Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.

PURPOSE: To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. MATERIAL AND METHODS: A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. RESULTS: The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001). CONCLUSIONS: This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists' PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further. CRITICAL RELEVANCE STATEMENT: The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans. KEY POINTS: • The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists. • The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool. • The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.

The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy.

BACKGROUND: The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. METHODS: A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. RESULTS: There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. CONCLUSIONS: Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists' PI-RR assessment.

Alcohol Consumption During Adolescence Alters the Cognitive Function in Adult Male Mice by Persistently Increasing Levels of DUSP6.

Binge alcohol drinking during adolescence has long-term effects on the adult brain that alter brain structure and behaviors, but the underlying mechanisms remain poorly understood. Extracellular signal-regulated kinase (ERK) is involved in the synaptic plasticity and pathological brain injury by regulating the expression of cyclic adenosine monophosphate response element binding protein (CREB) and brain-derived neurotrophic factor (BDNF). Dual-specificity phosphatase 6 (DUSP6) is a critical effector that dephosphorylates ERK1/2 to control the basal tone, amplitude, and duration of ERK signaling. To explore DUSP6 as a regulator of ERK signaling in the mPFC and its impact on long-term effects of alcohol, a male mouse model of adolescent intermittent alcohol (AIA) exposure was established. Behavioral experiments showed that AIA did not affect anxiety-like behavior or sociability in adulthood, but significantly damaged new object recognition and social recognition memory. Molecular studies further found that AIA reduced the levels of pERK-pCREB-BDNF-PSD95/NR2A involved in synaptic plasticity, while DUSP6 was significantly increased. Intra-mPFC infusion of AAV-DUSP6-shRNA restored the dendritic spine density and postsynaptic density thickness by reversing the level of p-ERK and its downstream molecular expression, and ultimately repaired adult cognitive impairment caused by chronic alcohol exposure during adolescence. These findings indicate that AIA exposure inhibits ERK-CREB-BDNF-PSD95/NR2A by increasing DUSP6 in the mPFC in adulthood that may be associated with long-lasting cognitive deficits.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study.

Background: Extracapsular extension (ECE) of prostate cancer (PCa) is closely related to the treatment and prognosis of patients, and radiomics has been widely used in the study of PCa. This study aimed to evaluate the value of a combined model considering magnetic resonance imaging (MRI)-based radiomics and clinical parameters for predicting ECE in PCa. Methods: A total of 392 PCa patients enrolled in this retrospective study were randomly divided into the training and validation sets at a ratio of 7:3. Radiologists assessed all lesions by Mehralivand grade. Radiomics features were extracted and selected to build a radiomics model, while clinical parameters were noted to construct the clinical model. The combined model was constructed by the integration of the radiomics model and clinical model. Meanwhile, the nomogram for predicting ECE was constructed based on the combined model. Then, the area under the receiver operating characteristic (ROC) curve (AUC), Delong test and the decision curve analysis (DCA) were used to compare the performance among the combined model, radiomics model, clinical model and Mehralivand grade. Results: The AUC of the combined model in the validation set was comparable to that of the radiomics model [AUC =0.894 (95% confidence interval (CI): 0.837-0.950) vs. 0.835 (95% CI: 0.763-0.908), P>0.05]. In addition, the sensitivity of the combined model and radiomics model was 90.7% and 77.8%, with an accuracy of 81.4% and 76.3%, respectively. On the other hand, the AUCs of the Mehralivand grade of radiologists and clinical model were 0.774 (95% CI: 0.691-0.857) and 0.749 (95% CI: 0.658-0.840), respectively, in the validation set, which were lower than those in the combined model (P<0.05). The DCA implied that the combined model could obtain the maximum net clinical benefits compared with the clinical model, the Mehralivand grade and radiomics model. Conclusions: The combined model has a satisfactory predictive value for ECE in PCa patients compared with the clinical model, Mehralivand grade of radiologists, and the radiomics model.

DNA Methylation in Alcohol Use Disorder.

