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Biology subjects

Weinshilboum, R. M.

Publications and source records attributed to Weinshilboum, R. M..

11 recordsLinked to original sources

Alcohol Use Disorder Associated Gene FNDC4 Alters Glutamatergic and GABAergic Neurogenesis

Large-cohort genome-wide association studies (GWAS) for alcohol use disorder (AUD) and AUD-related phenotypes have identified more than one hundred genetic loci. Functional study of those GWAS-identified loci might represent an important step toward understanding AUD pathophysiology. We found that genetic loci which are splicing quantitative trait loci (sQTLs) for the fibronectin III domain containing 4 (FNDC4) gene in the brain were identified by GWAS for both AUD drug treatment outcomes and AUD risk. However, FNDC4 function in the brain and how it might contribute to AUD pathophysiology remain unknown. In the present study, we characterized GWAS locus-associated FNDC4 splice isoforms, studies which suggested that FNDC4 alternative splicing results in loss-of-function for FNDC4. We also investigated FNDC4 function using CRISPR/cas9 gene editing, and the creation of human induced pluripotent stem cell (iPSC)-derived neural organoids joined with single-nucleus RNA sequencing. We observed that knock-out (KO) of FNDC4 resulted in a striking shift in the relative proportions of glutamatergic and GABAergic neurons in iPSC-derived neural organoids, suggesting a possible important role for FNDC4 in neurogenesis. We also explored potential mechanism(s) of FNDC4-dependent neurogenesis with results that suggested a role for FNDC4 in mediating neural cell-cell interaction. In summary, this series of experiments indicates that FNDC4 plays a role in regulating cerebral cortical neurogenesis in the brain. This regulation may contribute to the response to AUD pharmacotherapy as well as the effects of alcohol on the brain.

genomics↗

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing Deep Neural Networks and incorporating the SHapley Additive exPlanations (SHAP) algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas (TCGA) data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high Area Under Curve (AUC) scores-- 0.98{+/-}0.02 for lung cancer subtype differentiation, 0.83{+/-}0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient, and interpretable approach that contributes to a deeper understanding of disease mechanisms.

bioinformatics↗

Identification of transcriptional network disruptions in drug-resistant prostate cancer with TraRe

Metastatic castration-resistant prostate cancer (mCRPC) presents very low survival rates due to lack of response or acquired resistance to the available therapies. To date no molecular mechanisms of resistance have been identified, pointing out their complex dynamics. To identify key genes and processes associated with phenotypically-driven regulatory differences, we developed TraRe, a computational method that provides a three-tier analysis: i) at the network level, inferring differentially regulated modules; ii) at the regulon level, identifying regulatory relationships linked to phenotypic differences; and iii) at the single gene level, identifying TFs consistently linked to rewired modules. We applied TraRe (available in Bioconductor with full documentation) to transcriptomic data from 46 mCRPC patients with Abiraterone-response clinical data and uncovered abrogated immune response regulatory modules that showed strong differential regulation in Abi-resistant patients. These modules were replicated in an independent mCRPC study. Further, we experimentally validated key rewiring predictions and their associated transcription factors. Among them, ELK3, MXD1, and MYB were found to have a differential role in cell survival for Abi-response-specific settings. Moreover, we identified the role of ELK3 in cell migration capacity, which could have direct impact on mCRPC. Collectively, these findings shed light on the underlying regulatory mechanisms driving abiraterone response.

bioinformatics↗

Glucocorticoids Mediate Transcriptome-wide Alternative Polyadenylation: Mechanisms and Functional Implications

Alternative polyadenylation (APA) is a common genetic regulatory mechanism that generates distinct 3' ends for RNA transcripts. Changes in APA have been associated with multiple biological processes and disease phenotypes. However, the role of hormones and their drug analogs in APA remains largely unknown. In this study, we investigated transcriptome-wide the impact of glucocorticoids on APA in 30 human B-lymphoblastoid cell lines. We found that glucocorticoids could regulate APA of a subset of genes, possibly by changing the expression of 142 RNA-binding proteins, some with known APA-regulating properties. Interestingly, genes with glucocorticoid-mediated APA were enriched in viral translation-related pathways, while genes with glucocorticoid-mediated expression were enriched in interferon and interleukin pathways, suggesting that glucocorticoid-mediated APA might result in functional consequences distinct from gene expression. Glucocorticoid-mediated APA was also cell-type-specific, suggesting an action of glucocorticoids that may be unique to immune regulation. We also observed evidence for genotype-dependent glucocorticoid-mediated APA, providing potential functional mechanisms for a series of common genetic variants that had previously been associated with immune disorders, but without a clear mechanism. In summary, this study reports a series of novel observations regarding the impact of glucocorticoids on APA, suggesting novel avenues for mechanistic studies of hormonal biology.

genomics↗

Glucocorticoids Unmask Silent Non-Coding Genetic Risk Variants for Common Diseases

Understanding the function of non-coding genetic variants represents a formidable challenge for biomedicine. We previously identified genetic variants that influence gene expression only after exposure to a hormone or drug. Using glucocorticoid signaling as a model system, we have now demonstrated, in a genome-wide manner, that exposure to glucocorticoids triggered disease risk variants with previously unclear function to influence the expression of genes involved in autoimmunity, metabolic and mood disorders, osteoporosis and cancer. Integrating a series of pharmacogenomic and pharmacoepigenomic datasets, we identified the cis-regulatory elements and 3-dimensional interactions underlying the ligand-dependent associations between those genetic variants and distant risk genes. These observations increase our understanding of mechanisms of non-coding genetic variant-chemical environment interactions and advance the fine-mapping of disease risk and pharmacogenomic loci.

