Integration and biological interpretation of microarry gene expression profiling data

Many different strategies have been developed for the analysis of microarray data and these have a significant influence on the level and quality of knowledge that may be achieved from a microarray-based experiment. Two such strategies are explored in this thesis. Part A of this thesis describes explorations of a resource-efficient strategy that could allow for large-scale integration of microarray data in an unsupervised fashion. For this purpose, comparisons were carried out between a series of genelists manually extracted from the literature, representing a disparate set of microarray experiments. Initial results were highly unexpected, and are likely to have been caused by violations of the assumptions of the hypergeometric test used for assessing comparisons. Statistical modelling was found to successfully simulate these results however the estimated net effect of these violations was found to be considerable. These findings strongly caution against the comparison of microarray experiments using their genelists. Part B then describes the development of Gene Set Discovery (GSD), a novel methodology to perform threshold-free gene set analysis of microarray datasets without requiring sample class information. This was achieved by deriving a novel metric that allows for the selection of those gene sets that exhibit significant discrimination between samples. GSD was implemented on four microarray datasets and the results were found to be biologically plausible and/or in agreement with prior analyses of these datasets. These findings suggested that GSD could be a potentially useful tool for biological theme discovery in microarray datasets, particularly in studies of cancer where sample classification is problematic. Also described is a related methodology for extraction of informative genes from within selected gene sets, and a scheme for visualization of results in an integrated format

Performance Evaluation of Navigation Approaches on High-resolution Displays

We conducted a study to discover if the data navigation techniques suitable for high-resolution displays differed significantly from those traditionally used for single-screen desktop displays. The high-resolution capability of the former display makes it possible to show more data at once without having the user drill-down to get to the details. At the same time, the larger physical size makes it difficult for the user to interact with such a display using current day interaction techniques. Given these factors, we compare the performance of users on tasks that involve navigating into hierarchically-structured data. The specific visualization we use is a cushion treemap, displayed at multiple resolutions—on a 3x3, 17” tiled screen display; on a 2x2, 17” tiled screen display; on a single 17” screen display, and on a 66” SMART Board™. Through the performance evaluation of 24 users, we show that beyond a certain resolution and physical screen size, the drill-down technique fares relatively poorly, while the straightforward technique of displaying all the data at once results in better performance at the tasks we studied

Metabolomic changes during cellular transformation monitored by metabolite-metabolite correlation analysis and correlated with gene expression.

To investigate metabolic changes during cellular transformation, we used a 1H NMR based metabolite-metabolite correlation analysis (MMCA) method, which permits analysis of homeostatic mechanisms in cells at the steady state, in an inducible cell transformation model. Transcriptomic data were used to further explain the results. Transformed cells showed many more metabolite-metabolite correlations than control cells. Some had intuitively plausible explanations: a shift from glycolysis to amino acid oxidation after transformation was accompanied by a strongly positive correlation between glucose and glutamine and a strongly negative one between lactate and glutamate; there were also many correlations between the branched chain amino acids and the aromatic amino acids. Others remain puzzling: after transformation strong positive correlations developed between choline and a group of five amino acids, whereas the same amino acids showed negative correlations with phosphocholine, a membrane phospholipid precursor. MMCA in conjunction with transcriptome analysis has opened a new window into the metabolome.We acknowledge the support of The University of Cambridge, Cancer Research UK (C14303/A17197) and Hutchison Whampoa Limited.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s11306-015-0838-

Activating transcription factor-2 (ATF2) is a key determinant of resistance to endocrine treatment in an in vitro model of breast cancer.

BACKGROUND: Activating transcription factor-2 (ATF2), a member of the leucine zipper family of DNA binding proteins, has been implicated as a tumour suppressor in breast cancer. However, its exact role in breast cancer endocrine resistance is still unclear. We have previously shown that silencing of ATF2 leads to a loss in the growth-inhibitory effects of tamoxifen in the oestrogen receptor (ER)-positive, tamoxifen-sensitive MCF7 cell line and highlighted that this multi-faceted transcription factor is key to the effects of tamoxifen in an endocrine sensitive model. In this work, we explored further the in vitro role of ATF2 in defining the resistance to endocrine treatment. MATERIALS AND METHODS: We knocked down ATF2 in TAMR, LCC2 and LCC9 tamoxifen-resistant breast cancer cell lines as well as the parental tamoxifen sensitive MCF7 cell line and investigated the effects on growth, colony formation and cell migration. We also performed a microarray gene expression profiling (Illumina Human HT12_v4) to explore alterations in gene expression between MCF7 and TAMRs after ATF2 silencing and confirmed gene expression changes by quantitative RT-PCR. RESULTS: By silencing ATF2, we observed a significant growth reduction of TAMR, LCC2 and LCC9 with no such effect observed with the parental MCF7 cells. ATF2 silencing was also associated with a significant inhibition of TAMR, LCC2 and LCC9 cell migration and colony formation. Interestingly, knockdown of ATF2 enhanced the levels of ER and ER-regulated genes, TFF1, GREB1, NCOA3 and PGR, in TAMR cells both at RNA and protein levels. Microarray gene expression identified a number of genes known to mediate tamoxifen resistance, to be differentially regulated by ATF2 in TAMR in relation to the parental MCF7 cells. Moreover, differential pathway analysis confirmed enhanced ER activity after ATF2 knockdown in TAMR cells. CONCLUSION: These data demonstrate that ATF2 silencing may overcome endocrine resistance and highlights further the dual role of this transcription factor that can mediate endocrine sensitivity and resistance by modulating ER expression and activity

