Oligonucleotide Microarray for the Study of Functional Gene Diversity in the Nitrogen Cycle in the Environment

Abstract

The analysis of functional diversity and its dynamics in the environment is essential for understanding the microbial ecology and biogeochemistry of aquatic systems. Here we describe the development and optimization of a DNA microarray method for the detection and quantification of functional genes in the environment and report on their preliminary application to the study of the denitrification gene nirS in the Choptank River-Chesapeake Bay system. Intergenic and intragenic resolution constraints were determined by an oligonucleotide (70-mer) microarray approach. Complete signal separation was achieved when comparing unrelated genes within the nitrogen cycle (amoA, nifH, nirK, and nirS) and detecting different variants of the same gene, nirK, corresponding to organisms with two different physiological modes, ammonia oxidizers and denitrifying halobenzoate degraders. The limits of intragenic resolution were investigated with a microarray containing 64 nirS sequences comprising 14 cultured organisms and 50 clones obtained from the Choptank River in Maryland. The nirS oligonucleotides covered a range of sequence identities from approximately 40 to 100%. The threshold values for specificity were determined to be 87% sequence identity and a target-to-probe perfect match-to-mismatch binding free-energy ratio of 0.56. The lower detection limit was 10 pg of DNA (equivalent to approximately 10(7) copies) per target per microarray. Hybridization patterns on the microarray differed between sediment samples from two stations in the Choptank River, implying important differences in the composition of the denitirifer community along an environmental gradient of salinity, inorganic nitrogen, and dissolved organic carbon. This work establishes a useful set of design constraints (independent of the target gene) for the implementation of functional gene microarrays for environmental applications.

GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes

“…Microarrays are a recently developed, powerful genomic technology and are widely used to study gene expression (Schena et al, 1995;Lockhart et al, 1996;DeRisi et al, 1997;Liu et al, 2003;Gao et al, 2004), monitor environmental processes (Loy et al, 2002;Taroncher-Oldenburg et al, 2003;Zhou, 2003;Bodrossy and Sessitsch, 2004;Rhee et al, 2004;Steward et al, 2004;Tiquia et al, 2004;Zhou et al, 2004;Wu et al, 2006a), and potentially apply to clinic diagnosis (Lesko et al, 2003). Similar to the situation in which microprocessors have increased the speed of computation, microarraybased genomic technologies have revolutionized genetic analyses of biological systems.…”

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

“…The COMPO CHIP microarray was applied to the three compost types (green, manure mix, and anaerobic digestate composts), each sampled at different maturity stages (2, 8, and 16 weeks), so to determine if the array was able to discriminate between the various composts and to determine which bacteria were present in each of them. Because a linear correlation between signal intensity of a particular probe and target concentration has been reported by others [45,48], the results obtained with the COMPOCHIP microarray were analyzed semi-quantitatively. However, because different probes have different affinities for their targets and because of the inherent bias involved with PCR, the information on the relative abundance of different microorganisms derived from the microarrays needs to be interpreted with some caution.…”

Introduction to Microarray‐Based Detection Methods

“…pmoA, amoA, nifH, nirK, nirS). Many of these have been successfully applied on various microarray platforms [21,86,171,191]. The major limitation of these alternative marker genes is the limited organism coverage of published sequence databases.…”

“…According to the nature of the marker gene, one can further distinguish phylogenetic [30,110,145] and functional MDMs, also referred to as functional gene arrays [21,171,191].…”