植物功能基因组研究进展

随着植物基因组计划的深入,植物基因组学研究的重点已经转变为基因组功能的研究,即利用基因组序列的信息和高通量的系统分析技术,在基因组水平研究植物结构和组织与植物功能在细胞、有机体和进化上的关系.对功能基因组学研究的内容、方法以及最新研究进展进行了综述.     

Genomic differentiation and patterns of gene flow between two long‐tailed tit species (Aegithalos)

Patterns of heterogeneous genomic differentiation have been well documented between closely related species, with some highly differentiated genomic regions (“genomic differentiation islands”) spread throughout the genome. Differential levels of gene flow are proposed to account for this pattern, as genomic differentiation islands are suggested to be resistant to gene flow. Recent studies have also suggested that genomic differentiation islands could be explained by linked selection acting on genomic regions with low recombination rates. Here, we investigate genomic differentiation and gene‐flow patterns for autosomes using RAD‐seq data between two closely related species of long‐tailed tits (Aegithalos bonvaloti and A. fuliginosus) in both allopatric and contact zone populations. The results confirm recent or ongoing gene flow between these two species. However, there is little evidence that the genomic regions that were found to be highly differentiated between the contact zone populations are resistant to gene flow, suggesting that differential levels of gene flow is not the cause of the heterogeneous genomic differentiation. Linked selection may be the cause of genomic differentiation islands between the allopatric populations with no or very limited gene flow, but this could not account for the heterogeneous genomic differentiation between the contact zone populations, which show evidence of recent or ongoing gene flow.     

Genomic strategies to identify mammalian regulatory sequences

With the continuing accomplishments of the human genome project, high-throughput strategies to identify DNA sequences that are important in mammalian gene regulation are becoming increasingly feasible. In contrast to the historic, labour-intensive, wet-laboratory methods for identifying regulatory sequences, many modern approaches are heavily focused on the computational analysis of large genomic data sets. Data from inter-species genomic sequence comparisons and genome-wide expression profiling, integrated with various computational tools, are poised to contribute to the decoding of genomic sequence and to the identification of those sequences that orchestrate gene regulation. In this review, we highlight several genomic approaches that are being used to identify regulatory sequences in mammalian genomes.     

Genome-wide identification of novel genomic islands that contribute to Salmonella virulence in mouse systemic infection

Salmonella pathogenicity islands are inserted into the genome by horizontal gene transfer and are required for expression of full virulence. Here, we performed tRNA scanning of the genome of Salmonella enterica serovar Typhimurium and compared it with that of nonpathogenic Escherichia coli in order to identify genomic islands that contribute to Salmonella virulence. Using deletion analysis, we identified four genomic islands that are required for virulence in the mouse infection model. One of the newly identified pathogenicity islands was the pheV- tRNA-located genomic island, which is comprised of 26 126 bp, and encodes 22 putative genes, including STM3117–STM3138. We also showed that the pheV tRNA-located genomic island is widely distributed among different nontyphoid Salmonella serovars. Furthermore, genes including STM3118–STM3121 were identified as novel virulence-associated genes within the pheV- tRNA-located genomic island. These results indicate that a Salmonella -specific pheV- tRNA genomic island is involved in Salmonella pathogenesis among the nontyphoid Salmonella serovars.     

Genomic selection of milk cattle. The practical application over five years

Genomic selection is a method based on the use of single nucleotide polymorphisms (SNPs) as markers for detecting animal or plant genotype values. The review describes the genomic selection of milk cattle 5 years after the design of dense SNP chips. References to the application of genomic selection to other animal and plant species are given. The main principles of constructing linear and nonlinear mathematical models that allow one to determine genomic estimates in animals are briefly described. Particular attention is focused on the accuracy and the phenomenon of the additivity of genomic estimates, as well as to the prospective use of various genomic selection schemes that consider it over dozens of generations. Information including international organizations that provide the consolidation of genomic information from different countries aimed at designing global reference populations of milk cattle is reported. The results of the practical application of genomic selection to detecting of the breeding value of milk cattle over 5 years are demonstrated in the table, which makes it possible to visually assess the achievements of this highly technological field of cattle breeding.     

