Genome-wide matching of genes to cellular roles using guilt-by-association models derived from single sample analysis
© Klomp and Furge; licensee BioMed Central Ltd. 2012
Received: 2 February 2012
Accepted: 25 June 2012
Published: 23 July 2012
High-throughput methods that ascribe a cellular or physiological function for each gene product are useful to understand the roles of genes that have not been extensively characterized by molecular or genetic approaches. One method to infer gene function is "guilt-by-association", in which the expression pattern of a poorly characterized gene is shown to co-vary with the expression of better-characterized genes. The function of the poorly characterized gene is inferred from the known function(s) of the well-described genes. For example, genes co-expressed with transcripts that vary during the cell cycle, development, environmental stresses, and with oncogenesis have been implicated in those processes.
While examining the expression characteristics of several poorly characterized genes, we noted that we could associate each of the genes with a cellular phenotype by correlating individual gene expression changes with gene set enrichment scores from individual samples. We evaluated the effectiveness of this approach using a modest sized gene expression data set (expO) and a compendium of gene expression phenotypes (MSigDBv3.0). We found the transcripts that correlated best with enrichment in mitochondrial and lysosomal gene sets were mostly related to those processes (89/100 and 44/50, respectively). The reciprocal evaluation, ranking gene sets according to correlation of enrichment with an individual gene’s expression, also reflected known associations for prominent genes in the biomedical literature (16/19). In evaluating the model, we also found that 4% of the genome encodes proteins that are associated with small molecule and small peptide signal transduction gene sets, implicating a large number of genes in both internal and external environmental sensing.
Our results show that this approach is useful to infer functions of disparate sets of genes. This method mirrors the biological experimental approaches used by others to associate individual genes with defined gene expression changes. Moreover, the approach can be used beyond discovering genes related to a cellular process to discover meaningful expression phenotypes from a compendium that are associated with a given gene. The effectiveness, versatility, and breadth of this approach make possible its application in a variety of contexts and with a variety of downstream analyses.
KeywordsGene set enrichment analysis Gene module Co-regulated gene sets Gene prediction Protein function Olfactory receptors Mitochondria Lysosome Oxidative phosphorylation
Groups of genes that are co-expressed in a subset of samples, sometimes termed gene modules, can reflect an underlying biological activity. This type of "guilt-by-association" can be a powerful method to infer a gene's cellular role. Sets of genes that are co-expressed in varying cellular states, such as transcripts that are co-expressed during different phases of the cell cycle, transcripts that vary upon certain environmental exposures, transcripts that vary during cellular or tissue differentiation, and transcripts that show conserved coregulation across different organisms can be especially insightful [1–7].
Many of the current models that describe gene co-regulation are probabilistic and can be grouped into one of two general categories: 1) models that associate gene expression changes with response to a defined experimental or biological condition (such as those described above) and 2) models that group genes by similarities in expression patterns across a compendia of gene expression profiles, often as a genomic-approach. An example of a model that falls into the first category is the work described by Hughes et al., in which expression profiles from yeast that contained targeted deletion of genes with unknown functions were compared to expression profiles from yeast with targeted deletion of genes with known functions . The functions of these uncharacterized genes were subsequently inferred based on similarity to the transcriptional responses of inactivation of genes with known functions. In the second category, gene pairs that are co-expressed across a series of expression profiles are used to infer similarities in biological functions. Gene networks are often constructed to relate genes to one another in a pair-wise fashion. However, the inferred functions of transcriptionally related genes are not discovered based on similarity to a defined biological perturbation as described in the first category of models [8, 9]. Together, these guilt-by-association approaches have been useful to identify functions of transcripts ranging from disease-associated genes to microRNA function [10, 11]. In addition, a number of other context-specific co-regulation associations have been proposed [12–17] and several guilt-by-association models have been extended to incorporate a wider variety of genomic information with the gene pair associations, such as location data , motif profiles , phenotypic data  and GWAS signals .
Gene pair associations, networks, and gene modules fit well with our understanding of the regulation of biological systems . Although gene modules constructed from tumor gene expression data have proven to capture important characteristics of tumors [14, 23, 24], interpretation of these modules can be difficult if the genes making up the modules are not clearly linked to a cellular phenotype. Hence, a disadvantage of guilt-by-association approaches is that once a gene module is defined, the biological significance of the module is not immediately clear. While expert examination and literature curation of the genes in the module can provide insight into the biological role of the module, this is inefficient if a large number of modules are under examination or if a module contains a high proportion of genes with unknown roles. Consequently, gene set enrichment analysis approaches are used to allow researchers to understand the gene expression variability observed in their analyses in terms of pre-defined lists of genes with known biological or experimental underpinnings, termed gene sets [25, 26]. Gene sets associated with a given biological process are useful tools to investigate coordinated changes in cellular processes. Large collections of gene sets have been made publicly available (e.g., MSigDB, GO, KEGG) and can be used with gene set enrichment approaches to find which gene sets best capture the difference in transcriptional characteristics between various environmental exposures, disease states, developmental states, or other experimental comparisons. It is therefore possible to identify the biological process associated with a transcriptional profile by comparison to well-defined gene sets.
To make explicit associations between genes and the biological processes captured by gene sets, we examined the correlations between individual fold-change gene expression values and gene set enrichment statistics in human tumor tissue samples. We explored the effectiveness of this guilt-by-association approach by genome-wide interrogation of individual genes for strongly correlated gene sets. Specifically, we evaluated the effectiveness of finding individual genes that correlate with pre-defined mitochondrial and lysosomal gene sets and the more broad utility of this approach to associate genes with other cellular components and processes. Of the gene sets examined, those relating to environmental sensing were associated with the largest fraction of the genome. We found implementation of this method to be straightforward and computationally efficient, while often producing meaningful associations consistent with the biological literature. Further, we propose that a variety of secondary analyses that incorporate orthogonal data can be applied to aid in the understanding of biological networks and gene regulation.
To implement this approach for a given gene set  and a given tumor sample, the expression levels of the genes in the gene set were extracted, transformed to log2-space (fold-change), and an enrichment score was produced that summarizes the expression levels of those genes in that particular sample (Figures 1A). The enrichment score in this analysis is the score proposed by Kim and Volsky  (Figure 1B) which comprises the average expression value of the genes in the set, weighted by the variability of expression and the number of genes in the set (Z-score). However, other similarly calculated parametric enrichment scores could also be used [28, 29]. For a given gene set, this process was repeated for every sample in the expression data series to yield sample-wise enrichment scores. The fold-change expression value for an individual gene was then compared to the gene set enrichment score across all samples using a Spearman correlation coefficient.
