Translational Psychiatry最新文献

{"title":"A genetic exploration of the relationship between posttraumatic stress disorder and cardiovascular diseases.","authors":"Eva Lukas, Rada R Veeneman, Dirk J A Smit, Tarunveer S Ahluwalia, Jentien M Vermeulen, Gita A Pathak, Renato Polimanti, Karin J H Verweij, Jorien L Treur","doi":"10.1038/s41398-024-03197-z","DOIUrl":"https://doi.org/10.1038/s41398-024-03197-z","url":null,"abstract":"<p><p>Experiencing a traumatic event may lead to Posttraumatic Stress Disorder (PTSD), including symptoms such as flashbacks and hyperarousal. Individuals suffering from PTSD are at increased risk of cardiovascular disease (CVD), but it is unclear why. This study assesses shared genetic liability and potential causal pathways between PTSD and CVD. We leveraged summary-level data of genome-wide association studies (PTSD: N = 1,222,882; atrial fibrillation (AF): N = 482,409; coronary artery disease (CAD): N = 1,165,690; hypertension (HT): N = 458,554; heart failure (HF): N = 977,323). First, we estimated genetic correlations and utilized genomic structural equation modeling to identify a common genetic factor for PTSD and CVD. Next, we assessed biological, behavioural, and psychosocial factors as potential mediators. Finally, we employed multivariable Mendelian randomization to examine causal pathways between PTSD and CVD, incorporating the same potential mediators. Significant genetic correlations were found between PTSD and CAD, HT, and HF (r_g = 0.21-0.32, p ≤ 3.08 · 10^-16), but not between PTSD and AF. Insomnia, smoking, alcohol dependence, waist-to-hip ratio, and inflammation (IL6, C-reactive protein) partly mediated these associations. Mendelian randomization indicated that PTSD causally increases CAD (IVW OR = 1.53, 95% CIs = 1.19-1.96, p = 0.001), HF (OR = 1.44, CIs = 1.08-1.92, p = 0.012), and to a lesser degree HT (OR = 1.25, CIs = 1.05-1.49, p = 0.012). While insomnia, smoking, alcohol, and inflammation were important mediators, independent causal effects also remained. In addition to shared genetic liability between PTSD and CVD, we present strong evidence for causal effects of PTSD on CVD. Crucially, we implicate specific lifestyle and biological mediators (insomnia, substance use, inflammation) which has important implications for interventions to prevent CVD in PTSD patients.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"15 1","pages":"1"},"PeriodicalIF":5.8,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.","authors":"Annika Wiebe, Benjamin Selaskowski, Martha Paskin, Laura Asché, Julian Pakos, Behrem Aslan, Silke Lux, Alexandra Philipsen, Niclas Braun","doi":"10.1038/s41398-024-03217-y","DOIUrl":"10.1038/s41398-024-03217-y","url":null,"abstract":"<p><p>Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive utility of multimodal data, including eye tracking, EEG, actigraphy, and behavioral indices, in differentiating adults with ADHD from healthy individuals. Using a support vector machine model, we analyzed independent training (n = 50) and test (n = 36) samples from two clinically controlled studies. In both studies, participants performed an attention task (continuous performance task) in a virtual reality seminar room while encountering virtual distractions. Task performance, head movements, gaze behavior, EEG, and current self-reported inattention, hyperactivity, and impulsivity were simultaneously recorded and used for model training. Our final model based on the optimal number of features (maximal relevance minimal redundancy criterion) achieved a promising classification accuracy of 81% in the independent test set. Notably, the extracted EEG-based features had no significant contribution to this prediction and therefore were not included in the final model. Our results suggest the potential of applying ecologically valid virtual reality environments and integrating different data modalities for enhancing robustness of ADHD diagnosis.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"508"},"PeriodicalIF":5.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142910908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach.","authors":"Xi Zhang, Lan Yang, Jiayu Lu, Yuting Yuan, Dandan Li, Hui Zhang, Rong Yao, Jie Xiang, Bin Wang","doi":"10.1038/s41398-024-03212-3","DOIUrl":"10.1038/s41398-024-03212-3","url":null,"abstract":"<p><p>Bipolar disorder (BD) is a neuropsychiatric disorder characterized by severe disturbance and fluctuation in mood. Dynamic functional connectivity (dFC) has the potential to more accurately capture the evolving processes of emotion and cognition in BD. Nevertheless, prior investigations of dFC typically centered on larger time scales, limiting the sensitivity to transient changes. This study employed hidden Markov model (HMM) analysis to delve deeper into the moment-to-moment temporal patterns of brain activity in BD. We utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 43 BD patients and 51 controls to evaluate the altered dynamic spatiotemporal architecture of the whole-brain network and identify unique activation patterns in BD. Additionally, we investigated the relationship between altered brain dynamics and structural disruption through the ridge regression (RR) algorithm. The results demonstrated that BD spent less time in a hyperconnected state with higher network efficiency and lower segregation. Conversely, BD spent more time in anticorrelated states featuring overall negative correlations, particularly among pairs of default mode network (DMN) and sensorimotor network (SMN), DMN and insular-opercular ventral attention networks (ION), subcortical network (SCN) and SMN, as well as SCN and ION. Interestingly, the hypoactivation of the cognitive control network in BD may be associated with the structural disruption primarily situated in the frontal and parietal lobes. This study investigated the dynamic mechanisms of brain network dysfunction in BD and offered fresh perspectives for exploring the physiological foundation of altered brain dynamics.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"507"},"PeriodicalIF":5.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11685458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of α-klotho concentrations with cardiovascular and all-cause mortality in American adults with depression: a national prospective cohort study.","authors":"Jiayu Zhao, Tong Zhou, Yang Jing, Jiarui Shao, Cailin Xie, Yingying Huang, Tian Long, Jiaming Luo","doi":"10.1038/s41398-024-03215-0","DOIUrl":"10.1038/s41398-024-03215-0","url":null,"abstract":"<p><p>This study examines α-klotho levels in depressed American adults and their association with cardiovascular disease and all-cause mortality, utilizing data from the National Health and Nutrition Examination Survey (2007-2016) and mortality details from the National Death Index up to December 31, 2019. Including 3329 participants with depression, findings revealed 485 all-cause and 113 cardiovascular deaths. To investigate the nonlinear association between α-klotho and mortality, the Cox proportional hazards regression model, restricted cubic splines, and two-piecewise Cox proportional hazards model were developed. Analyzes indicated an \"L-shaped\" relationship between ln-transformed α-klotho levels and all-cause mortality, with a significant threshold effect at 6.53 ln(pg/ml). Below this threshold, ln-transformed α-klotho levels were inversely related to all-cause mortality (adjusted HR 0.33, 95%CI = 0.19-0.56), with no significant association above it (adjusted HR 1.41, 95%CI = 0.84-2.36). Cardiovascular mortality showed no link to α-klotho levels. Subgroup analysis shown that, the association between ln-transformed α-klotho concentration and all-cause mortality was consistent in subgroups according to gender, age, BMI, race, and depression(adjusted P > 0.05). The study uncovers a non-linear \"L-shaped\" association between ln-transformed α-klotho levels and all-cause mortality in depressed individuals, suggesting α-klotho assessment as a tool for identifying high-risk patients and guiding preventive strategies to enhance survival.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"505"},"PeriodicalIF":5.8,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142898486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenome-wide investigation of bidirectional causal relationships between major depressive disorder and common human diseases.","authors":"Wenxi Sun, Ancha Baranova, Dongming Liu, Hongbao Cao, Xiaobin Zhang, Fuquan Zhang","doi":"10.1038/s41398-024-03216-z","DOIUrl":"10.1038/s41398-024-03216-z","url":null,"abstract":"<p><p>The high comorbidity of major depressive disorder (MDD) with other diseases has been well-documented. However, the pairwise causal connections for MDD comorbid networks are poorly characterized. We performed Phenome-wide Mendelian randomization (MR) analyses to explore bidirectional causal associations between MDD (N = 807,553) and 877 common diseases from FinnGen datasets (N = 377,277). The inverse variance weighting method was the primary technique, and other methods (weighted median and MR-Egger) were used for sensitivity analyses. Our MR analyses showed that the genetic liability to MDD is causally associated with the risks of 324 disease phenotypes (average b: 0.339), including 46 psychiatric and behavioral disorders (average b: 0.618), 18 neurological diseases (average b: 0.348), 44 respiratory diseases (average b: 0.345), 40 digestive diseases (average b: 0.281), 18 circulatory diseases (average b: 0.237), 37 genitourinary diseases (average b: 0.271), 66 musculoskeletal and connective diseases (average b: 0.326), 22 endocrine diseases (average b: 0.302), and others. In a reverse analysis, a total of 51 genetic components predisposing to various diseases were causally associated with MDD risk (average b: 0.086), including 5 infectious diseases (average b: 0.056), 11 neurological diseases (average b: 0.106), 14 oncological diseases (average b: 0.108), and 5 psychiatric and behavioral disorders (average b: 0.114). Bidirectional causal associations were identified between MDD and 15 diseases. For most MR analyses, little evidence of heterogeneity and pleiotropy was detected. Our findings confirmed the extensive and significant causal role of genetic predisposition to MDD in contributing to human disease phenotypes, which were more pronounced than those seen in the reverse analysis of the causal influences of other diseases on MDD.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"506"},"PeriodicalIF":5.8,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11680865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142898489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of clinical phenotypes of depression with comorbid conditions, treatment patterns and outcomes: a 10-year region-based cohort study.","authors":"Ting Zhu, Di Mu, Yao Hu, Yang Cao, Minlan Yuan, Jia Xu, Heng-Qing Ye, Wei Zhang","doi":"10.1038/s41398-024-03213-2","DOIUrl":"10.1038/s41398-024-03213-2","url":null,"abstract":"<p><p>Depression is a heterogeneous and complex psychological syndrome with highly variable manifestations, which poses difficulties for treatment and prognosis. Depression patients are prone to developing various comorbidities, which stem from different pathophysiological mechanisms, remaining largely understudied. The current study focused on identifying comorbidity-specific phenotypes, and whether these clustered phenotypes are associated with different treatment patterns, clinical manifestations, physiological characteristics, and prognosis. We have conducted a 10-year retrospective observational cohort study using electronic medical records (EMR) for 11,818 patients diagnosed with depression and hospitalized at a large academic medical center in Chengdu, China. K-means clustering and visualization methods were performed to identify phenotypic categories. The association between phenotypic categories and clinical outcomes was evaluated using adjusted Cox proportional hazards model. We classified patients with depression into five stable phenotypic categories, including 15 statistically driven clusters in the discovery cohort (n = 9925) and the validation cohort (n = 1893), respectively. The categories include: (Category A) the lowest incidence of comorbidity, with prominent suicide, psychotic, and somatic symptoms (n = 3493/9925); (Category B) moderate comorbidity rate, with prominent anhedonia and anxious symptoms (n = 1795/9925); (Category C) the highest incidence of comorbidity of endocrine/metabolic and digestive system diseases (n = 1702/9925); (Category D) the highest incidence of comorbidity of neurological, mental and behavioral diseases (n = 881/9925); (Category E) other diseases comorbid with depression (n = 2054/9925). Patients in Category E had the lowest risk of psychiatric rehospitalization within 60-day follow-up, followed by Category C (HR, 1.57; 95% CI, 1.07-2.30), Category B (HR, 1.61; 95% CI, 1.10-2.40), Category A (HR, 1.82; 95% CI, 1.28-2.60), and Category D (HR, 2.38; 95% CI, 1.59-3.60) with P < 0.05, after adjustment for comorbidities, medications, and age. Regarding other longer observation windows (90-day, 180-day and 365-day), patients in Category D showed the highest rehospitalization risk all the time while there were notable shifts in rankings observed for Categories A, B and C over time. The results indicate that the higher the severity of mental illness in patients with five phenotypic categories, the greater the risk of rehospitalization. These phenotypes are associated with various pathways, including the cardiometabolic system, chronic inflammation, digestive system, neurological system, and mental and behavioral disorders. These pathways play a crucial role in connecting depression with other psychiatric and somatic diseases. The identified phenotypes exhibit notable distinctions in terms of comorbidity patterns, symptomology, biological characteristics, treatment approaches, and clinical outcomes.