Systems genetics in “-omics” era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP–SNP genetic interactions, LD block–block interactions, miRNA–mRNA interactions, DNA–protein interactions, protein–protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate “-omics” datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.     

Gene expression signatures in primary immunodeficiencies: the experience from human disease and mouse models

Extensive research on molecular genetics in recent decades has provided a wealth of information regarding the underlying mechanisms of primary immunodeficiency diseases. The microarray technology has made its entry into the molecular biology research area and hereby enabled signature expression profiling of whole species genomes. Perhaps no other methodological approach has transformed molecular biology more in recent years than the use of microarrays. Microarray technology has led the way from studies of the individual biological functions of a few related genes, proteins or, at best, pathways towards more global investigations of cellular activity. The development of this technology immediately yielded new and interesting information, and has produced more data than can be currently dealt with. It has also helped to realize that even a 'horizontally exhaustive' molecular analysis is insufficient. Applications of this tool in primary immunodeficiency studies have generated new information, which has led to a better understanding of the underlying basic biology of the diseases. Also, the technology has been used as an exploratory tool to disease genes in immunodeficiency diseases of unknown cause as in the case of the CD3Delta-chain and the MAPBPIP deficiency. For X-linked agammaglobulinemia, the technique has provided better understanding of the genes influenced by Btk. There is considerable hope that the microarray technology will lead to a better understanding of disease processes and the molecular phenotypes obtained from microarray experiments may represent a new tool for diagnosis of the disease.     

A microarray system for Y chromosomal and mitochondrial single nucleotide polymorphism analysis in chimpanzee populations

Chimpanzee populations are diminishing as a consequence of human activities, and as a result this species is now endangered. In the context of conservation programmes, genetic data can add vital information, for instance on the genetic diversity and structure of threatened populations. Single nucleotide polymorphisms (SNP) are biallelic markers that are widely used in human molecular studies and can be implemented in efficient microarray systems. This technology offers the potential of robust, multiplexed SNP genotyping at low reagent cost in other organisms than humans, but it is not commonly used yet in wild population studies. Here, we describe the characterization of new SNPs in Y-chromosomal intronic regions in chimpanzees and also identify SNPs from mitochondrial genes, with the aim of developing a microarray system that permits the simultaneous study of both paternal and maternal lineages. Our system consists of 42 SNPs for the Y chromosome and 45 SNPs for the mitochondrial genome. We demonstrate the applicability of this microarray in a captive population where genotypes accurately reflected its large pedigree. Two wild-living populations were also analysed and the results show that the microarray will be a useful tool alongside microsatellite markers, since it supplies complementary information about population structure and ecology. SNP genotyping using microarray technology, therefore, is a promising approach and may become an essential tool in conservation genetics to help in the management and study of captive and wild-living populations. Moreover, microarrays that combine SNPs from different genomic regions could replace microsatellite typing in the future.     

Yeast proteomics and protein microarrays

Our understanding of biological processes as well as human diseases has improved greatly thanks to studies on model organisms such as yeast. The power of scientific approaches with yeast lies in its relatively simple genome, its facile classical and molecular genetics, as well as the evolutionary conservation of many basic biological mechanisms. However, even in this simple model organism, systems biology studies, especially proteomic studies had been an intimidating task. During the past decade, powerful high-throughput technologies in proteomic research have been developed for yeast including protein microarray technology. The protein microarray technology allows the interrogation of protein–protein, protein–DNA, protein–small molecule interaction networks as well as post-translational modification networks in a large-scale, high-throughput manner. With this technology, many groundbreaking findings have been established in studies with the budding yeast Saccharomyces cerevisiae, most of which could have been unachievable with traditional approaches. Discovery of these networks has profound impact on explicating biological processes with a proteomic point of view, which may lead to a better understanding of normal biological phenomena as well as various human diseases.     

Using worms to better understand how Bacillus thuringiensis kills insects

Bacillus thuringiensis is widely used as a biological pesticide to control insects that either cause damage to crops or transmit disease. That it can also target the model organism Caenorhabditis elegans has not only provided exciting new insights into how the toxins produced by the bacterium target their victims but also how target insects counter the attack. Modern approaches such as reverse genetics and microarray technology have revealed novel receptors for the toxins and possible signal transduction pathways induced within the host following intoxication. This article will discuss how these findings fit in with current models and how they might influence future studies.     