Processing of gene expression datasets
Gene expression data from the human tumor data series of the Expression Project for Oncology (expO) were used and are publicly available from the GEO database (GSE2109). This data series contains gene expression data sets representing 1949 tumor samples of various origins and classifications, conducted with Affymetrix HG-U133 Plus2 arrays. Control samples were chosen from a compendium of array data for non-diseased human tissue, also publicly available from the GEO database (GSE3526, N=163) and Affymetrix , N=33). Sample datasets used in the analysis were hand-selected such that the tumor sample data was paired with tissue-matched control data for a total of 1949 tumor samples and 196 controls and only relative log2-transformed (fold-change) values were used as expression values in the subsequent analyses ( Additional file 2: Table S1). The data analyses were performed in the R statistical environment v2.11.11 [31, 32] with software available from the BioConductor Project (version 2.5). Robust Multichip Average (RMA) preprocessing was used for background adjustment, normalization, and summarization of raw expression image intensities, as implemented in the Affy package (1.24.2) with updated probeset mappings [33, 34].
Gene set enrichment analysis
Parametric gene set enrichment scores (Z-scores) were computed as implemented in the PGSEA package (version 1.20.1)  following standardization of each gene expression value to the median expression value of that gene in tissue-matched controls. Using the formula from Kim and Volsky (2005), the Z score was calculated as Z = (S m -μ) * m 1/2 / δ, where μ is the mean of fold-change gene expression values from an individual sample data set, δ is the standard deviation of the fold-change values from the individual sample data set, S m is the mean of fold-change values for gene set members in that data set, and m is the size of the gene set (Figure 1B). Gene sets were obtained from the Molecular Signatures Database  (MSigDB v.3.0 , curated from online pathway databases, biomedical literature, positional information, and microarray studies). Gene sets with fewer than 22 or greater than 800 genes were removed to limit biases due to very small and very large gene sets.
Using the preprocessed and standardized expO gene expression data and the matrix of enrichment scores derived from that data (gene sets listed as rows and samples as columns), Spearman correlation was conducted for all combinations of genes measured on the array and gene sets that were used to compute the enrichment scores. This produced an 81 million-element matrix of rho correlation coefficients through the comparison of 18,185 genes and 4,438 gene set enrichment scores (see Additional file 3: Table S2). A correlation statistic was thus found for each possible pair of fold-change expression values and enrichment scores across all tumor samples. To calculate a “relative rank”, the distribution of these correlation statistics was translated to have a mean of 0 and the transformed correlation statistics were ranked in magnitude. In this manner, a strong correlation corresponded to a low relative rank and gene sets associated with each gene were filtered according to the relative ranking. When examining individual gene sets for co-regulated genes, enrichment scores were computed after removing each gene from the gene set. This step was necessary in order to eliminate the possibility of autocorrelation between a gene, G, that is a constituent of gene set S that might produce an enrichment score heavily influenced by that gene.
Ranking and filtering results (and relative rank calculation)
To find genes that were associated with a given gene set, genes were ranked by rho correlation coefficients to produce an ordered list of genes whose expression was correlated with the enrichment scores of the gene set across the tumor samples. To find gene sets that were associated with a given gene, a similar process was used. Importantly, gene sets that were directional down (e.g., GENESET_EXAMPLE_DN) were grouped as the opposite correlation sign for consistency in interpretation. To gain a measure of confidence in the associations observed compared to the distribution of all possible associations in the data set, we calculated a relative rank for each gene-by-gene set association. The relative rank was computed using the rank of the correlation coefficient for the gene with each gene set compared to the distribution of all possible correlation coefficients (Additional file 1: Figure S2). The lists of gene sets associated with each gene were thus sorted by the relative ranks. Further, gene sets such as certain cancer “modules” were left out of the rankings if they were not clearly interpretable [14, 36]. Not all genes described in the book Genome were examined nor were all of the chromosomes represented. 5SRNA genes were described for chromosome 1 and NROB1 for the X chromosome, but were not measured on the array. Genes were also not explicitly provided for chromosomes 2, 21, and 22.
GO annotation enrichment
To further test whether genes whose expression was found to be correlated with enrichment in the REACTOME_OLFACTORY_SIGNALING_PATHWAY and the SHEN_SMARCA2_TARGETS_DN gene sets were related to olfactory signaling and environmental sensing, we computed and ranked posterior probabilities for enrichment in GO terms. The posterior probabilities were calculated after controlling for multiple testing and using all GO categories with greater than five members, as implemented in the MGSA package . Gene sets with posterior probabilities greater than 0.50 were regarded as active, as suggested by Bauer et al. (2011).
To determine if similar gene set associations were evident for STYXL1 and DUSP6 using gene-network model approaches, we input the STYXL1 and DUSP6 gene names into the online tools GeneMANIA  and COXPRESdb . The GeneMANIA tool uses available evidence from physical interactions, co-expression, predictions, pathways, co-localization, shared protein domains, and genetic interactions to create a network of genes related to the gene(s) input into the model  while the COXPRESdb tool uses coexpression of genes across seven animal species to build gene networks . We noted that explicit pathway associations were not available for STYXL1 using these tools. Thus, to determine pathway associations for these two genes, we extracted the 20 most significantly associated genes using each tool and compared them to the same compendium of gene sets described above, ranking according to significance with a hypergeometric distribution. Using GeneMANIA, the gene sets most related to STYXL1 were KEGG_PEROXISOME (p=0.003) and RICKMAN_METASTASIS_UP (p=0.004). In contrast to the frequent mitochondrial gene set associations (18 of the top 20 correlating gene sets) when using the gene expression and enrichment score correlations (Figure.2A and 2C), only one mitochondrial gene set appeared in the 20 most strongly associated gene sets using this gene-network model. When using COXPRESdb, the gene sets most related to STYXL1 were found to be chr7q22 (p<0.001) and MORF_XPC (p<0.001). Three mitochondrial gene sets were evident in the 20 most-significantly associated gene sets with this model. Both of the models showed a variety of gene sets related to DUSP6- primarily MAPK signaling, growth factor signaling, and phosphorylation activity.
