Using DNA microarrays to study natural variation

The relationship between protein modified folding molecular network and Alzheimer's disease pathogenesis based on BAG2-HSC70-STUB1-MAPT expression patterns analysis

Background

Alzheimer's disease (AD) is the most common cause of dementia and cognitive decline, while its pathological mechanism remains unclear. Tauopathies is one of the most widely accepted hypotheses. In this study, the molecular network was established and the expression pattern of the core gene was analyzed, confirming that the dysfunction of protein folding and degradation is one of the critical factors for AD.

Methods

This study analyzed 9 normal people and 22 AD patients' microarray data obtained from GSE1297 in Gene Expression Omnibus (GEO) database. The matrix decomposition analysis was used to identify the correlation between the molecular network and AD. The mathematics of the relationship between the Mini-Mental State Examination (MMSE) and the expression level of the genes involved in the molecular network was found by Neural Network (NN). Furthermore, the Support Vector Machine (SVM) model was for classification according to the expression value of genes.

Results

The difference of eigenvalues is small in first three stages and increases dramatically in the severe stage. For example, the maximum eigenvalue changed to 0.79 in the severe group from 0.56 in the normal group. The sign of the elements in the eigenvectors of biggest eigenvalue reversed. The linear function of the relationship between clinical MMSE and gene expression values was observed. Then, the model of Neural Network (NN) is designed to predict the value of MMSE based on the linear function, and the predicted accuracy is up to 0.93. For the SVM classification, the accuracy of the model is 0.72.

Conclusion

This study shows that the molecular network of protein folding and degradation represented by "BAG2-HSC70-STUB1-MAPT" has a strong relationship with the occurrence and progression of AD, and this degree of correlation of the four genes gradually weakens with the progression of AD. The mathematical mapping of the relationship between gene expression and clinical MMSE was found, and it can be used in predicting MMSE or classification with high accuracy. These genes are expected to be potential biomarkers for early diagnosis and treatment of AD.

Growth and food consumption of whiting Merlangius merlangus

In the western Baltic Sea (WBS), whiting Merlangius merlangus is the main piscivorous fish together with cod Gadus morhua. In the present study, we investigate the growth and food consumption rates of WBS M. merlangus and compare the growth rates of males and females with those of M. merlangus in the North Sea (NS). Food consumption rates are estimated directly from sampled stomach contents in the WBS using a gastric evacuation rate model and indirectly by using a static energy-budget model together with the growth rates. The results indicate that male and female M. merlangus in the WBS have similar feeding and growth strategies, while in the NS M. merlangus show more pronounced differences in food consumption and growth dynamics between the sexes. Female WBS M. merlangus grow significantly slower than their conspecifics in the NS, but there is no significant difference for males. Sexual size dimorphism is seen in both areas, but for M. merlangus in the WBS the difference is less pronounced. Food consumption rates in the WBS differ between seasons, with the lowest food intake in the first 2 quarters of the year and the highest in the 3rd quarter. No differences in consumption rates were seen between males and females, which could be related to the more similar growth pattern seen for M. merlangus in the WBS.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Brassica villosa, a system for studying non-glandular trichomes and genes in the Brassicas

Brassica villosa is a wild Brassica C genome species with very dense trichome coverage and strong resistance to many insect pests of Brassica oilseeds and vegetables. Transcriptome analysis of hairy B. villosa leaves indicated higher expression of several important trichome initiation genes compared with glabrous B. napus leaves and consistent with the Arabidopsis model of trichome development. However, transcripts of the TRY inhibitory gene in hairy B. villosa were surprisingly high relative to B. napus and relative transcript levels of SAD2, EGL3, and several XIX genes were low, suggesting potential ancillary or less important trichome-related roles for these genes in Brassica species compared with Arabidopsis. Several antioxidant, calcium, non-calcium metal and secondary metabolite genes also showed differential expression between these two species. These coincided with accumulation of two alkaloid-like compounds, high levels of calcium, and other metals in B. villosa trichomes that are correlated with the known tolerance of B. villosa to high salt and the calcium-rich natural habitat of this wild species. This first time report on the isolation of large amounts of pure B. villosa trichomes, on trichome content, and on relative gene expression differences in an exceptionally hairy Brassica species compared with a glabrous species opens doors for the scientific community to understand trichome gene function in the Brassicas and highlights the potential of B. villosa as a trichome research platform.