Excessive drinking damages the central nervous system of individuals and can even cause alcohol use disorder (AUD). AUD is regulated by both genetic and environmental factors. Genes determine susceptibility to alcohol, and the dysregulation of epigenome drives the abnormal transcription program and promotes the occurrence and development of AUD. DNA methylation is one of the earliest and most widely studied epigenetic mechanisms that can be inherited stably. In ontogeny, DNA methylation pattern is a dynamic process, showing differences and characteristics at different stages. DNA dysmethylation is prevalent in human cancer and alcohol-related psychiatric disorders, resulting in local hypermethylation and transcriptional silencing of related genes. Here, we summarize recent findings on the roles and regulatory mechanisms of DNA methylation, the development of methyltransferase inhibitors, methylation alteration during alcohol exposure at different stages of life, and possible therapeutic options for targeting methylation in human and animal studies.

CYP3A4 and CYP3A5 Expression is Regulated by CYP3A4*1G in CRISPR/Cas9-Edited HepG2 Cells.

Functional CYP3A4*1G (G>A, rs2242480) in cytochrome P450 3A4 (CYP3A4) regulates the drug-metabolizing enzyme CYP3A4 expression. The objective of this study was to investigate whether CYP3A4*1G regulates both basal and rifampicin (RIF)-induced expression and enzyme activity of CYP3A4 and CYP3A5 in gene-edited human HepG2 cells. CYP3A4*1G GG and AA genotype HepG2 cells were established using the clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) single nucleotide polymorphism technology and homology-directed repair in the CYP3A4*1G GA HepG2 cell line. In CYP3A4*1G GG, GA, and AA HepG2 cells, CYP3A4*1G regulated expression of CYP3A4 and CYP3A5 mRNA and protein in an allele-dependent manner. Of note, significantly decreased expression level of CYP3A4 and CYP3A5 was observed in CYP3A4*1G AA HepG2 cells. Moreover, the results after RIF treatment showed that CYP3A4*1G decreased the induction level of CYP3A4 and CYP3A5 mRNA expression in CYP3A4*1G AA HepG2 cells. At the same time, CYP3A4*1G decreased CYP3A4 enzyme activity and tacrolimus metabolism, especially in CYP3A4*1G GA HepG2 cells. In summary, we successfully constructed CYP3A4*1G GG and AA homozygous HepG2 cell models and found that CYP3A4*1G regulates both basal and RIF-induced expression and enzyme activity of CYP3A4 and CYP3A5 in CRISPR/Cas9 CYP3A4*1G HepG2 cells. SIGNIFICANCE STATEMENT: Cytochrome P450 (CYP) 3A4*1G regulates both basal and rifampicin (RIF)-induced expression and enzyme activity of CYP3A4 and CYP3A5. This study successfully established CYP3A4*1G (G>A, rs2242480), GG, and AA HepG2 cell models using CRISPR/Cas9, thus providing a powerful tool for studying the mechanism by which CYP3A4*1G regulates the basal and RIF-induced expression of CYP3A4 and CYP3A5.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). METHODS: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. RESULTS: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). CONCLUSION: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

Dual attention guided multiscale neural network trained with curriculum learning for noninvasive prediction of Gleason Grade Group from MRI.

BACKGROUND: The Gleason Grade Group (GG) is essential in assessing the malignancy of prostate cancer (PCa) and is typically obtained by invasive biopsy procedures in which sampling errors could lead to inaccurately scored GGs. With the gradually recognized value of bi-parametric magnetic resonance imaging (bpMRI) in PCa, it is beneficial to noninvasively predict GGs from bpMRI for early diagnosis and treatment planning of PCa. However, it is challenging to establish the connection between bpMRI features and GGs. PURPOSE: In this study, we propose a dual attention-guided multiscale neural network (DAMS-Net) to predict the 5-scored GG from bpMRI and design a training curriculum to further improve the prediction performance. METHODS: The proposed DAMS-Net incorporates a feature pyramid network (FPN) to fully extract the multiscale features for lesions of varying sizes and a dual attention module to focus on lesion and surrounding regions while avoiding the influence of irrelevant ones. Furthermore, to enhance the differential ability for lesions with the inter-grade similarity and intra-grade variation in bpMRI, the training process employs a specially designed curriculum based on the differences between the radiological evaluations and the ground truth GGs. RESULTS: Extensive experiments were conducted on a private dataset of 382 patients and the public PROSTATEx-2 dataset. For the private dataset, the experimental results showed that the proposed network performed better than the plain baseline model for GG prediction, achieving a mean quadratic weighted Kappa (Kw ) of 0.4902 and a mean positive predictive value of 0.9098 for predicting clinically significant cancer (PPVGG>1 ). With the application of curriculum learning, the mean Kw and PPVGG>1 further increased to 0.5144 and 0.9118, respectively. For the public dataset, the proposed method achieved state-of-the-art results of 0.5413 Kw and 0.9747 PPVGG>1 . CONCLUSION: The proposed DAMS-Net trained with curriculum learning can effectively predict GGs from bpMRI, which may assist clinicians in early diagnosis and treatment planning for PCa patients.