In addition, we compared the 100 genes with the highest rho correlation coefficients using our method to the scores generated by Baughman et al. who calculated the probability that a gene is co-expressed with the OXPHOS gene set (Figure 3C). The method proposed by Baughman et al. requires the curation and downloading of 1,427 individual human and mouse gene expression datasets, the preprocessing of each data set, calculation of correlation within each dataset, and combination of the resulting correlations to build a weighted summary score. The result of this activity is a single gene-gene set pair score. Our approach is comparatively simple, using only the human expO microarray data set and tissue-matched normal data. The initial findings from our approach were qualitatively similar to those obtained using the computationally intensive approach of Baughman et al. The top 100 genes we found strongly correlated with the MOOTHA_VOXPHOS gene set were also ranked highly in the Baughman et al. list of OXPHOS probability scores for each gene. Conversely, comparison of the top 100 genes identified in the Baughman et al. list showed high rankings within our ranked list of all genes in the datasets (Additional file 1: Figure S4).
In order to test this model beyond mitochondria, we assessed the co-regulation of individual lysosomal genes with enrichment in the lysosome gene set, KEGG_LYSOSOME. Lysosomal genes also show a high degree of transcriptional co-regulation in tissue culture cells (Sardiello et al 2009). The 50 genes that correlated strongest with this gene set were identified and compared with genes that were either identified as being lysosomal via a proteomic survey or associated with lysosomal GO annotation (Accession GO:0005764) . Fifty percent (25/50) of genes identified encode for lysosome-associated proteins (Additional file 1: Figure S5A). Of the twenty-five genes associated with the KEGG_LYSOSOME gene set that were not in the proteomic survey or identified with the lysosomal GO annotation, 19 (76%) possessed GO terms related to the endoplasmic reticulum, golgi, or vacuoles (Additional file 1: Figure S5B). Therefore, 88% (44/50) of genes associated with the KEGG_LYSOME gene set were related to vesicle transport.
Five genes shown in the list- HTT, L1TD1, ABO, HOXC4, and PRNP, have poorly defined or ambiguous biological roles. To determine whether these genes associate strongly with any gene sets, we examined the top ranked gene sets (Figure 4B-D). For the huntingtin gene, HTT, an association was found with the brain specific kinase LTK (Figure 4B and 4D). This association does not appear to be driven by a single gene since most of the genes of the LTK gene set show a similar trend in expression as HTT across the tumor samples (Figure 4D). Though there is evidence that LTK has a role in apoptosis , additional gene sets appear to be related to immune function and development pathways for which there is also evidence HTT is involved [57–59]. Expression of the gene ABO was strongly correlated with several gene sets related to the activity of transmembrane channels, indicating a potential role of this gene (Figure 4C and 4D). For three genes, L1TD1, HOXC4, and PRNP, a single biological role is not apparent in this analysis. In summary, in 16 of 19 (84%) cases, the predictions generated by the integration of fold-change gene expression and gene set enrichment scores were associated with consistent biological processes (Additional file 3: Table S2).
The olfactory receptor protein family consists of mainly seven-transmembrane receptors that are linked to ion flux through the cAMP-signaling pathway. Binding of small molecules to the receptor induces a signaling cascade whereby the activated receptor activates a G-protein, leading to the production of adenylyl cyclase. Subsequently, adenylyl cyclase binds to a cyclic-nucleotide-gated-ion channel, inducing channel opening and depolarization of the cell membrane. To determine whether the genes co-regulated with the REACTOME_OLFACTORY_SIGNALING_PATHWAY gene set were involved in the olfactory signaling process, we placed the genes with GO annotations into groups based on those annotations (Figure 5B). Consistent with this signaling cascade, the genes that were not members of the gene set could be classified predominantly to membrane signaling with activities related to GPCR activity, ion transport, ATP-binding, and signal transduction. Interestingly, many of the genes associated with the REACTOME_OLFACTORY_SIGNALING_PATHWAY gene set were uncharacterized proteins or open reading frames (196/1182 16.6%). The association with this number of uncharacterized proteins is nearly double what would be expected by chance (p<0.00001).
The second most prevalent gene set association involved SHEN_SMARCA2_TARGETS_DN related genes (Figure 5A). Although the two gene sets SHEN_SMARCA2_TARGETS_DN and REACTOME_OLFACTORY_SIGNALING_PATHWAY had little overlap (N=74, N=327, respectively; overlap=3 genes), the genes whose expression was correlated with enrichment in these gene sets were somewhat redundant (Venn diagram, Figures 3 and 4A). SMARCA2 is a component of the large SWI/SNF ATP-dependent chromatin-remodeling complex required for transcriptional activation of genes repressed by chromatin. Similar to the olfactory signaling gene set, of the 1,130 genes whose expression correlated best with SHEN_SMARCA2_TARGETS_DN, only 99 genes were constituent members of the gene set (9%). Also, like the olfactory signaling gene set, the associated non-constituent genes could be primarily classified to membrane signaling, GPCR activities, ion transport, ATP-binding, and signal transduction GO and PFAM annotations (Figure 5B). As with the REACTOME_OLFACTORY_SIGNALING_PATHWAY, more of the genes associated with SHEN_SMARCA2_TARGETS_DN coded for uncharacterized proteins than would be expected by chance (162/1130, 14.3%, p<0.00001).
We examined the genomic positions of the genes associated with these two gene sets to determine if shared cis-regulatory elements might exist between the correlated genes and the constituent genes of the gene sets. However, neither the genes co-regulated with REACTOME_OLFACTORY_SIGNALING_PATHWAY nor the genes co-regulated with SHEN_SMARCA2_TARGETS_DN were consistently mapped near the constituent genes of the respective gene sets (p=0.49 and p=0.66, respectively). The SHEN_SMARCA2_TARGETS_DN gene set was constructed by examining genes that correlated inversely with SMARCA2 expression in prostate cancer samples . The association of this gene set with olfactory signaling indicates a potential link between chromatin remodeling and the expression of chemo-sensing genes as recently described by Magklara et al..