Variation of gene expression associated with colonisation of an anthropized environment: comparison between African and European populations of Drosophila simulans

The comparison of transcriptome profiles among populations is a powerful tool for investigating the role of gene expression change in adaptation to new environments. In this study, we use massively parallel sequencing of 3' cDNAs obtained from large samples of adult males, to compare a population of Drosophila simulans from a natural reserve within its ancestral range (eastern Africa) with a derived population collected in the strongly anthropized Rhône valley (France). The goal was to scan for adaptation linked to the invasion of new environments by the species. Among 15,090 genes retained for the analysis, 794 were found to be differentially expressed between the two populations. We observed an increase in expression of reproduction-related genes in eastern Africa, and an even stronger increase in expression of Cytochrome P450, Glutathione transferase and Glucuronosyl transferase genes in the derived population. These three gene families are involved in detoxification processes, which suggests that pesticides are a major environmental pressure for the species in this area. The survey of the Cyp6g1 upstream region revealed the insertion of a transposable element, Juan, in the regulatory sequence that is almost fixed in the Rhône Valley, but barely present in Mayotte. This shows that Cyp6g1 has undergone parallel evolution in derived populations of D. simulans as previously shown for D. melanogaster. The increasing amount of data produced by comparative population genomics and transcriptomics should permit the identification of additional genes associated with functional divergence among those differentially expressed.

Vitis phylogenomics: hybridization intensities from a SNP array outperform genotype calls

Understanding relationships among species is a fundamental goal of evolutionary biology. Single nucleotide polymorphisms (SNPs) identified through next generation sequencing and related technologies enable phylogeny reconstruction by providing unprecedented numbers of characters for analysis. One approach to SNP-based phylogeny reconstruction is to identify SNPs in a subset of individuals, and then to compile SNPs on an array that can be used to genotype additional samples at hundreds or thousands of sites simultaneously. Although powerful and efficient, this method is subject to ascertainment bias because applying variation discovered in a representative subset to a larger sample favors identification of SNPs with high minor allele frequencies and introduces bias against rare alleles. Here, we demonstrate that the use of hybridization intensity data, rather than genotype calls, reduces the effects of ascertainment bias. Whereas traditional SNP calls assess known variants based on diversity housed in the discovery panel, hybridization intensity data survey variation in the broader sample pool, regardless of whether those variants are present in the initial SNP discovery process. We apply SNP genotype and hybridization intensity data derived from the Vitis9kSNP array developed for grape to show the effects of ascertainment bias and to reconstruct evolutionary relationships among Vitis species. We demonstrate that phylogenies constructed using hybridization intensities suffer less from the distorting effects of ascertainment bias, and are thus more accurate than phylogenies based on genotype calls. Moreover, we reconstruct the phylogeny of the genus Vitis using hybridization data, show that North American subgenus Vitis species are monophyletic, and resolve several previously poorly known relationships among North American species. This study builds on earlier work that applied the Vitis9kSNP array to evolutionary questions within Vitis vinifera and has general implications for addressing ascertainment bias in array-enabled phylogeny reconstruction.

Optimized probe masking for comparative transcriptomics of closely related species

Microarrays are commonly applied to study the transcriptome of specific species. However, many available microarrays are restricted to model organisms, and the design of custom microarrays for other species is often not feasible. Hence, transcriptomics approaches of non-model organisms as well as comparative transcriptomics studies among two or more species often make use of cost-intensive RNAseq studies or, alternatively, by hybridizing transcripts of a query species to a microarray of a closely related species. When analyzing these cross-species microarray expression data, differences in the transcriptome of the query species can cause problems, such as the following: (i) lower hybridization accuracy of probes due to mismatches or deletions, (ii) probes binding multiple transcripts of different genes, and (iii) probes binding transcripts of non-orthologous genes. So far, methods for (i) exist, but these neglect (ii) and (iii). Here, we propose an approach for comparative transcriptomics addressing problems (i) to (iii), which retains only transcript-specific probes binding transcripts of orthologous genes. We apply this approach to an Arabidopsis lyrata expression data set measured on a microarray designed for Arabidopsis thaliana, and compare it to two alternative approaches, a sequence-based approach and a genomic DNA hybridization-based approach. We investigate the number of retained probe sets, and we validate the resulting expression responses by qRT-PCR. We find that the proposed approach combines the benefit of sequence-based stringency and accuracy while allowing the expression analysis of much more genes than the alternative sequence-based approach. As an added benefit, the proposed approach requires probes to detect transcripts of orthologous genes only, which provides a superior base for biological interpretation of the measured expression responses.

Group normalization for genomic data

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets.

The relationship between phenotypic and environmental variation: do physiological responses reduce interindividual differences?