Latent regulatory potential of human-specific repetitive elements

At least half of the human genome is derived from repetitive elements, which are often lineage specific and silenced by a variety of genetic and epigenetic mechanisms. Using a transchromosomic mouse strain that transmits an almost complete single copy of human chromosome 21 via the female germline, we show that a heterologous regulatory environment can transcriptionally activate transposon-derived human regulatory regions. In the mouse nucleus, hundreds of locations on human chromosome 21 newly associate with activating histone modifications in both somatic and germline tissues, and influence the gene expression of nearby transcripts. These regions are enriched with primate and human lineage-specific transposable elements, and their activation corresponds to changes in DNA methylation at CpG dinucleotides. This study reveals the latent regulatory potential of the repetitive human genome and illustrates the species specificity of mechanisms that control it

Transcriptional silencing of long noncoding RNA GNG12-AS1 uncouples its transcriptional and product-related functions.

Long noncoding RNAs (lncRNAs) regulate gene expression via their RNA product or through transcriptional interference, yet a strategy to differentiate these two processes is lacking. To address this, we used multiple small interfering RNAs (siRNAs) to silence GNG12-AS1, a nuclear lncRNA transcribed in an antisense orientation to the tumour-suppressor DIRAS3. Here we show that while most siRNAs silence GNG12-AS1 post-transcriptionally, siRNA complementary to exon 1 of GNG12-AS1 suppresses its transcription by recruiting Argonaute 2 and inhibiting RNA polymerase II binding. Transcriptional, but not post-transcriptional, silencing of GNG12-AS1 causes concomitant upregulation of DIRAS3, indicating a function in transcriptional interference. This change in DIRAS3 expression is sufficient to impair cell cycle progression. In addition, the reduction in GNG12-AS1 transcripts alters MET signalling and cell migration, but these are independent of DIRAS3. Thus, differential siRNA targeting of a lncRNA allows dissection of the functions related to the process and products of its transcription.The authors acknowledge all the members of Murrell, Rinn, Odom and Gergely laboratory as well as Massimiliano di Pietro, Klaas Mulder, Anna Git, Jason Carroll in Cambridge and Laurence Hurst (University of Bath) for reading and providing helpful comments on the manuscript. We also thank the Genomics, Microscopy and Bioinformatics core facilities at the Cambridge Institute for support, Christina Ernst for thumbnail image design, Ezgi Hacisuleyman for the design of the negative control vector, Cole Trapnell and David Hendrickson for providing us with lincExpress vector, Arjun Raj with the RNA FISH and Alaisdair Russell with the lentiviral work. This research was supported by The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. The authors have no conflicting financial interests.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1040

Modeling human pancreatic beta cell dedifferentiation

Objective: Dedifferentiation could explain reduced functional pancreatic β-cell mass in type 2 diabetes (T2D). Methods: Here we model human β-cell dedifferentiation using growth factor stimulation in the human β-cell line, EndoC-βH1, and human pancreatic islets. Results: Fibroblast growth factor 2 (FGF2) treatment reduced expression of β-cell markers, (INS, MAFB, SLC2A2, SLC30A8, and GCK) and activated ectopic expression of MYC, HES1, SOX9, and NEUROG3. FGF2-induced dedifferentiation was time- and dose-dependent and reversible upon wash-out. Furthermore, FGF2 treatment induced expression of TNFRSF11B, a decoy receptor for RANKL and protected β-cells against RANKL signaling. Finally, analyses of transcriptomic data revealed increased FGF2 expression in ductal, endothelial, and stellate cells in pancreas from T2D patients, whereas FGFR1, SOX,9 and HES1 expression increased in islets from T2D patients. Conclusions: We thus developed an FGF2-induced model of human β-cell dedifferentiation, identified new markers of dedifferentiation, and found evidence for increased pancreatic FGF2, FGFR1, and β-cell dedifferentiation in T2D

