Myc deletion rescues Apc deficiency in the small intestine.

The APC gene encodes the adenomatous polyposis coli tumour suppressor protein, germline mutation of which characterizes familial adenomatous polyposis (FAP), an autosomal intestinal cancer syndrome. Inactivation of APC is also recognized as the key early event in the development of sporadic colorectal cancers, and its loss results in constitutive activity of the beta-catenin-Tcf4 transcription complex. The proto-oncogene c-MYC has been identified as a target of the Wnt pathway in colorectal cancer cells in vitro, in normal crypts in vivo and in intestinal epithelial cells acutely transformed on in vivo deletion of the APC gene; however, the significance of this is unclear. Therefore, to elucidate the role Myc has in the intestine after Apc loss, we have simultaneously deleted both Apc and Myc in the adult murine small intestine. Here we show that loss of Myc rescued the phenotypes of perturbed differentiation, migration, proliferation and apoptosis, which occur on deletion of Apc. Remarkably, this rescue occurred in the presence of high levels of nuclear beta-catenin. Array analysis revealed that Myc is required for the majority of Wnt target gene activation following Apc loss. These data establish Myc as the critical mediator of the early stages of neoplasia following Apc loss.

Hypoxia-mediated control of HIF/ARNT machinery in epidermal keratinocytes.

Transcriptional activity of hypoxia-induced factor 1 (HIF1) - a heterodimer of HIF1α and ARNT (HIF1ß) - is essential for cellular adaptation to environmental stress and plays an important role in skin development, wound healing, tumorigenesis and barrier function. Using primary mouse and human epidermal keratinocytes at ambient or hypoxic (1% O(2)) conditions we studied effects of hypoxia upon HIF protein expression. Significant nuclear levels of ARNT and HIF1α along with high HIF1 activity in normoxic keratinocytes suggest an as yet uncharacterised oxygen-independent role for HIF pathway in the epidermis. Acute hypoxia results in an instant but transient increase of HIF1α protein accompanied by a gradual decrease in its mRNA, while ARNT expression remains unchanged. In prolonged (chronic) hypoxia both HIF1α and Arnt are downregulated along with decline of HIF1 activity. However, expression of classical HIF1 targets such as Selenbp1 and Vegfa remains high. Thus, keratinocytes respond to acute hypoxia with immediate block of HIF1α protein degradation and concomitant increase of HIF activity, while under chronic hypoxia pro-angiogenic signalling is maintained through HIF1-independent pathway(s). Decline of HIF1α during chronic exposure is controlled at both mRNA and protein levels, while Arnt is downregulated post-translationally. Distinct transcription levels of Hif1α and Hif3α splice variants under normoxia and their differential response to hypoxia suggest functional diversity of Hif-α isoforms and highlight the complexity of HIF machinery control in epidermal keratinocytes.

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands.

BACKGROUND: Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA). RESULTS: MLDA was programmed in R (version 2.7.0) and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing. CONCLUSION: MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

Transcriptional analysis of quiescent and proliferating CD34+ human hemopoietic cells from normal and chronic myeloid leukemia sources.

Quiescent and dividing hemopoietic stem cells (HSC) display marked differences in their ability to move between the peripheral circulation and the bone marrow. Specifically, long-term engraftment potential predominantly resides in the quiescent HSC subfraction, and G-CSF mobilization results in the preferential accumulation of quiescent HSC in the periphery. In contrast, stem cells from chronic myeloid leukemia (CML) patients display a constitutive presence in the circulation. To understand the molecular basis for this, we have used microarray technology to analyze the transcriptional differences between dividing and quiescent, normal, and CML-derived CD34+ cells. Our data show a remarkable transcriptional similarity between normal and CML dividing cells, suggesting that the effects of BCR-ABL on the CD34+ cell transcriptome are more limited than previously thought. In addition, we show that quiescent CML cells are more similar to their dividing counterparts than quiescent normal cells are to theirs. We also show these transcriptional differences to be reflected in the altered proliferative activity of normal and CML CD34+ cells. Of the most interest is that the major class of genes that is more abundant in the quiescent cells compared with the dividing cells encodes members of the chemokine family. We propose a role for chemokines expressed by quiescent HSC in the orchestration of CD34+ cell mobilization. Disclosure of potential conflicts of interest is found at the end of this article.

Divergent routes to oral cancer.

