Biology subjects

Morosini, M. I.

Publications and source records attributed to Morosini, M. I..

2 recordsLinked to original sources

bioRxiv2022

The Impact of Colistin Resistance on the Activation of Innate Immunity by Lipopolysaccharide Modification

Colistin resistance is caused by different lipopolysaccharide (LPS) modifications, and we propose to evaluate the effect on the innate immune response of in vivo and in vitro colistin resistance acquisition. We used 2 pairs of isogenic strains: (1) Escherichia coli ATCC25922, susceptible to colistin and its isogenic transconjugant-carrying mcr-1 gene; and (2) OXA-48, CTX-M-15 K. pneumoniae susceptible to colistin (CS-Kp) isolated from a urinary infection and its colistin-resistant variant (CR-Kp) from the same patient after prolonged treatment with colistin. No mutation of described genes for colistin resistance (pmrA, pmrB, mgrB. phoP/Q and crrAB) were found in the CR-Kp genome; however, LPS modifications were characterized by negative-ion MALDI-TOF. The strains were co-cultured with human monocytes to determine their survival after phagocytosis and induction to apoptosis. Also, monocytes were stimulated with bacterial LPS to study cytokine and immunecheckpoint production. The addition of 4-amino-4-deoxy-l-arabinose (Ara4N) to lipid A of CR-Kp accounted for the colistin resistance. CR-Kp survived significantly longer inside human monocytes after being phagocytosed compared with the CS-Kp strain, whereas no significant differences were observed for the E. coli isogenic strains. In addition, LPS from CR-Kp induced both higher apoptosis in monocytes and higher levels of cytokine and immune checkpoint production than LPS from CS-Kp. This effect was strictly the opposite for E. coli. Our data reveal a variable impact of colistin resistance on the innate immune system, depending on the responsible mechanism. Adding Ara4N to LPS increases bacterial survival after phagocytosis and elicits a higher inflammatory response than its colistin-susceptible counterpart.

microbiology↗

bioRxiv2021

Discrimination of species within the Enterobacter cloacae complex using MALDI-TOF Mass Spectrometry and Fourier-Transform Infrared Spectroscopy coupled with Machine Learning tools

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may host different acquired antimicrobial resistance and virulence mechanisms and their identification are challenging. This study aims to develop predictive models based on MALDI-TOF MS spectral profiles and machine learning for species-level identification. A total of 198 ECC and 116 K. aerogenes clinical isolates from the University Hospital Ramon y Cajal (Spain) and the University Hospital Basel (Switzerland) were included. The capability of the proposed method to differentiate the most common ECC species (E. asburiae, E. kobei, E. hormaechei, E. roggenkampii, E. ludwigii, E. bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with PCA pre-processing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development dataset. Thus, we developed supervised, non-linear predictive models (Support Vector Machine with Radial Basis Function and Random Forest). The external validation of these models with protein spectra from the two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species. Similar results were obtained with the MSI database developed recently (https://msi.happy-dev.fr/) except in the case of E. hormaechei, which was more accurately identified by Random Forest. In short, MALDI-TOF MS combined with machine learning demonstrated to be a rapid and accurate method for the differentiation of ECC species.

microbiology↗