Defining genome maintenance pathways using functional genomic approaches

Genome maintenance activities including DNA repair, cell division cycle control, and checkpoint signaling pathways preserve genome integrity and prevent disease. Defects in these pathways cause birth defects, neurodegeneration, premature aging, and cancer. Recent technical advances in functional genomic approaches such as expression profiling, proteomics, and RNA interference (RNAi) technologies have rapidly expanded our knowledge of the proteins that work in these pathways. In this review, we examine the use of these high-throughput methodologies in higher eukaryotic organisms for the interrogation of genome maintenance activities.     

Visualization of genomic changes by segmented smoothing using an L0 penalty

Copy number variations (CNV) and allelic imbalance in tumor tissue can show strong segmentation. Their graphical presentation can be enhanced by appropriate smoothing. Existing signal and scatterplot smoothers do not respect segmentation well. We present novel algorithms that use a penalty on the L(0) norm of differences of neighboring values. Visualization is our main goal, but we compare classification performance to that of VEGA.     

Utilization of spline functions for smoothing fermentation data and for estimation of specific rates

A response surface method of smoothing fermentation data with spline functions is presented. The available electron balance is used to optimally select the values of the smoothing parameters associated with the spline functions. The method is applied to six sets of anaerobic fermentation data in which pure and mixed cultures are grown in batch followed by fed batch culture. Lactobacillus bulgaricus and Streptococcus thermophilus are cultured on 3% dry milk. Measured concentrations of biomass, lactose, galactose, lactic acid, and other acid products are smoothed using spline functions. Values of specific growth rate, specific lactose consumption rate, specific galactose formation rate, and specific acid product formation rate are estimated and the consistency of the results is examined using the available electron balance. The results show that the method works reasonably well, but that an upper bound should be used for the smoothing parameters to obtain accurate estimates of the derivative quantities.     

Prostate cancer and the genomic revolution: Advances using microarray analyses

The emerging technology of microarray analysis allows the establishment of molecular portraits of prostate cancer and the discovery of novel genes involved in the carcinogenesis process. Many novel genes have already been identified using this technique, and functional analyses of these genes are currently being tested. The combination of microarray analysis with other recently developed high-throughput techniques, such as proteomics, tissue arrays, and gene promoter-methylation, especially using tissue microdissection methods, will provide us with more comprehensive insights into how prostate cancer develops and responds to gene-targeted therapies. Animal models of prostate cancer are being characterized by high throughput techniques to better define the similarities and differences between those models and the human disease, and to determine whether particular models may be useful for specific targeted therapies in pre-clinical studies. Although profiling of mRNA expression provides important information of gene expression, the development of proteomic technologies will allow for an even more precise global insight into cellular signaling and structural alterations during prostate carcinogenesis. Not only will the "omic" revolution change basic science, but it will lead to a new era of molecular medicine.     

Functional materials for microscale genomic and proteomic analyses

The design of functional materials for genomic and proteomic analyses in microscale systems has begun to mature, from materials designed for capillary-based electrophoresis systems to those tailored for microfluidic-based or 'chip-based' platforms. In particular, recent research has focused on evaluating different polymer chemistries for microchannel surface passivation and improved DNA separation matrix performance. Additionally, novel bioconjugate materials designed specifically for electrophoretic separations in microscale channels are facilitating new separation modalities.     

Super-sparse principal component analyses for high-throughput genomic data

Background  

Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients.     

Mimotopes and proteome analyses using human genomic and cDNA epitope phage display

