Mixed-Model Reanalysis of Primate Data Suggests Tissue and Species Biases in Oligonucleotide-Based Gene Expression Profiles

The simple classification of multiple cancer types using a small number of significant genes

BACKGROUND AND OBJECTIVE

The problems involved in the classification of cancers have recently received a great deal of attention in the context of DNA microarrays. We propose a simple procedure for classifying or predicting the cancer types of test samples when multiple cancer types and many genes are present.

METHOD

The procedure sequentially combines a gene-sort algorithm and a predictive likelihood-based classifier. Genes that have homogeneous patterns of expression measurements across cancer types are of limited interest. Therefore, this algorithm orders genes on the basis of strong heterogeneous patterns. The proposed classifier then selects the first few genes, which are sufficient to classify most training samples correctly via cross validation. Test samples were classified using only the selected genes.

RESULTS AND CONCLUSION

This predictive likelihood-based classifier performs well and is simple to understand. Empirical examination revealed good classification accuracy using relatively few genes.

Gene expression profiling of liver metastases and tumour invasion in pancreatic cancer using an orthotopic SCID mouse model

The prognosis of pancreatic adenocarcinoma is affected by early metastases and local tumour invasion beyond surgical margins. Gene expression profiling in pancreatic cancer tissue is complicated due to the high amount of RNAses being present in human tissue and that of suitable models. In order to demonstrate early metastases, the models should take into account the anatomical environment of the tumour. Using the orthotopic transplantation of pancreatic tumour cells in SCID (severe combined immunodeficiency) mice, these interactions are taken into consideration. In order to identify genes associated with local tumour invasion and metastases in ductal pancreatic cancer, we investigated a human pancreatic tumour cell line derived from an orthopic pancreatic tumour model in SCID mice. Differential gene expression was performed on the basis of microarray technique. The human MiaPaca-2 cell line was implanted orthotopically in SCID mice. Transcriptional profiling was performed on fresh frozen tissue derived from the primary tumour, the tumour invasion front and the liver metastases. Differentially expressed genes were identified using statistical analyses, and were validated with external databases and with immunohistochemistry. A total of 1066 of 14 500 genes were significantly differentially expressed. Comparing the primary tumour with the tumour invasion front, there were 614 statistically significant up- and 348 downregulated genes. Twenty-five statistically significant up- and 181 downregulated genes were identified comparing the liver metastases with the primary tumour. Eight genes (PAI-1, BNIP3l, VEGF, NSE, RGS4, HSP27, GADD45A, PTPN14) were chosen and validated in a semi-quantitative immunohistochemical analysis, which revealed a positive correlation to the array data. Overrepresentation analyses revealed a total of 66 significantly regulated pathways associated with cell proliferation, cell stress, cell communication metabolic and cytokine function. In conclusion, model marker genes for local invasion and liver metastases can be identified using transcriptional profiling in the SCID mouse. Overrepresentation analysis secures a good and fast overview about the significantly regulated genes and can assign genes to certain pathways. These marker genes can be related to the apoptotic cascade, angiogenesis and cell interaction.

Genomic Comparisons of Humans and Chimpanzees

The genome consists of the entire DNA present in the nucleus of the fertilized embryo, which is then duplicated in every cell in the body. A draft sequence of the chimpanzee genome is now available, providing opportunities to better understand genetic contributions to human evolution, development, and disease. Sequence differences from the human genome were confirmed to be ∼1% in areas that can be precisely aligned, representing ∼35 million single base-pair differences. Some 45 million nucleotides of insertions and deletions unique to each lineage were also discovered, making the actual difference between the two genomes ∼4%. We discuss the opportunities and challenges that arise from this information and the need for comparison with additional species, as well as population genetic studies. Finally, we present a few examples of interesting findings resulting from genome-wide analyses, candidate gene studies, and combined approaches, emphasizing the pros and cons of each approach.

MicroRNA regulation and interspecific variation of gene expression

MicroRNAs (miRNAs) modulate expression of their target genes in various tissues and at different developmental stages, but it is unclear whether they drive cross-species variation in gene expression. By comparing data from mammal and fly species we found that the cross-species expression variation of miRNA targets is significantly lower than that of other genes. This implies that miRNAs can affect gene expression by reducing stochastic noise, buffering cross-species variation and constraining evolutionary gene expression variation.

Sequence polymorphisms cause many false cis eQTLs

Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as “cis-acting” or “cis” and can be successfully detected and eliminated with this approach.

Species differences in brain gene expression profiles associated with adult behavioral maturation in honey bees

Background

Honey bees are known for several striking social behaviors, including a complex pattern of behavioral maturation that gives rise to an age-related colony division of labor and a symbolic dance language, by which successful foragers communicate the location of attractive food sources to their nestmates. Our understanding of honey bees is mostly based on studies of the Western honey bee, Apis mellifera, even though there are 9–10 other members of genus Apis, showing interesting variations in social behavior relative to A. mellifera. To facilitate future in-depth genomic and molecular level comparisons of behavior across the genus, we performed a microarray analysis of brain gene expression for A. mellifera and three key species found in Asia, A. cerana, A. florea and A. dorsata.

Results

For each species we compared brain gene expression patterns between foragers and adult one-day-old bees on an A. mellifera cDNA microarray and calculated within-species gene expression ratios to facilitate cross-species analysis. The number of cDNA spots showing hybridization fluorescence intensities above the experimental threshold was reduced by an average of 16% in the Asian species compared to A. mellifera, but an average of 71% of genes on the microarray were available for analysis. Brain gene expression profiles between foragers and one-day-olds showed differences that are consistent with a previous study on A. mellifera and were comparable across species. Although 1772 genes showed significant differences in expression between foragers and one-day-olds, only 218 genes showed differences in forager/one-day-old expression between species (p < 0.001). Principal Components Analysis revealed dominant patterns of expression that clearly distinguished between the four species but did not reflect known differences in behavior and ecology. There were species differences in brain expression profiles for functionally related groups of genes.

