Constructing and validating pan-apoptosis-related features for predicting prognosis and immunotherapy response in hepatocellular carcinoma.

The study aimed to develop a prognostic model for Hepatocellular Carcinoma (HCC) based on pan-apoptosis-related genes, a novel inflammatory programmed cell death form intricately linked to HCC progression. Utilizing transcriptome sequencing and clinical data from the TCGA database, we identified six crucial pan-apoptosis-related genes through statistical analyses. These genes were then employed to construct a prognostic model that accurately predicts overall survival rates in HCC patients. Our findings revealed a strong correlation between the model's risk scores and tumor microenvironment (TME) status, immune cell infiltration, and immune checkpoint expression. Furthermore, we screened for drugs with potential therapeutic efficacy in high- and low-risk HCC groups. Notably, PPP2R5B gene knockdown was found to inhibit HCC cell proliferation and clonogenic capacity, suggesting its role in HCC progression. In conclusion, this study presents a novel pan-apoptosis gene-based prognostic risk model for HCC, providing valuable insights into patient TME status and guiding the selection of targeted therapies and immunotherapies.

Targeting STING oligomerization with licochalcone D ameliorates STING-driven inflammatory diseases.

The development of STING inhibitors for the treatment of STING-related inflammatory diseases continues to encounter significant challenges. The activation of STING is a multi-step process that includes binding with cGAMP, self-oligomerization, and translocation from the endoplasmic reticulum to the Golgi apparatus, ultimately inducing the expression of IRF3 and NF-κB-mediated interferons and inflammatory cytokines. It has been demonstrated that disruption of any of these steps can effectively inhibit STING activation. Traditional structure-based drug screening methodologies generally focus on specific binding sites. In this study, a TransformerCPI model based on protein primary sequences and independent of binding sites is employed to identify compounds capable of binding to the STING protein. The natural product Licochalcone D (LicoD) is identified as a potent and selective STING inhibitor. LicoD does not bind to the classical ligand-binding pocket; instead, it covalently modifies the Cys148 residue of STING. This modification inhibits STING oligomerization, consequently suppressing the recruitment of TBK1 and the nuclear translocation of IRF3 and NF-κB. LicoD treatment ameliorates the inflammatory phenotype in Trex1-1- mice and inhibits the progression of DSS-induced colitis and AOM/DSS-induced colitis-associated colon cancer (CAC). In summary, this study reveals the potential of LicoD in treating STING-driven inflammatory diseases. It also demonstrates the utility of the TransformerCPI model in discovering allosteric compounds beyond the conventional binding pockets.

Exosomal circPVT1 promotes angiogenesis in laryngeal cancer by activating the Rap1b-VEGFR2 signaling pathway.

Laryngeal cancer (LC) is the second most common head and neck cancer and has a decreasing 5-year survival rate worldwide. Circular RNAs (circRNAs) regulate cancer development in diverse ways based on their distinct biogenesis mechanisms and expansive regulatory roles. However, currently, there is little research on how exosomal circRNAs are involved in the development of LC. Here, we demonstrated that circPVT1, a circRNA derived from the well-studied long noncoding RNA PVT1, is correlated with disease progression in LC and promotes angiogenesis both in vivo and in vitro. Mechanistically, circPVT1 is loaded into LC cell-secreted exosomes and taken up by vascular epithelium cells. By sponging miR-30c-5p, exosomal circPVT1 promotes Rap1b expression, which dramatically enhances vascular endothelial growth factor receptor 2 and the phosphatidylinositol 3-kinase (PI3K)/AKT pathway activation, ultimately resulting in the induction of angiogenesis. Furthermore, our xenograft models demonstrated that the combination of short hairpin RNA-circPVT1 and cetuximab showed high efficacy in inhibiting tumor growth and angiogenesis. Collectively, these findings uncover a novel mechanism of exosomal circRNA-mediated angiogenesis modulation and provide a preclinical rationale for testing this analogous combination in patients with LC.

Survival benefits of gastrectomy in patients with metastatic gastric cancer: A meta-analysis.

