Mathematical Modeling of 16S Ribosomal DNA Amplification Reveals Optimal Conditions for the Interrogation of Complex Microbial Communities with Phylogenetic Microarrays
Motivation: Many current studies of complex microbial communities rely on the isolation of community genomic DNA, amplification of 16S ribosomal RNA genes (rDNA) and subsequent examination of community structure through interrogation of the amplified 16S rDNA pool by high-throughput sequencing, phylogenetic microarrays or quantitative PCR.
Results: Here we describe the development of a mathematical model aimed to simulate multitemplate amplification of 16S ribosomal DNA sample and subsequent detection of these amplified 16S rDNA species by phylogenetic microarray. Using parameters estimated from the experimental results obtained in the analysis of intestinal microbial communities with Microbiota Array, we show that both species detection and the accuracy of species abundance estimates depended heavily on the number of PCR cycles used to amplify 16S rDNA. Both parameters initially improved with each additional PCR cycle and reached optimum between 15 and 20 cycles of amplification. The use of more than 20 cycles of PCR amplification and/or more than 50 ng of starting genomic DNA template was, however, detrimental to both the fraction of detected community members and the accuracy of abundance estimates. Overall, the outcomes of the model simulations matched well available experimental data. Our simulations also showed that species detection and the accuracy of abundance measurements correlated positively with the higher sample-wide PCR amplification rate, lower template-to-template PCR bias and lower number of species in the interrogated community. The developed model can be easily modified to simulate other multitemplate DNA mixtures as well as other microarray designs and PCR amplification protocols.
& Foy, B.
(2011). Mathematical Modeling of 16S Ribosomal DNA Amplification Reveals Optimal Conditions for the Interrogation of Complex Microbial Communities with Phylogenetic Microarrays. Bioinformatics, 27 (15), 2134-2140.