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Bioinformation. 2007; 1(10): 406–413. Published online 2007 April 10. | PMCID: PMC1896055 |
Copyright © 2006 Biomedical Informatics Publishing Group Integrative analysis of the mouse embryonic transcriptome Amar V Singh, Kenneth B Knudsen, and Thomas B Knudsen* Department of Molecular, Cellular and Craniofacial Biology, University of Louisville, School of Dentistry, Louisville, KY 40202 Received December 5, 2006; Accepted January 20, 2007. This is an open-access article, which permits unrestricted use, distribution, and reproduction
in any medium, for non-commercial purposes, provided the original author and source are credited. Monitoring global gene expression provides insight into how genes and regulatory signals work together to guide embryo
development. The fields of developmental biology and teratology are now confronted with the need for automated access to a
reference library of gene-expression signatures that benchmark programmed (genetic) and adaptive (environmental) regulation of the
embryonic transcriptome. Such a library must be constructed from highly-distributed microarray data. Birth Defects Systems Manager
(BDSM), an open access knowledge management system, provides custom software to mine public microarray data focused on
developmental health and disease. The present study describes tools for seamless data integration in the BDSM library ( MetaSample,
MetaChip, CIAeasy) using the QueryBDSM module. A field test of the prototype was run using published microarray data series
derived from a variety of laboratories, experiments, microarray platforms, organ systems, and developmental stages. The datasets
focused on several developing systems in the mouse embryo, including preimplantation stages, heart and nerve development, testis
and ovary development, and craniofacial development. Using BDSM data integration tools, a gene-expression signature for 346 genes
was resolved that accurately classified samples by organ system and developmental sequence. The module builds a potential for the
BDSM approach to decipher a large number developmental processes through comparative bioinformatics analysis of embryological
systems at-risk for specific defects, using multiple scenarios to define the range of probabilities leading from molecular phenotype to
clinical phenotype. We conclude that an integrative analysis of global gene-expression of the developing embryo can form the
foundation for constructing a reference library of signaling pathways and networks for normal and abnormal regulation of the
embryonic transcriptome. These tools are available free of charge from the web-site http://systemsanalysis.louisville.edu requiring
only a short registration process. Keywords: transcriptome, mouse, expression, embryo, integrative analysis, birth defects Animal development is fashioned by conserved signaling pathways that orchestrate morphogenesis, pattern formation, and cell differentiation - complex processes operating jointly in
different parts of an embryo and in stages associated with sequential gene activation. Monitoring local and temporal changes in gene expression can provide insight into how genes
and regulatory signals work together to guide development. [ 1] This knowledge is important for understanding the pathogenesis
of birth defects and to the central problems of defining precursor target cell susceptibility and the causal mechanisms of abnormal development triggered by diverse environmental and genetic
perturbations to maternal-fetal unit. [ 2] Profiling gene expression on a global scale has become an important source of information for biological knowledge discovery. Despite well-known challenges confronting
technology development, the analysis of global gene expression data can reveal themes in the biologically robust response patterns in gene activity. [ 3] Gene Expression Omnibus (GEO)
is one of the main national repositories for high-information content transcript data from microarray analysis and serial analysis of gene expression (SAGE). [ 4] GEO has grown from
18,235 records in June 2004 to 115,415 records in December 2006, reflecting an average growth of over 100 new entries per day. A subset of this information describes the embryo proper
and can be mined for major biological themes in developmental health and disease. Using keyword searches and trend analysis to mine PubMed and Medline literature databases, the public
availability of embryo-based microarrays currently numbers 500-600 (564), mostly studies on mouse embryos and differentiating human cell lines. The increasing volume of gene expression data on local and temporal states confronts the developmental biologist with the need for reference libraries and information management
