Microarrays最新文献

{"title":"Small-Molecule Inhibition of Rho/MKL/SRF Transcription in Prostate Cancer Cells: Modulation of Cell Cycle, ER Stress, and Metastasis Gene Networks.","authors":"Chris R Evelyn,&nbsp;Erika M Lisabeth,&nbsp;Susan M Wade,&nbsp;Andrew J Haak,&nbsp;Craig N Johnson,&nbsp;Elizabeth R Lawlor,&nbsp;Richard R Neubig","doi":"10.3390/microarrays5020013","DOIUrl":"https://doi.org/10.3390/microarrays5020013","url":null,"abstract":"<p><p>Metastasis is the major cause of cancer deaths and control of gene transcription has emerged as a critical contributing factor. RhoA- and RhoC-induced gene transcription via the actin-regulated transcriptional co-activator megakaryocytic leukemia (MKL) and serum response factor (SRF) drive metastasis in breast cancer and melanoma. We recently identified a compound, CCG-1423, which blocks Rho/MKL/SRF-mediated transcription and inhibits PC-3 prostate cancer cell invasion. Here, we undertook a genome-wide expression study in PC-3 cells to explore the mechanism and function of this compound. There was significant overlap in the genes modulated by CCG-1423 and Latrunculin B (Lat B), which blocks the Rho/MKL/SRF pathway by preventing actin polymerization. In contrast, the general transcription inhibitor 5,6-dichloro-1-β-d-ribofuranosyl-1H-benzimidazole (DRB) showed a markedly different pattern. Effects of CCG-1423 and Lat B on gene expression correlated with literature studies of MKL knock-down. Gene sets involved in DNA synthesis and repair, G1/S transition, and apoptosis were modulated by CCG-1423. It also upregulated genes involved in endoplasmic reticulum stress. Targets of the known Rho target transcription factor family E2F and genes related to melanoma progression and metastasis were strongly suppressed by CCG-1423. These results confirm the ability of our compound to inhibit expression of numerous Rho/MKL-dependent genes and show effects on stress pathways as well. This suggests a novel approach to targeting aggressive cancers and metastasis. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays5020013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34365919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Living Cell Microarrays: An Overview of Concepts.","authors":"Rebecca Jonczyk, Tracy Kurth, Antonina Lavrentieva, Johanna-Gabriela Walter, Thomas Scheper, Frank Stahl","doi":"10.3390/microarrays5020011","DOIUrl":"10.3390/microarrays5020011","url":null,"abstract":"Living cell microarrays are a highly efficient cellular screening system. Due to the low number of cells required per spot, cell microarrays enable the use of primary and stem cells and provide resolution close to the single-cell level. Apart from a variety of conventional static designs, microfluidic microarray systems have also been established. An alternative format is a microarray consisting of three-dimensional cell constructs ranging from cell spheroids to cells encapsulated in hydrogel. These systems provide an in vivo-like microenvironment and are preferably used for the investigation of cellular physiology, cytotoxicity, and drug screening. Thus, many different high-tech microarray platforms are currently available. Disadvantages of many systems include their high cost, the requirement of specialized equipment for their manufacture, and the poor comparability of results between different platforms. In this article, we provide an overview of static, microfluidic, and 3D cell microarrays. In addition, we describe a simple method for the printing of living cell microarrays on modified microscope glass slides using standard DNA microarray equipment available in most laboratories. Applications in research and diagnostics are discussed, e.g., the selective and sensitive detection of biomarkers. Finally, we highlight current limitations and the future prospects of living cell microarrays.","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays5020011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stromal Activation by Tumor Cells: An in Vitro Study in Breast Cancer.","authors":"Giuseppe Merlino,&nbsp;Patrizia Miodini,&nbsp;Biagio Paolini,&nbsp;Maria Luisa Carcangiu,&nbsp;Massimiliano Gennaro,&nbsp;Matteo Dugo,&nbsp;Maria Grazia Daidone,&nbsp;Vera Cappelletti","doi":"10.3390/microarrays5020010","DOIUrl":"https://doi.org/10.3390/microarrays5020010","url":null,"abstract":"<p><strong>Background: </strong>The tumor microenvironment participates in the regulation of tumor progression and influences treatment sensitivity. In breast cancer, it also may play a role in determining the fate of non-invasive lesions such as ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive diseases, which is aggressively treated despite its indolent nature in many patients since no biomarkers are available to predict the progression of DCIS to invasive disease. In vitro models of stromal activation by breast tumor cells might provide clues as to specific stromal genes crucial for the transition from DCIS to invasive disease.