Overexpression of the E2F target gene CENPI promotes chromosome instability and predicts poor prognosis in estrogen receptor-positive breast cancer.

During cell division, chromosome segregation is facilitated by the mitotic checkpoint, or spindle assembly checkpoint (SAC), which ensures correct kinetochore-microtubule attachments and prevents premature sister-chromatid separation. It is well established that misexpression of SAC components on the outer kinetochores promotes chromosome instability (CIN) and tumorigenesis. Here, we study the expression of CENP-I, a key component of the HIKM complex at the inner kinetochores, in breast cancer, including ductal, lobular, medullary and male breast carcinomas. CENPI mRNA and protein levels are significantly elevated in estrogen receptor-positive (ER+) but not in estrogen receptor-negative (ER-) breast carcinoma. Well-established prognostic tests indicate that CENPI overexpression constitutes a powerful independent marker for poor patient prognosis and survival in ER+ breast cancer. We further demonstrate that CENPI is an E2F target gene. Consistently, it is overexpressed in RB1-deficient breast cancers. However, CENP-I overexpression is not purely due to cell cycle-associated expression. In ER+ breast cancer cells, CENP-I overexpression promotes CIN, especially chromosome gains. In addition, in ER+ breast carcinomas the degree of CENPI overexpression is proportional to the level of aneuploidy and CENPI overexpression is one of the strongest markers for CIN identified to date. Our results indicate that overexpression of the inner kinetochore protein CENP-I promotes CIN and forecasts poor prognosis for ER+ breast cancer patients. These observations provide novel mechanistic insights and have important implications for breast cancer diagnostics and potentially therapeutic targeting.

Increased expression of H19/miR-675 is associated with a low fat-free mass index in patients with COPD.

BACKGROUND: Loss of muscle mass and strength is a significant comorbidity in patients with chronic obstructive pulmonary disease (COPD) that limits their quality of life and has prognostic implications but does not affect everyone equally. To identify mechanisms that may contribute to the susceptibility to a low muscle mass, we investigated microRNA (miRNA) expression, methylation status, and regeneration in quadriceps muscle from COPD patients and the effect of miRNAs on myoblast proliferation in vitro. The relationships of miRNA expression with muscle mass and strength was also determined in a group of healthy older men. METHODS: We identified miRNAs associated with a low fat-free mass (FFM) phenotype in a small group of patients with COPD using a PCR screen of 750 miRNAs. The expression of two differentially expressed miRNAs (miR-675 and miR-519a) was determined in an expanded group of COPD patients and their associations with FFM and strength identified. The association of these miRNAs with FFM and strength was also explored in a group of healthy community-dwelling older men. As the expression of the miRNAs associated with FFM could be regulated by methylation, the relative methylation of the H19 ICR was determined. Furthermore, the proportion of myofibres with centralized nuclei, as a marker of muscle regeneration, in the muscle of COPD patients was identified by immunofluorescence. RESULTS: Imprinted miRNAs (miR-675 and from a cluster, C19MC which includes miR-519a) were differentially expressed in the quadriceps of patients with a low fat-free mass index (FFMI) compared to those with a normal FFMI. In larger cohorts, miR-675 and its host gene (H19) were higher in patients with a low FFMI and strength. The association of miR-519a expression with FFMI was present in male patients with severe COPD. Similar associations of miR expression with lean mass and strength were not observed in healthy community dwelling older men participating in the Hertfordshire Sarcopenia Study. Relative methylation of the H19 ICR was reduced in COPD patients with muscle weakness but was not associated with FFM. In vitro, miR-675 inhibited myoblast proliferation and patients with a low FFMI had fewer centralized nuclei suggesting miR-675 represses regeneration. CONCLUSIONS: The data suggest that increased expression of miR-675/H19 and altered methylation of the H19 imprinting control region are associated with a low FFMI in patients with COPD but not in healthy community dwelling older men suggesting that epigenetic control of this loci may contribute to a susceptibility to a low FFMI.

Overexpression of Ran GTPase Components Regulating Nuclear Export, but not Mitotic Spindle Assembly, Marks Chromosome Instability and Poor Prognosis in Breast Cancer.

BACKGROUND: Ran GTPase regulates nuclear import, nuclear export, and mitotic spindle assembly. The multifunctional involvement of seventeen Ran GTPase components in these processes has complicated research into how each contributes to cancer development. OBJECTIVE: To assess whether individual and process-specific misexpression of Ran GTPase components contribute to chromosome instability (CIN) and worsen breast cancer patient prognosis. METHODS: Using publicly available datasets, we studied the degree of misexpression of all Ran GTPase signaling components in breast cancer, assessed their involvement in CIN and used four clinical tests to evaluate whether their misregulation may constitute independent prognostic predictors. RESULTS: A significant majority of Ran GTPase signaling components is overexpressed in breast cancer. Strikingly, spindle assembly components are overexpressed and associated with CIN with only marginal significance and four independent tests indicate that this does not worsen patient outcome. Overexpression of nuclear import components is neither CIN-associated nor clinically significant. In sharp contrast, overexpression of nuclear export components constitutes a strong independent marker for both CIN and poor patient prognosis. We identify Exportin 2/CSE1L, Exportin 3/XPOT, Exportin 5/XPO5, and RANBP1 as novel potential targets. CONCLUSIONS: We find that overexpression of Ran GTPase components involved in nuclear export, but not nuclear import or mitotic spindle assembly, is a strong CIN-associated marker for poor breast cancer prognosis. This could mean that increased nuclear export (of, for instance, pRb, p53, p73, BRCA1, p21, p27, E2F4, IκB, survivin), rather than spindle defects, mainly drives CIN and tumorigenesis. Hence, selective inhibitors of nuclear export may be effective for treating the most aggressive and chromosomally unstable breast cancers.

A draft of the genome and four transcriptomes of a medicinal and pesticidal angiosperm Azadirachta indica.

BACKGROUND: The Azadirachta indica (neem) tree is a source of a wide number of natural products, including the potent biopesticide azadirachtin. In spite of its widespread applications in agriculture and medicine, the molecular aspects of the biosynthesis of neem terpenoids remain largely unexplored. The current report describes the draft genome and four transcriptomes of A. indica and attempts to contextualise the sequence information in terms of its molecular phylogeny, transcript expression and terpenoid biosynthesis pathways. A. indica is the first member of the family Meliaceae to be sequenced using next generation sequencing approach. RESULTS: The genome and transcriptomes of A. indica were sequenced using multiple sequencing platforms and libraries. The A. indica genome is AT-rich, bears few repetitive DNA elements and comprises about 20,000 genes. The molecular phylogenetic analyses grouped A. indica together with Citrus sinensis from the Rutaceae family validating its conventional taxonomic classification. Comparative transcript expression analysis showed either exclusive or enhanced expression of known genes involved in neem terpenoid biosynthesis pathways compared to other sequenced angiosperms. Genome and transcriptome analyses in A. indica led to the identification of repeat elements, nucleotide composition and expression profiles of genes in various organs. CONCLUSIONS: This study on A. indica genome and transcriptomes will provide a model for characterization of metabolic pathways involved in synthesis of bioactive compounds, comparative evolutionary studies among various Meliaceae family members and help annotate their genomes. A better understanding of molecular pathways involved in the azadirachtin synthesis in A. indica will pave ways for bulk production of environment friendly biopesticides.

Customisation of the exome data analysis pipeline using a combinatorial approach.

The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.