Most head and neck squamous cell carcinoma (HNSCC) patients present with late-stage cancers, which are difficult to treat. Therefore, early diagnosis of high-risk premalignant lesions and incipient cancers is important. HNSCC is currently perceived as a single progression mechanism, resulting in immortal invasive cancers. However, we have found that approximately 40% of primary oral SCCs are mortal in culture, and these have a better prognosis. About 60% of oral premalignancies (dysplasias) are also mortal. The mortal and immortal tumors are generated in vivo as judged by p53 mutations and loss of p16(INK4A) expression being found only in the original tumors from which the immortal cultures were derived. To investigate the relationships of dysplasias to SCCs, we did microarray analysis of primary cultures of 4 normal oral mucosa biopsies, 19 dysplasias, and 16 SCCs. Spectral clustering using the singular value decomposition and other bioinformatic techniques showed that development of mortal and immortal SCCs involves distinct transcriptional changes. Both SCC classes share most of the transcriptional changes found in their respective dysplasias but have additional changes. Moreover, high-risk dysplasias that subsequently progress to SCCs more closely resemble SCCs than nonprogressing dysplasias. This indicates for the first time that there are divergent mortal and immortal pathways for oral SCC development via intermediate dysplasias. We believe that this new information may lead to new ways of classifying HNSCC in relation to prognosis.

Invasion of normal human fibroblasts induced by v-Fos is independent of proliferation, immortalization, and the tumor suppressors p16INK4a and p53.

Invasion is generally perceived to be a late event during the progression of human cancer, but to date there are no consistent reports of alterations specifically associated with malignant conversion. We provide evidence that the v-Fos oncogene induces changes in gene expression that render noninvasive normal human diploid fibroblasts highly invasive, without inducing changes in growth factor requirements or anchorage dependence for proliferation. Furthermore, v-Fos-stimulated invasion is independent of the pRb/p16(INK4a) and p53 tumor suppressor pathways and telomerase. We have performed microarray analysis using Affymetrix GeneChips, and the gene expression profile of v-Fos transformed cells supports its role in the regulation of invasion, independent from proliferation. We also demonstrate that invasion, but not proliferation, is dependent on the activity of the up-regulated epidermal growth factor receptor. Taken together, these results indicate that AP-1-directed invasion could precede deregulated proliferation during tumorigenesis and that sustained activation of AP-1 could be the epigenetic event required for conversion of a benign tumor into a malignant one, thereby explaining why many malignant human tumors present without an obvious premalignant hyperproliferative dysplastic lesion.

Spectral analysis of two-signed microarray expression data.

We give a simple and informative derivation of a spectral algorithm for clustering and reordering complementary DNA microarray expression data. Here, expression levels of a set of genes are recorded simultaneously across a number of samples, with a positive weight reflecting up-regulation and a negative weight reflecting down-regulation. We give theoretical support for the algorithm based on a biologically justified hypothesis about the structure of the data, and illustrate its use on public domain data in the context of unsupervised tumour classification. The algorithm is derived by considering a discrete optimization problem and then relaxing to the continuous realm. We prove that in the case where the data have an inherent 'checkerboard' sign pattern, the algorithm will automatically reveal that pattern. Further, our derivation shows that the algorithm may be regarded as imposing a random graph model on the expression levels and then clustering from a maximum likelihood perspective. This indicates that the output will be tolerant to perturbations and will reveal 'near-checkerboard' patterns when these are present in the data. It is interesting to note that the checkerboard structure is revealed by the first (dominant) singular vectors--previous work on spectral methods has focussed on the case of nonnegative edge weights, where only the second and higher singular vectors are relevant. We illustrate the algorithm on real and synthetic data, and then use it in a tumour classification context on three different cancer data sets. Our results show that respecting the two-signed nature of the data (thereby distinguishing between up-regulation and down-regulation) reveals structures that cannot be gleaned from the absolute value data (where up- and down-regulation are both regarded as 'changes').

Identification from public data of molecular markers of adenocarcinoma characteristic of the site of origin.