</p><p><strong>Methods: </strong>normal human dermal fibroblasts (NHDF) were treated under serum-free conditions with cell culture media conditioned by breast cancer cell lines (SkBr3, MDA-MB-468, T47D) for 72 h and subjected to gene expression profiling with Illumina platform.</p><p><strong>Results: </strong>TGM2, coding for a tissue transglutaminase, was identified as candidate gene for stromal activation. In public transcriptomic datasets of invasive breast tumors TGM2 expression proved to provide prognostic information. Conversely, its role as an early biosensor of tumor invasiveness needs to be further investigated by in situ analyses.</p><p><strong>Conclusion: </strong>Stromal TGM2 might probably be associated with precancerous evolution at earlier stages compared to DCIS.</p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays5020010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34421562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microarray-Based Comparative Genomic and Transcriptome Analysis of Borrelia burgdorferi.","authors":"Radha Iyer,&nbsp;Ira Schwartz","doi":"10.3390/microarrays5020009","DOIUrl":"https://doi.org/10.3390/microarrays5020009","url":null,"abstract":"<p><p>Borrelia burgdorferi, the spirochetal agent of Lyme disease, is maintained in nature in a cycle involving a tick vector and a mammalian host. Adaptation to the diverse conditions of temperature, pH, oxygen tension and nutrient availability in these two environments requires the precise orchestration of gene expression. Over 25 microarray analyses relating to B. burgdorferi genomics and transcriptomics have been published. The majority of these studies has explored the global transcriptome under a variety of conditions and has contributed substantially to the current understanding of B. burgdorferi transcriptional regulation. In this review, we present a summary of these studies with particular focus on those that helped define the roles of transcriptional regulators in modulating gene expression in the tick and mammalian milieus. By performing comparative analysis of results derived from the published microarray expression profiling studies, we identified composite gene lists comprising differentially expressed genes in these two environments. Further, we explored the overlap between the regulatory circuits that function during the tick and mammalian phases of the enzootic cycle. Taken together, the data indicate that there is interplay among the distinct signaling pathways that function in feeding ticks and during adaptation to growth in the mammal. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays5020009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34421561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-Resolved Study of Nanoparticle Induced Apoptosis Using Microfabricated Single Cell Arrays.","authors":"Peter J F Röttgermann,&nbsp;Kenneth A Dawson,&nbsp;Joachim O Rädler","doi":"10.3390/microarrays5020008","DOIUrl":"https://doi.org/10.3390/microarrays5020008","url":null,"abstract":"<p><p>Cell fate decisions like apoptosis are heterogeneously implemented within a cell population and, consequently, the population response is recognized as sum of many individual dynamic events. Here, we report on the use of micro-patterned single-cell arrays for real-time tracking of nanoparticle-induced (NP) cell death in sets of thousands of cells in parallel. Annexin (pSIVA) and propidium iodide (PI), two fluorescent indicators of apoptosis, are simultaneously monitored after exposure to functionalized polystyrene (PS - NH 2) nanobeads as a model system. We find that the distribution of Annexin onset times shifts to later times and broadens as a function of decreasing NP dose. We discuss the mean time-to-death as a function of dose, and show how the EC 50 value depends both on dose and time of measurement. In addition, the correlations between the early and late apoptotic markers indicate a systematic shift from apoptotic towards necrotic cell death during the course of the experiment. Thus, our work demonstrates the potential of array-based single cell cytometry for kinetic analysis of signaling cascades in a high-throughput format. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays5020008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34421560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aptamer-Based Screens of Human Body Fluids for Biomarkers.","authors":"Dania Albaba,&nbsp;Sanam Soomro,&nbsp;Chandra Mohan","doi":"10.3390/microarrays4030424","DOIUrl":"https://doi.org/10.3390/microarrays4030424","url":null,"abstract":"<p><p>In recent years, aptamers have come to replace antibodies in high throughput multiplexed experiments. The aptamer-based biomarker screening technology, which kicked off in 2010, is capable of interrogating thousands of proteins in a very small sample volume. With this new technology, researchers hope to find clinically appropriate biomarkers for a myriad of illnesses by screening human body fluids. In this work, we have reviewed a total of eight studies utilizing aptamer-based biomarker screens of human body fluids, and have highlighted novel protein biomarkers discovered. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"424-31"},"PeriodicalIF":0.0,"publicationDate":"2015-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Constitutional Copy Number Variants in Breast Cancer.","authors":"Logan C Walker,&nbsp;George A R Wiggins,&nbsp;John F Pearson","doi":"10.3390/microarrays4030407","DOIUrl":"https://doi.org/10.3390/microarrays4030407","url":null,"abstract":"<p><p>Constitutional copy number variants (CNVs) include inherited and de novo deviations from a diploid state at a defined genomic region. These variants contribute significantly to genetic variation and disease in humans, including breast cancer susceptibility. Identification of genetic risk factors for breast cancer in recent years has been dominated by the use of genome-wide technologies, such as single nucleotide polymorphism (SNP)-arrays, with a significant focus on single nucleotide variants. To date, these large datasets have been underutilised for generating genome-wide CNV profiles despite offering a massive resource for assessing the contribution of these structural variants to breast cancer risk. Technical challenges remain in determining the location and distribution of CNVs across the human genome due to the accuracy of computational prediction algorithms and resolution of the array data. Moreover, better methods are required for interpreting the functional effect of newly discovered CNVs. In this review, we explore current and future application of SNP array technology to assess rare and common CNVs in association with breast cancer risk in humans. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"407-23"},"PeriodicalIF":0.0,"publicationDate":"2015-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery.","authors":"Christopher J Walsh,&nbsp;Pingzhao Hu,&nbsp;Jane Batt,&nbsp;Claudia C Dos Santos","doi":"10.3390/microarrays4030389","DOIUrl":"https://doi.org/10.3390/microarrays4030389","url":null,"abstract":"<p><p>The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"389-406"},"PeriodicalIF":0.0,"publicationDate":"2015-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology.","authors":"Javier Arsuaga,&nbsp;Tyler Borrman,&nbsp;Raymond Cavalcante,&nbsp;Georgina Gonzalez,&nbsp;Catherine Park","doi":"10.3390/microarrays4030339","DOIUrl":"10.3390/microarrays4030339","url":null,"abstract":"<p><p>DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"339-69"},"PeriodicalIF":0.0,"publicationDate":"2015-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.","authors":"Clare Coveney,&nbsp;David J Boocock,&nbsp;Robert C Rees,&nbsp;Suha Deen,&nbsp;Graham R Ball","doi":"10.3390/microarrays4030324","DOIUrl":"https://doi.org/10.3390/microarrays4030324","url":null,"abstract":"<p><p>The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10(-11), the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient's response to treatment or be used as a novel target for therapy. </p>","PeriodicalId":56355,"journal":{"name":"Microarrays","volume":" ","pages":"324-38"},"PeriodicalIF":0.0,"publicationDate":"2015-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3390/microarrays4030324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34366397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}