A novel immune-related gene-based prognostic signature to predict biochemical recurrence in patients with prostate cancer after radical prostatectomy.

Accumulating evidences indicates that the immune landscape signature dramatically correlates with tumorigenesis and prognosis of prostate cancer (PCa). Here, we identified a novel immune-related gene-based prognostic signature (IRGPS) to predict biochemical recurrence (BCR) after radical prostatectomy. We also explored the correlation between IRGPS and tumor microenvironment. We identified an IRGPS consisting of seven immune-related genes (PPARGC1A, AKR1C2, COMP, EEF1A2, IRF5, NTM, and TPX2) that were related to the BCR-free survival of PCa patients. The high-risk patients exhibited a higher fraction of regulatory T cells and M2 macrophages than the low-risk BCR patients (P < 0.05) as well as a lower fraction of resting memory CD4 T cells and resting mast cells. These high-risk patients also had higher expression levels of CTLA4, TIGIT, PDCD1, LAG3, and TIM3. Finally, a strong correlation was detected between IRGPS and specific clinicopathological features, including Gleason scores and tumor stage. In conclusion, our study reveals the clinical significance and potential functions of the IRGPS, provides more data for predicting outcomes, and suggests more effective immunotherapeutic target strategies for PCa.

Identifying the novel key genes in renal cell carcinoma by bioinformatics analysis and cell experiments.

BACKGROUND: Although major driver gene have been identified, the complex molecular heterogeneity of renal cell cancer (RCC) remains unclear. Therefore, more relevant genes need to be identified to explain the pathogenesis of renal cancer. METHODS: Microarray datasets GSE781, GSE6344, GSE53000 and GSE68417 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by employing GEO2R tool, and function enrichment analyses were performed by using DAVID. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Survival analysis was performed using GEPIA. Differential expression was verified in Oncomine. Cell experiments (cell viability assays, transwell migration and invasion assays, wound healing assay, flow cytometry) were utilized to verify the roles of the hub genes on the proliferation of kidney cancer cells (A498 and OSRC-2 cell lines). RESULTS: A total of 215 DEGs were identified from four datasets. Six hub gene (SUCLG1, PCK2, GLDC, SLC12A1, ATP1A1, PDHA1) were identified and the overall survival time of patients with RCC were significantly shorter. The expression levels of these six genes were significantly decreased in six RCC cell lines(A498, OSRC-2, 786- O, Caki-1, ACHN, 769-P) compared to 293t cell line. The expression level of both mRNA and protein of these genes were downregulated in RCC samples compared to those in paracancerous normal tissues. Cell viability assays showed that overexpressions of SUCLG1, PCK2, GLDC significantly decreased proliferation of RCC. Transwell migration, invasion, wound healing assay showed overexpression of three genes(SUCLG1, PCK2, GLDC) significantly inhibited the migration, invasion of RCC. Flow cytometry analysis showed that overexpression of three genes(SUCLG1, PCK2, GLDC) induced G1/S/G2 phase arrest of RCC cells. CONCLUSION: Based on our current findings, it is concluded that SUCLG1, PCK2, GLDC may serve as a potential prognostic marker of RCC.

Infection and RNA-seq analysis of a zebrafish tlr2 mutant shows a broad function of this toll-like receptor in transcriptional and metabolic control and defense to Mycobacterium marinum infection.

BACKGROUND: The function of Toll-like receptor 2 (TLR2) in host defense against pathogens, especially Mycobacterium tuberculosis (Mtb) is poorly understood. To investigate the role of TLR2 during mycobacterial infection, we analyzed the response of tlr2 zebrafish mutant larvae to infection with Mycobacterium marinum (Mm), a close relative to Mtb, as a model for tuberculosis. We measured infection phenotypes and transcriptome responses using RNA deep sequencing in mutant and control larvae. RESULTS: tlr2 mutant embryos at 2 dpf do not show differences in numbers of macrophages and neutrophils compared to control embryos. However, we found substantial changes in gene expression in these mutants, particularly in metabolic pathways, when compared with the heterozygote tlr2+/- control. At 4 days after Mm infection, the total bacterial burden and the presence of extracellular bacteria were higher in tlr2-/- larvae than in tlr2+/-, or tlr2+/+ larvae, whereas granuloma numbers were reduced, showing a function of Tlr2 in zebrafish host defense. RNAseq analysis of infected tlr2-/- versus tlr2+/- shows that the number of up-regulated and down-regulated genes in response to infection was greatly diminished in tlr2 mutants by at least 2 fold and 10 fold, respectively. Analysis of the transcriptome data and qPCR validation shows that Mm infection of tlr2 mutants leads to decreased mRNA levels of genes involved in inflammation and immune responses, including il1b, tnfb, cxcl11aa/ac, fosl1a, and cebpb. Furthermore, RNAseq analyses revealed that the expression of genes for Maf family transcription factors, vitamin D receptors, and Dicps proteins is altered in tlr2 mutants with or without infection. In addition, the data indicate a function of Tlr2 in the control of induction of cytokines and chemokines, such as the CXCR3-CXCL11 signaling axis. CONCLUSION: The transcriptome and infection burden analyses show a function of Tlr2 as a protective factor against mycobacteria. Transcriptome analysis revealed tlr2-specific pathways involved in Mm infection, which are related to responses to Mtb infection in human macrophages. Considering its dominant function in control of transcriptional processes that govern defense responses and metabolism, the TLR2 protein can be expected to be also of importance for other infectious diseases and interactions with the microbiome.