BACKGROUND: Individuals with metastatic gastric cancer (MGC) are incurable and have a poor prognosis. To date, surgical resection with curative intent is the only treatment providing hope for a cure, but the role of surgical resection is still controversial. OBJECTIVES: To assess the effects of gastrectomy compared to non-resection on MGC patient survival. MATERIAL AND METHODS: PubMed, Embase, Cochrane Library, and Web of Science databases were searched up to October 10, 2023. Primary outcomes were 1-, 2-, 3-, and 5-year overall survival (OS), OS, and OS time. RESULTS: Forty-six studies with 7,152 MGC patients were included. Compared to MGC patients receiving no resection, MGC patients with gastrectomy had significantly improved 1-year OS (pooled relative risk (RR):1.90, 95% confidence intervals (95% CIs): 1.50, 2.41), 2-year OS (pooled RR: 2.23, 95% CI: 1.40, 3.53), 3-year OS (pooled RR: 6.09, 95% CI: 3.12, 11.87), 5-year OS (pooled RR: 4.30, 95% CI: 1.35, 13.74), and reduced risk of death (pooled hazard ratio (HR): 0.49, 95% CI: 0.37, 0.65). Gastrectomy combined with metastasectomy or not also revealed similar results regarding OS and risk of death. Additionally, OS time was significantly longer in patients receiving gastrectomy than patients not receiving resection (pooled weighted mean difference (WMD): 6.06, 95% CI: 1.36, 10.760). No significant difference in postoperative morbidity was detected between the patients receiving gastrectomy and patients not receiving resection (pooled RR: 2.54, 95% CI: 0.13, 51.39). CONCLUSION: Gastrectomy, with or metastasectomy, may provide MGC patients with survival benefits.

Impact of vector richness on the risk of vector-borne disease: The role of vector competence.

A central goal of disease ecology is to identify the factors that drive the spread of infectious diseases. Changes in vector richness can have complex effects on disease risk, but little is known about the role of vector competence in the relationship between vector richness and disease risk. In this study, we firstly investigated the combined effects of vector competence, interspecific competition, and feeding interference on disease risk through a two-vector, one-host SIR-SI model, and obtained threshold conditions for the occurrence of dilution and amplification effects. Secondly, we extended the above model to the case of N vectors and assumed that all vectors were homogeneous to obtain analytic expressions for disease risk. It was found that in the two-vector model, disease risk declined more rapidly as interspecific competition of the high-competence vector increased. When vector richness increases, the positive effects of adding a high-competence vector species on disease transmission may outweigh the negative effects of feeding interference due to increased vector richness, making an amplification effect more likely to occur. While the addition of a highly competitive vector species may exacerbate the negative effects of feeding interference, making a dilution effect more likely to occur. In the N-vector model, the effect of increased vector richness on disease risk was fully driven by the strength of feeding interference and interspecific competition, and changes in vector competence only quantitatively but not qualitatively altered the vector richness-disease risk relationship. This work clarifies the role of vector competence in the relationship between vector richness and disease risk and provides a new perspective for studying the diversity-disease relationship. It also provides theoretical guidance for vector management and disease prevention strategies.

RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications.

Although over 170 chemical modifications have been identified, their prevalence, mechanism and function remain largely unknown. To enable integrated analysis of diverse RNA modification profiles, we have developed RMBase v3.0 (http://bioinformaticsscience.cn/rmbase/), a comprehensive platform consisting of eight modules. These modules facilitate the exploration of transcriptome-wide landscape, biogenesis, interactome and functions of RNA modifications. By mining thousands of epitranscriptome datasets with novel pipelines, the 'RNA Modifications' module reveals the map of 73 RNA modifications of 62 species. the 'Genes' module allows to retrieve RNA modification profiles and clusters by gene and transcript. The 'Mechanisms' module explores 23 382 enzyme-catalyzed or snoRNA-guided modified sites to elucidate their biogenesis mechanisms. The 'Co-localization' module systematically formulates potential correlations between 14 histone modifications and 6 RNA modifications in various cell-lines. The 'RMP' module investigates the differential expression profiles of 146 RNA-modifying proteins (RMPs) in 18 types of cancers. The 'Interactome' integrates the interactional relationships between 73 RNA modifications with RBP binding events, miRNA targets and SNPs. The 'Motif' illuminates the enriched motifs for 11 types of RNA modifications identified from epitranscriptome datasets. The 'Tools' introduces a novel web-based 'modGeneTool' for annotating modifications. Overall, RMBase v3.0 provides various resources and tools for studying RNA modifications.

Multipartite entanglement generation with high-order non-Hermitian exceptional points from dressing-controlled atomic nonlinearity.

