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Optimisation of 16S rRNA gut microbiota profiling of extremely low birth weight infants

Overview of attention for article published in BMC Genomics, November 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

blogs
1 blog
twitter
45 tweeters

Citations

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43 Dimensions

Readers on

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136 Mendeley
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Title
Optimisation of 16S rRNA gut microbiota profiling of extremely low birth weight infants
Published in
BMC Genomics, November 2017
DOI 10.1186/s12864-017-4229-x
Pubmed ID
Authors

Cristina Alcon-Giner, Shabhonam Caim, Suparna Mitra, Jennifer Ketskemety, Udo Wegmann, John Wain, Gusztav Belteki, Paul Clarke, Lindsay J. Hall

Abstract

Infants born prematurely, particularly extremely low birth weight infants (ELBW) have altered gut microbial communities. Factors such as maternal health, gut immaturity, delivery mode, and antibiotic treatments are associated with microbiota disturbances, and are linked to an increased risk of certain diseases such as necrotising enterocolitis. Therefore, there is a requirement to optimally characterise microbial profiles in this at-risk cohort, via standardisation of methods, particularly for studying the influence of microbiota therapies (e.g. probiotic supplementation) on community profiles and health outcomes. Profiling of faecal samples using the 16S rRNA gene is a cost-efficient method for large-scale clinical studies to gain insights into the gut microbiota and additionally allows characterisation of cohorts were sample quantities are compromised (e.g. ELBW infants). However, DNA extraction method, and the 16S rRNA region targeted can significantly change bacterial community profiles obtained, and so confound comparisons between studies. Thus, we sought to optimise a 16S rRNA profiling protocol to allow standardisation for studying ELBW infant faecal samples, with or without probiotic supplementation. Using ELBW faecal samples, we compared three different DNA extraction methods, and subsequently PCR amplified and sequenced three hypervariable regions of the 16S rRNA gene (V1 + V2 + V3), (V4 + V5) and (V6 + V7 + V8), and compared two bioinformatics approaches to analyse results (OTU and paired end). Paired shotgun metagenomics was used as a 'gold-standard'. Results indicated a longer bead-beating step was required for optimal bacterial DNA extraction and that sequencing regions (V1 + V2 + V3) and (V6 + V7 + V8) provided the most representative taxonomic profiles, which was confirmed via shotgun analysis. Samples sequenced using the (V4 + V5) region were found to be underrepresented in specific taxa including Bifidobacterium, and had altered diversity profiles. Both bioinformatics 16S rRNA pipelines used in this study (OTU and paired end) presented similar taxonomic profiles at genus level. We determined that DNA extraction from ELBW faecal samples, particularly those infants receiving probiotic supplementation, should include a prolonged beat-beating step. Furthermore, use of the 16S rRNA (V1 + V2 + V3) and (V6 + V7 + V8) regions provides reliable representation of ELBW microbiota profiles, while inclusion of the (V4 + V5) region may not be appropriate for studies where Bifidobacterium constitutes a resident microbiota member.

Twitter Demographics

The data shown below were collected from the profiles of 45 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 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 136 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 21%
Student > Ph. D. Student 24 18%
Student > Bachelor 17 13%
Student > Master 15 11%
Other 8 6%
Other 19 14%
Unknown 25 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 19%
Biochemistry, Genetics and Molecular Biology 25 18%
Medicine and Dentistry 19 14%
Immunology and Microbiology 13 10%
Nursing and Health Professions 4 3%
Other 19 14%
Unknown 30 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 11 February 2020.
All research outputs
#1,059,273
of 23,007,053 outputs
Outputs from BMC Genomics
#182
of 10,698 outputs
Outputs of similar age
#24,121
of 329,244 outputs
Outputs of similar age from BMC Genomics
#4
of 199 outputs
Altmetric has tracked 23,007,053 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,698 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 98% 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 329,244 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 199 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.