TRIM33 Is a Co-Regulator of Estrogen Receptor Alpha.

Estrogen receptor alpha (ER)-positive breast cancer is responsible for over 60% of breast cancer cases in the U.S. Among patients diagnosed with early-stage ER+ disease, 1/3 will experience recurrence despite treatment with adjuvant endocrine therapy. ER is a nuclear hormone receptor responsible for estrogen-driven tumor growth. ER transcriptional activity is modulated by interactions with coregulators. Dysregulation of the levels of these coregulators is involved in the development of endocrine resistance. To identify ER interactors that modulate transcriptional activity in breast cancer, we utilized biotin ligase proximity profiling of ER interactomes. Mass spectrometry analysis revealed tripartite motif containing 33 (TRIM33) as an estrogen-dependent interactor of ER. shRNA knockdown showed that TRIM33 promoted ER transcriptional activity and estrogen-induced cell growth. Despite its known role as an E3 ubiquitin ligase, TRIM33 increased the stability of endogenous ER in breast cancer cells. TRIM33 offers a novel target for inhibiting estrogen-induced cancer cell growth, particularly in cases of endocrine resistance driven by ER (ESR1) gene amplification or overexpression.

Potential Health Risk of Aluminum in Four Camellia sinensis Cultivars and Its Content as a Function of Leaf Position.

Tea plants can accumulate aluminum (Al) in their leaves to a greater extent than most other edible plants. Few studies, however, address the Al concentration in leaves at different positions, which is important information for tea quality control. Leaves from four different cultivars of Camellia sinensis L. grown in Hawaii were analyzed for Al concentrations at 10 different leaf positions. Each cultivar was harvested in the winter and summer to determine seasonal variations of Al concentrations in the leaves. The results showed that Al concentrations in the winter leaves were an average of 1.2-fold higher than those in the summer leaves, although the seasonal variations were not statistically significant. The total Al concentration of successively lower leaves showed an exponential increase (R2 ≥ 0.900) for all four cultivars in the summer season, whereas those of the winter leaves fit a bi-phase linear regression (R2 ≥ 0.968). The regression of the Al concentrations against the top-5 leaf positions in the winter season fit one linear regression, while that against leaf positions 6-11 fit another linear regression. The average Al concentrations between the third leaf and the shoot plus first two leaves increased approximately 2.7-fold and 1.9-fold for all cultivars in the winter and summer months, respectively. The Al concentrations in the rest of the leaves increased approximately 1.5-fold in a sequential order. The target hazard quotient being between 1.69 × 10-2 and 5.06 × 10-1 in the tea leaf samples of the four cultivars in Hawaii were all less than 1, suggesting negligible health risks for consumers. The results of this study may be useful for directing harvest practices and estimating tea quality.

A network pharmacology-integrated metabolomics strategy for clarifying the action mechanisms of Schisandrae Chinensis Fructus for treating drug-induced liver injury by acetaminophen.

Schisandrae Chinensis Fructus (SCF) was a Traditional Chinese Medicine (TCM) for protecting liver. However, underlying therapeutic mechanisms of SCF for drug-induced liver injury (DILI) by acetaminophen (APAP) are still unclear. This study aims to discover the potential regulation mechanisms of SCF in the treatment of DILI by APAP using the integrated network pharmacology, plasma metabolomics profiling with UPLC-Q-TOF-MS approach. The key targets in the shared pathways of network pharmacology and metabolomics were screened and experimentally validated by Quantitative Real-time PCR analysis. The results showed that SCF could exert excellent effects on DILI by APAP probably through regulating ErbB signaling pathway and Arachidonic acid metabolism pathway, which was reflected by the reduced gene expression of TNF-α, IL-6, IL-1ß, COX-2 and EGFR, as well as the increased gene expression of Nrf2, HO-1, MDM2, MAPK8, SRC, PLD1, CYP2E1, CYP1A2, CYP3A1. This study systematically explored the pharmacological mechanisms of SCF in the treatment of DILI, meanwhile, metabolomics combine with network pharmacology approach might be a useful strategy for early diagnosis of DILI by APAP.

