Early p53 alterations in mouse skin carcinogenesis by UVB radiation: immunohistochemical detection of mutant p53 protein in clusters of preneoplastic epidermal cells.

High levels of the p53 protein are immunohistochemically detectable in a majority of human nonmelanoma skin cancers and UVB-induced murine skin tumors. These increased protein levels are often associated with mutations in the conserved domains of the p53 gene. To investigate the timing of the p53 alterations in the process of UVB carcinogenesis, we used a well defined murine model (SKH:HR1 hairless mice) in which the time that tumors appear is predictable from the UVB exposures. The mice were subjected to a series of daily UVB exposures, either for 17 days or for 30 days, which would cause skin tumors to appear around 80 or 30 weeks, respectively. In the epidermis of these mice, we detected clusters of cells showing a strong immunostaining of the p53 protein, as measured with the CM-5 polyclonal antiserum. This cannot be explained by transient accumulation of the normal p53 protein as a physiological response to UVB-induced DNA damage. In single exposure experiments the observed transient CM-5 immunoreactivity lasted for only 3 days and was not clustered, whereas these clusters were still detectable as long as 56 days after 17 days of UVB exposure. In addition, approximately 70% of these patches reacted with the mutant-specific monoclonal antibody PAb240, whereas transiently induced p53-positive cells did not. In line with indicative human data, these experimental results in the hairless mouse model unambiguously demonstrate that constitutive p53 alterations are causally related to chronic UVB exposure and that they are a very early event in the induction of skin cancer by UVB radiation.

Fasciclin II controls proneural gene expression in Drosophila.

Fasciclin II (Fas II), an NCAM-like cell adhesion molecule in Drosophila, is expressed on a subset of embryonic axons and controls selective axon fasiculation. Fas II is also expressed in imaginal discs. Here we use genetic analysis to show that Fas II is required for the control of proneural gene expression. Clusters of cells in the eye-antennal imaginal disc express the achaete proneural gene and give rise to mechanosensory neurons; other clusters of cells express the atonal gene and give rise to ocellar photoreceptor neurons. In fasII loss-of-function mutants, the expression of both proneural genes is absent in certain locations, and, as a result, the corresponding sensory precursors fail to develop. In fasII gain-of-function conditions, extra sensory structures arise from this same region of the imaginal disc. Mutations in the Abelson tyrosine kinase gene show dominant interactions with fasII mutations, suggesting that Abl and Fas II function in a signaling pathway that controls proneural gene expression.

Activity-Regulated microRNAs: Modulators of Synaptic Growth at the Drosophila Neuromuscular Junction

It is well established that long-term changes in synaptic structure and function are mediated by rapid activity-dependent gene transcription and new protein synthesis. A growing body of evidence supports the involvement of the microRNA (miRNA) pathway in these processes. We have used the Drosophila neuromuscular junction (NMJ) as a model synapse to characterize activity-regulated miRNAs and their important mRNA targets. Here, we have identified five neuronal miRNAs (miRs-1, -8, -289, -314, and -958) that are significantly downregulated in response to neuronal activity. Furthermore we have discovered that neuronal misexpression of three of these miRNAs (miR-8, -289, and -958) is capable of suppressing new synaptic growth in response to activity suggesting that these miRNAs control the translation of biologically relevant target mRNAs. Putative targets of the activity-regulated miRNAs-8 and -289 are significantly enriched in clusters mapping to functional processes including axon development, pathfinding, and axon growth. We demonstrate that activity-regulated miR-8 regulates the 3'UTR of wingless, a presynaptic regulatory protein involved in the process of activity-dependent axon terminal growth. Additionally, we show that the 3'UTR of the protein tyrosine phosophatase leukocyte antengen related (lar), a protein required for axon guidance and synaptic growth, is regulated by activity-regulated miRNAs-8, -289, and -958 in vitro. Both wg and lar were identified as relevant putative targets for co-regulation based through our functional cluster analysis. One putative target of miR-289 is the Ca2+/calmodulin-dependent protein kinase II (CamKII). While CamKII is not predicted as a target for co-regulation by multiple activity-regulated miRNAs we identified it as an especially pertinent target for analysis in our system for two reasons. First, CamKII has an extremely well characterized role in postsynaptic plasticity, but its presynaptic role is less well characterized and bears further analysis. Second, local translation of CamKII mRNA is regulated in part by the miRNA pathway in an activity-dependent manner in dendrites. We find that the CamKII 3'UTR is regulated by miR-289 in-vitro and this regulation is alleviated by mutating the `seed region' of the miR-289 binding site within the CamKII 3'UTR. Furthermore, we demonstrate a requirement for local translation of CamKII in motoneurons in the process of activity-regulated axon terminal growth.

