Protective symbiosis has been reported in many organisms, but the molecular mechanisms of the mutualistic interactions between the symbionts and their hosts are unclear. Here, we sequenced the 424-kbp genome of " Spiroplasma holothuricola," which dominated the hindgut microbiome of a sea cucumber, a major scavenger captured in the Mariana Trench (6,140 m depth). Phylogenetic relationships indicated that the dominant bacterium in the hindgut was derived from a basal group of species. In this organism, the genes responsible for the biosynthesis of amino acids, glycolysis, and sugar transporters were lost, strongly suggesting endosymbiosis. The highly decayed genome consists of two chromosomes and harbors genes coding for proteolysis, microbial toxin, restriction-methylation systems, and clustered regularly interspaced short palindromic repeats (CRISPRs), composed of three genes and 76 CRISPR spacers. The holothurian host is probably protected against invading viruses from sediments by the CRISPRs/Cas and restriction systems of the endosymbiotic spiroplasma. The protective endosymbiosis indicates the important ecological role of the ancient symbiont in the maintenance of hadal ecosystems. Sea cucumbers are major inhabitants in hadal trenches. They collect microbes in surface sediment and remain tolerant against potential pathogenic bacteria and viruses. This study presents the genome of endosymbiotic spiroplasmas in the gut of a sea cucumber captured in the Mariana Trench. The extreme reduction of the genome and loss of essential metabolic pathways strongly support its endosymbiotic lifestyle. Moreover, a considerable part of the genome was occupied by a CRISPR/Cas system to provide immunity against viruses and antimicrobial toxin-encoding genes for the degradation of microbes. This novel species of is probably an important protective symbiont for the sea cucumbers in the hadal zone.
Gathering vast data sets of cancer genomes requires more efficient and autonomous procedures to classify cancer types and to discover a few essential genes to distinguish different cancers. Because protein expression is more stable than gene expression, we chose reverse phase protein array (RPPA) data, a powerful and robust antibody-based high-throughput approach for targeted proteomics, to perform our research. In this study, we proposed a computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method. A careful feature selection procedure was employed to select 23 important proteins from the total of 187 proteins by mRMR (minimum Redundancy Maximum Relevance Feature Selection) and IFS (Incremental Feature Selection) on the training set. By using the 23 proteins, we successfully classified the ten cancer types with an MCC (Matthews Correlation Coefficient) of 0.904 on the training set, evaluated by 10-fold cross-validation, and an MCC of 0.936 on an independent test set. Further analysis of these 23 proteins was performed. Most of these proteins can present the hallmarks of cancer; Chk2, for example, plays an important role in the proliferation of cancer cells. Our analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification. We also believe our methods and findings may shed light on the discoveries of specific biomarkers of different types of cancers.
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