Clinical implication of novel drug resistance-conferring mutations in resistant tuberculosis

Mnyambwa, Nicholaus P.; Kim, D.-J.; Ngadaya, Esther; Kazwala, R. R.; Petrucka, Pammla; Mfinanga, Sayoki

doi:10.1007/s10096-017-3027-3

Cited by 11 publications

(14 citation statements)

References 59 publications

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“…4 Clinically, the emergence of AMR is primarily driven by non-compliance with prescribed antimicrobials therapy, which in turn promotes spontaneous mutations in chromosome or control genes leading to new mutant pathogens with selective pressure in the presence of antimicrobials. 5 Further accumulation of such beneficial mutations catalyzes the evolution of multidrug-resistant strains, 6 which necessitate the use of broadspectrum antibiotics as the ultimate choice. Especially alarming is the emergence and spread of multi-drug resistant bacteria which are hard to treat with the available antibiotics.…”

Section: Introductionmentioning

confidence: 99%

Antibiotic Susceptibility Patterns of Bacterial Isolates from Routine Clinical Specimens from Referral Hospitals in Tanzania: A Prospective Hospital-Based Observational Study

Mnyambwa¹,

Mahende²,

Wilfred³

et al. 2021

IDR

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Introduction Antimicrobial resistance is one of the biggest threats of modern public health. Although sub-Saharan Africa is highly burdened with infectious diseases, current data on antimicrobial resistance are sparse. Methods A prospective study was conducted between October 2018 and September 2019 to assess the antibiotic susceptibility patterns of clinical bacterial isolates obtained from four referral hospitals in Tanzania. We used standard media and Kirby-Bauer disc diffusion methods as per Clinical and Laboratory Standards Institute (CLSI) standards. Results We processed a total of 2620 specimens of which 388 (14.8%) were culture-positive from patients with a median (IQR) age of 28 (12–44) years. Of the positive cultures, 52.3% (203) were from females. Most collected specimens were ear pus 28.6% (111), urine 24.0% (93), wound pus 20.6% (80), stool 14.9% (58), and blood 8.3% (32). Predominant isolates were S. aureus 28.4% (110), E. coli 15.2% (59), P. aeruginosa 10.6% (41), P. mirabilis 7.0% (27), V. cholerae 01 Ogawa 6.2% (24), Klebsiella spp. 5.2% (20) and Streptococcus spp. 4.6% (18). Generally, the isolates exhibited a high level of resistance to commonly used antibiotics such as Ampicillin, Amoxicillin-Clavulanic acid, Erythromycin, Gentamicin, Tetracycline, Trimethoprim, third-generation Cephalosporins (Ceftriaxone and Ceftazidime), and reserved drugs (Clindamycin and Meropenem). S. aureus isolates were resistant to most of the antibiotics tested; 66.7% were classified as MRSA infections. Conclusion Antibiotic resistance to commonly prescribed antibiotics was alarmingly high. Our findings emphasize the need for comprehensive national control programs to combat antibiotic resistance.

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Section: Introductionmentioning

confidence: 99%

Antibiotic Susceptibility Patterns of Bacterial Isolates from Routine Clinical Specimens from Referral Hospitals in Tanzania: A Prospective Hospital-Based Observational Study

Mnyambwa¹,

Mahende²,

Wilfred³

et al. 2021

IDR

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“…For most of the samples, drug resistance status was known only for the first-line drugs. Typically, if a sample is resistant to one of the first line drugs it is resistant to the others due to stepwise acquisition of resistance-conferring mutations during long ineffective treatment by multiple drugs [58, 59]. This co-resistance decreases the power of statistical methods to find new genes and pathways weakly associated with resistance.…”

Section: Discussionmentioning

confidence: 99%

Feature selection and aggregation for antibiotic resistance GWAS inMycobacterium tuberculosis: a comparative study

