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Browsing by Author "Shekhar, A."
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Item Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs(Nature Publishing Group, 2019-12-02) Niculescu, A. B.; Le-Niculescu, H.; Roseberry, K.; Wang, S.; Hart, J.; Kaur, A.; Robertson, H.; Jones, T.; Strasburger, A.; Williams, A.; Kurian, S. M.; Lamb, B.; Shekhar, A.; Lahiri, D. K.; Saykin, A. J.; Psychiatry, School of MedicineShort-term memory dysfunction is a key early feature of Alzheimer’s disease (AD). Psychiatric patients may be at higher risk for memory dysfunction and subsequent AD due to the negative effects of stress and depression on the brain. We carried out longitudinal within-subject studies in male and female psychiatric patients to discover blood gene expression biomarkers that track short term memory as measured by the retention measure in the Hopkins Verbal Learning Test. These biomarkers were subsequently prioritized with a convergent functional genomics approach using previous evidence in the field implicating them in AD. The top candidate biomarkers were then tested in an independent cohort for ability to predict state short-term memory, and trait future positive neuropsychological testing for cognitive impairment. The best overall evidence was for a series of new, as well as some previously known genes, which are now newly shown to have functional evidence in humans as blood biomarkers: RAB7A, NPC2, TGFB1, GAP43, ARSB, PER1, GUSB, and MAPT. Additional top blood biomarkers include GSK3B, PTGS2, APOE, BACE1, PSEN1, and TREM2, well known genes implicated in AD by previous brain and genetic studies, in humans and animal models, which serve as reassuring de facto positive controls for our whole-genome gene expression discovery approach. Biological pathway analyses implicate LXR/RXR activation, neuroinflammation, atherosclerosis signaling, and amyloid processing. Co-directionality of expression data provide new mechanistic insights that are consistent with a compensatory/scarring scenario for brain pathological changes. A majority of top biomarkers also have evidence for involvement in other psychiatric disorders, particularly stress, providing a molecular basis for clinical co-morbidity and for stress as an early precipitant/risk factor. Some of them are modulated by existing drugs, such as antidepressants, lithium and omega-3 fatty acids. Other drug and nutraceutical leads were identified through bioinformatic drug repurposing analyses (such as pioglitazone, levonorgestrel, salsolidine, ginkgolide A, and icariin). Our work contributes to the overall pathophysiological understanding of memory disorders and AD. It also opens new avenues for precision medicine- diagnostics (assement of risk) as well as early treatment (pharmacogenomically informed, personalized, and preventive).Item Convergent Functional Genomics of Schizophrenia: From Comprehensive Understanding to Genetic Risk Prediction(Office of the Vice Chancellor for Research, 2012-04-13) Ayalew, M.; Le-Niculescu, H.; Levey, D.F.; Jain, N.; Changala, B.; Patel, S.D.; Winiger, E.; Breier, A.; Shekhar, A.; Amdur, R.; Koller, D.; Nurnberger, J.I.; Corvin, A.; Geyer, M.; Tsuang, M.T.; Salomon, D.; Schork, N.; Fanous, A.H.; O’ Donovan, M.C.; Niculescu, A.B.We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study (GWAS) data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein coupled receptor signaling and cAMP- mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data is consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European-American (EA) and one African-American (AA), increasing overlap, reproducibility and consistency of findings from SNPs to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Lastly, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics, and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.Item Guanfacine treatment improves ADHD phenotypes of impulsivity and hyperactivity in a neurofibromatosis type 1 mouse model(BMC, 2020-01-15) Lukkes, J. L.; Drozd, H. P.; Fitz, S. D.; Molosh, A. I.; Clapp, D. W.; Shekhar, A.; Psychiatry, School of MedicineBACKGROUND: Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder with a mutation in one copy of the neurofibromin gene (NF1+/-). Even though approximately 40-60% of children with NF1 