Pathway aberrations of murine melanoma cells observed in Paired-End diTag transcriptomes

Antimelanoma effect of manool in 2D cell cultures and reconstructed human skin models

Melanoma is the most aggressive and lethal type of skin cancer, characterized by therapeutic resistance. In this context, the present study aimed to investigate the cytotoxic potential of manool, a diterpene from Salvia officinalis L., in human (A375) and murine (B16F10) melanoma cell lines. The analysis of cytotoxicity using the XTT assay showed the lowest IC₅₀ after 48 h of treatment with the manool, being 17.6 and 18.2 µg/ml for A375 and B16F10, respectively. A selective antiproliferative effect of manool was observed on the A375 cells based on the colony formation assay, showing an IC₅₀ equivalent to 5.6 µg/ml. The manool treatments led to 43.5% inhibition of the A375 cell migration at a concentration of 5.0 µg/ml. However, it did not affect cell migration in the B16F10 cells. Cell cycle analysis revealed that the manool interfered in the cell cycle of the A375 cells, blocking the G2/M phase. No changes in the cell cycle were observed in the B16F10 cells. Interestingly, manool did not induce apoptosis in the A375 cells, but apoptosis was observed after treatment of the B16F10 cells. Additionally, manool showed an antimelanoma effect in a reconstructed human skin model. Furthermore, in silico studies, showed that manool is stabilized in the active sites of the tubulin dimer with comparable energy concerning taxol, indicating that both structures can inhibit the proliferation of cancer cells. Altogether, it is concluded that manool, through the modulation of the cell cycle, presents a selective antiproliferative activity and a potential antimelanoma effect.

Gene-gene interaction analysis incorporating network information via a structured Bayesian approach

Increasing evidence has shown that gene-gene interactions have important effects in biological processes of human diseases. Due to the high dimensionality of genetic measurements, interaction analysis usually suffers from a lack of sufficient information and has unsatisfactory results. Biological network information has been massively accumulated, allowing researchers to identify biomarkers while taking a system perspective, conducting network selection (of functionally related biomarkers), and accommodating network structures. In main-effect-only analysis, network information has been incorporated. However, effort has been limited in interaction analysis. Recently, link networks that describe the relationships between genetic interactions have been demonstrated as effective for revealing multiscale hierarchical organizations in networks and providing interesting findings beyond node networks. In this study, we develop a novel structured Bayesian interaction analysis approach to effectively incorporate network information. This study is among the first to identify gene-gene interactions with the assistance of network selection, while simultaneously accommodating the underlying network structures of both main effects and interactions. It innovatively respects multiple hierarchies among main effects, interactions, and networks. The Bayesian technique is adopted, which may be more informative for estimation and prediction over some other techniques. An efficient variational Bayesian expectation-maximization algorithm is developed to explore the posterior distribution. Extensive simulation studies demonstrate the practical superiority of the proposed approach. The analysis of TCGA data on melanoma and lung cancer leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

ctPath: Demixing pathway crosstalk effect from transcriptomics data for differential pathway identification

Identifying differentially expressed pathways (DEPs) plays important roles in understanding tumor etiology and promoting clinical treatment of cancer or other diseases. By assuming gene expression to be a sparse non-negative linear combination of hidden pathway signals, we propose a pathway crosstalk-based transcriptomics data analysis method (ctPath) for identifying differentially expressed pathways. Biologically, pathways of different functions work in concert at the systematic level. The proposed method interrogates the crosstalks between pathways and discovers hidden pathway signals by mapping high-dimensional transcriptomics data into a low-dimensional pathway space. The resulted pathway signals reflect the activity level of pathways after removing pathway crosstalk effect and allow a robust identification of DEPs from inherently complex and noisy transcriptomics data. CtPath can also correct incomplete and inaccurate pathway annotations which frequently occur in public repositories. Experimental results on both simulation data and real-world cancer data demonstrate the superior performance of ctPath over other popular approaches. R code for ctPath is available for non-commercial use at the URL http://micblab.iim.ac.cn/Download/.

1H NMR Metabolomics Study of Metastatic Melanoma in C57BL/6J Mouse Spleen

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.