Prediction of genomic islands in seven human pathogens using the Z-Island method

We adopted the method of Zhang and Zhang (the Z-Island method) to identify genomic islands in seven human pathogens, analyzing their chromosomal DNA sequences. The Z-Island method is a theoretical method for predicting genomic islands in bacterial genomes; it consists of determination of the cumulative GC profile and computation of codon usage bias. Thirty-one genomic islands were found in seven pathogens using this method. Further analysis demonstrated that most have the known conserved features; this increases the probability that they are real genomic islands. Eleven genomic islands were found to code for products involved in causing disease (virulence factors) or in resistance to antibiotics (resistance factors). This finding could be useful for research on the pathogenicity of these bacteria and helpful in the treatment of the diseases that they cause. In a comparison of the distribution of mobility elements in genomic islands predicted by different methods, the Z-Island method gave lower false-positive rates. The Z-Island method was found to detect more known genomic islands than the two methods that we compared it with, SIGI-HMM and IslandPick. Furthermore, it maintained a better balance between specificity and sensitivity. The only inconvenience is that the steps for finding genomic islands by the Z-Island method are semi-automatic.     

A new experimental approach for studying bacterial genomic island evolution identifies island genes with bacterial host-specific expression patterns

Background  

Genomic islands are regions of bacterial genomes that have been acquired by horizontal transfer and often contain blocks of genes that function together for specific processes. Recently, it has become clear that the impact of genomic islands on the evolution of different bacterial species is significant and represents a major force in establishing bacterial genomic variation. However, the study of genomic island evolution has been mostly performed at the sequence level using computer software or hybridization analysis to compare different bacterial genomic sequences. We describe here a novel experimental approach to study the evolution of species-specific bacterial genomic islands that identifies island genes that have evolved in such a way that they are differentially-expressed depending on the bacterial host background into which they are transferred.     

Genome sequence comparisons: hurdles in the fast lane to functional genomics

An important computational technique for extracting the wealth of information hidden in human genomic sequence data is to compare the sequence with that from the corresponding region of the mouse genome, looking for segments that are conserved over evolutionary time. Moreover, the approach generalises to comparison of sequences from any two related species. The underlying rationale (which is abundantly confirmed by observation) is that a random mutation in a functional region is usually deleterious to the organism, and hence unlikely to become fixed in the population, whereas mutations in a non-functional region are free to accumulate over time.The potential value of this approach is so attractive that the public and private projects to sequence the human genome are now turning to sequencing the mouse, and you will soon be able to compare the human and mouse sequences of your favourite genomic region.We are currently witnessing an explosion of computer tools for comparative analysis of two genomic sequences. Here the capabilities of two new network servers for comparing genomic sequences from any pair of closely related species are sketched.The Syntenic Gene Prediction Program SGP-I utilises sequence comparisons to enhance the ability to locate protein coding segments in genomic data. PipMaker attempts to determine all conserved genomic regions, regardless of their function.     

Evidence of a large novel gene pool associated with prokaryotic genomic islands

Microbial genes that are “novel” (no detectable homologs in other species) have become of increasing interest as environmental sampling suggests that there are many more such novel genes in yet-to-be-cultured microorganisms. By analyzing known microbial genomic islands and prophages, we developed criteria for systematic identification of putative genomic islands (clusters of genes of probable horizontal origin in a prokaryotic genome) in 63 prokaryotic genomes, and then characterized the distribution of novel genes and other features. All but a few of the genomes examined contained significantly higher proportions of novel genes in their predicted genomic islands compared with the rest of their genome (Paired t test = 4.43E-14 to 1.27E-18, depending on method). Moreover, the reverse observation (i.e., higher proportions of novel genes outside of islands) never reached statistical significance in any organism examined. We show that this higher proportion of novel genes in predicted genomic islands is not due to less accurate gene prediction in genomic island regions, but likely reflects a genuine increase in novel genes in these regions for both bacteria and archaea. This represents the first comprehensive analysis of novel genes in prokaryotic genomic islands and provides clues regarding the origin of novel genes. Our collective results imply that there are different gene pools associated with recently horizontally transmitted genomic regions versus regions that are primarily vertically inherited. Moreover, there are more novel genes within the gene pool associated with genomic islands. Since genomic islands are frequently associated with a particular microbial adaptation, such as antibiotic resistance, pathogen virulence, or metal resistance, this suggests that microbes may have access to a larger “arsenal” of novel genes for adaptation than previously thought.     

Genomic prediction when some animals are not genotyped

Background

The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs). A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have been genotyped. In practice, not all animals are genotyped, and the methods have to be adapted to this situation.