What is the effect of a variable environment on phenotypic variation? Does the physiological response to a new environment increase or decrease the differences among individuals? We provide a speculative hypothesis suggesting that the induction of a physiological response to environmental change minimizes phenotypic differences among individuals in outbred genetically variable populations. Although this suggestion runs counter to the general idea that environmental variation induces phenotypic variation, we provide evidence that this is not always the case. One explanation for this counterintuitive hypothesis is that in a variable environment, the physiological mechanism that maintains homeostasis changes the concentrations of active transcription factors (TFs). This change in TFs reduces the effectiveness of nucleotide polymorphisms in TF binding sites and thus reduces the variation among individuals in mRNA expression and in the phenotypes affected by these mRNA transcripts. Thus, there are fewer differences among individuals in a variable environment compared with the variation observed in a constant environment. Our conjecture is that the physiological mechanisms that maintain homeostasis in response to environmental variation canalize phenotypic variation. If our hypothesis is correct, then the physiological canalization of gene expression in a variable environment hides genetic variation and thereby reduces the evolutionary costs of polymorphism. This hypothesis provides a new perspective on the mechanisms by which high levels of genetic variation can persist in real-world populations.

Genome-wide transcription analysis of clinal genetic variation in Drosophila

Clinal variation in quantitative traits is widespread, but its genetic basis awaits identification. Drosophila melanogaster shows adaptive, clinal variation in traits such as body size along latitudinal gradients on multiple continents. To investigate genome wide transcription differentiation between North and South that might contribute to the clinal phenotypic variation, we compared RNA expression patterns during development of D. melanogaster from tropical northern and temperate southern populations using whole genome tiling arrays. We found that genes that were differentially expressed between the cline ends were generally associated with metabolism and growth, and experimental alteration of expression of a sample of them generally resulted in altered body size in the predicted direction, sometimes significantly so. We further identified the serpent (srp) transcription factor binding sites to be enriched near genes up-regulated in expression in the south. Analysis of clinal populations revealed a significant cline in the expression level of srp. Experimental over-expression of srp increased body size, as predicted from its clinal expression pattern, suggesting that it may be involved in regulating adaptive clinal variation in Drosophila. This study identified a handful of genes that contributed to clinal phenotypic variation through altered gene expression level, yet misexpression of individual gene led to modest body size change.

Advances in genetical genomics of plants

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research.

Population transcriptomics: insights from Drosophila simulans, Drosophila sechellia and their hybrids

Sequence differentiation has been widely studied between populations and species, whereas interest in expression divergence is relatively recent. Using microarrays, we compared four geographically distinct populations of Drosophila simulans and a population of Drosophila sechellia, and interspecific hybrids. We observed few differences between populations, suggesting a slight population structure in D. simulans. This structure was observed in direct population comparisons, as well as in interspecific comparisons (hybrids vs. parents, D. sechellia vs. D. simulans). Expression variance is higher in the French and Zimbabwean populations than in the populations from the ancestral range of D. simulans (Kenya and Seychelles). This suggests a large scale phenomenon of decanalization following the invasion of a new environment. Comparing D. simulans and D. sechellia, we revealed 304 consistently differentially expressed genes, with striking overrepresentation of genes of the cytochrome P450 family, which could be related to their role in detoxification as well as in hormone regulation. We also revealed differences in genes involved in Juvenile hormone and Dopamine differentiation. We finally observed very few differentially expressed genes between hybrids and parental populations, with an overrepresentation of X-linked genes.

High-resolution genotyping via whole genome hybridizations to microarrays containing long oligonucleotide probes

To date, microarray-based genotyping of large, complex plant genomes has been complicated by the need to perform genome complexity reduction to obtain sufficiently strong hybridization signals. Genome complexity reduction techniques are, however, tedious and can introduce unwanted variables into genotyping assays. Here, we report a microarray-based genotyping technology for complex genomes (such as the 2.3 GB maize genome) that does not require genome complexity reduction prior to hybridization. Approximately 200,000 long oligonucleotide probes were identified as being polymorphic between the inbred parents of a mapping population and used to genotype two recombinant inbred lines. While multiple hybridization replicates provided ∼97% accuracy, even a single replicate provided ∼95% accuracy. Genotyping accuracy was further increased to >99% by utilizing information from adjacent probes. This microarray-based method provides a simple, high-density genotyping approach for large, complex genomes.

A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species

RNA-Seq has emerged as a revolutionary technology for transcriptome analysis. In this article, we report a systematic comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. On a panel of human/chimpanzee/rhesus cerebellum RNA samples previously examined by the high-density human exon junction array (HJAY) and real-time qPCR, we generated 48.68 million RNA-Seq reads. Our results indicate that RNA-Seq has significantly improved gene coverage and increased sensitivity for differentially expressed genes compared with the high-density HJAY array. Meanwhile, we observed a systematic increase in the RNA-Seq error rate for lowly expressed genes. Specifically, between-species DEGs detected by array/qPCR but missed by RNA-Seq were characterized by relatively low expression levels, as indicated by lower RNA-Seq read counts, lower HJAY array expression indices and higher qPCR raw cycle threshold values. Furthermore, this issue was not unique to between-species comparisons of gene expression. In the RNA-Seq analysis of MicroArray Quality Control human reference RNA samples with extensive qPCR data, we also observed an increase in both the false-negative rate and the false-positive rate for lowly expressed genes. These findings have important implications for the design and data interpretation of RNA-Seq studies on gene expression differences between and within species.

