The cancer genome anatomy project: building an annotated gene index

Mapping the brain basis of feelings, emotions and much more: A special issue focused on 'The Human Affectome'

The Human Affectome Project was launched by the non-profit organization Neuroqualia (www.neuroqualia.org) in 2015 with the seemingly impossible goal: To map a psychological process and form possible definitions and working models for affective states and related emotions. Twelve reviews based on emotions, feelings and motivation were written dedicated to mapping the brain basis of affect. A capstone piece 'The Human Affectome' provides a foundation for the special issue by giving detailed up-to-date definitions for key terms including feeling, affect, emotion and mood. Critically, the piece offers an overall model synthesizing three main features of affect: valence, motivation, and arousal. Affect itself is explored as the main umbrella function capturing all feeling states and related processes. Overall, the project and the special issue has been a highly successful interdisciplinary effort producing a novel approach that can be used to understand, guide and revise contemporary research on the brain basis of feeling and how diverse feeling states interact with each other in typical and atypical fashions.

Optimisation of an Electrochemical DNA Sensor for Measuring KRAS G12D and G13D Point Mutations in Different Tumour Types

Circulating tumour DNA (ctDNA) is widely used in liquid biopsies due to having a presence in the blood that is typically in proportion to the stage of the cancer and because it may present a quick and practical method of capturing tumour heterogeneity. This paper outlines a simple electrochemical technique adapted towards point-of-care cancer detection and treatment monitoring from biofluids using a label-free detection strategy. The mutations used for analysis were the KRAS G12D and G13D mutations, which are both important in the initiation, progression and drug resistance of many human cancers, leading to a high mortality rate. A low-cost DNA sensor was developed to specifically investigate these common circulating tumour markers. Initially, we report on some developments made in carbon surface pre-treatment and the electrochemical detection scheme which ensure the most sensitive measurement technique is employed. Following pre-treatment of the sensor to ensure homogeneity, DNA probes developed specifically for detection of the KRAS G12D and G13D mutations were immobilized onto low-cost screen printed carbon electrodes using diazonium chemistry and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride/N-hydroxysuccinimide coupling. Prior to electrochemical detection, the sensor was functionalised with target DNA amplified by standard and specialist PCR methodologies (6.3% increase). Assay development steps and DNA detection experiments were performed using standard voltammetry techniques. Sensitivity (as low as 0.58 ng/μL) and specificity (>300%) was achieved by detecting mutant KRAS G13D PCR amplicons against a background of wild-type KRAS DNA from the representative cancer sample and our findings give rise to the basis of a simple and very low-cost system for measuring ctDNA biomarkers in patient samples. The current time to receive results from the system was 3.5 h with appreciable scope for optimisation, thus far comparing favourably to the UK National Health Service biopsy service where patients can wait for weeks for biopsy results.

One-step isothermal detection of multiple KRAS mutations by forming SNP specific hairpins on a gold nanoshell

We developed a one-step isothermal method for typing multiple KRAS mutations using a designed set of primers to form a hairpin on a gold nanoshell upon being ligated by a SNP specific DNA ligase after binding of targets. As a result, we could detect as low as 20 attomoles of KRAS mutations within 1 h.

Rhazyaminine from Rhazya stricta Inhibits Metastasis and Induces Apoptosis by Downregulating Bcl-2 Gene in MCF7 Cell Line

Background: The role of alkaloids isolated from Rhazya stricta Decne (Apocynaceae family) (RS) in targeting genes involved in cancer and metastasis remains to be elucidated. Objective: Identify and characterize new compounds from RS, which inhibit gene(s) involved in the survival, invasion, self-renewal, and metastatic processes of cancer cells. Methods: Bioinformatics study was performed using HISAT2, stringtie, and ballgown pipeline to understand expressional differences between a normal epithelial cell line-MCF10A and MCF7. NMR and ATR-FTIR were performed to elucidate the structure of rhazyaminine (R.A), isolated from R stricta. Cell viability assay was performed using 0, 25, and 50 μg/mL of total extract of R stricta (TERS) and R.A, respectively, for 0, 24, and 48 hours, followed by scratch assay. In addition, total RNA was isolated for RNA-seq analysis of MCF7 cell line treated with R.A followed by qRT-PCR analysis of Bcl-2 gene. Results: Deptor, which is upregulated in MCF7 compared with MCF10A as found in our bioinformatics study was downregulated by R.A. Furthermore, R.A effectively reduced cell viability to around 50% (P < .05) and restricted cell migration in scratch assay. Thirteen genes, related to metastasis and cancer stem cells, were downregulated by R.A according to RNA-seq analysis. Additionally, qRT-PCR validated the downregulation of Bcl-2 gene in R.A-treated cells by less than 0.5 folds (P < .05). Conclusion: R.A successfully downregulated key genes involved in apoptosis, cell survival, epithelial-mesenchymal transition, cancer stem cell proliferation, and Wnt signal transduction pathway making it an excellent “lead candidate” molecule for in vivo proof-of-concept studies.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Lgr5 expression, cancer stem cells and pancreatic cancer: results from biological and computational analyses

AIMS

To determine the relationship between Lgr5 and other stemness markers and pathologic features in pancreatic ductal adenocarcinoma (PDAC) samples.

MATERIALS & METHODS

In 69 samples, Lgr5 was analyzed by qRT-PCR together with a panel of 29 genes. Bioinformatic analysis was carried out to identify a possible pathway regulating Lgr5 expression in PDAC.

RESULTS

Lgr5 expression was not associated with the expression of tested cancer stem cell markers. Moreover, it was not an independent predictor of survival neither at univariate analysis (p = 0.21) nor at multivariate analysis (p = 0.225).

