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1.
Int J Mol Sci ; 23(6)2022 Mar 18.
Article in English | MEDLINE | ID: covidwho-1760652

ABSTRACT

The presence of co-infections or superinfections with bacterial pathogens in COVID-19 patients is associated with poor outcomes, including increased morbidity and mortality. We hypothesized that SARS-CoV-2 and its components interact with the biofilms generated by commensal bacteria, which may contribute to co-infections. This study employed crystal violet staining and particle-tracking microrheology to characterize the formation of biofilms by Streptococcus pneumoniae and Staphylococcus aureus that commonly cause secondary bacterial pneumonia. Microrheology analyses suggested that these biofilms were inhomogeneous soft solids, consistent with their dynamic characteristics. Biofilm formation by both bacteria was significantly inhibited by co-incubation with recombinant SARS-CoV-2 spike S1 subunit and both S1 + S2 subunits, but not with S2 extracellular domain nor nucleocapsid protein. Addition of spike S1 and S2 antibodies to spike protein could partially restore bacterial biofilm production. Furthermore, biofilm formation in vitro was also compromised by live murine hepatitis virus, a related beta-coronavirus. Supporting data from LC-MS-based proteomics of spike-biofilm interactions revealed differential expression of proteins involved in quorum sensing and biofilm maturation, such as the AI-2E family transporter and LuxS, a key enzyme for AI-2 biosynthesis. Our findings suggest that these opportunistic pathogens may egress from biofilms to resume a more virulent planktonic lifestyle during coronavirus infections. The dispersion of pathogens from biofilms may culminate in potentially severe secondary infections with poor prognosis. Further detailed investigations are warranted to establish bacterial biofilms as risk factors for secondary pneumonia in COVID-19 patients.


Subject(s)
Antibiosis , Biofilms , Coronavirus/physiology , SARS-CoV-2/physiology , Spike Glycoprotein, Coronavirus/metabolism , Staphylococcus aureus/physiology , Streptococcus pneumoniae/physiology , Animals , Coinfection , Gene Expression Regulation, Bacterial , Humans , Mice , Microbial Interactions , Serogroup , Staphylococcus aureus/classification , Streptococcus pneumoniae/classification
2.
Sci Rep ; 11(1): 18444, 2021 09 16.
Article in English | MEDLINE | ID: covidwho-1415956

ABSTRACT

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.


Subject(s)
Methicillin Resistance , Staphylococcus aureus/classification , Staphylococcus aureus/growth & development , Deep Learning , Discriminant Analysis , Humans , Metal Nanoparticles/chemistry , Microbial Sensitivity Tests , Neural Networks, Computer , Signal-To-Noise Ratio , Silver/chemistry , Spectrum Analysis, Raman , Staphylococcus aureus/drug effects , Support Vector Machine
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