Peptide aptamers: tools for biology and drug discovery.

Peptide aptamer technology is relatively youthful. It has the advantage over existing techniques that the reagents identified are designed for expression in eukaryotic cells. This allows the construction of molecular tools that allow the logic of genetics, from knockouts to extragenic suppressors, to be applied to studies of proteins in tissue culture cells. Until recently, the available tools have limited our understanding of cell biology. The same limitation restricts out ability to validate the numerous candidate drug targets emerging from genome-wide approaches to cellular biology. Peptide aptamers represent a stride forwards in the evolution of a modular, molecular tool kit for cell biology and for drug target validation. The authors predict that they will also play a role in the transition from genomic to proteomic microarray technology.     

Cutaneous melanoma: fishing with chips

DNA microarray technology is a versatile platform that allows rapid genetic analysis to take place on a genome-wide scale and has revolutionized the way cancers are studied. This platform has enabled researchers to characterize mechanisms central to tumorigenesis and understand important molecular events in the multi-step tumor progression model of cutaneous melanoma and other cancers. In melanoma, multiple global gene expression profiling studies using various DNA microarray platforms and various experimental designs have been performed. Each study has been able to capture and characterize either the involvement of a novel pathway or a novel cause-effect-relationship. The use of microarrays to define subclasses, to identify differentially regulated genes within a mutational context to analyze epigenetically regulated genes has resulted in an unprecedented understanding of the biology of cutaneous melanoma that may lead to more accurate diagnosis, more comprehensive prognosis, prediction and more effective therapeutic interventions. Related DNA microarray platforms like array-comparative genomic hybridization (CGH) have also been instrumental to identify many non-random chromosomal alterations; however, studies identifying validated targets as a result of CGH are limited. Thus, there exists significant opportunity to discover novel melanoma genes and translate such discoveries into meaningful clinical endpoints. In this review, we focus on various DNA microarray-based studies performed in cutaneous melanoma and summarize our current understanding of the genetics and biology of melanoma progression derived from accumulating genomic information.     

Using biplots to interpret gene expression patterns in plants

Plant biologists in fields of ecology, evolution, genetics and breeding frequently use multivariate methods. This paper illustrates Principal Component Analysis (PCA) and Gabriel's biplot as applied to microarray expression data from plant pathology experiments.     

蛋白微阵列研究进展

蛋白微阵列是随着基因微阵列技术发展起来的,用于基因微阵列的制备方法、信号的检测及分析系统,也可用于蛋白微阵列。各种蛋白微阵列基质的发展,提高了蛋白的固定效率。放射性同位数、化学发光、激光共聚焦荧光扫描等技术都已用于微阵列的检测。重组蛋白技术的发展,提高了蛋白微阵列检测的通量和灵敏度。蛋白微阵列具有通量高、使用样品少、重复性好、可定量的特点,使其在生物医药科学研究中得到了广泛应用。本综述了蛋白微阵列的制备及其在免疫检测、医学诊断及蛋白组研究中的应用。     

Taking arrays from the lab to the field: trying to make sense of the unknown

With the rapid pace of nucleic acid microarray technology development and a renewed national emphasis on detecting and characterizing microorganisms in environmental samples, there is a rush to operationalize existing microarray technologies and apply them to uncharacterized environmental backgrounds. The purpose of this article is to pause and ask a basic question: what do microarray data actually mean in the face of uncharacterized sample backgrounds? In attempting to answer this question, we draw a clear distinction between hypothesis-driven fundamental science and operational uses of microarray technology; assess microarray technology assumptions in the face of uncharacterized environments; offer an environmental microbiologist's perspective on technology needs and requirements for quantitatively analyzing microbial communities; and hopefully stimulate a scientific and technical dialogue around the concept of analytical environmental microbiology and future technology development.     