Common and specific downstream signaling targets controlled by Tlr2 and Tlr5 innate immune signaling in zebrafish.

BACKGROUND: Although the responses to many pathogen associated molecular patterns (PAMPs) in cell cultures and extracted organs are well characterized, there is little known of transcriptome responses to PAMPs in whole organisms. To characterize this in detail, we have performed RNAseq analysis of responses of zebrafish embryos to injection of PAMPs in the caudal vein at one hour after exposure. We have compared two ligands that in mammals have been shown to specifically activate the TLR2 and TLR5 receptors: Pam3CSK4 and flagellin, respectively. RESULTS: We identified a group of 80 common genes that respond with high stringency selection to stimulations with both PAMPs, which included several well-known immune marker genes such as il1b and tnfa. Surprisingly, we also identified sets of 48 and 42 genes that specifically respond to either Pam3CSK4 or flagellin, respectively, after a comparative filtering approach. Remarkably, in the Pam3CSK4 specific set, there was a set of transcription factors with more than 2 fold-change, as confirmed by qPCR analyses, including cebpb, fosb, nr4a1 and egr3. We also showed that the regulation of the Pam3CSK4 and flagellin specifically responding sets is inhibited by knockdown of tlr2 or tlr5, respectively. CONCLUSIONS: Our studies show that Pam3CSK4 and flagellin can stimulate the Tlr2 and Tlr5 signaling pathways leading to common and specific responses in the zebrafish embryo system.

Balanced influence maximization in social networks based on deep reinforcement learning.

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users' historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy.

Personalized differential expression analysis in triple-negative breast cancer.

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Therapeutic potential of targeting polo-like kinase 4.

Polo-like kinase 4 (PLK4), a highly conserved serine/threonine kinase, masterfully regulates centriole duplication in a spatiotemporal manner to ensure the fidelity of centrosome duplication and proper mitosis. Abnormal expression of PLK4 contributes to genomic instability and associates with a poor prognosis in cancer. Inhibition of PLK4 is demonstrated to exhibit significant efficacy against various types of human cancers, further highlighting its potential as a promising therapeutic target for cancer treatment. As such, numerous small-molecule inhibitors with distinct chemical scaffolds targeting PLK4 have been extensively investigated for the treatment of different human cancers, with several undergoing clinical evaluation (e.g., CFI-400945). Here, we review the structure, distribution, and biological functions of PLK4, encapsulate its intricate regulatory mechanisms of expression, and highlighting its multifaceted roles in cancer development and metastasis. Moreover, the recent advancements of PLK4 inhibitors in patent or literature are summarized, and their therapeutic potential as monotherapies or combination therapies with other anticancer agents are also discussed.

Discovery of novel ocotillol derivatives modulating glucocorticoid receptor/NF-κB signaling for the treatment of sepsis.

Glucocorticoids (GCs) have been used in the treatment of sepsis because of their potent anti-inflammatory effects. However, their clinical efficacy against sepsis remains controversial because of glucocorticoid receptor (GR) downregulation and side effects. Herein, we designed and synthesized 30 ocotillol derivatives and evaluated their anti-inflammatory activities. Ocotillol 24(R/S) differential isomers were stereoselective in their pharmacological action. Specifically, 24(S) derivatives had better anti-inflammatory activity than their corresponding 24(R) derivatives. Compound 20 most effectively inhibited NO release (85.97% reduction), and it exerted dose-dependent inhibitory effects on interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) levels. Mechanistic studies revealed that compound 20 reduces the degradation of GR mRNA and GR protein. Meanwhile, compound 20 inhibited the activation of nuclear factor-κB (NF-κB) signaling, thereby inhibiting the nuclear translocation of p65 and attenuating the inflammatory response. In vivo studies revealed that compound 20 attenuated hepatic, pulmonary, and renal pathology damage in mice with sepsis and suppressed the production of inflammatory mediators. These results indicated that compound 20 is a promising lead compound for designing and developing anti-sepsis drugs.

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.

Using DeepContact with Amira graphical user interface.