systems to handle optimal-scale gene-expression signatures and facilitate biological knowledge discovery. For example, Lamb et al., [ 5] created a prototype reference collection of geneexpression
signatures from cultured human cells exposed to bioactive molecules, which serves as a platform for pattern-matching software to establish a ‘connectivity map’ between
drugs, genes, and diseases. Another example is the integrative analysis of multi-study tumor profiles. [ 6, 7]
The emerging database model for tumor classification based on molecular abundance profiles has implied a 67-gene core ‘common transcriptional program’ in multiple cancers. [ 8] In developmentteratogenesis,
a preliminary meta-analysis across microarray studies in the mouse embryo returned a gene-expression signature of 512 developmentally regulated genes of which 16%
(~82-genes) changed during exposure to teratogenic agents. [ 2] Given the promises and pitfalls of computational methods for
solving gene expression problems, automated access to a reference collection of gene-expression signatures to benchmark the programmed (genetic) and adaptive (environmental)
regulation of the embryonic transcriptome is scientifically needed. ‘Birth Defects Systems Manager’ (BDSM) is a knowledge management system that provides custom software to mine public microarray data for interesting patterns across
developmental stages, organ systems, and disease phenotypes. [ 2, 9]
This open resource enables: consolidation of communal data and metadata relevant to developmental health and disease; interactions with current builds of national databases and data
repositories; efficient algorithms for cross-species annotation of symbolic gene annotations using the NCBI sequence homologybased annotations for corresponding homologues and
orthologues; specific queries across experiments to facilitate secondary analysis; and data formats interoperable with analysis software for phenetic clustering, chromosomal mapping, gene
ontology classification, pathway evaluation, and network identification. Since a comprehensive reference collection of gene-expression signatures for developing structures must be constructed from highly-distributed data, the present study was designed to
empower BDSM with tools for seamless data integration: MetaSample, MetaChip, and CIAeasy. These tools are accessible
at http://systemsanalysis.louisville.edu requiring only a short
registration process to BDSM. A field test of the prototype run with published microarray data illustrates proof of concept for integrative analysis of the mouse embryonic transcriptome. Dataset collections Search of PubMed using keywords ‘embryo’ and ‘microarray’ returned 495 records of which 193 actually used the technology to study developing animal systems. GEO data sets (GDS) narrowed the list to 47 nonredundant
microarray datasets, and including the keyword ‘teratogen’ added a few more datasets, for a total of 564 public microarrays on the embryo. Raw and/or processed microarray
sample-data files and associated metadata were parsed onto the server using LoadBDSM. [ 9] BDSM currently holds 25
developmental series containing 537 samples that are derived from the public domain and 3 series containing 43 samples which are private. These data represent 15 developing organ
systems, 6 chemical exposures, and 5 drug interventions across 42 development stages. Tracking provenance For the present study, we restricted the analysis to 160 arrays in the BDSM library based on wellannotated experiments published on normal mouse
embryogenesis using an Affymetrix technology platform. These conditions are identified by GEO Series Accession number (GSE) and/or literature citation as follows: preimplantation
mouse embryo ( GSE1749) [ 10]; heart ( GSE1479) GD10 - GD18 [ 11]; nerve ( GSE972) GD9.5 - birth [ 12];
ovary ( GSE1359) and testis ( GSE1358) between GD11.5 - birth [ 13]; orofacial region
( GSE1624) [ 14] and secondary palate [ 15] between GD13-15.
The platforms for these series included: MG-U74Av2 (12488 probes), MG-U74Bv2 (12478 probes), MG430Av2 (45104 probes), MOE430Av2 (22690 probes), and MOE430Bv2 (22576
probes). Internal annotation of Affymetrix probe identifiers was performed to standardize gene labels across samples and improve cross-platform interoperability, as discussed previously.
[ 2] Data integration Individual microarray sample-data files from the aforementioned developmental series were integrated using QueryBDSM, a module that for merging pre-processed,
normalized samples from the BDSM library. Three metaanalysis tools written in PHP were designed to compare and analyze expression data across multiple chips and platforms:
MetaSample, MetaChip, and CIAeasy. The workflow schema is diagrammed in . Individual sample-data files are
selected from the BDSM library and added to a queue for integration. The formatted input files are tab-delimited expression ratios of probes (rows) x samples (columns).