Patients presenting with metastatic adenocarcinoma of unknown origin are a common clinical problem. Their optimal management and therapy are facilitated by identification of the primary site, yet histologically these tumors are almost identical. Better tumor markers are needed to enable the assignment of metastases to likely sites of origin. In this study, hierarchical clustering of public serial analysis of gene expression data showed that adenocarcinomas and their metastases cluster according to their site of origin. A novel bioinformatic approach was developed to exploit the differences between these clusters, using diverse sources: public expression data from serial analysis of gene expression and digital differential display; and the published literature, including microarray studies. Sixty-one candidate tumor markers with expression predicted to be characteristic of the site of origin were identified. Eleven genes were tested by reverse transcription-PCR in primary adenocarcinomas from a range of sites, and seven (64%) were site-restricted. Analysis of public gene expression data sets is a powerful method for the identification of clinically relevant tumor markers.

Profiling, comparison and validation of gene expression in gastric carcinoma and normal stomach.

Gastric carcinoma is the fourth most common cause of cancer death worldwide but its molecular biology is poorly understood. We catalogued the genes expressed in two gastric adenocarcinomas and normal stomach, using serial analysis of gene expression (SAGE), and compared the profiles on-line with other glandular epithelia. Candidates were validated by Northern blotting and immunohistochemistry. A total of 29 480 transcripts, derived from 10 866 genes, were identified. In all, 1% of the genes were differentially expressed (>/=fivefold difference plus P-value

Influence of an asparagine to lysine mutation at amino acid 3516 of apolipoprotein B on low-density lipoprotein receptor binding.

BACKGROUND: Three mutations in the apolipoprotein B (apoB) gene have previously been established as important causes of impaired receptor binding of LDL and, hence, Familial Defective Apolipoprotein B 100 (FDB). Previously, undescribed mutations were sought. METHODS: Using denaturing gradient gel electrophoresis for mutation detection, DNA from 1852 new patients was examined. RESULTS: A previously undiscovered mutation was found in codon 3516, located between known FDB mutations at codons 3500 and 3531. The new mutation introduces a positively charged amino acid-lysine-while other FDB mutations remove a positively charged residue, arginine. The phenotype was intriguing, LDL derived from N3516K heterozygotes allowed only poor growth of an LDL cholesterol-dependent cell line. ApoB-100-specific antibody MB47 bound to LDL from N3516K heterozygotes with increased affinity indicating a probable conformational change caused by the substitution. In contrast to these results, a competitive displacement assay in fibroblasts showed normal (or better) binding affinity to LDL receptors and using dynamic laser scattering no preferential accumulation of 3516K LDL particles in plasma was found. CONCLUSION: Discovery of the mutation and characterisation of N3516K LDL reveals another naturally occurring apoB mutation that influences conformation of LDL apoB and its interaction with the LDL receptor.

Spectral algorithms for heterogeneous biological networks.

Spectral methods, which use information relating to eigenvectors, singular vectors and generalized singular vectors, help us to visualize and summarize sets of pairwise interactions. In this work, we motivate and discuss the use of spectral methods by taking a matrix computation view and applying concepts from applied linear algebra. We show that this unified approach is sufficiently flexible to allow multiple sources of network information to be combined. We illustrate the methods on microarray data arising from a large population-based study in human adipose tissue, combined with related information concerning metabolic pathways.

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology.

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process.

Discretization provides a conceptually simple tool to build expression networks.

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes "microarray" data using known relationships which are successfully identified by our method.We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS.The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic--identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs.

Hematopoietic Lineage Transcriptome Stability and Representation in PAXgene Collected Peripheral Blood Utilising SPIA Single-Stranded cDNA Probes for Microarray.

Peripheral blood as a surrogate tissue for transcriptome profiling holds great promise for the discovery of diagnostic and prognostic disease biomarkers, particularly when target tissues of disease are not readily available. To maximize the reliability of gene expression data generated from clinical blood samples, both the sample collection and the microarray probe generation methods should be optimized to provide stabilized, reproducible and representative gene expression profiles faithfully representing the transcriptional profiles of the constituent blood cell types present in the circulation. Given the increasing innovation in this field in recent years, we investigated a combination of methodological advances in both RNA stabilisation and microarray probe generation with the goal of achieving robust, reliable and representative transcriptional profiles from whole blood. To assess the whole blood profiles, the transcriptomes of purified blood cell types were measured and compared with the global transcriptomes measured in whole blood. The results demonstrate that a combination of PAXgene() RNA stabilising technology and single-stranded cDNA probe generation afforded by the NuGEN Ovation RNA amplification system V2() enables an approach that yields faithful representation of specific hematopoietic cell lineage transcriptomes in whole blood without the necessity for prior sample fractionation, cell enrichment or globin reduction. Storage stability assessments of the PAXgene() blood samples also advocate a short, fixed room temperature storage time for all PAXgene() blood samples collected for the purposes of global transcriptional profiling in clinical studies.