In the post-genomic era, validation of candidate gene targets frequently requires proteinbased strategies. Phage display is a powerful tool to define protein-protein interactions by generating peptide binders against target antigens. Epitope phage display libraries have the potential to enrich coding exon sequences from human genomic loci. We evaluated genomic and cDNA phage display strategies to identify genes in the 5q31 Interleukin gene cluster and to enrich cell surface receptor tyrosine kinase genes from a breast cancer cDNA library. A genomic display library containing 2 x 106 clones with exon-sized inserts was selected with antibodies specific for human Interleukin-4 (IL-4) and Interleukin-13. The library was enriched significantly after two selection rounds and DNA sequencing revealed unique clones. One clone matched a cognate IL-4 epitope; however, the majority of clone insert sequences corresponded to E. coli genomic DNA. These bacterial sequences act as 'mimotopes' (mimetic sequences of the true epitope), correspond to open reading frames, generate displayed peptides, and compete for binding during phage selection. The specificity of these mimotopes for IL-4 was confirmed by competition ELISA. Other E. coli mimotopes were generated using additional antibodies. Mimotopes for a receptor tyrosine kinase gene were also selected using a breast cancer SKBR-3 cDNA phage display library, screened against an anti-erbB2 monoclonal antibody. Identification of mimotopes in genomic and cDNA phage libraries is essential for phage display-based protein validation assays and two-hybrid phage approaches that examine protein-protein interactions. The predominance of E. coli mimotopes suggests that the E. coli genome may be useful to generate peptide diversity biased towards protein coding sequences.ABBREVIATIONS USED: IL, interleukin; ELISA, enzyme linked immunoabsorbant assay; PBS, phospho-buffered saline; cfu, colony forming units.     

Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses

Population-based genomic association analyses are more powerful than within-family analyses. However, population stratification (unknown or ignored origin of individuals from multiple source populations) and cryptic relatedness (unknown or ignored covariance between individuals because of their relatedness) are confounding factors in population-based genomic association analyses, which inflate the false-positive rate. As a consequence, false association signals may arise in genomic data association analyses for reasons other than true association between the tested genomic factor (marker genotype, gene or protein expression) and the study phenotype. It is therefore important to correct or account for these confounders in population-based genomic data association analyses. The common correction techniques for population stratification and cryptic relatedness problems are presented here in the phenotype–marker association analysis context, and comments on their suitability for other types of genomic association analyses (for example, phenotype–expression association) are also provided. Even though many of these techniques have originally been developed in the context of human genetics, most of them are also applicable to model organisms and breeding populations.     

Functional analyses of human DNA repair proteins important for aging and genomic stability using yeast genetics

Model systems have been extremely useful for studying various theories of aging. Studies of yeast have been particularly helpful to explore the molecular mechanisms and pathways that affect aging at the cellular level in the simple eukaryote. Although genetic analysis has been useful to interrogate the aging process, there has been both interest and debate over how functionally conserved the mechanisms of aging are between yeast and higher eukaryotes, especially mammalian cells. One area of interest has been the importance of genomic stability for age-related processes, and the potential conservation of proteins and pathways between yeast and human. Translational genetics have been employed to examine the functional roles of mammalian proteins using yeast as a pliable model system. In the current review recent advancements made in this area are discussed, highlighting work which shows that the cellular functions of human proteins in DNA repair and maintenance of genomic stability can be elucidated by genetic rescue experiments performed in yeast.     

Breakpoint identification and smoothing of array comparative genomic hybridization data

SUMMARY: We describe a tool, called aCGH-Smooth, for the automated identification of breakpoints and smoothing of microarray comparative genomic hybridization (array CGH) data. aCGH-Smooth is written in visual C++, has a user-friendly interface including a visualization of the results and user-defined parameters adapting the performance of data smoothing and breakpoint recognition. aCGH-Smooth can handle array-CGH data generated by all array-CGH platforms: BAC, PAC, cosmid, cDNA and oligo CGH arrays. The tool has been successfully applied to real-life data. AVAILABILITY: aCGH-Smooth is free for researchers at academic and non-profit institutions at http://www.few.vu.nl/~vumarray/.     

单细胞基因组学分析的技术前沿

基因组学已经深刻地改变了生命科学的诸多领域的面貌。目前它的主要内容是新的全基因组碱基序列的测定和在全基因组范围内鉴定那些在不同水平上影响生命活动的基因群的功能和相互作用。为达此要求,近年出现的第二代测序(深度测序)技术和基因芯片技术发挥了关键作用,但是两者都需要足够的高质量的核酸样品。所以,在只有或只能用单细胞或极少量细胞的情况下,如果没有特殊手段,上述分析往往不能常规、方便地进行。文章以DNA扩增为主线,综合阐述了目前在单细胞(特别是微生物)全基因组测序和大基因组的靶向重测序,以及对单细胞或微量细胞进行的基于深度测序或芯片杂交的功能基因组分析,如转录组、ChIP和DNA的CpG甲基化分析等的最新策略和技术,评价了单细胞基因组测序和功能基因组学各技术的特点并对发展前景进行了展望。     

Generation of RCAS vectors useful for functional genomic analyses.

