Performance of a highly successful outbreak strain of Mycobacterium tuberculosis in a multifaceted approach to bacterial fitness assessment

Yokobori, Noemí; López, Beatriz; Monteserin, Johana; Paul, Roxana; Groll, Andréa von; Martin, Anandi; Marquina‐Castillo, Brenda; Palomino, Juan Carlos; Hernández-Pando, Rogelio; Sasiain, Marı́a del Carmen; Ritacco, Viviana

doi:10.1016/j.ijmm.2018.01.006

Cited by 5 publications

(3 citation statements)

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“…Haarlem was the predominant lineage and LAM represented the smallest group of genotypes. In fact, Haarlem has been identified as responsible for outbreaks, with determinate isolates eliciting a differential immune response that explains some advantage in causing TB [38]. However, in the present study, no lineage was more prone to form clusters, since the clusters were distributed in different lineages.…”

Section: Discussioncontrasting

confidence: 54%

Tuberculosis cases in a prison in the extreme south of Brazil

Bierhals

Busatto

Silveira³

et al. 2021

Journal of Medical Microbiology

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Introduction. Tuberculosis (TB) control is a challenge, especially in vulnerable populations, such as prisoners. Hypothesis. In prison houses, the transmission of micro-organisms that cause infectious diseases can occur due to the susceptibility and immune compromise of prisoners, and due to the precarious physical conditions of the prison houses. However, strategies such as monitoring by health professionals, can mitigate the transmission of these micro-organisms, as well as, reduce the number of coinfections and antimicrobials resistance. Aim. This study attempted to analyse the dynamics of transmission and the antimicrobial resistance profile of Mycobacterium tuberculosis strains obtained from prisoners and to characterize the epidemiological, clinical and laboratory profiles of prisoners diagnosed with TB. Methodology. A cross-sectional and retrospective study was conducted with sputum samples collected from 228 distinct prisoners who were treated at the Health Unit located in the Regional Penitentiary of Rio Grande, Rio Grande do Sul, Brazil. The antimicrobial resistance profile of the strains was evaluated using the Resazurin Microtiter Assay and the transmission dynamics was investigated using 15-loci MIRU-VNTR. Results. Thirty-five patients (15.4 %) were diagnosed with TB, and when a TB/HIV coinfection was assessed, 8.6 % (3/35) of the patients were positive. In addition, all patients with results available for HBV, HCV, syphilis and diabetes mellitus were negative. Based on the genotypic profile, 55.9 % of the clinical isolates were grouped into five groups. One isolate with mono-resistance to isoniazid and two with mono-resistance to streptomycin were found. Conclusion. The presence of a Health Unit may have influenced the low numbers of TB/HIV, TB/HBV, TB/HCV, TB/syphilis coinfections and TB cases resistant to antimicrobials. Recent M. tuberculosis transmission can be inferred based on the high percentage of formatting of clusters. This situation stresses the need to improve active and passive detection, the screening of individuals for TB upon entrance into prison for early detection, and the implementation of prophylactic measures to reduce M. tuberculosis transmission.

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Section: Discussioncontrasting

confidence: 54%

Tuberculosis cases in a prison in the extreme south of Brazil

Bierhals

Busatto

Silveira³

et al. 2021

Journal of Medical Microbiology

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“…Again, very little is known about the significance of these effects and the balance of outcomes in the field, that is, on livestock‐associated premises. Assessments of fitness that involve bioassays (Yokobori et al., ) or simulations of field environments may be helpful in this area.…”

Section: Discussionmentioning

confidence: 99%

Antimicrobial Resistance on Farms: A Review Including Biosecurity and the Potential Role of Disinfectants in Resistance Selection

