Genes, Demography, and Life Span: The Contribution of Demographic Data in Genetic Studies on Aging and Longevity

In population studies on aging, the data on genetic markers are often collected for individuals from different age groups. The purpose of such studies is to identify, by comparison of the frequencies of selected genotypes, "longevity" or "frailty" genes in the oldest and in younger groups of individuals. To address questions about more-complicated aspects of genetic influence on longevity, additional information must be used. In this article, we show that the use of demographic information, together with data on genetic markers, allows us to calculate hazard rates, relative risks, and survival functions for respective genes or genotypes. New methods of combining genetic and demographic information are discussed. These methods are tested on simulated data and then are applied to the analysis of data on genetic markers for two haplogroups of human mtDNA. The approaches suggested in this article provide a powerful tool for analyzing the influence of candidate genes on longevity and survival. We also show how factors such as changes in the initial frequencies of candidate genes in subsequent cohorts, or secular trends in cohort mortality, may influence the results of an analysis.     

Gene mapping in the wild with SNPs: guidelines and future directions

One of the biggest challenges facing evolutionary biologists is to identify and understand loci that explain fitness variation in natural populations. This review describes how genetic (linkage) mapping with single nucleotide polymorphism (SNP) markers can lead to great progress in this area. Strategies for SNP discovery and SNP genotyping are described and an overview of how to model SNP genotype information in mapping studies is presented. Finally, the opportunity afforded by new generation sequencing and typing technologies to map fitness genes by genome-wide association studies is discussed.     

The mother centriole plays an instructive role in defining cell geometry

Centriole positioning is a key step in establishment and propagation of cell geometry, but the mechanism of this positioning is unknown. The ability of pre-existing centrioles to induce formation of new centrioles at a defined angle relative to themselves suggests they may have the capacity to transmit spatial information to their daughters. Using three-dimensional computer-aided analysis of cell morphology in Chlamydomonas, we identify six genes required for centriole positioning relative to overall cell polarity, four of which have known sequences. We show that the distal portion of the centriole is critical for positioning, and that the centriole positions the nucleus rather than vice versa. We obtain evidence that the daughter centriole is unable to respond to normal positioning cues and relies on the mother for positional information. Our results represent a clear example of "cytotaxis" as defined by Sonneborn, and suggest that centrioles can play a key function in propagation of cellular geometry from one generation to the next. The genes documented here that are required for proper centriole positioning may represent a new class of ciliary disease genes, defects in which would be expected to cause disorganized ciliary position and impaired function.     

Modelling the evolution of genomes with integrated external and internal functions

The genomes that organisms transmit between generations contain information about different kinds of functions. The genome with the "best" mix and number of genes for these functions is the one that natural selection favours. Here I introduce a new way to model simple organisms with genes for external and internal functions, and use it to study the evolution of genome size. The external functions are exemplified by resource use and the internal functions by mutation control (repair). It is shown that even with a suitable proportion of genes for mutation control, the genomes in the organisms do not forever incorporate genes that increase resource use. Instead they evolve towards an optimal genome of limited size. The optimal proportion of genes for mutation control is shown to have an upper limit given by the ease with which transmission accuracy is improved by adding extra genes for this purpose to the genome. The model illustrates how natural selection on genomes integrates systems for the transmission of genetic information with systems relating to the external adaptation of the organism. It also opens up for other, more detailed theoretical investigations of genome functions.     

Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology

Background

Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.

Results

We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term''s distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure.

Conclusion

Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.     

Molecular approaches in pig breeding to improve meat quality.