Conclusion

We conclude that the A. mellifera cDNA microarray can be used effectively for cross-species comparisons within the genus. Our results indicate that there is a widespread conservation of the molecular processes in the honey bee brain underlying behavioral maturation. Species differences in brain expression profiles for functionally related groups of genes provide possible clues to the basis of behavioral variation in the genus.

Conservation of regional gene expression in mouse and human brain

Many neurodegenerative diseases have a hallmark regional and cellular pathology. Gene expression analysis of healthy tissues may provide clues to the differences that distinguish resistant and sensitive tissues and cell types. Comparative analysis of gene expression in healthy mouse and human brain provides a framework to explore the ability of mice to model diseases of the human brain. It may also aid in understanding brain evolution and the basis for higher order cognitive abilities. Here we compare gene expression profiles of human motor cortex, caudate nucleus, and cerebellum to one another and identify genes that are more highly expressed in one region relative to another. We separately perform identical analysis on corresponding brain regions from mice. Within each species, we find that the different brain regions have distinctly different expression profiles. Contrasting between the two species shows that regionally enriched genes in one species are generally regionally enriched genes in the other species. Thus, even when considering thousands of genes, the expression ratios in two regions from one species are significantly correlated with expression ratios in the other species. Finally, genes whose expression is higher in one area of the brain relative to the other areas, in other words genes with patterned expression, tend to have greater conservation of nucleotide sequence than more widely expressed genes. Together these observations suggest that region-specific genes have been conserved in the mammalian brain at both the sequence and gene expression levels. Given the general similarity between patterns of gene expression in healthy human and mouse brains, we believe it is reasonable to expect a high degree of concordance between microarray phenotypes of human neurodegenerative diseases and their mouse models. Finally, these data on very divergent species provide context for studies in more closely related species that address questions such as the origins of cognitive differences.

Animal models of human neurodegenerative and psychiatric disorders, particularly mouse models, have assumed a central role in biomedical research aimed at discovering the causes of disease and generating novel, mechanism-based treatments. But to what degree can a mouse brain serve as a model for a human brain? Here we begin to address this question by looking at patterns of gene expression across three corresponding regions of mouse and human brains. We find that within each species, the different regions (motor cortex, striatum, and cerebellum) have very distinct gene expression profiles. It is likely that these differences reflect distinctions in regional neurochemistry and function. We then show that genes that are enriched in one of the three areas relative to the other two in mice have the same pattern of expression in humans. Looking at the relationship between conservation of expression and amino acid sequence, we find that genes showing patterned expression generally have been more conserved than more uniformly expressed genes. This suggests that in the brain, constraints on the evolution of DNA sequence and gene expression can also be particularly high for genes with regional or tissue-specific expression.

Evolution of 2-DE protein patterns in a mussel hybrid zone

Protein expression patterns were compared in mussels from a hybrid zone between the two species Mytilus edulis and M. galloprovincialis using 2-DE. Significant differences in expression pattern were observed between species and between mussels within species. Hybrid mussels had more variable protein expression patterns than mussels of each species. This could be due to segregation at expression modifier loci in the hybrids. It is proposed that unusual hybrid expression patterns might contribute to postzygotic isolation, if such patterns reduce fitness. The use of proteomic data for testing evolutionary models, as in many transcriptomics studies, is explored with results consistent with the expectations of neutral theory.

Evaluating the role of natural selection in the evolution of gene regulation

Surveys of gene expression reveal extensive variability both within and between a wide range of species. Compelling cases have been made for adaptive changes in gene regulation, but the proportion of expression divergence attributable to natural selection remains unclear. Distinguishing adaptive changes driven by positive selection from neutral divergence resulting from mutation and genetic drift is critical for understanding the evolution of gene expression. Here, we review the various methods that have been used to test for signs of selection in genomic expression data. We also discuss properties of regulatory systems relevant to neutral models of gene expression. Despite some potential caveats, published studies provide considerable evidence for adaptive changes in gene expression. Future challenges for studies of regulatory evolution will be to quantify the frequency of adaptive changes, identify the genetic basis of expression divergence and associate changes in gene expression with specific organismal phenotypes.

Understanding the recent evolution of the human genome: insights from human-chimpanzee genome comparisons

The sequencing of the chimpanzee genome and the comparison with its human counterpart have begun to reveal the spectrum of genetic changes that has accompanied human evolution. In addition to gross karyotypic rearrangements such as the fusion that formed human chromosome 2 and the human-specific pericentric inversions of chromosomes 1 and 18, there is considerable submicroscopic structural variation involving deletions, duplications, and inversions. Lineage-specific segmental duplications, detected by array comparative genomic hybridization and direct sequence comparison, have made a very significant contribution to this structural divergence, which is at least three-fold greater than that due to nucleotide substitutions. Since structural genomic changes may have given rise to irreversible functional differences between the diverging species, their detailed analysis could help to identify the biological processes that have accompanied speciation. To this end, interspecies comparisons have revealed numerous human-specific gains and losses of genes as well as changes in gene expression. The very considerable structural diversity (polymorphism) evident within both lineages has, however, hampered the analysis of the structural divergence between the human and chimpanzee genomes. The concomitant evaluation of genetic divergence and diversity at the nucleotide level has nevertheless served to identify many genes that have evolved under positive selection and may thus have been involved in the development of human lineage-specific traits. Genes that display signs of weak negative selection have also been identified and could represent candidate loci for complex genomic disorders. Here, we review recent progress in comparing the human and chimpanzee genomes and discuss how the differences detected have improved our understanding of the evolution of the human genome.