We showed that correlation of fold-change gene expression values and gene set enrichment scores is an effective method of inferring cellular roles of genes. Using this correlation procedure has allowed us to confirm known roles for well-characterized genes by ranking the correlations of the gene expression with a compendium of gene sets. The approach described is computationally efficient and simple to implement and we demonstrated that its ability to identify mitochondria-related genes is as effective as approaches recently devised by Baughman et al.. Notably, the association matrix took approximately 57 hours of computational time to generate using a 2.2GHz (8-cores) processor with 9.6 Gb of RAM. Therefore, this type of association matrix can be computed from a variety of gene expression datasets using a variety of gene sets. Likewise, we have also implicated a mitochondrial role for several genes not currently known to be associated with mitochondria through the “guilt-by-association” paradigm. Following this paradigm, we found that a variety of individual genes were associated with gene sets functionally related to the known roles of those genes and that genes with unknown roles tended to be associated with gene sets that represent biological themes. We have generated a catalog of co-regulated gene sets (CGs) that includes all 18,185 genes in the dataset (Additional file 3: Table S2). This has the potential to inform a variety of high throughput genomic approaches.
Although olfactory receptors are in a large family of G-protein coupled receptors (GPCRs), the association of many other genes with the REACTOME_OLFACTORY_SIGNALING_PATHWAY gene set was somewhat surprising. Olfactory receptors are commonly associated with environmental sensing, including the perception of tastes, odors, and chemical moieties through specialized sensory receptor cells in the nose, mouth, tongue, and skin. Information about a stimulus is transduced through a cascade of molecular and cellular events into electrical signals that are recognized by the nervous system. However, recent reports indicate that olfactory receptors may play a much more diverse role in chemosensing [65–68]. Epithelial cells along the entire gastrointestinal tract are involved in chemosensing and proposed nutrient absorption . Outside of the gastrointestinal epithelium, olfactory receptors have been found to play important roles in myogenesis, and at least one receptor, MOR32, could direct myocyte migration through a yet to be identified soluble chemo-attractant secreted by fusing muscle cultures . Multiple studies also indicate the importance of olfactory receptors in sperm cell chemotaxis [70, 71]. Perhaps most interestingly, an olfactory receptor over-expressed in prostate cancer, OR51E2, interacts with androstenone derivatives and this interaction regulates cell proliferation . These findings suggest that chemosensing mechanisms operate in diverse cell types. Our results support this possibility and implicate a significant fraction of the coding genome in support of this type of signal transduction cascade. Several somatic mutations that have been identified in cancer cells are associated with olfactory signal transduction using the method put forth in this report (Additional file 3: Table S2). Whether these mutations lead to disrupted signal transduction important to tumorigenesis or proliferation of cancer cells remains to be explored.
As with all computationally-derived guilt-by-association correlations, we also point out limitations to this type of analysis which include 1) the trends may be dependent on the expression data used, 2) the analysis may be affected by low quality gene sets or gene sets with ambiguous biological underpinnings, and 3) the enrichment statistic can be influenced by expression of a gene if it resides within the actual gene set (i.e. self-fulfilling associations). However, in the latter case the calculation used for the enrichment statistic should at least partially compensate for this effect. Though we used tissue-matched normal tissues to standardize the tumor tissues in our analysis, we also conducted similar analyses using only normal tissues and using only tumor tissues. Many of the gene expression-by-gene set enrichment correlations were similar to our initial findings when we used these smaller, median-standardized subsets of expression data. However, there tended to be less expression variability for a given gene across the restricted data sets and genes with low expression variability tended to produce incorrect gene set associations. We also noted this effect when using a smaller set of tumor-normal gene expression data as described in . Perhaps intuitively, a sample set with sufficient expression variability in the gene of interest should be used. Two obvious sources of expression variability may come from using a greater number of samples or increasing the heterogeneity of the tissue types used.
The general effectiveness of computationally-derived guilt-by-association approaches is difficult to evaluate. Without detailed in vitro and in vivo experimental manipulations and follow-up studies, genes used for validation of an association must be supported by a large body of literature explicitly detailing both their molecular functions and biological roles. Some of the best-studied genes are those with strong links to human health and disease, as described in the book Genome. Though we examined these genes with our guilt-by-association model, it is possible that biomedically prominent genes are biased for their pleiotropic effects and the model might be less effective for genes with more subtle roles. Further, interpretation of the literature can be subjective, especially with the burgeoning numbers of publications and bias in not publishing “negative” data. In spite of these challenges, guilt-by-association models have served to inform a variety of hypotheses over the past two decades and have been extended in this report with the generation of sample-wise enrichment scores.
In addition to guilt-by-association, the generation of sample-wise enrichment scores for numerous, diverse gene sets lays the foundation for analyses similar to those recently devised for gene expression array data. Data mining approaches that are commonly used for gene expression analysis (clustering approaches , discriminate analysis , and outlier analysis ) can be applied at the level of pathways/gene sets and can be integrated with other types of orthogonal data sets such as genotype information (e.g. “ping-pong” analysis ). We have used a variation of outlier profile analysis (meta-COPA ) to identify tumor samples in this dataset that contained a gene signature indicative of activation of the NRF2 transcription factor. The identification of subsets of samples that share a similar biological activity, such as NRF2 activity, can assist in the identification of disease subgroups that may not be well described or may respond differently to treatments. Several other gene set enrichment approaches require that the samples of interest be partitioned into a priori defined groups for the application of the enrichment analysis. Unless a signal transduction transcriptional phenotype is stronger than other transcriptional effects related to the a prior group assignments (such as tissue type or proliferation rate), more subtle pathophysiological transcriptional effects can be overwhelmed and consequently overlooked with those traditional approaches.
We have outlined a computational approach that has proven useful to predict gene function using transcriptional data. The approach we describe was successfully used to identify molecular functions of genes that were previously not well characterized [42, 73]. We found that gene transcripts that correlated best with enrichment in mitochondrial and lysosomal gene sets often possessed mitochondrial and lysosomal roles as evidenced in the literature. Furthermore, gene sets reflective of a gene’s known cellular role were more strongly correlated with the gene’s expression than unrelated gene sets. Using this method, we noted the high number of genes whose transcripts correlated strongly with enrichment in gene sets representing small molecule and small peptide signal transduction. Our results show that this approach is useful to infer functions of disparate sets of genes with prominent roles in the biomedical literature and may complement current methods in situations where other databases are unable to provide predictions of gene functions. Further, the effectiveness, versatility, and breadth of this approach make possible its application across a variety of contexts and with a variety of downstream analyses.