5-hydroxymethylcytosine and gene activity in mouse intestinal differentiation

Abstract: Cytosine hydroxymethylation (5hmC) in mammalian DNA is the product of oxidation of methylated cytosines (5mC) by Ten-Eleven-Translocation (TET) enzymes. While it has been shown that the TETs influence 5mC metabolism, pluripotency and differentiation during early embryonic development, the functional relationship between gene expression and 5hmC in adult (somatic) stem cell differentiation is still unknown. Here we report that 5hmC levels undergo highly dynamic changes during adult stem cell differentiation from intestinal progenitors to differentiated intestinal epithelium. We profiled 5hmC and gene activity in purified mouse intestinal progenitors and differentiated progeny to identify 43425 differentially hydroxymethylated regions and 5325 differentially expressed genes. These differentially marked regions showed both losses and gains of 5hmC after differentiation, despite lower global levels of 5hmC in progenitor cells. In progenitors, 5hmC did not correlate with gene transcript levels, however, upon differentiation the global increase in 5hmC content showed an overall positive correlation with gene expression level as well as prominent associations with histone modifications that typify active genes and enhancer elements. Our data support a gene regulatory role for 5hmC that is predominant over its role in controlling DNA methylation states

Phenotype specific analyses reveal distinct regulatory mechanism for chronically activated p53.

The downstream functions of the DNA binding tumor suppressor p53 vary depending on the cellular context, and persistent p53 activation has recently been implicated in tumor suppression and senescence. However, genome-wide information about p53-target gene regulation has been derived mostly from acute genotoxic conditions. Using ChIP-seq and expression data, we have found distinct p53 binding profiles between acutely activated (through DNA damage) and chronically activated (in senescent or pro-apoptotic conditions) p53. Compared to the classical 'acute' p53 binding profile, 'chronic' p53 peaks were closely associated with CpG-islands. Furthermore, the chronic CpG-island binding of p53 conferred distinct expression patterns between senescent and pro-apoptotic conditions. Using the p53 targets seen in the chronic conditions together with external high-throughput datasets, we have built p53 networks that revealed extensive self-regulatory 'p53 hubs' where p53 and many p53 targets can physically interact with each other. Integrating these results with public clinical datasets identified the cancer-associated lipogenic enzyme, SCD, which we found to be directly repressed by p53 through the CpG-island promoter, providing a mechanistic link between p53 and the 'lipogenic phenotype', a hallmark of cancer. Our data reveal distinct phenotype associations of chronic p53 targets that underlie specific gene regulatory mechanisms.This work was supported by the University of Cambridge; Cancer Research UK (C14303/A17197); Hutchison Whampoa. In addition, MasasN and TO were supported by the Human Frontier Science Program (RGY0078/2010); HK was supported by MEXT KAKENHI (Grant Numbers 25116005 and 26291071); KT was supported by the Japan Society for the Promotion of Science (24–8563).This is the final version of the article. It first appeared at http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.100505

Phenotype specific analyses reveal distinct regulatory mechanism for chronically activated p53

This work was supported by the University of Cambridge; Cancer Research UK (C14303/A17197); Hutchison Whampoa. In addition, MasasN and TO were supported by the Human Frontier Science Program (RGY0078/2010); HK was supported by MEXT KAKENHI (Grant Numbers 25116005 and 26291071); KT was supported by the Japan Society for the Promotion of Science (24–8563).The downstream functions of the DNA binding tumor suppressor p53 vary depending on the cellular context, and persistent p53 activation has recently been implicated in tumor suppression and senescence. However, genome-wide information about p53-target gene regulation has been derived mostly from acute genotoxic conditions. Using ChIP-seq and expression data, we have found distinct p53 binding profiles between acutely activated (through DNA damage) and chronically activated (in senescent or pro-apoptotic conditions) p53. Compared to the classical ‘acute’ p53 binding profile, ‘chronic’ p53 peaks were closely associated with CpG-islands. Furthermore, the chronic CpG-island binding of p53 conferred distinct expression patterns between senescent and pro-apoptotic conditions. Using the p53 targets seen in the chronic conditions together with external high-throughput datasets, we have built p53 networks that revealed extensive self-regulatory ‘p53 hubs’ where p53 and many p53 targets can physically interact with each other. Integrating these results with public clinical datasets identified the cancer-associated lipogenic enzyme, SCD, which we found to be directly repressed by p53 through the CpG-island promoter, providing a mechanistic link between p53 and the ‘lipogenic phenotype’, a hallmark of cancer. Our data reveal distinct phenotype associations of chronic p53 targets that underlie specific gene regulatory mechanisms.Publisher PDFPeer reviewe