Reshetnikov

Bykova

Kuleshov

et al. 2022

Preprint

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Drug resistance (DR) remains a global healthcare concern. In contrast to other human bacterial pathogens, acquiring mutations in the genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics resistance of a particular isolate can be predicted with high confidence knowing whether specific mutations occurred, but for some antibiotics our knowledge of resistance mechanism is moderate. Statistical machine learning (ML) methods are used in attempts to infer new genes implicated in drug resistance. These methods use large collections of isolates with known whole-genome sequences and resistance status for different drugs. However, high correlations between the presence or absence of resistance to drugs that are used together in one treatment regimen complicate inference of causal mutations by traditional ML. Recently, several new methods were suggested to deal with the problem of correlations of response variables in training data. In this study, we applied the following methods to tackle the confounding effect of resistance co-occurrence in a dataset of approximately 13 000 complete genomes of MTB with characterized resistance status for 13 drugs: logistic regression with different regularization penalty functions, a polynomial-time algorithm for best-subset selection problem (ABESS), and "Hungry, Hungry SNPos" (HHS) method. We compared these methods by the ability to select known causal mutations for the resistance to each particular drug and not to select mutations in genes that are known to be associated with resistance to other drugs. ABESS significantly outperformed the others selecting more relevant sets of mutations. We also showed that aggregation of rare mutations into features indicating changes of PFAM domains increased the quality of prediction and these features were majorly selected by ABESS.

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“…Chromosome mutations comprise the major mechanism causing drug resistance in M. tuberculosis (Miotto et al, 2018). Drug resistance in strains could be caused by other resistance mechanisms such as antibiotic modifications or neutralization, augmented efflux pumps, porin alterations, and the downregulation of cell-wall permeability (Mnyambwa et al, 2017;Sharma et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

A Glutamine Insertion at Codon 432 of RpoB Confers Rifampicin Resistance in Mycobacterium tuberculosis

et al. 2020

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Tuberculosis (TB) is an infectious respiratory disease caused by Mycobacterium tuberculosis and one of the top 10 causes of death worldwide. Treating TB is challenging; successful treatment requires a long course of multiple antibiotics. Rifampicin (RIF) is a first-line drug for treating TB, and the development of RIF-resistant M. tuberculosis makes treatment even more difficult. To determine the mechanism of RIF resistance in these strains, we searched for novel mutations by sequencing. Four isolates, CDC-1, CDC-2, CDC-3, and CDC-4, had high-level RIF resistance and unique mutations encoding RpoB G 158 R, RpoB V 168 A, RpoB S 188 P, and RpoB Q 432 insQ, respectively. To evaluate their correlation with RIF resistance, plasmids carrying rpoB genes encoding these mutant proteins were transfected into the H 37 Rv reference strain. The plasmid complementation of RpoB indicated that G 158 R, V 168 A, and S 188 P did not affect the MIC of RIF. However, the MIC of RIF was increased in H 37 Rv carrying RpoB Q 432 insQ. To confirm the correlation between RIF resistance and Q 432 insQ, we cloned an rpoB fragment carrying the insertion (encoding RpoB Q 432 insQ) into H 37 Rv by homologous recombination using a suicide vector. All replacement mutants expressing RpoB Q 432 insQ were resistant to RIF (MIC > 1 mg/L). These results indicate that RpoB Q 432 insQ causes RIF resistance in M. tuberculosis.

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Clinical implication of novel drug resistance-conferring mutations in resistant tuberculosis

Cited by 11 publications

References 59 publications

Antibiotic Susceptibility Patterns of Bacterial Isolates from Routine Clinical Specimens from Referral Hospitals in Tanzania: A Prospective Hospital-Based Observational Study

Antibiotic Susceptibility Patterns of Bacterial Isolates from Routine Clinical Specimens from Referral Hospitals in Tanzania: A Prospective Hospital-Based Observational Study

Feature selection and aggregation for antibiotic resistance GWAS inMycobacterium tuberculosis: a comparative study

A Glutamine Insertion at Codon 432 of RpoB Confers Rifampicin Resistance in Mycobacterium tuberculosis

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