Browsing by Author "Niculescu, Alexander"

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    MicroRNA-298 reduces levels of human amyloid-β precursor protein (APP), β-site APP-converting enzyme 1 (BACE1) and specific tau protein moieties
    (Springer Nature, 2021) Chopra, Nipun; Wang, Ruizhi; Maloney, Bryan; Nho, Kwangsik; Beck, John S.; Pourshafie, Naemeh; Niculescu, Alexander; Saykin, Andrew J.; Rinaldi, Carlo; Counts, Scott E.; Lahiri, Debomoy K.; Psychiatry, School of Medicine
    Alzheimer's disease (AD) is the most common age-related form of dementia, associated with deposition of intracellular neuronal tangles consisting primarily of hyperphosphorylated microtubule-associated protein tau (p-tau) and extracellular plaques primarily comprising amyloid- β (Aβ) peptide. The p-tau tangle unit is a posttranslational modification of normal tau protein. Aβ is a neurotoxic peptide excised from the amyloid-β precursor protein (APP) by β-site APP-cleaving enzyme 1 (BACE1) and the γ-secretase complex. MicroRNAs (miRNAs) are short, single-stranded RNAs that modulate protein expression as part of the RNA-induced silencing complex (RISC). We identified miR-298 as a repressor of APP, BACE1, and the two primary forms of Aβ (Aβ40 and Aβ42) in a primary human cell culture model. Further, we discovered a novel effect of miR-298 on posttranslational levels of two specific tau moieties. Notably, miR-298 significantly reduced levels of ~55 and 50 kDa forms of the tau protein without significant alterations of total tau or other forms. In vivo overexpression of human miR-298 resulted in nonsignificant reduction of APP, BACE1, and tau in mice. Moreover, we identified two miR-298 SNPs associated with higher cerebrospinal fluid (CSF) p-tau and lower CSF Aβ42 levels in a cohort of human AD patients. Finally, levels of miR-298 varied in postmortem human temporal lobe between AD patients and age-matched non-AD controls. Our results suggest that miR-298 may be a suitable target for AD therapy.
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    Polyphenic risk score shows robust predictive ability for long-term future suicidality
    (Discover Mental Health, 2022-06-13) Cheng, M; Roseberry, Kyle; Choi, Y; Quast, L; Gaines, Madelynn; Sandusky, George; Kline, JA; Bogdan, Paul; Niculescu, Alexander
    Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient's risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.
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    Towards personalized medicine in psychiatry : focus on suicide
    (2016-12-13) Levey, Daniel F.; Saykin, Andrew J.; Breier, Alan F.; Oxford, Gerry S.; Shekhar, Anantha; Niculescu, Alexander
    Psychiatric disorders cost an estimated $273 billion annually. This cost comes largely in the form of lost income and the chronic disability that often strikes people when they are young and can last decades. While the monetary costs are quantifiable, the suffering of each individual patient is no less vital. As many as 1 in 5 persons diagnosed with mental illness will commit suicide, a contributing factor in suicide being the second leading cause of death of people age 15-34. There is a critical need to find better ways to identify and help those who are at risk. Understanding mental illness and improving treatment has been difficult due to the heterogeneous and complex etiology of these illnesses. A significant challenge for the field is integrating findings from diverse laboratories all over the world contributing to the ever expanding literature and translating them into actionable treatment. Our lab employs a convergent functional genomics approach which incorporates multiple independent lines of evidence provided by genetic and functional genomic data published in the primary literature as a Bayesian strategy to prioritize experimental findings. Heritability and genetics clearly play an important role in psychiatric disorders. We looked at schizophrenia and alcoholism in separate case-control analyses in order to identify and prioritize genes related to these disorders. We were able to reproduce these findings in additional independent cohorts using polygenic risk scores. We found overlap in these disorders, and identified possible underlying biological processes. Genetics play an important role in identifying clinical risk, particularly at the population level. At the level of the individual, gene expression may provide more proximal association to disease state, assimilating environmental, genetic, as well as epigenetic influence. We undertook N of 1 analyses in a longitudinally followed cohort of psychiatric participants, identifying genes which change in expression tracking an individual’s change in suicidal ideation. These genes were able to predict suicidal behavior in independent cohorts. When combined with simple clinical instruments these predictions were improved. This work shows how multi level integration of genetic, gene expression, and clinical data could be used to enable precision medicine in psychiatry.