Browsing by Author "Kurian, S. M."

Now showing 1 - 6 of 6
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    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 Medicine
    Short-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).
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    Discovery and validation of blood biomarkers for suicidality
    (Springer Nature, 2013) Le-Niculescu, H.; Levey, D. F.; Ayalew, M.; Palmer, L.; Gavrin, L. M.; Jain, N.; Winiger, E.; Bhosrekar, S.; Shankar, G.; Radel, M.; Bellanger, E.; Duckworth, H.; Olesek, K.; Vergo, J.; Schweitzer, R.; Yard, M.; Ballew, A.; Shekhar, A.; Sandusky, G. E.; Schork, N. J.; Kurian, S. M.; Salomon, D. R.; Niculescu, A. B., III; Psychiatry, School of Medicine
    Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case-case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence-genetic and functional genomic data-as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner's office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1-acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past hospitalizations with suicidality, in a live cohort of bipolar disorder subjects, and exhibited a similar but weaker pattern in a live cohort of psychosis (schizophrenia/schizoaffective disorder) subjects. Three other (phosphatase and tensin homolog (PTEN), myristoylated alanine-rich protein kinase C substrate (MARCKS), and mitogen-activated protein kinase kinase kinase 3 (MAP3K3)) of the six biomarkers that survived Bonferroni correction showed similar but weaker effects. Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 might be not only state biomarkers but trait biomarkers as well. Fifth, we show how a multi-dimensional approach using SAT1 blood expression levels and two simple visual-analog scales for anxiety and mood enhances predictions of future hospitalizations for suicidality in the bipolar cohort (receiver-operating characteristic curve with area under the curve of 0.813). Of note, this simple approach does not directly ask about SI, which some individuals may deny or choose not to share with clinicians. Lastly, we conducted bioinformatic analyses to identify biological pathways, mechanisms and medication targets. Overall, suicidality may be underlined, at least in part, by biological mechanisms related to stress, inflammation and apoptosis.
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    Next-generation precision medicine for suicidality prevention
    (Springer Nature, 2024-09-06) Bhagar, R.; Gill, S. S.; Le-Niculescu, H.; Yin, C.; Roseberry, K.; Mullen, J.; Schmitz, M.; Paul, E.; Cooke, J.; Tracy, C.; Tracy, Z.; Gettelfinger, A. S.; Battles, D.; Yard, M.; Sandusky, G.; Shekhar, A.; Kurian, S. M.; Bogdan, P.; Niculescu, A. B.; Psychiatry, School of Medicine
    Suicidality remains a clear and present danger in society in general, and for mental health patients in particular. Lack of widespread use of objective and/or quantitative information has hampered treatment and prevention efforts. Suicidality is a spectrum of severity from vague thoughts that life is not worth living, to ideation, plans, attempts, and completion. Blood biomarkers that track suicidality risk provide a window into the biology of suicidality, as well as could help with assessment and treatment. Previous studies by us were positive. Here we describe new studies we conducted transdiagnostically in psychiatric patients, starting with the whole genome, to expand the identification, prioritization, validation and testing of blood gene expression biomarkers for suicidality, using a multiple independent cohorts design. We found new as well as previously known biomarkers that were predictive of high suicidality states, and of future psychiatric hospitalizations related to them, using cross-sectional and longitudinal approaches. The overall top increased in expression biomarker was SLC6A4, the serotonin transporter. The top decreased biomarker was TINF2, a gene whose mutations result in very short telomeres. The top biological pathways were related to apoptosis. The top upstream regulator was prednisolone. Taken together, our data supports the possibility that biologically, suicidality is an extreme stress-driven form of active aging/death. Consistent with that, the top subtypes of suicidality identified by us just based on clinical measures had high stress and high anxiety. Top therapeutic matches overall were lithium, clozapine and ketamine, with lithium stronger in females and clozapine stronger in males. Drug repurposing bioinformatic analyses identified the potential of renin-angiotensin system modulators and of cyclooxygenase inhibitors. Additionally, we show how patient reports for doctors would look based on blood biomarkers testing, personalized by gender. We also integrated with the blood biomarker testing social determinants and psychological measures (CFI-S, suicidal ideation), showing synergy. Lastly, we compared that to machine learning approaches, to optimize predictive ability and identify key features. We propose that our findings and comprehensive approach can have transformative clinical utility.
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    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 Medicine
    Anxiety 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.
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    Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs
    (Springer Nature, 2019-04) Niculescu, A. B.; Le-Niculescu, H.; Levey, D. F.; Roseberry, K.; Soe, K. C.; Rogers, J.; Khan, F.; Jones, T.; Judd, S.; McCormick, M.A.; Wessel, A. R.; Williams, A.; Kurian, S. M.; White, F. A.; Psychiatry, School of Medicine
    We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful within-subject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic.
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    Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs
    (Springer Nature, 2019-03-12) Le-Niculescu, H.; Roseberry, K.; Levey, D. F.; Rogers, J.; Kosary, K.; Prabha, S.; Jones, T.; Judd, S.; McCormick, M. A.; Wessel, A. R.; Williams, A.; Phalen, P. L.; Mamdani, F.; Sequeira, A.; Kurian, S. M.; Niculescu, A. B.; Department of Psychiatry, School of Medicine
    The biological fingerprint of environmental adversity may be key to understanding health and disease, as it encompasses the damage induced as well as the compensatory reactions of the organism. Metabolic and hormonal changes may be an informative but incomplete window into the underlying biology. We endeavored to identify objective blood gene expression biomarkers for psychological stress, a subjective sensation with biological roots. To quantify the stress perception at a particular moment in time, we used a simple visual analog scale for life stress in psychiatric patients, a high-risk group. Then, using a stepwise discovery, prioritization, validation, and testing in independent cohort design, we were successful in identifying gene expression biomarkers that were predictive of high-stress states and of future psychiatric hospitalizations related to stress, more so when personalized by gender and diagnosis. One of the top biomarkers that survived discovery, prioritization, validation, and testing was FKBP5, a well-known gene involved in stress response, which serves as a de facto reassuring positive control. We also compared our biomarker findings with telomere length (TL), another well-established biological marker of psychological stress and show that newly identified predictive biomarkers such as NUB1, APOL3, MAD1L1, or NKTR are comparable or better state or trait predictors of stress than TL or FKBP5. Over half of the top predictive biomarkers for stress also had prior evidence of involvement in suicide, and the majority of them had evidence in other psychiatric disorders, providing a molecular underpinning for the effects of stress in those disorders. Some of the biomarkers are targets of existing drugs, of potential utility in patient stratification, and pharmacogenomics approaches. Based on our studies and analyses, the biomarkers with the best overall convergent functional evidence (CFE) for involvement in stress were FKBP5, DDX6, B2M, LAIR1, RTN4, and NUB1. Moreover, the biomarker gene expression signatures yielded leads for possible new drug candidates and natural compounds upon bioinformatics drug repurposing analyses, such as calcium folinate and betulin. Our work may lead to improved diagnosis and treatment for stress disorders such as PTSD, that result in decreased quality of life and adverse outcomes, including addictions, violence, and suicide.