The core spindle pole body scaffold Ppc89 links the pericentrin ortholog Pcp1 to the fission yeast spindle pole body via an evolutionarily conserved interface.

Centrosomes and spindle pole bodies (SPBs) are important for mitotic spindle formation and serve as cellular signaling platforms. Although centrosomes and SPBs differ in morphology, many mechanistic insights into centrosome function have been gleaned from SPB studies. In the fission yeast Schizosaccharomyces pombe, the ɑ-helical protein Ppc89, identified based on its interaction with the septation initiation network scaffold Sid4, comprises the SPB core. High-resolution imaging has suggested that SPB proteins assemble on the Ppc89 core during SPB duplication, but such interactions are undefined. Here, we define a connection between Ppc89 and the essential pericentrin Pcp1. Specifically, we found that a predicted third helix within Ppc89 binds the Pcp1 PACT domain complexed with calmodulin. Ppc89 helix 3 contains similarity to PINC motifs found in the centrosomal proteins fly SAS-6 and human Cep57 and also to the S. cerevisiae SPB protein Spc42. These motifs bind pericentrin-calmodulin complexes and AlphaFold2 models suggest a homologous complex assembles in all four organisms. Mutational analysis of the S. pombe complex supports the importance of Ppc89-Pcp1 binding interface in vivo. Our studies provide insight into the core architecture of the S. pombe SPB and suggest an evolutionarily conserved mechanism of scaffolding pericentrin-calmodulin complexes for mitotic spindle formation.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb.

BACKGROUND: Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS: Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS: In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

Transient PP2A SIP complex localization to mitotic SPBs for SIN inhibition is mediated solely by the Csc1 FHA domain.

Many organisms utilize an actin- and myosin-based cytokinetic ring (CR) to help complete cytokinesis. In Schizosaccharomyces pombe, the Septation Initiation Network (SIN) promotes proper CR function and stability. The SIN is a conserved and essential signaling network consisting of a GTPase and a cascade of kinases assembled at the spindle pole body (SPB). The PP2A SIN inhibitory phosphatase (SIP) complex related to the STRIPAK phosphatase complex is one inhibitor of SIN signaling. The SIP consists of Csc1, Csc2, Csc3, Csc4, Paa1, and the phosphatase subunit Ppa3. Here, we determine that the SIP is anchored at the SPB via the Csc1 FHA domain and that constitutive SPB localization of the SIP is lethal due to persistent SIN inhibition. Disrupting SIP docking at the SPB with a point mutation within the FHA domain or eliminating phosphatase activity by introducing a point mutation within Ppa3 resulted in intact SIP complexes without SIN inhibitory function. Lastly, we defined the unique features of Ppa3 that allow it, but not two other PP2A catalytic subunits, to incorporate into the SIP. Overall, we provide insight into how the SIP complex assembles, localizes, and functions to counteract the SIN with spatiotemporal precision during cytokinesis.

Characterization of temperature-sensitive Schizosaccharomyces pombe mutants in the septation initiation network Spg1 GTPase.

The Schizosaccharomyces pombe GTPase, Spg1 , activates the septation initiation network (SIN) protein kinase cascade to trigger septation. In the absence of functional Spg1 , cells fail cytokinesis and become multinucleate. In this study, we characterize a set of temperature-sensitive spg1 alleles isolated in the 1990s. We identify the mutations within each new and previously characterized allele, characterize the extent of relative growth defects, and assess their interaction with other SIN alleles.

Prevotella copri exhausts intrinsic indole-3-pyruvic acid in the host to promote breast cancer progression: inactivation of AMPK via UHRF1-mediated negative regulation.

