Extracellular Matrix Remodeling in Atopic Dermatitis Harnesses the Onset of an Asthmatic Phenotype and Is a Potential Contributor to the Atopic March.

The development of atopic dermatitis in infancy, and subsequent allergies, such as asthma in later childhood, is known as the atopic march. The mechanism is largely unknown, however the course of disease indicates an inter-epithelial crosstalk, through the onset of inflammation in the skin and progression to other mucosal epithelia. In this study, we investigated if and how skin-lung epithelial crosstalk contributes to the development of the atopic march. First, we emulated inter-epithelial crosstalk through indirect coculture of bioengineered atopic-like skin disease models and three-dimensional bronchial epithelial models triggering an asthma-like phenotype in the latter. A subsequent secretome analysis identified thrombospondin-1, CD44, complement factor C3, fibronectin, and syndecan-4 as potentially relevant skin-derived mediators. Because these mediators are extracellular matrix-related proteins, we then studied the involvement of the extracellular matrix, unveiling distinct proteomic, transcriptomic, and ultrastructural differences in atopic samples. The latter indicated extracellular matrix remodeling triggering the release of the above-mentioned mediators. In vivo mouse data showed that exposure to these mediators dysregulated activated circadian clock genes which are increasingly discussed in the context of atopic diseases and asthma development. Our data point toward the existence of a skin-lung axis that could contribute to the atopic march driven by skin extracellular matrix remodeling.

Post-COVID symptoms are associated with endotypes reflecting poor inflammatory and hemostatic modulation.

Introduction: Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID. Methods: RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development. Results: Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification. Discussion: This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment.

Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets.

Background: Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective: Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods: PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results: After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions: SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures.

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

Surviving the host: Microbial metabolic genes required for growth of Pseudomonas aeruginosa in physiologically-relevant conditions.

Pseudomonas aeruginosa, like other pathogens, adapts to the limiting nutritional environment of the host by altering patterns of gene expression and utilizing alternative pathways required for survival. Understanding the genes essential for survival in the host gives insight into pathways that this organism requires during infection and has the potential to identify better ways to treat infections. Here, we used a saturated transposon insertion mutant pool of P. aeruginosa strain PAO1 and transposon insertion sequencing (Tn-Seq), to identify genes conditionally important for survival under conditions mimicking the environment of a nosocomial infection. Conditions tested included tissue culture medium with and without human serum, a murine abscess model, and a human skin organoid model. Genes known to be upregulated during infections, as well as those involved in nucleotide metabolism, and cobalamin (vitamin B12) biosynthesis, etc., were required for survival in vivo- and in host mimicking conditions, but not in nutrient rich lab medium, Mueller Hinton broth (MHB). Correspondingly, mutants in genes encoding proteins of nucleotide and cobalamin metabolism pathways were shown to have growth defects under physiologically-relevant media conditions, in vivo, and in vivo-like models, and were downregulated in expression under these conditions, when compared to MHB. This study provides evidence for the relevance of studying P. aeruginosa fitness in physiologically-relevant host mimicking conditions and identified metabolic pathways that represent potential novel targets for alternative therapies.

Host Response of Human Epidermis to Methicillin-Resistant Staphylococcus aureus Biofilm Infection and Synthetic Antibiofilm Peptide Treatment.

Bacterial biofilm infections associated with wounded skin are prevalent, recalcitrant, and in urgent need of treatments. Additionally, host responses in the skin to biofilm infections are not well understood. Here we employed a human organoid skin model to explore the transcriptomic changes of thermally-injured epidermis to methicillin-resistant Staphylococcus aureus (MRSA) biofilm colonization. MRSA biofilm impaired skin barrier function, enhanced extracellular matrix remodelling, elicited inflammatory responses including IL-17, IL-12 family and IL-6 family interleukin signalling, and modulated skin metabolism. Synthetic antibiofilm peptide DJK-5 effectively diminished MRSA biofilm and associated skin inflammation in wounded human ex vivo skin. In the epidermis, DJK-5 shifted the overall skin transcriptome towards homeostasis including modulating the biofilm induced inflammatory response, promoting the skin DNA repair function, and downregulating MRSA invasion of thermally damaged skin. These data clarified the underlying immunopathogenesis of biofilm infections and revealed the intrinsic promise of synthetic peptides in reducing inflammation and biofilm infections.