RASSF1A elicits apoptosis through an MST2 pathway directing proapoptotic transcription by the p73 tumor suppressor protein.

RASSF1A is a tumor suppressor gene that is epigenetically silenced in a wide variety of sporadic human malignancies. Expression of alternative RASSF1 isoforms cannot substitute for RASSF1A-promoted cell-cycle arrest and apoptosis. Apoptosis can be driven by either activating Bax or by activation of MST kinases. The Raf1 proto-oncogene binds to MST2, preventing its activation and proapoptotic signaling. Here we show that key steps in RASSF1A-induced apoptosis are the disruption of the inhibitory Raf1-MST2 complex by RASSF1A and the concomitant enhancement of MST2 interaction with its substrate, LATS1. Subsequently, RASSF1A-activated LATS1 phosphorylates and releases the transcriptional regulator YAP1, allowing YAP1 to translocate to the nucleus and associate with p73, resulting in transcription of the proapoptotic target gene puma. Our results describe an MST2-dependent effector pathway for RASSF1A proapoptotic signaling and indicate that silencing of RASSF1A in tumors removes a proapoptotic signal emanating from p73.

Demethylation of the MCJ gene in stage III/IV epithelial ovarian cancer and response to chemotherapy.

OBJECTIVE: Methylation of a CpG island within the Methylation controlled DNAJ (MCJ) gene results in loss of expression in normal and neoplastic cells. Normal ovarian surface epithelial cells are methylated at the MCJ CpG island and do not express the MCJ gene. Furthermore, re-expression of the MCJ gene, in ovarian cancer cell lines, has been correlated with increased sensitivity to several important chemotherapeutic drugs. The objective of this study was to determine the extent of MCJ promoter methylation in epithelial ovarian cancer patients and address the possible role of MCJ methylation levels in response to chemotherapy in ovarian cancer patients. METHODS: The methylation status of 35 CpG sites within the MCJ CpG island was determined by sequencing of sodium bisulfite modified tumor DNA in 41 patients with stage III/IV epithelial ovarian tumors. Levels of methylation of the MCJ CpG island were then compared with response to therapy and overall survival in the patients. RESULTS: The analysis identified frequent loss of MCJ methylation in ovarian tumors, with only a subset retaining high methylation levels. While 93% (38/41) of tumors examined showed some level of MCJ methylation, only 17% (7/41) retained very high levels (>90% methylation). The presence of such high levels of CpG island methylation correlated significantly with poor response of patients' tumors to therapy (P = 0.027) and poor overall survival (P = 0.023, hazard ratio = 2.9). CONCLUSIONS: These results suggest that MCJ methylation may be useful as a marker of response to chemotherapy in ovarian cancer and are consistent with previous in vitro data linking loss of MCJ expression with drug resistance.

Design, validation, and application of a seven-strain Staphylococcus aureus PCR product microarray for comparative genomics.

Bacterial comparative genomics has been revolutionized by microarrays, but the power of any microarray is dependent on the number and diversity of gene reporters it contains. Staphylococcus aureus is an important human pathogen causing a wide range of invasive and toxin-mediated diseases, and more than 20% of the genome of any isolate consists of variable genes. Seven whole-genome sequences of S. aureus are available, and we exploited this rare opportunity to design, build, and validate a comprehensive, nonredundant PCR product microarray carrying reporters that represent every predicted open reading frame (3,623 probes). Such a comprehensive microarray necessitated a novel design strategy. Validation with the seven sequenced strains showed correct identification of 93.9% of genes present or absent/divergent but was dependent on the method of analysis chosen. Microarray data were highly reproducible, reducing the need for many replicate slides. Interpretation of microarray data was enhanced by focusing on the major areas of variation--the presence or absence of mobile genetic elements (MGEs). We compiled "composite genomes" of every individual MGE and visualized their distribution. This allowed the sensitive discrimination of related isolates, including the first clear description of how isolates of the same clone of epidemic methicillin-resistant S. aureus differ substantially in their carriage of MGEs. These MGEs carry virulence and resistance genes, suggesting differences in pathogenic potential. The novel methods of design and interpretation of data generated from this microarray will enable further studies of S. aureus evolution, epidemiology, and pathogenesis.