Machine learning-based CT texture analysis in the differentiation of testicular masses.

Purpose: To evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and identifying seminoma in testicular germ cell tumors, respectively. Methods: We retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers. Results: 985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively. Conclusion: Until now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.

Epigenetic Regulation of Neuroinflammation in Alzheimer's Disease.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disease and clinically manifests with cognitive decline and behavioral disabilities. Over the past years, mounting studies have demonstrated that the inflammatory response plays a key role in the onset and development of AD, and neuroinflammation has been proposed as the third major pathological driving factor of AD, ranking after the two well-known core pathologies, amyloid ß (Aß) deposits and neurofibrillary tangles (NFTs). Epigenetic mechanisms, referring to heritable changes in gene expression independent of DNA sequence alterations, are crucial regulators of neuroinflammation which have emerged as potential therapeutic targets for AD. Upon regulation of transcriptional repression or activation, epigenetic modification profiles are closely involved in inflammatory gene expression and signaling pathways of neuronal differentiation and cognitive function in central nervous system disorders. In this review, we summarize the current knowledge about epigenetic control mechanisms with a focus on DNA and histone modifications involved in the regulation of inflammatory genes and signaling pathways in AD, and the inhibitors under clinical assessment are also discussed.

Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions.

Purpose: To determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. Materials and Methods: A retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0-T MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images of each patient. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set. Results: A total of four radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: p < 0.001; testing set: p = 0.035). Multivariable logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical-radiomic model produced the receiver operating characteristic (ROC) curve (AUC) in the testing set was 0.88 (95%CI, 0.75-1.00), which was similar to clinical model (AUC = 0.85; 95%CI, 0.52-0.90) (p = 0.048) and higher than the radiomic model (AUC = 0.71; 95%CI, 0.68-1.00) (p < 0.001). The decision curve analysis implies that the clinical-radiomic model could be beneficial in identifying csPCa among PI-RADS 3 lesions. Conclusion: The clinical-radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions and thus could help avoid unnecessary biopsy and improve the life quality of patients.

Modafinil rescues repeated morphine-induced synaptic and behavioural impairments via activation of D1R-ERK-CREB pathway in medial prefrontal cortex.

Long-term opioid abuse causes a variety of long-lasting cognitive impairments such as attention, impulsivity and working memory. These cognitive impairments undermine behavioural treatment for drug abuse and lead to poor treatment retention and outcomes. Modafinil is a wake-promoting drug that shows potential in improving attention and memory in humans and animals. However, modafinil's effect on opioid-induced cognitive impairments remains unclear, and the underlying mechanism is poorly understood. This study showed that repeated morphine administration significantly impairs attention, increases impulsivity and reduces motivation to natural rewards in mice. Systemic modafinil treatment at low dose efficiently ameliorates morphine-induced attention dysfunction and improves motivation and working memory in mice. High dose of modafinil has adverse effects on impulsive action and attention. Local infusion of D1R antagonist SCH-23390 reverses the morphine-induced synaptic abnormalities and activation of the D1R-ERK-CREB pathway in medial prefrontal cortex (mPFC). This study demonstrated a protective effect of modafinil in mPFC neurons and offered a therapeutic potential for cognitive deficits in opioid abuse.

Decitabine improves MMS-induced retinal photoreceptor cell damage by targeting DNMT3A and DNMT3B.

