Dataset Information

Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data.

ABSTRACT: Cross-species comparison of gene expression profiles allows deciphering fundamental and species-specific transcriptional programs of cells and offers insight into organization and evolution of the genome and genetic network. Here, we propose an algorithm for comparing microarray data from different species to unravel transcriptional modules that are conserved or divergent through evolution. The proposed algorithm is based on cross-species matrix decomposition that includes a nonlinear independent component analysis followed a generalized probabilistic sparse matrix factorization on microarray data from different species. The proposed algorithm captures transcriptional modularity that might result from highly nonlinear interactions among genes, and partitions genes into mutually non-exclusive transcriptional modules. The conserved transcriptional modules are identified by the latent variables that are associated with predominant biological prototypes shared across species. We illustrated the application of the proposed algorithm by an analysis of human and mouse embryonic stem cell (ESC) data. The analysis uncovered conserved and divergent transcriptional modules in the ESC transcriptomes, shedding light on the understanding of fundamental and species-specific regulatory mechanisms controlling ESC development.

SUBMITTER: Li H

PROVIDER: S-EPMC2817969 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data.

Li Huai H Zhan Ming M

Journal of proteomics & bioinformatics 20090301

Cross-species comparison of gene expression profiles allows deciphering fundamental and species-specific transcriptional programs of cells and offers insight into organization and evolution of the genome and genetic network. Here, we propose an algorithm for comparing microarray data from different species to unravel transcriptional modules that are conserved or divergent through evolution. The proposed algorithm is based on cross-species matrix decomposition that includes a nonlinear independen ...[more]

PMID: 20148181

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Project description:A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes.We propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos.The R package containing the implementation of the proposed method in C?++?is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/jinbo@engr.uconn.edu.

Project description:There is mounting evidence to suggest that the epigenetic reprogramming capacity of the oocyte is superior to that of the current factor-based reprogramming approaches and that some factor-reprogrammed induced pluripotent stem cells (iPSCs) retain a degree of epigenetic memory that can influence differentiation capacity and may be linked to the observed expression of immunogenicity genes in iPSC derivatives. One hypothesis for this differential reprogramming capacity is the "chromatin loosening/enhanced reprogramming" concept, as previously described by John Gurdon and Ian Wilmut, as well as others, which postulates that the oocyte possesses factors that loosen the somatic cell chromatin structure, providing the epigenetic and transcriptional regulatory factors more ready access to repressed genes and thereby significantly increasing epigenetic reprogramming. However, to empirically test this hypothesis a list of candidate oocyte reprogramming factors (CORFs) must be ascertained that are significantly expressed in metaphase II oocytes. Previous studies have focused on intraspecies or cross-species transcriptional analysis of up to two different species of oocytes. In this study, we have identified eight CORFs (ARID2, ASF1A, ASF1B, DPPA3, ING3, MSL3, H1FOO, and KDM6B) based on unbiased global transcriptional analysis of oocytes from three different species (human, rhesus monkey, and mouse) that both demonstrate significant (p<0.05, FC>3) expression in oocytes of all three species and have well-established roles in loosening/opening up chromatin structure. We also identified an additional 15 CORFs that fit within our proposed "chromatin opening/fate transformative" (COFT) model. These CORFs may be able to augment Shinya Yamanaka's previously identified reprogramming factors (OCT4, SOX2, KLF4, and cMYC) and potentially facilitate the removal of epigenetic memory in iPSCs and/or reduce the expression of immunogenicity genes in iPSC derivatives, and may have applications in future personalized pluripotent stem cell based therapeutics.

Project description:BACKGROUND: Comparative genomic analysis using cDNA microarray is a new approach and a useful tool to identify important genetic sequences or genes that are conserved throughout evolution. Identification of these conserved sequences will help elucidate important molecular mechanisms or pathways common to many species. For example, the stockpiled transcripts in the oocyte necessary for successful fertilization and early embryonic development still remain relatively unknown. The objective of this study was to identify genes expressed in oocytes and conserved in three evolutionarily distant species. RESULTS: In this study we report the construction of a multi-species cDNA microarray containing 3,456 transcripts from three distinct oocyte-libraries from bovine, mouse and Xenopus laevis. Following the cross-species hybridizations, data analysis revealed that 1,541 positive hybridization signals were generated by oocytes of all three species, and 268 of these are preferentially expressed in the oocyte. Data reproducibility analyses comparing same-species to cross-species hybridization indicates that cross-species hybridizations are highly reproducible, thus increasing the confidence level in their specificity. A validation by RT-PCR using gene- and species-specific primers confirmed that cross-species hybridization allows the production of specific and reliable data. Finally, a second validation step through gene-specific microarray hybridizations further supported the validity of our cross-species microarray results. Results from these cross-species hybridizations on our multi-species cDNA microarray revealed that SMFN (Small fragment nuclease), Spin (Spindlin), and PRMT1 (Protein arginine methyltransferase 1) are transcripts present in oocytes and conserved in three evolutionarily distant species. CONCLUSION: Cross-species hybridization using a multi-species cDNA microarray is a powerful tool for the discovery of genes involved in evolutionarily conserved molecular mechanisms. The present study identified conserved genes in the oocytes of three distant species that will help understand the unique role of maternal transcripts in early embryonic development.

Dataset Information

Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data.

Publications

Identifying Conserved and Divergent Transcriptional Modules by Cross-species Matrix Decomposition on Microarray Data.

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