Mouse Genome Sequencing Consortium Research Articles

Phosphatidylinositol 3-Kinase (PI3K) Signaling via Glycogen Synthase Kinase-3 (Gsk-3) Regulates DNA Methylation of Imprinted Loci

Glycogen synthase kinase-3 (Gsk-3) isoforms, Gsk-3α and Gsk-3β, are constitutively active, largely inhibitory kinases involved in signal transduction. Underscoring their biological significance, altered Gsk-3 activity has been implicated in diabetes, Alzheimer disease, schizophrenia, and bipolar disorder. Here, we demonstrate that deletion of both Gsk-3α and Gsk-3β in mouse embryonic stem cells results in reduced expression of the de novo DNA methyltransferase Dnmt3a2, causing misexpression of the imprinted genes Igf2, H19, and Igf2r and hypomethylation of their corresponding imprinted control regions. Treatment of wild-type embryonic stem cells and neural stem cells with the Gsk-3 inhibitor, lithium, phenocopies the DNA hypomethylation at these imprinted loci. We show that inhibition of Gsk-3 by phosphatidylinositol 3-kinase (PI3K)-mediated activation of Akt also results in reduced DNA methylation at these imprinted loci. Finally, we find that N-Myc is a potent Gsk-3-dependent regulator of Dnmt3a2 expression. In summary, we have identified a signal transduction pathway that is capable of altering the DNA methylation of imprinted loci.

Differences in DBA/1J and DBA/2J reveal lipid QTL genes

Recent advances in mouse genomics have revealed considerable variation in the form of single-nucleotide polymorphisms (SNPs) among common inbred strains. This has made it possible to characterize closely related strains and to identify genes that differ; such genes may be causal for quantitative phenotypes. The mouse strains DBA/1J and DBA/2J differ by just 5.6% at the SNP level. These strains exhibit differences in a number of metabolic and lipid phenotypes, such as plasma levels of triglycerides (TGs) and HDL. A cross between these strains revealed multiple quantitative trait loci (QTLs) in 294 progeny. We identified significant TG QTLs on chromosomes (Chrs) 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 16, and 19, and significant HDL QTLs on Chrs 3, 9, and 16. Some QTLs mapped to chromosomes with limited variability between the two strains, thus facilitating the identification of candidate genes. We suggest that Tshr is the QTL gene for Chr 12 TG and HDL levels and that Ihh may account for the TG QTL on Chr 1. This cross highlights the advantage of crossing closely related strains for subsequent identification of QTL genes.