A transcriptomic analysis of the effects of macrophage polarization and endotoxin tolerance on the response to Salmonella.

Salmonella is an intracellular pathogen causing significant morbidity and mortality. Its ability to grow inside macrophages is important to virulence, and is dependent on the activation state of the macrophages. Classically activated M1 macrophages are non-permissive for Salmonella growth, while alternatively activated M2 macrophages are permissive for Salmonella growth. Here we showed that endotoxin-primed macrophages (MEP), such as those associated with sepsis, showed similar levels of Salmonella resistance to M1 macrophages after 2 hr of intracellular infection, but at the 4 hr and 24 hr time points were susceptible like M2 macrophages. To understand this mechanistically, transcriptomic sequencing, RNA-Seq, was performed. This showed that M1 and MEP macrophages that had not been exposed to Salmonella, demonstrated a process termed here as primed activation, in expressing relatively higher levels of particular anti-infective genes and pathways, including the JAK-STAT (Janus kinase-signal transducer and activator of transcription) pathway. In contrast, in M2 macrophages these genes and pathways were largely expressed only in response to infection. Conversely, in response to infection, M1 macrophages, but not MEP macrophages, modulated additional genes known to be associated with susceptibility to Salmonella infection, possibly contributing to the differences in resistance at later time points. Application of the JAK inhibitor Ruxolitinib before infection reduced resistance in M1 macrophages, supporting the importance of early JAK-STAT signalling in M1 resistance to Salmonella.

BosR: A novel biofilm-specific regulator in Pseudomonas aeruginosa.

Biofilms are the most common cause of bacterial infections in humans and notoriously hard to treat due to their ability to withstand antibiotics and host immune defenses. To overcome the current lack of effective antibiofilm therapies and guide future design, the identification of novel biofilm-specific gene targets is crucial. In this regard, transcriptional regulators have been proposed as promising targets for antimicrobial drug design. Therefore, a Transposon insertion sequencing approach was employed to systematically identify regulators phenotypically affecting biofilm growth in Pseudomonas aeruginosa PA14 using the TnSeq analysis tools Bio-TraDIS and TRANSIT. A screen of a pool of 300,000 transposon insertion mutants identified 349 genes involved in biofilm growth on hydroxyapatite, including 47 regulators. Detection of 19 regulatory genes participating in well-established biofilm pathways validated the results. An additional 28 novel prospective biofilm regulators suggested the requirement for multiple one-component transcriptional regulators. Biofilm-defective phenotypes were confirmed for five one-component transcriptional regulators and a protein kinase, which did not affect motility phenotypes. The one-component transcriptional regulator bosR displayed a conserved role in P. aeruginosa biofilm growth since its ortholog in P. aeruginosa strain PAO1 was also required for biofilm growth. Microscopic analysis of a chromosomal deletion mutant of bosR confirmed the role of this regulator in biofilm growth. Overall, our results highlighted that the gene network driving biofilm growth is complex and involves regulators beyond the primarily studied groups of two-component systems and cyclic diguanylate signaling proteins. Furthermore, biofilm-specific regulators, such as bosR, might constitute prospective new drug targets to overcome biofilm infections.

Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response.

