Abstracts


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Functional Properties and Genomics of Glucose Transporters
F.-Q. Zhao and A.F. Keating

Glucose is the major energy source for mammalian cells as well as an important substrate for protein and lipid synthesis. Mammalian cells take up glucose from extracellular fluid into the cell through two families of structurally-related glucose transporters. The facilitative glucose transporter family (solute carriers SLC2A, protein symbol GLUT) mediates a bidirectional and energy-independent process of glucose transport in most tissues and cells, while the Na⁺/glucose cotransporter family (solute carriers SLC5A, protein symbol SGLT) mediates an active, Na⁺-linked transport process against an electrochemical gradient. The GLUT family consists of thirteen members (GLUT1-12 and HMIT). Phylogenetically, the members of the GLUT family are split into three classes based on protein similarities. Up to now, at least six members of the SGLT family have been cloned (SGLT1-6). In this review, we report both the genomic structure and function of each transporter as well as intra-species comparative genomic analysis of some of these transporters. The affinity for glucose and transport kinetics of each transporter differs and ranges from 0.2 to 17mM. The ability of each protein to transport alternative substrates also differs and includes substrates such as fructose and galactose. In addition, the tissue distribution pattern varies between species. There are different regulation mechanisms of these transporters. Characterization of transcriptional control of some of the gene promoters has been investigated and alternative promoter usage to generate different protein isoforms has been demonstrated. We also introduce some pathophysiological roles of these transporters in human.


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Enjoy the Silence: The Story of let-7 MicroRNA and Cancer
J. Torrisani, L. Parmentier, L. Buscail and P. Cordelier

Cancer is a multi-step disease involving dynamic changes in the genome. However, studies on cancer genome so far have focused most heavily on protein-coding genes, and our knowledge on alterations of the functional noncoding sequences in cancer is largely absent. MicroRNAs (miRNAs) are endogenous small noncoding RNAs weighing 20 to 23 nucleotides that negatively regulate gene expression at the posttranscriptional level by base pairing to the 3' untranslated region of target messenger RNAs. Hundreds of miRNAs have been identified in humans and are evolutionarily conserved from plants to animals. These tiny but potent molecules regulate various physiological and pathological pathways such as cell differentiation and cell proliferation. Recently, miRNA alterations have been linked to the initiation and the progression of human cancer. As a consequence, MiRNA-expression profiling of human tumors has identified signatures associated with diagnosis, staging, progression, prognosis and response to treatment. In addition, profiling has been exploited to identify miRNA genes that might represent downstream targets of activated oncogenic pathways, or that target protein-coding genes involved in cancer. Of importance, pioneering studies described let-7 miRNA as a negative regulator of the oncogenic family of Ras guanosine triphosphatases in both Caenorhabditis elegans and human tumor cell lines. Later, let-7 expression deregulation was reported in several cancers, suggesting that let-7 may act as a tumor suppressor. This review will discuss the late insights in let-7 function, the relationship between let-7 and tumorigenesis, and the potential for modulating let-7 expression for the treatment of cancer.


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Real-Time PCR: Revolutionizing Detection and Expression Analysis of Genes
S.A. Deepak, K.R. Kottapalli, R. Rakwal, G. Oros, K.S. Rangappa, H. Iwahashi, Y. Masuo and G.K. Agrawal

Invention of polymerase chain reaction (PCR) technology by Kary Mullis in 1984 gave birth to real-time PCR. Real-time PCR — detection and expression analysis of gene(s) in real-time — has revolutionized the 21^st century biological science due to its tremendous application in quantitative genotyping, genetic variation of inter and intra organisms, early diagnosis of disease, forensic, to name a few. We comprehensively review various aspects of real-time PCR, including technological refinement and application in all scientific fields ranging from medical to environmental issues, and to plant.