Multipartite entanglement has emerged as a valuable quantum resource for constructing large-scale quantum networks. However, the presence of non-Hermitian features induced by natural microscopic quantum systems significantly modifies the overall response of nonlinear parametric processes, thereby enabling direct manipulation of multipartite entanglement properties. In this study, we demonstrate the generation of multimode entanglement through atomic four-wave mixing (FWM) and analyze the properties of exceptional points (EP) under dressing control in non-Hermitian systems. By leveraging dressing-controlled atomic nonlinearity, we achieve versatile EPs and higher-order EPs by carefully tuning the atomic multi-parameter in the cascading FWM system. Additionally, we investigate the entanglement properties of various permutations of the output signal modes using the positive partial transpose (PPT) criterion. Notably, under non-Hermitian control, the application of single-, double-, and N-dressing splits leads to coherent multichannel control and further extends the scale of quantum entanglement. The outcomes of our research offer a novel approach to actively control non-Hermitian quantum phenomena without relying on artificial photonic structures. Furthermore, this paves the way for the realization of complex quantum information tasks by exploiting the non-Hermitian characteristics of the light-matter interaction.

Learning protein fitness landscapes with deep mutational scanning data from multiple sources.

One of the key points of machine learning-assisted directed evolution (MLDE) is the accurate learning of the fitness landscape, a conceptual mapping from sequence variants to the desired function. Here, we describe a multi-protein training scheme that leverages the existing deep mutational scanning data from diverse proteins to aid in understanding the fitness landscape of a new protein. Proof-of-concept trials are designed to validate this training scheme in three aspects: random and positional extrapolation for single-variant effects, zero-shot fitness predictions for new proteins, and extrapolation for higher-order variant effects from single-variant effects. Moreover, our study identified previously overlooked strong baselines, and their unexpectedly good performance brings our attention to the pitfalls of MLDE. Overall, these results may improve our understanding of the association between different protein fitness profiles and shed light on developing better machine learning-assisted approaches to the directed evolution of proteins. A record of this paper's transparent peer review process is included in the supplemental information.

Sequence-based drug design as a concept in computational drug design.

Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

Flexible Synergistic MoS2 Quantum Dots/PEDOT: PSS Film Sensor for Acetaldehyde Sensing at Room Temperature.

Acetaldehyde (CH3CHO) is known as a primary carcinogen, and the development of wearable gas sensors for its detection at room temperature has rarely been rarely reported. Herein, MoS2 quantum dots (MoS2 QDs) have been employed to dope with poly(3,4-ethylenedioxythiophene): polystyrenesulfonate (PEDOT: PSS) via a simple in situ polymerization technique, and the CH3CHO gas-sensing properties of the resultant flexible and transparent film were investigated. MoS2 QDs had been evenly dispersed into the polymer, and it was shown that PEDOT: PSS doped with the 20 wt % MoS2 QDs sensor exhibited the highest response value of 78.8% against 100 ppm CH3CHO and its detection limit reached 1 ppm. Moreover, the sensor response remained stable for more than 3 months. In particular, the different bending angles (from 60 to 240°) had little effect on the sensor response to CH3CHO. The possible reason for the enhanced sensing properties was attributed to the large number of reaction sites on the MoS2 QDs and the direct charge transfer between the MoS2 QDs and PEDOT: PSS. This work suggested a platform to inspire MoS2 QDs-doping PEDOT: PSS materials as wearable gas sensors for highly sensitive chemoresistive sensors to detect CH3CHO at room temperature.

Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer.

Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.

Goliath induces inflammation in obese mice by linking fatty acid ß-oxidation to glycolysis.

Obesity is associated with metabolic disorders and chronic inflammation. However, the obesity-associated metabolic contribution to inflammatory induction remains elusive. Here, we show that, compared with lean mice, CD4+ T cells from obese mice exhibit elevated basal levels of fatty acid ß-oxidation (FAO), which promote T cell glycolysis and thus hyperactivation, leading to enhanced induction of inflammation. Mechanistically, the FAO rate-limiting enzyme carnitine palmitoyltransferase 1a (Cpt1a) stabilizes the mitochondrial E3 ubiquitin ligase Goliath, which mediates deubiquitination of calcineurin and thus enhances activation of NF-AT signaling, thereby promoting glycolysis and hyperactivation of CD4+ T cells in obesity. We also report the specific GOLIATH inhibitor DC-Gonib32, which blocks this FAO-glycolysis metabolic axis in CD4+ T cells of obese mice and reduces the induction of inflammation. Overall, these findings establish a role of a Goliath-bridged FAO-glycolysis axis in mediating CD4+ T cell hyperactivation and thus inflammation in obese mice.

Single-base resolution mapping of 2'-O-methylation sites by an exoribonuclease-enriched chemical method.