Systems-level analysis and evolution of the phototransduction network in Drosophila

Networks of interacting genes are responsible for generating life's complexity and for mediating how organisms respond to their environment. Thus, a basic understanding of genetic variation in gene networks in natural populations is important for elucidating how changes at the genetic level map to higher levels of biological organization. Here, using the well-characterized phototransduction network in Drosophila, we analyze variation in gene expression within and between two closely related species, Drosophila melanogaster and Drosophila simulans, under different environmental conditions. Gene expression levels in the pathway are largely conserved between these two sibling species. For most genes in the network, differences in level of gene expression between species are correlated with degree of polymorphism within species. However, one gene encoding the light-induced ion channel TRPL (transient receptor potential-like) shows an excess of expression divergence relative to polymorphism, suggesting a possible role for natural selection in shaping this expression difference between species. Finally, this difference in TRPL expression likely has significant functional consequences, because it is known that a high level of rhabdomeral TRPL leads to increased sensitivity to dim background light and an increased response to a wider range of light intensities. These results provide a preliminary quantification of variation and divergence of gene expression between species in a known gene network and provide a foundation for a system-level understanding of functional and evolutionary change.

Ancient and recent positive selection transformed opioid cis-regulation in humans

Changes in the cis-regulation of neural genes likely contributed to the evolution of our species' unique attributes, but evidence of a role for natural selection has been lacking. We found that positive natural selection altered the cis-regulation of human prodynorphin, the precursor molecule for a suite of endogenous opioids and neuropeptides with critical roles in regulating perception, behavior, and memory. Independent lines of phylogenetic and population genetic evidence support a history of selective sweeps driving the evolution of the human prodynorphin promoter. In experimental assays of chimpanzee-human hybrid promoters, the selected sequence increases transcriptional inducibility. The evidence for a change in the response of the brain's natural opioids to inductive stimuli points to potential human-specific characteristics favored during evolution. In addition, the pattern of linked nucleotide and microsatellite variation among and within modern human populations suggests that recent selection, subsequent to the fixation of the human-specific mutations and the peopling of the globe, has favored different prodynorphin cis-regulatory alleles in different parts of the world.

Structural divergence between the human and chimpanzee genomes

The structural microheterogeneity evident between the human and chimpanzee genomes is quite considerable and includes inversions and duplications as well as deletions, ranging in size from a few base-pairs up to several megabases (Mb). Insertions and deletions have together given rise to at least 150 Mb of genomic DNA sequence that is either present or absent in humans as compared to chimpanzees. Such regions often contain paralogous sequences and members of multigene families thereby ensuring that the human and chimpanzee genomes differ by a significant fraction of their gene content. There is as yet no evidence to suggest that the large chromosomal rearrangements which serve to distinguish the human and chimpanzee karyotypes have influenced either speciation or the evolution of lineage-specific traits. However, the myriad submicroscopic rearrangements in both genomes, particularly those involving copy number variation, are unlikely to represent exclusively neutral changes and hence promise to facilitate the identification of genes that have been important for human-specific evolution.

Evolution of candidate transcriptional regulatory motifs since the human-chimpanzee divergence

BACKGROUND

Despite the recent completion of the chimpanzee genome project, few functionally significant sequence differences between humans and chimpanzees have thus far been identified. Alteration in transcriptional regulatory mechanisms represents an important platform for evolutionary change, suggesting that a significant proportion of functional human-chimpanzee sequence differences may affect regulatory elements.

RESULTS

To explore this hypothesis, we performed genome-wide identification of conserved candidate transcription-factor binding sites that have evolved since the divergence of humans and chimpanzees. Analysis of candidate transcription-factor binding sites conserved between mouse and chimpanzee yet absent in human indicated that loss of candidate transcription-factor binding sites in the human lineage was not random but instead correlated with the biologic functions of associated genes.

CONCLUSION

Our data support the notion that changes in transcriptional regulation have contributed to the recent evolution of humans. Moreover, genes associated with mutated candidate transcription-factor binding sites highlight potential pathways underlying human-chimpanzee divergence.

Using DNA microarrays to study natural variation

The emerging field of genomics examines the relationship between genetic and phenotypic variation by describing and analyzing patterns of natural variation on a genome-wide scale. In this endeavor, an important tool is the use of microarrays, which enable simultaneous screening of thousands of assays. Microarrays were originally designed for the detection of differences between samples and are thus ideally suited to high-throughput studies of natural variation. Novel microarray platforms enable the high throughput survey of variation at multiple levels, including DNA sequences, gene expression, protein binding, and methylation. However, most microarray data analysis tools, notably normalization methods, were developed for experiments in which only few features differed between samples. In studies of natural variation, this assumption does not always hold, raising a number of new challenges.

Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution

Background

DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have been developed for evaluating the significance of the observed differences in gene expression. However, until now little attention has been given to the characterization of dispersion of DNA microarray data.

Results

Here we examine the expression data obtained from 682 Affymetrix GeneChips^® with 22 different types and we demonstrate that the Gaussian (normal) frequency distribution is characteristic for the variability of gene expression values. However, typically 5 to 15% of the samples deviate from normality. Furthermore, it is shown that the frequency distributions of the difference of expression in subsets of ordered, consecutive pairs of genes (consecutive samples) in pair-wise comparisons of replicate experiments are also normal. We describe a consecutive sampling method, which is employed to calculate the characteristic function approximating standard deviation and show that the standard deviation derived from the consecutive samples is equivalent to the standard deviation obtained from individual genes. Finally, we determine the boundaries of probability intervals and demonstrate that the coefficients defining the intervals are independent of sample characteristics, variability of data, laboratory conditions and type of chips. These coefficients are very closely correlated with Student's t-distribution.

Conclusion

In this study we ascertained that the non-systematic variations possess Gaussian distribution, determined the probability intervals and demonstrated that the K_α coefficients defining these intervals are invariant; these coefficients offer a convenient universal measure of dispersion of data. The fact that the K_α distributions are so close to t-distribution and independent of conditions and type of arrays suggests that the quantitative data provided by Affymetrix technology give "true" representation of physical processes, involved in measurement of RNA abundance.

