Molecular Mechanism of Capacitative Calcium Entry Deficits in Familial Alzheimer’s Disease

Poster PresentationPresenilin (PS) is the catalytic subunit of the gamma-secretase which is responsible for the cleavage of amyloid precursor protein to form beta amyloid (Aβ). Mutations in PS associated with familial Alzheimer’s disease (FAD) increase the Aβ plaques formation in the brain and cause neurodegeneration. Apart from this, FAD-linked PS mutations have been demonstrated to disrupt intracellular calcium (Ca2+) regulation. Accumulating evidence suggests that Ca2+ disruption may play a proximal role in the AD pathogenesis. Mutant PS exaggerated Ca2+ release from the endoplasmic reticulum (ER). It also attenuated Ca2+ entry through the capacitative Ca2+ entry (CCE) pathway, yet, the mechanism is not fully understood. Using a human neuroblast cell line SH-SY5Y and Ca2+ imaging technique, we observed CCE deficits in FAD-linked PS1-M146L retroviral infected cell. The attenuation of CCE in PS1 mutant cells was not mediated by the down-regulation of STIM1 and Orai1 expression, the known essential molecular players in the CCE pathway. Instead, we identified a molecular interaction between PS and STIM1 proteins by immunoprecipitation. On the other hand, immunofluorescence staining showed a significant reduction in puncta formation after ER Ca2+ depleted by thapsigargin in cells infected with PS1-M146L as compared to the wild type PS1 infected cells. Taken together, our results suggest a molecular mechanism for the CCE deficits in FAD associated with PS1 mutations. The interaction of mutant PS1 with STIM1 exerts a negative impact on its oligomerization and/or its interaction with Orai1. Our results may suggest molecular targets for the development of therapeutic agents that help to treat the disease.published_or_final_versio

Myocardial Recovery in Recent Onset Dilated Cardiomyopathy: Role of CDCP1 and Cardiac Fibrosis

Background: Dilated cardiomyopathy (DCM) is a major cause of heart failure and carries a high mortality rate. Myocardial recovery in DCM-related heart failure patients is highly variable, with some patients having little or no response to standard drug therapy. A genome-wide association study may agnostically identify biomarkers and provide novel insight into the biology of myocardial recovery in DCM. Methods: A genome-wide association study for change in left ventricular ejection fraction was performed in 686 White subjects with recent-onset DCM who received standard pharmacotherapy. Genome-wide association study signals were subsequently functionally validated and studied in relevant cellular models to understand molecular mechanisms that may have contributed to the change in left ventricular ejection fraction. Results: The genome-wide association study identified a highly suggestive locus that mapped to the 5'-flanking region of the CDCP1 (CUB domain containing protein 1) gene (rs6773435; P=7.12×10-7). The variant allele was associated with improved cardiac function and decreased CDCP1 transcription. CDCP1 expression was significantly upregulated in human cardiac fibroblasts (HCFs) in response to the PDGF (platelet-derived growth factor) signaling, and knockdown of CDCP1 significantly repressed HCF proliferation and decreased AKT phosphorylation. Transcriptomic profiling after CDCP1 knockdown in HCFs supported the conclusion that CDCP1 regulates HCF proliferation and mitosis. In addition, CDCP1 knockdown in HCFs resulted in significantly decreased expression of soluble ST2, a prognostic biomarker for heart failure and inductor of cardiac fibrosis. Conclusions: CDCP1 may play an important role in myocardial recovery in recent-onset DCM and mediates its effect primarily by attenuating cardiac fibrosis

Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication

We set out to determine whether machine learning-based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine-learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P 69% (P <= 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers

Epigenetic regulation of GABA catabolism in iPSC-derived neurons: The molecular links between FGF21 and histone methylation

Objective: Fibroblast growth factor 21 (FGF21) analogs have been tested as potential therapeutics for substance use disorders. Prior research suggests that FGF21 administration might affect alcohol consumption and reward behaviors. Our recent report showed that plasma FGF21 levels were positively correlated with alcohol use in patients with alcohol use disorder (AUD). FGF21 has a short half-life (0.5–2 h) and crosses the blood–brain barrier. Therefore, we set out to identify molecular mechanisms for both the naïve form of FGF21 and a long-acting FGF21 molecule (PF-05231023) in induced pluripotent stem cell (iPSC)-derived forebrain neurons. Methods: We performed RNA-seq in iPSC-derived forebrain neurons treated with naïve FGF21 or PF-05231023 at physiologically relevant concentrations. We obtained plasma levels of FGF21 and GABA from our previous AUD clinical trial (n = 442). We performed ELISA for FGF21 in both iPSC-derived forebrain neurons and forebrain organoids. We determined protein interactions using co-immunoprecipitation. Finally, we applied ChIP assays to confirm the occupancy of REST, EZH2 and H3K27me3 by FGF21 using iPSC-derived forebrain neurons with and without drug exposure. Results: We identified 4701 and 1956 differentially expressed genes in response to naïve FGF21 or PF-05231023, respectively (FDR < 0.05). Notably, 974 differentially expressed genes overlapped between treatment with naïve FGF21 and PF-05231023. REST was the most important upstream regulator of differentially expressed genes. The GABAergic synapse pathway was the most significant pathway identified using the overlapping genes. We also observed a significant positive correlation between plasma FGF21 and GABA concentrations in AUD patients. In parallel, FGF21 and PF-05231023 significantly induced GABA levels in iPSC-derived neurons. Finally, functional genomics studies showed a drug-dependent occupancy of REST, EZH2, and H3K27me3 in the promoter regions of genes involved in GABA catabolism which resulted in transcriptional repression. Conclusions: Our results highlight a significant role in the epigenetic regulation of genes involved in GABA catabolism related to FGF21 action.(The ClinicalTrials.gov Identifier: NCT00662571

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming

Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.

We set out to determine whether machine learning-based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine-learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P  69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD17). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD17 ≤ 7) and those who gained no meaningful benefits (&lt;30% reduction in HRSD17). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

10.1038/s41386-020-00943-xNeuropsychopharmacology4671272-128