Constitutive heterochromatin reorganization during somatic cell reprogramming.

Induced pluripotent stem (iPS) cell reprogramming is a gradual epigenetic process that reactivates the pluripotent transcriptional network by erasing and establishing repressive epigenetic marks. In contrast to loci-specific epigenetic changes, heterochromatin domains undergo epigenetic resetting during the reprogramming process, but the effect on the heterochromatin ultrastructure is not known. Here, we characterize the physical structure of heterochromatin domains in full and partial mouse iPS cells by correlative electron spectroscopic imaging. In somatic and partial iPS cells, constitutive heterochromatin marked by H3K9me3 is highly compartmentalized into chromocentre structures of densely packed chromatin fibres. In contrast, chromocentre boundaries are poorly defined in pluripotent embryonic stem and full iPS cells, and are characterized by unusually dispersed 10 nm heterochromatin fibres in high Nanog-expressing cells, including pluripotent cells of the mouse blastocyst before differentiation. This heterochromatin reorganization accompanies retroviral silencing during conversion of partial iPS cells by MEK/GSK3 2i inhibitor treatment. Thus, constitutive heterochromatin is compacted in partial iPS cells but reorganizes into dispersed 10 nm chromatin fibres as the fully reprogrammed iPS cell state is acquired.

Differential genomic targeting of the transcription factor TAL1 in alternate haematopoietic lineages.

TAL1/SCL is a master regulator of haematopoiesis whose expression promotes opposite outcomes depending on the cell type: differentiation in the erythroid lineage or oncogenesis in the T-cell lineage. Here, we used a combination of ChIP sequencing and gene expression profiling to compare the function of TAL1 in normal erythroid and leukaemic T cells. Analysis of the genome-wide binding properties of TAL1 in these two haematopoietic lineages revealed new insight into the mechanism by which transcription factors select their binding sites in alternate lineages. Our study shows limited overlap in the TAL1-binding profile between the two cell types with an unexpected preference for ETS and RUNX motifs adjacent to E-boxes in the T-cell lineage. Furthermore, we show that TAL1 interacts with RUNX1 and ETS1, and that these transcription factors are critically required for TAL1 binding to genes that modulate T-cell differentiation. Thus, our findings highlight a critical role of the cellular environment in modulating transcription factor binding, and provide insight into the mechanism by which TAL1 inhibits differentiation leading to oncogenesis in the T-cell lineage.

miRNA-132 orchestrates chromatin remodeling and translational control of the circadian clock.

Mammalian circadian rhythms are synchronized to the external time by daily resetting of the suprachiasmatic nucleus (SCN) in response to light. As the master circadian pacemaker, the SCN coordinates the timing of diverse cellular oscillators in multiple tissues. Aberrant regulation of clock timing is linked to numerous human conditions, including cancer, cardiovascular disease, obesity, various neurological disorders and the hereditary disorder familial advanced sleep phase syndrome. Additionally, mechanisms that underlie clock resetting factor into the sleep and physiological disturbances experienced by night-shift workers and travelers with jet lag. The Ca(2+)/cAMP response element-binding protein-regulated microRNA, miR-132, is induced by light within the SCN and attenuates its capacity to reset, or entrain, the clock. However, the specific targets that are regulated by miR-132 and underlie its effects on clock entrainment remained elusive until now. Here, we show that genes involved in chromatin remodeling (Mecp2, Ep300, Jarid1a) and translational control (Btg2, Paip2a) are direct targets of miR-132 in the mouse SCN. Coordinated regulation of these targets underlies miR-132-dependent modulation of Period gene expression and clock entrainment: the mPer1 and mPer2 promoters are bound to and transcriptionally activated by MeCP2, whereas PAIP2A and BTG2 suppress the translation of the PERIOD proteins by enhancing mRNA decay. We propose that miR-132 is selectively enriched for chromatin- and translation-associated target genes and is an orchestrator of chromatin remodeling and protein translation within the SCN clock, thereby fine-tuning clock entrainment. These findings will further our understanding of mechanisms governing clock entrainment and its involvement in human diseases.

Association of genes to genetically inherited diseases using data mining.

Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.

Dual role for the methyltransferase G9a in the maintenance of beta-globin gene transcription in adult erythroid cells.

