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Genomic epidemiology of Lineage 4 Mycobacterium tuberculosis subpopulations in New York City and New Jersey, 1999–2009

Overview of attention for article published in BMC Genomics, November 2016
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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Title
Genomic epidemiology of Lineage 4 Mycobacterium tuberculosis subpopulations in New York City and New Jersey, 1999–2009
Published in
BMC Genomics, November 2016
DOI 10.1186/s12864-016-3298-6
Pubmed ID
Authors

Tyler S. Brown, Apurva Narechania, John R. Walker, Paul J. Planet, Pablo J. Bifani, Sergios-Orestis Kolokotronis, Barry N. Kreiswirth, Barun Mathema

Abstract

Whole genome sequencing (WGS) has rapidly become an important research tool in tuberculosis epidemiology and is likely to replace many existing methods in public health microbiology in the near future. WGS-based methods may be particularly useful in areas with less diverse Mycobacterium tuberculosis populations, such as New York City, where conventional genotyping is often uninformative and field epidemiology often difficult. This study applies four candidate strategies for WGS-based identification of emerging M. tuberculosis subpopulations, employing both phylogenomic and population genetics methods. M. tuberculosis subpopulations in New York City and New Jersey can be distinguished via phylogenomic reconstruction, evidence of demographic expansion and subpopulation-specific signatures of selection, and by determination of subgroup-defining nucleotide substitutions. These methods identified known historical outbreak clusters and previously unidentified subpopulations within relatively monomorphic M. tuberculosis endemic clone groups. Neutrality statistics based on the site frequency spectrum were less useful for identifying M. tuberculosis subpopulations, likely due to the low levels of informative genetic variation in recently diverged isolate groups. In addition, we observed that isolates from New York City endemic clone groups have acquired multiple non-synonymous SNPs in virulence- and growth-associated pathways, and relatively few mutations in drug resistance-associated genes, suggesting that overall pathoadaptive fitness, rather than the acquisition of drug resistance mutations, has played a central role in the evolutionary history and epidemiology of M. tuberculosis subpopulations in New York City. Our results demonstrate that some but not all WGS-based methods are useful for detection of emerging M. tuberculosis clone groups, and support the use of phylogenomic reconstruction in routine tuberculosis laboratory surveillance, particularly in areas with relatively less diverse M. tuberculosis populations. Our study also supports the use of wider-reaching phylogenomic and population genomic methods in tuberculosis public health practice, which can support tuberculosis control activities by identifying genetic polymorphisms contributing to epidemiological success in local M. tuberculosis populations and possibly explain why certain isolate groups are apparently more successful in specific host populations.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 55 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Ph. D. Student 10 18%
Student > Master 10 18%
Student > Bachelor 4 7%
Student > Doctoral Student 3 5%
Other 5 9%
Unknown 12 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 30%
Immunology and Microbiology 11 20%
Agricultural and Biological Sciences 8 14%
Medicine and Dentistry 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 7%
Unknown 13 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 02 September 2017.
All research outputs
#7,436,625
of 22,903,988 outputs
Outputs from BMC Genomics
#3,581
of 10,674 outputs
Outputs of similar age
#136,048
of 414,929 outputs
Outputs of similar age from BMC Genomics
#77
of 241 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 10,674 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 66% 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 414,929 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 241 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.