Anaerobic oxidation of methane at a marine methane seep in a forearc sediment basin off Sumatra, Indian Ocean

A cold methane seep was discovered in a forearc sediment basin off the island Sumatra, exhibiting a methane-seep adapted microbial community. A defined seep center of activity, like in mud volcanoes, was not discovered. The seep area was rather characterized by a patchy distribution of active spots. The relevance of anaerobic oxidation of methane (AOM) was reflected by C-13-depleted isotopic signatures of dissolved inorganic carbon. The anaerobic conversion of methane to CO2 was confirmed in a C-13-labeling experiment. Methane fueled a vital microbial community with cell numbers of up to 4 x 10(9) cells cm(-3) sediment. The microbial community was analyzed by total cell counting, catalyzed reporter deposition fluorescence in situ hybridization (CARD FISH), quantitative real-time PCR (qPCR), and denaturing gradient gel electrophoresis (DGGE). CARD FISH cell counts and qPCR measurements showed the presence of Bacteria and Archaea, but only small numbers of Eukarya. The archaeal community comprised largely members of ANME-1 and ANME-2. Furthermore, members of the Crenarchaeota were frequently detected in the DGGE analysis. Three major bacterial phylogenetic groups (delta-Proteobacteria, candidate division OP9, and Anaerolineaceae) were abundant across the study area. Several of these sequences were closely related to the genus Desulfococcus of the family Desulfobacteraceae, which is in good agreement with previously described AOM sites. In conclusion, the majority of the microbial community at the seep consisted of AOM-related microorganisms, while the relevance of higher hydrocarbons as microbial substrates was negligible.

Identification of superior reference genes for data normalisation of expression studies via quantitative PCR in hybrid roses (Rosa hybrida)

Background: Gene expression studies are a prerequisite for understanding the biological function of genes. Because of its high sensitivity and easy use, quantitative PCR (qPCR) has become the gold standard for gene expression quantification. To normalise qPCR measurements between samples, the most prominent technique is the use of stably expressed endogenous control genes, the so called reference genes. However, recent studies show there is no universal reference gene for all biological questions. Roses are important ornamental plants for which there has been no evaluation of useful reference genes for gene expression studies. Results: We used three different algorithms (BestKeeper, geNorm and NormFinder) to validate the expression stability of nine candidate reference genes in different rose tissues from three different genotypes of Rosa hybrida and in leaves treated with various stress factors. The candidate genes comprised the classical "housekeeping genes" (Actin, EF-1α, GAPDH, Tubulin and Ubiquitin), and genes showing stable expression in studies in Arabidopsis (PP2A, SAND, TIP and UBC). The programs identified no single gene that showed stable expression under all of the conditions tested, and the individual rankings of the genes differed between the algorithms. Nevertheless the new candidate genes, specifically, PP2A and UBC, were ranked higher as compared to the other traditional reference genes. In general, Tubulin showed the most variable expression and should be avoided as a reference gene. Conclusions: Reference genes evaluated as suitable in experiments with Arabidopsis thaliana were stably expressed in roses under various experimental conditions. In most cases, these genes outperformed conventional reference genes, such as EF1-α and Tubulin. We identified PP2A, SAND and UBC as suitable reference genes, which in different combinations may be used for normalisation in expression analyses via qPCR for different rose tissues and stress treatments. However, the vast genetic variation found within the genus Rosa, including differences in ploidy levels, might also influence expression stability of reference genes, so that future research should also consider different genotypes and ploidy levels.