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Journal ArticleDOI

The genome sequence of an anaerobic aromatic-degrading denitrifying bacterium, strain EbN1.

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TLDR
The first complete genome of a metabolically versatile representative, strain EbN1, which metabolizes various aromatic compounds, including hydrocarbons is presented, which indicates a finely tuned regulatory network able to respond to the fluctuating availability of organic substrates and electron acceptors in the environment.
Abstract
Recent research on microbial degradation of aromatic and other refractory compounds in anoxic waters and soils has revealed that nitrate-reducing bacteria belonging to the Betaproteobacteria contribute substantially to this process. Here we present the first complete genome of a metabolically versatile representative, strain EbN1, which metabolizes various aromatic compounds, including hydrocarbons. A circular chromosome (4.3 Mb) and two plasmids (0.21 and 0.22 Mb) encode 4603 predicted proteins. Ten anaerobic and four aerobic aromatic degradation pathways were recognized, with the encoding genes mostly forming clusters. The presence of paralogous gene clusters (e.g., for anaerobic phenylacetate oxidation), high sequence similarities to orthologs from other strains (e.g., for anaerobic phenol metabolism) and frequent mobile genetic elements (e.g., more than 200 genes for transposases) suggest high genome plasticity and extensive lateral gene transfer during metabolic evolution of strain EbN1. Metabolic versatility is also reflected by the presence of multiple respiratory complexes. A large number of regulators, including more than 30 two-component and several FNR-type regulators, indicate a finely tuned regulatory network able to respond to the fluctuating availability of organic substrates and electron acceptors in the environment. The absence of genes required for nitrogen fixation and specific interaction with plants separates strain EbN1 ecophysiologically from the closely related nitrogen-fixing plant symbionts of the Azoarcus cluster. Supplementary material on sequence and annotation are provided at the Web page http://www.micro-genomes.mpg.de/ebn1/.

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Indole-3-acetic acid in microbial and microorganism-plant signaling.

TL;DR: The fact that bacteria use this phytohormone to interact with plants as part of their colonization strategy, including phyto-stimulation and circumvention of basal plant defense mechanisms, is highlighted.
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Marine microorganisms make a meal of oil

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Microbial degradation of aromatic compounds — from one strategy to four

TL;DR: These strategies are based on different ring activation mechanisms that consist of either formation of a non-aromatic ring-epoxide under oxic conditions, or reduction of the aromatic ring under anoxic conditions using one of two completely different systems.
Book ChapterDOI

Geobacter: The Microbe Electric's Physiology, Ecology, and Practical Applications

TL;DR: The study of Geobacter species has revealed a remarkable number of microbial physiological properties that had not previously been described in any microorganism, which might contribute to the field of bioelectronics.
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Anaerobic Catabolism of Aromatic Compounds: a Genetic and Genomic View

TL;DR: This review focuses on the recent findings that standard molecular biology approaches together with new high-throughput technologies have provided regarding the genetics, regulation, ecophysiology, and evolution of anaerobic aromatic degradation pathways, revealing that the anaerobia catabolism of aromatic compounds is more diverse and widespread than previously thought.
References
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TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Journal ArticleDOI

Base-calling of automated sequencer traces using Phred. I. accuracy assessment

TL;DR: In this article, a base-calling program for automated sequencer traces, phred, with improved accuracy was proposed. But it was not shown to achieve a lower error rate than the ABI software, averaging 40%-50% fewer errors in the data sets examined independent of position in read, machine running conditions, or sequencing chemistry.
Journal ArticleDOI

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

TL;DR: A new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence that performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets.
Journal ArticleDOI

Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities

TL;DR: The ability to estimate a probability of error for each base-call, as a function of certain parameters computed from the trace data, is developed and implemented in the base-calling program.

SHORT COMMUNICATION Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

TL;DR: In this paper, a new method for the identification of in performance compared with the weight matrix method signal peptides and their cleavage sites based on neural (Arrigo et al., 1991; Ladunga et al, 1991; Schneider and networks trained on separate sets of prokaryotic and eukaryotic sequence.
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