Reviewers

This article was reviewed by Yoav Gilad (nominated by Doron Lancet), Sach Mukherjee (nominated by Sandrine Dudoit) and Amir Niknejad and Shmuel Friedland (nominated by Neil Smalheiser).

Evolution of primate gene expression

It has been suggested that evolutionary changes in gene expression account for most phenotypic differences between species, in particular between humans and apes. What general rules can be described governing expression evolution? We find that a neutral model where negative selection and divergence time are the major factors is a useful null hypothesis for both transcriptome and genome evolution. Two tissues that stand out with regard to gene expression are the testes, where positive selection has exerted a substantial influence in both humans and chimpanzees, and the brain, where gene expression has changed less than in other organs but acceleration might have occurred in human ancestors.

Genetic diversity contribution to errors in short oligonucleotide microarray analysis

DNA arrays based on short oligonucleotide (< or = 25-mer) probes are being developed for many species, and are being applied to quantify transcript abundance variation in species with high genetic diversity. To define the parameters necessary to design short oligo arrays for maize (Zea mays L.), a species with particularly high nucleotide (single nucleotide polymorphism, SNP) and insertion-deletion (indel) polymorphism frequencies, we analysed gene expression estimates generated for four maize inbred lines using a custom Affymetrix DNA array, and identified biases associated with high levels of polymorphism between lines. Statistically significant interactions between probes and maize inbreds were detected, affecting five or more probes (out of 30 probes per transcript) in the majority of cases. SNPs and indels were identified by re-sequencing; they are the primary source of probe-by-line interactions, affecting probeset level estimates and reducing the power of detecting transcript level variation between maize inbreds. This analysis identified 36,196 probes in 5118 probesets containing markers that may be used for genotyping in natural and segregating populations for association gene analysis and genetic mapping.

Group testing for pathway analysis improves comparability of different microarray datasets

MOTIVATION

The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different datasets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, e.g. metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis, functional class scoring (FCS), or global tests.

RESULTS

Three public prostate cancer datasets obtained with the same microarray platform (HGU95A/HGU95Av2) were analyzed. Each dataset was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays was applied to the vsn-normalized data and mixed model analysis to the data normalized by MMN. For multiple testing adjustment the false discovery rate was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different datasets showed overlaps between 42 and 52%, while lists from different methods applied to the same dataset had between 63 and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three datasets was then subjected to group testing by Fisher's exact test. Group testing by GSEA and global test was applied to the three datasets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different datasets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single dataset.

Natural selection on gene expression

Changes in genetic regulation contribute to adaptations in natural populations and influence susceptibility to human diseases. Despite their potential phenotypic importance, the selective pressures acting on regulatory processes in general and gene expression levels in particular are largely unknown. Studies in model organisms suggest that the expression levels of most genes evolve under stabilizing selection, although a few are consistent with adaptive evolution. However, it has been proposed that gene expression levels in primates evolve largely in the absence of selective constraints. In this article, we discuss the microarray-based observations that led to these disparate interpretations. We conclude that in both primates and model organisms, stabilizing selection is likely to be the dominant mode of gene expression evolution. An important implication is that mutations affecting gene expression will often be deleterious and might underlie many human diseases.

Variation within and among species in gene expression: raw material for evolution

Heritable variation in regulatory or coding regions is the raw material for evolutionary processes. The advent of microarrays has recently promoted examination of the extent of variation in gene expression within and among taxa and examination of the evolutionary processes affecting variation. This review examines these issues. We find: (i) microarray-based measures of gene expression are precise given appropriate experimental design; (ii) there is large inter-individual variation, which is composed of a minor nongenetic component and a large heritable component; (iii) variation among populations and species appears to be affected primarily by neutral drift and stabilizing selection, and to a lesser degree by directional selection; and (iv) neutral evolutionary divergence in gene expression becomes nonlinear with greater divergence times due to functional constraint. Evolutionary analyses of gene expression reviewed here provide unique insights into partitioning of regulatory variation in nature. However, common limitations of these studies include the tendency to assume a linear relationship between expression divergence and species divergence, and failure to test explicit hypotheses that involve the ecological context of evolutionary divergence.

Segregating variation in the transcriptome: cis regulation and additivity of effects

Properties of genes underlying variation in complex traits are largely unknown, especially for variation that segregates within populations. Here, we evaluate allelic effects, cis and trans regulation, and dominance patterns of transcripts that are genetically variable in a natural population of Drosophila melanogaster. Our results indicate that genetic variation due to the third chromosome causes mainly additive and nearly additive effects on gene expression, that cis and trans effects on gene expression are numerically about equal, and that cis effects account for more genetic variation than do trans effects. We also evaluated patterns of variation in different functional categories and determined that genes involved in metabolic processes are overrepresented among variable transcripts, but those involved in development, transcription regulation, and signal transduction are underrepresented. However, transcripts for proteins known to be involved in protein-protein interactions are proportionally represented among variable transcripts.

Expression profiling in primates reveals a rapid evolution of human transcription factors

Although it has been hypothesized for thirty years that many human adaptations are likely to be due to changes in gene regulation, almost nothing is known about the modes of natural selection acting on regulation in primates. Here we identify a set of genes for which expression is evolving under natural selection. We use a new multi-species complementary DNA array to compare steady-state messenger RNA levels in liver tissues within and between humans, chimpanzees, orangutans and rhesus macaques. Using estimates from a linear mixed model, we identify a set of genes for which expression levels have remained constant across the entire phylogeny (approximately 70 million years), and are therefore likely to be under stabilizing selection. Among the top candidates are five genes with expression levels that have previously been shown to be altered in liver carcinoma. We also find a number of genes with similar expression levels among non-human primates but significantly elevated or reduced expression in the human lineage, features that point to the action of directional selection. Among the gene set with a human-specific increase in expression, there is an excess of transcription factors; the same is not true for genes with increased expression in chimpanzee.