This article reviews the advances in molecular genetics that have led to the identification of genes and markers associated with meat quality in pig. The development of a considerable number of annotated livestock genome sequences represents an incredibly rich source of information that can be used to identify candidate genes responsible for complex traits and quantitative trait loci effects. In pig, the huge amount of information emerging from the study of the genome has helped in the acquisition of new knowledge concerning biological systems and it is opening new opportunities for the genetic selection of this specie. Among the new fields of genomics recently developed, functional genomics and proteomics that allow considering many genes and proteins at the same time are very useful tools for a better understanding of the function and regulation of genes, and how these participate in complex networks controlling the phenotypic characteristics of a trait. In particular, global gene expression profiling at the mRNA and protein level can provide a better understanding of gene regulation that underlies biological functions and physiology related to the delivery of a better pig meat quality. Moreover, the possibility to realize an integrated approach of genomics and proteomics with bioinformatics tools is essential to obtain a complete exploitation of the available molecular genetics information. The development of this knowledge will benefit scientists, industry and breeders considering that the efficiency and accuracy of the traditional pig selection schemes will be improved by the implementation of molecular data into breeding programs.     

Using Functional Genetics to Understand Breast Cancer Biology

Genetic screens were for long the prerogative of those that studied model organisms. The discovery in 2001 that gene silencing through RNA interference (RNAi) can also be brought about in mammalian cells paved the way for large scale loss-of-function genetic screens in higher organisms. In this article, we describe how functional genetic studies can help us understand the biology of breast cancer, how it can be used to identify novel targets for breast cancer therapy, and how it can help in the identification of those patients that are most likely to respond to a given therapy.Much remains to be learned regarding the function of mammalian genes. Only some quarter of all human genes have well-described functions. It is likely that quite a few of these currently unannotated genes will turn out to play key parts in cancer biology. For example, a 70-gene gene signature that can discriminate breast tumors of good and poor prognosis contained some 20 genes of currently unknown function (van ‘t Veer et al. 2002). The fact that these genes of unknown function foretell breast cancer prognosis hints at a role for at least some of these genes in breast cancer biology. The unbiased search for genes that contribute to breast cancer development is therefore likely to yield a rich harvest of new insights. RNA interference allows us to suppress genes systematically on a large scale and study the effects of gene suppression on specific cellular processes or signaling pathways. Consequently, RNA interference-based genetic screens have the potential to deepen our understanding of the molecular events that cause breast cancer, to find novel targets for therapy and to find biomarkers of drug responsiveness. In this article, we will describe the technologies available to perform both gain-of-function and loss-of-function genetic screens and will illustrate how such functional genetic screens have been used in the recent past to study a variety of outstanding questions in the biology of breast cancer.     

Market Movements: Nongovernmental Organization Strategies to Influence Global Production and Consumption

This article analyzes nongovernmental organization (NGO) "market campaigns" that seek to motivate changes in global consumption and production patterns. Through campaigns targeting products as diverse as paper, shoes, and computers, advocacy groups seek to use existing concerns of consumers to influence producers, and simultaneously, to expand and deepen consumer demand for more sustainable products and services. NGOs deploy both negative information to critique leading brands, and positive information to help build new markets for improved products. Successful market campaigns construct networks of actors that identify points of leverage within global production and trading regimes; coordinate research, exposure, direct action, and negotiations with brands; identify solutions; advance new multi-stakeholder standards and monitoring and certification schemes; build new nongovernmental regulatory institutions; and occasionally attempt to strengthen state regulation. Through an assessment of three market campaigns focused on Staples, Nike, and Dell, this article describes the nature of these campaigns, discusses how they function, assesses their central strategies and tactics, and analyzes whether they are actually having an impact. The article concludes by discussing the relevance and implications of these campaigns for the field of industrial ecology, and how industrial ecology might support future efforts to advance more sustainable production and consumption.     

Functional Analysis of Prognostic Gene Expression Network Genes in Metastatic Breast Cancer Models

Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+) breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.     