Availability and requirements
Project name: Genome-wide matching of genes to cellular roles using guilt-by-association models derived from single sample analysis
Project home page: NA
Operating system(s): platform independent
Programming language: NA
Other requirements: R, Bioconductor
Any restrictions to use by non-academics: NA
Availability of supporting data
All data is from publicly available sources (refer to Additional file 2: Table S1).
We gratefully acknowledge the Van Andel Research Institute and the Van Andel Education Institute for supporting these studies.
- Stuart JM, Segal E, Koller D, Kim SK: A gene-coexpression network for global discovery of conserved genetic modules. Science. 2003, 302 (5643): 249-255.PubMedView ArticleGoogle Scholar
- Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I: The transcriptional program of sporulation in budding yeast. Science. 1998, 282 (5389): 699-705.PubMedView ArticleGoogle Scholar
- Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM: Systematic determination of genetic network architecture. Nat Genet. 1999, 22 (3): 281-285.PubMedView ArticleGoogle Scholar
- Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell. 2000, 11 (12): 4241-4257.PubMedPubMed CentralView ArticleGoogle Scholar
- Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stephaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH: Functional discovery via a compendium of expression profiles. Cell. 2000, 102 (1): 109-126.PubMedView ArticleGoogle Scholar
- Pe’er D, Regev A, Elidan G, Friedman N: Inferring subnetworks from perturbed expression profiles. Bioinformatics. 2001, 17 (Suppl 1): S215-S224.PubMedView ArticleGoogle Scholar
- Wen X, Fuhrman S, Michaels GS, Carr DB, Smith S, Barker JL, Somogyi R: Large-scale temporal gene expression mapping of central nervous system development. Proc Natl Acad Sci U S A. 1998, 95 (1): 334-339.PubMedPubMed CentralView ArticleGoogle Scholar
- Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P: Coexpression analysis of human genes across many microarray data sets. Genome Res. 2004, 14 (6): 1085-1094.PubMedPubMed CentralView ArticleGoogle Scholar
- Hess DC, Myers CL, Huttenhower C, Hibbs MA, Hayes AP, Paw J, Clore JJ, Mendoza RM, Luis BS, Nislow C, Giaever G, Costanzo M, Troyanskaya OG, Caudy AA: Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis. PLoS Genet. 2009, 5 (3): e1000407-PubMedPubMed CentralView ArticleGoogle Scholar
- Walker MG, Volkmuth W, Sprinzak E, Hodgson D, Klingler T: Prediction of gene function by genome-scale expression analysis: prostate cancer-associated genes. Genome Res. 1999, 9 (12): 1198-1203.PubMedPubMed CentralView ArticleGoogle Scholar
- Gennarino VA, Sardiello M, Avellino R, Meola N, Maselli V, Anand S, Cutillo L, Ballabio A, Banfi S: MicroRNA target prediction by expression analysis of host genes. Genome Res. 2009, 19 (3): 481-490.PubMedPubMed CentralView ArticleGoogle Scholar
- Huttenhower C, Hibbs M, Myers C, Troyanskaya OG: A scalable method for integration and functional analysis of multiple microarray datasets. Bioinformatics. 2006, 22 (23): 2890-2897.PubMedView ArticleGoogle Scholar
- Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG: Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics. 2007, 23 (20): 2692-2699.PubMedView ArticleGoogle Scholar
- Segal E, Friedman N, Koller D, Regev A: A module map showing conditional activity of expression modules in cancer. Nat Genet. 2004, 36 (10): 1090-1098.PubMedView ArticleGoogle Scholar
- Pujana MA, Han JD, Starita LM, Stevens KN, Tewari M, Ahn JS, Rennert G, Moreno V, Kirchhoff T, Gold B, Assmann V, Elshamy WM, Rual JF, Levine D, Rozek LS, Gelman RS, Gunsalus KC, Greenberg RA, Sobhian B, Bertin N, Venkatesan K, Ayivi-Guedehoussou N, Sole X, Hernandez P, Lazaro C, Nathanson KL, Weber BL, Cusick ME, Hill DE, Offit K, Livingston DM, Gruber SB, Parvin JD, Vidal M: Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet. 2007, 39 (11): 1338-1349.PubMedView ArticleGoogle Scholar
- Obayashi T, Hayashi S, Shibaoka M, Saeki M, Ohta H, Kinoshita K: COXPRESdb: a database of coexpressed gene networks in mammals. Nucleic Acids Res. 2008, 36 (Database issue): D77-D82.PubMedPubMed CentralGoogle Scholar
- Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH: Functional organization of the transcriptome in human brain. Nat Neurosci. 2008, 11 (11): 1271-1282.PubMedPubMed CentralView ArticleGoogle Scholar
- Hartemink AJ, Gifford DK, Jaakkola TS, Young RA: Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput. 2002: 437-449.