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Validation of Computational Methods in Genomics
E.R. Dougherty, J. Hua and M.L. Bittner

High-throughput technologies for genomics provide tens of thousands of genetic measurements, for instance, gene-expression measurements on microarrays, and the availability of these measurements has motivated the use of machine learning (inference) methods for classification, clustering, and gene networks. Generally, a design method will yield a model that satisfies some model constraints and fits the data in some manner. On the other hand, a scientific theory consists of two parts: (1) a mathematical model to characterize relations between variables, and (2) a set of relations between model variables and observables that are used to validate the model via predictive experiments. Although machine learning algorithms are constructed to hopefully produce valid scientific models, they do not ipso facto do so. In some cases, such as classifier estimation, there is a well-developed error theory that relates to model validity according to various statistical theorems, but in others such as clustering, there is a lack of understanding of the relationship between the learning algorithms and validation. The issue of validation is especially problematic in situations where the sample size is small in comparison with the dimensionality (number of variables), which is commonplace in genomics, because the convergence theory of learning algorithms is typically asymptotic and the algorithms often perform in counter-intuitive ways when used with samples that are small in relation to the number of variables. For translational genomics, validation is perhaps the most critical issue, because it is imperative that we understand the performance of a diagnostic or therapeutic procedure to be used in the clinic, and this performance relates directly to the validity of the model behind the procedure. This paper treats the validation issue as it appears in two classes of inference algorithms relating to genomics – classification and clustering. It formulates the problem and reviews salient results.


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Molecular Genetics of Alcohol Dependence and Related Endophenotypes
Y.L. Strat, N. Ramoz, G. Schumanna and P. Gorwood

Alcohol dependence is a worldwide public health problem, and involves both environmental and genetic vulnerability factors. The heritability of alcohol dependence is rather high, ranging between 50% and 60%, although alcohol dependence is a polygenic, complex disorder.

Genome-wide scans on large cohorts of multiplex families, including the collaborative study on genetics of alcoholism (COGA), emphasized the role of many chromosome regions and some candidate genes. The genes encoding the alcohol-metabolizing enzymes, or those involved in brain reward pathways, have been involved. Since dopamine is the main neurotransmitter in the reward circuit, genes involved in the dopaminergic pathway represent candidates of interest. Furthermore, gamma-amino-butyric acid (GABA) neurotransmitter mediates the acute actions of alcohol and is involved in withdrawal symptomatology. Numerous studies showed an association between variants within GABA receptors genes and the risk of alcohol dependence.

In accordance with the complexity of the “alcohol dependence” phenotype, another field of research, related to the concept of endophenotypes, received more recent attention. The role of vulnerability genes in alcohol dependence is therefore re-assessed focusing on different phenotypes and endophenotypes. The latter include brain oscillations, EEG alpha and beta variants and alpha power, and amplitude of P300 amplitude elicited from a visual oddball task.

Recent enhancement on global characterizations of the genome by high-throughput approach for genotyping of polymorphisms and studies of transcriptomics and proteomics in alcohol dependence is also reviewed.


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A Systematic Review of Meta-Analyses on Gene Polymorphisms and Gastric Cancer Risk
F. Gianfagna, E. De Feo, C.M. van Duijn, G. Ricciardi and S. Boccia

Background. Individual variations in gastric cancer risk have been associated in the last decade with specific variant alleles of different genes that are present in a significant proportion of the population. Polymorphisms may modify the effects of environmental exposures, and these gene-environment interactions could partly explain the high variation of gastric cancer incidence around the world. The aim of this report is to carry out a systematic review of the published meta-analyses of studies investigating the association between gene polymorphisms and gastric cancer risk, and describe their impact at population level. Priorities on the design of further primary studies are then provided.

Methods. A structured bibliographic search on Medline and EMBASE databases has been performed to identify meta-analyses on genetic susceptibility to gastric cancer, without restriction criteria. We report the main results of the meta-analyses and we describe the subgroup analyses performed, focusing on the detection of statistical heterogeneity. We investigated publication bias by pooling the primary studies included in the meta-analyses, and we computed the population attributable risk (PAR) for each polymorphism.

Results. Twelve meta-analyses and one pooled-analysis of community based genetic association studies were included, focusing on nine genes involved in inflammation (IL-1β, IL-1RN, IL-8), detoxification of carcinogens (GSTs, CYP2E1), folate metabolism (MTHFR), intercellular adhesion (E-cadherin) and cell cycle regulation (p53). According to their ran-dom-Odds Ratios, individuals carrying one of the IL-1RN *2, IL-1β -511T variant alleles or homozygotes for MTHFR 677T are significantly at higher risk of gastric cancer than those with the wild type homozygote genotypes, showing high PARs. The main sources of heterogeneity in the meta-analyses were ethnicity, quality of the primary study, and selected environmental co-exposures. Effect modification by Helicobacter pylori infection for subjects carrying the unfavourable variant of IL-1 polymorphisms and by low folate intake for individuals homozygotes for MTHFR 677T allele has been reported, while genes involved in the detoxification of carcinogens show synergistic interactions. Publication bias was observed (Egger test, p = 0.03).

