E-BUGS-129 - Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera

Status
Released on 23 January 2012, last updated on 2 June 2014
Organism
Neisseria meningitidis
Samples (5)
Array (1)
Protocols (14)
Description
Background Neisseria meningitidis is an important human commensal and pathogen that causes several thousand deaths each year, mostly in young children. How the pathogen replicates and causes disease in the host is largely unknown, particularly the role of metabolism in colonization and disease. Completed genome sequences are available for several strains but our understanding of how these data relate to phenotype remains limited. Results To investigate the metabolism of N. meningitidis we generated and selected a representative Tn5 library on rich medium, a minimal defined medium and in human serum to identify genes essential for growth under these conditions. To relate these data to a systems-wide understanding of the pathogens biology we constructed a genome-scale metabolic network: GSMN-Nm. This model was able to distinguish essential and non-essential genes as predicted by the global mutagenesis. These essentiality data, the library and the GSMN-Nm model are powerful and widely applicable resources for the study of meningococcal metabolism and physiology. We demonstrate the utility of these resources by predicting and demonstrating metabolic requirements on minimal medium such as a requirement for PEP carboxylase, and by describing the nutritional and biochemical status of N. meningitidis when grown in serum, including a requirement for both the synthesis and transport of amino acids. Conclusions This study describes the application of a genome scale transposon library combined with an experimentally validated genome-scale metabolic network of N. meningitidis to identify essential genes and provide novel insight to the pathogens metabolism both in vitro and during infection. Data is also available from BuG@Sbase
Experiment types
transcription profiling by array, growth condition
Contacts
Citation
MIAME
PlatformsProtocolsVariablesProcessedRaw
Files
Investigation descriptionE-BUGS-129.idf.txt
Sample and data relationshipE-BUGS-129.sdrf.txt
Raw data (1)E-BUGS-129.raw.1.zip
Processed data (2)E-BUGS-129.processed.1.zip, E-BUGS-129.processed.2.zip
Array designA-BUGS-30.adf.txt
R ExpressionSetE-BUGS-129.eSet.r
Links