Thioridazine reverses trastuzumab resistance in gastric cancer by inhibiting S-phase kinase associated protein 2-mediated aerobic glycolysis.

BACKGROUND: Trastuzumab constitutes the fundamental component of initial therapy for patients with advanced human epidermal growth factor receptor 2 (HER-2)-positive gastric cancer (GC). However, the efficacy of this treatment is hindered by substantial challenges associated with both primary and acquired drug resistance. While S-phase kinase associated protein 2 (Skp2) overexpression has been implicated in the malignant progression of GC, its role in regulating trastuzumab resistance in this context remains uncertain. Despite the numerous studies investigating Skp2 inhibitors among small molecule compounds and natural products, there has been a lack of successful commercialization of drugs specifically targeting Skp2. AIM: To discover a Skp2 blocker among currently available medications and develop a therapeutic strategy for HER2-positive GC patients who have experienced progression following trastuzumab-based treatment. METHODS: Skp2 exogenous overexpression plasmids and small interfering RNA vectors were utilized to investigate the correlation between Skp2 expression and trastuzumab resistance in GC cells. Q-PCR, western blot, and immunohistochemical analyses were conducted to evaluate the regulatory effect of thioridazine on Skp2 expression. A cell counting kit-8 assay, flow cytometry, a amplex red glucose/glucose oxidase assay kit, and a lactate assay kit were utilized to measure the proliferation, apoptosis, and glycolytic activity of GC cells in vitro. A xenograft model established with human GC in nude mice was used to assess thioridazine's effectiveness in vivo. RESULTS: The expression of Skp2 exhibited a negative correlation with the sensitivity of HER2-positive GC cells to trastuzumab. Thioridazine demonstrated the ability to directly bind to Skp2, resulting in a reduction in Skp2 expression at both the transcriptional and translational levels. Moreover, thioridazine effectively inhibited cell proliferation, exhibited antiapoptotic properties, and decreased the glucose uptake rate and lactate production by suppressing Skp2/protein kinase B/mammalian target of rapamycin/glucose transporter type 1 signaling pathways. The combination of thioridazine with either trastuzumab or lapatinib exhibited a more pronounced anticancer effect in vivo, surpassing the efficacy of either monotherapy. CONCLUSION: Thioridazine demonstrates promising outcomes in preclinical GC models and offers a novel therapeutic approach for addressing trastuzumab resistance, particularly when used in conjunction with lapatinib. This compound has potential benefits for patients with Skp2-proficient tumors.

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer.

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.

Biofilm stratification in counter-diffused membrane biofilm bioreactors (MBfRs) for aerobic methane oxidation coupled to aerobic/anoxic denitrification: Effect of oxygen pressure.

Aerobic methane oxidation coupled with denitrification (AME-D) executed in membrane biofilm bioreactors (MBfRs) provides a high promise for simultaneously mitigating methane (CH4) emissions and removing nitrate in wastewater. However, systematically experimental investigation on how oxygen partial pressure affects the development and characteristics of counter-diffusional biofilm, as well as its spatial stratification profiles, and the cooperative interaction of the biofilm microbes, is still absent. In this study, we combined Optical Coherence Tomography (OCT) with Confocal Laser Scanning Microscopy (CLSM) to in-situ characterize the development of counter-diffusion biofilm in the MBfR for the first time. It was revealed that oxygen partial pressure onto the MBfR was capable of manipulating biofilm thickness and spatial stratification, and then managing the distribution of functional microbes. With the optimized oxygen partial pressure of 5.5 psig (25% oxygen content), the manipulated counter-diffusional biofilm in the AME-D process obtained the highest denitrification efficiency, due mainly to that this biofilm had the proper dynamic balance between the aerobic-layer and anoxic-layer where suitable O2 gradient and sufficient aerobic methanotrophs were achieved in aerobic-layer to favor methane oxidation, and complete O2 depletion and accessible organic sources were kept to avoid constraining denitrification activity in anoxic-layer. By using metagenome analysis and Fluorescence in situ hybridization (FISH) staining, the spatial distribution of the functional microbes within counter-diffused biofilm was successfully evidenced, and Rhodocyclaceae, one typical aerobic denitrifier, was found to survive and gradually enriched in the aerobic layer and played a key role in denitrification aerobically. This in-situ biofilm visualization and characterization evidenced directly for the first time the cooperative path of denitrification for AME-D in the counter-diffused biofilm, which involved aerobic methanotrophs, heterotrophic aerobic denitrifiers, and heterotrophic anoxic denitrifiers.