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"504"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142885968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gut microbiota dysbiosis promotes cognitive impairment via bile acid metabolism in major depressive disorder.","authors":"Min Jia, Yajuan Fan, Qingyan Ma, Ding Yang, Yunpeng Wang, Xiaoyan He, Binbin Zhao, Xianyan Zhan, Zhiyang Qi, Yifan Ren, Ziqing Dong, Feng Zhu, Wei Wang, Yuan Gao, Xiancang Ma","doi":"10.1038/s41398-024-03211-4","DOIUrl":"10.1038/s41398-024-03211-4","url":null,"abstract":"<p><p>Evidence suggests that complex interactions among the gut microbiome, metabolic abnormalities, and brain have important etiological and therapeutic implications in major depressive disorder (MDD). However, the influence of microbiome-gut-brain cross-talk on cognitive impairment in MDD remains poorly characterized. We performed serum metabolomic profiling on 104 patients with MDD and 77 healthy controls (HCs), and also performed fecal metagenomic sequencing on a subset of these individuals, including 79 MDD patients and 60 HCs. The findings were validated in a separate cohort that included 40 patients with MDD and 40 HCs using serum-targeted metabolomics. Abnormal bile acid metabolism was observed in patients with MDD, which is related to cognitive dysfunction. The following gut microbiota corresponded to changes in bile acid metabolism and enzyme activities involved in the bile acid metabolic pathway, including Lachnospiraceae (Blautia_massiliensis, Anaerostipes_hadrus, Dorea_formicigenerans, and Fusicatenibacter_saccharivorans), Ruminococcaceae (Ruminococcus_bromii, Flavonifractor_plautii, and Ruthenibacterium_lactatiformans), and Escherichia_coli. Furthermore, a combinatorial marker classifier that robustly differentiated patients with MDD from HCs was identified. In conclusion, this study provides insights into the gut-brain interactions in the cognitive phenotype of MDD, indicating a potential therapeutic strategy for MDD-associated cognitive impairment by targeting the gut microbiota and bile acid metabolism.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"503"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142885977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings.","authors":"Chunyu Liu, Elliot S Gershon","doi":"10.1038/s41398-024-03195-1","DOIUrl":"10.1038/s41398-024-03195-1","url":null,"abstract":"<p><p>Recent genetic studies have linked numerous loci to psychiatric disorders. However, the biological pathways that connect these genetic associations to psychiatric disorders' specific pathophysiological processes are largely unclear. Endophenotypes, first defined over five decades ago, are heritable traits, independent of disease state that are associated with a disease, encompassing a broad range of neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, and neuropsychological characteristics. Considering the advancements in genetics and genomics over recent decades, we propose a revised definition of endophenotypes as 'genetically influenced phenotypes linked to disease or treatment characteristics and their related events.' We also updated endophenotype criteria to include (1) reliable measurement, (2) association with the disease or its related events, and (3) genetic mediation. 'Genetic mediation' is introduced to differentiate between causality and pleiotropic effects and allows non-linear relationships. Furthermore, this updated Endophenotype 2.0 framework expands to encompass genetically regulated responses to disease-related factors, including environmental risks, illness progression, treatment responses, and resilience phenotypes, which may be state-dependent. This broadened definition paves the way for developing new endophenotypes crucial for genetic analyses in psychiatric disorders. Integrating genetics, genomics, and diverse endophenotypes into multi-dimensional mechanistic models is vital for advancing our understanding of psychiatric disorders. Crucially, elucidating the biological underpinnings of endophenotypes will enhance our grasp of psychiatric genetics, thereby improving disease risk prediction and treatment approaches.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"502"},"PeriodicalIF":5.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11668880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142885940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individualized identification value of stress-related network structural-functional properties and HPA axis reactivity for subthreshold depression.","authors":"Youze He, Baoru Zhao, Zhihan Liu, Yudie Hu, Jian Song, Jingsong Wu","doi":"10.1038/s41398-024-03210-5","DOIUrl":"10.1038/s41398-024-03210-5","url":null,"abstract":"<p><p>Accumulating studies have highlighted the links between stress-related networks and the HPA axis for emotion regulation and proved the mapping associations between altered structural and functional networks (called SC-FC coupling) in depression. However, the signatures of