13
views
0
recommends
+1 Recommend
2 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Streptococcus pneumoniae is a leading cause of invasive disease in infants, especially in low-income settings. Asymptomatic carriage in the nasopharynx is a prerequisite for disease, but variability in its duration is currently only understood at the serotype level. Here we developed a model to calculate the duration of carriage episodes from longitudinal swab data, and combined these results with whole genome sequence data. We estimated that pneumococcal genomic variation accounted for 63% of the phenotype variation, whereas the host traits considered here (age and previous carriage) accounted for less than 5%. We further partitioned this heritability into both lineage and locus effects, and quantified the amount attributable to the largest sources of variation in carriage duration: serotype (17%), drug-resistance (9%) and other significant locus effects (7%). A pan-genome-wide association study identified prophage sequences as being associated with decreased carriage duration independent of serotype, potentially by disruption of the competence mechanism. These findings support theoretical models of pneumococcal competition and antibiotic resistance.

          eLife digest

          Microorganisms live in most parts of our body, including the inside of our nose. Most of the microbes are harmless and can even be beneficial to our health. However, some microbes can cause diseases – although they often go unnoticed, as our immune system can remove them before we show any symptoms. For example, the bacterium Streptococcus pneumoniae can cause diseases such as pneumonia and meningitis, but generally, it lives harmlessly in the nose, and is particularly common in children and the elderly.

          The longer the bacteria live in the nose before being killed by the immune system, the more likely they are to be transmitted to another person. The amount of time it takes for the immune system to clear the bacteria depends on various factors, such as the age of the person or the bacterium’s defense mechanism and its genetic material. A particularly important aspect is to what subtype, also known as serotype, a bacterium belongs to, which is characterized by differences in the structure of the sugar coating that surrounds the microbe. However, until now, it was not known how much each of these factors contributes.

          Now, Lees et al. have developed a mathematical model to calculate how long the bacteria are carried in the nose before they are cleared away, and compared it with the genomic data of the bacteria. For this, over 14,000 nose swabs from almost 600 children were collected over a two-year period. In their model, Lees et al. calculated that the bacteria’s genetics explained over 60% of the variability in survival time. They also found that the serotype was the most important individual factor that influenced how long a bacterium could survive. The age of the child was less important and only accounted for 5%. In addition, Lees et al. also found that when viruses infected some S. pneumoniae, the bacteria died sooner.

          A next step will be to confirm the effect of a viral infection on the bacteria’s survival time in a controlled model system, and also replicate the findings in separate population study.Understanding how long people can carry bacteria and transmit them to others may help to develop new vaccination or treatment strategies to control infections. Moreover, the discovery that viruses can negatively affect how long a bacterium lives, could motivate studies to investigate these findings further.

          Related collections

          Most cited references94

          • Record: found
          • Abstract: not found
          • Article: not found

          Regularization Paths for Generalized Linear Models via Coordinate Descent

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A new look at the statistical model identification

            IEEE Transactions on Automatic Control, 19(6), 716-723
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

              Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N 2) space and O(N 2 L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
                Bookmark

                Author and article information

                Contributors
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                25 July 2017
                2017
                : 6
                : e26255
                Affiliations
                [1 ]deptInfection Genomics Wellcome Trust Sanger Institute HinxtonUnited Kingdom
                [2 ]deptDepartment of Infectious Disease Epidemiology St. Mary’s Campus, Imperial College London LondonUnited Kingdom
                [3 ]deptInstitute of Child Health University College London LondonUnited Kingdom
                [4 ]deptShoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine Mahidol University Mae SotThailand
                [5 ]deptCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine University of Oxford OxfordUnited Kingdom
                University of Chicago United States
                University of Chicago United States
                Author notes
                [‡]

                Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia.

                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0001-5360-1254
                http://orcid.org/0000-0001-6303-8768
                http://orcid.org/0000-0002-7951-0745
                http://orcid.org/0000-0002-1013-7815
                Article
                26255
                10.7554/eLife.26255
                5576492
                28742023
                ac256073-19c9-4db4-8787-c11cbfbf2bc1
                © 2017, Lees et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 22 February 2017
                : 21 July 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 098051
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: 1365620
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000288, Royal Society;
                Award ID: 104169/Z/14/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 104169/Z/14/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 083735/Z/07/Z
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Genomics and Evolutionary Biology
                Microbiology and Infectious Disease
                Custom metadata
                Sequence changes in the pneumococcal genome explain most of the variability in duration of asymptomatic carriage with serotype, antibiotic resistance and prophage accounting for the largest effects.

                Life sciences
                s. pneumoniae,gwas,carriage duration,epidemiology,heritability,other
                Life sciences
                s. pneumoniae, gwas, carriage duration, epidemiology, heritability, other

                Comments

                Comment on this article