Large scale comparison of global gene expression patterns in human and mouse
TL;DR: The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse.
Abstract: It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress. In a principal component analysis (PCA) of combined data from human and mouse samples merged on orthologous probesets, samples largely form distinctive clusters based on their tissue sources when projected onto the top principal components. The most prominent groups are the nervous system, muscle/heart tissues, liver and cell lines. Despite the great differences in sample characteristics and experiment conditions, the overall patterns of these prominent clusters are strikingly similar for human and mouse. We further analyzed data for each tissue separately and found that the most variable genes in each tissue are highly enriched with human-mouse tissue-specific orthologs and the least variable genes in each tissue are enriched with human-mouse housekeeping orthologs. The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse. The expression of groups of orthologous genes co-varies in the two species, both for the most variable genes and the most ubiquitously expressed genes.
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TL;DR: It was shown that adaptive convergence in all 3 high-altitude species seldom occurred at the amino acid substitution level but was more common on positively selected genes, and an altitude-related expression pattern was observed in genes differentially expressed between all 3 species pairs and genes associated with altitude, suggesting that the high-ALTitude environment may drive similar expression shifts in the 3high-altitudes species.
Abstract: High-altitude environments present strong stresses for living organisms, which have driven striking phenotypic and genetic adaptations. While previous studies have revealed multiple genetic adaptations in high-altitude species, how evolutionary history (i.e., phylogenetic background) contributes to similarity in genetic adaptations to high-altitude environments is largely unknown, in particular in a group of birds. We explored this in 3 high-altitude passerine birds from the Qinghai-Tibet Plateau and their low-altitude relatives in lowland eastern China. We generated transcriptomic data for 5 tissues across these species and compared sequence changes and expression shifts between high- and low-altitude pairs. Sequence comparison revealed that similarity in all 3 high-altitude species was high for genes under positive selection (218 genes) but low in amino acid substitutions (only 4 genes sharing identical amino acid substitutions). Expression profiles for all genes identified a tissue-specific expression pattern (i.e., all species clustered by tissue). By contrast, an altitude-related pattern was observed in genes differentially expressed between all 3 species pairs and genes associated with altitude, suggesting that the high-altitude environment may drive similar expression shifts in the 3 high-altitude species. Gene expression level, gene connectivity, and the interactions of these 2 factors with altitude were correlated with evolutionary rates. Our results provide evidence for how gene sequence changes and expression shifts work in a concerted way in a group of high-altitude birds, leading to similar evolution routes in response to high-altitude environmental stresses.
54 citations
TL;DR: Find In Translation (FIT) is presented, a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition and predicted novel disease-associated genes.
Abstract: Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org
), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20–50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost. The machine learning approach FIT leverages public mouse and human expression data to improve the translation of mouse model results to analogous human disease.
52 citations
TL;DR: Evidence is provided that species differences exist in chemokine/cytokine subnetworks in brain cells that may be relevant to stroke pathophysiology and further investigation of differential gene pathways across species is warranted.
Abstract: Mice and rats are the most commonly used animals for preclinical stroke studies, but it is unclear whether targets and mechanisms are always the same across different species. Here, we mapped the b...
51 citations
TL;DR: This study represents the first use of global gene expression profiling from healthy human brain to develop a disease gene prediction model and this generic methodology can be applied to study any neurological disorder.
Abstract: Brain function is governed by precise regulation of gene expression across its anatomically distinct structures; however, the expression patterns of genes across hundreds of brain structures are not clearly understood. Here, we describe a gene expression model, which is representative of the healthy human brain transcriptome by using data from the Allen Brain Atlas. Our in-depth gene expression profiling revealed that 84% of genes are expressed in at least one of the 190 brain structures studied. Hierarchical clustering based on gene expression profiles delineated brain regions into structurally tiered spatial groups and we observed striking enrichment for region-specific processes. Further, weighted co-expression network analysis identified 19 robust modules of highly correlated genes enriched with functional associations for neurogenesis, dopamine signaling, immune regulation and behavior. Also, structural distribution maps of major neurotransmission systems in the brain were generated. Finally, we developed a supervised classification model, which achieved 84% and 81% accuracies for predicting autism- and Parkinson's-implicated genes, respectively, using our expression model as a baseline. This study represents the first use of global gene expression profiling from healthy human brain to develop a disease gene prediction model and this generic methodology can be applied to study any neurological disorder.
