Anaerobic Carbon Monoxide Dehydrogenase Diversity in the Homoacetogenic Hindgut Microbial Communities of Lower Termites and the Wood Roach

Anaerobic carbon monoxide dehydrogenase (CODH) is a key enzyme in the Wood-Ljungdahl (acetyl-CoA) pathway for acetogenesis performed by homoacetogenic bacteria. Acetate generated by gut bacteria via the acetyl-CoA pathway provides considerable nutrition to wood-feeding dictyopteran insects making CODH important to the obligate mutualism occurring between termites and their hindgut microbiota. To investigate CODH diversity in insect gut communities, we developed the first degenerate primers designed to amplify cooS genes, which encode the catalytic (β) subunit of anaerobic CODH enzyme complexes. These primers target over 68 million combinations of potential forward and reverse cooS primer-binding sequences. We used the primers to identify cooS genes in bacterial isolates from the hindgut of a phylogenetically lower termite and to sample cooS diversity present in a variety of insect hindgut microbial communities including those of three phylogenetically-lower termites, Zootermopsis nevadensis, Reticulitermes hesperus, and Incisitermes minor, a wood-feeding cockroach, Cryptocercus punctulatus, and an omnivorous cockroach, Periplaneta americana. In total, we sequenced and analyzed 151 different cooS genes. These genes encode proteins that group within one of three highly divergent CODH phylogenetic clades. Each insect gut community contained CODH variants from all three of these clades. The patterns of CODH diversity in these communities likely reflect differences in enzyme or physiological function, and suggest that a diversity of microbial species participate in homoacetogenesis in these communities

Adaptive process and measurement noise identification for recursive Bayesian estimation

The optimality of recursive Bayesian estimators which have been extensively studied and implemented, for problems of state and parameter estimation, as well as for state estimation of systems with unknown inputs, is closely related to the quality of prior information about the process and measurement noise terms. These are typically treated as tuning parameters and therefore adjusted in an ad hoc and rather heuristic manner. Such an approach might be adequate for systems under stable environmental and operational conditions, but is proven insufficient for systems operating in a dynamic environment, where adaptive schemes are required. In this work, a new leave-one-out (LOO) metric is proposed for innovation-based adaptation of noise covariance matrices with the aim of robustly quantifying the actual model errors and properly describing the measurement-related uncertainties.ISSN:2191-5644ISSN:2191-565