Response and resistance to BET inhibitors in triple negative breast cancer

Introduction: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and urgently needs new potent therapies. Bromodomain and extra-terminal proteins (BET) are primary regulators of RNA polymerase II, which also regulate gene transcription. For cancer patients, numerous BET inhibitors are now undergoing clinical trials. Unfortunately, the responses to BET inhibitors varied between preclinical and clinical investigations, necessitating more research into their functions in cancer. Materials and methods: Gene expression data were acquired from The Cancer Genome Atlas the (TCGA), Cancer Cell Line Encyclopedia (CCLE), and the Human Protein Atlas (HPA) to analyze the gene and protein expression of BET family members in TNBC patients and cell lines. One cell line (MDA-MB-231R) resistant to the BET inhibitor JQ1 was established from the parental cell line MDA-MB-231. The cytotoxicity of the medications following treatments was assessed using the MTT test. The cell cycle, cell apoptosis, and BH3 profiling analyses were carried out using FACS analysis. Western blotting was used to detect protein expression before and after treatments. Results: Particularly in TNBC tissues and cell lines with the low rate of mutations, BRD4 was overexpressed in breast cancer. The BET inhibitor JQ1 could induce cell cycle G1 arrest, senescence, and minor cell apoptosis in TNBC. When combined with CDK9 inhibitors, JQ1 showed a strong synergistic effect in TNBC cell lines by inducing apoptosis. It was discovered through the use of the BH3 profiling assay and the MTT assay that BCL-xL was necessary for the survival of MDA-MB-231 and MDA-MB-231R cells, whereas that of MDA-MB-436 cells was dependent on BCL-xL and MCL-1. According to Western blot analysis the combination of JQ1 and CDK9 inhibitor dramatically reduced the expression of BCL-xL and MCL-1 in TNBC cell lines. Therefore it is likely that apoptosis induced by combination of both substances is a result of reduction of BCL-xL and/or MCL-1. Summary/conclusions: Promising Targets in TNBC include BET proteins, particularly BRD4. The BET inhibitor JQ1 is a potential therapeutic agent in treatment of TNBC treatment. Moreover, the combination of BET and CDK9 inhibitors has a synergistic inhibitory effect in TNBC treatment not only in JQ1-sensitive cell but also in primary and secondary resistant cell lines by inducing cell apoptosis possibly through suppressing MCL-1 and BCL-xL.Zusammenfassung Einleitung: Das triple-negative Mammakarzinom (triple negative breast cancer, TNBC) gilt als der aggressivste Subtyp von Mammakarzinomen und es werden für diese Entität dringend neue wirksamere Therapieansätze gesucht. Bromodomain und Extra-Terminal (BET)-Proteine sind Schlüsselregulatoren der RNA-Polymerase II und kontrollieren die Gentranskription. Derzeit werden BET-Inhibitoren in klinischen Studien für Krebspatienten untersucht. BET-Inhibitoren zeigten bis dato unterschiedlichen Ergebnissen in präklinischen Studien untersucht, weshalb ihre Rolle bei Tumortherapien, insbesondere beim TNBC, weiter erforscht und definiert sollte. Material und Methoden: Genexpressionsdaten wurden vom Cancer Genome Atlas (TCGA), der Cancer Cell Line Encyclopedia (CCLE) und dem Human Protein Atlas (HPA) heruntergeladen, um die Expression der BET-Familie bei TNBC-Patienten und Zelllinien zu analysieren. Eine Zelllinie (MDA-MB-231R), die gegen den BET-Inhibitor JQ1 resistent war, wurde aus der ursprünglichen Zelllinie MDA-MB-231 entwickelt. An TNBC-Zelllinien wurde die Zytotoxizität der Substanzen mittels MTT-Assay untersucht. Zellzyklus, Zellapoptose und BH3-Profiling wurden mittels FACS analysiert. Die Proteinexpression nach der Behandlung wurde mittels Western Blot analysiert. Ergebnisse: BRD4 wird bei Brustkrebs überexprimiert, besonders in TNBC-Gewebe und Zelllinien mit zugleich niedriger Mutationsrate. Der BET-Inhibitor JQ1 induzierte Zellzyklus-G1-Arrest, Seneszenz und geringe Zellapoptose in TNBC. In Kombination mit CDK9-Inhibitoren zeigte der Bromodomain-Inhibitor JQ1 jedoch einen starken synergistischen Effekt auf TNBC-Zelllinien bei der Apoptose-Induktion. Mit Hilfe des BH3-Profiling-Assays und MTT-Assays konnte gezeigt werden, dass das Überleben von MDA-MB-231- und MDA-MB-231R-Zellen von BCL-xL abhängt, während die Überlebensrate der MDA-MB-436-Zellen von BCL-xL und MCL-1 abhängig ist. Die Western-Blot-Analyse zeigte, dass die Kombination von JQ1 und CDK9-Inhibitoren eine signifikante Herabregulierung der BCL-xL- und MCL-1-Expression in TNBC-Zelllinien bewirkte. Daher wat es wahrscheinlich, dass die durch die Kombination der beiden Substanzen induzierte Apoptose auf eine Verringerung von BCL-xL und/oder MCL-1 zurückzuführen ist. Zusammenfassung/Schlussfolgerungen: BET-Proteine, insbesondere BRD4, sind vielversprechende therapeutische Zielstrukturen beim TNBC. Der BET-Inhibitor JQ1 erwies sich als effektiv in die Behandlung von TNBC-Zelllinien. Darüber hinaus hat die Kombination von BET- und CDK9-Inhibitoren eine synergistische hemmende Wirkung bei TNBC, nicht nur bei JQ1-empfindlichen Zellen, sondern auch bei primär und sekundär resistenten Zelllinien, möglicherweise durch Apoptoseinduktion über die Unterdrückung von MCL-1 und BCL-xL

Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm

IntroductionActive tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.MethodologyUtilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model.ResultsIn executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787.ConclusionThe present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis

Age-related increase of mitochondrial content in human memory CD4+ T cells contributes to ROS-mediated increased expression of proinflammatory cytokines

Cellular metabolism modulates effector functions in human CD4+ T (Th) cells by providing energy and building blocks. Conversely, cellular metabolic responses are modulated by various influences, e.g., age. Thus, we hypothesized that metabolic reprogramming in human Th cells during aging modulates effector functions and contributes to “inflammaging”, an aging-related, chronic, sterile, low-grade inflammatory state characterized by specific proinflammatory cytokines. Analyzing the metabolic response of human naive and memory Th cells from young and aged individuals, we observed that memory Th cells exhibit higher glycolytic and mitochondrial fluxes than naive Th cells. In contrast, the metabolism of the latter was not affected by donor age. Memory Th cells from aged donors showed a higher respiratory capacity, mitochondrial content, and intracellular ROS production than those from young donors without altering glucose uptake and cellular ATP levels, which finally resulted in higher secreted amounts of proinflammatory cytokines, e.g., IFN-γ, IP-10 from memory Th cells taken from aged donors after TCR-stimulation which were sensitive to ROS inhibition. These findings suggest that metabolic reprogramming in human memory Th cells during aging results in an increased expression of proinflammatory cytokines through enhanced ROS production, which may contribute to the pathogenesis of inflammaging

Traffic Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE

In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in traffic flow can timely alleviate traffic congestion and reduce the occurrence of accidents by vehicle scheduling. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an excellent ability in dealing with spatial dependence. Existing works typically make use of graph neural networks (GNNs) and temporal convolutional networks (TCNs) to model spatial and temporal dependencies respectively. However, how to extract as much valid information as possible from nodes is a challenge for GNNs. Therefore, we propose a multi-mode spatial-temporal convolution of mixed hop diffuse ODE (MHODE) for modeling traffic flow prediction. First, we design an adaptive spatial-temporal convolution module that combines Gate TCN and graph convolution to capture more short-term spatial-temporal dependencies and features. Secondly, we design a mixed hop diffuse ordinary differential equation(ODE) spatial-temporal convolution module to capture long-term spatial-temporal dependencies using the receptive field of the mixed hop diffuse ODE. Finally, we design a multi spatial-temporal fusion module to integrate the different spatial-temporal dependencies extracted from two different spatial-temporal convolutions. To capture more spatial-temporal features of traffic flow, we use the multi-mode spatial-temporal fusion module to get more abundant traffic features by considering short-term and long-term two different features. The experimental results on two public traffic datasets (METR-LA and PEMS-BAY) demonstrate that our proposed algorithm performs better than the existing methods in most of cases