Functional synergies yet distinct modulators affected by genetic alterations in common human cancers

An important general concern in cancer research is how diverse genetic alterations and regulatory pathways can produce common signaling outcomes. In this study, we report the construction of cancer models that combine unique regulation and common signaling. We compared and functionally analyzed sets of genetic alterations, including somatic sequence mutations and copy number changes, in breast, colon, and pancreatic cancer and glioblastoma that had been determined previously by global exon sequencing and SNP (single nucleotide polymorphism) array analyses in multiple patients. The genes affected by the different types of alterations were mostly unique in each cancer type, affected different pathways, and were connected with different transcription factors, ligands, and receptors. In our model, we show that distinct amplifications, deletions, and sequence alterations in each cancer resulted in common signaling pathways and transcription regulation. In functional clustering, the impact of the type of alteration was more pronounced than the impact of the kind of cancer. Several pathways such as TGF-β/SMAD signaling and PI3K (phosphoinositide 3-kinase) signaling were defined as synergistic (affected by different alterations in all four cancer types). Despite large differences at the genetic level, all data sets interacted with a common group of 65 "universal cancer genes" (UCG) comprising a concise network focused on proliferation/apoptosis balance and angiogenesis. Using unique nodal regulators ("overconnected" genes), UCGs, and synergistic pathways, the cancer models that we built could combine common signaling with unique regulation. Our findings provide a novel integrated perspective on the complex signaling and regulatory networks that underlie common human cancers.

Pseudogene: lessons from PCR bias, identification and resurrection

Pseudogenes are fragments of non-functional genomic DNA with high sequences similarity to normal functional genes. They are a kind of non-coding DNA produced by gene duplications or retrotranspositions. Pseudogenes exist in human genome at a large quantity which is nearly as much as that of normal functional genes. They could cause PCR bias in molecular biology experiments and confuse related analysis. On the other hand, pesudogenes are important elements in genomics study for getting an integral picture of genome annotation. They give diverse information of evolutionary history and are regarded as genome fossils. Worldwide research project "encyclopedia of DNA elements"(ENCODE) founded in recent years have enhanced our understanding of pseudogenes. Approaches established to identify pseudogenes include PseudoPipe, HAVANA method, PseudoFinder, RetroFinder, GIS-PET method and consensus method. This paper discuss pseudogenes with respect to the formation mechanisms, distribution, and problems for PCR, importance and identification of pseudogenes. Furthermore, potential resurrection of pseudogenes and their potential function are discussed.

Large-scale RACE approach for proactive experimental definition of C. elegans ORFeome

Although a highly accurate sequence of the Caenorhabditis elegans genome has been available for 10 years, the exact transcript structures of many of its protein-coding genes remain unsettled. Approximately two-thirds of the ORFeome has been verified reactively by amplifying and cloning computationally predicted transcript models; still a full third of the ORFeome remains experimentally unverified. To fully identify the protein-coding potential of the worm genome including transcripts that may not satisfy existing heuristics for gene prediction, we developed a computational and experimental platform adapting rapid amplification of cDNA ends (RACE) for large-scale structural transcript annotation. We interrogated 2000 unverified protein-coding genes using this platform. We obtained RACE data for approximately two-thirds of the examined transcripts and reconstructed ORF and transcript models for close to 1000 of these. We defined untranslated regions, identified new exons, and redefined previously annotated exons. Our results show that as much as 20% of the C. elegans genome may be incorrectly annotated. Many annotation errors could be corrected proactively with our large-scale RACE platform.

Genomic convergence analysis of schizophrenia: mRNA sequencing reveals altered synaptic vesicular transport in post-mortem cerebellum

Schizophrenia (SCZ) is a common, disabling mental illness with high heritability but complex, poorly understood genetic etiology. As the first phase of a genomic convergence analysis of SCZ, we generated 16.7 billion nucleotides of short read, shotgun sequences of cDNA from post-mortem cerebellar cortices of 14 patients and six, matched controls. A rigorous analysis pipeline was developed for analysis of digital gene expression studies. Sequences aligned to approximately 33,200 transcripts in each sample, with average coverage of 450 reads per gene. Following adjustments for confounding clinical, sample and experimental sources of variation, 215 genes differed significantly in expression between cases and controls. Golgi apparatus, vesicular transport, membrane association, Zinc binding and regulation of transcription were over-represented among differentially expressed genes. Twenty three genes with altered expression and involvement in presynaptic vesicular transport, Golgi function and GABAergic neurotransmission define a unifying molecular hypothesis for dysfunction in cerebellar cortex in SCZ.