Quantifying whole transcriptome size, a prerequisite for understanding transcriptome evolution across species: an example from a plant allopolyploid

Evolutionary biologists are increasingly comparing gene expression patterns across species. Due to the way in which expression assays are normalized, such studies provide no direct information about expression per gene copy (dosage responses) or per cell and can give a misleading picture of genes that are differentially expressed. We describe an assay for estimating relative expression per cell. When used in conjunction with transcript profiling data, it is possible to compare the sizes of whole transcriptomes, which in turn makes it possible to compare expression per cell for each gene in the transcript profiling data set. We applied this approach, using quantitative reverse transcriptase-polymerase chain reaction and high throughput RNA sequencing, to a recently formed allopolyploid and showed that its leaf transcriptome was approximately 1.4-fold larger than either progenitor transcriptome (70% of the sum of the progenitor transcriptomes). In contrast, the allopolyploid genome is 94.3% as large as the sum of its progenitor genomes and retains > or =93.5% of the sum of its progenitor gene complements. Thus, "transcriptome downsizing" is greater than genome downsizing. Using this transcriptome size estimate, we inferred dosage responses for several thousand genes and showed that the majority exhibit partial dosage compensation. Homoeologue silencing is nonrandomly distributed across dosage responses, with genes showing extreme responses in either direction significantly more likely to have a silent homoeologue. This experimental approach will add value to transcript profiling experiments involving interspecies and interploidy comparisons by converting expression per transcriptome to expression per genome, eliminating the need for assumptions about transcriptome size.

Evolution of alternative splicing in primate brain transcriptomes

Alternative splicing is a predominant form of gene regulation in higher eukaryotes. The evolution of alternative splicing provides an important mechanism for the acquisition of novel gene functions. In this work, we carried out a genome-wide phylogenetic survey of lineage-specific splicing patterns in the primate brain, via high-density exon junction array profiling of brain transcriptomes of humans, chimpanzees and rhesus macaques. We identified 509 genes showing splicing differences among these species. RT-PCR analysis of 40 exons confirmed the predicted splicing evolution of 33 exons. Of these 33 exons, outgroup analysis using rhesus macaques confirmed 13 exons with human-specific increase or decrease in transcript inclusion levels after humans diverged from chimpanzees. Some of the human-specific brain splicing patterns disrupt domains critical for protein-protein interactions, and some modulate translational efficiency of their host genes. Strikingly, for exons showing splicing differences across species, we observed a significant increase in the rate of silent substitutions within exons, coupled with accelerated sequence divergence in flanking introns. This indicates that evolution of cis-regulatory signals is a major contributor to the emergence of human-specific splicing patterns. In one gene (MAGOH), using minigene reporter assays, we demonstrated that the combination of two human-specific cis-sequence changes created its human-specific splicing pattern. Together, our data reveal widespread human-specific changes of alternative splicing in the brain and suggest an important role of splicing in the evolution of neuronal gene regulation and functions.

Single feature polymorphism (SFP)-based selective sweep identification and association mapping of growth-related metabolic traits in Arabidopsis thaliana

Background

Natural accessions of Arabidopsis thaliana are characterized by a high level of phenotypic variation that can be used to investigate the extent and mode of selection on the primary metabolic traits. A collection of 54 A. thaliana natural accession-derived lines were subjected to deep genotyping through Single Feature Polymorphism (SFP) detection via genomic DNA hybridization to Arabidopsis Tiling 1.0 Arrays for the detection of selective sweeps, and identification of associations between sweep regions and growth-related metabolic traits.

Results

A total of 1,072,557 high-quality SFPs were detected and indications for 3,943 deletions and 1,007 duplications were obtained. A significantly lower than expected SFP frequency was observed in protein-, rRNA-, and tRNA-coding regions and in non-repetitive intergenic regions, while pseudogenes, transposons, and non-coding RNA genes are enriched with SFPs. Gene families involved in plant defence or in signalling were identified as highly polymorphic, while several other families including transcription factors are depleted of SFPs. 198 significant associations between metabolic genes and 9 metabolic and growth-related phenotypic traits were detected with annotation hinting at the nature of the relationship. Five significant selective sweep regions were also detected of which one associated significantly with a metabolic trait.

