Research and Discovery
What mechanisms shape the activity of our genes?
How does gene activity shape brain function, behavior and disease risks?
Genomic Imprinting – Maternally and Paternally Inherited Epigenetic Effects
Genomic imprinting is a heritable form of epigenetic gene regulation that causes the maternally and paternally inherited copies (alleles) for some genes to be silenced in the offspring. As a result, only one parent’s allele is expressed. The Gregg lab has described novel genomic imprinting effects that we call “Non-canonical genomic imprinting“. Rather than manifesting as the silencing of one parent’s allele, non-canonical imprinted genes exhibit a bias at the tissue level to express one parent’s allele at a higher level than the other. Non-canonical imprinting is especially enriched in the brain and we are testing the idea that this is a highly cell-type specific form of imprinting through which mothers and fathers differentially shape specific brain functions in offspring.
Bonthuis PJ, Huang WC, Stacher Hörndli CN, Ferris E, Cheng T, Gregg C. Noncanonical genomic imprinting effects in offspring. Cell Reports. 2015 Aug 11; 12(6):979-91. PMID: 26235621 (research article)
Gregg C, Ferris E. Methods for Detecting Rapidly Processed Introns to Evaluate Allelic Expression. U.S. Patent No. 62/494,162. (2016) Washington, D.C.:U.S. Patent and Trademark Office.
Novel Epigenetic Mechanisms at the Allele and Cellular Level
The Gregg lab recently developed new genomics methods that uncovered thousands of genes that differentially express their maternal and paternal alleles in vivo in a developmentally regulated manner in the mouse and primate brain (Huang et al., 2017). We refer to these novel effects as differential allele expression effects (DAEEs). DAEEs are epigenetic in origin and are not due to genetic variation or genomic imprinting. At the cellular level, DAEEs involve random monoallelic expression in subpopulations of brain cells. In the mouse, DAEEs are prevalent during early stages of postnatal brain development (postnatal day (P)5), impacting ~85% of genes, but subsequently decline by weaning, impacting ~10% of genes in the juvenile (P15) and adult brain. We found that DAEEs can interact with heterozygous mutations to cause mosaics of monoallelic brain cells that differentially express mutant (MT) versus wildtype (WT) alleles, which we refer to as monoallelic-MT and monoallelic-WT cells, respectively. These studies have uncovered a new landscape of in vivo gene regulatory effects at the allele and cellular level that we do not understand. Currently, we do not know the function of DAEEs, the mechanisms involved, whether specific developmental stages, brain regions and cell-types are impacted, or whether DAEEs are stable or dynamic allelic states in brain cells. The answers to these questions could improve our understanding of how genes shape phenotypes and influence disease risks.
Huang* WC, Ferris* E, Cheng T, Hörndli CS, Gleason K, Tamminga C, Wagner JD, Boucher KM, Christian JL, Gregg C. Diverse Non-genetic, Allele-Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain. Neuron. 2017. 93(5):1094-1109.e7. doi: 10.1016/j.neuron.2017.01.033. PMID: 28238550 *co-authors (research article)
Phylogenomics Approaches to Discover Key Regulatory Elements in the Mammalian Genome for Clinically Important Phenotypes
The identity of most functional noncoding elements in the mammalian genome and the phenotypes they impact are unclear. Here, we perform genome-wide comparative analyses of patterns of accelerated evolution in species with highly distinctive traits to discover candidate functional elements for clinically important phenotypes. We identified accelerated regions (ARs) in the elephant, hibernating bats, orca, dolphin, naked mole rat and thirteen-lined ground squirrel lineages in mammalian conserved regions, uncovering ~33,000 elements that bind hundreds of different regulatory proteins in humans and mice. ARs in the elephant, the largest land mammal, are uniquely enriched at elephant DNA damage response genes and changed conserved regulatory sites. The genomic hotspot for elephant ARs is the E3 ligase subunit of the Fanconi Anemia Complex, a master regulator of DNA repair. Additionally, ARs in the six species are associated with specific human clinical phenotypes that have apparent concordance with overt traits in each species. My lab’s phylogenomics approach has revealed novel putative functional elements in the genome for cancer resistance, blood clot resistance, reversible insulin resistance, social behaviors, activity, motivated behavior, hibernation-related phenotypes and seasonal gene expression. Currently, we are using genomics methods and CRISPR-Cas9 based genome engineering strategies to investigate the functions of these novel noncoding elements in the mammalian genome.
Ferris E, Abegglen L, Schiffman JD, Gregg C. Accelerated Evolution in Distinctive Species Reveals Candidate Elements for Clinically Relevant Traits, including Mutation and Cancer Resistance. Cell Reports. 2018 22(10):2742-2755. (research article)
Computational Ethology: Deconstructing Complex Patterns of Behavior
The analysis of behavior in mammalian model organisms, such as mice, is fundamental to understanding the molecular and neural mechanisms that underlie brain function. These assays are also used in pre-clinical studies to test the efficacy of drug treatments. However, current behavioral paradigms are limited in that the investigator must have a predetermined hypothesis regarding the phenotypes that they are interested in evaluating in their studies. In addition, for several paradigms the ecological relevance and conservation of the assayed phenotype is unclear. Other fields, such as the genomics field, have developed methods that allow for unbiased screens to facilitate new discoveries. Here, we are developing novel methods for unbiased behavioral phenotyping that involve automated video-tracking and machine learning based methods to deconstruct complex patterns of ecologically relevant and highly conserved behaviors. Currently, we have developed new methods to screen hundreds of features of behavior during foraging and applied this method to study parental and genetic mechanisms regulating behavioral development. This work is opening a new field that we refer to as computational ethology, and it is expected to improve our ability to study the mechanisms that shape behavioral phenotypes and discover new disease mechanisms and therapeutics.
Stacher Horndli CN, Wong E, Rhodes AN, Ferris E, Fletcher PT, Gregg C. An Ethomics Approach to Study Behavioral Development and Foraging Reveals Age-Dependent Parental and Genetic Effects. (under review)
Stacher Horndli CN, Wong E, Palande S, Fletcher T, Wang B, Gregg C. Parental and Genetic Influences on Offspring Personality Architecture and Economic Strategies Revealed by a Computational Approach to Ethology. (In Preparation)