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual vaccine effectiveness suggests that a better mechanistic understanding of vaccine-induced immune responses could improve vaccine design and efficacy. We have previously shown that protective antibody levels could be elicited in a subset of recipients with only a single dose of the hepatitis B virus (HBV) vaccine and that a wide range of antibody levels were elicited after three doses. The immune mechanisms responsible for this vaccine response variability is unclear. Using single cell RNA sequencing of sorted innate immune cell subsets, we identified two distinct myeloid dendritic cell subsets (NDRG1-expressing mDC2 and CDKN1C-expressing mDC4), the ratio of which at baseline (pre-vaccination) correlated with the immune response to a single dose of HBV vaccine. Our results suggest that the participants in our vaccine study were in one of two different dendritic cell dispositional states at baseline - an NDRG2-mDC2 state in which the vaccine elicited an antibody response after a single immunization or a CDKN1C-mDC4 state in which the vaccine required two or three doses for induction of antibody responses. To explore this correlation further, genes expressed in these mDC subsets were used for feature selection prior to the construction of predictive models using supervised canonical correlation machine learning. The resulting models showed an improved correlation with serum antibody titers in response to full vaccination. Taken together, these results suggest that the propensity of circulating dendritic cells toward either activation or suppression, their "dispositional endotype" at pre-vaccination baseline, could dictate response to vaccination.

RNase III and RNase E Influence Posttranscriptional Regulatory Networks Involved in Virulence Factor Production, Metabolism, and Regulatory RNA Processing in Bordetella pertussis.

Bordetella pertussis has been shown to encode regulatory RNAs, yet the posttranscriptional regulatory circuits on which they act remain to be fully elucidated. We generated mutants lacking the endonucleases RNase III and RNase E and assessed their individual impact on the B. pertussis transcriptome. Transcriptome sequencing (RNA-Seq) analysis showed differential expression of ∼25% of the B. pertussis transcriptome in each mutant, with only 28% overlap between data sets. Both endonucleases exhibited substantial impact on genes involved in amino acid uptake (e.g., ABC transporters) and in virulence (e.g., the type III secretion system and the autotransporters vag8, tcfA, and brkA). Interestingly, mutations in RNase III and RNase E drove the stability of many transcripts, including those involved in virulence, in opposite directions, a result that was validated by qPCR and immunoblotting for tcfA and brkA. Of note, whereas similar mutations to RNase E in Escherichia coli have subtle effects on transcript stability, a striking >20-fold reduction in four gene transcripts, including tcfA and vag8, was observed in B. pertussis. We further compared our data set to the regulon controlled by the RNA chaperone Hfq to identify B. pertussis loci influenced by regulatory RNAs. This analysis identified ∼120 genes and 19 operons potentially regulated at the posttranscriptional level. Thus, our findings revealed how changes in RNase III- and RNase E-mediated RNA turnover influence pathways associated with virulence and cellular homeostasis. Moreover, we highlighted loci potentially influenced by regulatory RNAs, providing insights into the posttranscriptional regulatory networks involved in fine-tuning B. pertussis gene expression. IMPORTANCE Noncoding, regulatory RNAs in bacterial pathogens are critical components required for rapid changes in gene expression profiles. However, little is known about the role of regulatory RNAs in the growth and pathogenesis of Bordetella pertussis. To address this, mutants separately lacking ribonucleases central to regulatory RNA processing, RNase III and RNase E, were analyzed by RNA-Seq. Here, we detail the first transcriptomic analysis of the impact of altered RNA degradation in B. pertussis. Each mutant showed approximately 1,000 differentially expressed genes, with significant changes in the expression of pathways associated with metabolism, bacterial secretion, and virulence factor production. Our analysis suggests an important role for these ribonucleases during host colonization and provides insights into the breadth of posttranscriptional regulation in B. pertussis, further informing our understanding of B. pertussis pathogenesis.