- Segal E, Yelensky R, Koller D: Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics. 2003, 19 (Suppl 1): i273-i282.PubMedView ArticleGoogle Scholar
- Segal E, Taskar B, Gasch A, Friedman N, Koller D: Rich probabilistic models for gene expression. Bioinformatics. 2001, 17 (Suppl 1): S243-S252.PubMedView ArticleGoogle Scholar
- Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH: Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011, 474 (7351): 380-384.PubMedPubMed CentralView ArticleGoogle Scholar
- Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks. BMC Bioinforma. 2005, 6: 227-View ArticleGoogle Scholar
- Segal E, Friedman N, Kaminski N, Regev A, Koller D: From signatures to models: understanding cancer using microarrays. Nat Genet. 2005, 37 (Suppl): S38-S45.PubMedView ArticleGoogle Scholar
- Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003, 34 (2): 166-176.PubMedView ArticleGoogle Scholar
- Kim SY, Volsky DJ: PAGE: parametric analysis of gene set enrichment. BMC Bioinforma. 2005, 6: 144-View ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005, 102 (43): 15545-15550.PubMedPubMed CentralView ArticleGoogle Scholar
- Molecular Signatures Database v3.0.http://www.broadinstitute.org/gsea/msigdb/index.jsp,
- Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ: GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinforma. 2009, 10: 161-View ArticleGoogle Scholar
- Irizarry RA, Wang C, Zhou Y, Speed TP: Gene set enrichment analysis made simple. Stat Methods Med Res. 2009, 18 (6): 565-575.PubMedPubMed CentralView ArticleGoogle Scholar
- Exon 1.0 ST Array Sample Dataset.http://www.affymetrix.com/support/technical/sample_data/exon_array_data.affx,
- Ihaka R, Gentleman R: R: A language for data analysis and graphics. J Comput Graph Stat. 1996, 5: 299-314.Google Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80-PubMedPubMed CentralView ArticleGoogle Scholar
- Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003, 31 (4): e15-PubMedPubMed CentralView ArticleGoogle Scholar
- Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, Watson SJ, Meng F: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005, 33 (20): e175-PubMedPubMed CentralView ArticleGoogle Scholar
- Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP: Molecular Signatures Database (MSigDB) 3.0. Bioinformatics. 2011, 27 (12): 1739-1740.PubMedPubMed CentralView ArticleGoogle Scholar
- A 'module map' showing conditional activity of expression modules in cancer.http://robotics.stanford.edu/~erans/cancer/index.html,
- Bauer S, Robinson PN, Gagneur J: Model-based gene set analysis for Bioconductor. Bioinformatics. 2011, 27 (13): 1882-1883.PubMedPubMed CentralView ArticleGoogle Scholar
- MacKeigan JP, Murphy LO, Blenis J: Sensitized RNAi screen of human kinases and phosphatases identifies new regulators of apoptosis and chemoresistance. Nat Cell Biol. 2005, 7 (6): 591-600.PubMedView ArticleGoogle Scholar
- Hinton SD, Myers MP, Roggero VR, Allison LA, Tonks NK: The pseudophosphatase MK-STYX interacts with G3BP and decreases stress granule formation. Biochem J. 2010, 427 (3): 349-357.PubMedPubMed CentralView ArticleGoogle Scholar
- Dickinson RJ, Keyse SM: Diverse physiological functions for dual-specificity MAP kinase phosphatases. J Cell Sci. 2006, 119 (Pt 22): 4607-4615.PubMedView ArticleGoogle Scholar
- Bauer S, Gagneur J, Robinson PN: GOing Bayesian: model-based gene set analysis of genome-scale data. Nucleic Acids Res. 2010, 38 (11): 3523-3532.PubMedPubMed CentralView ArticleGoogle Scholar
- Niemi NM, Lanning NJ, Klomp JA, Tait SW, Xu Y, Dykema KJ, Murphy LO, Gaither LA, Xu HE, Furge KA, Green DR, MacKeigan JP: MK-STYX, a catalytically inactive phosphatase regulating mitochondrial dependent apoptosis. Mol Cell Biol. 2010, 31 (7): 1357-1368.View ArticleGoogle Scholar
- COXPRESdb v4.1.http://coxpresdb.jp/,
- Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q: GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 2008, 9 (Suppl 1): S4-PubMedPubMed CentralView ArticleGoogle Scholar
- Obayashi T, Kinoshita K: COXPRESdb: a database to compare gene coexpression in seven model animals. Nucleic Acids Res. 2010, 39 (Database issue): D1016-D1022.PubMedPubMed CentralGoogle Scholar
- Hibbs MA, Myers CL, Huttenhower C, Hess DC, Li K, Caudy AA, Troyanskaya OG: Directing experimental biology: a case study in mitochondrial biogenesis. PLoS Comput Biol. 2009, 5 (3): e1000322-PubMedPubMed CentralView ArticleGoogle Scholar
- Baughman JM, Nilsson R, Gohil VM, Arlow DH, Gauhar Z, Mootha VK: A computational screen for regulators of oxidative phosphorylation implicates SLIRP in mitochondrial RNA homeostasis. PLoS Genet. 2009, 5 (8): e1000590-PubMedPubMed CentralView ArticleGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273.PubMedView ArticleGoogle Scholar
- Lubke T, Lobel P, Sleat DE: Proteomics of the lysosome. Biochim Biophys Acta. 2009, 1793 (4): 625-635.PubMedPubMed CentralView ArticleGoogle Scholar
- Ridley M: Genome: the autobiography of a species in 23 chapters. 1st U. S. edition. 1999, HarperCollins, New YorkGoogle Scholar
- Wu D, Sharan C, Yang H, Goodwin JS, Zhou L, Grabowski GA, Du H, Guo Z: Apolipoprotein E-deficient lipoproteins induce foam cell formation by downregulation of lysosomal hydrolases in macrophages. J Lipid Res. 2007, 48 (12): 2571-2578.PubMedView ArticleGoogle Scholar
- Han X: The role of apolipoprotein E in lipid metabolism in the central nervous system. Cell Mol Life Sci. 2004, 61 (15): 1896-1906.PubMedView ArticleGoogle Scholar
- Fisher SE, Scharff C: FOXP2 as a molecular window into speech and language. Trends Genet. 2009, 25 (4): 166-177.PubMedView ArticleGoogle Scholar
- Tamura S, Morikawa Y, Iwanishi H, Hisaoka T, Senba E: Foxp1 gene expression in projection neurons of the mouse striatum. Neuroscience. 2004, 124 (2): 261-267.PubMedView ArticleGoogle Scholar
- Honda H, Harada K, Komuro I, Terasaki F, Ueno H, Tanaka Y, Kawamura K, Yazaki Y, Hirai H: Heart-specific activation of LTK results in cardiac hypertrophy, cardiomyocyte degeneration and gene reprogramming in transgenic mice. Oncogene. 1999, 18 (26): 3821-3830.PubMedView ArticleGoogle Scholar
- Soulet D, Cicchetti F: The role of immunity in Huntington’s disease. Mol Psychiatry. 16 (9): 889-902.