CONCLUSION

Based on the lack of correlation between Lgr5 and tested cancer stem cell markers, Lgr5 does not seem to be a potential stemness marker or prognostic factor in PDAC.

PCR free multiple ligase reactions and probe cleavages for the SNP detection of KRAS mutation with attomole sensitivity

A method to produce multiple ligated primers without PCR for a target DNA containing a single point mutation is presented.

Predicting disease-related proteins based on clique backbone in protein-protein interaction network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

Constitutive patterns of gene expression regulated by RNA-binding proteins

Background

RNA-binding proteins regulate a number of cellular processes, including synthesis, folding, translocation, assembly and clearance of RNAs. Recent studies have reported that an unexpectedly large number of proteins are able to interact with RNA, but the partners of many RNA-binding proteins are still uncharacterized.

Results

We combined prediction of ribonucleoprotein interactions, based on catRAPID calculations, with analysis of protein and RNA expression profiles from human tissues. We found strong interaction propensities for both positively and negatively correlated expression patterns. Our integration of in silico and ex vivo data unraveled two major types of protein–RNA interactions, with positively correlated patterns related to cell cycle control and negatively correlated patterns related to survival, growth and differentiation. To facilitate the investigation of protein–RNA interactions and expression networks, we developed the catRAPID express web server.

Conclusions

Our analysis sheds light on the role of RNA-binding proteins in regulating proliferation and differentiation processes, and we provide a data exploration tool to aid future experimental studies.

Transcriptome-wide expansion of non-coding regulatory switches: evidence from co-occurrence of Alu exonization, antisense and editing

Non-coding RNAs from transposable elements of human genome are gaining prominence in modulating transcriptome dynamics. Alu elements, as exonized, edited and antisense components within same transcripts could create novel regulatory switches in response to different transcriptional cues. We provide the first evidence for co-occurrences of these events at transcriptome-wide scale through integrative analysis of data sets across diverse experimental platforms and tissues. This involved the following: (i) positional anchoring of Alu exonization events in the UTRs and CDS of 4663 transcript isoforms from RefSeq mRNAs and (ii) mapping on to them A→I editing events inferred from ∼7 million ESTs from dbEST and antisense transcripts identified from virtual serial analysis of gene expression tags represented in Cancer Genome Anatomy Project next-generation sequencing data sets across 20 tissues. We observed significant enrichment of these events in the 3'UTR as well as positional preference within the embedded Alus. More than 300 genes had co-occurrence of all these events at the exon level and were significantly enriched in apoptosis and lysosomal processes. Further, we demonstrate functional evidence of such dynamic interactions between Alu-mediated events in a time series data from Integrated Personal Omics Profiling during recovery from a viral infection. Such 'single transcript-multiple fate' opportunity facilitated by Alu elements may modulate transcriptional response, especially during stress.

dbDEPC 2.0: updated database of differentially expressed proteins in human cancers

A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do.

Human cancer long non-coding RNA transcriptomes

Once thought to be a part of the 'dark matter' of the genome, long non-coding RNAs (lncRNAs) are emerging as an integral functional component of the mammalian transcriptome. LncRNAs are a novel class of mRNA-like transcripts which, despite no known protein-coding potential, demonstrate a wide range of structural and functional roles in cellular biology. However, the magnitude of the contribution of lncRNA expression to normal human tissues and cancers has not been investigated in a comprehensive manner. In this study, we compiled 272 human serial analysis of gene expression (SAGE) libraries to delineate lncRNA transcription patterns across a broad spectrum of normal human tissues and cancers. Using a novel lncRNA discovery pipeline we parsed over 24 million SAGE tags and report lncRNA expression profiles across a panel of 26 different normal human tissues and 19 human cancers. Our findings show extensive, tissue-specific lncRNA expression in normal tissues and highly aberrant lncRNA expression in human cancers. Here, we present a first generation atlas for lncRNA profiling in cancer.

The PathOlogist: an automated tool for pathway-centric analysis

Background

The PathOlogist is a new tool designed to transform large sets of gene expression data into quantitative descriptors of pathway-level behavior. The tool aims to provide a robust alternative to the search for single-gene-to-phenotype associations by accounting for the complexity of molecular interactions.

Results

Molecular abundance data is used to calculate two metrics - 'activity' and 'consistency' - for each pathway in a set of more than 500 canonical molecular pathways (source: Pathway Interaction Database, http://pid.nci.nih.gov). The tool then allows a detailed exploration of these metrics through integrated visualization of pathway components and structure, hierarchical clustering of pathways and samples, and statistical analyses designed to detect associations between pathway behavior and clinical features.

Conclusions

The PathOlogist provides a straightforward means to identify the functional processes, rather than individual molecules, that are altered in disease. The statistical power and biologic significance of this approach are made easily accessible to laboratory researchers and informatics analysts alike. Here we show as an example, how the PathOlogist can be used to establish pathway signatures that robustly differentiate breast cancer cell lines based on response to treatment.

