Further issues in 16S rDNA analysis occur in steps prior to the sequencing. The usage of primers for the amplification may induce a bias towards the replicated fragments. Comparing the taxonomic classifications of 16S rDNA amplicon sequences with the profile of the metatranscriptome 16S rRNA fragments shows the absence of Methanobac- terium. So far, the reason for the absence of sequences in the amplicon dataset is not clear. A missing taxon may shift the relative abundances. Another issue is the phylo- genetic marker itself that may influence the relative abundances. In some microbes, multiple 16S rRNA genes occur. Thus, the amplicon sequences would overestimate the abundances of these species. A normalization of the taxonomic results would be necessary to obtain reliable abundances of the corresponding species. Although there are several pipelines and tools available, manual inspections were required during the performed analysis, as the tools have missed artifact sequences in the quality control step. Nevertheless, 16S rDNA amplicon sequencing is a valuable and cheap method to get a global view of the microbes that reside in a habitat of interest.
7.3 Analysis of a metatranscriptome of a biogas-producing
community by means of MeTra
The results of the first metatranscriptome approach of a biogas-producing community have been demonstrated in this thesis. In this respect, the pipeline MeTra has been de- signed that allows the annotation of different RNA types including rRNA, mRNA and non-coding RNA. Most of these were assigned to the phyla Firmicutes and Euryarchaeota. This tendency was confirmed by a profile based on expressed mRNA tags indicating that these phyla contribute most of the transcripts in the biogas plant. Transcripts for enzymes functioning in methanogenesis are among the most abundant mRNA tags indicating that the corresponding pathway is very active in the methanogenic sub- community. As the metatranscriptome was not enriched for mRNA tags, the number of sequences encoding proteins is very low. Nevertheless, genes for enzymes partici- pating in major steps of anaerobic digestion were identified among the mRNA tags. To obtain a deeper functional profile, mRNA enrichment or rRNA depletion would be required. Recently, techniques for removal of ribosomal RNA in a metatranscriptome RNA preparation were outlined [He et al., 2010a].
In addition, a detailed study of non-coding RNA was carried out. The identification of non-coding RNAs enables to broaden the research field of metatranscriptomics. Studies of non-coding RNAs in metatranscriptomes are very rare [Shi et al., 2009, Gosalbes et al., 2011]. However, the most abundant non-coding RNA families are also highly abundant in the transcriptome of microbial communities from differ- ent habitats [Shi et al., 2009, Gosalbes et al., 2011] as well as of single microorganisms [Block et al., 2011] indicating that the pipeline produces valuable results. Unfortunately, functional, non-coding RNAs are so far not well described making it challenging to decipher the regulation process based on non-coding RNAs. The taxonomic analysis
based on non-coding RNAs showed that they are highly transcribed by species par- ticipating in the anaerobic digestion process. Therefore, understanding the functions and regulations of non-coding RNAs in the biogas plant might help to reconstruct the biogas formation process and control the methane yield.
Several factors have to be considered as intrinsic biases during the metatranscriptome analysis implicating that the data might not represent the whole complexity of tran- scripts synthesized by a microbial community [Velculescu et al., 1995]. Transcript-based analysis may be influenced by the instability, rapid turnover and short cellular half-life of the RNA [Poretsky et al., 2005]. In addition, biases may be introduced during RNA extraction and enzymatic conversion of RNA into cDNA. As a consequence, functional and taxonomic information might remain unexplored in the data analysis. Similarly to the metagenome-based analysis, the taxonomic and functional profiles are dependent on existing databases, which are likely biased towards cultured species. Despite these pitfalls, metatranscriptomics is a potential approach to address questions regarding active organisms and functions of the biogas-producing community.
Overall, three approaches, namely whole metagenome shotgun, 16S rDNA amplicon and metatranscriptomics, were performed to study a biogas-producing community. Each of these approaches has advantages and disadvantages. However, a combination of the outcomes gives a comprehensive understanding of the organisms residing in a biogas plant and their metabolic functions.
7.4 Identi cation of laccases using hidden Markov models
Nature has invented a variety of enzymes, which are potentially useful for biotechno- logical applications. Instead of engineering industrially optimal enzymes, it is possible to search for genes of interest encoded by microorganisms that live in environments matching industrial conditions. Herein, a method based on profile hidden Markov models (HMM) [Eddy, 2011] has been designed and applied to identify genes en- coding laccases-like enzymes in metagenomes obtained from the biogas-producing community as well as an ocean sampling project. Such probabilistic models of pro- tein families are commonly used in the analysis of high-throughput sequencing data [Krause et al., 2008a, Pope et al., 2010]. The main advantage of a profile HMM-based approach is the high accuracy in detecting conserved domains compared to other methods such as BLAST. As laccase proteins are conserved in the four copper binding regions, the usage of profile HMMs is suitable for a sequence-based search.
Since salt- and pH-tolerant laccases are desired for industrial applications, marine metagenomes are promising to identify laccases with desired characteristics. Using in silico screenings, novel putative laccase genes were discovered that might be relevant for industrial applications. Moreover, reads were identified that covered all central regions of the small bacterial laccases (two-domain laccase). In the metagenome from