Recurrent fusion transcripts detected by whole-transcriptome sequencing of 120 primary breast cancer samples.

Relatively few recurrent gene fusion events have been associated with breast cancer to date. In an effort to uncover novel fusion transcripts, we performed whole-transcriptome sequencing of 120 fresh-frozen primary breast cancer samples and five adjacent normal breast tissues using the Illumina HiSeq2000 platform. Three different fusion-detecting tools (deFuse, Chimerascan, and TopHatFusion) were used, and the results were compared. These tools detected 3,831, 6,630 and 516 fusion transcripts (FTs) overall. We primarily focused on the results obtained using the deFuse software. More FTs were identified from HER2 subtype breast cancer samples than from the luminal or triple-negative subtypes (P < 0.05). Seventy fusion candidates were selected for validation, and 32 (45.7%) were confirmed by RT-PCR and Sanger sequencing. Of the validated fusions, six were recurrent (found in 2 or more samples), three were in-frame (PRDX1-AKR1A1, TACSTD2-OMA1, and C2CD2-TFF1) and three were off-frame (CEACAM7-CEACAM6, CYP4X1-CYP4Z2P, and EEF1DP3-FRY). Notably, the novel read-through fusion, EEF1DP3-FRY, was identified and validated in 6.7% (8/120) of the breast cancer samples. This off-frame fusion results in early truncation of the FRY gene, which plays a key role in the structural integrity during mitosis. Three previously reported fusions, PPP1R1B-STARD3, MFGE8-HAPL, and ETV6-NTRK3, were detected in 8.3, 3.3, and 0.8% of the 120 samples, respectively, by both deFuse and Chimerascan. The recently reported MAGI3-AKT3 fusion was not detected in our analysis. Although future work will be needed to examine the biological significance of our new findings, we identified a number of novel fusions and confirmed some previously reported fusions.

Genome-scale DNA methylation pattern profiling of human bone marrow mesenchymal stem cells in long-term culture.

Human bone marrow mesenchymal stem cells (MSCs) expanded in vitro exhibit not only a tendency to lose their proliferative potential, homing ability and telomere length but also genetic or epigenetic modifications, resulting in senescence. We compared differential methylation patterns of genes and miRNAs between early-passage [passage 5 (P5)] and late-passage (P15) cells and estimated the relationship between senescence and DNA methylation patterns. When we examined hypermethylated genes (methylation peak ≥ 2) at P5 or P15, 2,739 genes, including those related to fructose and mannose metabolism and calcium signaling pathways, and 2,587 genes, including those related to DNA replication, cell cycle and the PPAR signaling pathway, were hypermethylated at P5 and P15, respectively. There was common hypermethylation of 1,205 genes at both P5 and P15. In addition, genes that were hypermethylated at P5 (CPEB1, GMPPA, CDKN1A, TBX2, SMAD9 and MCM2) showed lower mRNA expression than did those hypermethylated at P15, whereas genes that were hypermethylated at P15 (MAML2, FEN1 and CDK4) showed lower mRNA expression than did those that were hypermethylated at P5, demonstrating that hypermethylation at DNA promoter regions inhibited gene expression and that hypomethylation increased gene expression. In the case of hypermethylation on miRNA, 27 miRNAs were hypermethylated at P5, whereas 44 miRNAs were hypermethylated at P15. These results show that hypermethylation increases at genes related to DNA replication, cell cycle and adipogenic differentiation due to long-term culture, which may in part affect MSC senescence.

Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis.

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required.

The genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens.

The rumen represents the first section of a ruminant animal's stomach, where feed is collected and mixed with microorganisms for initial digestion. The major gas produced in the rumen is CO(2) (65.5 mol%), yet the metabolic characteristics of capnophilic (CO(2)-loving) microorganisms are not well understood. Here we report the 2,314,078 base pair genome sequence of Mannheimia succiniciproducens MBEL55E, a recently isolated capnophilic Gram-negative bacterium from bovine rumen, and analyze its genome contents and metabolic characteristics. The metabolism of M. succiniciproducens was found to be well adapted to the oxygen-free rumen by using fumarate as a major electron acceptor. Genome-scale metabolic flux analysis indicated that CO(2) is important for the carboxylation of phosphoenolpyruvate to oxaloacetate, which is converted to succinic acid by the reductive tricarboxylic acid cycle and menaquinone systems. This characteristic metabolism allows highly efficient production of succinic acid, an important four-carbon industrial chemical.

BioSilico: an integrated metabolic database system.

BioSilico is a web-based database system that facilitates the search and analysis of metabolic pathways. Heterogeneous metabolic databases including LIGAND, ENZYME, EcoCyc and MetaCyc are integrated in a systematic way, thereby allowing users to efficiently retrieve the relevant information on enzymes, biochemical compounds and reactions. In addition, it provides well-designed view pages for more detailed summary information. BioSilico is developed as an extensible system with a robust systematic architecture.