Dataset Information

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

ABSTRACT: Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

SUBMITTER: Lam M

PROVIDER: S-EPMC8357785 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

Lam Max M Chen Chia-Yen CY Ge Tian T Xia Yan Y Hill David W DW Trampush Joey W JW Yu Jin J Knowles Emma E Davies Gail G Stahl Eli A EA Huckins Laura L Liewald David C DC Djurovic Srdjan S Melle Ingrid I Christoforou Andrea A Reinvang Ivar I DeRosse Pamela P Lundervold Astri J AJ Steen Vidar M VM Espeseth Thomas T Räikkönen Katri K Widen Elisabeth E Palotie Aarno A Eriksson Johan G JG Giegling Ina I Konte Bettina B Hartmann Annette M AM Roussos Panos P Giakoumaki Stella S Burdick Katherine E KE Payton Antony A Ollier William W Chiba-Falek Ornit O Koltai Deborah C DC Need Anna C AC Cirulli Elizabeth T ET Voineskos Aristotle N AN Stefanis Nikos C NC Avramopoulos Dimitrios D Hatzimanolis Alex A Smyrnis Nikolaos N Bilder Robert M RM Freimer Nelson B NB Cannon Tyrone D TD London Edythe E Poldrack Russell A RA Sabb Fred W FW Congdon Eliza E Conley Emily Drabant ED Scult Matthew A MA Dickinson Dwight D Straub Richard E RE Donohoe Gary G Morris Derek D Corvin Aiden A Gill Michael M Hariri Ahmad R AR Weinberger Daniel R DR Pendleton Neil N Bitsios Panos P Rujescu Dan D Lahti Jari J Le Hellard Stephanie S Keller Matthew C MC Andreassen Ole A OA Deary Ian J IJ Glahn David C DC Huang Hailiang H Liu Chunyu C Malhotra Anil K AK Lencz Todd T

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20210525 10

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent ...[more]

PMID: 34035472

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Project description:BackgroundEndometriosis (EM) is a chronic painful condition that predominantly affects women of reproductive age. Currently, surgery or medication can only provide limited symptom relief. This study used a comprehensive genetic analytical approach to explore potential drug targets for EM in the plasma proteome.MethodsIn this study, 2,923 plasma proteins were selected as exposure and EM as outcome for two-sample Mendelian randomization (MR) analyses. The plasma proteomic data were derived from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), while the EM dataset from the FinnGen consortium R10 release data. Several sensitivity analyses were performed, including summary-data-based MR (SMR) analyses, heterogeneity in dependent instruments (HEIDI) test, reverse MR analyses, steiger detection test, and bayesian co-localization analyses. Furthermore, proteome-wide association study (PWAS) and single-cell transcriptomic analyses were also conducted to validate the findings.ResultsSix significant (p < 3.06 × 10-5) plasma protein-EM pairs were identified by MR analyses. These included EPHB4 (OR = 1.40, 95% CI: 1.20 - 1.63), FSHB (OR = 3.91, 95% CI: 3.13 - 4.87), RSPO3 (OR = 1.60, 95% CI: 1.38 - 1.86), SEZ6L2 (OR = 1.44, 95% CI: 1.23 - 1.68) and WASHC3 (OR = 2.00, 95% CI: 1.54 - 2.59) were identified as risk factors, whereas KDR (OR = 0.80, 95% CI: 0.75 - 0.90) was found to be a protective factor. All six plasma proteins passed the SMR test (P < 8.33 × 10-3), but only four plasma proteins passed the HEIDI heterogeneity test (PHEIDI > 0.05), namely FSHB, RSPO3, SEZ6L2 and EPHB4. These four proteins showed strong evidence of co-localization (PPH4 > 0.7). In particular, RSPO3 and EPHB4 were replicated in the validated PWAS. Single-cell analyses revealed high expression of SEZ6L2 and EPHB4 in stromal and epithelial cells within EM lesions, while RSPO3 exhibited elevated expression in stromal cells and fibroblasts.ConclusionOur study identified FSHB, RSPO3, SEZ6L2, and EPHB4 as potential drug targets for EM and highlighted the critical role of stromal and epithelial cells in disease development. These findings provide new insights into the diagnosis and treatment of EM.

Project description:BackgroundPromiscuity in molecular interactions between small-molecules, including drugs, and proteins is widespread. Such unintended interactions can be exploited to suggest drug repurposing possibilities as well as to identify potential molecular mechanisms responsible for observed side-effects.MethodsWe perform a large-scale analysis to detect binding-site molecular interaction field similarities between the binding-sites of the primary target of 400 drugs against a dataset of 14082 cavities within 7895 different proteins representing a non-redundant dataset of all proteins with known structure. Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target. Such cases are further analysed with docking simulations to verify if indeed the drug could, in principle, bind the off-target. Diverse sources of data are integrated to associated potential cross-reactivity targets with side-effects.ResultsWe observe that promiscuous binding-sites tend to display higher levels of hydrophobic and aromatic similarities. Focusing on the most statistically significant similarities (Z-score ≥ 3.0) and corroborating docking results (RMSD < 2.0 Å), we find 2923 cases involving 140 unique drugs and 1216 unique potential cross-reactivity protein targets. We highlight a few cases with a potential for drug repurposing (acetazolamide as a chorismate pyruvate lyase inhibitor, raloxifene as a bacterial quorum sensing inhibitor) as well as to explain the side-effects of zanamivir and captopril. A web-interface permits to explore the detected similarities for each of the 400 binding-sites of the primary drug targets and visualise them for the most statistically significant cases.ConclusionsThe detection of molecular interaction field similarities provide the opportunity to suggest drug repurposing opportunities as well as to identify potential molecular mechanisms responsible for side-effects. All methods utilized are freely available and can be readily applied to new query binding-sites. All data is freely available and represents an invaluable source to identify further candidates for repurposing and suggest potential mechanisms responsible for side-effects.

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Dataset Information

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

Publications

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

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