α-Ketoglutarate prevents hyperlipidemia-induced fatty liver mitochondrial dysfunction and oxidative stress by activating the AMPK-pgc-1α/Nrf2 pathway.

α-Ketoglutarate (AKG), a crucial intermediate in the tricarboxylic acid cycle, has been demonstrated to mitigate hyperlipidemia-induced dyslipidemia and endothelial damage. While hyperlipidemia stands as a major trigger for non-alcoholic fatty liver disease, the protection of AKG on hyperlipidemia-induced hepatic metabolic disorders remains underexplored. This study aims to investigate the potential protective effects and mechanisms of AKG against hepatic lipid metabolic disorders caused by acute hyperlipidemia. Our observations indicate that AKG effectively alleviates hepatic lipid accumulation, mitochondrial dysfunction, and loss of redox homeostasis in P407-induced hyperlipidemia mice, as well as in palmitate-injured HepG2 cells and primary hepatocytes. Mechanistic insights reveal that the preventive effects are mediated by activating the AMPK-PGC-1α/Nrf2 pathway. In conclusion, our findings shed light on the role and mechanism of AKG in ameliorating abnormal lipid metabolic disorders in hyperlipidemia-induced fatty liver, suggesting that AKG, an endogenous mitochondrial nutrient, holds promising potential for addressing hyperlipidemia-induced fatty liver conditions.

The ECL enhancement of MBene QDs with nanoparticle-on-mirror structure for sensitive detection of exosomal miRNA.

Thyroid cancer is the most prevalent endocrine malignancy. The development of sensitive and reliable methods to detect the thyroid cancer is the currently urgent requirement. Herein, we developed an electrochemiluminescence (ECL) biosensor based on MBene derivative quantum dots (MoB QDs) and Ag NP-on-mirror (NPoM) nanocavity structure. On the one hand, MBene QDs as a novel luminescent material in the ECL process was reported for the first time, which can react with H2O2 as co-reactant. On the other hand, the NPoM nanostructure was successfully constructed with the Ag mirror and Ag NPs to provide highly localized hot spots. The NPoM structure had high degree of light field confinement and electromagnetic field enhancement, which can amplify the ECL signal as the signal modulator. Therefore, the synergistic effect of the nanocavity and localized surface plasmon resonance (LSPR) mode in the NPoM facilitated the enhancement of the ECL signal of MoB QDs over 21.7 times. Subsequently, the proposed ECL biosensing system was employed to analyze the expression level of miRNA-222-3p in the thyroid cancer exosome. The results indicated the relative association between miRNA-222-3p and BRAFV600E mutation. The MoB QDs/NPoM biosensor displayed the ideal potential in assessing thyroid cancer progression for advancing clinical diagnosis applications.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

Molecular Dynamics Simulations Combined with Markov Model to Explore the Effect of Allosteric Inhibitor Binding on Bromodomain-Containing Protein 4.

Bromodomain-Containing Protein 4 (BRD4) can play an important role in gene transcriptional regulation of tumor development and survival by participating in histone modification epigenetic mechanism. Although it has been reported that novel allosteric inhibitors such as ZL0590 have a high affinity with target protein BRD4 and good efficacy, their inhibitory mechanism has not been studied further. The aim of this study was to reveal the inhibition mechanism of allosteric inhibitor ZL0590 on Free-BRD4 and BRD4 binding MS436 (orthosteric inhibitor) by molecular dynamics simulation combined with a Markov model. Our results showed that BRD4-ZL0590 led to α-helices formation of 100-105 compared with Free-BRD4; the combination of MS436 caused residues 30-40 and 95-105 to form α-helices, while the combination of allosteric inhibitors untangled the α-helices formed by the MS436. The results of Markov flux analysis showed that the binding process of inhibitors mainly involved changes in the degree of α-helices at ZA loop. The binding of ZL0590 reduced the distance between ZA loop and BC loop, blocked the conformation at the active site, and inhibited the binding of MS436. After the allosteric inhibitor binding, the MS436 that could normally penetrate into the interior of the pocket was floating on the edge of the active pocket and did not continue to penetrate into the active pocket as expected. In summary, we provide a theoretical basis for the inhibition mechanism of ZL0590 against BRD4, which can be used as a reference for improving the development of drug targets for cancer therapy.

Hydrogen Protection Boosts the Bioactivity of Chrysanthemum morifolium Extract in Preventing Palmitate-Induced Endothelial Dysfunction by Restoring MFN2 and Alleviating Oxidative Stress in HAEC Cells.

