Molecular mechanism for activation of the 26S proteasome by ZFAND5.

Various hormones, kinases, and stressors (fasting, heat shock) stimulate 26S proteasome activity. To understand how its capacity to degrade ubiquitylated proteins can increase, we studied mouse ZFAND5, which promotes protein degradation during muscle atrophy. Cryo-electron microscopy showed that ZFAND5 induces large conformational changes in the 19S regulatory particle. ZFAND5's AN1 Zn-finger domain interacts with the Rpt5 ATPase and its C terminus with Rpt1 ATPase and Rpn1, a ubiquitin-binding subunit. Upon proteasome binding, ZFAND5 widens the entrance of the substrate translocation channel, yet it associates only transiently with the proteasome. Dissociation of ZFAND5 then stimulates opening of the 20S proteasome gate. Using single-molecule microscopy, we showed that ZFAND5 binds ubiquitylated substrates, prolongs their association with proteasomes, and increases the likelihood that bound substrates undergo degradation, even though ZFAND5 dissociates before substrate deubiquitylation. These changes in proteasome conformation and reaction cycle can explain the accelerated degradation and suggest how other proteasome activators may stimulate proteolysis.

The Hippo pathway noncanonically drives autophagy and cell survival in response to energy stress.

The Hippo pathway is known for its crucial involvement in development, regeneration, organ size control, and cancer. While energy stress is known to activate the Hippo pathway and inhibit its effector YAP, the precise role of the Hippo pathway in energy stress response remains unclear. Here, we report a YAP-independent function of the Hippo pathway in facilitating autophagy and cell survival in response to energy stress, a process mediated by its upstream components MAP4K2 and STRIPAK. Mechanistically, energy stress disrupts the MAP4K2-STRIPAK association, leading to the activation of MAP4K2. Subsequently, MAP4K2 phosphorylates ATG8-family member LC3, thereby facilitating autophagic flux. MAP4K2 is highly expressed in head and neck cancer, and its mediated autophagy is required for head and neck tumor growth in mice. Altogether, our study unveils a noncanonical role of the Hippo pathway in energy stress response, shedding light on this key growth-related pathway in tissue homeostasis and cancer.

Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases.

MOTIVATION: Identifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases. RESULTS: In this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA-miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP.

Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis.

Survival analysis is critical to cancer prognosis estimation. High-throughput technologies facilitate the increase in the dimension of genic features, but the number of clinical samples in cohorts is relatively small due to various reasons, including difficulties in participant recruitment and high data-generation costs. Transcriptome is one of the most abundantly available OMIC (referring to the high-throughput data, including genomic, transcriptomic, proteomic and epigenomic) data types. This study introduced a multitask graph attention network (GAT) framework DQSurv for the survival analysis task. We first used a large dataset of healthy tissue samples to pretrain the GAT-based HealthModel for the quantitative measurement of the gene regulatory relations. The multitask survival analysis framework DQSurv used the idea of transfer learning to initiate the GAT model with the pretrained HealthModel and further fine-tuned this model using two tasks i.e. the main task of survival analysis and the auxiliary task of gene expression prediction. This refined GAT was denoted as DiseaseModel. We fused the original transcriptomic features with the difference vector between the latent features encoded by the HealthModel and DiseaseModel for the final task of survival analysis. The proposed DQSurv model stably outperformed the existing models for the survival analysis of 10 benchmark cancer types and an independent dataset. The ablation study also supported the necessity of the main modules. We released the codes and the pretrained HealthModel to facilitate the feature encodings and survival analysis of transcriptome-based future studies, especially on small datasets. The model and the code are available at http://www.healthinformaticslab.org/supp/.

MolFeSCue: enhancing molecular property prediction in data-limited and imbalanced contexts using few-shot and contrastive learning.

MOTIVATION: Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models. RESULTS: This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm's effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery. AVAILABILITY AND IMPLEMENTATION: We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper.

Structural Basis for Shelterin Bridge Assembly.

