Molecular archaeology

Advances in technologies such as mass spectrometry and capillary electrophoresis have encouraged the study of ancient lipids and other ancient biomolecules. Now, microarray technology looks set to revolutionise the study of ancient DNA, perhaps with as much impact as that of PCR.

Characterization of mouse profilaggrin: Evidence for nuclear engulfment and translocation of the profilaggrin B-domain during epidermal differentiation

Filaggrin is a keratin filament associated protein that is expressed in granular layer keratinocytes and derived by sequential proteolysis from a polyprotein precursor termed profilaggrin. Depending on the species, each profilaggrin molecule contains between 10 and 20 filaggrin subunits organized as tandem repeats with a calcium-binding domain at the N-terminal end. We now report the characterization of the complete mouse gene. The structural organization of the mouse gene is identical to the human profilaggrin gene and consists of three exons with a 4 kb intron within the 5' noncoding region and a 1.7 kb intron separating the sequences encoding the calcium-binding EF-hand motifs. A processed pseudogene was found embedded within the second intron. The third and largest exon encodes the second EF-hand, a basic domain (designated the B-domain) followed by 12 filaggrin repeats and a unique C-terminal tail domain. A polyclonal anti-body raised against the conceptually translated sequence of the B-domain specifically stained keratohyalin granules and colocalized with a filaggrin antibody in granular layer cells. In upper granular layer cells, B-domain containing keratohyalin granules were in close apposition to the nucleus and, in some cells, appeared to be completely engulfed by the nucleus. In transition layer cells, B-domain staining was evident in the nucleus whereas filaggrin staining remained cytoplasmic. Nuclear staining of the B-domain was also observed in primary mouse keratinocytes induced to differentiate. This study has also revealed significant sequence homology between the mouse and human promoter sequences and in the calcium-binding domain but the remainder of the protein-coding region shows substantial divergence.

Expression analysis of delta-catenin and prostate-specific membrane antigen: Their potential as diagnostic markers for prostate cancer

The current approach to prostate cancer diagnosis has major limitations including the inability of prostate-specific antigen (PSA) assays to accurately differentiate between prostate cancer and benign prostate hyperplasia (BPH) and the imprecision of transrectal ultrasound (TRUS) biopsy sampling. We have employed cDNA microarray screening to compare gene expression patterns in BPH and tumour samples to identify expression markers that may be useful in discriminating between these conditions. Screening of 3 individual cDNA arrays identified 8 genes with expression 3-fold greater in 6 tumour tissues than in 1 nontumour sample and I BPH sample. Real-time PCR was used to confirm the overexpression of these 8 genes and 12 genes selected from the literature against a panel of 17 tumours and I 1 BPH samples. Two genes, delta-catenin (delta-catenin; CTNND2) and prostate-specific membrane antigen (PSMA; FOLH1), were significantly overexpressed in prostate cancer compared to BPH. Prostate epithelial cells stained positively for S-catenin and PSMA in our prostate cancer tissues, whereas the majority of our BPH tissues were negative for both markers. Thus we have identified delta-catenin (not previously associated with prostatic adenocarcinoma) and confirmed the potential of PSMA as potential candidates for the diagnosis and management of prostate cancer. (C) 2002 Wiley-Liss. Inc.

Novel genes regulated by Sonic Hedgehog in pluripotent mesenchymal cells

Sonic Hedgehog is a secreted morphogen involved in patterning a wide range of structures in the developing embryo. Disruption of the Hedgehog signalling cascade leads to a number of developmental disorders and plays a key role in the formation of a range of human cancers. The identification of genes regulated by Hedgehog is crucial to understanding how disruption of this pathway leads to neoplastic transformation. We have used a Sonic Hedgehog (Shh) responsive mouse cell line, C3H/10T1/2, to provide a model system for hedgehog target gene discovery. Following activation of cell cultures with Shh, RNA was used to interrogate microarrays to investigate downstream transcriptional consequences of hedgehog stimulation. As a result 11 target genes have been identified, seven of which are induced (Thrombomodulin, GILZ, BF-2, Nr4a1, IGF2, PMP22, LASP1) and four of which are repressed (SFRP-1, SFRP-2, Mip1-gamma, Amh) by Shh. These targets have a diverse range of putative functions and include transcriptional regulators and molecules known to be involved in regulating cell growth or apoptosis. The corroboration of genes previously implicated in hedgehog signalling, along with the finding of novel targets, demonstrates both the validity and power of the C3H/10T1/2 system for Shh target gene discovery.