Global analysis of carbohydrate utilization by Lactobacillus acidophilus using cDNA microarrays

The transport and catabolic machinery involved in carbohydrate utilization by Lactobacillus acidophilus was characterized genetically by using whole-genome cDNA microarrays. Global transcriptional profiles were determined for growth on glucose, fructose, sucrose, lactose, galactose, trehalose, raffinose, and fructooligosaccharides. Hybridizations were carried out by using a round-robin design, and microarray data were analyzed with a two-stage mixed model ANOVA. Differentially expressed genes were visualized by hierarchical clustering, volcano plots, and contour plots. Overall, only 63 genes (3% of the genome) showed a >4-fold induction. Specifically, transporters of the phosphoenolpyruvate:sugar transferase system were identified for uptake of glucose, fructose, sucrose, and trehalose, whereas ATP-binding cassette transporters were identified for uptake of raffinose and fructooligosaccharides. A member of the LacS subfamily of galactoside-pentose hexuronide translocators was identified for uptake of galactose and lactose. Saccharolytic enzymes likely involved in the metabolism of monosaccharides, disaccharides, and polysaccharides into substrates of glycolysis were also found, including enzymatic machinery of the Leloir pathway. The transcriptome appeared to be regulated by carbon catabolite repression. Although substrate-specific carbohydrate transporters and hydrolases were regulated at the transcriptional level, genes encoding regulatory proteins CcpA, Hpr, HprK/P, and EI were consistently highly expressed. Genes central to glycolysis were among the most highly expressed in the genome. Collectively, microarray data revealed that coordinated and regulated transcription of genes involved in sugar uptake and metabolism is based on the specific carbohydrate provided. L. acidophilus's adaptability to environmental conditions likely contributes to its competitive ability for limited carbohydrate sources available in the human gastrointestinal tract.

Application of microarray technology in primate behavioral neuroscience research

Gene expression profiling of brain tissue samples applied to DNA microarrays promises to provide novel insights into the neurobiological bases of primate behavior. The strength of the microarray technology lies in the ability to simultaneously measure the expression levels of all genes in defined brain regions that are known to mediate behavior. The application of microarrays presents, however, various limitations and challenges for primate neuroscience research. Low RNA abundance, modest changes in gene expression, heterogeneous distribution of mRNA among cell subpopulations, and individual differences in behavior all mandate great care in the collection, processing, and analysis of brain tissue. A unique problem for nonhuman primate research is the limited availability of species-specific arrays. Arrays designed for humans are often used, but expression level differences are inevitably confounded by gene sequence differences in all cross-species array applications. Tools to deal with this problem are currently being developed. Here we review these methodological issues, and provide examples from our experiences using human arrays to examine brain tissue samples from squirrel monkeys. Until species-specific microarrays become more widely available, great caution must be taken in the assessment and interpretation of microarray data from nonhuman primates. Nevertheless, the application of human microarrays in nonhuman primate neuroscience research recovers useful information from thousands of genes, and represents an important new strategy for understanding the molecular complexity of behavior and mental health.

Comparing the human and chimpanzee genomes: searching for needles in a haystack

The chimpanzee genome sequence is a long-awaited milestone, providing opportunities to explore primate evolution and genetic contributions to human physiology and disease. Humans and chimpanzees shared a common ancestor approximately 5-7 million years ago (Mya). The difference between the two genomes is actually not approximately 1%, but approximately 4%--comprising approximately 35 million single nucleotide differences and approximately 90 Mb of insertions and deletions. The challenge is to identify the many evolutionarily, physiologically, and biomedically important differences scattered throughout these genomes while integrating these data with emerging knowledge about the corresponding "phenomes" and the relevant environmental influences. It is logical to tackle the genetic aspects via both genome-wide analyses and candidate gene studies. Genome-wide surveys could eliminate the majority of genomic sequence differences from consideration, while simultaneously identifying potential targets of opportunity. Meanwhile, candidate gene approaches can be based on such genomic surveys, on genes that may contribute to known differences in phenotypes or disease incidence/severity, or on mutations in the human population that impact unique aspects of the human condition. These two approaches will intersect at many levels and should be considered complementary. We also cite some known genetic differences between humans and great apes, realizing that these likely represent only the tip of the iceberg.

The eloquent ape: genes, brains and the evolution of language

The human capacity to acquire complex language seems to be without parallel in the natural world. The origins of this remarkable trait have long resisted adequate explanation, but advances in fields that range from molecular genetics to cognitive neuroscience offer new promise. Here we synthesize recent developments in linguistics, psychology and neuroimaging with progress in comparative genomics, gene-expression profiling and studies of developmental disorders. We argue that language should be viewed not as a wholesale innovation, but as a complex reconfiguration of ancestral systems that have been adapted in evolutionarily novel ways.

Transcriptomic analyses support the similarity of gene expression between brain and testis in human as well as mouse

We previously revealed similarity in gene expression patterns between human brain and testis, based on digital differential display analyses of 760 human Unigenes. In the present work, we reanalyzed the gene expression data in many tissues of human and mouse for a large number of genes almost covering the respective whole genomes. The results indicated that both in human and in mouse, the gene expression profiles exhibited by brain, cerebellum and testis are most similar to each other compared with other tissues.

[11] Analysis of Variance of Microarray Data

Analysis of variance (ANOVA) is an approach used to identify differentially expressed genes in complex experimental designs. It is based on testing for the significance of the magnitude of effect of two or more treatments taking into account the variance within and between treatment classes. ANOVA is a highly flexible analytical approach that allows investigators to simultaneously assess the contributions of multiple factors to gene expression variation, including technical (dye, batch) effects and biological (sex, genotype, drug, time) ones, as well as interactions between factors. This chapter provides an overview of the theory of linear mixture modeling and the sequence of steps involved in fitting gene-specific models and discusses essential features of experimental design. Commercial and open-source software for performing ANOVA is widely available.