Peptide 1018 inhibits swarming and influences Anr-regulated gene expression downstream of the stringent stress response in Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen that causes considerable human morbidity and mortality, particularly in nosocomial infections and individuals with cystic fibrosis. P. aeruginosa can adapt to surface growth by undergoing swarming motility, a rapid multicellular movement that occurs on viscous soft surfaces with amino acids as a nitrogen source. Here we tested the small synthetic host defense peptide, innate defense regulator 1018, and found that it inhibited swarming motility at concentrations as low as 0.75 µg/ml, well below the MIC for strain PA14 planktonic cells (64 µg/ml). A screen of the PA14 transposon insertion mutant library revealed 29 mutants that were more tolerant to peptide 1018 during swarming, five of which demonstrated significantly greater swarming than the WT in the presence of peptide. Transcriptional analysis (RNA-Seq) of cells that were inoculated on swarming plates containing 1.0 µg/ml peptide revealed differential expression of 1,190 genes compared to cells swarming on plates without peptide. Furthermore, 1018 treatment distinctly altered the gene expression profile of cells when compared to that untreated cells in the centre of the swarm colonies. Peptide-treated cells exhibited changes in the expression of genes implicated in the stringent stress response including those regulated by anr, which is involved in anaerobic adaptation, indicative of a mechanism by which 1018 might inhibit swarming motility. Overall, this study illustrates potential mechanisms by which peptide 1018 inhibits swarming surface motility, an important bacterial adaptation associated with antibiotic resistance, virulence, and dissemination of P. aeruginosa.

Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort.

Background: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. Methods: We applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres. Results: Using both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response. Conclusion: This study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.

Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults.

Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches-NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.

Characterization of immune responses and the lung transcriptome in a murine model of IL-33 challenge.

IL-33 induces airway inflammation and hyper-responsiveness in respiratory diseases. Although defined as a therapeutic target, there are limited studies that have comprehensively investigated IL-33-mediated responses in the lungs in vivo. In this study, we characterized immunological and physiological responses induced by intranasal IL-33 challenge, in a mouse model. We identified specific cytokines, IL-4, IL-5, IL-6, IL-10, IP-10 and MIP1-α, that are increased in bronchoalveolar lavage and lung tissues by IL-33. Using transcriptomics (RNA-Seq) we demonstrated that 2279 transcripts were up-regulated and 1378 downregulated (≥ 2-fold, p < 0.01) in lung tissues, in response to IL-33. Bioinformatic interrogation of the RNA-Seq data was used to predict biological pathways and upstream regulators involved in IL-33-mediated responses. We showed that the mRNA and protein of STAT4, a predicted upstream regulator of IL-33-induced transcripts, was significantly enhanced in the lungs following IL-33 challenge. Overall, this study provides specific IL-33-induced molecular targets and endpoints that can be used as a resource for in vivo studies, e.g. in preclinical murine models examining novel interventions to target downstream effects of IL-33.

The Stringent Stress Response Controls Proteases and Global Regulators under Optimal Growth Conditions in Pseudomonas aeruginosa.

The bacterial stringent stress response, mediated by the signaling molecule guanosine tetraphosphate, ppGpp, has recently gained attention as being important during normal cellular growth and as a potential new therapeutic target, which warrants detailed mechanistic understanding. Here, we used intracellular protein tracking in Pseudomonas aeruginosa PAO1, which indicated that RelA was bound to the ribosome, while SpoT localized at the cell poles. Transcriptome sequencing (RNA-Seq) was used to investigate the transcriptome of a ppGpp-deficient strain under nonstressful, nutrient-rich broth conditions where the mutant grew at the same rate as the parent strain. In the exponential growth phase, the lack of ppGpp led to >1,600 transcriptional changes (fold change cutoff of ±1.5), providing further novel insights into the normal physiological role of ppGpp. The stringent response was linked to gene expression of various proteases and secretion systems, including aprA, PA0277, impA, and clpP2 The previously observed reduction in cytotoxicity toward red blood cells in a stringent response mutant appeared to be due to aprA Investigation of an aprA mutant in a murine skin infection model showed increased survival rates of mice infected with the aprA mutant, consistent with previous observations that stringent response mutants have reduced virulence. In addition, the overexpression of relA, but not induction of ppGpp with serine hydroxamate, dysregulated global transcriptional regulators as well as >30% of the regulatory networks controlled by AlgR, OxyR, LasR, and AmrZ. Together, these data expand our knowledge about ppGpp and its regulatory network and role in environmental adaptation. It also confirms its important role throughout the normal growth cycle of bacteria.IMPORTANCE Microorganisms need to adapt rapidly to survive harsh environmental changes. Here, we showed the broad influence of the highly studied bacterial stringent stress response under nonstressful conditions that indicate its general physiological importance and might reflect the readiness of bacteria to respond to and activate acute stress responses. Using RNA-Seq to investigate the transcriptional network of Pseudomonas aeruginosa cells revealed that >30% of all genes changed expression in a stringent response mutant under optimal growth conditions. This included genes regulated by global transcriptional regulators and novel downstream effectors. Our results help to understand the importance of this stress regulator in bacterial lifestyle under relatively unstressed conditions. As such, it draws attention to the consequences of targeting this ubiquitous bacterial signaling molecule.