Discussion. The published meta-analyses included in our systematic review focused on polymorphisms having a small effect in increasing gastric cancer risk per se. Nevertheless, the risk increase by interacting with environmental exposures and in combination with additional unfavourable polymorphisms. Unfortunately meta-analyses are underpowered for many subgroup analyses, so additional primary studies performed on larger population and collecting data on environmental and genetic co-exposures are demanded.


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MicroRNA and Cancer: Tiny Molecules with Major Implications
T.G. VandenBoom II, Y. Li, P.A. Philip and F.H. Sarkar

Cancer is currently a major public health problem and, as such, emerging research is making significant progress in identifying major players in its biology. One recent topic of interest involves microRNAs (miRNAs) which are small, non-coding RNA molecules that inhibit gene expression post-transcriptionally. They accomplish this by binding to the 3’ untranslated region (3’UTR) of target messengerRNA (mRNA), resulting in either their degradation or inhibition of translation, depending on the degree of complementary base pairing. They are transcribed by RNA polymerase II and are formed into mature miRNAs via two steps, each catalyzed by a different ribonuclease III (RNaseIII). Cross-species comparisons demonstrate that miRNAs are evolutionarily conserved and play important roles in a wide array of normal biological processes. Importantly, aberrant miRNA expression is correlated with human disease, especially in the development of cancer. Recent research has identified targets and functions of miRNAs, illustrating that some are oncogenic in nature while others show tumor suppressor activity. The miRNAs have also been characterized as having high potential in the clinical arena and, as such, have been a target for exploitation toward cancer therapy. Not only has it been shown that miRNA expression profiles may prove useful as diagnostic and prognostic markers in cancer, various miRNA-based therapies show promise as well. It is anticipated that further research will elucidate the benefits of using miRNAs as clinical agents in the battle against cancer and other chronic diseases.


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Validation of Inference Procedures for Gene Regulatory Networks
E.R. Dougherty

The availability of high-throughput genomic data has motivated the development of numerous algorithms to infer gene regulatory networks. The validity of an inference procedure must be evaluated relative to its ability to infer a model network close to the ground-truth network from which the data have been generated. The input to an inference algorithm is a sample set of data and its output is a network. Since input, output, and algorithm are mathematical structures, the validity of an inference algorithm is a mathematical issue. This paper formulates validation in terms of a semi-metric distance between two networks, or the distance between two structures of the same kind deduced from the networks, such as their steady-state distributions or regulatory graphs. The paper sets up the validation framework, provides examples of distance functions, and applies them to some discrete Markov network models. It also considers approximate validation methods based on data for which the generating network is not known, the kind of situation one faces when using real data.


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Comprehensive Resources for Tomato Functional Genomics Based on the Miniature Model Tomato Micro-Tom
C. Matsukura, K. Aoki, N. Fukuda, T. Mizoguchi, E. Asamizu, T. Saito, D. Shibata and H. Ezura

Tomato (Solanum lycopersicum L., Solanaceae) is an excellent model plant for genomic research of solanaceous plants, as well as for studying the development, ripening, and metabolism of fruit. In 2003, the International Solanaceae Project (SOL, www.sgn.cornell.edu) was initiated by members from more than 30 countries, and the tomato genome-sequencing project is currently underway. Genome sequence of tomato obtained by this project will provide a firm foundation for forthcoming genomic studies such as the comparative analysis of genes conserved among the Solanaceae species and the elucidation of the functions of unknown tomato genes. To exploit the wealth of the genome sequence information, there is an urgent need for novel resources and analytical tools for tomato functional genomics. Here, we present an overview of the development of genetic and genomic resources of tomato in the last decade, with a special focus on the activities of Japan SOL and the National Bio-Resource Project in the development of functional genomic resources of a model cultivar, Micro-Tom.


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Mass Spectrometry-Based Approaches Toward Absolute Quantitative Proteomics
K. Kito and T. Ito

Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.