Energetics of small hearts

Little is known about the energetics of the failing heart. In man it is well known that energy metabolism is affected in chronic heart failure (Ingwall & Weiss, 2004). Genetically modified mice are useful in the study of heart disease. A persistent headache has been the lack of basic energetic data. In this issue of The Journal of PhysiologyWiden & Barclay (2006) give us both a method and the first data. For the first time, the basic energetic characteristics of mouse heart muscle activation and contraction have been measured in papillary muscle. Mice can be obtained relatively easily and the mouse genome has been sequenced. The proportion of mouse genes without a human homologue is probably less than 1% (Mouse Genome Sequencing Consortium, 2002). Numerous mouse models for studying heart disease exist already and the number is increasing (Wang et al. 2004). Gene products are processed in cells resulting in many more proteins than genes and a staggering number of possible interactions between the different proteins exist. One important question is then: How does one relate gene function to the final contractile characteristics of cardiomyocytes? Muscles produce mechanical work and heat in different amounts and proportions, depending on the type of muscle and the load (Hill, 1965; Woledge et al. 1985). ATP required for ion pumps and cardiomyocyte shortening is buffered by the Lohmann reaction in the myoplasm. This keeps the ATP concentration constant while the phosphocreatine concentration decreases when the muscle is activated. Net splitting of phosphocreatine releases 34 kJ mol−1 (Woledge et al. 1985). This energy is partly converted into mechanical work during systole when the phosphocreatine concentration decreases and the rest is liberated as heat. A metabolic steady state can only be maintained when creatine is rephosphorylated during one heart cycle. The energy for this is produced by oxidation of fat or carbohydrate, 439 or 473 kJ (mol O2)−1, respectively. Depending on the efficiency of the oxidation (the ∼P/O2 ratio), part of the energy is used to rephosphorylate creatine, and the remainder is liberated as heat. Heat production increases the temperature of the heart muscle preparation, which can be measured using a thermopile. The myothermic technique has a higher time resolution than oxygen consumption or 31P-NMR which are too slow to determine energy fluxes during a single contraction. The interpretation of heat production data requires that the biochemical reaction producing it is known. This is complicated in heart muscle because contraction-related processes, i.e. heat production due to ion pumps and cross-bridge cycling, and oxidative phosphorylation, usually occur simultaneously. Another challenge is that mouse hearts are small (about 0.1–0.2 g) and operate at high metabolic rates (Gibbs & Loiselle, 2001). The risk that the preparation becomes anoxic is considerable. This is a problem because heat production by partly anoxic preparations cannot be interpreted reliably, and because reoxygenation can damage anoxic cardiomyocytes. The maximum rate of oxygen consumption of mouse heart muscle is not known, but can be estimated on the basis of volume density of mitochondria and the rate of oxygen consumption of rat myocardial trabeculae, 0.58 nmol mm−3 s−1 at the maximum heart rate (van der Laarse et al. 2005), 600 beats min−1, which is the resting rate in mice. Mouse cardiomyocytes contain 38% (v/v) mitochondria and rat cardiomyocytes contain 32% (Barth et al. 1992). Assuming the maximum rate of oxygen consumption is proportional to volume density of mitochondria, the maximum rate of oxygen consumption of mouse trabeculae is about 0.7 nmol mm−3 s−1. At this rate of oxygen consumption, the mouse heart becomes anoxic a few seconds after perfusion stops and superfused preparations, e.g. papillary muscles or trabeculae at experimental PO2 of 500 mmHg, must have diameters of less than 160 µm to prevent an anoxic core. Widen & Barclay (2006) solved these problems and determined heat produced by calcium cycling and heat produced by cross-bridge cycling in papillary muscles of Swiss mice. Contraction-related heat was determined during the first three contractions of a series, making use of the relatively slow onset of recovery metabolism at 27°C. Hypoxia was prevented using stimulus periods of 20 s, sufficiently short not to deplete oxygen stored in the muscle, and contraction and activation-related heat were separated by partial inhibition of cross-bridges using butanedione monoxime (Alpert et al. 1989). The results indicate that about one Ca2+ cycles per three cross-bridge cycles, and that the number of cross-bridge cycles per cardiac twitch is half the number of available cross-bridges. How these numbers vary with genotype and intervention remains to be determined.

Multiple Promoters Direct Expression of Three AKAP12 Isoforms with Distinct Subcellular and Tissue Distribution Profiles

A Kinase Anchoring Protein 12 (AKAP12; also known as src-suppressed C kinase substrate (SSeCKS) and Gravin) is a multivalent anchoring protein with tumor suppressor activity. Although expression of AKAP12 has been examined in a number of contexts, its expression control remains to be elucidated. Herein, we characterize the genomic organization of the AKAP12 locus, its regulatory regions, and the spatial distribution of the proteins encoded by the AKAP12 gene. Using comparative genomics and various wet-lab assays, we show that the AKAP12 locus is organized as three separate transcription units that are governed by non-redundant promoters coordinating distinct tissue expression profiles. The proteins encoded by the three AKAP12 isoforms (designated alpha, beta, and gamma) share >95% amino acid sequence identity but differ at their N termini. Analysis of the targeting of each isoform reveals distinct spatial distribution profiles. An N-terminal myristoylation motif present in AKAP12alpha is shown to be necessary and sufficient for targeted expression of this AKAP12 isoform to the endoplasmic reticulum, a novel subcellular compartment for AKAP12. Our results demonstrate heretofore unrecognized complexity within the AKAP12 locus and suggest a mechanism for genetic control of signaling specificity through distinct regulation of alternately targeted anchoring protein isoforms.