Telomere elongation through telomerase enables chromosome survival during cellular proliferation. The conserved multifunctional shelterin complex associates with telomeres to coordinate multiple telomere activities, including telomere elongation by telomerase. Similar to the human shelterin, fission yeast shelterin is composed of telomeric sequence-specific double- and single-stranded DNA-binding proteins, Taz1 and Pot1, respectively, bridged by Rap1, Poz1, and Tpz1. Here, we report the crystal structure of the fission yeast Tpz1475-508-Poz1-Rap1467-496 complex that provides the structural basis for shelterin bridge assembly. Biochemical analyses reveal that shelterin bridge assembly is a hierarchical process in which Tpz1 binding to Poz1 elicits structural changes in Poz1, allosterically promoting Rap1 binding to Poz1. Perturbation of the cooperative Tpz1-Poz1-Rap1 assembly through mutation of the "conformational trigger" in Poz1 leads to unregulated telomere lengthening. Furthermore, we find that the human shelterin counterparts TPP1-TIN2-TRF2 also assemble hierarchically, indicating cooperativity as a conserved driving force for shelterin assembly.

Meeting Report on the 2nd Chinese American Society for Mass Spectrometry Conference: Advancing Biological and Pharmaceutical Mass Spectrometry.

The 2nd CASMS conference was held virtually through Gather. Town platform from October 17 to 21, 2022, with a total of 363 registrants including an outstanding and diverse group of scientists at the forefront of their research fields from both academia and industry worldwide, especially in the United States and China. The conference offered a 5-day agenda with an exciting scientific program consisting of two plenary lectures, 14 parallel symposia, and 4 special sessions in which a total of 97 invited speakers presented technological innovations and their applications in proteomics & biological mass spectrometry and metabo-lipidomics & pharmaceutical mass spectrometry. In addition, 18 invited speakers/panelists presented at 3 research-focused and 2 career development workshops. Moreover, 144 posters, 54 lightning talks, 5 sponsored workshops, and 14 exhibitions were presented, from which 20 posters and 8 lightning talks received presentation awards. Furthermore, the conference featured 1 MCP lectureship and 5 young investigator awardees for the first time to highlight outstanding mid-career and early-career rising stars in mass spectrometry from our society. The conference provided a unique scientific platform for young scientists (i.e., graduate students, postdocs and junior faculty/investigators) to present their research, meet with prominent scientists, and learn about career development and job opportunities (http://casms.org).

MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples.

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Proteomics Platform Reveals Broad-Spectrum Nanobodies for SARS-CoV-2 Variant Neutralization.

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of different variants of concerns with immune evasion that have been prevalent over the past three years. Nanobodies, the functional variable regions of camelid heavy-chain-only antibodies, have garnered interest in developing neutralizing antibodies due to their smaller size, structural stability, ease of production, high affinity, and low immunogenicity, among other characteristics. In this work, we describe an integrated proteomics platform for the high-throughput screening of nanobodies against different SARS-CoV-2 spike variants. To demonstrate this platform, we immunized a camel with subunit 1 (S1) of the wild-type spike protein and constructed a nanobody phage library. The binding and neutralizing activities of the nanobodies against 72 spike variants were then measured, resulting in the identification of two nanobodies (C-282 and C-39) with broad neutralizing activity against six non-Omicron variants (D614G, Alpha, Beta, Gamma, Delta, Kappa) and five Omicron variants (BA.1-5). Their neutralizing capability was validated using in vitro pseudovirus-based neutralization assays. All these results demonstrate the utility of our proteomics platform to identify new nanobodies with broad neutralizing capability and to develop a treatment for patients with SARS-CoV-2 variant infection in the future.

Research progress on the regulatory mechanism of integrin-mediated mechanical stress in cells involved in bone metabolism.