- Zeitlin S, Liu JP, Chapman DL, Papaioannou VE, Efstratiadis A: Increased apoptosis and early embryonic lethality in mice nullizygous for the Huntington's disease gene homologue. Nat Genet. 1995, 11 (2): 155-163.PubMedView ArticleGoogle Scholar
- Nasir J, Floresco SB, O'Kusky JR, Diewert VM, Richman JM, Zeisler J, Borowski A, Marth JD, Phillips AG, Hayden MR: Targeted disruption of the Huntington's disease gene results in embryonic lethality and behavioral and morphological changes in heterozygotes. Cell. 1995, 81 (5): 811-823.PubMedView ArticleGoogle Scholar
- Kaupp UB: Olfactory signalling in vertebrates and insects: differences and commonalities. Nat Rev Neurosci. 2010, 11 (3): 188-200.PubMedGoogle Scholar
- Mombaerts P: Seven-transmembrane proteins as odorant and chemosensory receptors. Science. 1999, 286 (5440): 707-711.PubMedView ArticleGoogle Scholar
- Buck L, Axel R: A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell. 1991, 65 (1): 175-187.PubMedView ArticleGoogle Scholar
- Shen H, Powers N, Saini N, Comstock CE, Sharma A, Weaver K, Revelo MP, Gerald W, Williams E, Jessen WJ, Aronow BJ, Rosson G, Weissman B, Muchardt C, Yaniv M, Knudsen KE: The SWI/SNF ATPase Brm is a gatekeeper of proliferative control in prostate cancer. Cancer Res. 2008, 68 (24): 10154-10162.PubMedPubMed CentralView ArticleGoogle Scholar
- Magklara A, Yen A, Colquitt BM, Clowney EJ, Allen W, Markenscoff-Papadimitriou E, Evans ZA, Kheradpour P, Mountoufaris G, Carey C, Barnea G, Kellis M, Lomvardas S: An epigenetic signature for monoallelic olfactory receptor expression. Cell. 2011, 145 (4): 555-570.PubMedPubMed CentralView ArticleGoogle Scholar
- De la Cruz O, Blekhman R, Zhang X, Nicolae D, Firestein S, Gilad Y: A signature of evolutionary constraint on a subset of ectopically expressed olfactory receptor genes. Mol Biol Evol. 2009, 26 (3): 491-494.PubMedPubMed CentralView ArticleGoogle Scholar
- Ichimura A, Kadowaki T, Narukawa K, Togiya K, Hirasawa A, Tsujimoto G: In silico approach to identify the expression of the undiscovered molecules from microarray public database: identification of odorant receptors expressed in non-olfactory tissues. Naunyn Schmiedebergs Arch Pharmacol. 2008, 377 (2): 159-165.PubMedView ArticleGoogle Scholar
- Pick H, Etter S, Baud O, Schmauder R, Bordoli L, Schwede T, Vogel H: Dual activities of odorants on olfactory and nuclear hormone receptors. J Biol Chem. 2009, 284 (44): 30547-30555.PubMedPubMed CentralView ArticleGoogle Scholar
- Sternini C, Anselmi L, Rozengurt E: Enteroendocrine cells: a site of 'taste' in gastrointestinal chemosensing. Curr Opin Endocrinol Diabetes Obes. 2008, 15 (1): 73-78.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffin CA, Kafadar KA, Pavlath GK: MOR23 promotes muscle regeneration and regulates cell adhesion and migration. Dev Cell. 2009, 17 (5): 649-661.PubMedPubMed CentralView ArticleGoogle Scholar
- Spehr M, Schwane K, Riffell JA, Zimmer RK, Hatt H: Odorant receptors and olfactory-like signaling mechanisms in mammalian sperm. Mol Cell Endocrinol. 2006, 250 (1–2): 128-136.PubMedView ArticleGoogle Scholar
- Veitinger T, Riffell JR, Veitinger S, Nascimento JM, Triller A, Chandsawangbhuwana C, Schwane K, Geerts A, Wunder F, Berns MW, Neuhaus EM, Zimmer RK, Spehr M, Hatt H: Chemosensory Ca2+ dynamics correlate with diverse behavioral phenotypes in human sperm. J Biol Chem. 2011, 286 (19): 17311-17325.PubMedPubMed CentralView ArticleGoogle Scholar
- Neuhaus EM, Zhang W, Gelis L, Deng Y, Noldus J, Hatt H: Activation of an olfactory receptor inhibits proliferation of prostate cancer cells. J Biol Chem. 2009, 284 (24): 16218-16225.PubMedPubMed CentralView ArticleGoogle Scholar
- Klomp JA, Petillo D, Niemi NM, Dykema KJ, Chen J, Yang XJ, Saaf A, Zickert P, Aly M, Bergerheim U, Nordenskjold M, Gad S, Giraud S, Denoux Y, Yonneau L, Mejean A, Vasiliu V, Richard S, MacKeigan JP, Teh BT, Furge KA: Birt-Hogg-Dube renal tumors are genetically distinct from other renal neoplasias and are associated with up-regulation of mitochondrial gene expression. BMC Med Genomics. 2010, 3: 59-PubMedPubMed CentralView ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998, 95 (25): 14863-14868.PubMedPubMed CentralView ArticleGoogle Scholar
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999, 286 (5439): 531-537.PubMedView ArticleGoogle Scholar
- Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM: Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science. 2005, 310 (5748): 644-648.PubMedView ArticleGoogle Scholar
- Kutalik Z, Beckmann JS, Bergmann S: A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nat Biotechnol. 2008, 26 (5): 531-539.PubMedView ArticleGoogle Scholar
- Tomlins SA, Rhodes DR, Yu J, Varambally S, Mehra R, Perner S, Demichelis F, Helgeson BE, Laxman B, Morris DS, Cao Q, Cao X, Andren O, Fall K, Johnson L, Wei JT, Shah RB, Al-Ahmadie H, Eastham JA, Eggener SE, Fine SW, Hotakainen K, Stenman UH, Tsodikov A, Gerald WL, Lilja H, Reuter VE, Kantoff PW, Scardino PT, Rubin MA, Bjartell AS, Chinnaiyan AM: The role of SPINK1 in ETS rearrangement-negative prostate cancers. Cancer Cell. 2008, 13 (6): 519-528.PubMedPubMed CentralView ArticleGoogle Scholar