Epiplakin Gene Analysis in Mouse Reveals a Single Exon Encoding a 725-kDa Protein with Expression Restricted to Epithelial Tissues

Based on cDNA cloning and sequencing, human epiplakin has been classified as a member of the plakin protein family of cytolinkers. We report here the characterization of the mouse epiplakin gene locus and the isolation of full-length mouse epiplakin cDNA using BAC vectors. We found that the protein is encoded by a single remarkably large exon (>20 kb) that consists of a series of 0.8-1.5-kb-long DNA repeats, eight of which are virtually identical. Consequently, mouse epiplakin contains 16 plakin repeat domains, three more than reported for the human protein and eight more than predicted for the mouse protein based on the contig characterized by the Mouse Genome Sequencing Consortium. Using antibodies raised to a highly conserved repeating epiplakin sequence domain, we show that the protein in cells is expressed in its full length (725 kDa), and we provide evidence that the size of human epiplakin previously may have been underestimated. In addition we show on transcript and protein levels that epiplakin is restricted to epithelial tissues and that its gene maps to mouse chromosome 15 (human chromosome 8). This study lays the groundwork for future genetic approaches aimed at defining the biological role of this unique protein.

Exon Structure Analysis, Ortholog Identification, and SNP Candidate Screening by Mapping Mouse RIKEN Sequences to Multiple Genome Assemblies

Mapping RIKEN full-length cDNAs (The FANTOM Consortium and the RIKEN Genome Exploration Research Group Phase I and II Team 2002) to the genome assemblies enables a variety of analyses to be performed. First, exon structure can be determined, and coding and noncoding regions can be inferred, due to different average exon length. In some cases, the alternative exon structures may be identified at this stage. Second, chromosome position can be used to identify the correct ortholog in human, for which functional data may be available. Third, intronless genes can be identified and examined carefully to determine whether they are retransposition events, pseudogenes, or genomic contamination. Finally, high-quality sequence discrepancies can be identified as potential SNPs by use of the fact that RIKEN and Mouse Genome Sequencing Consortium sequenced the C57BL/6J mouse strain, whereas the four mouse strains sequenced by Celera included 129X1/SvJ, 129S1/SvImJ, DBA/2J, and A/J. We have mapped 60,770 RIKEN clones by BLAT (Kent 2002) to the MGSC (Mouse Genome Sequencing Consortium 2002) genome assembly versions 1 and 3, Ensembl human genome assembly v.28 and the Celera mouse genome assembly releases R12 and R13, and human assembly Release R26i (http://cds.celera.com/; Table 1). The single-exon clones longer than 1 Kb are candidates for further investigation as bona fide intronless genes, retransposition events, or possible genomic DNA contamination. For this investigation, the expression profile of an intronless clone can be very informative (Su et al. 2002). This analysis also allows us to roughly compare the completeness of the assemblies. Of 60,172 RIKEN cDNAs containing >100 non-masked bases, >99% were mapped at >70% length to both latest assemblies, much more complete than the earlier assemblies. Figure 1 illustrates the comparison of the mapping to four assemblies. MGSC v.3 in green as left bars, Celera R13 in blue as right bars for each chromosome. Where RIKEN clones mapped to both assemblies, a cyan line connects the mapping positions, whereas triangles mark clones mapped exclusively to one assembly. The large-scale discrepancies are marked in red. One can observe a 10-Mb contig inversion on chromosome X (later detected and corrected by MGSC) and smaller ones on chr.5, 17, 18, 19, etc. The up-to-date scalable version of the mapping comparison is available at http://www.gnf.org/RIKEN/. The extra bars represent superimposed syntenic regions identified by mapping to human assemblies, NCBI v.28 left from MGSC v.3, Celera R26h right from the mouse Celera R13. The two-digit electric color code for human chromosomes is shown at the bottom. Several cases of different syntenic assignment deserve further investigation.

