Dataset Information

Multi-omics analysis of a drug-induced model of bipolar disorder in zebrafish.

ABSTRACT: Emerging studies demonstrate that inflammation plays a crucial role in the pathogenesis of bipolar disorder (BD), but the underlying mechanism remains largely unclear. Given the complexity of BD pathogenesis, we performed high-throughput multi-omic profiling (metabolomics, lipidomics, and transcriptomics) of the BD zebrafish brain to comprehensively unravel the molecular mechanism. Our research proved that in BD zebrafish, JNK-mediated neuroinflammation altered metabolic pathways involved in neurotransmission. On one hand, disturbed metabolism of tryptophan and tyrosine limited the participation of the monoamine neurotransmitters serotonin and dopamine in synaptic vesicle recycling. On the other hand, dysregulated metabolism of the membrane lipids sphingomyelin and glycerophospholipids altered the synaptic membrane structure and neurotransmitter receptors (chrnα7, htr1b, drd5b, and gabra1) activity. Our findings revealed that disturbance of serotonergic and dopaminergic synaptic transmission mediated by the JNK inflammatory cascade was the key pathogenic mechanism in a zebrafish model of BD, provides critical biological insights into the pathogenesis of BD.

SUBMITTER: Li Y

PROVIDER: S-EPMC10189518 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Multi-omics analysis of a drug-induced model of bipolar disorder in zebrafish.

Li Yameng Y Zhang Lin L Mao Mingcai M He Linjuan L Wang Tiancai T Pan Yecan Y Zhao Xiaoyu X Li Zishu Z Mu Xiyan X Qian Yongzhong Y Qiu Jing J

iScience 20230425 5

Emerging studies demonstrate that inflammation plays a crucial role in the pathogenesis of bipolar disorder (BD), but the underlying mechanism remains largely unclear. Given the complexity of BD pathogenesis, we performed high-throughput multi-omic profiling (metabolomics, lipidomics, and transcriptomics) of the BD zebrafish brain to comprehensively unravel the molecular mechanism. Our research proved that in BD zebrafish, JNK-mediated neuroinflammation altered metabolic pathways involved in neu ...[more]

PMID: 37207274

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Project description:Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Project description:BackgroundWith the expansion of animal production, parasitic helminths are gaining increasing economic importance. However, application of several established deworming agents can harm treated hosts and environment due to their low specificity. Furthermore, the number of parasite strains showing resistance is growing, while hardly any new anthelminthics are being developed. Here, we present a bioinformatics workflow designed to reduce the time and cost in the development of new strategies against parasites. The workflow includes quantitative transcriptomics and proteomics, 3D structure modeling, binding site prediction, and virtual ligand screening. Its use is demonstrated for Acanthocephala (thorny-headed worms) which are an emerging pest in fish aquaculture. We included three acanthocephalans (Pomphorhynchus laevis, Neoechinorhynchus agilis, Neoechinorhynchus buttnerae) from four fish species (common barbel, European eel, thinlip mullet, tambaqui).ResultsThe workflow led to eleven highly specific candidate targets in acanthocephalans. The candidate targets showed constant and elevated transcript abundances across definitive and accidental hosts, suggestive of constitutive expression and functional importance. Hence, the impairment of the corresponding proteins should enable specific and effective killing of acanthocephalans. Candidate targets were also highly abundant in the acanthocephalan body wall, through which these gutless parasites take up nutrients. Thus, the candidate targets are likely to be accessible to compounds that are orally administered to fish. Virtual ligand screening led to ten compounds, of which five appeared to be especially promising according to ADMET, GHS, and RO5 criteria: tadalafil, pranazepide, piketoprofen, heliomycin, and the nematicide derquantel.ConclusionsThe combination of genomics, transcriptomics, and proteomics led to a broadly applicable procedure for the cost- and time-saving identification of candidate target proteins in parasites. The ligands predicted to bind can now be further evaluated for their suitability in the control of acanthocephalans. The workflow has been deposited at the Galaxy workflow server under the URL tinyurl.com/yx72rda7 .

Dataset Information

Multi-omics analysis of a drug-induced model of bipolar disorder in zebrafish.

Publications

Multi-omics analysis of a drug-induced model of bipolar disorder in zebrafish.

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