Novel copper-containing membrane monooxygenases (CuMMOs) encoded by alkane-utilizing Betaproteobacteria.

Copper-containing membrane monooxygenases (CuMMOs) are encoded by xmoCAB(D) gene clusters and catalyze the oxidation of methane, ammonia, or some short-chain alkanes and alkenes. In a metagenome constructed from an oilsands tailings pond we detected an xmoCABD gene cluster with <59% derived protein sequence identity to genes from known bacteria. Stable isotope probing experiments combined with a specific xmoA qPCR assay demonstrated that the bacteria possessing these genes were incapable of methane assimilation, but did grow on ethane and propane. Single-cell amplified genomes (SAGs) from propane-enriched samples were screened with the specific PCR assay to identify bacteria possessing the target gene cluster. Multiple SAGs of Betaproteobacteria belonging to the genera Rhodoferax and Polaromonas possessed homologues of the metagenomic xmoCABD gene cluster. Unexpectedly, each of these two genera also possessed other xmoCABD paralogs, representing two additional lineages in phylogenetic analyses. Metabolic reconstructions from SAGs predicted that neither bacterium encoded enzymes with the potential to support catabolic methane or ammonia oxidation, but that both were capable of higher n-alkane degradation. The involvement of the encoded CuMMOs in alkane oxidation was further suggested by reverse transcription PCR analyses, which detected elevated transcription of the xmoA genes upon enrichment of water samples with propane as the sole energy source. Enrichments, isotope incorporation studies, genome reconstructions, and gene expression studies therefore all agreed that the unknown xmoCABD operons did not encode methane or ammonia monooxygenases, but rather n-alkane monooxygenases. This study broadens the known diversity of CuMMOs and identifies these enzymes in non-nitrifying Betaproteobacteria

The effect of switchgrass loadings on feedstock solubilization and biofuel production by Clostridium thermocellum

Abstract Background Efficient deconstruction and bioconversion of solids at high mass loadings is necessary to produce industrially relevant titers of biofuels from lignocellulosic biomass. To date, only a few studies have investigated the effect of solids loadings on microorganisms of interest for consolidated bioprocessing. Here, the effects that various switchgrass loadings have on Clostridium thermocellum solubilization and bioconversion are investigated. Results Clostridium thermocellum was grown for 10 days on 10, 25, or 50 g/L switchgrass or Avicel at equivalent glucan loadings. Avicel was completely consumed at all loadings, but total cellulose solubilization decreased from 63 to 37% as switchgrass loadings increased from 10 to 50 g/L. Washed, spent switchgrass could be additionally hydrolyzed and fermented in second-round fermentations suggesting that access to fermentable substrates was not the limiting factor at higher feedstock loadings. Results from fermentations on Avicel or cellobiose using culture medium supplemented with 50% spent fermentation broth demonstrated that compounds present in the supernatants from the 25 or 50 g/L switchgrass loadings were the most inhibitory to continued fermentation. Conclusions Recalcitrance alone cannot fully account for differences in solubilization and end-product formation between switchgrass and Avicel at increased substrate loadings. Experiments aimed at separating metabolic inhibition from inhibition of hydrolysis suggest that C. thermocellum’s hydrolytic machinery is more vulnerable to inhibition from switchgrass-derived compounds than its fermentative metabolism

Linking genome content to biofuel production yields: a meta-analysis of major catabolic pathways among select H₂ and ethanol-producing bacteria

Abstract Background Fermentative bacteria offer the potential to convert lignocellulosic waste-streams into biofuels such as hydrogen (H2) and ethanol. Current fermentative H2 and ethanol yields, however, are below theoretical maxima, vary greatly among organisms, and depend on the extent of metabolic pathways utilized. For fermentative H2 and/or ethanol production to become practical, biofuel yields must be increased. We performed a comparative meta-analysis of (i) reported end-product yields, and (ii) genes encoding pyruvate metabolism and end-product synthesis pathways to identify suitable biomarkers for screening a microorganism’s potential of H2 and/or ethanol production, and to identify targets for metabolic engineering to improve biofuel yields. Our interest in H2 and/or ethanol optimization restricted our meta-analysis to organisms with sequenced genomes and limited branched end-product pathways. These included members of the Firmicutes, Euryarchaeota, and Thermotogae. Results Bioinformatic analysis revealed that the absence of genes encoding acetaldehyde dehydrogenase and bifunctional acetaldehyde/alcohol dehydrogenase (AdhE) in Caldicellulosiruptor, Thermococcus, Pyrococcus, and Thermotoga species coincide with high H2 yields and low ethanol production. Organisms containing genes (or activities) for both ethanol and H2 synthesis pathways (i.e. Caldanaerobacter subterraneus subsp. tengcongensis, Ethanoligenens harbinense, and Clostridium species) had relatively uniform mixed product patterns. The absence of hydrogenases in Geobacillus and Bacillus species did not confer high ethanol production, but rather high lactate production. Only Thermoanaerobacter pseudethanolicus produced relatively high ethanol and low H2 yields. This may be attributed to the presence of genes encoding proteins that promote NADH production. Lactate dehydrogenase and pyruvate:formate lyase are not conducive for ethanol and/or H2 production. While the type(s) of encoded hydrogenases appear to have little impact on H2 production in organisms that do not encode ethanol producing pathways, they do influence reduced end-product yields in those that do. Conclusions Here we show that composition of genes encoding pathways involved in pyruvate catabolism and end-product synthesis pathways can be used to approximate potential end-product distribution patterns. We have identified a number of genetic biomarkers for streamlining ethanol and H2 producing capabilities. By linking genome content, reaction thermodynamics, and end-product yields, we offer potential targets for optimization of either ethanol or H2 yields through metabolic engineering.</p