A mutation accumulation assay reveals a broad capacity for rapid evolution of gene expression

Mutation is the ultimate source of biological diversity because it generates the variation that fuels evolution. Gene expression is the first step by which an organism translates genetic information into developmental change. Here we estimate the rate at which mutation produces new variation in gene expression by measuring transcript abundances across the genome during the onset of metamorphosis in 12 initially identical Drosophila melanogaster lines that independently accumulated mutations for 200 generations. We find statistically significant mutational variation for 39% of the genome and a wide range of variability across corresponding genes. As genes are upregulated in development their variability decreases, and as they are downregulated it increases, indicating that developmental context affects the evolution of gene expression. A strong correlation between mutational variance and environmental variance shows that there is the potential for widespread canalization. By comparing the evolutionary rates that we report here with differences between species, we conclude that gene expression does not evolve according to strictly neutral models. Although spontaneous mutations have the potential to generate abundant variation in gene expression, natural variation is relatively constrained.

Is ectopic expression caused by deregulatory mutations or due to gene-regulation leaks with evolutionary potential?

It has long been thought that gene expression is tightly regulated in multicellular eukaryotes, so that expression profiles match functional profiles. This conception emerged from the assumption that gene activity is synonymous with gene function. This paradigm was first challenged by comparative protein electrophoresis studies showing extensive differences in expression patterns among related species. The paradigm is now being challenged by evolutionary transcriptomics using microarray technologies. Most gene expression profiles display features that lack any obvious functional significance. The so-called "ectopic" expression refers to the expression of genes at times and locations where the target gene is not known to have a function. However, ectopic expression might be associated with genuine function even if this function is not essential or has yet to be ascertained. Alternatively, ectopic expression might come about as a superfluous by-product of regulatory systems, which would call for a revision of prevailing ideas about the specificity of gene regulation. We herein review available evidence for ectopic expression and the hypotheses proposed for its origin and evolution. We propose that ectopic expression must be regarded as part of an integrated phenotypic whole. It seems likely that ectopic expression represents a leak in the evolution of regulatory systems, but one that is endowed with considerable evolutionary possibilities.

Evolutionary Conservation of Expression Profiles Between Human and Mouse Orthologous Genes

Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.

Genetic analysis of variation in gene expression in Arabidopsis thaliana

In Arabidopsis thaliana, significant efforts to determine the extent of genomic variation between phenotypically divergent accessions are under way, but virtually nothing is known about variation at the transcription level. We used microarrays to examine variation in transcript abundance among three inbred lines and two pairs of reciprocal F1 hybrids of the highly self-fertilizing species Arabidopsis. Composite additive genetic effects for gene expression were estimated from pairwise comparisons of the three accessions Columbia (Col), Landsberg erecta (Ler), and Cape Verde Islands (Cvi). For the pair Col and Ler, 27.0% of the 4876 genes exhibited additive genetic effects in their expression (alpha = 0.001) vs. 32.2 and 37.5% for Cvi with Ler and Col, respectively. Significant differential expression ranged from 32.45 down to 1.10 in fold change and typically differed by a factor of 1.56. Maternal or paternal transmission affected only a few genes, suggesting that the reciprocal effects observed in the two crosses analyzed were minimal. Dominance effects were estimated from the comparisons of hybrids with the corresponding midparent value. The percentage of genes showing dominance at the expression level in the F1 hybrids ranged from 6.4 to 21.1% (alpha = 0.001). Breakdown of these numbers of genes according to the magnitude of the dominance ratio revealed heterosis for expression for on average 9% of the genes. Further advances in the genetic analysis of gene expression variation may contribute to a better understanding of its role in affecting quantitative trait variation at the phenotypic level.

Uncovering evolutionary patterns of gene expression using microarrays

The advent of microarray technology is providing new insights into fundamental questions in evolutionary biology. Here, we review the recent literature on the use of microarrays to study the evolution of genome-wide patterns of gene expression within and between species. Large levels of variation in gene expression patterns have been observed at the intra and interspecific level, and a substantial fraction of transcriptional variation has a genetic component that is contributed by changes in both cis-acting and trans-acting regulatory elements. We argue that there is solid evidence to show that the temporal dynamics of transcriptional variation is largely determined by natural selection, with the fraction of the transcriptome more closely related to sex and reproduction evolving more rapidly.

Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees

The determination of the chimpanzee genome sequence provides a means to study both structural and functional aspects of the evolution of the human genome. Here we compare humans and chimpanzees with respect to differences in expression levels and protein-coding sequences for genes active in brain, heart, liver, kidney, and testis. We find that the patterns of differences in gene expression and gene sequences are markedly similar. In particular, there is a gradation of selective constraints among the tissues so that the brain shows the least differences between the species whereas liver shows the most. Furthermore, expression levels as well as amino acid sequences of genes active in more tissues have diverged less between the species than have genes active in fewer tissues. In general, these patterns are consistent with a model of neutral evolution with negative selection. However, for X-chromosomal genes expressed in testis, patterns suggestive of positive selection on sequence changes as well as expression changes are seen. Furthermore, although genes expressed in the brain have changed less than have genes expressed in other tissues, in agreement with previous work we find that genes active in brain have accumulated more changes on the human than on the chimpanzee lineage.

Challenges for identifying functionally important genetic variation: the promise of combining complementary research strategies

Strategies for the identification of functional genetic variation underlying phenotypic traits of ecological and evolutionary importance have received considerable attention in the literature recently. This paper aims to bring together and compare the relative strengths and limitations of various potentially useful research strategies for dissecting functionally important genetic variation in a wide range of organisms. We briefly explore the relative strengths and limitations of traditional and emerging approaches and evaluate their potential use in free-living populations. While it is likely that much of the progress in functional genetic analyses will rely on progress in traditional model species, it is clear that with prudent choices of methods and appropriate sampling designs, much headway can be also made in a diverse range of species. We suggest that combining research approaches targeting different functional and biological levels can potentially increase understanding the genetic basis of ecological and evolutionary processes both in model and non-model organisms.