SC-FC coupling in subthreshold depression (StD) individuals and their relationships with HPA axis reactivity, as well as the predictive power of these combinations for discriminating StD, remain unclear. This cross-sectional study enrolled 160 adults, including 117 StD and 43 healthy controls (HC). The propensity score matching method was applied for match-pair analysis between StD and HC. Herein, we measured depression level, cortisol level, and brain imaging outcomes. The functional MRI and diffusion tensor imaging methods were employed to acquire the network SC-FC couplings and topological attributes. Support vector machine models were employed to discriminate StD from HC. Herein, 43 pairs were matched, but four participants were excluded due to over-threshold head motion, leaving 41 participants in each group. General linear model results revealed a significant SC-FC coupling increase in the default mode network (DMN) and decrements of global efficiency in DMN and frontoparietal control network (P < 0.05), while the cortisol secretion significantly increased (P < 0.001) in StD individuals. Partial correlation analysis revealed positive associations between DMN coupling and cortisol values (r = 0.298, P = 0.033), and their combination provided greater power for discriminating StD than another single model, with the classification accuracy and AUC value up to 85.71% and 0.894, respectively. In summary, this study clarified the relationship between stress-related network SC-FC coupling and cortisol secretion in influencing depressive symptoms, whose combination would contribute to discriminating subthreshold depressive states in the future.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"501"},"PeriodicalIF":5.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142882874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroinflammatory history results in overlapping transcriptional signatures with heroin exposure in the nucleus accumbens and alters responsiveness to heroin in male rats.","authors":"Gabriele Floris, Mary Tresa Zanda, Konrad R Dabrowski, Stephanie E Daws","doi":"10.1038/s41398-024-03203-4","DOIUrl":"10.1038/s41398-024-03203-4","url":null,"abstract":"<p><p>Recent progress in psychiatric research has highlighted neuroinflammation in the pathophysiology of opioid use disorder (OUD), suggesting that heightened immune responses in the brain may exacerbate opioid-related mechanisms. However, the molecular mechanisms resulting from neuroinflammation that impact opioid-induced behaviors and transcriptional pathways remain poorly understood. In this study, we have begun to address this critical knowledge gap by exploring the intersection between neuroinflammation and exposure to the opioid heroin, utilizing lipopolysaccharide (LPS)-induced neuroinflammation, to investigate transcriptional changes in the nucleus accumbens (NAc), an essential region in the mesolimbic dopamine system that mediates opioid reward. By integrating RNA sequencing with bioinformatic and statistical analyses, we observed significant transcriptional overlaps between neuroinflammation and experimenter-administered heroin exposure in the NAc. Furthermore, we identified a subset of NAc genes synergistically regulated by LPS and heroin, suggesting that LPS history may exacerbate some heroin-induced molecular neuroadaptations. We extended our findings to examine the impact of neuroinflammatory history on responsiveness to heroin in a locomotor sensitization assay and observed LPS-induced exacerbation of heroin sensitization, indicating that neuroinflammation may increase sensitivity to opioids' behavioral effects. Lastly, we performed comparative analysis of the NAc transcriptional profiles of LPS-heroin rats with those obtained from voluntary heroin intake in a rat model of heroin self-administration (SA) and published human OUD datasets. We observed significant convergence of the three datasets and identified transcriptional patterns in the preclinical models that recapitulated human OUD neuropathology, highlighting the utility of preclinical models to further investigate molecular mechanisms of OUD pathology. Overall, our study elucidates transcriptional interconnections between neuroinflammation and heroin exposure, and also provides evidence of the behavioral ramifications of such interactions. By bridging the gap between neuroinflammation and heroin exposure at the transcriptional level, our work provides valuable insights for future research aimed at mitigating the influence of inflammatory pathways in OUD.</p>","PeriodicalId":23278,"journal":{"name":"Translational Psychiatry","volume":"14 1","pages":"500"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659471/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}