Transcriptomic determinants of the response of ST-111 Pseudomonas aeruginosa AG1 to ciprofloxacin identified by a top-down systems biology approach

A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1

IMPORTANCE

We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

Differentially Expressed Genes of Pseudomonas aeruginosa Isolates from Eyes with Keratitis and Healthy Conjunctival Sacs

Background

Pseudomonas aeruginosa (P. aeruginosa) is the second-most common commensal bacterium in healthy conjunctival sacs. When the corneal epithelial barrier is damaged, P. aeruginosa in a healthy conjunctival sac can cause infectious keratitis, which can result in the loss of vision. This study was designed to investigate the differentially expressed genes (DEGs) of P. aeruginosa isolates from eyes with keratitis and from healthy conjunctival sacs to predict their functions and pathways through Illumina high-throughput RNA sequencing (RNA-seq).

Methods

P. aeruginosa isolates from keratitis and healthy conjunctival sacs were obtained. The transcriptome profile of P. aeruginosa was characterized by a high throughput RNA-seq strategy using the Illumina HiSeq 2500 platform. The DEGs were analyzed with DESeq and validated through quantitative real-time polymerase chain reaction (PCR) and with experimental mice. GO enrichment and the KEGG pathway were also analyzed.

Results

In genome-wide transcriptional analysis, 557 genes (332 upregulated and 225 downregulated) were found to be differentially expressed (fold change ≥ 2, p ≤ 0.05) in the strains from keratitis. GO enrichment analysis suggested that DEGs tended to be associated with cellular and metabolic processes. KEGG pathway analysis revealed the DEGs were typically associated with the pathways of the bacterial secretion system and pyoverdine metabolism. Eleven DEGs were validated using quantitative reverse-transcription PCR and verified with experimental mice. The results were consistent with those obtained in RNA-seq.

Conclusion

The DEGs related to pilin, T2SS, T3SS, and pyoverdine metabolisms were significantly altered in the strains from keratitis. The findings may be helpful for further investigations on genes or pathways related to the pathogenesis of and therapeutic targets for P. aeruginosa keratitis.

Antibiotic-producing Micrococcales govern the microbiome that inhabits the fur of two- and three-toed sloths

Sloths have a dense coat on which insects, algae, and fungi coexist in a symbiotic relationship. This complex ecosystem requires different levels of control, however, most of these mechanisms remain unknown. We investigated the bacterial communities inhabiting the hair of two- (Choloepus Hoffmani) and three-toed (Bradypus variegatus) sloths and evaluated their potential for producing antibiotic molecules capable of exerting control over the hair microbiota. The analysis of 16S rRNA amplicon sequence variants (ASVs) revealed that the communities in both host species are dominated by Actinobacteriota and Firmicutes. The most abundant genera were Brevibacterium, Kocuria/Rothia, Staphylococcus, Rubrobacter, Nesterenkonia, and Janibacter. Furthermore, we isolated nine strains of Brevibacterium and Rothia capable of producing substances that inhibited the growth of common mammalian pathogens. The analysis of the biosynthetic gene clusters (BCGs) of these nine isolates suggests that the pathogen-inhibitory activity could be mediated by the presence of siderophores, terpenes, beta-lactones, Type III polyketide synthases (T3PKS), ribosomally synthesized, and post-translationally modified peptides (RiPPs), non-alpha poly-amino acids (NAPAA) like e-Polylysin, ectoine or nonribosomal peptides (NRPs). Our data suggest that Micrococcales that inhabit sloth hair could have a role in controlling microbial populations in that habitat, improving our understanding of this highly complex ecosystem. This article is protected by copyright. All rights reserved.