Comparative Analysis of Carbohydrate Active Enzymes in <i>Clostridium termitidis</i> CT1112 Reveals Complex Carbohydrate Degradation Ability

<div><p><i>Clostridium termitidis</i> strain CT1112 is an anaerobic, gram positive, mesophilic, cellulolytic bacillus isolated from the gut of the wood-feeding termite, <i>Nasutitermes lujae</i>. It produces biofuels such as hydrogen and ethanol from cellulose, cellobiose, xylan, xylose, glucose, and other sugars, and therefore could be used for biofuel production from biomass through consolidated bioprocessing. The first step in the production of biofuel from biomass by microorganisms is the hydrolysis of complex carbohydrates present in biomass. This is achieved through the presence of a repertoire of secreted or complexed carbohydrate active enzymes (CAZymes), sometimes organized in an extracellular organelle called cellulosome. To assess the ability and understand the mechanism of polysaccharide hydrolysis in <i>C. termitidis</i>, the recently sequenced strain CT1112 of <i>C. termitidis</i> was analyzed for both CAZymes and cellulosomal components, and compared to other cellulolytic bacteria. A total of 355 CAZyme sequences were identified in <i>C. termitidis</i>, significantly higher than other Clostridial species. Of these, high numbers of glycoside hydrolases (199) and carbohydrate binding modules (95) were identified. The presence of a variety of CAZymes involved with polysaccharide utilization/degradation ability suggests hydrolysis potential for a wide range of polysaccharides. In addition, dockerin-bearing enzymes, cohesion domains and a cellulosomal gene cluster were identified, indicating the presence of potential cellulosome assembly.</p></div

Phylogenetic analysis of selected Clostridial species based on <i>cpn60</i> gene sequences.

<p>The phylogenetic tree was obtained using neighbor-joining <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104260#pone.0104260-Saitou1" target="_blank">[29]</a> provided in MEGA version 4 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104260#pone.0104260-Tamura1" target="_blank">[30]</a>. Bootstrap tests with 1000 replications were conducted to examine the reliability of the interior branches. Asterisks (*) indicates the other <i>Clostridium</i> species used in CAZy comparison.</p

Predicted extracellular glycoside hydrolases of <i>C.termitidis</i> based on PSORTb.3 analysis.

<p>Bold: GH genes with dockerin domain.</p><p>^*: Genes with SLH domain.</p

Modular structure of putative cohesin I domain containing proteins identified in the <i>C. termitidis</i> CT1112 genome.

<p>(a) Cter_0001; (b) Cter_0520; (c) Cter_0526; (d) Cter_3731, and, (e) Cter_0525. CBM3-carbohydrate binding module. X2- domain of unknown function which may play a role in attachment of the putative cellulosome to the cell wall. Cohesin I proteins have dockerin binding surfaces, which bind cellulosomal enzymes and are considered important in the formation of a cellulosome. Cohesins a, c and d show putative truncated ends. Cohesins b, c and e are components of a putative cellulosome related gene cluster.</p

Cellulosome components of <i>C. thermocellum</i>.

<p>Enzymatic components (colored differently to indicate enzyme variety) produced by anaerobic bacteria contain a dockerin domain. Dockerins bind the cohesins of a non-catalytic scaffoldin, providing a mechanism for cellulosome assembly. Scaffoldins also contain a cellulose-specific family 3 CBM (cellulose binding module) and a C-terminal dockerin domain II that targets the cellulosome to cellulose and the bacterial cell envelope, respectively.</p

General features of genomes of select <i>Clostridium</i> species.

<p>*Based on PSORTb 3.0 prediction and includes GHs, PLs, and CEs.</p