The regulation of Hox gene expression during animal development

Hox genes encode a family of transcriptional regulators that elicit distinct developmental programmes along the head-to-tail axis of animals. The specific regional functions of individual Hox genes largely reflect their restricted expression patterns, the disruption of which can lead to developmental defects and disease. Here, we examine the spectrum of molecular mechanisms controlling Hox gene expression in model vertebrates and invertebrates and find that a diverse range of mechanisms, including nuclear dynamics, RNA processing, microRNA and translational regulation, all concur to control Hox gene outputs. We propose that this complex multi-tiered regulation might contribute to the robustness of Hox expression during development.

MicroRNAs in pathology and as therapeutic target in gene therapy?

Dissertação para obtenção do grau de Mestre no Instituto Superior de Ciências da Saúde Egas Moniz

Using multiple landscape genetic approaches to test the validity of genetic clusters in a species characterized by an isolation-by-distance pattern

Bayesian clustering methods are typically used to identify barriers to gene flow, but they are prone to deduce artificial subdivisions in a study population characterized by an isolation-by-distance pattern (IbD). Here we analysed the landscape genetic structure of a population of wild boars (Sus scrofa) from south-western Germany. Two clustering methods inferred the presence of the same genetic discontinuity. However, the population in question was characterized by a strong IbD pattern. While landscape-resistance modelling failed to identify landscape features that influenced wild boar movement, partial Mantel tests and multiple regression of distance matrices (MRDMs) suggested that the empirically inferred clusters were separated by a genuine barrier. When simulating random lines bisecting the study area, 60% of the unique barriers represented, according to partial Mantel tests and MRDMs, significant obstacles to gene flow. By contrast, the random-lines simulation showed that the boundaries of the inferred empirical clusters corresponded to the most important genetic discontinuity in the study area. Given the degree of habitat fragmentation separating the two empirical partitions, it is likely that the clustering programs correctly identified a barrier to gene flow. The differing results between the work published here and other studies suggest that it will be very difficult to draw general conclusions about habitat permeability in wild boar from individual studies.

Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling

Thesis (Ph.D.)--University of Washington, 2016-06

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.

Resistance gene analogues in sugarcane and sorghum and their association with quantitative trait loci for rust resistance

Fifty-four different sugarcane resistance gene analogue (RGA) sequences were isolated, characterized, and used to identify molecular markers linked to major disease-resistance loci in sugarcane. Ten RGAs were identified from a sugarcane stem expressed sequence tag (EST) library; the remaining 44 were isolated from sugarcane stem, leaf, and root tissue using primers designed to conserved RGA motifs. The map location of 31 of the RGAs was determined in sugarcane and compared with the location of quantitative trait loci (QTL) for brown rust resistance. After 2 years of phenotyping, 3 RGAs were shown to generate markers that were significantly associated with resistance to this disease. To assist in the understanding of the complex genetic structure of sugarcane, 17 of the 31 RGAs were also mapped in sorghum. Comparative mapping between sugarcane and sorghum revealed syntenic localization of several RGA clusters. The 3 brown rust associated RGAs were shown to map to the same linkage group (LG) in sorghum with 2 mapping to one region and the third to a region previously shown to contain a major rust-resistance QTL in sorghum. These results illustrate the value of using RGAs for the identification of markers linked to disease resistance loci and the value of simultaneous mapping in sugarcane and sorghum.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Clusters of internally primed transcripts reveal novel long noncoding RNAs