Combination of DNA ligase reaction and gold nanoparticle-quenched fluorescent oligonucleotides: a simple and efficient approach for fluorescent assaying of single-nucleotide polymorphisms

A new fluorescent sensing approach for detection of single-nucleotide polymorphisms (SNPs) is proposed based on the ligase reaction and gold nanoparticle (AuNPs)-quenched fluorescent oligonucleotides. The design exploits the strong fluorescence quenching of AuNPs for organic dyes and the difference in noncovalent interactions of the nanoparticles with single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA), where ssDNA can be adsorbed onto the surface of AuNPs while dsDNA cannot be. In the assay, two half primer DNA probes, one being labeled with a dye and the other being phosphorylated, were first incubated with a target DNA template. In the presence of DNA ligase, the two captured ssDNAs are linked for the perfectly matched DNA target to form a stable duplex, but the duplex could not be formed by the single-base mismatched DNA template. After addition of AuNPs, the fluorescence of dye-tagged DNA probe will be efficiently quenched unless the perfectly matched DNA target is present. To demonstrate the feasibility of this design, the performance of SNP detection using two different DNA ligases, T4 DNA ligase and Escherichia coli DNA ligase, were investigated. In the case of T4 DNA ligase, the signal enhancement of the dye-tagged DNA for perfectly matched DNA target is 4.6-fold higher than that for the single-base mismatched DNA. While in the presence of E. coli DNA ligase, the value raises to be 30.2, suggesting excellent capability for SNP discrimination.

Personalized medicine: marking a new epoch in cancer patient management

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.

Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer

Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.

Speckled-like pattern in the germinal center (SLIP-GC), a nuclear GTPase expressed in activation-induced deaminase-expressing lymphomas and germinal center B cells

We identified a novel GTPase, SLIP-GC, with expression limited to a few tissues, in particular germinal center B cells. It lacks homology to any known proteins, indicating that it may belong to a novel family of GTPases. SLIP-GC is expressed in germinal center B cells and in lymphomas derived from germinal center B cells such as large diffuse B cell lymphomas. In cell lines, SLIP-GC is expressed in lymphomas that express activation-induced deaminase (AID) and that likely undergo somatic hypermutation. SLIP-GC is a nuclear protein, and it localizes to replication factories. Reduction of SLIP-GC levels in the Burkitt lymphoma cell line Raji and in non-Hodgkin lymphoma cell lines resulted in an increase in DNA breaks and apoptosis that was AID-dependent, as simultaneous reduction of AID abrogated the deleterious effects of SLIP-GC reduction. These results strongly suggest that SLIP-GC is a replication-related protein in germinal center B cells whose reduction is toxic to cells through an AID-dependent mechanism.

Transcriptome-guided characterization of genomic rearrangements in a breast cancer cell line

We have identified new genomic alterations in the breast cancer cell line HCC1954, using high-throughput transcriptome sequencing. With 120 Mb of cDNA sequences, we were able to identify genomic rearrangement events leading to fusions or truncations of genes including MRE11 and NSD1, genes already implicated in oncogenesis, and 7 rearrangements involving other additional genes. This approach demonstrates that high-throughput transcriptome sequencing is an effective strategy for the characterization of genomic rearrangements in cancers.

Transcriptome sequencing to detect gene fusions in cancer

Recurrent gene fusions, typically associated with hematological malignancies and rare bone and soft tissue tumors1, have been recently described in common solid tumors2–9. Here we employ an integrative analysis of high-throughput long and short read transcriptome sequencing of cancer cells to discover novel gene fusions. As a proof of concept we successfully utilized integrative transcriptome sequencing to “re-discover” the BCR-ABL110 gene fusion in a chronic myelogenous leukemia cell line and the TMPRSS2-ERG2,3 gene fusion in a prostate cancer cell line and tissues. Additionally, we nominated, and experimentally validated, novel gene fusions resulting in chimeric transcripts in cancer cell lines and tumors. Taken together, this study establishes a robust pipeline for the discovery of novel gene chimeras using high throughput sequencing, opening up an important class of cancer-related mutations for comprehensive characterization.

TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining

Background

Traditional Chinese Medicine (TCM), a complementary and alternative medical system in Western countries, has been used to treat various diseases over thousands of years in East Asian countries. In recent years, many herbal medicines were found to exhibit a variety of effects through regulating a wide range of gene expressions or protein activities. As available TCM data continue to accumulate rapidly, an urgent need for exploring these resources systematically is imperative, so as to effectively utilize the large volume of literature.

Methods

TCM, gene, disease, biological pathway and protein-protein interaction information were collected from public databases. For association discovery, the TCM names, gene names, disease names, TCM ingredients and effects were used to annotate the literature corpus obtained from PubMed. The concept to mine entity associations was based on hypothesis testing and collocation analysis. The annotated corpus was processed with natural language processing tools and rule-based approaches were applied to the sentences for extracting the relations between TCM effecters and effects.

Results

We developed a database, TCMGeneDIT, to provide association information about TCMs, genes, diseases, TCM effects and TCM ingredients mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information are also available for exploring the regulations of genes associated with TCM curative effects. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in understanding the possible therapeutic mechanisms of TCMs via gene regulations and deducing synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. The database is now available at .

Conclusion

TCMGeneDIT is a unique database that offers diverse association information on TCMs. This database integrates TCMs with biomedical studies that would facilitate clinical research and elucidate the possible therapeutic mechanisms of TCMs and gene regulations.

Identification of candidate cancer genes involved in human retinoblastoma by data mining

OBJECTIVE

The objective of this study was to discover potential cancer-related genes involved in retinoblastoma (RB) tumorigenesis.

MATERIALS AND METHODS

Using a data-mining tool called cDNA Digital Gene Expression Displayer (DGED) and serial analysis of gene expression DGED from the Cancer Genome Anatomy Project (CGAP) database, eight cDNA libraries and five serial analysis of gene expression libraries from retinoblastoma (RB) solid tumors and normal retina tissues were analyzed. The deregulated genes were classified into major families using information from Gene Ontology. Several candidate cancer-related genes were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) on tissue microarrays (TMA) of RB and human normal retina samples.