Results

In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result, in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions (BLUPs). The method is illustrated using a simulated data set.

Conclusions

The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated breeding values and has the potential to become the standard tool for genomic prediction of breeding values in future practical evaluations in pig and cattle breeding.     

Genome architecture, rearrangements and genomic disorders

An increasing number of human diseases are recognized to result from recurrent DNA rearrangements involving unstable genomic regions. These are termed genomic disorders, in which the clinical phenotype is a consequence of abnormal dosage of gene(s) located within the rearranged genomic fragments. Both inter- and intrachromosomal rearrangements are facilitated by the presence of region-specific low-copy repeats (LCRs) and result from nonallelic homologous recombination (NAHR) between paralogous genomic segments. LCRs usually span approximately 10-400 kb of genomic DNA, share >or= 97% sequence identity, and provide the substrates for homologous recombination, thus predisposing the region to rearrangements. Moreover, it has been suggested that higher order genomic architecture involving LCRs plays a significant role in karyotypic evolution accompanying primate speciation.     

A systematic method to identify genomic islands and its applications in analyzing the genomes of Corynebacterium glutamicum and Vibrio vulnificus CMCP6 chromosome I

MOTIVATION: Some genomic islands contain horizontally transferred genes, which play critical roles in altering the genotypes and phenotypes of organisms, and horizontal gene transfer has been recognized as a universal event throughout bacterial evolution. A windowless method to display the distribution of genomic GC content, the cumulative GC profile, is proposed to identify genomic islands in genomes whose complete genome sequences are available. Two new indices are proposed to assess the codon usage bias and amino acid usage bias in genomic islands. RESULTS: A 211 kb genomic island (CGGI-1) has been identified in the genome of Corynebacterium glutamicum, and three genomic islands VVGI-1, VVGI-2 and VVGI-3, with lengths 167, 40 and 33 kb, respectively, have been identified in the genome of Vibrio vulnificus CMCP6 chromosome I. The CGGI-1 is flanked by two approximately 500 bp direct repeats, and utilizes a Val-tRNA as the integration site. For the VVGI-1 and VVGI-2, each has an integrase gene at 5' junction. All the identified genomic islands show unusual GC content, codon usage and amino acid usage, compared with the rest of the genomes. In addition, it is found that genomic islands are fairly homogenous in terms of GC content variation. An index, h, to quantify the homogeneity of GC content for genomic islands is proposed, and it is shown that h is less than 0.1 for all the genomic islands analyzed. The cumulative GC profile, as well as various indices to assess the codon usage bias, amino acid usage bias and homogeneity of the genomic islands, will be useful in the analysis of other genomes. AVAILABILITY: Programs used in this work and numerical results are available upon request.     

Triggers for genomic rearrangements: insights into genomic, cellular and environmental influences

Genomic rearrangements are associated with many human genomic disorders, including cancers. It was previously thought that most genomic rearrangements formed randomly but emerging data suggest that many are nonrandom, cell type-, cell stage- and locus-specific events. Recent studies have revealed novel cellular mechanisms and environmental cues that influence genomic rearrangements. In this Review, we consider the multitude of influences on genomic rearrangements by grouping these influences into four categories: proximity of chromosomal regions in the nucleus, cellular stress, inappropriate DNA repair or recombination, and DNA sequence and chromatin features. The synergy of these triggers can poise a cell for rearrangements and here we aim to provide a conceptual framework for understanding the genesis of genomic rearrangements.     

Widespread compensatory evolution conserves DNA-encoded nucleosome organization in yeast

Evolution maintains organismal fitness by preserving genomic information. This is widely assumed to involve conservation of specific genomic loci among species. Many genomic encodings are now recognized to integrate small contributions from multiple genomic positions into quantitative dispersed codes, but the evolutionary dynamics of such codes are still poorly understood. Here we show that in yeast, sequences that quantitatively affect nucleosome occupancy evolve under compensatory dynamics that maintain heterogeneous levels of A+T content through spatially coupled A/T-losing and A/T-gaining substitutions. Evolutionary modeling combined with data on yeast polymorphisms supports the idea that these substitution dynamics are a consequence of weak selection. This shows that compensatory evolution, so far believed to affect specific groups of epistatically linked loci like paired RNA bases, is a widespread phenomenon in the yeast genome, affecting the majority of intergenic sequences in it. The model thus derived suggests that compensation is inevitable when evolution conserves quantitative and dispersed genomic functions.     

