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Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance

Overview of attention for article published in BMC Medicine, March 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
8 tweeters

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
193 Mendeley
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Title
Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance
Published in
BMC Medicine, March 2016
DOI 10.1186/s12916-016-0575-9
Pubmed ID
Authors

Jody Phelan, Francesc Coll, Ruth McNerney, David B. Ascher, Douglas E. V. Pires, Nick Furnham, Nele Coeck, Grant A. Hill-Cawthorne, Mridul B. Nair, Kim Mallard, Andrew Ramsay, Susana Campino, Martin L. Hibberd, Arnab Pain, Leen Rigouts, Taane G. Clark

Abstract

Combating the spread of drug resistant tuberculosis is a global health priority. Whole genome association studies are being applied to identify genetic determinants of resistance to anti-tuberculosis drugs. Protein structure and interaction modelling are used to understand the functional effects of putative mutations and provide insight into the molecular mechanisms leading to resistance. To investigate the potential utility of these approaches, we analysed the genomes of 144 Mycobacterium tuberculosis clinical isolates from The Special Programme for Research and Training in Tropical Diseases (TDR) collection sourced from 20 countries in four continents. A genome-wide approach was applied to 127 isolates to identify polymorphisms associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. In addition, the effect of identified candidate mutations on protein stability and interactions was assessed quantitatively with well-established computational methods. The analysis revealed that mutations in the genes rpoB (rifampicin), katG (isoniazid), inhA-promoter (isoniazid), rpsL (streptomycin) and embB (ethambutol) were responsible for the majority of resistance observed. A subset of the mutations identified in rpoB and katG were predicted to affect protein stability. Further, a strong direct correlation was observed between the minimum inhibitory concentration values and the distance of the mutated residues in the three-dimensional structures of rpoB and katG to their respective drugs binding sites. Using the TDR resource, we demonstrate the usefulness of whole genome association and convergent evolution approaches to detect known and potentially novel mutations associated with drug resistance. Further, protein structural modelling could provide a means of predicting the impact of polymorphisms on drug efficacy in the absence of phenotypic data. These approaches could ultimately lead to novel resistance mutations to improve the design of tuberculosis control measures, such as diagnostics, and inform patient management.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 193 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 <1%
Unknown 192 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 42 22%
Researcher 36 19%
Student > Ph. D. Student 31 16%
Student > Bachelor 20 10%
Student > Postgraduate 12 6%
Other 32 17%
Unknown 20 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 52 27%
Agricultural and Biological Sciences 42 22%
Medicine and Dentistry 26 13%
Immunology and Microbiology 22 11%
Pharmacology, Toxicology and Pharmaceutical Science 7 4%
Other 19 10%
Unknown 25 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 January 2017.
All research outputs
#1,191,271
of 13,500,356 outputs
Outputs from BMC Medicine
#943
of 2,142 outputs
Outputs of similar age
#32,807
of 263,977 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 13,500,356 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,142 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 34.8. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,977 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them