meet the criteria for attention deficit hyperactivity disorder (ADHD), very few preclinical studies, if any, have investigated alterations in impulsivity and risk-taking behavior. Mice with deletion of a single NF1 gene (Nf1+/-) recapitulate many of the phenotypes of NF1 patients. METHODS: We compared wild-type (WT) and Nf1+/- mouse strains to investigate differences in impulsivity and hyperactivity using the delay discounting task (DDT), cliff avoidance reaction (CAR) test, and open field. We also investigated whether treatment with the clinically effective alpha-2A adrenergic receptor agonist, guanfacine (0.3 mg/kg, i.p.), would reverse deficits observed in behavioral inhibition. RESULTS: Nf1+/- mice chose a higher percentage of smaller rewards when both 10- and 20-s delays were administered compared to WT mice, suggesting Nf1+/- mice are more impulsive. When treated with guanfacine (0.3 mg/kg, i.p.), Nf1+/- mice exhibited decreased impulsive choice by waiting for the larger, delayed reward. Nf1+/- mice also exhibited deficits in behavioral inhibition compared to WT mice in the CAR test by repetitively entering the outer edge of the platform where they risk falling. Treatment with guanfacine ameliorated these deficits. In addition, Nf1+/- mice exhibited hyperactivity as increased distance was traveled compared to WT controls in the open field. This hyperactivity in Nf1+/- mice was reduced with guanfacine pre-treatment. CONCLUSIONS: Overall, our study confirms that Nf1+/- mice exhibit deficits in behavioral inhibition in multiple contexts, a key feature of ADHD, and can be used as a model system to identify alterations in neural circuitry associated with symptoms of ADHD in children with NF1.Item Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs(Springer Nature, 2021-07) Le-Niculescu, H.; Roseberry, K.; Gill, S.S.; Levey, D.F.; Phalen, P.L.; Mullen, J.; Williams, A.; Bhairo, S.; Voegtline, T.; Davis, H.; Shekhar, A.; Kurian, S.M.; Niculescu, A.B.; Psychiatry, School of MedicineMood disorders (depression, bipolar disorders) are prevalent and disabling. They are also highly co-morbid with other psychiatric disorders. Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level. Early pilot studies by us to discover blood biomarkers for mood state were promising [1], and validated by others [2]. Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality) [3-6]. These studies showed good reproducibility with subsequent independent genetic studies [7]. More recently, we have conducted such studies also for pain [8], for stress disorders [9], and for memory/Alzheimer's Disease [10]. We endeavored to use a similar comprehensive approach to identify more definitive biomarkers for mood disorders, that are transdiagnostic, by studying mood in psychiatric disorders patients. First, we used a longitudinal within-subject design and whole-genome gene expression approach to discover biomarkers which track mood state in subjects who had diametric changes in mood state from low to high, from visit to visit, as measured by a simple visual analog scale that we had previously developed (SMS-7). Second, we prioritized these biomarkers using a convergent functional genomics (CFG) approach encompassing in a comprehensive fashion prior published evidence in the field. Third, we validated the biomarkers in an independent cohort of subjects with clinically severe depression (as measured by Hamilton Depression Scale, (HAMD)) and with clinically severe mania (as measured by the Young Mania Rating Scale (YMRS)). Adding the scores from the first three steps into an overall convergent functional evidence (CFE) score, we ended up with 26 top candidate blood gene expression biomarkers that had a CFE score as good as or better than SLC6A4, an empirical finding which we used as a de facto positive control and cutoff. Notably, there was among them an enrichment in genes involved in circadian mechanisms. We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth. Fourth, we tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). We conducted our analyses across all patients, as well as personalized by gender and diagnosis, showing increased accuracy with the personalized approach, particularly in women. Again, using SLC6A4 as the cutoff, twelve top biomarkers had the strongest overall evidence for tracking and