Mechanical stress is an internal force between various parts of an object that resists external factors and effects that cause an object to deform, and mechanical stress is essential for various tissues that are constantly subjected to mechanical loads to function normally. Integrins are a class of transmembrane heterodimeric glycoprotein receptors that are important target proteins for the action of mechanical stress stimuli on cells and can convert extracellular physical and mechanical signals into intracellular bioelectrical signals, thereby regulating osteogenesis and osteolysis. Integrins play a bidirectional regulatory role in bone metabolism. In this paper, relevant literature published in recent years is reviewed and summarized. The characteristics of integrins and mechanical stress are introduced, as well as the mechanisms underlying responses of integrin to mechanical stress stimulation. The paper focuses on integrin-mediated mechanical stress in different cells involved in bone metabolism and its associated signalling mechanisms. The purpose of this review is to provide a theoretical basis for the application of integrin-mediated mechanical stress to the field of bone tissue repair and regeneration.

Mitochondrial genome provides species-specific targets for the rapid detection of early invasive populations of Hylurgus ligniperda in China.

BACKGROUND: Hylurgus ligniperda, a major international forestry quarantine pest, was recently found to have invaded and posed a serious threat to the Pinus forests of the Jiaodong Peninsula in China. Continuous monitoring and vigilance of the early population is imperative, and rapid molecular detection technology is urgently needed. We focused on developing a single-gene-based species-specific PCR (SS-PCR) method. RESULTS: We sequenced and assembled the mitochondrial genome of H. ligniperda to identify suitable target genes. We identified three closely related species for detecting the specificity of SS-PCR through phylogenetic analysis based on 13 protein-coding genes (PCGs). Subsequently, we analyzed the evolution of 13 PCGs and selected four mitochondrial genes to represent slow-evolving gene (COI) and faster-evolving genes (e.g. ND2, ND4, and ND5), respectively. We developed four species-specific primers targeting COI, ND2, ND4, and ND5 to rapidly identify H. ligniperda. The results showed that the four species-specific primers exhibited excellent specificity and sensitivity in the PCR assays, with consistent performance across a broader range of species. This method demonstrates the ability to identify beetles promptly, even during their larval stage. The entire detection process can be completed within 2-3 h. CONCLUSIONS: This method is suitable for large-scale species detection in laboratory settings. Moreover, the selection of target genes in the SS-PCR method is not affected by the evolutionary rate. SS-PCR can be widely implemented at port and forestry workstations, significantly enhancing early management strategies and quarantine measures against H. ligniperda. This approach will help prevent the spread of the pest and effectively preserve the resources of Chinese pine forests.

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction.

Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

Multi-channel graph attention autoencoders for disease-related lncRNAs prediction.

MOTIVATION: Predicting disease-related long non-coding RNAs (lncRNAs) can be used as the biomarkers for disease diagnosis and treatment. The development of effective computational prediction approaches to predict lncRNA-disease associations (LDAs) can provide insights into the pathogenesis of complex human diseases and reduce experimental costs. However, few of the existing methods use microRNA (miRNA) information and consider the complex relationship between inter-graph and intra-graph in complex-graph for assisting prediction. RESULTS: In this paper, the relationships between the same types of nodes and different types of nodes in complex-graph are introduced. We propose a multi-channel graph attention autoencoder model to predict LDAs, called MGATE. First, an lncRNA-miRNA-disease complex-graph is established based on the similarity and correlation among lncRNA, miRNA and diseases to integrate the complex association among them. Secondly, in order to fully extract the comprehensive information of the nodes, we use graph autoencoder networks to learn multiple representations from complex-graph, inter-graph and intra-graph. Thirdly, a graph-level attention mechanism integration module is adopted to adaptively merge the three representations, and a combined training strategy is performed to optimize the whole model to ensure the complementary and consistency among the multi-graph embedding representations. Finally, multiple classifiers are explored, and Random Forest is used to predict the association score between lncRNA and disease. Experimental results on the public dataset show that the area under receiver operating characteristic curve and area under precision-recall curve of MGATE are 0.964 and 0.413, respectively. MGATE performance significantly outperformed seven state-of-the-art methods. Furthermore, the case studies of three cancers further demonstrate the ability of MGATE to identify potential disease-correlated candidate lncRNAs. The source code and supplementary data are available at https://github.com/sheng-n/MGATE. CONTACT: huanglan@jlu.edu.cn, wy6868@jlu.edu.cn.