DeepContact is a deep learning software for high-throughput quantification of membrane contact site (MCS) in 2D electron microscopy images. This protocol will guide users through incorporating available DeepContact models in Amira's artificial intelligence module, thereby allowing invoking of DeepContact functions in organelle segmentation and quantifying of MCS with a user-friendly graphical user interface of Amira software. For complete details on the use and execution of this protocol, please refer to Liu et al. (2022).1.

Radiology report generation with a learned knowledge base and multi-modal alignment.

In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distill and restore medical knowledge from textual embedding without manual labor; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. Code is available at https://github.com/LX-doctorAI1/M2KT.

Hsa_circ_0063329 inhibits prostate cancer growth and metastasis by modulating the miR-605-5p/tgif2 axis.

Circular RNAs play crucial regulatory roles in the progression of various cancers. Nevertheless, the underlying mechanisms of circRNAs in prostate cancer (PCa) proliferation and metastasis remain largely uncertain. Here, we performed circRNA microarray analyses to identify differentially expressed circRNAs in a normal prostate epithelial cell line and PCa cell lines. We found that hsa_circ_0063329 was significantly downregulated in PCa. A series of in vitro and in vivo functional assays showed that overexpression of hsa_circ_0063329 inhibits PCa cell progression, while silencing of hsa_circ_0063329 achieved the opposite effects. Mechanistically, bioinformatics analysis, RNA pulldown, RNA-seq and dual-luciferase reporter assays demonstrated that hsa_circ_0063329 exerts its effect by sponging miR-605-5p to derepress TG-interacting factor 2 (TGIF2) and inactivate the TGF-ß pathway. In conclusion, hsa_circ_0063329 inhibits the proliferation and metastasis of PCa via modulation of the miR-605-5p/TGIF2 axis, and targeting hsa_circ_0063329 may provide a promising treatment strategy for aggressive PCa.

Apa is a trimeric autotransporter adhesin of Actinobacillus pleuropneumoniae responsible for autoagglutination and host cell adherence.

Actinobacillus pleuropneumoniae is the causative agent of porcine pleuropneumonia, and adherence to host cells is a key step in the pathogenic process. Although trimeric autotransporter adhesins (TAAs) were identified in many pathogenic bacteria in recent years, none in A. pleuropneumoniae have been characterized. In this study, we identified a TAA from A. pleuropneumoniae, Apa, and characterized the contribution of its amino acid residues to the adhesion process. Sequence analysis of the C-terminal amino acid residues of Apa revealed the presence of a putative translocator domain and six conserved HsfBD1-like or HsfBD2-like binding domains. Western blot analysis revealed that the 126 C-terminal amino acids of Apa could form trimeric molecules. By confocal laser scanning microscopy, one of these six domains (ApaBD3) was determined to mediate adherence to epithelial cells. Adherence assays and adherence inhibition assays using a recombinant E. coli- ApaBD3 strain which expressed ApaBD3 on the surface of E. coli confirmed that this domain was responsible for the adhesion activity. Moreover, cellular enzyme-linked immunosorbent assays demonstrated that ApaBD3 mediated high-level adherence to epithelial cell lines. Intriguingly, autoagglutination was observed with the E. coli- ApaBD3 strain, and this phenomenon was dependent upon the association of the expressed ApaBD3 with the C-terminal translocator domain.

Knowledge matters: Chest radiology report generation with general and specific knowledge.

Automatic chest radiology report generation is critical in clinics which can relieve experienced radiologists from the heavy workload and remind inexperienced radiologists of misdiagnosis or missed diagnose. Existing approaches mainly formulate chest radiology report generation as an image captioning task and adopt the encoder-decoder framework. However, in the medical domain, such pure data-driven approaches suffer from the following problems: 1) visual and textual bias problem; 2) lack of expert knowledge. In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for chest X-ray report generation. To fully utilize both the general and specific knowledge, we also propose a knowledge-enhanced multi-head attention mechanism. By merging the visual features of the radiology image with general knowledge and specific knowledge, the proposed model can improve the quality of generated reports. The experimental results on the publicly available IU-Xray dataset show that the proposed knowledge-enhanced approach outperforms state-of-the-art methods in almost all metrics. And the results of MIMIC-CXR dataset show that the proposed knowledge-enhanced approach is on par with state-of-the-art methods. Ablation studies also demonstrate that both general and specific knowledge can help to improve the performance of chest radiology report generation.

DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging.

Membrane contact site (MCS)-mediated organelle interactions play essential roles in the cell. Quantitative analysis of MCSs reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed DeepContact, a deep-learning protocol for optimizing organelle segmentation and contact analysis based on label-free EM. DeepContact presents high efficiency and flexibility in interactive visualizations, accommodating new morphologies of organelles and recognizing contacts in versatile width ranges, which enables statistical analysis of various types of MCSs in multiple systems. DeepContact profiled previously unidentified coordinative rearrangements of MCS types in cultured cells with combined nutritional conditions. DeepContact also unveiled a subtle wave of ER-mitochondrial entanglement in Sertoli cells during the seminiferous epithelial cycle, indicating its potential in bridging MCS dynamics to physiological and pathological processes.