Enhanced Biosynthesis of Fatty Acids Contributes to Ciprofloxacin Resistance in Pseudomonas aeruginosa

Antibiotic-resistant Pseudomonas aeruginosa is insensitive to antibiotics and difficult to deal with. An understanding of the resistance mechanisms is required for the control of the pathogen. In this study, gas chromatography-mass spectrometer (GC-MS)-based metabolomics was performed to identify differential metabolomes in ciprofloxacin (CIP)-resistant P. aeruginosa strains that originated from P. aeruginosa ATCC 27853 and had minimum inhibitory concentrations (MICs) that were 16-, 64-, and 128-fold (PA-R16_CIP, PA-R64_CIP, and PA-R128_CIP, respectively) higher than the original value, compared to CIP-sensitive P. aeruginosa (PA-S). Upregulation of fatty acid biosynthesis forms a characteristic feature of the CIP-resistant metabolomes and fatty acid metabolome, which was supported by elevated gene expression and enzymatic activity in the metabolic pathway. The fatty acid synthase inhibitor triclosan potentiates CIP to kill PA-R128_CIP and clinically multidrug-resistant P. aeruginosa strains. The potentiated killing was companied with reduced gene expression and enzymatic activity and the returned abundance of fatty acids in the metabolic pathway. Consistently, membrane permeability was reduced in the PA-R and clinically multidrug-resistant P. aeruginosa strains, which were reverted by triclosan. Triclosan also stimulated the uptake of CIP. These findings highlight the importance of the elevated biosynthesis of fatty acids in the CIP resistance of P. aeruginosa and provide a target pathway for combating CIP-resistant P. aeruginosa.

Adaptive resistance mutations at supra-inhibitory concentrations independent of SOS mutagenesis

Emergence of resistant bacteria during antimicrobial treatment is one of the most critical and universal health threats. It is known that several stress-induced mutagenesis and heteroresistance mechanisms can enhance microbial adaptation to antibiotics. Here, we demonstrate that the pathogen Bartonella can undergo stress-induced mutagenesis despite the fact it lacks error-prone polymerases, the rpoS gene and functional UV-induced mutagenesis. We demonstrate that Bartonella acquire de novo single mutations during rifampicin exposure at suprainhibitory concentrations at a much higher rate than expected from spontaneous fluctuations. This is while exhibiting a minimal heteroresistance capacity. The emerged resistant mutants acquired a single rpoB mutation, whereas no other mutations were found in their whole genome. Interestingly, the emergence of resistance in Bartonella occurred only during gradual exposure to the antibiotic, indicating that Bartonella sense and react to the changing environment. Using a mathematical model, we demonstrated that, to reproduce the experimental results, mutation rates should be transiently increased over 1,000-folds, and a larger population size or greater heteroresistance capacity is required. RNA expression analysis suggests that the increased mutation rate is due to downregulation of key DNA repair genes (mutS, mutY, and recA), associated with DNA breaks caused by massive prophage inductions. These results provide new evidence of the hazard of antibiotic overuse in medicine and agriculture.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model. Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements. We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.

Genomic context of the two integrons of ST-111 Pseudomonas aeruginosa AG1: A VIM-2-carrying old-acquaintance and a novel IMP-18-carrying integron

Pseudomonas aeruginosa is an opportunist and versatile organism responsible for infections mainly in immunocompromised hosts. This pathogen has high intrinsic resistance to most antimicrobials. P. aeruginosa AG1 (PaeAG1) is a Costa Rican high-risk ST-111 strain with resistance to multiple antibiotics, including carbapenems, due to the activity of VIM-2 and IMP-18 metallo-β-lactamases (MBLs). These genes are harbored in two class 1 integrons located inone out of the 57 PaeAG1 genomic islands. However, the genomic context associated to these determinants in PaeAG1 and other P. aeruginosa strains is unclear. Thus, we first assessed the transcriptional activity of VIM-2 and IMP-18 genes when exposed to imipenem (a carbapenem) by RT-qPCR. To select related genomes to PaeAG1, we implemented a pan-genome analysis to define and up-date the phylogenetic relationship among complete P. aeruginosa genomes. We also studied the PaeAG1 genomic islands content in the related strains and finally we described the architecture and possible evolutionary steps of the genomic regions around the VIM-2- and IMP-18-carrying integrons. Expression of VIM-2 and IMP-18 genes was demonstrated to be induced after imipenem exposure. In a subsequent comparative genomics analysis with 211 strains, the P. aeruginosa pan-genome revealed that complete genome sequences are able to separate clones by MLST profile, including a clear ST-111 cluster with PaeAG1. The PaeAG1 genomic islands were found to define a diverse presence/absence pattern among related genomes. Finally, landscape reconstruction of genomic regions showed that VIM-2-carrying integron (In59-like) is an old-acquaintance element harbored in the same known region found in other two ST-111 strains. Also, PaeAG1 has an exclusive genomic region containing a novel IMP-18-carrying integron (registered as In1666), with an arrangement never reported before. Altogether, we provide new insights about the genomic determinants associated with the resistance to carbapenems in this high-risk P. aeruginosa using comparative genomics.