Artificial intelligence in clinical research of cancers.

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

Discovery of Unusual Ajmaline-Macroline Type Bisindole Alkaloids from Alstonia macrophylla by Building Blocks-Based Molecular Networking.

Three unusual ajmaline-macroline type bisindole alkaloids, alsmaphylines A-C, together with their postulated biogenetic precursors, were isolated from the stem barks and leaves of Alstonia macrophylla via the building blocks-based molecular network (BBMN) strategy. Alsmaphyline A represents a rare ajmaline-macroline type bisindole alkaloid with an S-shape polycyclic ring system. Alsmaphylines B and C are two novel ajmaline-macroline type bisindole alkaloids with N-1-C-21' linkages, and the former possesses an unconventional stacked conformation due to the presence of intramolecular noncovalent interactions. The chemical structures including absolute configurations of alsmaphylines A-C were established by comprehensive spectroscopic analyses, electronic circular dichroism (ECD) calculations, and single-crystal X-ray crystallography. In addition, a plausible biosynthetic pathway of these bisindole alkaloids as well as their ability to promote the protein synthesis on HT22 cells were discussed.

GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning.

RNA modification, N4-acetylcytidine (ac4C), is enzymatically catalyzed by N-acetyltransferase 10 (NAT10) and plays an essential role across tRNA, rRNA, and mRNA. It influences various cellular functions, including mRNA stability and rRNA biosynthesis. Wet-lab detection of ac4C modification sites is highly resource-intensive and costly. Therefore, various machine learning and deep learning techniques have been employed for computational detection of ac4C modification sites. The known ac4C modification sites are limited for training an accurate and stable prediction model. This study introduces GANSamples-ac4C, a novel framework that synergizes transfer learning and generative adversarial network (GAN) to generate synthetic RNA sequences to train a better ac4C modification site prediction model. Comparative analysis reveals that GANSamples-ac4C outperforms existing state-of-the-art methods in identifying ac4C sites. Moreover, our result underscores the potential of synthetic data in mitigating the issue of data scarcity for biological sequence prediction tasks. Another major advantage of GANSamples-ac4C is its interpretable decision logic. Multi-faceted interpretability analyses detect key regions in the ac4C sequences influencing the discriminating decision between positive and negative samples, a pronounced enrichment of G in this region, and ac4C-associated motifs. These findings may offer novel insights for ac4C research. The GANSamples-ac4C framework and its source code are publicly accessible at http://www.healthinformaticslab.org/supp/.

Adipocyte pyroptosis occurs in omental tumor microenvironment and is associated with chemoresistance of ovarian cancer.

BACKGROUND: Ovarian carcinoma (OC) is a fatal malignancy, with most patients experiencing recurrence and resistance to chemotherapy. In contrast to hematogenous metastasizing tumors, ovarian cancer cells disseminate within the peritoneal cavity, especially the omentum. Previously, we reported omental crown-like structure (CLS) number is associated with poor prognosis of advanced-stage OC. CLS that have pathologic features of a dead or dying adipocyte was surrounded by several macrophages is well known a histologic hallmark for inflammatory adipose tissue. In this study, we attempted to clarify the interaction between metastatic ovarian cancer cells and omental CLS, and to formulate a therapeutic strategy for advanced-stage ovarian cancer. METHODS: A three-cell (including OC cells, adipocytes and macrophages) coculture model was established to mimic the omental tumor microenvironment (TME) of ovarian cancer. Caspase-1 activity, ATP and free fatty acids (FFA) levels were detected by commercial kits. An adipocyte organoid model was established to assess macrophages migration and infiltration. In vitro and in vivo experiments were performed for functional assays and therapeutic effect evaluations. Clinical OC tissue samples were collected for immunochemistry stain and statistics analysis. RESULTS: In three-cell coculture model, OC cells-derived IL-6 and IL-8 could induce the occurrence of pyroptosis in omental adipocytes. The pyroptotic adipocytes release ATP to increase macrophage infiltration, release FFA into TME, uptake by OC cells to increase chemoresistance. From OC tumor samples study, we demonstrated patients with high gasdermin D (GSDMD) expression in omental adipocytes is highly correlated with chemoresistance and poor outcome in advanced-stage OC. In animal model, by pyroptosis inhibitor, DSF, effectively retarded tumor growth and prolonged mice survival. CONCLUSIONS: Omental adipocyte pyroptosis may contribute the chemoresistance in advanced stage OC. Omental adipocytes could release FFA and ATP through the GSDMD-mediate pyroptosis to induce chemoresistance and macrophages infiltration resulting the poor prognosis in advanced-stage OC. Inhibition of adipocyte pyroptosis may be a potential therapeutic modality in advanced-stage OC with omentum metastasis.