A Guide to the Mammalian Genome: Figure 1

Sequencing of a Transcriptome The rapid completion and public release of the genome sequences of mouse and human has led to a downgrading of the number of “genes” predicted in the mammalian genome to the region of 30,000 (Mouse Genome Sequencing Consortium, Waterston et al. 2002). In simpler organisms such as yeast, the estimate of gene number is comparatively straightforward, because the majority of the genome clearly encodes proteins, and individual genes generally have a welldefined start and finish and a single mRNA output. In mammals, the task is muchmore complex. Only a small proportion of the genome encodes mRNAs that in turn encode protein, and protein-coding sequence is interspersed with large introns or intergenic regions. Even protein coding genes have proven difficult to annotate reliably (Kawai et al. 2001), and non–protein coding genes are essentially impossible to annotate a priori. The key to reliable annotation of a mammalian genome is the comprehensive characterization of the transcriptional output, the transcriptome. There are two approaches to this problem. The most common is highthroughput sequencing of cDNA ends (ESTs). In mouse and human, and to a lesser extent in many other mammals, there are millions of EST sequences in various repositories. EST sequences can be computationally assembled into clusters, as in the UniGene projects (http : / /www.ncbi .nlm.nih.gov/ UniGene). There are many drawbacks with this approach, both from the cDNA cloning and sequence quality and from computational perspectives, but the most compelling is that the sequences are generated in silico and are not necessarily supported by a physical clone. It is also rather inefficient, because even with the best subtraction and normalization, abundant transcripts have been sequenced thousands of times, whereas many rare transcripts are absent from EST databases. EST assemblies are particularly difficult to interpret when there are multigene families or complex alternative splicing. The alternative approach is to systematically isolate and sequence fulllength cDNAs. The logistics of this approach are daunting, and it is actually far more challenging than is genomic sequencing, especially using shotgun approaches because of the difficulties in the collection of the samples. Nevertheless, the RIKEN Mouse Gene Encyclopedia Project has taken this approach. In the process, the RIKEN team has provided a model for eukaryotic transcriptome projects. The task required a range of new technologies and approaches. In outline, the RIKEN team developed new approaches to production of full-length cDNAs (Carninci et al. 2003) that required (1) a novel reverse transcriptase reaction (to enable effective complete firststrand synthesis), (2) novel 5 end capture technology, and (3) novel approaches to normalization and subtraction of cDNA libraries. Starting with their first libraries, the RIKEN team sequenced 3 ends (and later 5 ends) in a Phase 1 sequencing pipeline and, for each individual clone, determined whether the sequence had been sequenced previously or could be ascribed to a new cluster. In the second phase, individual representatives of EST clusters were selected and fully sequenced to produce a full-length cDNA sequence representing the sequence of an individual physical clone. At a number of stages in the project, the RIKEN team assembled a set of cDNAs that had previously been sequenced and used them to subtract successive libraries. The success of the approach is outlined in detail in Carninci et al. (2003). The output of this pipeline was analyzed in the FANTOM2 meeting (April 29 to May 5, 2002, Yokohama, Japan), which is the basis of this special issue of Genome Research.

Connecting sequence and biology in the laboratory mouse.

The Mouse Genome Sequencing Consortium and the RIKEN Genome Exploration Research grouphave generated large sets of sequence data representing the mouse genome and transcriptome, respectively. These data provide a valuable foundation for genomic research. The challenges for the informatics community are how to integrate these data with the ever-expanding knowledge about the roles of genes and gene products in biological processes, and how to provide useful views to the scientific community. Public resources, such as the National Center for Biotechnology Information (NCBI; http://www.ncbi.nih.gov), and model organism databases, such as the Mouse Genome Informatics database (MGI; http://www.informatics.jax.org), maintain the primary data and provide connections between sequence and biology. In this paper, we describe how the partnership of MGI and NCBI LocusLink contributes to the integration of sequence and biology, especially in the context of the large-scale genome and transcriptome data now available for the laboratory mouse. In particular, we describe the methods and results of integration of 60,770 FANTOM2 mouse cDNAs with gene records in the databases of MGI and LocusLink.