QueryBDSM determines the number of distinct microarray platforms in the sample queue and merges the data as follows: if all samples come from the same microarray platform (number
=1), then QueryBDSM automatically runs MetaSample to create a merged-table having ‘columns’ of normalized expression data (samples) in ‘rows’ derived from the platform, with unique probe
identifiers (ProbeID) expanded to include GenBank accession (GeneID), and symbolic gene name (Gene Symbol). If multiple platforms are represented by samples in the queue (number >1),
then QueryBDSM automatically runs MetaChip. MetaChip merges data when probe identifiers are different but represent the same annotation, such as across microarray platforms or
phylogenetic species. The probe identifiers from each platform are converted to UniGene ID and then merged accordingly, with associated expression data, for those genes common across the
datasets. The probes are annotated based on reverse-engineering the sequence homology-based annotations from GenBank. [ 2]
For this purpose the system uses data flat files downloaded monthly from the HomoloGene and UniGene databases of NCBI. [ 16] In contrast to MetaSample and MetaChip, automated tools for merging samples, CIAeasy must be specified explicitly to
compare datasets for the same samples on different platforms. CIAeasy was created from the ADE-4 for R statistical
computing software package [ 17] and is adapted from ‘coinertia analysis’ (CIA) for microarrays. [ 18] With CIAeasy
users can perform CIA from the BDSM web site without detailed knowledge of R language programming. Since samples are aligned on a common space, CIA extracts
information about the joint trends in expression patterns of genes independent of probe or sequence annotation. [ 18]
CIAeasy automatically computes successive orthogonal axes with correspondence analysis and returns the percentage of total variance explained by each eigenvector to find the
strongest trends in the co-structured datasets. Data analysis One of the problems confronting meta-analysis is the normalcy and spread of expression data. In order to derive expression ratios from the Affymetrix data, we computed a
reference denominator, averaged for each gene at the earliest stage in a developmental series. These ratios were transformed to logarithm base 2 (log2) to produce a continuous spectrum of
values without biasing between up- and down-regulated genes and making the spread more normal. Each microarray set was centered to median of 0.00 and standardized by scaling to an
average standard deviation of 0.5. [ 2] The merged data were imported to GeneSpring v 7.2 using the UniGene cluster ID as
the unique gene identifier. The data were clustered using Pearson correlation for the gene tree and two-sided Spearman Confidence for the developmental conditions. Functional
annotation used the NIH/NIAID Database for Annotation, Visualization and Integrated Discovery (DAVID). [ 19] The
highest-ranking biological themes were stratified by Gene Ontology (GO) terms. Implementation of QueryBDSM For proof of concept we examined samples from GEO data source GSE1391, a series describing global gene-expression profiles of the mouse embryo
during preimplantation stages. [ 10] Samples included in this series represent development of the oocyte through fertilization
(1-cell embryo), activation of the zygotic genome (2-cell embryo) and first differentiation (8-cell embryo) leading to divergent embryonal (inner cell mass) and trophectodermal
(placental) lineages of the blastocyst. Biological replicates (3-4) arrayed at each stage used three different Affymetrix platforms: MOE430Av2 (22690 probes), MOE430Bv2 (22576 probes), and
MG-U74Av2 (12488 probes). Gene-expression profiles were normalized to the ‘oocyte’ in each platform as the earliest stage in the series. Derived data are log2-scale expression values
computed from the ratio of signals to the oocyte reference. Using QueryBDSM, we merged datasets from the different samples to create three distinct datasets for the MOE430Av2, MOE430Bv2, and MG-U74Av2 platforms. Statistical
(ANOVA) analysis, run at high stringency with Benjamini-Hochberg correction applied, returned 4417 probes (alpha = 0.0001), 1614 probes (alpha = 0.0001), and 2400 probes (alpha
= 0.001) that were differentially regulated. Aside from 34 probes that overlapped between the first two datasets, different genes were detected across these diverse platforms. Hierarchical
clustering revealed two basic trajectories of gene-expression in all three platforms (). One expression cluster contained genes that increased at the 2-cell stage to the blastocyst stage,
and the other cluster contained genes that decreased over these stages (not shown). These probes were mapped to the 307 reference pathways in the KEGG: Kyoto Encyclopedia of Genes
and Genomes ( http://www.genome.ad.jp/kegg/) library to
identify metabolic themes. The top significant KEGG pathways showed concordance between platforms MOE430Av2 and MGU74Av2 ( Table 1), whereas MOE430Bv2 detected different
pathways. Some pathways had marginal P-values with individual analysis that became significant in datasets joined by MetaChip (e.g., Adherens Junction, Tight Junction pathways).