SUMOylation of annexin A6 retards cell migration and tumor growth by suppressing RHOU/AKT1-involved EMT in hepatocellular carcinoma.

BACKGROUND: The protein annexin A6 (AnxA6) is involved in numerous membrane-related biological processes including cell migration and invasion by interacting with other proteins. The dysfunction of AnxA6, including protein expression abundance change and imbalance of post-translational modification, is tightly related to multiple cancers. Herein we focus on the biological function of AnxA6 SUMOylation in hepatocellular carcinoma (HCC) progression. METHODS: The modification sites of AnxA6 SUMOylation were identified by LC-MS/MS and amino acid site mutation. AnxA6 expression was assessed by immunohistochemistry and immunofluorescence. HCC cells were induced into the epithelial-mesenchymal transition (EMT)-featured cells by 100 ng/mL 12-O-tetradecanoylphorbol-13-acetate exposure. The ability of cell migration was evaluated under AnxA6 overexpression by transwell assay. The SUMO1 modified AnxA6 proteins were enriched from total cellular proteins by immunoprecipitation with anti-SUMO1 antibody, then the SUMOylated AnxA6 was detected by Western blot using anti-AnxA6 antibody. The nude mouse xenograft and orthotopic hepatoma models were established to determine HCC growth and tumorigenicity in vivo. The HCC patient's overall survival versus AnxA6 expression level was evaluated by the Kaplan-Meier method. RESULTS: Lys579 is a major SUMO1 modification site of AnxA6 in HCC cells, and SUMOylation protects AnxA6 from degradation via the ubiquitin-proteasome pathway. Compared to the wild-type AnxA6, its SUMO site mutant AnxA6K579R leads to disassociation of the binding of AnxA6 with RHOU, subsequently RHOU-mediated p-AKT1ser473 is upregulated to facilitate cell migration and EMT progression in HCC. Moreover, the SENP1 deSUMOylates AnxA6, and AnxA6 expression is negatively correlated with SENP1 protein expression level in HCC tissues, and a high gene expression ratio of ANXA6/SENP1 indicates a poor overall survival of patients. CONCLUSIONS: AnxA6 deSUMOylation contributes to HCC progression and EMT phenotype, and the combination of AnxA6 and SENP1 is a better tumor biomarker for diagnosis of HCC grade malignancy and prognosis.

Visible-Light Induced Radical Addition-Elimination Reaction for Constructing Allylic Sulfones from Sulfonyl Chlorides and Allyl Bromides.

Allyl sulfones are commonly present in bioactive compounds and organic building blocks. This work introduces a photocatalytic radical addition-elimination reaction involving readily accessible sulfonyl chlorides and allyl bromides. It delivers structurally diverse allylic sulfones in moderate to excellent yields, showcasing a high tolerance to functional groups. Notably, this method operates under mild reaction conditions without the need for oxidants, stoichiometric reducing metals, or additives.