MAP30 protein from Momordica charantia is therapeutic and has synergic activity with cisplatin against ovarian cancer in vivo by altering metabolism and inducing ferroptosis.

Increasing evidence shows that Traditional Chinese Medicine (TCM) has an obvious appeal for cancer treatment, but there is still a lack of scientific investigation of its underlying molecular mechanisms. Bitter melon or bitter gourd (Momordica charantia) is an edible fruit that is commonly consumed, and it is used to cure different diseases in various ancient folk medical practices. We report that a bioactive protein, MAP30, isolated from bitter melon seeds exhibited potent anticancer and anti-chemoresistant effects on ovarian cancer cells. Functional studies revealed that MAP30 inhibited cancer cell migration, cell invasion, and cell proliferation in various ovarian cancer cells but not normal immortalized ovarian epithelial cells. When administered with cisplatin, MAP30 produced a synergistic effect on cisplatin-induced cell cytotoxicity in ovarian cancer cells. When low doses of cisplatin and MAP30 were co-injected intraperitoneally, a remarkable reduction of tumor dissemination and tumor growth was observed in an ovarian cancer ascites mouse model. Notably, blood tests confirmed that MAP30 did not cause any adverse effects on liver and kidney functions in the treated mice. MAP30 activated AMP-activated protein kinase (AMPK) signaling via CaMKKß and induced cell cycle arrest in the S-phase. MAP30 modulated cell metabolism of ovarian cancer cells via suppression of GLUT-1/-3-mediated glucose uptake, adipogenesis, and lipid droplet formation in tumor development and progression. MAP30 also induced an increase in intracellular Ca2+ ion concentration, which triggered ROS-mediated cancer cell death via apoptosis and ferroptosis. Collectively, these findings suggest that natural MAP30 is a non-toxic supplement that may enhance chemotherapeutic outcomes and benefit ovarian cancer patients with peritoneal metastases.

Network pharmacology based investigation into the bioactive compounds and molecular mechanisms of Schisandrae Chinensis Fructus against drug-induced liver injury.

OBJECTIVE: To investigate the against Drug-induced liver injury ingredients and their functional mechanisms in S. Chinensis Fructus. METHODS: Liquid chromatograph-mass spectrometry analysis was performed on S. Chinensis Fructus extrac. The "Components-Target-Disease" network model was constructed by network pharmacology-based approaches. String analysis was performed to reveal enrichment of these target proteins, protein-protein interactions, pathways and related diseases. And experiment of APAP-induced drug-induced liver injury was to be verified. RESULTS: Cytoscape was used to determine the potential protein targets for these components in S. Chinensis Fructus, indicating that 17 against Drug-induced liver injury compounds in S. Chinensis Fructus regulate 52 diabetes-related proteins in 15 signal pathways and involve 14 core key targets. Verification experiment results that S. Chinensis Fructus prevented the elevation of serum biochemical parameters including aspartate aminotransferase (AST), alanine aminotransferase (ALT), purine nucleoside phosphorylase (PNP) and alkaline phosphatase (ALP) against acute liver failure. Additionally, S. Chinensis Fructus reduced the content of malondialdehyde (MDA), increased the levels of the Superoxide dismutase (SOD) and Glutathione (GSH), and inhibited the production of proinflammatory cytokines in APAP-induced hepatotoxicity. CONCLUSION: The mechanisms of S. Chinensis Fructus against Drug-induced liver injury were involved in the regulation of multiple targets, especially affecting the ErbB signaling pathways. The active ingredients of S. Chinensis Fructus may play a role against Drug-induced liver injury by participating in the regulation of inflammatory factors, oxidative stress.

Comparison of femur stiffness measured from DXA and QCT for assessment of hip fracture risk.