Surfing motility is a complex adaptation dependent on the stringent stress response in Pseudomonas aeruginosa LESB58.

Cystic fibrosis (CF) is a genetic disease that affects mucin-producing body organs such as the lungs. Characteristic of CF is the production of thick, viscous mucus, containing the glycoprotein mucin, that can lead to progressive airway obstruction. Recently, we demonstrated that the presence of mucin induced a rapid surface adaptation in motile bacteria termed surfing motility, which data presented here indicates is very different from swarming motility. Pseudomonas aeruginosa, the main colonizing pathogen in CF, employs several stress coping mechanisms to survive the highly viscous environment of the CF lung. We used motility-based assays and RNA-Seq to study the stringent stress response in the hypervirulent CF isolate LESB58 (Liverpool Epidemic Strain). Motility experiments revealed that an LESB58 stringent response mutant (ΔrelAΔspoT) was unable to surf. Transcriptional profiling of ΔrelAΔspoT mutant cells from surfing agar plates, when compared to wild-type cells from the surfing edge, revealed 2,584 dysregulated genes. Gene Ontology and KEGG enrichment analysis revealed effects of the stringent response on amino acid, nucleic acid and fatty acid metabolism, TCA cycle and glycolysis, type VI secretion, as well as chemotaxis, cell communication, iron transport, nitrogen metabolic processes and cyclic-di-GMP signalling. Screening of the ordered PA14 transposon library revealed 224 mutants unable to surf and very limited overlap with genes required for swarming. Mutants affecting surfing included two downstream effector genes of the stringent stress response, the copper regulator cueR and the quinolone synthase pqsH. Both the cueR and pqsH cloned genes complemented the surfing deficiency of ΔrelAΔspoT. Our study revealed insights into stringent stress dependency in LESB58 and showed that surfing motility is stringently-controlled via the expression of cueR and pqsH. Downstream factors of the stringent stress response are important to investigate in order to fully understand its ability to colonize and persist in the CF lung.

MetaBridge: An Integrative Multi-Omics Tool for Metabolite-Enzyme Mapping.

MetaBridge is a web-based tool designed to facilitate the integration of metabolomics with other "omics" data types such as transcriptomics and proteomics. It uses data from the MetaCyc metabolic pathway database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to map metabolite compounds to directly interacting upstream or downstream enzymes in enzymatic reactions and metabolic pathways. The resulting list of enzymes can then be integrated with transcriptomics or proteomics data via protein-protein interaction networks to perform integrative multi-omics analyses. MetaBridge was developed to be intuitive and easy to use, requiring little to no prior computational experience. The protocols described here detail all steps involved in the use of MetaBridge, from preparing input data and performing metabolite mapping to utilizing the results to build a protein-protein interaction network. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Mapping metabolite data using MetaCyc identifiers Basic Protocol 2: Mapping metabolite data using KEGG identifiers Support Protocol 1: Converting compound names to HMDB IDs Support Protocol 2: Submitting mapped genes produced by MetaBridge for protein-protein interaction (PPI) network construction.