RESULTS

A total of 260 genes with deregulated expression emerged when examined by DGED from the CGAP database. Functional classification of these genes not only provided an interesting insight into RB tumorigenesis but also facilitated target identification for RB therapeutics. Several candidate genes were confirmed by real-time RT-PCR and IHC analysis on TMA and were found to be associated with RB genesis through text-mining in Information Hyperlinked over Proteins. The results also implicated MCM7 and WIF1 as promising therapeutic targets for RB, but further validation is needed.

Human Proteinpedia as a resource for clinical proteomics

Clinical proteomics is an emerging field that deals with the use of proteomic technologies for medical applications. With a major objective of identifying proteins involved in pathological processes and as potential biomarkers, this field is already gaining momentum. Consequently, clinical proteomics data are being generated at a rapid pace, although mechanisms of sharing such data with the biomedical community lag far behind. Most of these data are either provided as supplementary information through journal web sites or directly made available by the authors through their own web resources. Integration of these data within a single resource that displays information in the context of individual proteins is likely to enhance the use of proteomic data in biomedical research. Human Proteinpedia is one such portal that unifies human proteomic data under a single banner. The goal of this resource is to ultimately capture and integrate all proteomic data obtained from individual studies on normal and diseased tissues. We anticipate that harnessing of these data will help prioritize experiments related to protein targets and also permit meta-analysis to uncover molecular signatures of disease. Finally, we encourage all biomedical investigators to maximize dissemination of their valuable proteomic data to rest of the community by active participation in existing repositories such as Human Proteinpedia.

How many human genes can be defined as housekeeping with current expression data?

Background

Housekeeping (HK) genes are ubiquitously expressed in all tissue/cell types and constitute a basal transcriptome for the maintenance of basic cellular functions. Partitioning transcriptomes into HK and tissue-specific (TS) genes relatively is fundamental for studying gene expression and cellular differentiation. Although many studies have aimed at large-scale and thorough categorization of human HK genes, a meaningful consensus has yet to be reached.

Results

We collected two latest gene expression datasets (both EST and microarray data) from public databases and analyzed the gene expression profiles in 18 human tissues that have been well-documented by both two data types. Benchmarked by a manually-curated HK gene collection (HK408), we demonstrated that present data from EST sampling was far from saturated, and the inadequacy has limited the gene detectability and our understanding of TS expressions. Due to a likely over-stringent threshold, microarray data showed higher false negative rate compared with EST data, leading to a significant underestimation of HK genes. Based on EST data, we found that 40.0% of the currently annotated human genes were universally expressed in at least 16 of 18 tissues, as compared to only 5.1% specifically expressed in a single tissue. Our current EST-based estimate on human HK genes ranged from 3,140 to 6,909 in number, a ten-fold increase in comparison with previous microarray-based estimates.

Conclusion

We concluded that a significant fraction of human genes, at least in the currently annotated data depositories, was broadly expressed. Our understanding of tissue-specific expression was still preliminary and required much more large-scale and high-quality transcriptomic data in future studies. The new HK gene list categorized in this study will be useful for genome-wide analyses on structural and functional features of HK genes.

MCAM: a database to accelerate the identification of functional cell adhesion molecules

In the post-genomic era, computational identification of cell adhesion molecules (CAMs) becomes important in defining new targets for diagnosis and treatment of various diseases including cancer. Lack of a comprehensive CAM-specific database restricts our ability to identify and characterize novel CAMs. Therefore, we developed a comprehensive mammalian cell adhesion molecule (MCAM) database. The current version is an interactive Web-based database, which provides the resources needed to search mouse, human and rat-specific CAMs and their sequence information and characteristics such as gene functions and virtual gene expression patterns in normal and tumor tissues as well as cell lines. Moreover, the MCAM database can be used for various bioinformatics and biological analyses including identifying CAMs involved in cell-cell interactions and homing of lymphocytes, hematopoietic stem cells and malignant cells to specific organs using data from high-throughput experiments. Furthermore, the database can also be used for training and testing existing transmembrane (TM) topology prediction methods specifically for CAM sequences. The database is freely available online at http://app1.unmc.edu/mcam.

Tissue microdissection

Procurement of pure populations of cells from heterogeneous histological sections can be accomplished utilizing tissue microdissection. At present, a variety of different manual and laser-based dissection tools are available and each method has particular strengths and weaknesses. The types of biomolecular analyses that can be performed on microdissected cells depend not only on the method of cell procurement, but also on the effects of upstream tissue handling and processing. Tissue preparation protocols include two major approaches; snap-freezing, or, fixation and embedding. Snap-freezing generally provides the best quality tissue for subsequent study, including proteomic analyses such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). Tissue fixatives include either precipitating reagents or biomolecular cross-linkers. The fixed samples are then further processed and embedded in a wax medium. In general, the biomolecules recovered from fixed and embedded tissue specimens are lower in both quantity and quality than those from snap-frozen specimens, although they are useful for certain types of analyses. The protocols provided here for tissue handling and processing, preparation of tissue sections, and microdissection are derived from our experience at the Pathogenetics Unit of the National Cancer Institute.

Comprehensive serial analysis of gene expression of the cervical transcriptome

Background

More than half of the approximately 500,000 women diagnosed with cervical cancer worldwide each year will die from this disease. Investigation of genes expressed in precancer lesions compared to those expressed in normal cervical epithelium will yield insight into the early stages of disease. As such, establishing a baseline from which to compare to, is critical in elucidating the abnormal biology of disease. In this study we examine the normal cervical tissue transcriptome and investigate the similarities and differences in relation to CIN III by Long-SAGE (L-SAGE).

Results

We have sequenced 691,390 tags from four L-SAGE libraries increasing the existing gene expression data on cervical tissue by 20 fold. One-hundred and eighteen unique tags were highly expressed in normal cervical tissue and 107 of them mapped to unique genes, most belong to the ribosomal, calcium-binding and keratinizing gene families. We assessed these genes for aberrant expression in CIN III and five genes showed altered expression. In addition, we have identified twelve unique HPV 16 SAGE tags in the CIN III libraries absent in the normal libraries.

