Browsing by Author "Niculescu, A.B."

Now showing 1 - 3 of 3
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    Polyphenic risk score shows robust predictive ability for long-term future suicidality
    (Springer, 2022) Cheng, M.; Roseberry, K.; Choi, Y.; Quast, L.; Gaines, M.; Sandusky, G.; Kline, J.A.; Bogdan, P.; Niculescu, A.B.; Psychiatry, School of Medicine
    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.
  • Thumbnail Image
    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 Medicine
    Mood 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.