2'-O-methylation (Nm) is one of the most abundant RNA epigenetic modifications and plays a vital role in the post-transcriptional regulation of gene expression. Current Nm mapping approaches are normally limited to highly abundant RNAs and have significant technical hurdles in mRNAs or relatively rare non-coding RNAs (ncRNAs). Here, we developed a new method for enriching Nm sites by using RNA exoribonuclease and periodate oxidation reactivity to eliminate 2'-hydroxylated (2'-OH) nucleosides, coupled with sequencing (Nm-REP-seq). We revealed several novel classes of Nm-containing ncRNAs as well as mRNAs in humans, mice, and drosophila. We found that some novel Nm sites are present at fixed positions in different tRNAs and are potential substrates of fibrillarin (FBL) methyltransferase mediated by snoRNAs. Importantly, we discovered, for the first time, that Nm located at the 3'-end of various types of ncRNAs and fragments derived from them. Our approach precisely redefines the genome-wide distribution of Nm and provides new technologies for functional studies of Nm-mediated gene regulation.

Discovery and characterization of a novel cGAS covalent inhibitor for the treatment of inflammatory bowel disease.

Cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, acts as a nucleotidyl transferase that catalyzes ATP and GTP to form cyclic GMP-AMP (cGAMP) and plays a critical role in innate immunity. Hyperactivation of cGAS-STING signaling contributes to hyperinflammatory responses. Therefore, cGAS is considered a promising target for the treatment of inflammatory diseases. Herein, we report the discovery and identification of several novel types of cGAS inhibitors by pyrophosphatase (PPiase)-coupled activity assays. Among these inhibitors, 1-(1-phenyl-3,4-dihydro-1H-pyrrolo[1,2-a]pyrazin-2-yl)prop-2-yn-1-one (compound 3) displayed the highest potency and selectivity at the cellular level. Compound 3 exhibited better inhibitory activity and pathway selectivity than RU.521, which is a selective cGAS inhibitor with anti-inflammatory effects in vitro and in vivo. Thermostability analysis, nuclear magnetic resonance and isothermal titration calorimetry assays confirmed that compound 3 directly binds to the cGAS protein. Mass spectrometry and mutation analysis revealed that compound 3 covalently binds to Cys419 of cGAS. Notably, compound 3 demonstrated promising therapeutic efficacy in a dextran sulfate sodium (DSS)-induced mouse colitis model. These results collectively suggest that compound 3 will be useful for understanding the biological function of cGAS and has the potential to be further developed for inflammatory disease therapies.

The ripple effect: How leader workplace anxiety shape follower job performance.

Although the dominant view in the literature suggests that work-related anxiety experienced by employees affects their behavior and performance, little research has focused on how and when leaders' workplace anxiety affects their followers' job performance. Drawing from Emotions as Social Information (EASI) theory, we propose dual mechanisms of cognitive interference and emotional exhaustion to explain the relationship between leader workplace anxiety and subordinate job performance. Specifically, cognitive interference is the mechanism that best explains the link between leader workplace anxiety and follower task performance, while emotional exhaustion is the mechanism that best explains the link between leader workplace anxiety and follower contextual performance. Additionally, we examine how follower epistemic motivation serves as a boundary condition for the effect of leader anxiety on follower performance outcomes. Results from a 2-wave study of 228 leader-follower dyads in a high-tech company mostly supported our theoretical model. We conclude the study with a discussion of the theoretical and practical implications of our findings.

Higher Risk of Acute Respiratory Distress Syndrome and Risk Factors among Patients with COVID-19: A Systematic Review, Meta-Analysis and Meta-Regression.