Introduction: Retinitis pigmentosa (RP) is a group of neurodegenerative retinopathies causing blindness due to progressive and irreversible photoreceptor cell death. The alkylating agent methyl methanesulfonate (MMS) can induce selective photoreceptor cell death, which is used to establish RP animal models. MMS induces DNA base damage by adding alkyl groups to DNA, and epigenetic modifications influence DNA damage response. Here, we aimed to explore the relationship between DNA methylation and DNA damage response in dying photoreceptors of RP. Methods: The mouse RP model was established by a single intraperitoneal injection of MMS. The retinal structure and function were assessed by H&E, OCT, TUNEL, and ERG at several time points. The expression of DNA methylation regulators was assessed by qPCR and Western blot. DNMT inhibitor 5-aza-dC was applied to inhibit the activity of DNA methyltransferases and improve the retinal photoreceptor damage. Results: The outer nuclear layer (ONL) and IS/OS layer were significantly thinner and the retinal function was impaired after MMS treatment. The cell death was mainly located in the ONL. The retinal damage induced by MMS was accompanied by hyperexpression of DNMT3A/3B. The application of DNMT inhibitor 5-aza-dC could suppress the expression level of DNMT3A/3B, resulting in the remission of MMS-induced photoreceptor cell damage. The ONL and IS/OS layers were thicker than that of the control group, and the retinal function was partially restored. This protective effect of 5-aza-dC was associated with the down-regulated expression of DNMT3A/3B. Conclusion: These findings identified a functional role of DNMT3A/3B in MMS-induced photoreceptor cell damage and provided novel evidence to support DNMTs as potential therapeutic targets in retinal degenerative diseases.Graphical Abstract.

Ethanol-Induced Neuronal and Cognitive/Emotional Impairments are Accompanied by Down-Regulated NT3-TrkC-ERK in Hippocampus.

AIMS: Ethanol ingestion affects cognition and emotion, which have been attributed to the dysfunction of specific brain structures. Studies of alcoholic patients and animal models consistently identify reduced hippocampal mass as a key ethanol-induced brain adaptation. This study evaluated how neuroadaptation in the hippocampus (Hip) produced by ethanol contributed to related behavioral deficits in male and female rats. METHODS: Effects of acute, short-term and long-term ethanol exposure on the anxiety-like behavior and recognition memory on adult male and female Sprague-Dawley rats were assessed using elevated plus maze test and novel object recognition test, respectively. In addition, in order to investigate the direct effect of ethanol on hippocampal neurons, primary culture of hippocampal neurons was exposed to ethanol (10, 30 and 90 mM; 1, 24 and 48 h), and viability (CCK-8) and morphology (immunocytochemistry) were analyzed at structural levels. Western blot assays were used to assess protein levels of NT3-TrkC-ERK. RESULTS: Acute and short-term ethanol exposure exerted anxiolytic effects, whereas long-term ethanol exposure induced anxiogenic responses in both sexes. Short-term ethanol exposure impaired spatial memory only in female rats, whereas long-term ethanol exposure impaired spatial and recognition memory in both sexes. These behavioral impairments and ethanol-induced loss of hippocampal neurons and decreased cell viability were accompanied by downregulated NT3-TrkC-ERK pathway. CONCLUSION: These results indicate that NT3-TrkC-ERK signaling in the Hip may play an important role in ethanol-induced structural and behavioral impairments.

Dual-specificity phosphatase 15 (DUSP15) in the nucleus accumbens is a novel negative regulator of morphine-associated contextual memory.

Drug relapse among addicts often occurs due to the learned association between drug-paired cues and the rewarding effects of these drugs, such as morphine. Contextual memory associated with morphine has a central role in maintenance and relapse. We showed that morphine-conditioned place preference (CPP) activates extracellular-regulated protein kinase (ERK) in the nucleus accumbens (NAc). The main enzymes that mediate ERK dephosphorylation are members of the dual-specificity phosphatase (DUSP) superfamily. It is unclear which members regulate the morphine CPP-induced activation of ERK. After screening, DUSP15 was found to be decreased during both morphine CPP expression and the reinstatement period. Intra-NAc infusions of AAV-DUSP15 (overexpression) not only prevented the expression of morphine-induced CPP but also facilitated extinction, inhibited reinstatement, and abolished ERK activation. However, after repeated morphine exposure and withdrawal in mice, there was no change in the expression of p-ERK and DUSP15, and the overexpression of DUSP15 in the NAc did not improve the impaired spatial memory or anxiety-like behaviour induced by morphine. Together, these findings indicate that DUSP15 not only prevents the expression of drug-paired contextual memory but also promotes the extinction of existing addiction memories, thus providing a novel therapeutic target for the treatment of drug addiction.

Neuroprotective Effects of Grape Seed Procyanidins on Ethanol-Induced Injury and Oxidative Stress in Rat Hippocampal Neurons.