Non- protein- coding RNAs ( ncRNAs) are increasingly being recognized as having important regulatory roles. Although much recent attention has focused on tiny 22- to 25- nucleotide microRNAs, several functional ncRNAs are orders of magnitude larger in size. Examples of such macro ncRNAs include Xist and Air, which in mouse are 18 and 108 kilobases ( Kb), respectively. We surveyed the 102,801 FANTOM3 mouse cDNA clones and found that Air and Xist were present not as single, full- length transcripts but as a cluster of multiple, shorter cDNAs, which were unspliced, had little coding potential, and were most likely primed from internal adenine- rich regions within longer parental transcripts. We therefore conducted a genome- wide search for regional clusters of such cDNAs to find novel macro ncRNA candidates. Sixty- six regions were identified, each of which mapped outside known protein- coding loci and which had a mean length of 92 Kb. We detected several known long ncRNAs within these regions, supporting the basic rationale of our approach. In silico analysis showed that many regions had evidence of imprinting and/ or antisense transcription. These regions were significantly associated with microRNAs and transcripts from the central nervous system. We selected eight novel regions for experimental validation by northern blot and RT- PCR and found that the majority represent previously unrecognized noncoding transcripts that are at least 10 Kb in size and predominantly localized in the nucleus. Taken together, the data not only identify multiple new ncRNAs but also suggest the existence of many more macro ncRNAs like Xist and Air.

The identification and characterisation of alleles of sucrose phosphate synthase gene family III in sugarcane

Little is known about the extent of allelic diversity of genes in the complex polyploid, sugarcane. Using sucrose phosphate synthase (SPS) Gene (SPS) Family III as an example, we have amplified and sequenced a 400 nt region from this gene from two sugarcane lines that are parents of a mapping population. Ten single nucleotide polymorphisms (SNPs) were identified within the 400 nt region of which seven were present in both lines. In the elite commercial cultivar Q165(A), 10 sequence haplotypes were identified, with four haplotypes recovered at 9% or greater frequency. Based on SNP presence, two clusters of haplotypes were observed. In IJ76-514, a Saccharum officinarum accession, 8 haplotypes were identified with 4 haplotypes recovered at 13% or greater frequency. Again, two clusters of haplotypes were observed. The results suggest that there may be two SPS Gene Family III genes per genome in sugarcane, each with different numbers of different alleles. This suggestion is supported by sequencing results in an elite parental sorghum line, 403463-2-1, in which 4 haplotypes, corresponding to two broad types, were also identified. Primers were designed to the sugarcane SNPs and screened over bulked DNA from high and low Sucrose-containing progeny from a cross between Q165(A) and IJ76-514. The SNP frequency did not vary in the two bulked DNA samples, suggesting that these SNPs from this SPS gene family are not associated with variation in sucrose content. Using an ecotilling approach, two of the SPS Gene Family III haplotypes were mapped to two different linkage groups in homology group 1 in Q165(A). Both haplotypes mapped near QTLs for increased sucrose content but were not themselves associated with any sugar-related trait.

