Evidence map›Paper›PMID 23607288›Full record

ArticleBMC genomics2013

Transcriptional pathways associated with the slow growth phenotype of transformed Anaplasma marginale.

Sebastián Aguilar Pierlé, Gena Kenitra Hammac, Guy H Palmer, Kelly A Brayton

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.3field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 13 citations in OpenAlex.

  1. Facultative mutualism betweenISME communications · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Understanding Anaplasmataceae pathogenesis using "Omics" approaches.Frontiers in cellular and infection microbiology · 2014
    Review
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Sebastián Aguilar PierléProgram in Genomics, Department of Veterinary Microbiology and Pathology, Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164-7040, USA. saguilar@vetmed.wsu.edu
Gena Kenitra Hammac
Guy H Palmer
Kelly A Brayton
Washington State University · US

Funding

Infectious Diseases and Microbial Immunology Training ProgramT32AI007025 · NIAID · WASHINGTON STATE UNIVERSITY · PI Thomas H KAWULA, M KARIUKI NJENGA · 1985 to 2026
$7.4M
Antigenic variation in microbial transmissionR37AI044005 · NIAID · WASHINGTON STATE UNIVERSITY · PI PALMER, GUY HUGHES · 2009 to 2018
$3.3M
Antigenic variation in rickettsial transmissionR01AI044005 · NIAID · WASHINGTON STATE UNIVERSITY · PI PALMER, GUY HUGHES · 1999 to 2008
$2.4M
NIAID NIH HHS AI44005NIAID NIH HHS R37 AI044005NIAID NIH HHS T32 AI007025Wellcome Trust GR075800M
6 · The paper itself

Abstract

backgroundThe ability to genetically manipulate bacteria has been fundamentally important for both basic biological discovery and translational research to develop new vaccines and antibiotics. Experimental alteration of the genetic content of prokaryotic pathogens has revealed both expected functional relationships and unexpected phenotypic consequences. Slow growth phenotypes have been reported for multiple transformed bacterial species, including extracellular and intracellular pathogens. Understanding the genes and pathways responsible for the slow growth phenotype provides the opportunity to develop attenuated vaccines as well as bacteriostatic antibiotics. Transformed Anaplasma marginale, a rickettsial pathogen, exhibits slow growth in vitro and in vivo as compared to the parent wild type strain, providing the opportunity to identify the underlying genes and pathways associated with this phenotype.

resultsWhole genome transcriptional profiling allowed for identification of specific genes and pathways altered in transformed A. marginale. Genes found immediately upstream and downstream of the insertion site, including a four gene operon encoding key outer membrane proteins, were not differentially transcribed between wild type and transformed A. marginale. This lack of significant difference in transcription of flanking genes and the large size of the insert relative to the genome were consistent with a trans rather than a cis effect. Transcriptional profiling across the complete genome identified the most differentially transcribed genes, including an iron transporter, an RNA cleaving enzyme and several genes involved in translation. In order to confirm the trend seen in translation-related genes, K-means clustering and Gene Set Enrichment Analysis (GSEA) were applied. These algorithms allowed evaluation of the behavior of genes as groups that share transcriptional status or biological function. Clustering and GSEA confirmed the initial observations and found additional pathways altered in transformed A. marginale. Three pathways were significantly altered as compared to the wild type: translation, translation elongation, and purine biosynthesis.

conclusionsIdentification of perturbed genes and networks through genome wide transcriptional profiling highlights the relevance of pathways such as nucleotide biosynthesis, translation, and translation elongation in the growth phenotype of obligate intracellular bacteria. These genes and pathways provide specific targets for development of slow growing attenuated vaccines and for bacteriostatic antibiotics.

Indexed as

TranscriptomeTransformation, BacterialAnaplasma marginaleBacillus subtilisEscherichia coliMetabolic Networks and PathwaysPhenotypeTranscription, Genetic

Identifiers

PMID23607288
PMCPMC3646689
OpenAlexW2039858404

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.