predicting depression after all four steps: NRG1, DOCK10, GLS, PRPS1, TMEM161B, GLO1, FANCF, HNRNPDL, CD47, OLFM1, SMAD7, and SLC6A4. Of them, six had the strongest overall evidence for tracking and predicting both depression and mania, hence bipolar mood disorders. There were also two biomarkers (RLP3 and SLC6A4) with the strongest overall evidence for mania. These panels of biomarkers have practical implications for distinguishing between depression and bipolar disorder. Next, we evaluated the evidence for our top biomarkers being targets of existing psychiatric drugs, which permits matching patients to medications in a targeted fashion, and the measuring of response to treatment. We also used the biomarker signatures to bioinformatically identify new/repurposed candidate drugs. Top drugs of interest as potential new antidepressants were pindolol, ciprofibrate, pioglitazone and adiphenine, as well as the natural compounds asiaticoside and chlorogenic acid. The last 3 had also been identified by our previous suicidality studies. Finally, we provide an example of how a report to doctors would look for a patient with depression, based on the panel of top biomarkers (12 for depression and bipolar, one for mania), with an objective depression score, risk for future depression, and risk for bipolar switching, as well as personalized lists of targeted prioritized existing psychiatric medications and new potential medications. Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.Item PSD95 and nNOS interaction as a novel molecular target to modulate conditioned fear: relevance to PTSD(Springer Nature, 2018-08-14) Li, L.P.; Dustrude, E.T.; Haulcomb, M.M.; Abreu, A.R.; Fitz, S.D.; Johnson, P.L.; Thakur, G.A.; Molosh, A.I.; Lai, Y.; Shekhar, A.; Psychiatry, School of MedicineStimulation of N-methyl-D-aspartic acid receptors (NMDARs) and the resulting increase of nitric oxide (NO) production are critical for fear memory formation. Following NMDAR activation, efficient production of NO requires linking the 95 kDa postsynaptic density protein (PSD95), a scaffolding protein to neuronal nitric oxide synthase (nNOS). A variety of previously studied NMDAR antagonists and NOS inhibitors can disrupt fear conditioning, but they also affect many other CNS functions such as motor activity, anxiety, and learning. We hypothesized that disrupting nNOS and PSD95 interaction in the amygdala, a critical site for fear memory formation, will reduce conditioned fear. Our results show that systemic treatment with ZL006, a compound that disrupts PSD95/nNOS binding, attenuates fear memory compared to its inactive isomer ZL007. Co-immunoprecipitation after fear conditioning showed a robust increase in the amygdala PSD95/nNOS binding, which was blocked by systemic pre-administration of ZL006. Treatment of amygdala slices with ZL006 also impaired long-term potentiation (LTP), a cellular signature of synaptic plasticity. Direct intra-amygdala infusion of ZL006 also attenuated conditioned fear. Finally, unlike NMDAR antagonist MK-801, ZL006 does not affect locomotion, social interaction, object recognition memory, and spatial memory. These findings support the hypothesis that disrupting the PSD95/nNOS interaction downstream of NMDARs selectively reduces fear memory, and highlights PSD95/nNOS interaction as a novel target for fear-related disorders, such as posttraumatic stress disorder.Item Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs(Springer Nature, 2023) Roseberry, K.; Le-Niculescu, H.; Levey, D. F.; Bhagar, R.; Soe, K.; Rogers, J.; Palkowitz, S.; Pina, N.; Anastasiadis, W. A.; Gill, S. S.; Kurian, S. M.; Shekhar, A.; Niculescu, A. B.; Psychiatry, School of MedicineAnxiety disorders are increasingly prevalent, affect people's ability to do things, and decrease quality of life. Due to lack of objective tests, they are underdiagnosed and sub-optimally treated, resulting in adverse life events and/or addictions. We endeavored to discover blood biomarkers for anxiety, using a four-step approach. First, we used a longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states. Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as a valproate, omega-3 fatty acids, fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is a urgent need for more precise and personalized approaches like the one we developed.