Toward a neutral evolutionary model of gene expression

We introduce a stochastic model that describes neutral changes of gene expression over evolutionary time as a compound Poisson process where evolutionary events cause changes of expression level according to a given probability distribution. The model produces simple estimators for model parameters and allows discrimination between symmetric and asymmetric distributions of evolutionary expression changes along an evolutionary lineage. Furthermore, we introduce two measures, the skewness of expression difference distributions and relative difference of evolutionary branch lengths, which are used to quantify deviation from clock-like behavior of gene expression distances. Model-based analyses of gene expression profiles in primate liver and brain samples yield the following results: (1) The majority of gene expression changes are consistent with a neutral model of evolution; (2) along evolutionary lineages, upward changes in expression are less frequent but of greater average magnitude than downward changes; and (3) the skewness measure and the relative branch length difference confirm that an acceleration of gene expression evolution occurred on the human lineage in brain but not in liver. We discuss the latter result with respect to a neutral model of transcriptome evolution and show that a small number of genes expressed in brain can account for the observed data.

Comparison of regional gene expression differences in the brains of the domestic dog and human

Comparison of the expression profiles of 2,721 genes in the cerebellum, cortex and pituitary gland of three American Staffordshire terriers, one beagle and one fox hound revealed regional expression differences in the brain but failed to reveal marked differences among breeds, or even individual dogs. Approximately 85 per cent (42 of 49 orthologue comparisons) of the regional differences in the dog are similar to those that differentiate the analogous human brain regions. A smaller percentage of human differences were replicated in the dog, particularly in the cortex, which may generally be evolving more rapidly than other brain regions in mammals. This study lays the foundation for detailed analysis of the population structure of transcriptional variation as it relates to cognitive and neurological phenotypes in the domestic dog.

Assessing statistical precision, power, and robustness of alternative experimental designs for two color microarray platforms based on mixed effects models

Recommendations on experimental designs for two color microarray systems have been generally conflicting as they pertain to the general choice between reference and non-reference loop designs. This conflict may currently exist because many previously published assessments may not have effectively connected design layout with the level of biological relative to technical replication. We reassess various reference and non-reference designs for statistical efficiency in terms of standard errors of mean differences, power of test, and robustness using recently developed mixed model software tools. In minimally replicated cases (n = 2), it appears that the reference design outperforms the classical loop design whereby a sample from each animal is used for only one particular array hybridization. Alternatively, the reference design was consistently inferior to those connected loop designs in which a sample from each animal is used in two different hybridizations. Nevertheless, the gap in power between these two designs diminished as the biological to residual variance ratio increased. The statistical efficiency of a single large classical loop design for the comparison of many treatments was demonstrated to be highly sensitive to missing arrays relative to a common reference design (n = 2). However, the use of two loops within an interwoven loop design was shown to be substantially more robust to missing arrays and statistically more efficient relative to a common reference design. Furthermore, the use of more than one loop leads to less disparity in precision and power comparisons between any two treatments.

The transcriptional consequences of mutation and natural selection in Caenorhabditis elegans

The evolutionary importance of gene-expression divergence is unclear: some studies suggest that it is an important mechanism for evolution by natural selection, whereas others claim that most between-species regulatory changes are neutral or nearly neutral. We examined global transcriptional divergence patterns in a set of Caenorhabditis elegans mutation-accumulation lines and natural isolate lines to provide insights into the evolutionary importance of transcriptional variation and to discriminate between the forces of mutation and natural selection in shaping the evolution of gene expression. We detected the effects of selection on transcriptional divergence patterns and characterized them with respect to coexpressed gene sets, chromosomal clustering of expression changes and functional gene categories. We directly compared observed transcriptional variation patterns in the mutation-accumulation and natural isolate lines to a neutral model of transcriptome evolution to show that strong stabilizing selection dominates the evolution of transcriptional change for thousands of C. elegans expressed sequences.

Contribution of transcriptional regulation to natural variations in Arabidopsis

BACKGROUND

Genetic control of gene transcription is a key component in genome evolution. To understand the transcriptional basis of natural variation, we have studied genome-wide variations in transcription and characterized the genetic variations in regulatory elements among Arabidopsis accessions.

RESULTS

Among five accessions (Col-0, C24, Ler, WS-2, and NO-0) 7,508 probe sets with no detectable genomic sequence variations were identified on the basis of the comparative genomic hybridization to the Arabidopsis GeneChip microarray, and used for accession-specific transcriptome analysis. Two-way ANOVA analysis has identified 60 genes whose mRNA levels differed in different accession backgrounds in an organ-dependent manner. Most of these genes were involved in stress responses and late stages of plant development, such as seed development. Correlation analysis of expression patterns of these 7,508 genes between pairs of accessions identified a group of 65 highly plastic genes with distinct expression patterns in each accession.

CONCLUSION

Genes that show substantial genetic variation in mRNA level are those with functions in signal transduction, transcription and stress response, suggesting the existence of variations in the regulatory mechanisms for these genes among different accessions. This is in contrast to those genes with significant polymorphisms in the coding regions identified by genomic hybridization, which include genes encoding transposon-related proteins, kinases and disease-resistance proteins. While relatively fewer sequence variations were detected on average in the coding regions of these genes, a number of differences were identified from the upstream regions, several of which alter potential cis-regulatory elements. Our results suggest that nucleotide polymorphisms in regulatory elements of genes encoding controlling factors could be primary targets of natural selection and a driving force behind the evolution of Arabidopsis accessions.