As the most important natural antioxidants in plant extracts, polyphenols demonstrate versatile bioactivities and are susceptible to oxidation. The commonly used ultrasonic extraction often causes oxidation reactions involving the formation of free radicals. To minimize the oxidation effects during the ultrasonic extraction process, we designed a hydrogen (H2)-protected ultrasonic extraction method and used it in Chrysanthemum morifolium extraction. Hydrogen-protected extraction improved the total antioxidant capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, and polyphenol content of Chrysanthemum morifolium water extract (CME) compared with air and nitrogen (N2) conditions. We further investigated the protective effects and mechanisms of CME on palmitate (PA)-induced endothelial dysfunction in human aorta endothelial cells (HAECs). We found that hydrogen-protected CME (H2-CME) best-prevented impairment in nitric oxide (NO) production, endothelial NO synthase (eNOS) protein level, oxidative stress, and mitochondrial dysfunction. In addition, H2-CME prevented PA-induced endothelial dysfunction by restoring mitofusin-2 (MFN2) levels and maintaining redox balance.

Computer-Aided Screening and Revealing Action Mechanism of Green Tea Polyphenols Intervention in Alzheimer's Disease.

The accumulation of cross-ß-sheet amyloid fibrils is a hallmark of the neurodegenerative process of Alzheimer's disease (AD). Although it has been reported that green tea substances such as epicatechin (EC), epicatechin-3-gallate (ECG), epigallocatechin (EGC) and epigallocatechin-3-gallate (EGCG) could alleviate the symptoms of AD and other neurodegenerative diseases, the pharmacological mechanism remains largely unexplored. This study aimed to reveal the underlying mechanism of EC, ECG, EGC and EGCG in AD using a computer-aided screening strategy. Our results showed that the four tea polyphenols interfered with the signaling pathways of AD via calcium signaling channels, neurodegeneration-multiple disease signal pathways and others. We also identified the key residues of the interaction between VEGFA and the four active components, which included Glu64 and Phe36. Overall, we have provided valuable insights into the molecular mechanism of tea polyphenols, which could be used as a reference to improve therapeutic strategies against AD.

Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data.

Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples. Although many studies have investigated this problem, there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples. Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries, we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and assembly approaches to obtain the relative abundance profiles of both known and novel genomes. The random forests (RF) classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance profiles. Based on within data cross-validation and cross-dataset prediction, we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and Bracken. We also develop a novel method to integrate the relative abundance profiles of both known and novel microbial organisms to further increase the prediction performance for colorectal cancer from metagenomes.

Amylin deposition activates HIF1α and 6-phosphofructo-2-kinase/fructose-2, 6-biphosphatase 3 (PFKFB3) signaling in failing hearts of non-human primates.

Hyperamylinemia induces amylin aggregation and toxicity in the pancreas and contributes to the development of type-2 diabetes (T2D). Cardiac amylin deposition in patients with obesity and T2D was found to accelerate heart dysfunction. Non-human primates (NHPs) have similar genetic, metabolic, and cardiovascular processes as humans. However, the underlying mechanisms of cardiac amylin in NHPs, particularly related to the hypoxia inducible factor (HIF)1α and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) signaling pathways, are unknown. Here, we demonstrate that in NHPs, amylin deposition in heart failure (HF) contributes to cardiac dysfunction via activation of HIF1α and PFKFB3 signaling. This was confirmed in two in vitro cardiomyocyte models. Furthermore, alterations of intracellular Ca2+, reactive oxygen species, mitochondrial function, and lactate levels were observed in amylin-treated cells. Our study demonstrates a pathological role for amylin in the activation of HIF1α and PFKFB3 signaling in NHPs with HF, establishing amylin as a promising target for heart disease patients.

A smart drug: a pH-responsive photothermal ablation agent for Golgi apparatus activated cancer therapy.

We report a pH-responsive photothermal ablation agent (pH-PTT) based on cyanine dyes for photothermal therapy (PTT). The nanoparticles formed by BSA and pH-PTT preferentially accumulated in the Golgi apparatus of cancer cells compared to normal cells, and thus can be specifically activated by the acidic Golgi apparatus in cancer cells for effective PTT both ex vivo and in vivo.

Amphiphilic PEGylated Lanthanide-Doped Upconversion Nanoparticles for Significantly Passive Accumulation in the Peritoneal Metastatic Carcinomatosis Models Following Intraperitoneal Administration.

Inorganic nanoparticles have emerged as attractive materials for cancer research, because of their exceptional physical properties and multifunctional engineering. However, inorganic nanoparticle accumulation in the tumors located in the abdominal cavity after intravenous (IV) administration is confined because of the peritoneum-plasma barrier. To improve this situation, we developed lanthanide-doped upconversion nanoparticles (UCNPs), coated by amphiphilic polyethylene glycol (P-PEG), serving as a representative of inorganic nanoparticles. Following intraperitoneal (IP) administration into the peritoneal metastatic carcinomatosis models, UCNPs coated by P-PEG (P-PEG-UCNPs) passively accumulated in the cancerous tissues at a larger amount than that in the main normal organs. On the basis of spatial proximity, P-PEG-UCNPs administrated via the IP route exhibited higher passive accumulation in the tumors in the abdominal cavity compared to that via the IV route. It is suggested that IP administration could be a promising strategy for inorganic nanoparticles to be efficaciously applied in peritoneal cancer research.