Combining microarrays and genetic analysis

Gene expression can be studied at a genome-wide scale with the aid of modern microarray technologies. Expression profiling of tens to hundreds of individuals in a genetic population can reveal the consequences of genetic variation. In this paper it is argued that the design and analysis of such a study is not a matter of simply applying the existing and more-or-less standard computational tools for microarrays to a new type of experimental data. It is shown how to fully exploit the power of genetics through optimal experimental design and analysis for two major microarray technologies, cDNA two-colour arrays and Affymetrix short oligonucleotide arrays.     

A family-based test for correlation between gene expression and trait values

Advances in microarray technology have made it attractive to combine information on clinical traits, marker genotypes, and comprehensive gene expression from family studies to dissect complex disease genetics. Without accounting for family structure, methods that test for association between a trait and gene-expression levels can be misleading. We demonstrate that the standard unstratified test based on Pearson's correlation coefficient can produce spurious results when applied to family data, and we present a stratified family expression association test (FEXAT). We illustrate the utility of the FEXAT via simulation and an application to gene-expression data from lymphoblastoid cell lines from four CEPH families. The FEXAT has a smaller estimated false-discovery rate than the standard test when within-family correlations are of interest, and it detects biologically plausible correlations between beta catenin and genes in the WNT-activation pathway in humans that the standard test does not.     

Novel and simple transformation algorithm for combining microarray data sets

Background  

With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis.     

A perspective on microarrays: current applications,pitfalls, and potential uses

With advances in robotics, computational capabilities, and the fabrication of high quality glass slides coinciding with increased genomic information being available on public databases, microarray technology is increasingly being used in laboratories around the world. In fact, fields as varied as: toxicology, evolutionary biology, drug development and production, disease characterization, diagnostics development, cellular physiology and stress responses, and forensics have benefiting from its use. However, for many researchers not familiar with microarrays, current articles and reviews often address neither the fundamental principles behind the technology nor the proper designing of experiments. Although, microarray technology is relatively simple, conceptually, its practice does require careful planning and detailed understanding of the limitations inherently present. Without these considerations, it can be exceedingly difficult to ascertain valuable information from microarray data. Therefore, this text aims to outline key features in microarray technology, paying particular attention to current applications as outlined in recent publications, experimental design, statistical methods, and potential uses. Furthermore, this review is not meant to be comprehensive, but rather substantive; highlighting important concepts and detailing steps necessary to conduct and interpret microarray experiments. Collectively, the information included in this text will highlight the versatility of microarray technology and provide a glimpse of what the future may hold.     

Network quantitative trait loci mapping of circadian clock outputs identifies metabolic pathway-to-clock linkages in Arabidopsis

Modern systems biology permits the study of complex networks, such as circadian clocks, and the use of complex methodologies, such as quantitative genetics. However, it is difficult to combine these approaches due to factorial expansion in experiments when networks are examined using complex methods. We developed a genomic quantitative genetic approach to overcome this problem, allowing us to examine the function(s) of the plant circadian clock in different populations derived from natural accessions. Using existing microarray data, we defined 24 circadian time phase groups (i.e., groups of genes with peak phases of expression at particular times of day). These groups were used to examine natural variation in circadian clock function using existing single time point microarray experiments from a recombinant inbred line population. We identified naturally variable loci that altered circadian clock outputs and linked these circadian quantitative trait loci to preexisting metabolomics quantitative trait loci, thereby identifying possible links between clock function and metabolism. Using single-gene isogenic lines, we found that circadian clock output was altered by natural variation in Arabidopsis thaliana secondary metabolism. Specifically, genetic manipulation of a secondary metabolic enzyme led to altered free-running rhythms. This represents a unique and valuable approach to the study of complex networks using quantitative genetics.     

Chasing the dream: plant EST microarrays

DNA microarray technology is poised to make an important contribution to the field of plant biology. Stimulated by recent funding programs, expressed sequence tag sequencing and microarray production either has begun or is being contemplated for most economically important plant species. Although the DNA microarray technology is still being refined, the basic methods are well established. The real challenges lie in data analysis and data management. To fully realize the value of this technology, centralized databases that are capable of storing microarray expression data and managing information from a variety of sources will be needed. These information resources are under development and will help usher in a new era in plant functional genomics.