genes of Pseudomonas aeruginosa and their network revealed by integrating dynamic data from inverted conditions

Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All

Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets.

Structure, function and regulation of Pseudomonas aeruginosa porins

Pseudomonas aeruginosa is a Gram-negative bacterium belonging to the γ-proteobacteria. Like other members of the Pseudomonas genus, it is known for its metabolic versatility and its ability to colonize a wide range of ecological niches, such as rhizosphere, water environments and animal hosts, including humans where it can cause severe infections. Another particularity of P. aeruginosa is its high intrinsic resistance to antiseptics and antibiotics, which is partly due to its low outer membrane permeability. In contrast to Enterobacteria, pseudomonads do not possess general diffusion porins in their outer membrane, but rather express specific channel proteins for the uptake of different nutrients. The major outer membrane 'porin', OprF, has been extensively investigated, and displays structural, adhesion and signaling functions while its role in the diffusion of nutrients is still under discussion. Other porins include OprB and OprB2 for the diffusion of glucose, the two small outer membrane proteins OprG and OprH, and the two porins involved in phosphate/pyrophosphate uptake, OprP and OprO. The remaining nineteen porins belong to the so-called OprD (Occ) family, which is further split into two subfamilies termed OccD (8 members) and OccK (11 members). In the past years, a large amount of information concerning the structure, function and regulation of these porins has been published, justifying why an updated review is timely.

Reorganization of gene network for degradation of polycyclic aromatic hydrocarbons (PAHs) in Pseudomonas aeruginosa PAO1 under several conditions

Although polycyclic aromatic hydrocarbons (PAHs) are harmful to human health, their elimination from the environment is not easy. Biodegradation of PAHs is promising since many bacteria have the ability to use hydrocarbons as their sole carbon and energy sources for growth. Of various microorganisms that can degrade PAHs, Pseudomonas aeruginosa is particularly important, not only because it causes a series of diseases including infection in cystic fibrosis patients, but also because it is a model bacterium in various studies. The genes that are responsible for degrading PAHs have been identified in P. aeruginosa, however, no gene acts alone as various stresses often initiate different metabolic pathways, quorum sensing, biofilm formation, antibiotic tolerance, etc. Therefore, it is important to study how PAH degradation genes behave under different conditions. In this study, we apply network analysis to investigating how 46 PAH degradation genes reorganized among 5549 genes in P. aeruginosa PAO1 under nine different conditions using publicly available gene coexpression data from GEO. The results provide six aspects of novelties: (i) comparing the number of gene clusters before and after stresses, (ii) comparing the membership in each gene cluster before and after stresses, (iii) defining which gene changed its membership together with PAH degradation genes before and after stresses, (iv) classifying membership-changed-genes in terms of category in Pseudomonas Genome Database, (v) postulating unknown gene’s function, and (vi) proposing new mechanisms for genes of interests. This study can shed light on understanding of cooperative mechanisms of PAH degradation from the level of entire genes in an organism, and paves the way to conduct the similar studies on other genes.

RNASeq Based Transcriptional Profiling of Pseudomonas aeruginosa PA14 after Short- and Long-Term Anoxic Cultivation in Synthetic Cystic Fibrosis Sputum Medium

The opportunistic human pathogen Pseudomonas aeruginosa can thrive under microaerophilic to anaerobic conditions in the lungs of cystic fibrosis patients. RNA_Seq based comparative RNA profiling of the clinical isolate PA14 cultured in synthetic cystic fibrosis medium was performed after planktonic growth (OD₆₀₀ = 2.0; P), 30 min after shift to anaerobiosis (A-30) and after anaerobic biofilm growth for 96h (B-96) with the aim to reveal differentially regulated functions impacting on sustained anoxic biofilm formation as well as on tolerance towards different antibiotics. Most notably, functions involved in sulfur metabolism were found to be up-regulated in B-96 cells when compared to A-30 cells. Based on the transcriptome studies a set of transposon mutants were screened, which revealed novel functions involved in anoxic biofilm growth.In addition, these studies revealed a decreased and an increased abundance of the oprD and the mexCD-oprJ operon transcripts, respectively, in B-96 cells, which may explain their increased tolerance towards meropenem and to antibiotics that are expelled by the MexCD-OprD efflux pump. The OprI protein has been implicated as a target for cationic antimicrobial peptides, such as SMAP-29. The transcriptome and subsequent Northern-blot analyses showed that the abundance of the oprI transcript encoding the OprI protein is strongly decreased in B-96 cells. However, follow up studies revealed that the susceptibility of a constructed PA14ΔoprI mutant towards SMAP-29 was indistinguishable from the parental wild-type strain, which questions OprI as a target for this antimicrobial peptide in strain PA14.

ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions

The increasing number of genome-wide assays of gene expression available from public databases presents opportunities for computational methods that facilitate hypothesis generation and biological interpretation of these data. We present an unsupervised machine learning approach, ADAGE (analysis using denoising autoencoders of gene expression), and apply it to the publicly available gene expression data compendium for Pseudomonas aeruginosa. In this approach, the machine-learned ADAGE model contained 50 nodes which we predicted would correspond to gene expression patterns across the gene expression compendium. While no biological knowledge was used during model construction, cooperonic genes had similar weights across nodes, and genes with similar weights across nodes were significantly more likely to share KEGG pathways. By analyzing newly generated and previously published microarray and transcriptome sequencing data, the ADAGE model identified differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes based on low-level gene expression differences. ADAGE compared favorably with traditional principal component analysis and independent component analysis approaches in its ability to extract validated patterns, and based on our analyses, we propose that these approaches differ in the types of patterns they preferentially identify. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments and open source code for use with other species and settings. Extraction of consistent patterns across large-scale collections of genomic data using methods like ADAGE provides the opportunity to identify general principles and biologically important patterns in microbial biology. This approach will be particularly useful in less-well-studied microbial species. IMPORTANCE The quantity and breadth of genome-scale data sets that examine RNA expression in diverse bacterial and eukaryotic species are increasing more rapidly than for curated knowledge. Our ADAGE method integrates such data without requiring gene function, gene pathway, or experiment labeling, making practical its application to any large gene expression compendium. We built a Pseudomonas aeruginosa ADAGE model from a diverse set of publicly available experiments without any prespecified biological knowledge, and this model was accurate and predictive. We provide ADAGE results for the complete P. aeruginosa GeneChip compendium for use by researchers studying P. aeruginosa and source code that facilitates ADAGE's application to other species and data types. Author Video: An author video summary of this article is available.

The OpdQ porin of Pseudomonas aeruginosa is regulated by environmental signals associated with cystic fibrosis including nitrate-induced regulation involving the NarXL two-component system

Pseudomonas aeruginosa is a versatile opportunistic pathogen that causes chronic infections in immunocompromised hosts. Multiple porins modulate outer membrane permeability under various environmental conditions. The lung environment of cystic fibrosis (CF) patients is unique with changes occurring in nutrient availability, osmolarity, and oxygen content. Although P. aeruginosa gene expression is modified under these conditions, little is known about how they influence porin regulation. In this study, we evaluated the regulation of the outer membrane porin OpdQ, a member of the OprD family of porins, with regard to oxygen, nitrate, and/or NaCl levels. We demonstrated using promoter::fusion clones of P. aeruginosa PAO1 and clinical strains collected from CF patients that OpdQ was transcriptionally repressed under low oxygen but increased in the presence of nitrate. The nitrate‐induced regulation of OpdQ was found to be dependent on the transcription factor NarL via the NarXL two‐component system. In addition, NaCl‐induced osmotic stress increased OpdQ production among most of the clinical strains evaluated. In conclusion, these data identify for the first time that specific environmental cues associated with the CF microenvironment influence porin regulation, and that the nitrate‐induced regulation of OpdQ is associated with nitrate metabolism via the NarXL two‐component system of P. aeruginosa.

Construction and verification of the transcriptional regulatory response network of Streptococcus mutans upon treatment with the biofilm inhibitor carolacton

Background

Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating time-resolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out.

Results

The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.

Conclusion

The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-362) contains supplementary material, which is available to authorized users.