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Characterization and genomic analysis of kraft lignin biodegradation by the beta-proteobacterium Cupriavidus basilensis B-8

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      Lignin materials are abundant and among the most important potential sources for biofuel production. Development of an efficient lignin degradation process has considerable potential for the production of a variety of chemicals, including bioethanol. However, lignin degradation using current methods is inefficient. Given their immense environmental adaptability and biochemical versatility, bacterial could be used as a valuable tool for the rapid degradation of lignin. Kraft lignin (KL) is a polymer by-product of the pulp and paper industry resulting from alkaline sulfide treatment of lignocellulose, and it has been widely used for lignin-related studies.


      Beta-proteobacterium Cupriavidus basilensis B-8 isolated from erosive bamboo slips displayed substantial KL degradation capability. With initial concentrations of 0.5–6 g L -1, at least 31.3% KL could be degraded in 7 days. The maximum degradation rate was 44.4% at the initial concentration of 2 g L -1. The optimum pH and temperature for KL degradation were 7.0 and 30°C, respectively. Manganese peroxidase (MnP) and laccase (Lac) demonstrated their greatest level of activity, 1685.3 U L -1 and 815.6 U L -1, at the third and fourth days, respectively. Many small molecule intermediates were formed during the process of KL degradation, as determined using GC-MS analysis. In order to perform metabolic reconstruction of lignin degradation in this bacterium, a draft genome sequence for C. basilensis B-8 was generated. Genomic analysis focused on the catabolic potential of this bacterium against several lignin-derived compounds. These analyses together with sequence comparisons predicted the existence of three major metabolic pathways: β-ketoadipate, phenol degradation, and gentisate pathways.


      These results confirmed the capability of C. basilensis B-8 to promote KL degradation. Whole genomic sequencing and systematic analysis of the C. basilensis B-8 genome identified degradation steps and intermediates from this bacterial-mediated KL degradation method. Our findings provide a theoretical basis for research into the mechanisms of lignin degradation as well as a practical basis for biofuel production using lignin materials.

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      Most cited references 39

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      RNAmmer: consistent and rapid annotation of ribosomal RNA genes

      The publication of a complete genome sequence is usually accompanied by annotations of its genes. In contrast to protein coding genes, genes for ribosomal RNA (rRNA) are often poorly or inconsistently annotated. This makes comparative studies based on rRNA genes difficult. We have therefore created computational predictors for the major rRNA species from all kingdoms of life and compiled them into a program called RNAmmer. The program uses hidden Markov models trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project. A pre-screening step makes the method fast with little loss of sensitivity, enabling the analysis of a complete bacterial genome in less than a minute. Results from running RNAmmer on a large set of genomes indicate that the location of rRNAs can be predicted with a very high level of accuracy. Novel, unannotated rRNAs are also predicted in many genomes. The software as well as the genome analysis results are available at the CBS web server.
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        Identifying bacterial genes and endosymbiont DNA with Glimmer.

        The Glimmer gene-finding software has been successfully used for finding genes in bacteria, archaea and viruses representing hundreds of species. We describe several major changes to the Glimmer system, including improved methods for identifying both coding regions and start codons. We also describe a new module of Glimmer that can distinguish host and endosymbiont DNA. This module was developed in response to the discovery that eukaryotic genome sequencing projects sometimes inadvertently capture the DNA of intracellular bacteria living in the host. The new methods dramatically reduce the rate of false-positive predictions, while maintaining Glimmer's 99% sensitivity rate at detecting genes in most species, and they find substantially more correct start sites, as measured by comparisons to known and well-curated genes. We show that our interpolated Markov model (IMM) DNA discriminator correctly separated 99% of the sequences in a recent genome project that produced a mixture of sequences from the bacterium Prochloron didemni and its sea squirt host, Lissoclinum patella. Glimmer is OSI Certified Open Source and available at
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          The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs

          Transfer RNAs (tRNAs) and small nucleolar RNAs (snoRNAs) are two of the largest classes of non-protein-coding RNAs. Conventional gene finders that detect protein-coding genes do not find tRNA and snoRNA genes because they lack the codon structure and statistical signatures of protein-coding genes. Previously, we developed tRNAscan-SE, snoscan and snoGPS for the detection of tRNAs, methylation-guide snoRNAs and pseudouridylation-guide snoRNAs, respectively. tRNAscan-SE is routinely applied to completed genomes, resulting in the identification of thousands of tRNA genes. Snoscan has successfully detected methylation-guide snoRNAs in a variety of eukaryotes and archaea, and snoGPS has identified novel pseudouridylation-guide snoRNAs in yeast and mammals. Although these programs have been quite successful at RNA gene detection, their use has been limited by the need to install and configure the software packages on UNIX workstations. Here, we describe online implementations of these RNA detection tools that make these programs accessible to a wider range of research biologists. The tRNAscan-SE, snoscan and snoGPS servers are available at , and , respectively.

            Author and article information

            [1 ]School of Metallurgical Science and Engineering, Central South University, Changsha, 410017, PR China
            [2 ]Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha, 410017, PR China
            Biotechnol Biofuels
            Biotechnol Biofuels
            Biotechnology for Biofuels
            BioMed Central
            8 January 2013
            : 6
            : 1
            Copyright ©2013 Shi et al.; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



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