AIMS: Ethanol is a small molecule capable of interacting with numerous targets in the brain, the mechanisms of which are complex and still poorly understood. Studies have revealed that ethanol-induced hippocampal neuronal injury is associated with oxidative stress. Grape seed procyanidin (GSP) is a new type of antioxidant that is believed to scavenge free radicals and be anti-inflammatory. This study evaluated the ability and mechanism by which the GSP improves ethanol-induced hippocampal neuronal injury. METHODS: Primary cultures of hippocampal neurons were exposed to ethanol (11, 33 and 66 mM, 1, 4, 8, 12 and 24 h) and the neuroprotective effects of GSP were assessed by evaluating the activity of superoxide dismutase (SOD), the levels of malondialdehyde (MDA) and lactate dehydrogenase (LDH) and cell morphology. RESULTS: Our results indicated that GSP prevented ethanol-induced neuronal injury by reducing the levels of MDA and LDH, while increasing the activity of SOD. In addition, GSP increased the number of primary dendrites and total dendritic length per cell. CONCLUSION: Together with previous findings, these results lend further support to the significance of developing GSP as a therapeutic tool for use in the treatment of alcohol use disorders.

PI3K-AKT-GSK3ß-CREB signaling pathway regulates anxiety-like behavior in rats following alcohol withdrawal.

BACKGROUND: Alcohol abuse and anxiety disorders often occur concurrently, but their underlying cellular mechanisms remain unclear. Neuroadaptation within the medial prefrontal cortex (mPFC) have been implicated in the molecular mechanisms underlying alcohol drinking behavior and withdrawal. METHODS: A chronic alcohol exposure rat model (35 consecutive days of 10% alcohol intake and 48 h of withdrawal) was established, then, wortmannin (0.5 µg/side) was injected bilaterally into the mPFC. The elevated plus maze (EPM) and open field test (OFT) were used to assess anxiety-like behavior. Western blot assays were used to assess protein levels. RESULTS: We found that anxiety-like behavior peaked approximately 6 h after alcohol withdrawal. However, wortmannin greatly decreased alcohol intake and attenuated anxiety-like behavior in the alcohol exposure rats. Moreover, the PI3K-AKT-GSK3ß signaling pathway was activated after alcohol withdrawal, and phosphorylation of the downstream cAMP response element-binding protein (CREB) was increased. Wortmannin uniformly reversed PI3K-AKT-GSK3ß-CREB pathway phosphorylation. LIMITATIONS: The downstream GSK3ß activity was not intervened and a single dose level of wortmannin was used. CONCLUSION: Our results suggest that activating the PI3K-AKT-GSK3ß-CREB pathway in the mPFC is an important contributor to the molecular mechanisms underlying alcohol withdrawal. PI3K signaling pathway inhibitors are thus potential candidates for treating alcohol abuse.

5-Aza-2'-deoxycytidine in the medial prefrontal cortex regulates alcohol-related behavior and Ntf3-TrkC expression in rats.

Recent studies have indicated that DNA methylation plays an important role in the development of alcohol abuse. 5-Aza-2'-deoxycytidine (5-Aza-dc), an inhibitor of DNA methyltransferases, was FDA approved for myelodysplastic syndrome treatment. However, it is unclear whether 5-Aza-dc is involved in alcohol abuse. In this study, using a chronic alcohol exposure model in rats, 5-Aza-dc was injected into the medial prefrontal cortex (mPFC). Alcohol-drinking behavior and the anxiety related behavior were evaluated by two-bottle choice and open field test. We found that 5-Aza-dc injection into the mPFC significantly decreased alcohol consumption and alcohol preference in alcohol-exposure rats, corresponding to the reduced blood alcohol levels. Although 5-Aza-dc potentiated the anxiety-like behavior of alcohol-exposure rats, it had no effect on the locomotor activity. Moreover, both of the mRNA and protein levels of DNA Methyltransferase 3A (DNMT3A) and DNMT3B in the mPFC were upregulated after 35 days of alcohol exposure and this upregulation could be reversed by 5-Aza-dc treatment. Additionally, 5-Aza-dc reversed the alcohol-induced downregulation of neurotrophin-3 (Ntf3), correspondingly the expression of its receptor-TrkC was reduced. These findings identified a functional role of 5-Aza-dc in alcohol-related behavioral phenotypes and one of the potential target genes, Ntf3. We also provide novel evidence for DNA methyltransferases as potential therapeutic targets in alcohol abuse.