Comparison of gene expression of Paramecium bursaria with and without Chlorella variabilis symbionts.

BACKGROUND: The ciliate Paramecium bursaria harbors several hundred cells of the green-alga Chlorella sp. in their cytoplasm. Irrespective of the mutual relation between P. bursaria and the symbiotic algae, both cells retain the ability to grow without the partner. They can easily reestablish endosymbiosis when put in contact with each other. Consequently, P. bursaria is an excellent model for studying cell-cell interaction and the evolution of eukaryotic cells through secondary endosymbiosis between different protists. Despite the importance of this organism, no genomic resources have been identified for P. bursaria to date. This investigation compared gene expressions through RNA-Seq analysis and de novo transcriptome assembly of symbiont-free and symbiont-bearing host cells. RESULTS: To expedite the process of gene discovery related to the endosymbiosis, we have undertaken Illumina deep sequencing of mRNAs prepared from symbiont-bearing and symbiont-free P. bursaria cells. We assembled the reads de novo to build the transcriptome. Sequencing using Illumina HiSeq2000 platform yielded 232.3 million paired-end sequence reads. Clean reads filtered from the raw reads were assembled into 68,175 contig sequences. Of these, 10,557 representative sequences were retained after removing Chlorella sequences and lowly expressed sequences. Nearly 90% of these transcript sequences were annotated by similarity search against protein databases. We identified differentially expressed genes in the symbiont-bearing P. bursaria cells relative to the symbiont-free cells, including heat shock 70 kDa protein and glutathione S-transferase. CONCLUSIONS: This is the first reported comprehensive sequence resource of Paramecium - Chlorella endosymbiosis. Results provide some keys for the elucidation of secondary endosymbiosis in P. bursaria. We identified P. bursaria genes that are differentially expressed in symbiont-bearing and symbiont-free conditions.

Genome-wide analysis of Chlamydophila pneumoniae gene expression at the late stage of infection.

Chlamydophila pneumoniae, an obligate intracellular eubacterium, changes its form from a vegetative reticulate body into an infectious elementary body during the late stage of its infection cycle. Comprehension of the molecular events in the morphological change is important to understand the switching mechanism between acute and chronic infection, which is deemed to relate to the pathogenesis of atherosclerosis. Herein, we have attempted to screen genes expressed in the late stage with a genome-wide DNA microarray, resulting in nomination of 17 genes as the late-stage genes. Fourteen of the 17 genes and six other genes predicted as late-stage genes were confirmed to be up-regulated in the late stage with a quantitative reverse transcriptase-polymerase chain reaction. These 20 late-stage genes were classified into two groups by clustering analysis: 'drastically induced' and 'moderately induced' genes. Out of eight drastically induced genes, four contain sigma(28) promoter-like sequences and the other four contain an upstream common sequence. It suggests that besides sigma(28), there are certain up-regulatory mechanisms at the late stage, which may be involved in the chlamydial morphological change and thus pathogenesis.

Performance improvement in protein N-myristoyl classification by BONSAI with insignificant indexing symbol.

Many N-myristoylated proteins play key roles in regulating cellular structure and function. In the previous study, we have applied the machine learning system BONSAI to predict patterns based on which positive and negative examples could be classified. Although BONSAI has helped establish 2 interesting rules regarding the requirements for N-myristoylation, the accuracy rates of these rules are not satisfactory. This paper suggests an enhancement of BONSAI by introducing an "insignificant indexing symbol" and demonstrates the efficiency of this enhancement by showing an improvement in the accuracy rates. We further examine the performance of this enhanced BONSAI by comparing the results of classification obtained the proposed method and an existing public method for the same sets of positive and negative examples.