This illustrates the strength of the meta-analysis approach. | Table 1Meta-analysis of top significant KEGG pathways altered during preimplantation mouse embryo development 1 |
We next used QueryBDSM to merge samples from platforms MOE430Av2 and MG-U74Av2 to illustrate the MetaChip and CIAeasy tools. These chips contained 13022 and 9562 nonredundant
UniGene cluster identifiers, respectively, of which 7278 were common between the two platforms when passed through MetaChip. Statistical (ANOVA) analysis with the same
parameters as before returned 3324 genes that were differentially regulated in this data subset. The top significant KEGG pathways for the combined 430A-U74A MetaChip are
concordant with the individual analysis ( Table 1). CIAeasy was used to compare the joint trend between 4417, 1614, and 2400 probes from MetaSample, resulting in a high-level of similarity
between these three platforms (). Of the 6812 nonredundant probes between MOE430Av2 and MG-U74Av2 (4417 + 2400), only five probes were common. Annotating the probes which were not in common gave 4551 unique DAVID
identifiers. Again, meta-analysis picked up most of the significant KEGG pathways identified in either of the singular analyses performed earlier and a few additional metabolic pathways ( Table 1). Comparative bioinformatics analysis across developing systems An obvious limitation in combining data from several different platforms is that as more platforms are included, fewer representative genes are found to be common amongst all
platforms. This problem increases when considering less comprehensive arrays, older arrays with outdated probe annotations, or arrays across animal species. Staging the entry of
data from the most versatile arrays first can lessen the problem of losing information when data are combined across platforms; however, in some data mining efforts the discriminating power
gained by increasing samples and conditions might outweigh the loss of information. For example, multi-platform datasets have been found to discriminate tumor classification by expression
profile with as few as 25 genes. [ 6] For this reason it may be possible to benchmark developmental stages using a limited
number of genes across many diverse platforms. To illustrate this point we used BDSM-derived data to compare expression profiles across six unrelated studies and developing systems. We
constructed a virtual meta-chip for probes common to all five technology platforms represented in these studies, yielding 346 genes. Unsupervised clustering and Pearson correlation of the
gene-expression profiles correctly ordered the samples first by organ system and then by developmental sequence within each system (). Within this hierarchy commonalities and
differences across organ systems were evident for the patterns of expression in subsets of genes. Unfortunately, the number of genes returned from 346 by statistical (ANOVA) analysis of
each individual system, or by K-means clustering of the entire matrix, was too small for insightful functional annotation. QueryBDSM is a simple and efficient solution that can be used
to construct a self-evolving reference collection of geneexpression profiles from highly-distributed data on mouse development. Although robust mapping of biological themes and pathways that are expressed at particular developmental
stages is straightforward when the same technology platform is considered [ 2] fuzzy-clustering methods will be needed when multiple platforms are considered. The MetaSample and MetaChip components of QueryBDSM are also available as standalone tools under the MetaBDSM module. In this way, the user can upload data outside the BDSM library.
The user supplies details of the technology platform, organism, and information about the file format. Once all the required fields are entered and submitted, the files are checked for unique
headers. Columns can be dropped by selecting the appropriate checkboxes. Clicking the Continue button combines the datasets with expression data and unique identifiers only for genes
common between the datasets. These tools have been tested on Internet Explorer 6.0 or greater and Mozilla-based browsers, such as Netscape 6.0 and Firefox. Other tools are available to assemble data when the platform is same, such as Microarray data assembler. [ 20] This Excel-based
program inherits Excel's limitation from file sizes and number of samples (256 columns and 65,000 data points) whereas the MetaSample and MetaChip tools do not have this limitation.
