Application of COMPOCHIP Microarray to Investigate the Bacterial Communities of Different Composts

Abstract

A microarray spotted with 369 different 16S rRNA gene probes specific to microorganisms involved in the degradation process of organic waste during composting was developed. The microarray was tested with pure cultures, and of the 30,258 individual probe-target hybridization reactions performed, there were only 188 false positive (0.62%) and 22 false negative signals (0.07%). Labeled target DNA was prepared by polymerase chain reaction amplification of 16S rRNA genes using a Cy5-labeled universal bacterial forward primer and a universal reverse primer. The COMPOCHIP microarray was applied to three different compost types (green compost, manure mix compost, and anaerobic digestate compost) of different maturity (2, 8, and 16 weeks), and differences in the microorganisms in the three compost types and maturity stages were observed. Multivariate analysis showed that the bacterial composition of the three composts was different at the beginning of the composting process and became more similar upon maturation. Certain probes (targeting Sphingobacterium, Actinomyces, Xylella/Xanthomonas/ Stenotrophomonas, Microbacterium, Verrucomicrobia, Planctomycetes, Low G + C and Alphaproteobacteria) were more influential in discriminating between different composts. Results from denaturing gradient gel electrophoresis supported those of microarray analysis. This study showed that the COMPOCHIP array is a suitable tool to study bacterial communities in composts.

Oligonucleotide microarray methodology for taxonomic and functional monitoringof microbial community composition

“…When a theory is to be tested for its general applicability, high number of samples must be processed and this may be achieved with microarray analysis, allowing high-throughput assessment of taxonomic or functional diversity of microorganisms in the environment, as well as genotypic characterization of community members. indeed, microarray analysis has proved effective to reach these goals with various types of microbial ecosystems, using microarrays focused on particular taxonomic groups or with a broad coverage of bacterial diversity (Deangelis et al 2009, Franke-Whittle et al 2009, Sanguin et al 2008, Zhou et al 2008). Yet, bioinformatics and biostatistics tools will have to be improved further to handle more effectively the huge amount of data produced, and to find significant features in noisy biological data.…”

Application of Multivariate Data Analyses in Waste Management

“…CCA was applied by Franke-Whittle et al [25] and ElSheikh et al [73]. Franke-Whittle et al [25] applied CCA to illustrate the similarities in microbial communities of three different composting processes.…”

“…CCA was applied by Franke-Whittle et al [25] and ElSheikh et al [73]. Franke-Whittle et al [25] applied CCA to illustrate the similarities in microbial communities of three different composting processes. El-Sheikh et al [73] investigated the ten-year primary succession on a newly created landfill at a lagoon of the Mediterranean Sea.…”

“…Furthermore CA has often been used to evaluate microbiological data, especially in compost science [25][26][27][28]31]. Innerebner et al [26] and Ros et al [27,28] used CA to identify related samples and similar groups of microorganisms.…”

“…Innerebner et al [26] and Ros et al [27,28] used CA to identify related samples and similar groups of microorganisms. Franke-Whittle et al [25] used CA to show the similarities of Denaturing Gradient Gel Electrophoresis (DGGE) data of three different compost types with proceeding compost maturity. Xiao et al [31] used a hierarchical cluster analysis of DGGE data to estimate the succession of bacterial communities during the active composting process.…”