The Gram-negative bacterium Pseudomonas aeruginosa uses a complex type III secretion apparatus to inject effector proteins into host cells. The configuration of this secretion machinery, the ...activities of the proteins that are injected by it and the consequences of this process for infection are now being elucidated. This Review summarizes our current knowledge of P. aeruginosa type III secretion, including the secretion and translocation machinery, the regulation of this machinery, and the associated chaperones and effector proteins. The features of this interesting secretion system have important implications for the pathogenesis of P. aeruginosa infections and for other type III secretion systems.
Pseudomonas aeruginosa is an important opportunistic pathogen responsible for many infections in hospitalized and immunocompromised patients. Previous reports estimated that approximately 10% of its ...6.6 Mbp genome varies from strain to strain and is therefore referred to as "accessory genome". Elements within the accessory genome of P. aeruginosa have been associated with differences in virulence and antibiotic resistance. As whole genome sequencing of bacterial strains becomes more widespread and cost-effective, methods to quickly and reliably identify accessory genomic elements in newly sequenced P. aeruginosa genomes will be needed.
We developed a bioinformatic method for identifying the accessory genome of P. aeruginosa. First, the core genome was determined based on sequence conserved among the completed genomes of twelve reference strains using Spine, a software program developed for this purpose. The core genome was 5.84 Mbp in size and contained 5,316 coding sequences. We then developed an in silico genome subtraction program named AGEnt to filter out core genomic sequences from P. aeruginosa whole genomes to identify accessory genomic sequences of these reference strains. This analysis determined that the accessory genome of P. aeruginosa ranged from 6.9-18.0% of the total genome, was enriched for genes associated with mobile elements, and was comprised of a majority of genes with unknown or unclear function. Using these genomes, we showed that AGEnt performed well compared to other publically available programs designed to detect accessory genomic elements. We then demonstrated the utility of the AGEnt program by applying it to the draft genomes of two previously unsequenced P. aeruginosa strains, PA99 and PA103.
The P. aeruginosa genome is rich in accessory genetic material. The AGEnt program accurately identified the accessory genomes of newly sequenced P. aeruginosa strains, even when draft genomes were used. As P. aeruginosa genomes become available at an increasingly rapid pace, this program will be useful in cataloging the expanding accessory genome of this bacterium and in discerning correlations between phenotype and accessory genome makeup. The combination of Spine and AGEnt should be useful in defining the accessory genomes of other bacterial species as well.
BACKGROUND
Prevalence estimates of the serious hazards of transfusion vary widely. We hypothesized that the current reporting infrastructure in the United States fails to capture many transfusion ...reactions and undertook a multicenter study using active surveillance, data review, and adjudication to test this hypothesis.
STUDY DESIGN AND METHODS
A retrospective record review was completed for a random sample of 17% of all inpatient transfusion episodes over 6 months at four academic tertiary care hospitals, with an episode defined as all blood products released to a patient in 6 hours. Data were recorded by trained clinical research nurses, and serious reactions were adjudicated by a panel of transfusion medicine experts.
RESULTS
Of 4857 transfusion episodes investigated, 1.1% were associated with a serious reaction. Transfusion‐associated circulatory overload was the most frequent serious reaction noted, being identified in 1% of transfusion episodes. Despite clinical notes describing a potential transfusion association in 59% of these cases, only 5.1% were reported to the transfusion service. Suspected transfusion‐related acute lung injury/possible transfusion‐related acute lung injury, anaphylactic, and hypotensive reactions were noted in 0.08, 0.02, and 0.02% of transfusion episodes, respectively. Minor reactions, including febrile nonhemolytic and allergic, were noted in 0.62 and 0.29% of transfusion episodes, respectively, with 30 and 50% reported to the transfusion service.
CONCLUSION
Underreporting of cardiopulmonary transfusion reactions is striking among academic, tertiary care hospitals. Complete and accurate reporting is essential to identify, define, establish pathogenesis, and mitigate/treat transfusion reactions. A better understanding of the failure to report may improve the accuracy of passive reporting systems.