Conclusions

We generated a high density polymorphism map for 54 A. thaliana accessions that highlights the variability of resistance genes across geographic ranges and used it to identify selective sweeps and associations between metabolic genes and metabolic phenotypes. Several associations show a clear biological relationship, while many remain requiring further investigation.

Kinetic effects on signal normalization in oligonucleotide microchips with labeled immobilized probes

Among various factors affecting operation of oligonucleotide microchips, the variations in concentration and in homogeneous distribution of immobilized probes over the cells are one of the most important. The labeling of immobilized probes ensures the complete current monitoring on the probe distribution and is reliable and convenient. Using hydrogel-based oligonucleotide microchips, the applicability of Cy3-labeled immobilized probes for quality control and signal normalization after hybridization with Cy5-labeled target DNA was investigated. This study showed that proper signal normalization should be different in thermodynamic conditions and in transient regime with hybridization far from saturation. This kinetic effect holds for both hydrogel-based and surface oligonucleotide microchips. Besides proving basic features, the technique was assessed on a sampling batch of 50 microchips developed for identifying mutations responsible for rifampicin and isoniazid resistance of Mycobacterium tuberculosis.

Characterizing natural variation using next-generation sequencing technologies

Progress in evolutionary genomics is tightly coupled with the development of new technologies to collect high-throughput data. The availability of next-generation sequencing technologies has the potential to revolutionize genomic research and enable us to focus on a large number of outstanding questions that previously could not be addressed effectively. Indeed, we are now able to study genetic variation on a genome-wide scale, characterize gene regulatory processes at unprecedented resolution, and soon, we expect that individual laboratories might be able to rapidly sequence new genomes. However, at present, the analysis of next-generation sequencing data is challenging, in particular because most sequencing platforms provide short reads, which are difficult to align and assemble. In addition, only little is known about sources of variation that are associated with next-generation sequencing study designs. A better understanding of the sources of error and bias in sequencing data is essential, especially in the context of studies of variation at dynamic quantitative traits.

The genetics of quantitative traits: challenges and prospects

A major challenge in current biology is to understand the genetic basis of variation for quantitative traits. We review the principles of quantitative trait locus mapping and summarize insights about the genetic architecture of quantitative traits that have been obtained over the past decades. We are currently in the midst of a genomic revolution, which enables us to incorporate genetic variation in transcript abundance and other intermediate molecular phenotypes into a quantitative trait locus mapping framework. This systems genetics approach enables us to understand the biology inside the 'black box' that lies between genotype and phenotype in terms of causal networks of interacting genes.

Using high-density exon arrays to profile gene expression in closely related species

Global comparisons of gene expression profiles between species provide significant insight into gene regulation, evolutionary processes and disease mechanisms. In this work, we describe a flexible and intuitive approach for global expression profiling of closely related species, using high-density exon arrays designed for a single reference genome. The high-density probe coverage of exon arrays allows us to select identical sets of perfect-match probes to measure expression levels of orthologous genes. This eliminates a serious confounding factor in probe affinity effects of species-specific microarray probes, and enables direct comparisons of estimated expression indexes across species. Using a newly designed Affymetrix exon array, with eight probes per exon for approximately 315 000 exons in the human genome, we conducted expression profiling in corresponding tissues from humans, chimpanzees and rhesus macaques. Quantitative real-time PCR analysis of differentially expressed candidate genes is highly concordant with microarray data, yielding a validation rate of 21/22 for human versus chimpanzee differences, and 11/11 for human versus rhesus differences. This method has the potential to greatly facilitate biomedical and evolutionary studies of gene expression in nonhuman primates and can be easily extended to expression array design and comparative analysis of other animals and plants.

Defining gene and QTL networks

Current technologies for high-throughput molecular profiling of large numbers of genetically different individuals offer great potential for elucidating the genotype-to-phenotype relationship. Variation in molecular and phenotypic traits can be correlated to DNA sequence variation using the methods of quantitative trait locus (QTL) mapping. In addition, the correlation structure in the molecular and phenotypic traits can be informative for inferring the underlying molecular networks. For this, new methods are emerging to distinguish among causality, reactivity, or independence of traits based upon logic involving underlying QTL. These methods are becoming increasingly popular in plant genetic studies as well as in studies on many other organisms.