Avian leukosis type A virus-derived retroviral vectors have been used to introduce genes into cells expressing the corresponding avian receptor tv-a. This includes the use of Replication-Competent Avian sarcoma-leukosis virus (ASLV) long terminal repeat (LTR) with Splice acceptor (RCAS) vectors in the analysis of avian development, human and murine cell cultures, murine cell lineage studies and cancer biology. Previously, cloning of genes into this virus was difficult due to the large size of the vector and sparse cloning sites. To overcome some of the disadvantages of traditional cloning using the RCASBP-Y vector, we have modified the RCASBP-Y to incorporate "Gateway" site-specific recombination cloning of genes into the construct, either with or without HA epitope tags. We have found the repetitive "att" sequences, which are the targets for site-specific recombination, do not impair the production of infectious viral particles or the expression of the gene of interest. This is the first instance of site-specific recombination being used to generate retroviral gene constructs. These viral constructs will allow for the efficient transfer and expression of cDNAs needed for functional genomic analyses.     

Depauperate genetic variability detected in the American and European bison using genomic techniques

 

A total of 929 polymorphic SNPs in EB (out of 54, 000 SNPs screened using a BovineSNP50 Illumina Genotyping BeadChip), and 1, 524 and 1, 403 polymorphic SNPs in WB and PB, respectively, were analysed. EB, WB and PB have all undergone recent drastic reductions in population size. Accordingly, they exhibited extremely depauperate genomes, deviations from genetic equilibrium and a genome organization consisting of a mosaic of haplotype blocks: regions with low haplotype diversity and high levels of linkage disequilibrium. No evidence for positive or stabilizing selection was found in EB, WB and PB, likely reflecting drift overwhelming selection. We suggest that utilization of genome-wide screening technologies, followed by utilization of less expensive techniques (e.g. VeraCode and Fluidigm EP1), holds large potential for genetic monitoring of populations. Additionally, these techniques will allow radical improvements of breeding practices in captive or managed populations, otherwise hampered by the limited availability of polymorphic markers. This result in improved possibilities for 1) estimating genetic relationships among individuals and 2) designing breeding strategies which attempt to preserve or reduce polymorphism in ecologically relevant genes and/or entire blocks.

Reviewers

This article was reviewed by: Fyodor Kondrashov and Shamil Sunyaev     

Deregressing estimated breeding values and weighting information for genomic regression analyses

Background

Genomic prediction of breeding values involves a so-called training analysis that predicts the influence of small genomic regions by regression of observed information on marker genotypes for a given population of individuals. Available observations may take the form of individual phenotypes, repeated observations, records on close family members such as progeny, estimated breeding values (EBV) or their deregressed counterparts from genetic evaluations. The literature indicates that researchers are inconsistent in their approach to using EBV or deregressed data, and as to using the appropriate methods for weighting some data sources to account for heterogeneous variance.

Methods

A logical approach to using information for genomic prediction is introduced, which demonstrates the appropriate weights for analyzing observations with heterogeneous variance and explains the need for and the manner in which EBV should have parent average effects removed, be deregressed and weighted.

Results

An appropriate deregression for genomic regression analyses is EBV/r² where EBV excludes parent information and r² is the reliability of that EBV. The appropriate weights for deregressed breeding values are neither the reliability nor the prediction error variance, two alternatives that have been used in published studies, but the ratio (1 - h²)/[(c + (1 - r²)/r²)h²] where c > 0 is the fraction of genetic variance not explained by markers.

Conclusions

Phenotypic information on some individuals and deregressed data on others can be combined in genomic analyses using appropriate weighting.     

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background  

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.