While most patients with myocardial infarction (MI) have underlying coronary atherosclerosis, not all patients with coronary artery disease (CAD) develop MI. We sought to address the hypothesis that ...some of the genetic factors which establish atherosclerosis may be distinct from those that predispose to vulnerable plaques and thrombus formation.
We carried out a genome-wide association study for MI in the UK Biobank (n∼472 000), followed by a meta-analysis with summary statistics from the CARDIoGRAMplusC4D Consortium (n∼167 000). Multiple independent replication analyses and functional approaches were used to prioritize loci and evaluate positional candidate genes. Eight novel regions were identified for MI at the genome wide significance level, of which effect sizes at six loci were more robust for MI than for CAD without the presence of MI. Confirmatory evidence for association of a locus on chromosome 1p21.3 harbouring choline-like transporter 3 (SLC44A3) with MI in the context of CAD, but not with coronary atherosclerosis itself, was obtained in Biobank Japan (n∼165 000) and 16 independent angiography-based cohorts (n∼27 000). Follow-up analyses did not reveal association of the SLC44A3 locus with CAD risk factors, biomarkers of coagulation, other thrombotic diseases, or plasma levels of a broad array of metabolites, including choline, trimethylamine N-oxide, and betaine. However, aortic expression of SLC44A3 was increased in carriers of the MI risk allele at chromosome 1p21.3, increased in ischaemic (vs. non-diseased) coronary arteries, up-regulated in human aortic endothelial cells treated with interleukin-1β (vs. vehicle), and associated with smooth muscle cell migration in vitro.
A large-scale analysis comprising ∼831 000 subjects revealed novel genetic determinants of MI and implicated SLC44A3 in the pathophysiology of vulnerable plaques.
The Accessory Genome of Pseudomonas aeruginosa KUNG, Vanderlene L; OZER, Egon A; HAUSER, Alan R
Microbiology and Molecular Biology Reviews,
12/2010, Letnik:
74, Številka:
4
Journal Article
•Per- and polyfluoroalkyl substances (PFASs) are associated with glucose intolerance.•PFAS is associated with increased lipolysis.•Lipid metabolism may contribute to the association of PFAS with ...glucose intolerance.
Per- and polyfluoroalkyl substances (PFASs) exposure is ubiquitous among the US population and has been linked to adverse health outcomes including cardiometabolic diseases, immune dysregulation and endocrine disruption. However, the metabolic mechanism underlying the adverse health effect of PFASs exposure is unknown.
The aim of this project is to investigate the association between PFASs exposure and altered metabolic pathways linked to increased cardiometabolic risk in young adults.
A total of 102 young adults with 82% overweight or obese participants were enrolled from Southern California between 2014 and 2017. Cardiometabolic outcomes were assessed including oral glucose tolerance test (OGTT) measures, body fat and lipid profiles. High-resolution metabolomics was used to quantify plasma exposure levels of three PFAS congeners and intensity profiles of the untargeted metabolome. Fasting concentrations of 45 targeted metabolites involved in fatty acid and lipid metabolism were used to verify untargeted metabolomics findings. Bayesian Kernel Machine Regression (BKMR) was used to examine the associations between PFAS exposure mixture and cardiometabolic outcomes adjusting for covariates. Mummichog pathway enrichment analysis was used to explore PFAS-associated metabolic pathways. Moreover, the effect of PFAS exposure on the metabolic network, including metabolomic profiles and cardiometabolic outcomes, was investigated.
Higher exposure to perfluorooctanoic acid (PFOA) was associated with higher 30-minute glucose levels and glucose area under the curve (AUC) during the OGTT (p < 0.001). PFAS exposure was also associated with altered lipid pathways, which contributed to the metabolic network connecting PFOA and higher glucose levels following the OGTT. Targeted metabolomics analysis indicated that higher PFOA exposure was associated with higher levels of glycerol (p = 0.006), which itself was associated with higher 30-minute glucose (p = 0.006).
Increased lipolysis and fatty acid oxidation could contribute to the biological mechanisms linking PFAS exposure and impaired glucose metabolism among young adults. Findings of this study warrants future experimental studies and epidemiological studies with larger sample size to replicate.
Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict ...cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6-2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk.
Background Cardiovascular risk models remain incomplete. Small-molecule metabolites may reflect underlying disease and, as such, serve as novel biomarkers of cardiovascular risk. Methods We studied ...2,023 consecutive patients undergoing cardiac catheterization. Mass spectrometry profiling of 69 metabolites and lipid assessments were performed in fasting plasma. Principal component analysis reduced metabolites to a smaller number of uncorrelated factors. Independent relationships between factors and time-to-clinical events were assessed using Cox modeling. Clinical and metabolomic models were compared using log-likelihood and reclassification analyses. Results At median follow-up of 3.1 years, there were 232 deaths and 294 death/myocardial infarction (MI) events. Five of 13 metabolite factors were independently associated with mortality: factor 1 (medium-chain acylcarnitines: hazard ratio HR 1.12 95% CI, 1.04-1.21, P = .005), factor 2 (short-chain dicarboxylacylcarnitines: HR 1.17 1.05-1.31, P = .005), factor 3 (long-chain dicarboxylacylcarnitines: HR 1.14 1.05-1.25, P = .002); factor 6 (branched-chain amino acids: HR 0.86 0.75-0.99, P = .03), and factor 12 (fatty acids: HR 1.19 1.06-1.35, P = .004). Three factors independently predicted death/MI: factor 2 (HR 1.11 1.01-1.23, P = .04), factor 3 (HR 1.13 1.04-1.22, P = .005), and factor 12 (HR 1.18 1.05-1.32, P = .004). For mortality, 27% of intermediate-risk patients were correctly reclassified (net reclassification improvement 8.8%, integrated discrimination index 0.017); for death/MI model, 11% were correctly reclassified (net reclassification improvement 3.9%, integrated discrimination index 0.012). Conclusions Metabolic profiles predict cardiovascular events independently of standard predictors.
Acinetobacter baumannii is a Gram-negative bacterium that causes diseases such as pneumonia, bacteremia, and soft tissue infections in hospitalized patients. Relatively little is known about how ...A. baumannii causes these infections. Thus, we used insertion sequencing (INSeq), a combination of transposon mutagenesis and massively parallel next-generation sequencing, to identify novel virulence factors of A. baumannii. To this end, we generated a random transposon mutant library containing 150,000 unique insertions in A. baumannii strain ATCC 17978. The INSeq analysis identified 453 genes required for growth in rich medium. The library was then used in a murine pneumonia model, and the relative levels of abundance of mutants before and after selection in the mouse were compared. When genes required for growth in rich medium were removed from the analysis, 157 genes were identified as necessary for persistence in the mouse lung. Several of these encode known virulence factors of A. baumannii, such as OmpA and ZnuB, which validated our approach. A large number of the genes identified were predicted to be involved in amino acid and nucleotide metabolism and transport. Other genes were predicted to encode an integration host factor, a transmembrane lipoprotein, and proteins involved in stress response and efflux pumps. Very few genes, when disrupted, resulted in an increase in A. baumannii numbers during host infection. The INSeq approach identified a number of novel virulence determinants of A. baumannii, which are candidate targets for therapeutic interventions.
A. baumannii has emerged as a frequent cause of serious infections in hospitals and community settings. Due to increasing antibiotic resistance, alternative approaches, such as antivirulence strategies, are desperately needed to fight A. baumannii infections. Thorough knowledge of A. baumannii pathogenicity is essential for such approaches but is currently lacking. With the increasingly widespread use of massively parallel sequencing, a class of techniques known as transposon insertion sequencing has been developed to perform comprehensive virulence screens of bacterial genomes in vivo. We have applied one of these approaches (INSeq) to uncover novel virulence factors in A. baumannii. We identified several such factors, including those predicted to encode amino acid and nucleotide metabolism proteins, an integration host factor protein, stress response factors, and efflux pumps. These results greatly expand the number of A. baumannii virulence factors and uncover potential targets for antivirulence treatments.
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