Davies

Wales

2019

Comp Rev Food Sci Food Safe

120

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Resistance to therapeutic antimicrobial agents is recognized as a growing problem for both human and veterinary medicine, and the need to address the issue in both of these linked domains is a current priority in public policy. Efforts to limit antimicrobial resistance (AMR) on farms have so far focused on control of the supply and use of antimicrobial drugs, plus husbandry measures to reduce infectious disease. In the United Kingdom and some other countries, substantial progress has been made recently against targets on agricultural antimicrobial drug use. However, evidence suggests that resistant pathogenic and commensal bacteria can persist and spread within and between premises despite declining or zero antimicrobial drug use. Reasons for this are likely complex and varied but may include: bacterial adaptations to ameliorate fitness costs associated with maintenance and replication of resistance genes and associated proteins, horizontal transmission of genetic resistance determinants between bacteria, physical transfer of bacteria via movement (of animals, workers, and equipment), ineffective cleaning and disinfection, and co‐selection of resistance to certain drugs by use of other antimicrobials, heavy metals, or biocides. Areas of particular concern for public health include extended‐spectrum cephalosporinases and fluoroquinolone resistance among Enterobacteriaceae, livestock‐associated methicillin‐resistant Staphylococcus aureus, and the emergence of transmissible colistin resistance. Aspects of biosecurity have repeatedly been identified as risk factors for the presence of AMR on farm premises, but there are large gaps in our understanding of the most important risk factors and the most effective interventions. The present review aims to summarize the present state of knowledge in this area, from a European perspective.

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“…. We extracted and normalized bacteria counts measured from an in vitro axenic culture of M. tuberculosis H37Rv from Yokobori et al (2018) corresponding to hour 300 for T(t) and hour 200 for t 0 , and counts for T(t) were subtracted with a sufficiently small ε to account for the small distance between T(t) and K at t. By substituting the resulting K 100, T(t) 100 − ε, t 0 32.6, and t 4.17, we obtain α 2 0.3423 1 d . For Tf, we assumed that α 3 α 2 .…”

Section: Bacterial Growth αmentioning

confidence: 99%

Mathematical Model of the Immunopathological Progression of Tuberculosis

et al. 2022

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Tuberculosis is a worldwide persistent infectious disease. It is caused by bacteria from the Mycobacterium tuberculosis complex that mainly affects the lungs and can be fatal. Using an integrative systems biology approach, we study the immunopathological progression of this disease, analyzing the key interactions between the cells involved in the different phases of the infectious process. We integrated multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays into a mechanistic mathematical model. The ordinary differential equation (ODE) model captures the regulatory interplay between the phenotypic variation of the main cells involved in the disease progression and the inflammatory microenvironment. The model reproduces in vivo time course data of an experimental model of progressive pulmonary TB in mouse, accurately reflecting the functional adaptations of the host–pathogen interactions as the disease progresses through three phenotypically different phases. We used the model to assess the effect of genotypic variations (encoded as changes in parameters) on disease outcomes. For all genotypes, we found an all-or-nothing response, where the virtual mouse either completely clears the infection or suffers uncontrolled Tb growth. Results show that it is 84% probable that a mouse submitted to a progressive pulmonary TB assay will end up with an uncontrolled infection. The simulations also showed how the genotypic variations shape the transitions across phases, showing that 100% of the genotypes evaluated eventually progress to phase two of the disease, suggesting that adaptive immune response activation was unavoidable. All the genotypes of the network that avoided progressing to phase 3 cleared the infection. Later, by analyzing the three different phases separately, we saw that the anti-inflammatory genotype of phase 3 was the one with the highest probability of leading to uncontrolled bacterial growth, and the proinflammatory genotype associated with phase 2 had the highest probability of bacterial clearance. Forty-two percent of the genotypes evaluated showed a bistable response, with one stable steady state corresponding to infection clearance and the other one to bacteria reaching its carrying capacity. Our mechanistic model can be used to predict the outcomes of different experimental conditions through in silico assays.

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Performance of a highly successful outbreak strain of Mycobacterium tuberculosis in a multifaceted approach to bacterial fitness assessment

Cited by 5 publications

References 48 publications

Tuberculosis cases in a prison in the extreme south of Brazil

Tuberculosis cases in a prison in the extreme south of Brazil

Antimicrobial Resistance on Farms: A Review Including Biosecurity and the Potential Role of Disinfectants in Resistance Selection

Mathematical Model of the Immunopathological Progression of Tuberculosis

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