Using a proteomics screen, we have identified the methyltransferase G9a as an interacting partner of the hematopoietic activator NF-E2. We show that G9a is recruited to the beta-globin locus in a NF-E2-dependent manner and spreads over the entire locus. While G9a is often regarded as a corepressor, knocking down this protein in differentiating adult erythroid cells leads to repression of the adult beta(maj) globin gene and aberrant reactivation of the embryonic beta-like globin gene E(y). While in adult cells G9a maintains E(y) in a repressed state via dimethylation of histone H3 at lysines 9 and 27, it activates beta(maj) transcription in a methyltransferase-independent manner. Interestingly, the demethylase UTX is recruited to the beta(maj) (but not the E(y)) promoter where it antagonizes G9a-dependent H3K27 dimethylation. Collectively, these results reveal a dual role for G9a in maintaining proper expression (both repression and activation) of the beta-globin genes in differentiating adult erythroid cells.

Quantitative proteomic analysis of dystrophic dog muscle.

Duchenne muscular dystrophy (DMD) is caused by null mutations in the dystrophin gene, leading to progressive and unrelenting muscle loss. Although the genetic basis of DMD is well resolved, the cellular mechanisms associated with the physiopathology remain largely unknown. Increasing evidence suggests that secondary mechanisms, as the alteration of key signaling pathways, may play an important role. In order to identify reliable biomarkers and potential therapeutic targets, and taking advantage of the clinically relevant Golden Retriever Muscular Dystrophy (GRMD) dog model, a proteomic study was performed. Isotope-coded affinity tag (ICAT) profiling was used to compile quantitative changes in protein expression profiles of the vastus lateralis muscles of 4-month old GRMD vs healthy dogs. Interestingly, the set of under-expressed proteins detected appeared primarily composed of metabolic proteins, many of which have been shown to be regulated by the transcriptional peroxisome proliferator-activated receptor-gamma co-activator 1 alpha (PGC-1α). Subsequently, we were able to showed that PGC1-α expression is dramatically reduced in GRMD compared to healthy muscle. Collectively, these results provide novel insights into the molecular pathology of the clinically relevant animal model of DMD, and indicate that defective energy metabolism is a central hallmark of the disease in the canine model.

Outer membrane pore protein prediction in mycobacteria using genomic comparison.

Proteins responsible for outer membrane transport across the unique membrane structure of Mycobacterium spp. are attractive drug targets in the treatment of human diseases caused by the mycobacterial pathogens, Mycobacterium tuberculosis, M. bovis, M. leprae and M. ulcerans. In contrast with Escherichia coli, relatively few outer-membrane proteins (OMPs) have been identified in Mycobacterium spp., largely due to the difficulties in isolating mycobacterial membrane proteins and our incomplete understanding of secretion mechanisms and cell wall structure in these organisms. To further expand our knowledge of these elusive proteins in mycobacteria, we have improved upon our previous method of OMP prediction in mycobacteria by taking advantage of genomic data from seven mycobacteria species. Our improved algorithm suggests 4333 sequences as putative OMPs in seven species with varying degrees of confidence. The most virulent pathogenic mycobacterial species are slightly enriched in these selected sequences. We present examples of predicted OMPs involved in horizontal transfer and paralogy expansion. Analysis of local secondary structure content allowed identification of small domains predicted to perform as OMPs; some examples show their involvement in events of tandem duplication and domain rearrangements. We discuss the taxonomic distribution of these discovered families and architectures, often specific to mycobacteria or the wider taxonomic class of Actinobacteria. Our results suggest that OMP functionality in mycobacteria is richer than expected and provide a resource to guide future research of these understudied proteins.

Update of the G2D tool for prioritization of gene candidates to inherited diseases.

G2D (genes to diseases) is a web resource for prioritizing genes as candidates for inherited diseases. It uses three algorithms based on different prioritization strategies. The input to the server is the genomic region where the user is looking for the disease-causing mutation, plus an additional piece of information depending on the algorithm used. This information can either be the disease phenotype (described as an online Mendelian inheritance in man (OMIM) identifier), one or several genes known or suspected to be associated with the disease (defined by their Entrez Gene identifiers), or a second genomic region that has been linked as well to the disease. In the latter case, the tool uses known or predicted interactions between genes in the two regions extracted from the STRING database. The output in every case is an ordered list of candidate genes in the region of interest. For the first two of the three methods, the candidate genes are first retrieved through sequence homology search, then scored accordingly to the corresponding method. This means that some of them will correspond to well-known characterized genes, and others will overlap with predicted genes, thus providing a wider analysis. G2D is publicly available at http://www.ogic.ca/projects/g2d_2/

Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes.