Conclusion

Establishing a baseline of gene expression in normal cervical tissue is key for identifying changes in cancer. We demonstrate the utility of this baseline data by identifying genes with aberrant expression in CIN III when compared to normal tissue.

The Wnt pathway regulator DKK1 is preferentially expressed in hormone-resistant breast tumours and in some common cancer types

In addition to new tumour antigens, new prognostic and diagnostic markers are needed for common cancers. In this study, we report the expression of Dickkopf-1 (DKK1) in multiple common cancers. This constitutes a comprehensive analysis of the DKK1 expression profile. Dickkopf-1 expression was evaluated by classical and quantitative reverse transcriptase–polymerase chain reaction (RT–PCR) and enzyme-linked immunosorbant assay for protein determination, in cancer lines and clinical specimens of several cancer origins. For breast cancer, expression was correlated with clinicopathological parameters. Dickkopf-1 expression was confirmed in several cancer cell lines derived from breast and other common cancers. Dickkopf-1 protein secretion was documented in breast, prostate and lung cancer lines, but was negligible in melanoma. Analysis of DKK1 expression in human cancer specimens revealed DKK1 expression in breast (21 out of 73), lung (11 out of 23) and kidney cancers (six out of 20). Interestingly, DKK1 was preferentially expressed in oestrogen and progesterone receptor-negative tumours (ER⁻/PR⁻; P=0.005) and in tumours from women with a family history of breast cancer (P=0.024). Importantly, DKK1 protein production was confirmed in multiple breast cancer specimens that were positive by RT–PCR. This work establishes DKK1 as a potential prognostic and diagnostic marker for cohorts of breast cancer patients with poor prognosis. Dickkopf-1 may also become a relevant candidate target for immunotherapy of different cancers.

DiscoverySpace: an interactive data analysis application

DiscoverySpace is a graphical application for bioinformatics data analysis. Users can seamlessly traverse references between biological databases and draw together annotations in an intuitive tabular interface. Datasets can be compared using a suite of novel tools to aid in the identification of significant patterns. DiscoverySpace is of broad utility and its particular strength is in the analysis of serial analysis of gene expression (SAGE) data. The application is freely available online.

Mutation analysis of five candidate genes in familial breast cancer

Most of the known breast cancer susceptibility genes (BRCA1, BRCA2, CHEK2 and ATM) are involved in the damage response pathway. Other members of this pathway are therefore good candidates for additional breast cancer susceptibility genes. ATR, along with ATM, plays a central role in DNA damage recognition and Chk1 relays checkpoint signals from both ATR and ATM. PPP2R1B and PPP2R5B code for subunits of protein phosphatase 2A (PP2A), which regulates autophosphorylation of ATM. In addition, EIF2S6/Int-6, which was originally identified as a common integration site for the mouse mammary tumour virus in virally induced mouse mammary tumours, is a candidate breast cancer susceptibility gene because of its putative role in maintaining chromosome stability. To investigate the role of ATR, CHK1, PPP2R1B, PPP2R5B and EIF2S6/Int-6, we carried out mutation analysis of these genes in the index cases from non-BRCA1/BRCA2 breast cancer families. We also screened sporadic breast tumours for somatic mutations in PPP2R1B and PPP2R5B. Although we identified many novel variants, we found no evidence that highly penetrant germline mutations in these five genes contribute to familial breast cancer susceptibility.

The UCSC genome browser database: update 2007

The University of California, Santa Cruz Genome Browser Database contains, as of September 2006, sequence and annotation data for the genomes of 13 vertebrate and 19 invertebrate species. The Genome Browser displays a wide variety of annotations at all scales from the single nucleotide level up to a full chromosome and includes assembly data, genes and gene predictions, mRNA and EST alignments, and comparative genomics, regulation, expression and variation data. The database is optimized for fast interactive performance with web tools that provide powerful visualization and querying capabilities for mining the data. In the past year, 22 new assemblies and several new sets of human variation annotation have been released. New features include VisiGene, a fully integrated in situ hybridization image browser; phyloGif, for drawing evolutionary tree diagrams; a redesigned Custom Track feature; an expanded SNP annotation track; and many new display options. The Genome Browser, other tools, downloadable data files and links to documentation and other information can be found at .

Meta-analysis of published transcriptional and translational fold changes reveals a preference for low-fold inductions

The goals of this study were to gain a better quantitative understanding of the dynamic range of transcriptional and translational response observed in biological systems and to examine the reporting of regulatory events for trends and biases. A straightforward pattern-matching routine extracted 3,408 independent observations regarding transcriptional fold-changes and 1,125 regarding translational fold-changes from over 15 million MEDLINE abstracts. Approximately 95% of reported changes were > or =2-fold. Further, the historical trend of reporting individual fold-changes is declining in favor of high-throughput methods for transcription but not translation. Where it was possible to compare the average fold-changes in transcription and translation for the same gene/product (203 examples), approximately 53% were a < or =2-fold difference, suggesting a loose tendency for the two to be coupled in magnitude. We found also that approximately three-fourths of reported regulatory events have been at the transcriptional level. The frequency distribution appears to be normally distributed and peaks near 2-fold, suggesting that nature selects for a low-energy solution to regulatory responses. Because high-throughput technologies ordinarily sacrifice measurement quality for quantity, this also suggests that many regulatory events may not be reliably detectable by such technologies. Text mining of regulatory events and responses provides additional information incorporable into microarray analysis, such as prior fold-change observations and flagging genes that are regulated post-transcription. All extracted regulation and response patterns can be downloaded at the following website: www.ou.edu/microarray/ oumcf/Meta_analysis.xls.