OBJECTIVE: To estimate the global risk and risk factors associated with acute respiratory distress syndrome (ARDS) among patients with COVID-19: Design: A systematic review, meta-analysis and meta-regression. SETTING AND PARTICIPANTS: Hospitals or nursing homes and patients with acute respiratory distress syndrome after COVID-19. METHODS: The literature review was systematically conducted on Embase, MEDLINE, CINAHL, and Web of Science, in addition to manual searches and reference list checking from 1 January 2019 to 2 March 2022. The search terms included coronavirus, acute respiratory syndrome, acute respiratory distress syndrome and observational studies. Three reviewers independently appraised the quality of the studies and extracted the relevant data using the Joanna Briggs Institute abstraction form and critical appraisal tools. A study protocol was registered in PROSPERO (CRD42022311957). Eligible studies were meta-analyzed and underwent meta-regression. RESULTS: A total of 12 studies were included, with 148,080 participants. The risk ratio (RR) of ARDS was 23%. Risk factors were age ≥ 41-64 years old (RR = 15.3%, 95% CI =0.14-2.92, p = 0.03); fever (RR = 10.3%, 95% CI = 0.03-2.03, p = 0.04); multilobe involvement of the chest (RR = 33.5%, 95% CI = 0.35-6.36, p = 0.02); lymphopenia (RR = 25.9%, 95% CI = 1.11-4.08, p = 0.01); mechanical ventilation with oxygen therapy (RR = 31.7%, 95% CI = 1.10-5.25, p = 0.002); European region (RR = 16.3%, 95% CI = 0.09-3.17, p = 0.03); sample size ≤ 500 (RR = 18.0%, 95% CI = 0.70-2.89, p = 0.001). CONCLUSIONS AND IMPLICATIONS: One in four patients experienced ARDS after having COVID-19. The age group 41-64 years old and the European region were high-risk groups. These findings can be used by policymakers to allocate resources for respiratory care facilities and can also provide scientific evidence in the design of protocols to manage COVID-19 worldwide.

The Thr105Ile Variant (rs11558538) of the Histamine N-methyltransferase Gene may be associated with Reduced Risk of Parkinson Disease: A Meta-analysis.

Background: Previous studies have reported conflicting results regarding the potential association between the risk of Parkinson's disease (PD) and the single nucleotide polymorphism, rs11558538 (Thr105Ile), in the histamine N-methyltransferase (HNMT) gene. We performed a systematic review and meta-analysis to improve our understanding of the association between them. Methods: We systematically searched several online databases to identify relevant studies regarding the association between rs11558538 and PD. We extracted data on the frequencies of genotypes (Thr/Thr, Thr/Ile, and Ile/Ile) and alleles (Thr and Ile) at the rs11558538 locus in patients with PD and healthy controls. Associations between genotype and PD risk were assessed in terms of odds ratios (OR) and 95% confidence intervals (CI). Results: The final meta-analysis included six case-control studies and data from the International Parkinson's Disease Genomics Consortium (IPDGC) data base on the association between HNMT rs11558538 and PD, involving 22,855 patients and 65,367 controls. Among the studies, substantial heterogeneity was observed (I2 = 84.42 for genotype and I2 = 73.39 for allele). Both the Ile (log OR: -0.31; 95% CI: -0.5 to -0.12; p < 0.001) and Thr/Ile+Ile genotypes (log OR: -0.32; 95% CI: -0.55 to -0.08; p < 0.001) were associated with a decreased risk of sporadic PD across all study populations. Subgroup analysis showed the protective effect of Thr/Ile+Ile genotypes in non-Chinese cohorts (log OR: -0.66; 95% CI: -0.67 to -0.04; p < 0.001) but not in Chinese cohorts (log OR: -0.26; 95% CI: -0.63 to 0.11; p = 0.13). Conclusion: Our findings suggest that the HNMT rs11558538T polymorphism may protect against PD, particularly in patients from the United States and Europe.

Plant community-mediated effects of grazing on plant diseases.

Grazing is one of the most important management practices for grasslands. To date, most studies on how grazing affects plant diseases have focused on a single plant species, ignoring plant community characteristics and phylogeny. We used data from a 6-year yak grazing experiment (0, 1, 2, and 3 yak(s) ha - 1 treatment) in an alpine meadow ecosystem of Qinghai-Tibetan Plateau, from which we tested grazing effects on foliar fungal diseases at both population and community levels. By measuring plant community variables (including richness, evenness, phylogenetic diversity, and composition) and disease severity, we evaluated the relative importance of plant community-mediated effects of yak grazing on community pathogen load with a multi-model inference approach. We found significant differences in pathogen load among different grazing treatments; we recorded the highest and lowest pathogen loads in the 1 yak ha - 1 treatment and in the 3 yaks ha - 1 treatment, respectively. Pielou's evenness index and community proneness (i.e., an estimate of the capacity of plant communities to support diseases) best explained variation in pathogen load, indicating that plant community-mediated effects (through evenness and proneness) of yak grazing determined pathogen load. Our study provides empirical evidence that grazing influences foliar fungal disease prevalence through plant community evenness and composition, which demonstrates the necessity of incorporating host plant community characteristics into disease load prediction frameworks.

Discovery of ARF1-targeting inhibitor demethylzeylasteral as a potential agent against breast cancer.

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