Gregory I. Elmer, PhD

Adolescent Trauma. Trauma, stress, and the subsequent development of mood disorders are pervasive themes in mental illness. There is a remarkably high incidence of childhood trauma in adult depression, generalized anxiety, obsessive-compulsive disorder, schizophrenia, and drug abuse suggesting that childhood trauma plays a causal role in increased liability to these disorders. Childhood trauma can dramatically alter treatment trajectories even within a diagnostic category. Unfortunately, the neurobiology underlying the psychiatric consequences of early trauma is poorly understood. Tangible treatment strategies for trauma-altered mental illness are even further removed from successful development. A better understanding of the neurobiological impact of childhood trauma could improve our discovery efforts for effectively tailored treatment interventions. The foundation our current work is the proposal that trauma impacts core neural regions of interest that in turn impact neurocircuitry involved in a range of behavioral constructs that cross diagnostic categories. We propose that a newly characterized region, the rostral medial tegmentum (RMTg), is a central component of a critical circuit mediating the consequences of childhood trauma. The combination of roles hypothesized to involve the RMTg and associated regions place a RMTg-centric circuit in an ideal position to mediate core symptom domains altered by trauma exposure (threat, fear, anxiety, avoidance, sleep, negative affect and cognition).

Reward and Anhedonia. Extensive formal and informal discussions with practicing research psychiatrists directed the focus of our work on addiction and schizophrenia toward the neural circuitry of reward (approach motivation) and its opposite pole, anhedonia. As part of work conducted in a translational grant focused on reward, we identified the habenula as a brain region ideally situated to influence reward processing. We used autoshaping as an assay that would probe important aspect of reward processing, temporal difference error signaling and approach motivation.  We characterized the role of dopamine (using 1^st and 2^nd generation antipsychotic) in autoshaping and reported a significant effect on salience attribution but not the strict predictive power of stimuli associated with reward (Danna & Elmer, '10).  In a circuitry oriented manuscript, lesion and discretely timed electrical stimulation of the habenula circuit was used to discover, for the first time, that the habenula is intimately involved in governing the attribution of incentive value to previously neutral cues. This finding is an important contribution to the original reports of the habenula's role in processing temporal difference errors and suggests a broader role in disease states in which approach motivation and anhenonia are key domains (addiction, depression and schizophrenia) (Danna et al, Frontiers in Human Neuroscience, 2013).  The work garnered a F1000 Prime citation. Our current work investigates the role of habenula associate circuitry in the expression of depression-like behavior. Several publications are in preparation to describe habunula-RMTg-VTA circuitry in learned helplessness, opioid withdrawal and pain.  

Addiction. Not long ago (to some…) the addiction field was reluctant to believe genetics played a significant role in determining susceptibility to drug addiction (1980’s). My career began with a series of experiments designed to break this perspective. Operant drug self-administration (SA) in carefully chosen genotypes demonstrated a clear quantitative and qualitative impact on drug-taking behavior (Elmer et al., '86; '87; '88; '90). These papers shook the prevailing mindset and helped establish evidence for strong genetic influence just prior to the wave of publications extolling the potential of genetically engineered animal models. We then began our long-standing investigation into genetic factors that contribute to pain, opioid potency, tolerance and addiction liability. We discovered an important relationship between that baseline pain sensitivity and analgesic potency (Elmer et al., Pain, '98) and revealed a genetic distinction between the analgesic potency and reinforcing properties of opioids (Ambrosio et al., '93; Elmer et al., '95a,b;'96).  To further these findings, our lab established proof-of-principle studies for a strategy designed to associate large-scale gene expression profiles with behavior (highlighted article in J Neuroscience, Letwin et al., '06, Reiner et al., Bioinformatics, '07). Among several findings, we were the first to highlight the potential involvement of miRNAs in analgesic tolerance using in vivo methodology (Tapocik et al., J Neuroscience,'09). and identify changes in gene expression uniquely associated with the reinforcing properties of morphine (Tapocik et al, Addiction Biology, '13).

Drug Discovery. The appreciation of data-mining’s power for discovery and the realized complexity of neuropharmacological response to drugs helped stimulate our development of a truly novel behavioral assay, termed Pattern Array. The foundation for this approach was methodically constructed by establishing highly heritable (and replicable) building blocks of behavior (Kafkafi et al., PNAS, '05; Kafkafi et al., '03a;'03b,'05a;'05b).  We then designed an algorithm to categorize combinations of these building blocks into >70,000 units amenable to data-mining (a "behavioral chip"). In a disease model setting, Pattern Array detected the onset of ALS in a genetic animal model significantly earlier than any standard behavioral measure (Kafkafi et al., '08). In the psychiatric drug discovery setting, we characterized more than 60 drugs across 6 drugs classes to develop a psychopharmacological classification model. The model provides a single behavioral assay capable of predicting, with a high rate of success, the therapeutic application of a test drug among multiple, therapeutically-relevant classes. The work has led to a UMB-sponsored patent application and two substantial publications (Kafkafi et al, Neuropsychopharmacology, '09; Psychopharmacology, '13). The model is currently being used as an in vivo screening procedure to identify the therapeutic potential of novel compounds and discover new uses for existing compounds (repurposing). In addition, very promising ongoing studies in disease models (depression, PTSD) utilize Pattern Array as a highly sensitive detector of altered behavior and a predictor of disease-related phenotypes.

• NIH Molecular Neurogenetics (MNG) Study Section (09/2012, 02/2013) 
• NIH Biobehavioral Regulation, Learning and Ethology (BRLE) Study Section (06/2013)
• NIH Biophysical and Physiological Neuroscience (F03B) study section (02/2011)         
• Standing Member, Merit Review Subcommittee for Neurobiology–A, Veterans Affairs (May 2004 - 2007)
• Additional: Participated in 4 NIH Special Emphasis Study sections, 3 NIH Center reviews, 2 NIH Neuropharmacology Study Sections (NIDA-A, ad-hoc), 4 NIH Cutting Edge Basic Research Awards, 1 F31 Review and 1 VA Program Review.
• Panel Member, Chronic Pain Management and Opioid Abuse: We Need a Fix, “UMSOM Cannabinoid Research Summit”, November 2016. The Symposium was designed to present current knowledge and future planning in biomedical research, public health, and government policy to address the prescription opioid abuse epidemic.
• Panel Member, NIDA Workshop on “Addiction, Microarrays, and Gene Discovery”, May 2007. The workshop was designed to bring researchers together to share their findings and to discuss obstacles and opportunities for using microarrays to identify genes involved in addiction