genomics↗

Alterations in Acylcarnitines, Amines, and Lipids Inform about 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- and long-chain acylcarnitines, suggesting an SSRI effect on {beta}-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[&le;]7) and those who gained no meaningful benefits (<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 {beta}-oxidation - and lipid membrane remodeling may play roles in SSRI treatment response.

neuroscience↗

Pilot Study of Metabolomic Clusters as State Markers of Major Depression and Outcomes to CBT Treatment

Major depressive disorder (MDD) is a common and disabling syndrome with multiple etiologies that is defined by clinically elicited signs and symptoms. In hopes of developing a list of candidate biological measures that reflect and relate closely to the severity of depressive symptoms, so-called \"state-dependent\" biomarkers of depression, this pilot study explored the biochemical underpinnings of treatment response to cognitive behavior therapy (CBT) in medication-freeMDD outpatients. Plasma samples were collected at baseline and week 12 from a subset of MDD patients (N=26) who completed a course of CBT treatment as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Targeted metabolomic profiling using the the AbsoluteIDQ(R) p180 Kit and LC-MS identified eight \"co-expressed\" metabolomic modules. Of these eight, three were significantly associated with change in depressive symptoms over the course of the 12-weeks. Metabolites found to be most strongly correlated with change in depressive symptoms were branched chain amino acids, acylcarnitines, methionine sulfoxide, and -aminoadipic acid (negative correlations with symptom change) as well as several lipids, particularly the phosphatidlylcholines (positive correlation). These results implicate disturbed bioenergetics as an important state marker in the pathobiology of MDD. Exploratory analyses contrasting remitters to CBT versus those who failed the treatment further suggest these metabolites may serve as mediators of recovery during CBT treatment. Larger studies examining metabolomic change patterns in patients treated with pharmacotherapy or psychotherapy will be necessary to elucidate the biological underpinnings of MDD and the -specific biologies of treatment response.

neuroscience↗

Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform

We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis and expression signature analysis. The system offers knowledge-guided data-mining and machine learning algorithms, where user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge-bases and encoded in a massive Knowledge Network. KnowEnG adheres to FAIR principles: its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution of compute-intensive and data-intensive algorithms, and are interoperable with other computing platforms. They are made available through multiple access modes including a web-portal, and include specialized visualization modules. We present use cases and re-analysis of published cancer data sets using KnowEnG tools and demonstrate its potential value in democratization of advanced tools for the modern genomics era.

cancer biology↗

Acylcarnitine Metabolomic Profiles Inform Clinically-Defined Major Depressive Phenotypes

BackgroundAcylcarnitines have important functions in mitochondrial energetics and {beta}-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD)--(core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+))--following treatment with a selective serotonin reuptake inhibitor (SSRI).\n\nMethodsDepressed outpatients (n=240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and eight weeks post-treatment were profiled for multiple-, short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ(R)p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminate the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differ between groups.\n\nResultsAt baseline, significantly lower concentrations of short- and long-chain acylcarnitines were found in CD+ and NVSM+ compared to ANX+, and the short-chain acylcarnitines remained lower after eight weeks. At eight weeks, the medium- and long-chain acylcarnitines were significantly lower in NVSM+ compared to ANX+. Regarding changes baseline to week eight, short-chain acylcarnitine levels significantly increased in CD+ and ANX+, and medium- and long-chain acylcarnitines significantly decreased in NVSM+ and CD+.\n\nConclusionsIn depressed patients treated with SSRIs, {beta}-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.

biochemistry↗

MIR2052HG regulates ERα levels and aromatase inhibitor resistance through LMTK3 by recruiting EGR1

Our previous GWAS using the MA.27 aromatase inhibitors (AIs) adjuvant trial identified SNPs in the lncRNA MIR2052HG associated with breast cancer free interval. Here we report that MIR2052HG depletion in breast cancer cells results in a decrease in LMTK3 expression and cell growth. Mechanistically, MIR2052HG interacts with EGR1 and facilitates its recruitment to the LMTK3 promoter. LMTK3 sustains ER levels by reducing PKC activity, resulting in increased ESR1 transcription mediated through AKT/FOXO3 and reduced ER degradation mediated by the PKC/MEK/ERK/RSK1 pathway. MIR2052HG regulated LMTK3 in a SNP- and aromatase inhibitor - dependent fashion: the variant SNP increased EGR1 binding to LMTK3 promoter in response to androstenedione, relative to wild-type genotype, a pattern that can be reversed by aromatase inhibitor treatment. Finally, LMTK3 overexpression abolished the effect of MIR2052HG on PKC activity and ER levels. These results reveal a direct role of MIR2052HG in LMTK3 regulation and raise the possibility of targeting MIR2052HG or LMTK3 in ER-positive breast cancer.

cancer biology↗

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: meta-analysis of data from genome-wide association studies

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects.\n\nCYP2C19 phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was percentage symptom improvement and remission. Side effect data were available at weeks 2-4, 6 and 9 in three of the investigated samples. A fixed-effects meta-analysis was performed using EM as the reference group.\n\nAnalysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD=0.43, CI=0.19-0.66) and higher remission rates (OR=1.55, CI=1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR=1.26, CI=1.08-1.47), neurological (OR=1.28, CI=1.07-1.53) and sexual side effects (OR=1.52, CI=1.23-1.87; week 6 values similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups.\n\nCYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs are relatively rare among Caucasians (~2%).

genetics↗