My group studies how transcriptional regulatory control is encrypted in genomic sequence. We seek to define the cellular contexts within which noncoding variants mediate their effects and how such variation in regulatory sequences may contribute to phenotype variation and disease risk/presentation. In this work my group employs cutting edge genomic and functional genetic approaches in mice, zebrafish and in vitro, integrating them with computational biology.
Regulatory sequences underlie the cellular diversity that arises during human development, and how cells respond to environmental and genetic insult. Regulatory mutations underlie an array of human diseases. They play a significant role in disease susceptibility and they form the basis of cellular response to insult, aging and stress.
Efforts in my lab are currently directed at developing cell-type dependent regulatory sequence catalogs and applying them in human population-based studies to predict, identify and validate likely functional variation that associates with disease. In some of our recent work we have generated large catalogs of putative enhancers in various cell types using ChIP-seq and ATAC-seq (melanoctytes, dopaminergic neurons [ventral midbrain, forebrain, olfactory bulb]). Further begun to explore how these data can be used to learn the vocabularies of cell-dependent control inform our understanding of functional non-coding variation and the molecular mechanisms of transcriptional control. Our emerging work has begun to integrate these analyses with studies of transcriptional heterogeneity within cell-types, using single cell RNA-seq to explore specific neuronal populations and define their Gene Regulatory Networks (GRNs).
Discernment of disease-relevant cell populations, pertinent biological states and stages, is essential for comprehensive functional investigation of their contribution to risk. We have begun to address this question by leveraging stratified LD [Linkage Disequilibrium] score regression (S-LDSC) to partition heritability from GWAS summary statistics to sets of cell-dependent biological signatures in order to identify cell types relevant to disease. We are evaluating common human traits using ATAC-seq data from a wide array of purified mouse cell populations. Our efforts to date have already revealed that OCR signatures from specific neuron sub-populations are highly enriched for schizophrenia heritability.
Collectively, our work sets a powerful precedent for our continued study of a wide assortment of common human neurological and neuropsychiatric phenotypes.
Gene regulation is the framework on which vertebrate cellular diversity is built. The substantial cellular diversity that characterizes complex integrated cell populations, such as the human central nervous system, must therefore require immense regulatory complexity. Similarly, the cells comprising the embryonic neural crest, a population that contributes craniofacial cartilage and bone, pigment cells of the skin and hair, neuroendocrine cells and the entire peripheral nervous system to the vertebrate embryo, must face similar challenges in choosing the correct fate. These cells go awry in a wide array of human disorders like Parkinson's disease, Hirschsprung disease, psychiatric disorders and melanoma, and comprise the focus of our efforts.
Although regulatory control acts at many levels, we focus on the roles played by cis-regulatory elements (REs) in controlling the timing, location and levels of gene activation (transcription). However, the biological relevance of non-coding sequences cannot be inferred by examination of sequence alone. Perhaps the most commonly used indicator of non-coding REs is evolutionary sequence conservation. Although conservation can uncover functionally constrained sequences, it cannot predict biological function, and regulatory function is not always confined to conserved sequences. At its simplest level, regulatory instructions are inscribed in transcription factor binding sites (TFBS) within REs. Yet, while many TFBS have been identified, TFBS combinations predictive of specific regulatory control have not yet emerged for vertebrates. We posit that motif combinations accounting for tissue-specific regulatory control can be identified in REs of genes expressed in those cell types. Our immediate goal is to begin to identify TFBS combinations that can predict REs with cell-specific biological control—a first step in developing true regulatory lexicons.
As a functional genetic laboratory, we develop and implement assays to rapidly determine the biological relevance of sequence elements within the human genome and the pathological relevance of variation therein. In recent years, we have developed a highly efficient reporter transgene system in zebrafish that can accurately evaluate the regulatory control of mammalian sequences, enabling characterization of reporter expression during development at a fraction of the cost of similar analyses in mice. We employ a range of strategies in model systems (zebrafish and mice), as well as analyses in the human population, to illuminate the genetic basis of disease processes. Our long-term objective is to use these approaches in contributing to improved diagnostic, prognostic and ultimately therapeutic strategies in patient care.
If you are interested in learning more about the work we do or would like to inquire about positions available within the lab, please contact Dr. McCallion (email@example.com).
Lab Website: Andrew McCallion Laboratory
Phenotyping (and Pathology) Core (Phenocore)
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Fisher S, Grice EA, Vinton RM, Bessling SL, McCallion AS. (2006) Conservation of RET Regulatory Function from Human to Zebrafish Without Sequence Similarity. Science. 312, 276-279.
David Gorkin, Dongwon Lee, Xylena Reed, Christopher Fletez-Brant, Stacie K. Loftus, Michael A. Beer, William J. Pavan and McCallion, A.S. Integration of ChIP-seq and Machine Learning Reveals Enhancers and a Predictive Regulatory Sequence Vocabulary in Melanocytes. Genome Res. 2012;22(11):2290-301.
Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion, A.S.‚† and Michael A. Beer‚† A method to predict the impact of regulatory variants from DNA sequence. Nature Genetics. 2015 Aug;47(8):955-61. ‚†, Co-corresponding authors
Hook PW, McClymont SA, Cannon GH, Law WD, Morton AJ, Goff LA‚, McCallion AS‚. Single-cell RNA-seq of dopaminergic neurons informs candidate gene selection for sporadic Parkinson's disease. American Journal of Human Genetics 2018 Mar 1;102(3):427-446. doi: 10.1016/j.ajhg.2018.02.001 (Cotterman Award winner - Outstanding AJHG paper of 2018)
McClymont SA, Hook PW, Briceno NJ, Reed X, Soto AI, Law WD, Ross OA, Visel A, Pennacchio L, Beer MA, McCallion AS. Parkinson-associated SNCA intronic enhancer variants revealed by open chromatin in mouse dopamine neurons. American Journal of Human Genetics - Am J Hum Genet. 2018 Dec 6;103(6):874-892. doi: 10.1016/j.ajhg.2018.10.018. (Cotterman Award winner - Outstanding AJHG paper of 2019)