Traffic Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE

In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in traffic flow can timely alleviate traffic congestion and reduce the occurrence of accidents by vehicle scheduling. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an excellent ability in dealing with spatial dependence. Existing works typically make use of graph neural networks (GNNs) and temporal convolutional networks (TCNs) to model spatial and temporal dependencies respectively. However, how to extract as much valid information as possible from nodes is a challenge for GNNs. Therefore, we propose a multi-mode spatial-temporal convolution of mixed hop diffuse ODE (MHODE) for modeling traffic flow prediction. First, we design an adaptive spatial-temporal convolution module that combines Gate TCN and graph convolution to capture more short-term spatial-temporal dependencies and features. Secondly, we design a mixed hop diffuse ordinary differential equation(ODE) spatial-temporal convolution module to capture long-term spatial-temporal dependencies using the receptive field of the mixed hop diffuse ODE. Finally, we design a multi spatial-temporal fusion module to integrate the different spatial-temporal dependencies extracted from two different spatial-temporal convolutions. To capture more spatial-temporal features of traffic flow, we use the multi-mode spatial-temporal fusion module to get more abundant traffic features by considering short-term and long-term two different features. The experimental results on two public traffic datasets (METR-LA and PEMS-BAY) demonstrate that our proposed algorithm performs better than the existing methods in most of cases

Genomic insights and prognostic significance of novel biomarkers in pancreatic ductal adenocarcinoma: A comprehensive analysis

Pancreatic ductal adenocarcinoma (PDAC) is a highly prevalent digestive system malignancy, with a significant impact on public health, especially in the elderly population. The advent of the Human Genome Project has opened new avenues for precision medicine, allowing researchers to explore genetic markers and molecular targets for cancer diagnosis and treatment. Despite significant advances in genomic research, early diagnosis of pancreatic cancer remains elusive due to the lack of highly sensitive and specific markers. Therefore, there is a need for in-depth research to identify more precise and reliable diagnostic markers for pancreatic cancer. In this study, we utilized a combination of public databases from different sources to meticulously screen genes associated with prognosis in pancreatic cancer. We used gene differential analysis, univariate cox regression analysis, least absolute selection and shrinkage operator (LASSO) regression, and multivariate cox regression analysis to identify genes associated with prognosis. Subsequently, we constructed a scoring system, validated its validity using survival analysis and ROC analysis, and further confirmed its reliability by nomogram and decision curve analysis (DCA). We evaluated the diagnostic value of this scoring system for pancreatic cancer prognosis and validated the function of the genes using single cell data analysis. Our analysis identifies six genes, including GABRA3, IL20RB, CDK1, GPR87, TTYH3, and KCNA2, that were strongly associated with PDAC prognosis. Clinical prognostic models based on these genes showed strong predictive power not only in the training set but also in external datasets. Functional enrichment analysis revealed significant differences between high- and low-risk groups mainly in immune-related functions. Additionally, we explored the potential of the risk score as a marker for immunotherapy response and identified key factors within the tumor microenvironment. The single-cell RNA sequencing analysis further enriched our understanding of cell clusters and six hub genes expressions. This comprehensive investigation provides valuable insights into pancreatic PDAC and its intricate immune landscape. The identified genes and their functional significance underscore the importance of continued research into improving diagnosis and treatment strategies for PDAC

A Rapid and Efficient Method for Isolation and Transformation of Cotton Callus Protoplast

Protoplasts, which lack cell walls, are ideal research materials for genetic engineering. They are commonly employed in fusion (they can be used for more distant somatic cell fusion to obtain somatic hybrids), genetic transformation, plant regeneration, and other applications. Cotton is grown throughout the world and is the most economically important crop globally. It is therefore critical to study successful extraction and transformation efficiency of cotton protoplasts. In the present study, a cotton callus protoplast extraction method was tested to optimize the ratio of enzymes (cellulase, pectinase, macerozyme R-10, and hemicellulase) used in the procedure. The optimized ratio significantly increased the quantity and activity of protoplasts extracted. We showed that when enzyme concentrations of 1.5% cellulase and 1.5% pectinase, and either 1.5% or 0.5% macerozyme and 0.5% hemicellulase were used, one can obtain increasingly stable protoplasts. We successfully obtained fluorescent protoplasts by transiently expressing fluorescent proteins in the isolated protoplasts. The protoplasts were determined to be suitable for use in further experimental studies. We also studied the influence of plasmid concentration and transformation time on protoplast transformation efficiency. When the plasmid concentration reaches 16 µg and the transformation time is controlled within 12–16 h, the best transformation efficiency can be obtained. In summary, this study presents efficient extraction and transformation techniques for cotton protoplasts