The mononuclear phagocyte system revisited

The mononuclear phagocyte system (MPS) was defined as a family of cells comprising bone marrow progenitors, blood monocytes, and tissue macrophages. In this review, we briefly consider markers for cells of this lineage in the mouse, especially the F4/80 surface antigen and the receptor for macrophage colony-stimulating factor. The concept of the MPS is challenged by evidence that there is a separate embryonic phagocyte lineage, the blurring of the boundaries between macrophages and other cells types arising from phenotypic plasticity and transdifferentiation, and evidence of local renewal of tissue macrophage populations as opposed to monocyte recruitment. Nevertheless, there is a unity to cells of the MPS suggested by their location, morphology, and shared markers. We discuss the origins of macrophage heterogeneity and argue that macrophages and antigen-representing dendritic cells are closely related and part of the MPS.

Modelling High-Dimensional Data by Mixtures of Factor Analyzers

We focus on mixtures of factor analyzers from the perspective of a method for model-based density estimation from high-dimensional data, and hence for the clustering of such data. This approach enables a normal mixture model to be fitted to a sample of n data points of dimension p, where p is large relative to n. The number of free parameters is controlled through the dimension of the latent factor space. By working in this reduced space, it allows a model for each component-covariance matrix with complexity lying between that of the isotropic and full covariance structure models. We shall illustrate the use of mixtures of factor analyzers in a practical example that considers the clustering of cell lines on the basis of gene expressions from microarray experiments. (C) 2002 Elsevier Science B.V. All rights reserved.

Gene Expression in Brain: A Window on Ethanol Dependence, Neuroadaptation, and Preference

This article represents the proceedings of a symposium at the 2002 joint RSA/ISBRA Conference in San Francisco, California. The organizer was Paula L. Hoffman and the co-chairs were Paula L. Hoffman and Michael Miles. The presentations were (1) Introduction and overview of the use of DNA microarrays, by Michael Miles; (2) DNA microarray analysis of gene expression in brains of P and NP rats, by Howard J. Edenberg; (3) Gene expression patterns in brain regions of AA and ANA rats, by Wolfgang Sommer; (4) Patterns of gene expression in brains of selected lines of mice that differ in ethanol tolerance, by Boris Tabakoff; (5) Gene expression profiling related to initial sensitivity and tolerance in gamma-protein kinase C mutants, by Jeanne Wehner; and (6) Gene expression patterns in human alcoholic brain: from microarrays to protein profiles, by Joanne Lewohl.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

Chromosomal gains and losses in ocular melanoma detected by comparative genomic hybridization in an Australian population-based study

To define the location of potential oncogenes and tumor suppressor genes in ocular melanoma we carried out comparative genomic hybridization (CGH) analysis on a population-based series of 25 formalin-fixed, paraffin-embedded primary tumors comprising 17 choroidal, 2 ciliary body, 4 iris, and 2 conjunctival melanomas. Twelve (48%) of the 25 melanomas showed no chromosomal changes and 13 (52%) had at least one chromosomal gain or loss. The mean number of CGH changes in all tumors was 3.3, with similar mean numbers of chromosomal gains (1.5) and losses (1.8). The highest number of chromosomal changes (i.e., nine) occurred in a conjunctival melanoma and included four changes not observed in tumors at any other ocular site (gains in 22q and 11p and losses in 6p and 17p). The most frequent gains in all primary ocular melanomas were on chromosome arm 8q (69%), 6p (31%) and 8p (23%) and the most frequent losses were on 6q (38%), 10q (23%), and 16q (23%). The most common pairing was gain in 8p and gain in 8q, implying a whole chromosome copy number increase; gains in 8p occurred only in conjunction with gains in 8q. The smallest regions of copy number alteration were mapped to gain of 8q21 and loss of 6q21, 10q21, and 16q22. Sublocalization of these chromosomal changes to single-band resolution should accelerate the identification of genes involved in the genesis of ocular melanoma.