DNA Mutation of the progranulin (GRN) gene in familial frontotemporal lobar degeneration (FTLD):a study of the pathology of nine cases

Frontotemporal lobar degeneration (FTLD) with transactive response (TAR) DNA-binding protein of 43kDa (TDP-43) proteinopathy (FTLD-TDP) is a neurodegenerative disease characterized by variable neocortical and allocortical atrophy principally affecting the frontal and temporal lobes. Histologically, there is neuronal loss, microvacuolation in the superficial cortical laminae, and a reactive astrocytosis. A variety of TDP-43 immunoreactive changes are present in FTLD-TDP including neuronal cytoplasmic inclusions (NCI), neuronal intranuclear inclusions (NII), dystrophic neurites (DN) and, oligodendroglial inclusions (GI). Many cases of familial FTLD-TDP are caused by DNA mutations of the progranulin (GRN) gene. Hence, the density, spatial patterns, and laminar distribution of the pathological changes were studied in nine cases of FLTD-TDP with GRN mutation. The densities of NCI and DN were greater in cases caused by GRN mutation compared with sporadic cases. In cortical regions, the commonest spatial pattern exhibited by the TDP-43 immunoreactive lesions was the presence of clusters of inclusions regularly distributed parallel to the pia mater. In approximately 50% of cortical gyri, the NCI exhibited a peak of density in the upper cortical laminae while the GI were commonly distributed across all laminae. The distribution of the NII and DN was variable, the most common pattern being a peak of NII density in the lower cortical laminae and DN in the upper cortical laminae. These results suggest in FTLD-TDP caused by GRN mutation: 1) there are greater densities of NCI and DN than in sporadic cases of the disease, 2) there is degeneration of the cortico-cortical and cortico-hippocampal pathways, and 3) cortical degeneration occurs across the cortical laminae, the various TDP-43 immunoreactive inclusions often being distributed in different cortical laminae.

Insulin gene therapy for the treatment of diabetes mellitus

Currently available treatments for insulin-dependent diabetes mellitus are often inadequate in terms of both efficacy and patient compliance. Gene therapy offers the possibility of a novel and improved method by which exogenous insulin can be delivered to a patient. This was approached in the present study by constructing a novel insulin-secreting cell line. For the purposes of this work immortalized cell lines were used. Fibroblasts and pituitary cells were transfected with the human preproisinulin gene to create stable lines of proinsulin- and insulin-secreting cells. The effect of known β-cell secretagogues on these cells were investigated, and found mostly to have no stimulatory effect, although IBMX, arginine and ZnSO4 each increased the rate of secretion. Cyclosporin (CyA) is currently the immunosuppresant of choice for transplant recipients; the effect of this treatment on endogenous β-cell function was assessed both in vivo and in vitro. Therapeutic doses of CyA were found to reduce plasma insulin concentrations and to impair glucose tolerance. The effect of immunoisolation on insulin release by HIT T15 cells was also investigated. The presence of an alginate membrane was found to severely impair insulin release. For the first implantation of the insulin-secreting cells, the animal model selected was the athymic nude mouse. This animal is immunoincompetent, and hence the use of an immunosuppressive regimen is circumvented. Graft function was assessed by measurement of plasma human C peptide concentrations, using a highly specific assay. Intraperitoneal implantation of genetically manipulated insulin-secreting pituitary cells into nude mice subsequently treated with a large dose of streptozotocin (STZ) resulted in a significantly delayed onset of hyperglycaemia when compared to control animals. Consumption of a ZnSO4 solution was shown to increase human C peptide release by the implant. Ensuing studies in nude mice examined the efficacy of different implantation sites, and included histochemical examination of the tumours. Aldehyde fuchsin staining and immunocytochemical processing demonstrated the presence of insulin containing cells within the excised tissue. Following initial investigations in nude mice, implantation studies were performed in CyA-immunosuppressed normal and STZ-diabetic mice. Graft function was found to be less efficacious, possibly due to the subcutaneous implantation site, or to the immunosuppresive regimen. Histochemical and transmission electron microscopic analysis of the tumour-like cell clusters found at autopsy revealed necrosis of cells at the core, but essentially normal cell morphology, with dense secretory granules in peripheral cells. The thesis provides evidence that gene therapy offers a feasibly new approach to insulin delivery.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.