Flexible joint parameters identification method based on improved tracking differentiator and adaptive differential evolution.

The assembly error of flexible joints and the change in joint stiffness during movement make the actual value of joint parameters inconsistent with the given value, which affects the joint control accuracy. In order to suppress the influence of parameters error, a parameters identification method for flexible joint combined offline identification and online compensation is proposed. First, the offline identification model of inertia, mass, and damping and the online identification model of joint stiffness are established, respectively. Then, a hybrid tracking differentiator based on an improved Sigmoid function is designed to track the differential signals of joint motion parameters, and the Lyapunov function is designed to prove its convergence. The adaptive differential evolution is used as the identification algorithm, and the improved adaptive crossover, mutation factor, and Metropolis acceptance criterion are designed to improve the convergence speed. Finally, a feedforward control structure based on identification is designed to compensate for the model deviation. Simulation and experimental results show that the improved differentiator can effectively improve the tracking speed and derivation accuracy of the signals. Compared with other algorithms, the proposed identification method has a faster convergence speed and higher identification accuracy, and feedforward compensation control can effectively correct model parameters and improve control accuracy.

An immuno-score signature of tumor immune microenvironment predicts clinical outcomes in locally advanced rectal cancer.

Background and purpose: Accumulating evidence indicates that neoadjuvant chemoradiotherapy(nCRT) success has an immune-associated constituent in locally advanced rectal cancer (LARC). The immune-associated configuration of the tumor microenvironment associated with responses to treatment was explored in LARC in this study. Material and methods: A novel analytic framework was developed based on within-sample relative expression orderings for identifying tumor immune-associated gene pairs and identified an immuno-score signature from bulk transcriptome profiling analysis of 200 LARC patients. And sequencing and microarray analysis of gene expression was conducted to investigate the association between the signature and response to nCRT, immunotherapy, and cell function of CD4 and CD8. The results were validated using 111 pretreated samples from publicly available datasets in multiple aspects and survival analyses. Results: The immuno-score signature of 18 immune-related gene pairs (referred to as IPS) was validated on bulk microarray and RNA-Seq data. According to the model's immune score, LARC patients were divided into high- and low-score groups. The patients with high-score were greater sensitivity to nCRT and immunotherapy, gaining a significantly improved prognosis. In addition, the immune-score gene pair signature was associated with type I anti-tumor T cell responses, positive regulators of T cell functions, and chromosomal instability while reflecting differences between CD8+ T cell subtypes. Conclusion: The immuno-score signature underlines a key role of tumor immune components in nCRT response, and predicts the prognosis of LARC patients as well.

COVID-19 inhibits spermatogenesis in the testes by inducing cellular senescence.

Introduction: COVID-19 (SARS-CoV-2) has been linked to organ damage in humans since its worldwide outbreak. It can also induce severe sperm damage, according to research conducted at numerous clinical institutions. However, the exact mechanism of damage is still unknown. Methods: In this study, testicular bulk-RNA-seq Data were downloaded from three COVID-19 patients and three uninfected controls from GEO to evaluate the effect of COVID-19 infection on spermatogenesis. Relative expression of each pathway and the correlation between genes or pathways were analyzed by bioinformatic methods. Results: By detecting the relative expression of each pathway and the correlation between genes or pathways, we found that COVID-19 could induce testicular cell senescence through MAPK signaling pathway. Cellular senescence was synergistic with MAPK pathway, which further affected the normal synthesis of cholesterol and androgen, inhibited the normal synthesis of lactate and pyruvate, and ultimately affected spermatogenesis. The medications targeting MAPK signaling pathway, especially MAPK1 and MAPK14, are expected to be effective therapeutic medications for reducing COVID-19 damage to spermatogenesis. Conclusion: These results give us a new understanding of how COVID-19 inhibits spermatogenesis and provide a possible solution to alleviate this damage.

Photoaffinity-Based Chemical Proteomics Reveals 7-Oxocallitrisic Acid Targets CPT1A to Trigger Lipogenesis Inhibition.

One of the natural terpenoids isolated from Resina Commiphora, 7-oxocallitrisic acid (7-OCA), has lipid metabolism regulatory activity. To uncover its lipogenesis inhibition mechanism, we developed a photoaffinity and clickable probe based on the 7-OCA scaffold and performed chemical proteomics to profile its potential cellular targets. It was found that 7-OCA could directly interact with carnitine palmitoyl transferase 1A (CPT1A) to promote its activity to reduce lipid accumulation. The present work reveals our understanding of the mode of lipid mebabolism regulation by abietic acids and provides new clues for antiobesity drug development with CPT1A as a main target.