These tools create temporary tables in the Oracle database and join them using the functionality of Oracle before putting it into a text file, reducing restrictions on the number of samples and
size of files. Although users can theoretically combine 100 files at a single time, it is not recommended to load more then 25 files at a time. The representation of experimental samples as developmentally contiguous groups is expected to yield a novel mosaic view of gene-expression signatures and genetic dependencies. Although
sufficient data exists for data-mining efforts to begin, the ultimate goal of an unabridged reference collection must be viewed as a long-term effort. Regarding the embryo, a search of
OVID MedLine using keywords ‘embryo’ OR ‘teratogen’ (136,146 records) AND ‘microarray’ OR ‘SAGE’ (16,906 records) returned 343 total records. At the current rate of 564
microarrays per 343 publication records (factor = 1.64), the trajectory of embryo-based microarray publications projects GEO to hold in excess of 1,476 microarrays relevant for
embryogenesis or teratogenesis by the year 2010. As studies unravel gene-expression signatures, the key principles in teratology – namely, chemical effects on biological mechanisms, dose-response relationships, factors underlying
genetic susceptibility, stage-dependent responses, and maternal influences, can be framed in a systems biology context to address an ‘experience database’ for ranking pathways and
networks by strength of association with anatomical landmarks and developmental abnormalities. [ 2] The BDSM resource
would parallel efforts toward molecular diagnostics in cancer biology ( http://www.oncomine.org/), which includes data sets
profiling human tumor samples. [ 21] Since interpreting geneexpression signatures in birth defects will be predicated on
posterior (prior) knowledge about developmental health and disease, an important payoff from this bioinformatics effort is to recognize and characterize how these biological states emerge
from adaptation or adverse regulation of the embryonic transcriptome. This work was supported by NIH grants RO1 AA13205 (National Institute on Alcohol Abuse and Alcoholism) and RO1 ES09120 (National Institute of Environmental Health Sciences). 1. Davidson EH, Erwin DH. Science. 2006;311:796. [PubMed] 2. Singh AV, et al. Reprod Toxicol. 2005;19:421. [PubMed] 3. Marshall E. Science. 2004;306:630. [PubMed] 4. Barrett T, et al. Nucleic Acids Res. 2005;33:D562. [PubMed] 5. Lamb J, et al. Science. 2006;313:1929. [PubMed] 6. Bloom G, et al. Am J Pathol. 2004. p. 9. [PubMed] 7. Rhodes DR, Chinnaiyan AM. Nat Genet. 2005. p. S31. [PubMed] 8. Rhodes DR, et al. Proc Natl Acad Sci. 2004. p. 9309. [PubMed] 9. Knudsen KB, et al. Reprod Toxicol. 2005. p. 369. [PubMed] 10. Zeng F, et al. Dev Biol. 2004. p. 483. [PubMed] 11. Tanaka M, et al. Cold Spring Harb Symp Quant Biol. 2002. p. 317. [PubMed] 12. Buchstaller J, et al. J Neurosci. 2004. p. 2357. [PubMed] 13. Small CL, et al. Biol Reprod. 2005. p. 492. [PubMed] 14. Mukhopadhyay P, et al. Birth Defects Res. 2004. p. 912. [PubMed] 15. Brown NL, et al. Dev Growth Differ. 2003. p. 153. [PubMed] 16. Wheeler DL, et al. Nucleic Acids Res. 2006. p. D173. [PubMed] 17. Thioulouse J, et al. Stat Comput. 1997. p. 75. [PubMed] 18. Culhane AC, et al. BMC Bioinformatics. 2003. p. 59. [PubMed] 19. Dennis G, et al. Genome Biol. 2001. p. P3. [PubMed] 20. Anbazhagan R. Bioinformatics. 2003. p. 157. [PubMed] 21. Rhodes DR, et al. Neoplasia. 2004. p. 1. [PubMed]
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