Recent segmental and gene duplications in the mouse genome.

The high quality of the mouse genome draft sequence and its associated annotations are an invaluable biological resource. Identifying recent duplications in the mouse genome, especially in regions containing genes, may highlight important events in recent murine evolution. In addition, detecting recent sequence duplications can reveal potentially problematic regions of the genome assembly. We use BLAST-based computational heuristics to identify large (>/= 5 kb) and recent (>/= 90% sequence identity) segmental duplications in the mouse genome sequence. Here we present a database of recently duplicated regions of the mouse genome found in the mouse genome sequencing consortium (MGSC) February 2002 and February 2003 assemblies. We determined that 33.6 Mb of 2,695 Mb (1.2%) of sequence from the February 2003 mouse genome sequence assembly is involved in recent segmental duplications, which is less than that observed in the human genome (around 3.5-5%). From this dataset, 8.9 Mb (26%) of the duplication content consisted of 'unmapped' chromosome sequence. Moreover, we suspect that an additional 18.5 Mb of sequence is involved in duplication artifacts arising from sequence misassignment errors in this genome assembly. By searching for genes that are located within these regions, we identified 675 genes that mapped to duplicated regions of the mouse genome. Sixteen of these genes appear to have been duplicated independently in the human genome. From our dataset we further characterized a 42 kb recent segmental duplication of Mater, a maternal-effect gene essential for embryogenesis in mice. Our results provide an initial analysis of the recently duplicated sequence and gene content of the mouse genome. Many of these duplicated loci, as well as regions identified to be involved in potential sequence misassignment errors, will require further mapping and sequencing to achieve accuracy. A Genome Browser database was set up to display the identified duplication content presented in this work. This data will also be relevant to the growing number of investigators who use the draft genome sequence for experimental design and analysis.

Whole-genome sequence assembly for mammalian genomes: Arachne 2.

We previously described the whole-genome assembly program Arachne, presenting assemblies of simulated data for small to mid-sized genomes. Here we describe algorithmic adaptations to the program, allowing for assembly of mammalian-size genomes, and also improving the assembly of smaller genomes. Three principal changes were simultaneously made and applied to the assembly of the mouse genome, during a six-month period of development: (1) Supercontigs (scaffolds) were iteratively broken and rejoined using several criteria, yielding a 64-fold increase in length (N50), and apparent elimination of all global misjoins; (2) gaps between contigs in supercontigs were filled (partially or completely) by insertion of reads, as suggested by pairing within the supercontig, increasing the N50 contig length by 50%; (3) memory usage was reduced fourfold. The outcome of this mouse assembly and its analysis are described in (Mouse Genome Sequencing Consortium 2002).

The phusion assembler.

The Phusion assembler has assembled the mouse genome from the whole-genome shotgun (WGS) dataset collected by the Mouse Genome Sequencing Consortium, at ~7.5x sequence coverage, producing a high-quality draft assembly 2.6 gigabases in size, of which 90% of these bases are in 479 scaffolds. For the mouse genome, which is a large and repeat-rich genome, the input dataset was designed to include a high proportion of paired end sequences of various size selected inserts, from 2-200 kbp lengths, into various host vector templates. Phusion uses sequence data, called reads, and information about reads that share common templates, called read pairs, to drive the assembly of this large genome to highly accurate results. The preassembly stage, which clusters the reads into sensible groups, is a key element of the entire assembler, because it permits a simple approach to parallelization of the assembly stage, as each cluster can be treated independent of the others. In addition to the application of Phusion to the mouse genome, we will also present results from the WGS assembly of Caenorhabditis briggsae sequenced to about 11x coverage. The C. briggsae assembly was accessioned through EMBL, http://www.ebi.ac.uk/services/index.html, using the series CAAC01000001-CAAC01000578, however, the Phusion mouse assembly described here was not accessioned. The mouse data was generated by the Mouse Genome Sequencing Consortium. The C. briggsae sequence was generated at The Wellcome Trust Sanger Institute and the Genome Sequencing Center, Washington University School of Medicine.