46 citations
Cites background from "Large scale comparison of global ge..."
...Other interesting studies have analyzed gene expression data from various tissues of both humans and chimpanzees, and humans and mice to get a deeper understanding of differences and similarities in the transcriptional regulation between species (19, 20)....
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TL;DR: It is found that only a small number of orthologous gene pairs show similar expression pattern in the dauer of the two species, while the non-orthologous fraction of genes is a major contributor to the active transcriptome in dauers.
Abstract: An organism can respond to changing environmental conditions by adjusting gene regulation and by forming alternative phenotypes. In nematodes, these mechanisms are coupled because many species will form dauer larvae, a stress-resistant and non-aging developmental stage, when exposed to unfavorable environmental conditions, and execute gene expression programs that have been selected for the survival of the animal in the wild. These dauer larvae represent an environmentally induced, homologous developmental stage across many nematode species, sharing conserved morphological and physiological properties. Hence it can be expected that some core components of the associated transcriptional program would be conserved across species, while others might diverge over the course of evolution. However, transcriptional and metabolic analysis of dauer development has been largely restricted to Caenorhabditis elegans. Here, we use a transcriptomic approach to compare the dauer stage in the evolutionary model system Pristionchus pacificus with the dauer stage in C. elegans. We have employed Agilent microarrays, which represent 20,446 P. pacificus and 20,143 C. elegans genes to show an unexpected divergence in the expression profiles of these two nematodes in dauer and dauer exit samples. P. pacificus and C. elegans differ in the dynamics and function of genes that are differentially expressed. We find that only a small number of orthologous gene pairs show similar expression pattern in the dauers of the two species, while the non-orthologous fraction of genes is a major contributor to the active transcriptome in dauers. Interestingly, many of the genes acquired by horizontal gene transfer and orphan genes in P. pacificus, are differentially expressed suggesting that these genes are of evolutionary and functional importance. Our data set provides a catalog for future functional investigations and indicates novel insight into evolutionary mechanisms. We discuss the limited conservation of core developmental and transcriptional programs as a common aspect of animal evolution.
46 citations
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Abstract: SUMMARY In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip R � system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip R � arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth’s Genetics Institute involving 95 HG-U95A human GeneChip R � arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip R � arrays. We display some familiar features of the perfect match and mismatch probe ( PM and MM )v alues of these data, and examine the variance–mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix’s (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multiarray average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities. ∗ To whom correspondence should be addressed
10,711 citations
"Large scale comparison of global ge..." refers methods in this paper
...The resulting 1,323 CEL files were pre-processed using Bioconductor’s RMA package [32] to create an integrated, normalized data matrix....
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TL;DR: In this paper, high-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale, and the authors have designed custom arrays that interrogate the expression of the vast majority of proteinencoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues.
Abstract: The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.
3,513 citations
"Large scale comparison of global ge..." refers background or result in this paper
...While studies suggested that orthologous genes do not share similar expression patterns [1-5], other groups reported the opposite observations [6-9]....
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...Alternatively, many other studies made use of species-specific arrays to identify coexpressed groups of orthologous genes [4-6,16,17]....
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TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.
Abstract: The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.
2,683 citations
"Large scale comparison of global ge..." refers methods in this paper
...PCA has been often used to study high-dimensional data generated by genome-wide gene expression studies [22-25]....
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Book•
27 Jan 2006
TL;DR: In this article, the authors present a detailed case study of R algorithms with publicly available data, and a major section of the book is devoted to fully worked case studies, with a companion website where readers can reproduce every number, figure and table on their own computers.
Abstract: Full four-color book.
Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R.
All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies.
Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
2,625 citations