Evidence map›Paper›PMID 23992299›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2013

Analysis of metabolic evolution in bacteria using whole-genome metabolic models.

William A Bryant, Ali A Faruqi, John W Pinney

Open access · greenAbstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Derivative processes for modelling metabolic fluxes.Bioinformatics (Oxford, England) · 2014
    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

3 authors at 1 institution in 1 country.

William A BryantDepartment of Life Sciences, Imperial College London , London, United Kingdom .
Ali A Faruqi
John W Pinney
Imperial College London · GB

Funding

Biotechnology and Biological Sciences Research Council BB/G020434/1
6 · The paper itself

Abstract

Recent advances in the automation of metabolic model reconstruction have led to the availability of draft-quality metabolic models (predicted reaction complements) for multiple bacterial species. These reaction complements can be considered as trait representations and can be used for ancestral state reconstruction to infer the most likely metabolic complements of common ancestors of all bacteria with generated metabolic models. We present here an ancestral state reconstruction for 141 extant bacteria and analyze the reaction gains and losses for these bacteria with respect to their lifestyles and pathogenic nature. A simulated annealing approach is used to look at coordinated metabolic gains and losses in two bacteria. The main losses of Onion yellows phytoplasma OY-M, an obligate intracellular pathogen, are shown (as expected) to be in cell wall biosynthesis. The metabolic gains made by Clostridium difficile CD196 in adapting to its current habitat in the human colon is also analyzed. Our analysis shows that the capability to utilize N-Acetyl-neuraminic acid as a carbon source has been gained, rather than having been present in the Clostridium ancestor, as has the capability to synthesize phthiocerol dimycocerosate, which could potentially aid the evasion of the host immune response. We have shown that the availability of large numbers of metabolic models, along with conventional approaches, has enabled a systematic method to analyze metabolic evolution in the bacterial domain.

Indexed as

Genome, BacterialAdaptation, BiologicalClostridioides difficileEvolution, MolecularHumansMetabolic Networks and PathwaysModels, GeneticPhylogenyPhytoplasmaRNA, BacterialRNA, Ribosomal, 23SRNA, BacterialRNA, Ribosomal, 23S

Identifiers

PMID23992299
PMCPMC3791050
OpenAlexW2009584152

What OpenQuestion holds

Textmetadata
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.