Emerging evidence has revealed the novel role of gut microbiota in the development of cancer. The characteristics of function and composition in the gut microbiota of patients with breast cancer patients has been reported, however the detailed causation between gut microbiota and breast cancer remains uncertain. In the present study, 16S rRNA sequencing revealed that Prevotella, particularly the dominant species Prevotella copri, is significantly enriched and prevalent in gut microbiota of breast cancer patients. Prior-oral administration of P. copri could promote breast cancer growth in specific pathogen-free mice and germ-free mice, accompanied with sharp reduction of indole-3-pyruvic acid (IPyA). Mechanistically, the present of excessive P. copri consumed a large amount of tryptophan (Trp), thus hampering the physiological accumulation of IPyA in the host. Our results revealed that IPyA is an intrinsic anti-cancer reagent in the host at physiological level. Briefly, IPyA directly suppressed the transcription of UHRF1, following by the declined UHRF1 and PP2A C in nucleus, thus inhibiting the phosphorylation of AMPK, which is just opposite to the cancer promoting effect of P. copri. Therefore, the exhaustion of IPyA by excessive P. copri strengthens the UHRF1-mediated negative control to inactivated the energy-controlling AMPK signaling pathway to promote tumor growth, which was indicated by the alternation in pattern of protein expression and DNA methylation. Our findings, for the first time, highlighted P. copri as a risk factor for the progression of breast cancer.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model.

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.

Generation and characterization of temperature-sensitive alleles encoding GPI anchored proteins Psu1 and Dfg502 in Schizosaccharomyces pombe.

Glycosyl-phosphatidylinositol (GPI) anchored proteins are implicated in remodeling of the yeast cell wall during growth and division. Schizosaccharomyces pombe proteins, Psu1 , Dfg501 , and Dfg502 are predicted GPI anchored proteins with likely cell wall modifying activity. Here, we isolated and characterized null and temperature-sensitive alleles that will allow further analysis of the function of these proteins and S. pombe cell wall formation. Our data confirm that Psu1 is necessary for cell separation, maintaining proper cell shape, and viability. Additionally, we found that Dfg501 and Dfg502 share a redundant and essential function necessary for cell separation and viability.

Light Enables the Cathodic Interface Reaction Reversibility in Solid-State Lithium-Oxygen Batteries.

Limited triple-phase boundaries arising from the accumulation of solid discharge product(s) in solid-state cathodes (SSCs) pose a challenge to high-property solid-state lithium-oxygen batteries (SSLOBs). Light-assisted SSLOBs have been gradually explored as an ingenious system; however, the fundamental mechanisms of the SSCs interface behavior remain unclear. Here, we discovered that light assistance can enhance the fast inner-sphere charge transfer in SSCs and regulate the discharge products with spherical particles generated via the surface growth model. Moreover, the high photoelectron excitation and transportation capabilities of SSCs can retard cathodic catalytic decay by avoiding structural degradation of the cathode with a reduced charge voltage. The light-induced SSLOBs exhibited excellent stability (170 cycles) with a low discharge-charge polarization overpotential (0.27 V). Furthermore, transparent SSLOBs with exceptional flexibility, mechanical stability, and multiform shapes were fabricated for theory-to-practical applications in sunlight-induced batteries. Our study opens new opportunities for the introduction of solar energy into energy storage systems.

MCSdb, a database of proteins residing in membrane contact sites.

Organelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called "membrane contact sites (MCSs)". And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking. Therefore, we developed MCSdb, a manually curated resource of MCS proteins and complexes from publications. MCSdb documents 7010 MCS protein entries and 263 complexes, involving 24 organelles and 44 MCSs across 11 species. Additionally, MCSdb orchestrates all data into different categories with multitudinous information for presenting MCS proteins. In summary, MCSdb provides a valuable resource for accelerating MCS functional interpretation and interorganelle communication deciphering.

ncRS: A resource of non-coding RNAs in sepsis.

Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments.

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy.