There is a rapid increase in the world-wide burden of disease attributed to metabolic syndrome, as defined by co-occurrence of an array of phenotypes including abdominal obesity, dysglycemia, hypertriglyceridemia, low levels of high density lipoprotein cholesterol, and hypertension. Familial studies clearly indicate a genetic component to the disease and many linkage studies have identified a large number of linked loci. No disease-causing genes, however, have been conclusively identified, most likely because this is a multigenic disease for which effects of many causative genes may be small and combined with environmental effects. To assist empirical identification of metabolic syndrome associated genes, we present here a novel computational approach to prioritize candidate genes. We have used linkage studies and the clinical and population-specific presentation of the disease to select a final candidate gene list of 19 most likely disease-causing genes. These are predominantly involved in chylomicron processing, transmembrane receptor activity, and signal transduction pathways. We propose here that information about the clinical presentation of a complex trait can be used to effectively inform computational prioritization of disease-causing genes for that trait.

K2D2: estimation of protein secondary structure from circular dichroism spectra.

BACKGROUND: Circular dichroism spectroscopy is a widely used technique to analyze the secondary structure of proteins in solution. Predictive methods use the circular dichroism spectra from proteins of known tertiary structure to assess the secondary structure contents of a protein with unknown structure given its circular dichroism spectrum. RESULTS: We developed K2D2, a method with an associated web server to estimate protein secondary structure from circular dichroism spectra. The method uses a self-organized map of spectra from proteins with known structure to deduce a map of protein secondary structure that is used to do the predictions. CONCLUSION: The K2D2 server is publicly accessible at http://www.ogic.ca/projects/k2d2/. It accepts as input a circular dichroism spectrum and outputs the estimated secondary structure content (alpha-helix and beta-strand) of the corresponding protein, as well as an estimated measure of error.

Resident endothelial precursors in muscle, adipose, and dermis contribute to postnatal vasculogenesis.

A novel population of tissue-resident endothelial precursors (TEPs) was isolated from small blood vessels in dermal, adipose, and skeletal muscle of mouse based on their ability to be grown as spheres. Cellular and molecular analyses of these cells revealed that they were highly related regardless of the tissue of origin and distinct from embryonic neural stem cells. Notably, TEPs did not express hematopoietic markers, but they expressed numerous characteristics of angiogenic precursors and their differentiated progeny, such as CD34, Flk-1, Tie-1, CD31, and vascular endothelial cadherin (VE-cadherin). TEPs readily differentiated into endothelial cells in newly formed vascular networks following transplantation into regenerating skeletal muscle. Taken together, these experiments suggest that TEPs represent a novel class of endothelial precursors that are closely associated with small blood vessels in muscle, adipose, and dermal tissue. This finding is of particular interest since it could bring new insight in cancer angiogenesis and collateral blood vessels developed following ischemia. Disclosure of potential conflicts of interest is found at the end of this article.

Systematic association of genes to phenotypes by genome and literature mining.

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene-phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.

Gene function in early mouse embryonic stem cell differentiation.

BACKGROUND: Little is known about the genes that drive embryonic stem cell differentiation. However, such knowledge is necessary if we are to exploit the therapeutic potential of stem cells. To uncover the genetic determinants of mouse embryonic stem cell (mESC) differentiation, we have generated and analyzed 11-point time-series of DNA microarray data for three biologically equivalent but genetically distinct mESC lines (R1, J1, and V6.5) undergoing undirected differentiation into embryoid bodies (EBs) over a period of two weeks. RESULTS: We identified the initial 12 hour period as reflecting the early stages of mESC differentiation and studied probe sets showing consistent changes of gene expression in that period. Gene function analysis indicated significant up-regulation of genes related to regulation of transcription and mRNA splicing, and down-regulation of genes related to intracellular signaling. Phylogenetic analysis indicated that the genes showing the largest expression changes were more likely to have originated in metazoans. The probe sets with the most consistent gene changes in the three cell lines represented 24 down-regulated and 12 up-regulated genes, all with closely related human homologues. Whereas some of these genes are known to be involved in embryonic developmental processes (e.g. Klf4, Otx2, Smn1, Socs3, Tagln, Tdgf1), our analysis points to others (such as transcription factor Phf21a, extracellular matrix related Lama1 and Cyr61, or endoplasmic reticulum related Sc4mol and Scd2) that have not been previously related to mESC function. The majority of identified functions were related to transcriptional regulation, intracellular signaling, and cytoskeleton. Genes involved in other cellular functions important in ESC differentiation such as chromatin remodeling and transmembrane receptors were not observed in this set. CONCLUSION: Our analysis profiles for the first time gene expression at a very early stage of mESC differentiation, and identifies a functional and phylogenetic signature for the genes involved. The data generated constitute a valuable resource for further studies. All DNA microarray data used in this study are available in the StemBase database of stem cell gene expression data 1 and in the NCBI's GEO database.