Influence of ageing, heat shock treatment and in vivo total antioxidant status on gene-expression profile and protein synthesis in human peripheral lymphocytes

Ageing results in a progressive, intrinsic and generalised imbalance of the control of regulatory systems. A key manifestation of this complex biological process includes the attenuation of the universal stress response. Here we provide the first global assessment of the ageing process as it affects the heat shock response, utilising human peripheral lymphocytes and cDNA microarray analysis. The genomic approach employed in our preliminary study was supplemented with a proteomic approach. In addition, the current study correlates the in vivo total antioxidant status with the age-related differential gene expression as well as the translational kinetics of heat shock proteins (hsps). Most of the genes encoding stress response proteins on the 4224 element microarray used in this study were significantly elevated after heat shock treatment of lymphocytes obtained from both young and old individuals albeit to a greater extent in the young. Cell signaling and signal transduction genes as well as some oxidoreductases showed varied response. Results from translational kinetics of induction of major hsps, from 0 to 24 It recovery period were broadly consistent with the differential expression of HSC 70 and HSP 40 genes. Total antioxidant levels in plasma from old individuals were found to be significantly lower by comparison with young, in agreement with the widely acknowledged role of oxidant homeostasis in the ageing process. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on samples subgroups

Background: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. Results: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. Conclusion: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.

Deregulated expression of selected histone methylases and demethylases in prostate carcinoma

Prostate cancer (PCa), a leading cause of cancer-related morbidity and mortality, arises through the acquisition of genetic and epigenetic alterations. Deregulation of histone methyltransferases (HMTs) or demethylases (HDMs) has been associated with PCa development and progression. However, the precise influence of altered HMTs or HDMs expression and respective histone marks in PCa onset and progression remains largely unknown. To clarify the role of HMTs and HDMs in prostate carcinogenesis, expression levels of 37 HMTs and 20 HDMs were assessed in normal prostate and PCa tissue samples by RT-qPCR. SMYD3, SUV39H2, PRMT6, KDM5A, and KDM6A were upregulated, whereas KMT2A-E (MLL1-5) and KDM4B were downregulated in PCa, compared with normal prostate tissues. Remarkably, PRMT6 was the histone modifier that best discriminated normal from tumorous tissue samples. Interestingly, EZH2 and SMYD3 expression levels significantly correlated with less differentiated and more aggressive tumors. Remarkably, SMYD3 expression levels were of independent prognostic value for the prediction of disease-specific survival of PCa patients with clinically localized disease submitted to radical prostatectomy. We concluded that expression profiling of HMTs and HDMs, especially SMYD3, might be of clinical usefulness for the assessment of PCa patients and assist in pre-therapeutic decision-making.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Role of the DHH1 Gene in the Regulation of Monocarboxylic Acids Transporters Expression in Saccharomyces cerevisiae

Previous experiments revealed that DHH1, a RNA helicase involved in the regulation of mRNA stability and translation, complemented the phenotype of a Saccharomyces cerevisiae mutant affected in the expression of genes coding for monocarboxylic-acids transporters, JEN1 and ADY2 (Paiva S, Althoff S, Casal M, Leao C. FEMS Microbiol Lett, 1999, 170∶301–306). In wild type cells, JEN1 expression had been shown to be undetectable in the presence of glucose or formic acid, and induced in the presence of lactate. In this work, we show that JEN1 mRNA accumulates in a dhh1 mutant, when formic acid was used as sole carbon source. Dhh1 interacts with the decapping activator Dcp1 and with the deadenylase complex. This led to the hypothesis that JEN1 expression is post-transcriptionally regulated by Dhh1 in formic acid. Analyses of JEN1 mRNAs decay in wild-type and dhh1 mutant strains confirmed this hypothesis. In these conditions, the stabilized JEN1 mRNA was associated to polysomes but no Jen1 protein could be detected, either by measurable lactate carrier activity, Jen1-GFP fluorescence detection or western blots. These results revealed the complexity of the expression regulation of JEN1 in S. cerevisiae and evidenced the importance of DHH1 in this process. Additionally, microarray analyses of dhh1 mutant indicated that Dhh1 plays a large role in metabolic adaptation, suggesting that carbon source changes triggers a complex interplay between transcriptional and post-transcriptional effects.

Epidemiological characterization, antimicrobial resistance and virulence mechanisms of human and animal streptococci

Dissertação para obtenção do Grau de Doutor em Biologia