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability. Therefore, in this study, we developed the Cm-siRPred algorithm based on a multi-view learning strategy. The algorithm employs a multi-view strategy to represent the double-strand sequences, chemical modifications, and physicochemical properties of cm-siRNA. It incorporates a cross-attention model to globally correlate different representation vectors and a two-layer CNN module to learn local correlation features. The algorithm demonstrates exceptional performance in cross-validation experiments, independent dataset, and case studies on approved siRNA drugs, and showcasing its robustness and generalization ability. In addition, we developed a user-friendly webserver that enables efficient prediction of cm-siRNA efficiency and assists in the design of siRNA drug chemical modifications. In summary, Cm-siRPred is a practical tool that offers valuable technical support for siRNA chemical modification and drug efficiency research, while effectively assisting in the development of novel small nucleic acid drugs. Cm-siRPred is freely available at https://cellknowledge.com.cn/sirnapredictor/.

Generation and characterization of temperature-sensitive alleles of the glucanosyltransferase Gas1 in Schizosaccharomyces pombe.

The Schizosaccharomyces pombe Gas family of ß-1,3-glucanosyltransferases modify the cell wall by elongating ß-1,3-glucan chains. While gas1Δ cells are inviable under standard laboratory growth conditions, they are viable in the presence of an osmotic stabilizer. Even under these conditions however, gas1Δ cells are slow-growing and display cell separation and morphology defects. Here, we isolated and characterized two gas1 temperature-sensitive alleles. Our data support that Gas1 is the primary S. pombe ß-1,3-glucanosyltransferase important for cell separation and cell viability and provide useful tools for further analysis of S. pombe cell wall formation.

Applications of single­cell omics and spatial transcriptomics technologies in gastric cancer (Review).

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

Towards the role of apprehension and anxiety as antecedents of EFL students' burnout.

How students experience the learning process is of great importance because it affects the enthusiasm they feel during this process. Although many studies have been conducted on apprehension, anxiety, and burnout in both teachers and students, few focus on the relationship between these three variables. To this end, the three variables in this review study are first defined. Then, the relationship between EFL learners' apprehension and their anxiety and burnout is discussed. It seems that the more apprehensive and anxious one feels, the more likely they feel burned out. Finally, the implications of this study and suggestions for further studies are presented.

The Cdc14 phosphatase, Clp1, does not affect genome expression.

Schizosaccharomyces pombe Clp1 is a Cdc14-family phosphatase that reverses mitotic Cdk1 phosphorylation. Despite evolutionary conservation, Clp1 's mammalian orthologs do not share this function. Rather, higher eukaryotic Cdc14 enzymes act in DNA repair, ciliogenesis, and gene regulation. To examine if Clp1 regulates gene expression, we compared the transcriptional profiles of cells lacking Clp1 function to that of wildtype. Because clp1∆ cells are sensitive to the actin depolymerizing drug, LatrunculinA, we also investigated whether a transcriptional response was involved. Our results indicate that Clp1 does not detectably affect gene expression and highlight the organism-specific functions of this conserved phosphatase family.

Improving astaxanthin-loaded chitosan/polyvinyl alcohol/graphene oxide nanofiber membranes and their application in periodontitis.

Periodontitis is a chronic inflammatory disease primarily driven by host inflammation and plaque-induced immune responses. Controlling the host inflammatory response and improving the periodontal inflammatory microenvironment are crucial to promoting periodontal tissue regeneration. In this study, the blended nanofiber membranes previously prepared by our research group were improved, and we developed multifunctional chitosan/polyvinyl alcohol/graphene oxide/astaxanthin coaxial nanofiber membranes. Scanning electron microscopy showed that the prepared nanofibers had a smooth surface and a uniform diameter distribution. The mechanical property test results showed that the coaxial nanofiber membranes exhibited higher tensile strength compared to the blended nanofiber membranes, which increased from 4.50 ± 0.32 and 3.70 ± 0.45 MPa to 7.12 ± 0.22 and 5.62 ± 0.79 MPa respectively. Drug release studies indicated that the "shell-core" structure of coaxial nanofibers significantly reduced the initial burst release of astaxanthin (ASTA), with only 13.49 % and 10.71 % release in the first 24 h, and drug release lasted for over a week. Animal experiments confirmed that the coaxial nanofiber membranes loaded with ASTA promoted periodontal bone defect repair while inhibiting periodontal inflammation. In conclusion, the prepared coaxial nanofiber membranes are a promising sustained-release drug system for treating periodontitis.

MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19.

In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent. In this study, we developed MetaboliteCOVID, a manually curated database of COVID-19-related metabolite markers. The database currently comprises 665 manually selected entries of significantly altered metabolites associated with early diagnosis, disease severity, prognosis, and drug response in COVID-19, encompassing 337 metabolites. Additionally, the database offers a user-friendly interface, containing abundant information for querying, browsing, and analyzing COVID-19-related abnormal metabolites in different body fluids. In summary, we believe that this database will effectively facilitate research on the functions and mechanisms of COVID-19-related metabolic biomarkers, thereby advancing both basic and clinical research on COVID-19. MetaboliteCOVID is free available at: https://cellknowledge.com.cn/MetaboliteCOVID.

Accurately identifying hemagglutinin using sequence information and machine learning methods.

Introduction: Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA. Methods: In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm. Results and discussion: The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.

P450Rdb: a manually curated database of reactions catalyzed by cytochrome P450 enzymes.

INTRODUCTION: Cytochrome P450 enzymes (P450s) are recognized as the most versatile catalysts worldwide, playing vital roles in numerous biological metabolism and biosynthesis processes across all kingdoms of life. Despite the vast number of P450 genes available in databases (over 300,000), only a small fraction of them (less than 0.2%) have undergone functional characterization. OBJECTIVES: To provide a convenient platform with abundant information on P450s and their corresponding reactions, we introduce the P450Rdb database, a manually curated resource compiles literature-supported reactions catalyzed by P450s. METHODS: All the P450s and Reactions were manually curated from the literature and known databases. Subsequently, the P450 reactions organized and categorized according to their chemical reaction type and site. The website was developed using HTML and PHP languages, with the MySQL server utilized for data storage. RESULTS: The current version of P450Rdb catalogs over 1,600 reactions, involving more than 590 P450s across a diverse range of over 200 species. Additionally, it offers a user-friendly interface with comprehensive information, enabling easy querying, browsing, and analysis of P450s and their corresponding reactions. P450Rdb is free available at http://www.cellknowledge.com.cn/p450rdb/. CONCLUSIONS: We believe that this database will significantly promote structural and functional research on P450s, thereby fostering advancements in the fields of natural product synthesis, pharmaceutical engineering, biotechnological applications, agricultural and crop improvement, and the chemical industry.

Characterization of Pik1 function in fission yeast reveals its conserved role in lipid synthesis and not cytokinesis.

Phosphatidylinositol (PI)-4-phosphate (PI4P) is a lipid found at the plasma membrane (PM) and Golgi in cells from yeast to humans. PI4P is generated from PI by PI4-kinases and can be converted into PI-4,5-bisphosphate [PI(4,5)P2]. Schizosaccharomyces pombe have two essential PI4-kinases - Stt4 and Pik1. Stt4 localizes to the PM, and its loss from the PM results in a decrease of PM PI4P and PI(4,5)P2. As a result, cells divide non-medially due to disrupted cytokinetic ring-PM anchoring. However, the localization and function of S. pombe Pik1 has not been thoroughly examined. Here, we found that Pik1 localizes exclusively to the trans-Golgi and is required for Golgi PI4P production. We determined that Ncs1 regulates Pik1, but unlike in other organisms, it is not required for Pik1 Golgi localization. When Pik1 function was disrupted, PM PI4P but not PI(4,5)P2 levels were reduced, a major difference compared with Stt4. We conclude that Stt4 is the chief enzyme responsible for producing the PI4P that generates PI(4,5)P2. Also, that cells with disrupted Pik1 do not divide asymmetrically highlights the specific importance of PM PI(4,5)P2 for cytokinetic ring-PM anchoring.