StemBase: a resource for the analysis of stem cell gene expression data.

StemBase is a database of gene expression data obtained from stem cells and derivatives mainly from mouse and human using DNA microarrays and Serial Analysis of Gene Expression. Here, we describe this database and indicate ways to use it for the study the expression of particular genes in stem cells or to search for genes with particular expression profiles in stem cells, which could be associated to stem cell function or used as stem cell markers.

Amplification of the Gene Ontology annotation of Affymetrix probe sets.

BACKGROUND: The annotations of Affymetrix DNA microarray probe sets with Gene Ontology terms are carefully selected for correctness. This results in very accurate but incomplete annotations which is not always desirable for microarray experiment evaluation. RESULTS: Here we present a protocol to amplify the set of Gene Ontology annotations associated to Affymetrix DNA microarray probe sets using information from related databases. CONCLUSION: Predicted novel annotations and the evidence producing them can be accessed at Probe2GO: http://www.ogic.ca/p2g. Scripts are available on demand.

Update on XplorMed: A web server for exploring scientific literature.

As scientific literature databases like MEDLINE increase in size, so does the time required to search them. Scientists must frequently inspect long lists of references manually, often just reading the titles. XplorMed is a web tool that aids MEDLINE searching by summarizing the subjects contained in the results, thus allowing users to focus on subjects of interest. Here we describe new features added to XplorMed during the last 2 years (http://www.bork.embl-heidelberg.de/xplormed/).

Inconsistencies over time in 5% of NetAffx probe-to-gene annotations.

BACKGROUND: DNA microarray probes are designed to match particular mRNA transcripts, often based on expressed sequences like ESTs, or cDNAs, many times incomplete. As a result, the relations between probes and genes can change as the sequence data are updated. However, it is frequent that the reported results of microarray analyses are given just as lists of genes without any reference to the underlying probes. RESULTS: We show for a particular commercial microarray design that the number of probes associated to some genes change with time. These changes concern approximately 5% of the probe sets across the history of annotation releases over a two year span. CONCLUSION: We recommend to report probe set identifiers when publishing microarray results, and to submit those analyses to microarray public databases to ensure that the interpretation of the data is updated with the latest set of annotations.

Study of stem cell function using microarray experiments.

DNA Microarrays are used to simultaneously measure the levels of thousands of mRNAs in a sample. We illustrate here that a collection of such measurements in different cell types and states is a sound source of functional predictions, provided the microarray experiments are analogous and the cell samples are appropriately diverse. We have used this approach to study stem cells, whose identity and mechanisms of control are not well understood, generating Affymetrix microarray data from more than 200 samples, including stem cells and their derivatives, from human and mouse. The data can be accessed online (StemBase; http://www.scgp.ca:8080/StemBase/).

G2D: a tool for mining genes associated with disease.

BACKGROUND: Human inherited diseases can be associated by genetic linkage with one or more genomic regions. The availability of the complete sequence of the human genome allows examining those locations for an associated gene. We previously developed an algorithm to prioritize genes on a chromosomal region according to their possible relation to an inherited disease using a combination of data mining on biomedical databases and gene sequence analysis. RESULTS: We have implemented this method as a web application in our site G2D (Genes to Diseases). It allows users to inspect any region of the human genome to find candidate genes related to a genetic disease of their interest. In addition, the G2D server includes pre-computed analyses of candidate genes for 552 linked monogenic diseases without an associated gene, and the analysis of 18 asthma loci. CONCLUSION: G2D can be publicly accessed at http://www.ogic.ca/projects/g2d_2/.