A Comprehensive Screening Strategy to Detect and Identify Novel Compounds: Using Secondary Metabolites from Myxobacteria as a Model
Event Date: October 16, 2012 at 01:00 PM EDT
The extraction of relevant information from complex data sets is a bottleneck in many “omics” studies, in particular for non-targeted metabolomics with the goal of the identifying all compounds produced by a bacterial strain. Many of these compounds are part of primary metabolism and therefore out of scope when research concentrates on secondary metabolites. Other metabolites may be very common for a certain genus of bacteria and already well known but the subset of new compounds is hard to identify. Here, we combine targeted and non-targeted metabolomic approaches, including searches against in-house and freely available databases, to identify numerous features derived from high resolution LC/MS/MS.

Myxobacteria represent an important source of novel natural products exhibiting a wide range of biological activities. "Metabolome-mining" approaches hold great promise for uncovering novel secondary metabolites from myxobacterial strains, some of which are investigated as potential leads for novel drugs. Non-targeted metabolite profiling, using statistical methods such as ANOVA, a t-test and PCA analysis identified 176 features related to the growth of the bacteria by comparing myxobacterial extracts to blanks. The presented statistical profiling workflow enables a rapid profiling of complex metabolite extracts and identification of relevant marker compounds – addressing two of the major bottleneck in metabolomics, sample throughput and compound identification.

Steve Brown.

Technical Editor


Michael D. Timmons, Ph.D.

ESI Application Scientist

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