Title MS/MS networking guided analysis of molecule and gene cluster families.
Year of Publication 2013
Authors D.D. Nguyen; C.H. Wu; W.J. Moree; A. Lamsa; M.H. Medema; X. Zhao; R.G. Gavilan; M. Aparicio; L. Atencio; C. Jackson; J. Ballesteros; J. Sanchez; J.D. Watrous; V.V. Phelan; C. Wiel; R.D. Kersten; S. Mehnaz; R. De Mot; E.A. Shank; P. Charusanti; H. Nagarajan; B.M. Duggan; B.S. Moore; N. Bandeira; B.Ø. Palsson; K. Pogliano; M. Gutiérrez; P.C. Dorrestein
Journal PLoS Comput Biol
Abstract The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779(T). The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.
URL http://www.ncbi.nlm.nih.gov/pubmed/23798442?dopt=Abstract
PubMed ID 23798442