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Abstract
Mutations in
mexZ, encoding a negative regulator of the expression of the
mexXY efflux pump genes, are frequently acquired by
Pseudomonas aeruginosa at early stages of lung infection. Although traditionally related to resistance to
the first-line drug tobramycin,
mexZ mutations are associated with low-level aminoglycoside resistance when determined
in the laboratory, suggesting that their selection during infection may not be necessarily,
or only, related to tobramycin therapy. Here, we show that
mexZ-mutated bacteria tend to accumulate inside the epithelial barrier of a human airway
infection model, thus colonising the epithelium while being protected against diverse
antibiotics. This phenotype is mediated by overexpression of
lecA, a quorum sensing-controlled gene, encoding a lectin involved in
P. aeruginosa tissue invasiveness. We find that
lecA overexpression is caused by a disrupted equilibrium between the overproduced MexXY
and another efflux pump, MexAB, which extrudes quorum sensing signals. Our results
indicate that
mexZ mutations affect the expression of quorum sensing-regulated pathways, thus promoting
tissue invasiveness and protecting bacteria from the action of antibiotics within
patients, something unnoticeable using standard laboratory tests.
Abstract
Mutations in
mexZ, encoding a negative regulator of efflux pump genes, are frequently acquired by
Pseudomonas aeruginosa during early lung infection, but do not confer high antibiotic resistance as measured
in lab tests. Here, Laborda et al. show that
mexZ mutations affect quorum sensing pathways, thus promoting tissue invasiveness and
protecting bacteria from the action of antibiotics within tissues.
The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT-PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.
[2
]GRID grid.5170.3, ISNI 0000 0001 2181 8870, The Novo Nordisk Foundation Center for Biosustainability, , Technical University of Denmark, ; Kgs. Lyngby, Denmark
[4
]GRID grid.9224.d, ISNI 0000 0001 2168 1229, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen Macarena, CSIC,
, Universidad de Sevilla, ; Sevilla, Spain
[5
]Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Instituto de
Salud Carlos III, (
https://ror.org/00ca2c886)
Madrid, Spain
[6
]Department of Otorhinolaryngology, Head and Neck Surgery & Audiology, Rigshospitalet,
(
https://ror.org/03mchdq19)
Copenhagen, Denmark
[7
]Department of Clinical Medicine, Faculty of Health and Medical Sciences, University
of Copenhagen, (
https://ror.org/035b05819)
Copenhagen, Denmark
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History
Date
received
: 15
September
2023
Date
accepted
: 14
March
2024
Funding
Funded by: PL was the recipient of an European Molecular Biology Organization (EMBO) Scientific
Exchange Grant (Ref. nr: 9112) and afterwards a of an ERS/EU RESPIRE4 Marie Skłodowska-Curie
Postdoctoral Research Fellowship (Ref. nr: R4202305-01047; this project has received
funding from the European Respiratory Society and the European Union’s H2020 research
and innovation programme under the Marie Skłodowska-Curie grant agreement No 847462).
Funded by: Novo Nordisk Foundation Challenge grant NNF19OC0056411 and a grant from THE JOHN AND
BIRTHE MEYER FOUNDATION (2022)
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