Category: Papers by CGEB labs

Gene Loss and Error-Prone RNA Editing in the Mitochondrion of Perkinsela, an Endosymbiotic Kinetoplastid.

MBio. 2015;6(6)

Authors: David V, Flegontov P, Gerasimov E, Tanifuji G, Hashimi H, Logacheva MD, Maruyama S, Onodera NT, Gray MW, Archibald JM, Lukeš J

Abstract
UNLABELLED: Perkinsela is an enigmatic early-branching kinetoplastid protist that lives as an obligate endosymbiont inside Paramoeba (Amoebozoa). We have sequenced the highly reduced mitochondrial genome of Perkinsela, which possesses only six protein-coding genes (cox1, cox2, cox3, cob, atp6, and rps12), despite the fact that the organelle itself contains more DNA than is present in either the host or endosymbiont nuclear genomes. An in silico analysis of two Perkinsela strains showed that mitochondrial RNA editing and processing machineries typical of kinetoplastid flagellates are generally conserved, and all mitochondrial transcripts undergo U-insertion/deletion editing. Canonical kinetoplastid mitochondrial ribosomes are also present. We have developed software tools for accurate and exhaustive mapping of transcriptome sequencing (RNA-seq) reads with extensive U-insertions/deletions, which allows detailed investigation of RNA editing via deep sequencing. With these methods, we show that up to 50% of reads for a given edited region contain errors of the editing system or, less likely, correspond to alternatively edited transcripts.
IMPORTANCE: Uridine insertion/deletion-type RNA editing, which occurs in the mitochondrion of kinetoplastid protists, has been well-studied in the model parasite genera Trypanosoma, Leishmania, and Crithidia. Perkinsela provides a unique opportunity to broaden our knowledge of RNA editing machinery from an evolutionary perspective, as it represents the earliest kinetoplastid branch and is an obligatory endosymbiont with extensive reductive trends. Interestingly, up to 50% of mitochondrial transcripts in Perkinsela contain errors. Our study was complemented by use of newly developed software designed for accurate mapping of extensively edited RNA-seq reads obtained by deep sequencing.

PMID: 26628723 [PubMed – in process]

Molecular Organization of the 5s rDna Gene Type II in Elasmobranchs.

RNA Biol. 2015 Oct 21;:0

Authors: Castro SI, Hleap JS, Cárdenas H, Blouin C

Abstract
The 5S rDNA gene is a non-coding RNA that can be found in two copies (type I and type II) in bony and cartilaginous fish. Previous studies have pointed out that type II gene is a paralog derived from type I. We analyzed the molecular organization of 5S rDNA type II in elasmobranchs. Although the structure of the 5S rDNA is supposed to be highly conserved, our results show that the secondary structure in this group possesses some variability and is different than the consensus secondary structure. One of these differences in Selachii is an internal loop at nucleotides 7 and 112. These mutations observed in the transcribed region suggest an independent origin of the gene among Batoids and Selachii. All promoters were highly conserved with the exception of BoxA, possibly due to its affinity to polymerase III. This latter enzyme recognizes a dT4 sequence as stop signal, however in Rajiformes this signal was doubled in length to dT8. This could be an adaptation towards a higher efficiency in the termination process. Our results suggest that there is no TATA box in elasmobranchs in the NTS region. We also provide some evidence suggesting that the complexity of the microsatellites present in the NTS region play an important role in the 5S rRNA gene since it is significantly correlated with the length of the NTS.

PMID: 26488198 [PubMed – as supplied by publisher]

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Phylogenetic approaches to microbial community classification.

Microbiome. 2015;3(1):47

Authors: Ning J, Beiko RG

Abstract
BACKGROUND: The microbiota from different body sites are dominated by different major groups of microbes, but the variations within a body site such as the mouth can be more subtle. Accurate predictive models can serve as useful tools for distinguishing sub-sites and understanding key organisms and their roles and can highlight deviations from expected distributions of microbes. Good classification depends on choosing the right combination of classifier, feature representation, and learning model. Machine-learning procedures have been used in the past for supervised classification, but increased attention to feature representation and selection may produce better models and predictions.
RESULTS: We focused our attention on the classification of nine oral sites and dental plaque in particular, using data collected from the Human Microbiome Project. A key focus of our representations was the use of phylogenetic information, both as the basis for custom kernels and as a way to represent sets of microbes to the classifier. We also used the PICRUSt software, which draws on phylogenetic relationships to predict molecular functions and to generate additional features for the classifier. Custom kernels based on the UniFrac measure of community dissimilarity did not improve performance. However, feature representation was vital to classification accuracy, with microbial clade and function representations providing useful information to the classifier; combining the two types of features did not yield increased prediction accuracy. Many of the best-performing clades and functions had clear associations with oral microflora.
CONCLUSIONS: The classification of oral microbiota remains a challenging problem; our best accuracy on the plaque dataset was approximately 81 %. Perfect accuracy may be unattainable due to the close proximity of the sites and intra-individual variation. However, further exploration of the space of both classifiers and feature representations is likely to increase the accuracy of predictive models.

PMID: 26437943 [PubMed – in process]