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The pathogen Mycoplasma dispar Shows High Minimum Inhibitory Concentrations for Antimicrobials Commonly Used for Bovine Respiratory Disease.

ABSTRACT: Mycoplasma dispar is an overlooked pathogen often involved in bovine respiratory disease (BRD), which affects cattle around the world. BRD results in lost production and high treatment and prevention costs. Additionally, chronic therapies with multiple antimicrobials may lead to antimicrobial resistance. Data on antimicrobial susceptibility to M. dispar is limited so minimum inhibitory concentrations (MIC) of a range of antimicrobials routinely used in BRD were evaluated using a broth microdilution technique for 41 M. dispar isolates collected in Italy between 2011-2019. While all isolates had low MIC values for florfenicol (<1 ?g/mL), many showed high MIC values for erythromycin (MIC90 ?8 ?g/mL). Tilmicosin MIC values were higher (MIC50 = 32 ?g/mL) than those for tylosin (MIC50 = 0.25 ?g/mL). Seven isolates had high MIC values for lincomycin, tilmicosin and tylosin (?32 ?g/mL). More, alarmingly, results showed more than half the strains had high MICs for enrofloxacin, a member of the fluoroquinolone class considered critically important in human health. A time-dependent progressive drift of enrofloxacin MICs towards high-concentration values was observed, indicative of an on-going selection process among the isolates.

SUBMITTER: Bottinelli M

PROVIDER: S-EPMC7459706 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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The pathogen Mycoplasma dispar Shows High Minimum Inhibitory Concentrations for Antimicrobials Commonly Used for Bovine Respiratory Disease.

Bottinelli Marco M Merenda Marianna M Gastaldelli Michele M Picchi Micaela M Stefani Elisabetta E Nicholas Robin A J RAJ Catania Salvatore S

Antibiotics (Basel, Switzerland) 20200729 8

Mycoplasma dispar is an overlooked pathogen often involved in bovine respiratory disease (BRD), which affects cattle around the world. BRD results in lost production and high treatment and prevention costs. Additionally, chronic therapies with multiple antimicrobials may lead to antimicrobial resistance. Data on antimicrobial susceptibility to M. dispar is limited so minimum inhibitory concentrations (MIC) of a range of antimicrobials routinely used in BRD were evaluated using a br ...[more]

PMID: 32751401

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Project description:Mycoplasma synoviae (MS) is a highly prevalent bacterial species in poultry causing disease and severe economic losses. Antibiotic treatment is one of the control strategies that can be applied to contain clinical outbreaks in MS-free flocks, especially because this bacterium can be transmitted in ovo. It becomes, then, very important for veterinarians to know the antibiotic susceptibility of the circulating strains in order to choose the most appropriate first-line antibiotic molecule as a proactive role in fighting antibiotic resistance. We evaluated the Minimum Inhibitory Concentrations (MICs) of enrofloxacin, oxytetracycline, doxycycline, erythromycin, tylosin, tilmicosin, spiramycin, tiamulin, florfenicol and lincomycin for MS isolates collected between 2012 and 2017 in Italy. A total of 154 MS isolates from different poultry commercial categories (broiler, layer, and turkey sectors) was tested using commercial MIC plates. All MS isolates showed very high MIC values of erythromycin (MIC90 ?8 ?g/mL) and enrofloxacin (MIC90 ?16 ?g/mL). MIC values of doxycycline and oxytetracycline obtained were superimposable to each other with only a one-fold dilution difference. Discrepancies between MIC values of tylosin and tilmicosin were observed. Interestingly, seven isolates showed very high MIC values of lincomycin and tilmicosin, but not all of them showed very high MIC values of tylosin. Most of the MS isolates showed low MIC values of spiramycin, but seven strains showed a MIC ?16 ?g/mL. In the observation period, the frequency of the different MIC classes varied dependently on the tested antibiotic. Interestingly, tilmicosin MICs clearly showed a time-dependent progressive shift towards high-concentration classes, indicative of an on-going selection process among MS isolates. Until standardized breakpoints become available to facilitate data interpretation, it will be fundamental to continue studying MIC value fluctuations in the meantime in order to create a significant database that would facilitate veterinarians in selecting the proper drug for treating this impactful Mycoplasma.

Project description:Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Project description:Background: Despite advances in therapeutics, outcomes for hepatocellular carcinoma (HCC) remain poor and there is an urgent need for efficacious systemic therapy. Unfortunately, drugs that are successful in preclinical studies often fail in the clinical setting, and we hypothesize that this is due to functional differences between primary tumors and commonly used preclinical models. In this study, we attempt to answer this question by comparing tumor morphology and gene expression profiles between primary tumors, xenografts and HCC cell lines. Methods: Hep G2 cell lines and tumor cells from patient tumor explants were subcutaneously (ectopically) injected into the flank and orthotopically into liver parenchyma of Mus Musculus SCID mice. The mice were euthanized after two weeks. RNA was extracted from the tumors, and gene expression profiling was performed using the Gene Chip Human Genome U133 Plus 2.0. Principal component analyses (PCA) and construction of dendrograms were conducted using Partek genomics suite. Results: PCA showed that the commonly used HepG2 cell line model and its xenograft counterparts were vastly different from all fresh, primary tumors. Expression profiles of primary tumors were also significantly divergent from their counterpart patient-derived xenograft (PDX) models, regardless of the site of implantation. Xenografts from the same primary tumors were more likely to cluster together regardless of site of implantation, although heat maps showed distinct differences in gene expression profiles between orthotopic and ectopic models. Conclusions: The data presented here challenges the utility of routinely used preclinical models. Models using HepG2 were vastly different from primary tumors and PDXs, suggesting that this is not clinically representative. Surprisingly, site of implantation (orthotopic versus ectopic) resulted in limited impact on gene expression profiles, and in both scenarios xenografts differed significantly from the original primary tumors, challenging the long-held notion that orthotopic PDX model is the gold standard preclinical model for HCC.

Dataset Information

The pathogen Mycoplasma dispar Shows High Minimum Inhibitory Concentrations for Antimicrobials Commonly Used for Bovine Respiratory Disease.

Publications

The pathogen <i>Mycoplasma dispar</i> Shows High Minimum Inhibitory Concentrations for Antimicrobials Commonly Used for Bovine Respiratory Disease.

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