Femur stiffness, for example axial and bending stiffness, integrates both geometric and material information of the bone, and thus can be an effective indicator of bone strength and hip fracture risk. Femur stiffness is ideally measured from quantitative computed tomography (QCT), but QCT is not recommended for routine clinical use due to the public concern about exposure to high-dosage radiation. Dual energy X-ray absorptiometry (DXA) is currently the primary imaging modality in clinic. However, DXA is two-dimensional and it is not clear whether DXA-estimated stiffness has adequate accuracy to replace its QCT counterpart for clinical application. This study investigated the accuracy of femur stiffness (axial and bending) estimated from CTXA (computed tomography X-ray absorptiometry) and DXA against those directly measured from QCT. Proximal-femur QCT and DXA from 67 subjects were acquired. For each femur, the QCT dataset was projected into CTXA using CTXA-Hip (Mindways Software, Inc., USA). Femur stiffness at the femoral neck and intertrochanter were then calculated from QCT, CTXA and DXA, respectively, and different elasticity-density relationships were considered in the calculation. Pearson correlations between QCT and CTXA/DXA measured stiffness were studied. The results showed that there were strong correlations between QCT and CTXA derived stiffness, although the correlations were affected by the adopted elasticity-density relationship. Correlations between QCT and DXA derived stiffness were much less strong, mainly caused by the inconsistence of femur orientation in QCT projection and in DXA positioning. Our preliminary clinical study showed that femur stiffness had slightly better performance than femur geometry in discrimination of hip fracture cases from controls.

Prediction and detection of seizures from simultaneous thalamic and scalp electroencephalography recordings.

OBJECTIVE The authors explored the feasibility of seizure detection and prediction using signals recorded from the anterior thalamic nucleus, a major target for deep brain stimulation (DBS) in the treatment of epilepsy. METHODS Using data from 5 patients (13 seizures in total), the authors performed a feasibility study and analyzed the performance of a seizure prediction and detection algorithm applied to simultaneously acquired scalp and thalamic electroencephalography (EEG). The thalamic signal was obtained from DBS electrodes. The applied algorithm used the similarity index as a nonlinear measure for seizure identification, with patient-specific channel and threshold selection. Receiver operating characteristic (ROC) curves were calculated using data from all patients and channels to compare the performance between DBS and EEG recordings. RESULTS Thalamic DBS recordings were associated with a mean prediction rate of 84%, detection rate of 97%, and false-alarm rate of 0.79/hr. In comparison, scalp EEG recordings were associated with a mean prediction rate of 71%, detection rate of 100%, and false-alarm rate of 1.01/hr. From the ROC curves, when considering all channels, DBS outperformed EEG for both detection and prediction of seizures. CONCLUSIONS This is the first study to compare automated seizure detection and prediction from simultaneous thalamic and scalp EEG recordings. The authors have demonstrated that signals recorded from DBS leads are more robust than EEG recordings and can be used to predict and detect seizures. These results indicate feasibility for future designs of closed-loop anterior nucleus DBS systems for the treatment of epilepsy.

Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection.

OBJECTIVE: Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. APPROACH: Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. MAIN RESULTS: Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. SIGNIFICANCE: These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.

Feature consistency-based model adaptation in session-to-session classification: a study using motor imagery of swallow EEG signals.

The performance degradation for session to session classification in brain computer interface is a critical problem. This paper proposes a novel method for model adaptation based on motor imagery of swallow EEG signal for dysphagia rehabilitation. A small amount of calibration testing data is utilized to select the model catering for test data. The features of the training and calibration testing data are firstly clustered and each cluster is labeled by the dominant label of the training data. The cluster with the minimum impurity is selected and the number of features consistent with the cluster label are calculated for both training and calibration testing data. Finally, the training model with the maximum number of consistent features is selected. Experiments conducted on motor imagery of swallow EEG data achieved an average accuracy of 74.29% and 72.64% with model adaptation for Laplacian derivates of power features and wavelet features, respectively. Further, an average accuracy increase of 2.9% is achieved with model adaptation using wavelet features, in comparison with that achieved without model adaptation, which is significant at 5% significance level as demonstrated in the statistical test.