Gene regulation in primates evolves under tissue-specific selection pressures

Regulatory changes have long been hypothesized to play an important role in primate evolution. To identify adaptive regulatory changes in humans, we performed a genome-wide survey for genes in which regulation has likely evolved under natural selection. To do so, we used a multi-species microarray to measure gene expression levels in livers, kidneys, and hearts from six humans, chimpanzees, and rhesus macaques. This comparative gene expression data allowed us to identify a large number of genes, as well as specific pathways, whose inter-species expression profiles are consistent with the action of stabilizing or directional selection on gene regulation. Among the latter set, we found an enrichment of genes involved in metabolic pathways, consistent with the hypothesis that shifts in diet underlie many regulatory adaptations in humans. In addition, we found evidence for tissue-specific selection pressures, as well as lower rates of protein evolution for genes in which regulation evolves under natural selection. These observations are consistent with the notion that adaptive circumscribed changes in gene regulation have fewer deleterious pleiotropic effects compared with changes at the protein sequence level.

Single-species microarrays and comparative transcriptomics

BACKGROUND

Prefabricated expression microarrays are currently available for only a few species but methods have been proposed to extend their application to comparisons between divergent genomes.

METHODOLOGY/PRINCIPAL FINDINGS

Here we demonstrate that the hybridization intensity of genomic DNA is a poor basis on which to select unbiased probes on Affymetrix expression arrays for studies of comparative transcriptomics, and that doing so produces spurious results. We used the Affymetrix Xenopus laevis microarray to evaluate expression divergence between X. laevis, X. borealis, and their F1 hybrids. When data are analyzed with probes that interrogate only sequences with confirmed identity in both species, we recover results that differ substantially analyses that use genomic DNA hybridizations to select probes.

CONCLUSIONS/SIGNIFICANCE

Our findings have implications for the experimental design of comparative expression studies that use single-species microarrays, and for our understanding of divergent expression in hybrid clawed frogs. These findings also highlight important limitations of single-species microarrays for studies of comparative transcriptomics of polyploid species.

Revealing the architecture of gene regulation: the promise of eQTL studies

Expression quantitative trait loci (eQTL) mapping studies have become a widely used tool for identifying genetic variants that affect gene regulation. In these studies, expression levels are viewed as quantitative traits, and gene expression phenotypes are mapped to particular genomic loci by combining studies of variation in gene expression patterns with genome-wide genotyping. Results from recent eQTL mapping studies have revealed substantial heritable variation in gene expression within and between populations. In many cases, genetic factors that influence gene expression levels can be mapped to proximal (putatively cis) eQTLs and, less often, to distal (putatively trans) eQTLs. Beyond providing great insight into the biology of gene regulation, a combination of eQTL studies with results from traditional linkage or association studies of human disease may help predict a specific regulatory role for polymorphic sites previously associated with disease.

Using Arabidopsis to explore zinc tolerance and hyperaccumulation

Identifying the particular gene or genes underlying a specific adaptation is a major challenge in modern biology. Currently, the study of naturally occurring variation in Arabidopsis thaliana provides a bridge between functional genetics and evolutionary analyses. Nevertheless, the use of A. thaliana to study adaptation is limited to those traits that have undergone selection. Therefore, to understand fully the genetics of adaptation, the vast arsenal of genetic resources developed in A. thaliana must be extended to other species that display traits absent in this model species. Here, we discuss how A. thaliana resources can significantly enhance the study of heavy-metal tolerance and hyperaccumulation in the wild species Arabidopsis halleri.

Gene expression analysis of the ovary of hybrid females of Xenopus laevis and X. muelleri

BACKGROUND

Interspecific hybrids of frogs of the genus Xenopus result in sterile hybrid males and fertile hybrid females. Previous work has demonstrated a dramatic asymmetrical pattern of misexpression in hybrid males compared to the two parental species with relatively few genes misexpressed in comparisons of hybrids and the maternal species (X. laevis) and dramatically more genes misexpressed in hybrids compared to the paternal species (X. muelleri). In this work, we examine the gene expression pattern in hybrid females of X. laevis x X. muelleri to determine if this asymmetrical pattern of expression also occurs in hybrid females.

RESULTS

We find a similar pattern of asymmetry in expression compared to males in that there were more genes differentially expressed between hybrids and X. muelleri compared to hybrids and X. laevis. We also found a dramatic increase in the number of misexpressed genes with hybrid females having about 20 times more genes misexpressed in ovaries compared to testes of hybrid males and therefore the match between phenotype and expression pattern is not supported.

CONCLUSION

We discuss these intriguing findings in the context of reproductive isolation and suggest that divergence in female expression may be involved in sterility of hybrid males due to the inherent sensitivity of spermatogenesis as defined by the faster male evolution hypothesis for Haldane's rule.

Diversity arrays technology (DArT) for pan-genomic evolutionary studies of non-model organisms

Background

High-throughput tools for pan-genomic study, especially the DNA microarray platform, have sparked a remarkable increase in data production and enabled a shift in the scale at which biological investigation is possible. The use of microarrays to examine evolutionary relationships and processes, however, is predominantly restricted to model or near-model organisms.