The LIFEdb database in 2006

LIFEdb () integrates data from large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. New features of LIFEdb include (i) an updated user interface with enhanced query capabilities, (ii) a configurable output table and the option to download search results in XML, (iii) the integration of data from cell-based screening assays addressing the influence of protein-overexpression on cell proliferation and (iv) the display of the relative expression (‘Electronic Northern’) of the genes under investigation using curated gene expression ontology information. LIFEdb enables researchers to systematically select and characterize genes and proteins of interest, and presents data and information via its user-friendly web-based interface.

Examples of the complex architecture of the human transcriptome revealed by RACE and high-density tiling arrays

Recently, we mapped the sites of transcription across approximately 30% of the human genome and elucidated the structures of several hundred novel transcripts. In this report, we describe a novel combination of techniques including the rapid amplification of cDNA ends (RACE) and tiling array technologies that was used to further characterize transcripts in the human transcriptome. This technical approach allows for several important pieces of information to be gathered about each array-detected transcribed region, including strand of origin, start and termination positions, and the exonic structures of spliced and unspliced coding and noncoding RNAs. In this report, the structures of transcripts from 14 transcribed loci, representing both known genes and unannotated transcripts taken from the several hundred randomly selected unannotated transcripts described in our previous work are represented as examples of the complex organization of the human transcriptome. As a consequence of this complexity, it is not unusual that a single base pair can be part of an intricate network of multiple isoforms of overlapping sense and antisense transcripts, the majority of which are unannotated. Some of these transcripts follow the canonical splicing rules, whereas others combine the exons of different genes or represent other types of noncanonical transcripts. These results have important implications concerning the correlation of genotypes to phenotypes, the regulation of complex interlaced transcriptional patterns, and the definition of a gene.

Evidence that public database records for many cancer-associated genes reflect a splice form found in tumors and lack normal splice forms

Alternative splicing is widespread in the human genome, and it appears that many genes display different splice forms in cancerous tissue than in normal human tissues. However, since cDNAs for many cancer-associated genes were originally cloned from tumor samples, it is important to ask whether this repertoire of cDNAs provides a complete or representative picture of the transcript isoforms found in normal tissues. To answer this, we used bioinformatics and RT-PCR to identify novel splice forms, focusing on in-frame exonskips, for a panel of 50 cancer-associated genes in normal tissue samples. These data show that in nearly two-thirds of the genes, normal tissues expressed previously unknown splice forms, of which 40% were normally a dominant splice form. Surprisingly, the tumor-associated splice forms were twice as likely to be represented in GenBank than their normal tissue-associated splice forms, most likely because 70% of the mRNAs in GenBank for these genes were cloned from tumor samples. As an example, we describe a novel normal splice form of IKBbeta, an important regulator of the NFkappaB pathway. Our data suggest that systematic re-evaluation of cancer genes' splice forms in normal tissue will yield insights into their distinct functions in normal tissues and in cancer. Our database contains 1308 novel normal splice forms, including many known cancer genes.

Allelic variation in gene expression identified through computational analysis of the dbEST database

Differential expression between the two alleles of an individual and between people with different genotypes has been commonly observed. Quantitative differences in gene expression between people may provide the genetic basis for the phenotypic difference between individuals and may be the primary cause of complex diseases. In this paper, we developed a computational method to identify genes that displayed allelic variation in gene expression in human EST libraries. To model allele-specific gene expression, we first identified EST libraries in which both A and B alleles were expressed and then identified allelic variation in gene expression based on the EST counts for each allele using a binomial test. Among 1107 SNPs that had a sufficient number of ESTs for the analysis, 524 (47%) displayed allelic variation in at least one cDNA library. We verified experimentally the allelic variation in gene expression for 6 of these SNPs. The frequency of allelic variation observed in EST libraries was similar to the previous studies using the SNP chip and primer extension method. We found that genes that displayed allelic variation were distributed throughout the human genome and were enriched in certain chromosome regions. The SNPs and genes identified in this study will provide a rich source for evaluating the effects of those SNPs and associated haplotypes in human health and diseases.

Enhancement of 5-Fluorouracil Sensitivity by an rTS Signaling Mimic in H630 Colon Cancer Cells

The rTSbeta protein has been hypothesized to synthesize signaling molecules that can down-regulate thymidylate synthase. These molecules share biological and chemical properties with acyl-homoserine lactones (AHL), suggesting some AHLs might act as rTS signaling mimics and down-regulate thymidylate synthase. We have determined that the AHL, 3-oxododecanoyl homoserine lactone (3-oxo-C12-(L)-HSL) can down-regulate thymidylate synthase protein at 10 micromol/L and reduce H630 (human colorectal cancer) growth by 50% at 23 micromol/L (IC50) in cell culture. At its IC50 concentration, 3-oxo-C12-(L)-HSL reduces the apparent IC50 of 5-fluorouracil (5-FU) from 1 micromol/L to 80 nmol/L (12-fold) in a colony formation assay. 3-Oxo-C12-(L)-HSL enhances the activity of 5-fluorodeoxyuridine, tomudex, and taxol but not the activity of 5-fluorouridine, methotrexate or Adriamycin. The unexpected interaction with taxol probably results from effects of the AHL on tubulin expression. Differences in taxol sensitivity, tubulin, and cellular morphology between H630 and the thymidylate synthase and rTSbeta-overproducing, 5-FU-resistant H630-1 cell line as determined by colony formation assays, Western analysis of one-dimensional and two-dimensional gels, and photomicroscopy confirm that cytoskeletal changes are induced by the AHL or by rTS signaling. Isozyme differences in thymidylate synthase and rTSbeta also exist in the two cell lines. Phosphorylation of rTSbeta amino acid S121 is shown to occur and is decreased at least 10-fold in the drug-resistant cells. The data presented provide support for further investigations of rTS signaling mimics as enhancers to thymidylate synthase-directed chemotherapy, evidence that the phosphorylation state of rTSbeta may be a marker for 5-FU resistance and a previously unrealized relationship between rTS signaling and the cytoskeleton.