Estimation of pathogenic potential of an environmental Pseudomonas aeruginosa isolate using comparative genomics

The isolation and sequencing of new strains of Pseudomonas aeruginosa created an extensive dataset of closed genomes. Many of the publicly available genomes are only used in their original publication while additional in silico information, based on comparison to previously published genomes, is not being explored. In this study, we defined and investigated the genome of the environmental isolate P. aeruginosa KRP1 and compared it to more than 100 publicly available closed P. aeruginosa genomes. By using different genomic island prediction programs, we could identify a total of 17 genomic islands and 8 genomic islets, marking the majority of the accessory genome that covers ~ 12% of the total genome. Based on intra-strain comparisons, we are able to predict the pathogenic potential of this environmental isolate. It shares a substantial amount of genomic information with the highly virulent PSE9 and LESB58 strains. For both of these, the increased virulence has been directly linked to their accessory genome before. Hence, the integrated use of previously published data can help to minimize expensive and time consuming wetlab work to determine the pathogenetic potential.

Molecular Determinants of Antibiotic Resistance in the Costa Rican Pseudomonas aeruginosa AG1 by a Multi-omics Approach: A Review of 10 Years of Study

Pseudomonas aeruginosa AG1 (PaeAG1) is a Costa Rican strain that was isolated in 2010 in a major Hospital. This strain has resistance to multiple antibiotics such as β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. PaeAG1 is considered critical (Priority 1) due to its resistance to carbapenems, and it was the first report of a P. aeruginosa isolate carrying both VIM-2 and IMP-18 genes encoding for metallo-β-lactamases (MBL) enzymes (both with carbapenemase activity). Owing to these traits, we have studied this model for 10 years using diverse approaches including multi-omics. In this review, we summarize the main points of the different steps that we have studied in PaeAG1: preliminary analyses of this strain at the genomic and phenomic levels revealed that this microorganism has particular features of antibiotic resistance. In the multi-omics approach, the genome assembly was the initial step to identify the genomic determinants of this strain, including virulence factors, antibiotic resistance genes, as well as a complex accessory genome. Second, a comparative genomic approach was implemented to define and update the phylogenetic relationship among complete P. aeruginosa genomes, the genomic island content in other strains, and the architecture of the two MBL-carrying integrons. Third, the proteomic profile of PaeAG1 was studied after exposure to antibiotics using 2-dimensional gel electrophoresis (2D-GE). Fourth, to study the central response to multiple perturbations in P. aeruginosa, i.e., the core perturbome, a machine learning approach was used. The analysis revealed biological functions and determinants that are shared by different disturbances. Finally, to evaluate the effects of ciprofloxacin (CIP) on PaeAG1, a growth curve comparison, differential expression analysis (RNA-Seq), and network analysis were performed. Using the results of the core perturbome (pathways that also were found in this perturbation with CIP), it was possible to identify the “exclusive” response and determinants of PaeAG1 after exposure to CIP. Altogether, after a decade of study using a multi-omics approach (at genomics, comparative genomics, perturbomics, transcriptomics, proteomics, and phenomics levels), we have provided new insights about the genomic and transcriptomic determinants associated with antibiotic resistance in PaeAG1. These results not only partially explain the high-risk condition of this strain that enables it to conquer nosocomial environments and its multi-resistance profile, but also this information may eventually be used as part of the strategies to fight this pathogen.