Methodology/Principal Findings

This study explores the utility of Diversity Arrays Technology (DArT) in evolutionary studies of non-model organisms. DArT is a hybridization-based genotyping method that uses microarray technology to identify and type DNA polymorphism. Theoretically applicable to any organism (even one for which no prior genetic data are available), DArT has not yet been explored in exclusively wild sample sets, nor extensively examined in a phylogenetic framework. DArT recovered 1349 markers of largely low copy-number loci in two lineages of seed-free land plants: the diploid fern Asplenium viride and the haploid moss Garovaglia elegans. Direct sequencing of 148 of these DArT markers identified 30 putative loci including four routinely sequenced for evolutionary studies in plants. Phylogenetic analyses of DArT genotypes reveal phylogeographic and substrate specificity patterns in A. viride, a lack of phylogeographic pattern in Australian G. elegans, and additive variation in hybrid or mixed samples.

Conclusions/Significance

These results enable methodological recommendations including procedures for detecting and analysing DArT markers tailored specifically to evolutionary investigations and practical factors informing the decision to use DArT, and raise evolutionary hypotheses concerning substrate specificity and biogeographic patterns. Thus DArT is a demonstrably valuable addition to the set of existing molecular approaches used to infer biological phenomena such as adaptive radiations, population dynamics, hybridization, introgression, ecological differentiation and phylogeography.

Quantitative Single-letter Sequencing: a method for simultaneously monitoring numerous known allelic variants in single DNA samples

Background

Pathogens such as fungi, bacteria and especially viruses, are highly variable even within an individual host, intensifying the difficulty of distinguishing and accurately quantifying numerous allelic variants co-existing in a single nucleic acid sample. The majority of currently available techniques are based on real-time PCR or primer extension and often require multiplexing adjustments that impose a practical limitation of the number of alleles that can be monitored simultaneously at a single locus.

Results

Here, we describe a novel method that allows the simultaneous quantification of numerous allelic variants in a single reaction tube and without multiplexing. Quantitative Single-letter Sequencing (QSS) begins with a single PCR amplification step using a pair of primers flanking the polymorphic region of interest. Next, PCR products are submitted to single-letter sequencing with a fluorescently-labelled primer located upstream of the polymorphic region. The resulting monochromatic electropherogram shows numerous specific diagnostic peaks, attributable to specific variants, signifying their presence/absence in the DNA sample. Moreover, peak fluorescence can be quantified and used to estimate the frequency of the corresponding variant in the DNA population.

Using engineered allelic markers in the genome of Cauliflower mosaic virus, we reliably monitored six different viral genotypes in DNA extracted from infected plants. Evaluation of the intrinsic variance of this method, as applied to both artificial plasmid DNA mixes and viral genome populations, demonstrates that QSS is a robust and reliable method of detection and quantification for variants with a relative frequency of between 0.05 and 1.

Conclusion

This simple method is easily transferable to many other biological systems and questions, including those involving high throughput analysis, and can be performed in any laboratory since it does not require specialized equipment.

Differential expression of genes important for adaptation in Capsella bursa-pastoris (Brassicaceae)

Understanding the genetic basis of natural variation is of primary interest for evolutionary studies of adaptation. In Capsella bursa-pastoris, a close relative of Arabidopsis (Arabidopsis thaliana), variation in flowering time is correlated with latitude, suggestive of an adaptation to photoperiod. To identify pathways regulating natural flowering time variation in C. bursa-pastoris, we have studied gene expression differences between two pairs of early- and late-flowering C. bursa-pastoris accessions and compared their response to vernalization. Using Arabidopsis microarrays, we found a large number of significant differences in gene expression between flowering ecotypes. The key flowering time gene FLOWERING LOCUS C (FLC) was not differentially expressed prior to vernalization. This result is in contrast to those in Arabidopsis, where most natural flowering time variation acts through FLC. However, the gibberellin and photoperiodic flowering pathways were significantly enriched for gene expression differences between early- and late-flowering C. bursa-pastoris. Gibberellin biosynthesis genes were down-regulated in late-flowering accessions, whereas circadian core genes in the photoperiodic pathway were differentially expressed between early- and late-flowering accessions. Detailed time-series experiments clearly demonstrated that the diurnal rhythm of CIRCADIAN CLOCK-ASSOCIATED1 (CCA1) and TIMING OF CAB EXPRESSION1 (TOC1) expression differed between flowering ecotypes, both under constant light and long-day conditions. Differential expression of flowering time genes was biologically validated in an independent pair of flowering ecotypes, suggesting a shared genetic basis or parallel evolution of similar regulatory differences. We conclude that genes involved in regulation of the circadian clock, such as CCA1 and TOC1, are strong candidates for the evolution of adaptive flowering time variation in C. bursa-pastoris.