FACT--a framework for the functional interpretation of high-throughput experiments

BACKGROUND

Interpreting the results of high-throughput experiments, such as those obtained from DNA-microarrays, is an often time-consuming task due to the high number of data-points that need to be analyzed in parallel. It is usually a matter of extensive testing and unknown beforehand, which of the possible approaches for the functional analysis will be the most informative.

RESULTS

To address this problem, we have developed the Flexible Annotation and Correlation Tool (FACT). FACT allows for detection of important patterns in large data sets by simplifying the integration of heterogeneous data sources and the subsequent application of different algorithms for statistical evaluation or visualization of the annotated data. The system is constantly extended to include additional annotation data and comparison methods.

CONCLUSION

FACT serves as a highly flexible framework for the explorative analysis of large genomic and proteomic result sets. The program can be used online; open source code and supplementary information are available at http://www.factweb.de.

Sam68-like mammalian protein 2, identified by digital differential display as expressed by podocytes, is induced in proteinuria and involved in splice site selection of vascular endothelial growth factor

Podocytes, the glomerular epithelial cells of the kidney, share important features with neuronal cells. In addition to phenotypical and functional similarities, a number of gene products have been found to be expressed exclusively or predominantly by both cell types. With the hypothesis of a common transcriptome shared by podocytes and neurons, digital differential display was used to identify novel podocyte-expressed gene products. Comparison of brain and kidney cDNA libraries with those of other organs identified Sam68-like mammalian protein 2 (SLM-2), a member of the STAR family of RNA processing proteins, as expressed by podocytes. SLM-2 expression was found to be restricted in the kidney to podocytes. In proteinuric diseases, SLM-2, a known regulator of neuronal mRNA splice site selection, was found significantly upregulated on mRNA and protein levels. Knockdown of SLM-2 by short interfering RNA in podocytes was performed to evaluate its biologic role. RNA splicing of vascular endothelial growth factor (VEGF), a key regulator of the filtration barrier and expressed as functionally distinct splice isoforms, was evaluated. VEGF(165) expression was found to be reduced by 25% after SLM-2 knockdown. In vivo, the glomerular expression of SLM-2 correlated with the mRNA levels of VEGF(165). This study demonstrates the power of digital differential display to predict cell type-specific gene expression by hypothesis-driven analysis of tissue cDNA libraries. SLM-2-dependent VEGF splicing indicates the importance of mRNA splice site selection for glomerular filtration barrier function.

SNP discovery using advanced algorithms and neural networks

Forage is an application which uses two neural networks for detecting single nucleotide polymorphisms (SNPs). Potential SNP candidates are identified in multiple alignments. Each candidate is then represented by a vector of features, which is classified as SNP or monomorphic by the networks. A validated dataset of SNPs was constructed from experimentally verified SNP data and used for network training and method evalutation.

AVAILABILITY

The package is available at biobase.biotech.kth.se/forage/

A methodology for analyzing SAGE libraries for cancer profiling

Serial Analysis of Gene Expression (SAGE) has proven to be an important alternative to microarray techniques for global profiling of mRNA populations. We have developed preprocessing methodologies to address problems in analyzing SAGE data due to noise caused by sequencing error, normalization methodologies to account for libraries sampled at different depths, and missing tag imputation methodologies to aid in the analysis of poorly sampled SAGE libraries. We have also used subspace selection using the Wilcoxon rank sum test to exclude tags that have similar expression levels regardless of source. Using these methodologies we have clustered, using the OPTICS algorithm, 88 SAGE libraries derived from cancerous and normal tissues as well as cell line material. Our results produced eight dense clusters representing ovarian cancer cell line, brain cancer cell line, brain cancer bulk tissue, prostate tissue, pancreatic cancer, breast cancer cell line, normal brain, and normal breast bulk tissue. The ovarian cancer and brain cancer cell lines clustered closely together, leading to a further investigation on possible associations between these two cancer types. We also investigated the utility of gene expression data in the classification between normal and cancerous tissues. Our results indicate that brain and breast cancer libraries have strong identities allowing robust discrimination from their normal counterparts. However, the SAGE expression data provide poor predictive accuracy in discriminating between prostate and ovarian cancers and their respective normal tissues.

Application of bioinformatics in cancer epigenetics

With the completion of the human genome sequence and the advent of high-throughput genomics-based technologies, it is now possible to study the entire human genome and epigenome. The challenge in the next decade of biomedical research is to functionally annotate the genome, epigenome, transcriptome, and proteome. High-throughput genome technology has already produced massive amounts of data including genome sequences, single nucleotide polymorphisms, and microarray gene expression. Our ability to manage and analyze data needs to match the speed of data acquisition. We will summarize our studies of allele-specific gene expression using genomic and computational approaches and identification of sequence motifs that are signature of imprinted genes. We will also discuss about how bioinformatics can facilitate epigenetic researches.

