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Søgeord (pseudomonas) valgt.
5 emner vises.
BMC Infectious Diseases, 2.05.2024
Tilføjet 2.05.2024
Abstract Background Pseudomonas aeruginosa (P. aeruginosa) is a life-threatening bacterium known for its rapid development of antibiotic resistance, posing significant challenges in clinical treatment, biosecurity, food safety, and environmental monitoring. Early and accurate identification of P. aeruginosa is crucial for effective intervention. Methods The lasB gene of P. aeruginosa was selected as the target for the detection. RPA primers for recombinase polymerase amplification (RPA) and crRNA for CRISPR/Cas12a detection were meticulously designed to target specific regions within the lasB gene. The specificity of the RPA/CRISPR/Cas12a detection platform was assessed using 15 strains. The detection limit of RPA/CRISPR/Cas12a detection platform was determined by utilizing a pseudo-dilution series of the P. aeruginosa DNA. The practical applicability of the RPA/CRISPR/Cas12a detection platform was validated by comparing it with qPCR on 150 samples (35 processed meat product samples, 55 cold seasoned vegetable dishes, 60 bottled water samples). Results The RPA/CRISPR/Cas12a detection platform demonstrates high specificity, with no cross-reactivity with non-P. aeruginosa strains. This assay exhibits remarkable sensitivity, with a limit of detection (LOD) of 100 copies/µL for fluorescence assay and 101 copies/µL for the LFTS method. Furthermore, the performance of the RPA/CRISPR/Cas12a detection platform is comparable to that of the well-established qPCR method, while offering advantages such as shorter reaction time, simplified operation, and reduced equipment requirements. Conclusions The RPA/CRISPR/Cas12a detection platform presents a straightforward, accurate, and sensitive approach for early P. aeruginosa detection and holds great promise for diverse applications requiring rapid and reliable identification.
Læs mere Tjek på PubMedVidmantas PetraitisRuta PetraitienePovilas KavaliauskasEthan NaingAndrew GarciaVilma ZigmantaiteRamune GrigaleviciuteAudrius KucinskasAlius PockeviciusRimantas StakauskasThomas J. Walsh1Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine of Cornell University, New York, New York, USA2The Biological Research Center, Lithuanian University of Health Sciences, Kaunas, Lithuania3Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania4Department of Veterinary Pathobiology, Veterinary Academy, Pathology Center, Lithuanian University of Health Sciences, Kaunas, Lithuania5Center for Innovative Therapeutics and Diagnostics, Richmond, Virginia, USA, Anne-Catrin Uhlemann
Antimicrobial Agents And Chemotherapy, 30.04.2024
Tilføjet 30.04.2024
Daniel E. Jacobsen, Makaela M. Montoya, Trent R. Llewellyn, Kaitlyn Martinez, Kristen M. Wilding, Kiersten D. Lenz, Carrie A. Manore, Jessica Z. Kubicek-Sutherland, Harshini Mukundan
PLoS One Infectious Diseases, 23.04.2024
Tilføjet 23.04.2024
by Daniel E. Jacobsen, Makaela M. Montoya, Trent R. Llewellyn, Kaitlyn Martinez, Kristen M. Wilding, Kiersten D. Lenz, Carrie A. Manore, Jessica Z. Kubicek-Sutherland, Harshini Mukundan Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.
Læs mere Tjek på PubMedAna D. VegaKailynn DeRondeAdriana JimenezMichael PiazzaChristine VuOctavio MartinezLaura J. RojasSteven MarshallMohamad YasminRobert A. BonomoLilian M. Abbo1Department of Pharmacy, Jackson Health System, Miami, Florida, USA2Department of Epidemiology, Florida International University, Miami, Florida, USA3Department of Medicine, Virtua Medical Group, Medford, New Jersey, USA4Department of Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA5Department of Molecular Biology and Microbiology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA6CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES), Cleveland, Ohio, USA7Department of Medicine, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, Ohio, USA8Departments of Proteomics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA9Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA10Department of Biochemistry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA, Pranita D. Tamma
Antimicrobial Agents And Chemotherapy, 11.04.2024
Tilføjet 11.04.2024
Bryan K. Lynn, Patrick De Leenheer, Martin Schuster
PLoS One Infectious Diseases, 10.04.2024
Tilføjet 10.04.2024
by Bryan K. Lynn, Patrick De Leenheer, Martin Schuster Cooperation via shared public goods is ubiquitous in nature, however, noncontributing social cheaters can exploit the public goods provided by cooperating individuals to gain a fitness advantage. Theory predicts that this dynamic can cause a Tragedy of the Commons, and in particular, a ‘Collapsing’ Tragedy defined as the extinction of the entire population if the public good is essential. However, there is little empirical evidence of the Collapsing Tragedy in evolutionary biology. Here, we experimentally demonstrate this outcome in a microbial model system, the public good-producing bacterium Pseudomonas aeruginosa grown in a continuous-culture chemostat. In a growth medium that requires extracellular protein digestion, we find that P. aeruginosa populations maintain a high density when entirely composed of cooperating, protease-producing cells but completely collapse when non-producing cheater cells are introduced. We formulate a mechanistic mathematical model that recapitulates experimental observations and suggests key parameters, such as the dilution rate and the cost of public good production, that define the stability of cooperative behavior. We combine model prediction with experimental validation to explain striking differences in the long-term cheater trajectories of replicate cocultures through mutational events that increase cheater fitness. Taken together, our integrated empirical and theoretical approach validates and parametrizes the Collapsing Tragedy in a microbial population, and provides a quantitative, mechanistic framework for generating testable predictions of social behavior.
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