Recent Development of Machine Learning Methods in Sumoylation Sites Prediction.

Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity, and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico has also attracted much attention for its accuracy, convenience, and speed. At present, many computational prediction models have been used to identify SUMO sites, but their contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We have briefly summarized the development of bioinformatics methods for sumoylation site prediction by mainly focusing on the benchmark dataset construction, feature extraction, machine learning method, published results, and online tools. We hope that this review will provide more help for wet-experimental scholars.

Bacillus amyloliquefaciens ameliorates high-carbohydrate diet-induced metabolic phenotypes by restoration of intestinal acetate-producing bacteria in Nile Tilapia.

Poor utilisation efficiency of carbohydrate always leads to metabolic phenotypes in fish. The intestinal microbiota plays an important role in carbohydrate degradation. Whether the intestinal bacteria could alleviate high-carbohydrate diet (HCD)-induced metabolic phenotypes in fish remains unknown. Here, a strain affiliated to Bacillus amyloliquefaciens was isolated from the intestine of Nile tilapia. A basal diet (CON), HCD or HCD supplemented with B. amy SS1 (HCB) was used to feed fish for 10 weeks. The beneficial effects of B. amy SS1 on weight gain and protein accumulation were observed. Fasting glucose and lipid deposition were decreased in the HCB group compared with the HCD group. High-throughput sequencing showed that the abundance of acetate-producing bacteria was increased in the HCB group relative to the HCD group. Gas chromatographic analysis indicated that the concentration of intestinal acetate was increased dramatically in the HCB group compared with that in the HCD group. Glucagon-like peptide-1 was also increased in the intestine and serum of the HCB group. Thus, fish were fed with HCD, HCD supplemented with sodium acetate at 900 mg/kg (HLA), 1800 mg/kg (HMA) or 3600 mg/kg (HHA) diet for 8 weeks, and the HMA and HHA groups mirrored the effects of B. amy SS1. This study revealed that B. amy SS1 could alleviate the metabolic phenotypes caused by HCD by enriching acetate-producing bacteria in fish intestines. Regulating the intestinal microbiota and their metabolites might represent a powerful strategy for fish nutrition modulation and health maintenance in future.

[Study of p16(INK4A) expression and DNA ploidy in HPV-negative cervical cancers and precursors].

OBJECTIVE: To investigate the clinicopathological significance of p16(INK4A) expression and DNA ploidy status in HPV-negative uterine cervical cancers and their precursors. METHODS: HPV-negative cervical lesions, including 20 cases of cervicitis, 20 cases of cervical intraepithelial neoplasm (CIN), 3 cases of cervical glandular intraepithelial neoplasm (CGIN), 38 cases of invasive squamous cell carcinoma (SCCs) and 15 cases of invasive adenocarcinoma were selected and subject to screening for HPV infection by PCR method. The p16(INK4A) protein expression and DNA ploidy status were studied by immunohistochemistry and flow cytometry respectively. RESULTS: Specific expression of p16(INK4A) was seen in both the nucleus and cytoplasm of the dysplastic and malignant cells of CIN, CGIN, cervical SCC and adenocarcinoma. In contrast, no expression was present in normal and inflammatory squamous or glandular epithelium. DNA aneuploidy was significantly more frequent in invasive SCCs and adenocarcinomas than in CIN (P < 0.01). Aneuploid was also more frequent in the lymph node positive group than lymph node negative group, although no statistic significance was found. Among the 8 cases of p16(INK4A) negative SCCs, two showed DNA aneuploidy. CONCLUSIONS: Immunohistochemical detection for p16(INK4A) can be an early diagnostic marker for HPV-negative cervical SCC and adenocarcinoma. DNA ploidy analysis may further assist the diagnosis of cervical malignancies.