A transcript finishing initiative for closing gaps in the human transcriptome

We report the results of a transcript finishing initiative, undertaken for the purpose of identifying and characterizing novel human transcripts, in which RT-PCR was used to bridge gaps between paired EST clusters, mapped against the genomic sequence. Each pair of EST clusters selected for experimental validation was designated a transcript finishing unit (TFU). A total of 489 TFUs were selected for validation, and an overall efficiency of 43.1% was achieved. We generated a total of 59,975 bp of transcribed sequences organized into 432 exons, contributing to the definition of the structure of 211 human transcripts. The structure of several transcripts reported here was confirmed during the course of this project, through the generation of their corresponding full-length cDNA sequences. Nevertheless, for 21% of the validated TFUs, a full-length cDNA sequence is not yet available in public databases, and the structure of 69.2% of these TFUs was not correctly predicted by computer programs. The TF strategy provides a significant contribution to the definition of the complete catalog of human genes and transcripts, because it appears to be particularly useful for identification of low abundance transcripts expressed in a restricted set of tissues as well as for the delineation of gene boundaries and alternatively spliced isoforms.

The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags

Whereas genome sequencing defines the genetic potential of an organism, transcript sequencing defines the utilization of this potential and links the genome with most areas of biology. To exploit the information within the human genome in the fight against cancer, we have deposited some two million expressed sequence tags (ESTs) from human tumors and their corresponding normal tissues in the public databases. The data currently define approximately 23,500 genes, of which only approximately 1,250 are still represented only by ESTs. Examination of the EST coverage of known cancer-related (CR) genes reveals that <1% do not have corresponding ESTs, indicating that the representation of genes associated with commonly studied tumors is high. The careful recording of the origin of all ESTs we have produced has enabled detailed definition of where the genes they represent are expressed in the human body. More than 100,000 ESTs are available for seven tissues, indicating a surprising variability of gene usage that has led to the discovery of a significant number of genes with restricted expression, and that may thus be therapeutically useful. The ESTs also reveal novel nonsynonymous germline variants (although the one-pass nature of the data necessitates careful validation) and many alternatively spliced transcripts. Although widely exploited by the scientific community, vindicating our totally open source policy, the EST data generated still provide extensive information that remains to be systematically explored, and that may further facilitate progress toward both the understanding and treatment of human cancers.

What can digital transcript profiling reveal about human cancers?

Important biological and clinical features of malignancy are reflected in its transcript pattern. Recent advances in gene expression technology and informatics have provided a powerful new means to obtain and interpret these expression patterns. A comprehensive approach to expression profiling is serial analysis of gene expression (SAGE), which provides digital information on transcript levels. SAGE works by counting transcripts and storing these digital values electronically, providing absolute gene expression levels that make historical comparisons possible. SAGE produces a comprehensive profile of gene expression and can be used to search for candidate tumor markers or antigens in a limited number of samples. The Cancer Genome Anatomy Project has created a SAGE database of human gene expression levels for many different tumors and normal reference tissues and provides online tools for viewing, comparing, and downloading expression profiles. Digital expression profiling using SAGE and informatics have been useful for identifying genes that have a role in tumor invasion and other aspects of tumor progression.

Ethical hurdles for translational research

The notion of translational research has gained considerable currency over the past few years. While such an approach promises great scientific and clinical advances, the penumbra of translational research tends to incorporate prioritizing scientific projects based upon their potential for translation; tight financial connections between sponsors, scientists and clinical investigators; and sometimes research involving biological approaches for which there is little experience determining safety. It is these aspects of translational research that raise some serious ethical challenges. In this report, we examine three specific areas that raise ethical questions: (1) the potential implications of prioritizing research objectives based on the potential for translation; (2) cautions related to moving from bench to bedside (and back again); and (3) unique questions for translational research initiatives in academic medical centers. Based on this examination, it is clear that the financial and ethical costs as well as benefits of taking a translational approach need to be considered. In the meantime, exquisite attention needs to be paid whenever translational research is likely to affect the traditional fiduciary responsibilities of scientists, clinicians and institutions to research subjects, patients and students. Successful mechanisms that might be developed to address any untoward effects should be shared and evaluated.

Use of transcriptome data to unravel the fine structure of genes involved in sepsis

The sequence of the human genome is providing researchers with the scaffold upon which genes are built. The definition of the boundaries of the genes themselves and of their complex architecture requires a mapping of the transcriptome to the genome. A methodology was developed for generating a detailed transcriptome map and for reconstituting transcripts by using the genome as a template. As a demonstration of the potential of this method, the structure of the human Toll-like receptor (TLR) genes was reevaluated. For all TLR genes for which a genomic sequence was available (i.e., all except TLR10), novel features of the gene structure were discovered. These features include multiple alternative polyadenylation sites, additional exons or splice variants, and overlaps with other genes. These findings have implications for the analysis of TLR gene expression and for the diversity of the proteins encoded by these genes.

Serial analysis of gene expression (SAGE): advances, analysis and applications to pigment cell research

As cells progress from normal to diseased states, they may undergo a series of gene expression changes. Advances in molecular biology allow us to examine a host of these changes at once, in a high throughput fashion. Serial analysis of gene expression (SAGE) allows for the expression profiling of the complete transcriptome of a given cell, and has the potential for identifying novel genes as well as those in low abundance. In this review, we will outline the technique, how one analyzes the massive amounts of data generated, and describe pigment cell libraries currently in the making.

Sequence-based cancer genomics: progress, lessons and opportunities

Technologies that provide a genome-wide view offer an unprecedented opportunity to scrutinize the molecular biology of the cancer cell. The information that is derived from these technologies is well suited to the development of public databases of alterations in the cancer genome and its expression. Here, we describe the synergistic efforts of research programmes in Brazil, the United Kingdom and the United States towards building integrated databases that are widely accessible to the research community, to enable basic and applied applications in cancer research.