Research interests

Computational Analysis of Gene Regulation and Genome Organization
The advent of genome-scale biology has provided biologists with enormous amounts of data to analyze, understand, and incorporate into ever-improving models of how organisms function at a molecular level.  A fundamental problem in these studies is the identification and characterization of an organism’s genes, especially those that code for proteins.  While a great deal of attention has understandably been focused on delineation of the function of the resulting proteins, it is equally important to ascertain the context in which the genes are (and are not) activated, a phenomenon broadly referred to as gene regulation.

Evolution and Functional Organization of Metazoan Chromosomes
The physical and functional organizations of the mammalian genome are correlated outcomes of evolution. Inbred strains of mice provide a unique opportunity for exploring these relationships, representing as they do, diverse genomes originally separated by millions of generations that were then scrambled in the laboratory and subjected to intense selection during inbreeding to homozygosity. In collaborative work with Senior Staff Scientists Ken Paigen and Gary Churchill and Associate Research Scientist Petko Petkov, we showed that the resulting pattern of chromosome organization includes local domains of functionally related elements that promote the co-inheritance and survival of compatible sets of alleles.  The evidence for this includes multiple, extensive domains of linkage disequilibrium (LD), as identified through cross-strain and genome-wide analysis of SNPs, and the reduced viability of offspring with recombination in these domains, as demonstrated by comparison of first generation crosses with their associated recombinant inbred lines.  There are also patterns of linkage disequilibrium between domains on separate chromosomes; these are distinctly non-random and form networks with scale-free architecture. Comparison of the LD patterns with available gene annotation has identified biological functions underlying some domains and networks.  The strong conservation of gene order among mammals indicates that the domains and networks we find arose prior to the mammalian radiation beginning some 90 million years and likely characterize all mammals. 

Our preliminary studies of genome organization have now been successfully parlayed into a new effort in systems biology: The Center for Genome Dynamics at The Jackson Laboratory.   This newly formed center will serve to unify a broad multi-disciplinary group of researchers bringing a variety of perspectives and associated experimental approaches to the study of chromosome function, organization, and evolution.  Our group is developing the mathematical framework in which multiple disparate data sources can be integrated to better delineate both local and global organization of the genome.  Successful modeling of this data requires a broad array of techniques, including, but not limited to, Hidden Markov Models, graph theory, and dynamic programming.

Computational Studies of Post-transcriptional Gene Regulation
Gene regulation is controlled by a complex mixture of forces that can act at any of the stages of gene activation: transcription of DNA to a precursor RNA, post-transcriptional processing of the RNA, translation of the RNA to a protein, or post-translational modification of the final protein product. Examples of post-transcriptional gene regulation are available across a wide range of organisms and biological processes, and include such varied phenomena as 3’-end formation (cleavage and polyadenylation), splicing, localization, degradation, editing, and translational suppression or enhancement.  As we improve our understanding of the fundamental mechanisms of gene regulation, we correspondingly improve our ability to understand how gene regulation can be disrupted, often resulting in either disease or developmental problems.

Post-transcriptional gene regulation is utilized extensively in early mammalian development.  We collaborated with Senior Staff Scientist Barbara Knowles and Research Scientist Mimi deVries in a study that showed striking and differential activities of various retrotransposons during the transition from oogenesis, through ovulation, fertilization, and early embryogenesis.  We carried this work forward with an analysis of a large (~19,000) EST library from the full-grown-oocyte (FGO), which, in comparison with the previously released two-cell embryo library, enables the identification of maternal transcripts with differential stability and translation control. Through comparative studies of the 3’-UTR sequences of stable and transient transcripts, we identified systematic differences in sequence content, which we hypothesize are related to mRNA degradation and translational control

Similar to oogenesis, spermatogenesis is characterized by tissue- and stage-specific mRNA processing.  In collaborative work with Clinton MacDonald of Texas Tech University, we used tissue-specific EST sequences to identify and characterize 3’-processing sites specific to various stages of spermatogenesis. The 3’-processing control is comprised of several distinct sequence elements, including the well-known canonical AAUAAA element.  Our studies indicate that the balance between these elements changes significantly during spermatogenesis, facilitating systematic selection between alternative 3’-processing sites for large groups of genes.  The use of alternative 3’-processing sites correspondingly changes the 3’-UTR sequence, which in turn can alter the post-transcriptional regulation of the affected genes.  This work also provided unexpected new insights on the details and pitfalls of using EST libraries to identify quantitative changes in gene processing and/or regulation.

We recently completed a broad survey of metazoan 3’-processing downstream regulatory elements, identifying and analyzing more than 1000 putative 3’-processing sites from ten organisms, including human, mouse, rat, dog, chicken, zebrafish, fugu, fruit fly, mosquito, and nematode.  In this analysis, we identified statistically significant patterns of both sequence content and positioning relative to the 3’-processing site. These patterns include elements common to all metazoans, as well as elements that are specific to sub-groups. To further inform these studies, we collected, multiply aligned, and analyzed protein sequences for CstF64 (which binds directly to the precursor RNA) from the same range of organisms, focusing on the RNA binding domain (RBD) and adjacent hinge domain.  Strikingly, we find correlations between amino acid changes in critical residues of the RBD that correlate with observable changes in the binding sites of the different organisms.  These correlations allowed us to propose a new model for protein-RNA interaction that encompasses and is consistent with a broad body of previous experimental studies.

Publications

• Betley JN, Frith MC, Graber JH, and Deshler JO. 2002. Identification of new localized mRNAs and RNA localization elements using a novel computational tool for sequence analysis. Curr Biol 12:1756-1761.
• Graber JH, McAllister GH, and Smith TF. 2002. Probabilistic prediction of S. cerevisiae mRNA 3’-processing sites. Nucleic Acids Res 30:1851-1858.
• Pervouchine DD, Graber JH, Kasif S. 2003. On the normalization of RNA equilibrium free energy to the length of the sequence. Nucleic Acids Res 31:e49.
• Graber JH. 2003. Variations of 3’-processing cis-elements in yeast correlate with transcript stability. Trends Genet 19:473-6.
• Peaston AE, Evsikov AV, Graber JH, de Vries WN, Holbrook AE, Solter D, Knowles BB. 2004. Retrotransposons regulate host genes in mouse oocytes and preimplantation embryos. Dev Cell 7:597-606.
• Brockman JM, Singh P, Liu D, Quinlan S, Salisbury J, Graber JH. 2005. PACdb: PolyA cleavage site and 3'-UTR database. Bioinformatics 21:3691-3693.
• Petkov PM, Graber JH, Churchill GA, Dipetrillo K, King BL, Paigen K. 2005. Evidence of a large-scale functional organization of mammalian chromosomes. PLoS Genet 1:e33.
• Liu D, Graber JH. 2006. Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation. BMC Bioinformatics 7:77.
• Salisbury J, Hutchison KW, Graber JH. 2006. A multispecies comparison of the metazoan 3'-processing downstream elements and the CstF-64 RNA recognition motif. BMC Genomics 7:55.

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