- de Bernabe-Beltran-Valero D, Jimenez FJ, Aquaron R, de Rodriguez Cordoba S: Analysis of alkaptonuria (AKU) mutations and polymorphisms reveals that the CCC sequence motif is a mutational hot spot in the homogentisate 1,2 dioxygenase gene (HGO). Am J Hum Genet. 1999, 64 (5): 1316-1322.View ArticleGoogle Scholar
- Fernandez-Canon JM, Granadino B, de Bernabe Beltran-Valero D, Renedo M, Fernandez-Ruiz E, Penalva MA, Rodriguez de Cordoba S: The molecular basis of alkaptonuria. Nat Genet. 1996, 14 (1): 19-24.PubMedView ArticleGoogle Scholar
- Knox WE, Edwards SW: Homogentisate oxidase of liver. J Biol Chem. 1955, 216 (2): 479-487.PubMedGoogle Scholar
- McLean RC, Hirsch GA, Becker LC, Kasch-Semenza L, Gerstenblith G, Schulman SP: Polymorphisms of the beta adrenergic receptor predict left ventricular remodeling following acute myocardial infarction. Cardiovasc Drugs Ther. 2011, 25 (3): 251-258.PubMedView ArticleGoogle Scholar
- Ikushima H, Munakata Y, Ishii T, Iwata S, Terashima M, Tanaka H, Schlossman SF, Morimoto C: Internalization of CD26 by mannose 6-phosphate/insulin-like growth factor II receptor contributes to T cell activation. Proc Natl Acad Sci U S A. 2000, 97 (15): 8439-8444.PubMedPubMed CentralView ArticleGoogle Scholar
- Sohar I, Sleat D, Gong Liu C, Ludwig T, Lobel P: Mouse mutants lacking the cation-independent mannose 6-phosphate/insulin-like growth factor II receptor are impaired in lysosomal enzyme transport: comparison of cation-independent and cation-dependent mannose 6-phosphate receptor-deficient mice. Biochem J. 1998, 330 (Pt 2): 903-908.PubMedPubMed CentralView ArticleGoogle Scholar
- Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP: A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001, 413 (6855): 519-523.PubMedView ArticleGoogle Scholar
- Konopka G, Bomar JM, Winden K, Coppola G, Jonsson ZO, Gao F, Peng S, Preuss TM, Wohlschlegel JA, Geschwind DH: Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature. 2009, 462 (7270): 213-217.PubMedPubMed CentralView ArticleGoogle Scholar
- Hisaoka T, Nakamura Y, Senba E, Morikawa Y: The forkhead transcription factors, Foxp1 and Foxp2, identify different subpopulations of projection neurons in the mouse cerebral cortex. Neuroscience. 2010, 166 (2): 551-563.PubMedView ArticleGoogle Scholar
- Dhir V, Reisch N, Bleicken CM, Lebl J, Kamrath C, Schwarz HP, Grotzinger J, Sippell WG, Riepe FG, Arlt W, Krone N: Steroid 17alpha-hydroxylase deficiency: functional characterization of four mutations (A174E, V178D, R440C, L465P) in the CYP17A1 gene. J Clin Endocrinol Metab. 2009, 94 (8): 3058-3064.PubMedView ArticleGoogle Scholar
- Taniguchi F, Couse JF, Rodriguez KF, Emmen JM, Poirier D, Korach KS: Estrogen receptor-alpha mediates an intraovarian negative feedback loop on thecal cell steroidogenesis via modulation of Cyp17a1 (cytochrome P450, steroid 17alpha-hydroxylase/17,20 lyase) expression. FASEB J. 2007, 21 (2): 586-595.PubMedPubMed CentralView ArticleGoogle Scholar
- Neve KA, Seamans JK, Trantham-Davidson H: Dopamine receptor signaling. J Recept Signal Transduct Res. 2004, 24 (3): 165-205.PubMedView ArticleGoogle Scholar
- Yoshida K, Miki Y: Role of BRCA1 and BRCA2 as regulators of DNA repair, transcription, and cell cycle in response to DNA damage. Cancer Sci. 2004, 95 (11): 866-871.PubMedView ArticleGoogle Scholar
- Vaughn JP, Cirisano FD, Huper G, Berchuck A, Futreal PA, Marks JR, Iglehart JD: Cell cycle control of BRCA2. Cancer Res. 1996, 56 (20): 4590-4594.PubMedGoogle Scholar
- Pendergrass WR, Penn PE, Li J, Wolf NS: Age-related telomere shortening occurs in lens epithelium from old rats and is slowed by caloric restriction. Exp Eye Res. 2001, 73 (2): 221-228.PubMedView ArticleGoogle Scholar
- Compton DA, Cleveland DW: NuMA is required for the proper completion of mitosis. J Cell Biol. 1993, 120 (4): 947-957.PubMedView ArticleGoogle Scholar
- Singhmar P, Kumar A: Angelman syndrome protein UBE3A interacts with primary microcephaly protein ASPM, localizes to centrosomes and regulates chromosome segregation. PLoS One. 2011, 6 (5): e20397-PubMedPubMed CentralView ArticleGoogle Scholar
- Ramos YF, Hestand MS, Verlaan M, Krabbendam E, Ariyurek Y, van Galen M, van Dam H, van Ommen GJ, den Dunnen JT, Zantema A, ‘t Hoen PA: Genome-wide assessment of differential roles for p300 and CBP in transcription regulation. Nucleic Acids Res. 2010, 38 (16): 5396-5408.PubMedPubMed CentralView ArticleGoogle Scholar
- Vousden KH, Lu X: Live or let die: the cell's response to p53. Nat Rev Cancer. 2002, 2 (8): 594-604.PubMedView ArticleGoogle Scholar
- Ding YQ, Kim JY, Xu YS, Rao Y, Chen ZF: Ventral migration of early-born neurons requires Dcc and is essential for the projections of primary afferents in the spinal cord. Development. 2005, 132 (9): 2047-2056.PubMedPubMed CentralView ArticleGoogle Scholar
- Gitai Z, Yu TW, Lundquist EA, Tessier-Lavigne M, Bargmann CI: The netrin receptor UNC-40/DCC stimulates axon attraction and outgrowth through enabled and, in parallel, Rac and UNC-115/AbLIM. Neuron. 2003, 37 (1): 53-65.PubMedView ArticleGoogle Scholar
- Dewing P, Chiang CW, Sinchak K, Sim H, Fernagut PO, Kelly S, Chesselet MF, Micevych PE, Albrecht KH, Harley VR, Vilain E: Direct regulation of adult brain function by the male-specific factor SRY. Curr Biol. 2006, 16 (4): 415-420.PubMedView ArticleGoogle Scholar
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