Genome-wide association and genomic selection in animal breeding

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.     

MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis

Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.     

Clinical application of array-based comparative genomic hybridization for the identification of prognostically important genetic alterations in chronic lymphocytic leukemia

Genomic aberrations have increasingly gained attention as prognostic markers in B-cell chronic lymphocytic leukemia (CLL). Fluorescence in situ hybridization (FISH) has improved the detection rate of genomic alterations in CLL from approximately 50% using conventional cytogenetics to greater than 80%. More recently, array comparative genomic hybridization (CGH) has gained popularity as a clinical tool that can be applied to detect genomic gains and losses of prognostic importance in CLL. Array CGH and FISH are particularly useful in CLL because genomic gains and losses are key events with both biologic and prognostic significance, while balanced translocations have limited prognostic value. Although FISH has a higher technical sensitivity, it requires separate, targeted hybridizations for the detection of alterations at genomic loci of interest. Array CGH, on the other hand, has the ability to provide a genome-wide survey of genomic aberrations with a single hybridization reaction. Array CGH is expanding the known genomic regions of importance in CLL and allows these regions to be evaluated in the context of a genome-wide perspective. Ongoing clinical trials are evaluating the use of genomic aberrations as tools for risk-stratifying patients for therapy, thus increasing the need for reliable and high-yield methods to detect these genomic changes. In this review, we consider the use of array CGH as a clinical tool for the identification of genomic alterations with prognostic significance in CLL, and suggest ways to integrate this test into the clinical molecular diagnostic laboratory work flow.     

Comparison of genomic fragment and clone sequences within a long interspersed repeated sequence of the mouse genome.

The 393bp nucleotide sequence of a HindIII genomic fragment mapping within the major long interspersed repeated sequence family (MIF-1, Bam, L1) of mouse is reported and compared to clone sequences of the same region of this repeated sequence. The consensus of the clone sequences significantly differs from the genomic fragment sequence by additions and deletions that are inconsistent with the physical and biochemical properties of the genomic fragment. While alternative explanations could account for some of these differences, several aspects of the experimental results imply that cloning artifacts contribute to the discrepancies. Despite the differences between the clone and genomic fragment sequences, the biologically interesting features previously noted in clone sequences (promoter-like signals and an open reading frame) are conserved in the genomic fragment sequence.     

Closing the Gap between Knowledge and Clinical Application: Challenges for Genomic Translation

Despite early predictions and rapid progress in research, the introduction of personal genomics into clinical practice has been slow. Several factors contribute to this translational gap between knowledge and clinical application. The evidence available to support genetic test use is often limited, and implementation of new testing programs can be challenging. In addition, the heterogeneity of genomic risk information points to the need for strategies to select and deliver the information most appropriate for particular clinical needs. Accomplishing these tasks also requires recognition that some expectations for personal genomics are unrealistic, notably expectations concerning the clinical utility of genomic risk assessment for common complex diseases. Efforts are needed to improve the body of evidence addressing clinical outcomes for genomics, apply implementation science to personal genomics, and develop realistic goals for genomic risk assessment. In addition, translational research should emphasize the broader benefits of genomic knowledge, including applications of genomic research that provide clinical benefit outside the context of personal genomic risk.     

Y enriched and Y specific DNA sequences from the genome of the mediterranean fruit fly,Ceratitis capitata

DNA sequences that are enriched or specific to the genome of the male medfly, Ceratitis capitata, have been isolated using a differential hybridization approach. Twelve phage clones from a genomic library have been identified that consistently display more intense hybridization with a genomic DNA probe from males as opposed to one from females. Southern DNA blot analysis reveals that these recombinant clones contain at least one EcoRI fragment that is either specific to the male genome, or more highly represented in it, as compared with the female genome. These EcoRI fragments, when used as probes, all generate a similar pattern of intense multiple bands in genomic DNA of males. This suggests the presence of repetitive sequences that are at least partially homologous in these regions of the genome that are specific to or enriched in males. In situ hybridization to mitotic chromosomes confirms a Y chromosomal origin for the male specific repetitive sequences. Data on the genomic organization, representation and evolutionary conservation of these sequences that are specific to or enriched in males are presented. Studies of the genomic organization and representation of flanking sequences that are not male specific are presented as well.by R. Appels