Gene expression profiles of arabidopsis under the stress of methyl viologen: a microarray analysis

Methyl viologen (MV) is the main ingredient of Paraquat. It is little known about how plants respond to this compound. To understand the mode of MV action and molecular mechanism of plant response, we performed experiments of microarray on Arabidopsis. In MV treated seedling, approximately 6 % genes were altered at mRNA levels, including 818 genes increased, whereas 1,440 genes decreased. Studies of these genes expression patterns provided some new information on the reaction process of plant after the treatment with MV. These included signaling molecules for MV response and reactive oxygen species formation, enzymes required for secondary metabolism and, cell wall maintenance and strategy of photostasis balance. The expression kinetics of the genes induced by MV will provides useful information for the abiotic stress defense mechanism in plants.     

Structure and regulation of human troponin genes

The recent completion of a first draft of the human genome has allowed "in silico" genome browsing to become routine. Such computer-based research is now a useful adjunct to experiments based at the bench, and is accelerating gene discovery and the analysis and understanding of genes in their genomic contexts. This review summarises recent findings on genes encoding proteins of the troponin complex. We describe the organization of the three pairs of genes which encode isoforms of troponins I and T, and discuss how this relates to their evolution and regulation. Detailed analysis of the chromosomal context of the cardiac troponin I and slow skeletal troponin T genes reveals a region of densely packed differentially expressed genes, including new genes identified by automatic genome annotation. This information is discussed within the context of detailed analysis of the best-studied gene in this region, cardiac troponin I. In this way, we illustrate the uses to which a combination of conventional bench experiments and "in silico" analyses may be put in understanding the relationship between structure and function within the genome.     

Streptococcus pneumoniae as a genomics platform for broad-spectrum antibiotic discovery

Streptococcus pneumoniae is a useful tool for the discovery of broad-spectrum antibiotics because of its genetic malleability and importance as a pathogen. Recent publications of complete chromosomal DNA sequences for S. pneumoniae facilitate rapid and effective use of genomics-based technology to identify essential genes encoding new targets for antibacterial drugs. These methods include computational comparative genomics, gene disruption studies to determine essentiality or identify essential genes, and gene expression analysis using microarrays and gel-based proteomics. We review how genomics has transformed the use of the pneumococcus for the pursuit of new antibiotics, and made it the best species for the identification and validation of new antibiotic targets.     

Microbial genomics for the improvement of natural product discovery

The quest for the discovery of novel natural products has entered a new chapter with the enormous wealth of genetic data that is now available. This information has been exploited by using whole-genome sequence mining to uncover cryptic pathways, or biosynthetic pathways for previously undetected metabolites. Alternatively, using known paradigms for secondary metabolite biosynthesis, genetic information has been 'fished out' of DNA libraries resulting in the discovery of new natural products and isolation of gene clusters for known metabolites. Novel natural products have been discovered by expressing genetic data from uncultured organisms or difficult-to-manipulate strains in heterologous hosts. Furthermore, improvements in heterologous expression have not only helped to identify gene clusters but have also made it easier to manipulate these genes in order to generate new compounds. Finally, and perhaps the most crucial aspect of the efficient and prosperous use of the abundance of genetic information, novel enzyme chemistry continues to be discovered, which has aided our understanding of how natural products are biosynthesized de novo, and enabled us to rework the current paradigms for natural product biosynthesis.     

miRNA signature associated with outcome of gastric cancer patients following chemotherapy

Background

Resistance to chemotherapy severely limits the effectiveness of chemotherapy drugs in treating cancer. Still, the mechanisms and critical pathways that contribute to chemotherapy resistance are relatively unknown. This study elucidates the chemoresistance-associated pathways retrieved from the integrated biological interaction networks and identifies signature genes relevant for chemotherapy resistance.

Methods

An integrated network was constructed by collecting multiple metabolic interactions from public databases and the k-shortest path algorithm was implemented to identify chemoresistant related pathways. The identified pathways were then scored using differential expression values from microarray data in chemosensitive and chemoresistant ovarian and lung cancers. Finally, another pathway database, Reactome, was used to evaluate the significance of genes within each filtered pathway based on topological characteristics.