Probe rank approaches for gene selection in oligonucleotide arrays with a small number of replicates

MOTIVATION

One major area of interest in analyzing oligonucleotide gene array data is identifying differentially expressed genes. A challenge to biostatisticians is to develop an approach to summarizing probe-level information that adequately reflects the true expression level while accounting for probe variation, chip variation and interaction effects. Various statistical tools, such as MAS and RMA, have been developed to address this issue. In these approaches, the probe level expression data are summarized into gene level data, which are then used for downstream statistical analysis. Since probe variation is often larger than chip variation and there is also a potential interaction effect between probe affinity and treatment effect, strategies such as a gene level analysis, may not be optimal. In this study, we propose a procedure to analyze probe level data for selecting differentially expressed genes under two treatment conditions (groups) with a small number of replicates. The probe level discrepancy between two groups can be measured by a difference of the percentiles of probe perfect-match (PM) ranks or of probe PM weighted ranks. The difference is then compared with a pre-specified threshold to determine differentially expressed genes. The probe level approach takes into account non-homogenous treatment effects and reduces possible cross-hybridization effects across a set of probes.

RESULTS

The proposed approach is compared with MAS and RMA using two benchmark gene array datasets. Positive predictivity and sensitivity are used for evaluation. Results show the proposed approach has higher positive predictivity and higher sensitivity.

AVAILABILITY

Available on request from the authors.

CONTACT

dtchen@uab.edu.

Further statistical analysis for genome-wide expression evolution in primate brain/liver/fibroblast tissues

In spite of only a 1-2 per cent genomic DNA sequence difference, humans and chimpanzees differ considerably in behaviour and cognition. Affymetrix microarray technology provides a novel approach to addressing a long-term debate on whether the difference between humans and chimpanzees results from the alteration of gene expressions. Here, we used several statistical methods (distance method, two-sample t-tests, regularised t-tests, ANOVA and bootstrapping) to detect the differential expression pattern between humans and great apes. Our analysis shows that the pattern we observed before is robust against various statistical methods; that is, the pronounced expression changes occurred on the human lineage after the split from chimpanzees, and that the dramatic brain expression alterations in humans may be mainly driven by a set of genes with increased expression (up-regulated) rather than decreased expression (down-regulated).

Comparative primate genomics: the year of the chimpanzee

This is the year of the chimpanzee genome. Chimpanzee chromosome 22 has been sequenced and soon will be followed by the whole genome, and thousands of chimpanzee cDNA sequences are available for comparative analysis. Not only does this genomic information allow us to identify human-specific changes in particular genes that are potentially under selection, but also to understand molecular evolutionary dynamics characterizing the two most closely related mammalian genomes sequenced so far. Studies comparing gene expression in chimpanzees and other closely related primates reveal significant species differences in brain, liver and fibroblasts. New empirical data, in combination with models of speciation, are giving insight into how humans and chimpanzees speciated.

Contribution of transcriptional regulation to natural variations in Arabidopsis

BACKGROUND

Genetic control of gene transcription is a key component in genome evolution. To understand the transcriptional basis of natural variation, we have studied genome-wide variations in transcription and characterized the genetic variations in regulatory elements among Arabidopsis accessions.

RESULTS

Among five accessions (Col-0, C24, Ler, WS-2, and NO-0) 7,508 probe sets with no detectable genomic sequence variations were identified on the basis of the comparative genomic hybridization to the Arabidopsis GeneChip microarray, and used for accession-specific transcriptome analysis. Two-way ANOVA analysis has identified 60 genes whose mRNA levels differed in different accession backgrounds in an organ-dependent manner. Most of these genes were involved in stress responses and late stages of plant development, such as seed development. Correlation analysis of expression patterns of these 7,508 genes between pairs of accessions identified a group of 65 highly plastic genes with distinct expression patterns in each accession.

CONCLUSION

Genes that show substantial genetic variation in mRNA level are those with functions in signal transduction, transcription and stress response, suggesting the existence of variations in the regulatory mechanisms for these genes among different accessions. This is in contrast to those genes with significant polymorphisms in the coding regions identified by genomic hybridization, which include genes encoding transposon-related proteins, kinases and disease-resistance proteins. While relatively fewer sequence variations were detected on average in the coding regions of these genes, a number of differences were identified from the upstream regions, several of which alter potential cis-regulatory elements. Our results suggest that nucleotide polymorphisms in regulatory elements of genes encoding controlling factors could be primary targets of natural selection and a driving force behind the evolution of Arabidopsis accessions.

Extensive sex-specific nonadditivity of gene expression in Drosophila melanogaster

Assessment of the degree to which gene expression is additive and heritable has important implications for understanding the maintenance of variation, adaptation, phenotypic divergence, and the mapping of genotype onto phenotype. We used whole-genome transcript profiling using Agilent long-oligonucleotide microarrays representing 12,017 genes to demonstrate that gene transcription is pervasively nonadditive in Drosophila melanogaster. Comparison of adults of two isogenic lines and their reciprocal F1 hybrids revealed 5820 genes as significantly different between at least two of the four genotypes in either males or females or across both sexes. Strikingly, while 25% of all genes differ between the two parents, 33% differ between both F1's and the parents, averaged across sexes. However, only 5% of genes show overdominance, suggesting that heterosis for expression is rare.

Variation in tissue-specific gene expression among natural populations

The expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish Fundulus heteroclitus was examined. Only a small subset (31%) of tissue-specific differences was consistent in all three populations, indicating that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types.

Background

Variation in gene expression is extensive among tissues, individuals, strains, populations and species. The interactions among these sources of variation are relevant for physiological studies such as disease or toxic stress; for example, it is common for pathologies such as cancer, heart failure and metabolic disease to be associated with changes in tissue-specific gene expression or changes in metabolic gene expression. But how conserved these differences are among outbred individuals and among populations has not been well documented. To address this we examined the expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish Fundulus heteroclitus using a highly replicated experimental design.

Results

Half of the genes (48%) were differentially expressed among individuals within a population-tissue group and 76% were differentially expressed among tissues. Differences among tissues reflected well established tissue-specific metabolic requirements, suggesting that these measures of gene expression accurately reflect changes in proteins and their phenotypic effects. Remarkably, only a small subset (31%) of tissue-specific differences was consistent in all three populations.

Conclusions

These data indicate that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types. We suggest that those subsets of treatment-specific gene expression patterns that are conserved between taxa are most likely to be functionally related to the physiological state in question.