Multidrug Adaptive Resistance of Pseudomonas aeruginosa Swarming Cells.

Swarming surface motility is a complex adaptation leading to multidrug antibiotic resistance and virulence factor production in Pseudomonas aeruginosa Here, we expanded previous studies to demonstrate that under swarming conditions, P. aeruginosa PA14 is more resistant to multiple antibiotics, including aminoglycosides, ß-lactams, chloramphenicol, ciprofloxacin, tetracycline, trimethoprim, and macrolides, than swimming cells, but is not more resistant to polymyxin B. We investigated the mechanism(s) of swarming-mediated antibiotic resistance by examining the transcriptomes of swarming cells and swarming cells treated with tobramycin by transcriptomics (RNA-Seq) and reverse transcriptase quantitative PCR (qRT-PCR). RNA-Seq of swarming cells (versus swimming) revealed 1,581 dysregulated genes, including 104 transcriptional regulators, two-component systems, and sigma factors, numerous upregulated virulence and iron acquisition factors, and downregulated ribosomal genes. Strain PA14 mutants in resistome genes that were dysregulated under swarming conditions were tested for their ability to swarm in the presence of tobramycin. In total, 41 mutants in genes dysregulated under swarming conditions were shown to be more resistant to tobramycin under swarming conditions, indicating that swarming-mediated tobramycin resistance was multideterminant. Focusing on two genes downregulated under swarming conditions, both prtN and wbpW mutants were more resistant to tobramycin, while the prtN mutant was additionally resistant to trimethoprim under swarming conditions; complementation of these mutants restored susceptibility. RNA-Seq of swarming cells treated with subinhibitory concentrations of tobramycin revealed the upregulation of the multidrug efflux pump MexXY and downregulation of virulence factors.

Evaluation of Molecular Methods for Identification of Salmonella Serovars.

Classification by serotyping is the essential first step in the characterization of Salmonella isolates and is important for surveillance, source tracking, and outbreak detection. To improve detection and reduce the burden of salmonellosis, several rapid and high-throughput molecular Salmonella serotyping methods have been developed.The aim of this study was to compare three commercial kits, Salm SeroGen (Salm Sero-Genotyping AS-1 kit), Check&Trace (Check-Points), and xMAP (xMAP Salmonella serotyping assay), to the Salmonella genoserotyping array (SGSA) developed by our laboratory. They were assessed using a panel of 321 isolates that represent commonly reported serovars from human and nonhuman sources globally. The four methods correctly identified 73.8% to 94.7% of the isolates tested. The methods correctly identified 85% and 98% of the clinically important Salmonella serovars Enteritidis and Typhimurium, respectively. The methods correctly identified 75% to 100% of the nontyphoidal, broad host range Salmonella serovars, including Heidelberg, Hadar, Infantis, Kentucky, Montevideo, Newport, and Virchow. The sensitivity and specificity of Salmonella serovars Typhimurium and Enteritidis ranged from 85% to 100% and 99% to 100%, respectively.It is anticipated that whole-genome sequencing will replace serotyping in public health laboratories in the future. However, at present, it is approximately three times more expensive than molecular methods. Until consistent standards and methodologies are deployed for whole-genome sequencing, data analysis and interlaboratory comparability remain a challenge. The use of molecular serotyping will provide a valuable high-throughput alternative to traditional serotyping. This comprehensive analysis provides a detailed comparison of commercial kits available for the molecular serotyping of Salmonella.

Draft Whole-Genome Sequences of 25 Salmonella enterica Strains Representing 24 Serovars.

We report the draft genome sequences of 25 Salmonella enterica strains representing 24 different serotypes, many of which were not available in public repositories during our selection process. These draft genomes will provide useful reference for the genetic variation between serotypes and aid in the development of molecular typing tools.