Sterility and gene expression in hybrid males of Xenopus laevis and X. muelleri

BACKGROUND

Reproductive isolation is a defining characteristic of populations that represent unique biological species, yet we know very little about the gene expression basis for reproductive isolation. The advent of powerful molecular biology tools provides the ability to identify genes involved in reproductive isolation and focuses attention on the molecular mechanisms that separate biological species. Herein we quantify the sterility pattern of hybrid males in African Clawed Frogs (Xenopus) and apply microarray analysis of the expression pattern found in testes to identify genes that are misexpressed in hybrid males relative to their two parental species (Xenopus laevis and X. muelleri).

METHODOLOGY/PRINCIPAL FINDINGS

Phenotypic characteristics of spermatogenesis in sterile male hybrids (X. laevis x X. muelleri) were examined using a novel sperm assay that allowed quantification of live, dead, and undifferentiated sperm cells, the number of motile vs. immotile sperm, and sperm morphology. Hybrids exhibited a dramatically lower abundance of mature sperm relative to the parental species. Hybrid spermatozoa were larger in size and accompanied by numerous undifferentiated sperm cells. Microarray analysis of gene expression in testes was combined with a correction for sequence divergence derived from genomic hybridizations to identify candidate genes involved in the sterility phenotype. Analysis of the transcriptome revealed a striking asymmetric pattern of misexpression. There were only about 140 genes misexpressed in hybrids compared to X. laevis but nearly 4,000 genes misexpressed in hybrids compared to X. muelleri.

CONCLUSIONS/SIGNIFICANCE

Our results provide an important correlation between phenotypic characteristics of sperm and gene expression in sterile hybrid males. The broad pattern of gene misexpression suggests intriguing mechanisms creating the dominance pattern of the X. laevis genome in hybrids. These findings significantly contribute to growing evidence for allelic dominance in hybrids and have implications for the mechanism of species differentiation at the transcriptome level.

The Culture Collection of Algae and Protozoa (CCAP): a biological resource for protistan genomics

CCAP, the largest European protistan culture collection, is based at the Scottish Association for Marine Science near Oban, Scotland (http://www.ccap.ac.uk). The Collection comprises more than 2700 strains in the public domain, of which 1050 are marine algae, 1300 freshwater algae, and 350 protozoa. The primary mission of CCAP is to maintain and distribute defined cultures and their associated information to its customers. It also has a support and advisory function on all aspects of protistan science. In addition, it is involved in the training of students and researchers in algal identification and culture techniques. In light of the increasing number of fully sequenced protists, the CCAP is striving to provide targeted services and support to workers involved in all aspects of genomic research. At present, the Collection holds several hundred strains of genomic model taxa including: Acanthamoeba, Cafeteria, Cercomonas, Chlamydomonas, Chlorella, Cyanophora, Dictyostelium, Dunaliella, Ectocarpus, Emiliania, Euglena, Micromonas, Naegleria, Nephroselmis, Paramecium, Pavlova, Phaeodactylum, Porphyra, Pseudendoclonium, Pylaiella, Rhodomonas, Scenedesmus, Staurastrum, Tetrahymena, Thalassiosira, Volvox and Zygnema. These strains provide a defined representation of natural variation within model organisms, an increasingly useful resource for post-genomics approaches. Our aim over the next 2-5 years is to add value to the Collection by increasing the number of genome model species, and by offering an integrated, up-to-date, easy-to-use resource that would provide curated information on our strain holdings. In collaboration with other major Biological Resource Centres worldwide, we intend to build a hub providing access to both protistan cultures and their associated bioinformatics data.

Using DNA microarrays to study gene expression in closely related species

MOTIVATION

Comparisons of gene expression levels within and between species have become a central tool in the study of the genetic basis for phenotypic variation, as well as in the study of the evolution of gene regulation. DNA microarrays are a key technology that enables these studies. Currently, however, microarrays are only available for a small number of species. Thus, in order to study gene expression levels in species for which microarrays are not available, researchers face three sets of choices: (i) use a microarray designed for another species, but only compare gene expression levels within species, (ii) construct a new microarray for every species whose gene expression profiles will be compared or (iii) build a multi-species microarray with probes from each species of interest. Here, we use data collected using a multi-primate cDNA array to evaluate the reliability of each approach.

RESULTS

We find that, for inter-species comparisons, estimates of expression differences based on multi-species microarrays are more accurate than those based on multiple species-specific arrays. We also demonstrate that within-species expression differences can be estimated using a microarray for a closely related species, without discernible loss of information.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.