Results

By this method, we discovered pathways specific to chemoresistance. Many of these pathways were consistent with or supported by known involvement in chemotherapy. Experimental results also indicated that integration of pathway structure information with gene differential expression analysis can identify dissimilar modes of gene reactions between chemosensitivity and chemoresistance. Several identified pathways can increase the development of chemotherapeutic resistance and the predicted signature genes are involved in drug resistant during chemotherapy. In particular, we observed that some genes were key factors for joining two or more metabolic pathways and passing down signals, which may be potential key targets for treatment.

Conclusions

This study is expected to identify targets for chemoresistant issues and highlights the interconnectivity of chemoresistant mechanisms. The experimental results not only offer insights into the mode of biological action of drug resistance but also provide information on potential key targets (new biological hypothesis) for further drug-development efforts.     

Data integration workflow for search of disease driving genes and genetic variants

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/     

Separation technologies for glycomics

Progress in genome projects has provided us with fundamentals on genetic information; however, the functions of a large number of genes remain to be elucidated. To understand the in vivo functions of eukaryotic genes, it is essential to grasp the features of their post-translational modifications. Among them, protein glycosylation is a central issue to be discussed, considering the predominant roles of glycoproteins in cell-cell and cell-substratum recognition events in multicellular organisms. In this context, it is necessary to establish a core strategy for analyzing glycosylated proteins under the concept of the "glycome" [Trends Glycosci. Glycotechnol. 12 (2000) 1]. Though the term glycome should be defined, in analogy to the genome and proteome, as "a whole set of glycans produced in a single organism", here we propose a glycome project specifically focusing on glycoproteins. Principal objectives in the project are to identify: (1) which genes encode glycoproteins (i.e. genome information); (2) which sites among potential glycosylation sites are actually glycosylated (i.e. glycosylation site information); (3) what are the structures of glycans (i.e. structural information); and (4) what are the effects (functions) of glycosylation (functional information). For these purposes, two affinity technologies have been introduced. One is named the "glyco-catch method" to identify genes encoding glycoproteins [Proteomics 1 (2001) 295], and the other is the recently reinforced "frontal affinity chromatography" [J. Chromatogr. A 890 (2000) 261]. By the former method, genes that encode glycoproteins as well as glycosylation sites are systematically identified by the efficient combination of conventional lectin-affinity chromatography and contemporary in silico database searching. The following three actions have been devised for rapid and systematic characterization of glycans: (1) mass spectrometry to acquire exact mass information; (2) 2-D/3-D mapping to obtain refined chemical information; and (3) reinforced frontal affinity chromatography to determine affinity constants (K(a)-values) for a set of lectins. Pyridylaminated glycans are used throughout the characterization processes. In this review, the concept and strategy of glycomic approaches are described referring to the on-going glycome project focused on the nematode Caenorhabditis elegans.     

Identification of new human cadherin genes using a combination of protein motif search and gene finding methods

We have combined protein motif search and gene finding methods to identify genes encoding proteins containing specific domains. Particularly, we have focused on finding new human genes of the cadherin superfamily proteins, which represent a major group of cell-cell adhesion receptors contributing to embryonic neuronal morphogenesis. Models for three cadherin protein motifs were generated from over 100 already annotated cadherin domains and used to search the complete translated human genome. The genomic sequence regions containing motif "hits" were analyzed by eukaryotic GeneMark.hmm to identify the exon-intron structure of new genes. Three new genes CDH-J, PCDH-J and FAT-J were found. The predicted proteins PCDH-J and FAT-J were classified into protocadherin and FAT-like subfamilies, respectively, based on the number and organization of cadherin domains and presence of subfamily-specific conserved amino acid residues. Expression of FAT-J was shown in almost all tested tissues. The exon-intron organization of CDH-J was experimentally verified by PCR with specifically designed primers and its tissue-specific expression was demonstrated. The described methodology can be applied to discover new genes encoding proteins from families with well-characterized structural and functional domains.