Coupling of Functional Gene Diversity and Geochemical Data from Environmental Samples

Abstract

Genomic techniques commonly used for assessing distributions of microorganisms in the environment often produce small sample sizes. We investigated artificial neural networks for analyzing the distributions of nitrite reductase genes (nirS and nirK) and two sets of dissimilatory sulfite reductase genes (dsrAB 1 and dsrAB 2 ) in small sample sets. Data reduction (to reduce the number of input parameters), cross-validation (to measure the generalization error), weight decay (to adjust model parameters to reduce generalization error), and importance analysis (to determine which variables had the most influence) were useful in developing and interpreting neural network models that could be used to infer relationships between geochemistry and gene distributions. A robust relationship was observed between geochemistry and the frequencies of genes that were not closely related to known dissimilatory sulfite reductase genes (dsrAB 2 ). Uranium and sulfate appeared to be the most related to distribution of two groups of these unusual dsrAB-related genes. For the other three groups, the distributions appeared to be related to pH, nickel, nonpurgeable organic carbon, and total organic carbon. The models relating the geochemical parameters to the distributions of the nirS, nirK, and dsrAB 1 genes did not generalize as well as the models for dsrAB 2 . The data also illustrate the danger (generating a model that has a high generalization error) of not using a validation approach in evaluating the meaningfulness of the fit of linear or nonlinear models to such small sample sizes.One of the goals of microbial ecology is to understand which abiotic factors control the abundance and distribution of microorganisms in the environment. Environmental microbial ecology is beginning to achieve this goal in a wide range of habitats (6,8,30,59) with the advent of molecular techniques that allow a significant part of the indigenous populations to be identified to some phylogenetic or functional level. For example, microbial distributions and diversity have been examined in relation to spatial factors (1), freshwater and ocean environments (51), and soil type (48, 50). Distribution or diversity has also been linked to dominant environmental characteristics or seasonal variations (29,43,57,63,68). To identify the critical factors that influence population distribution in complex environments, sophisticated data analysis techniques are needed to model the relationships between microbial distributions and environmental characteristics (14, 66).Cloning and sequencing of functional genes from environmental samples are powerful methods for investigating the ecology of microorganisms. These techniques have advanced our understanding of the types of microorganisms and degradation capabilities found in various habitats (6,12,15,43,51). However, relating the population data generated by these techniques to environmental characteristics, such as geochemical measurements, can be challenging. One problem is the small sample size that is typical in thes...

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