Plasma cell-free DNA methylome profiling in pre- and post-surgery oral cavity squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous disease that involves multiple anatomic sites, is a leading cause of cancer-related mortality worldwide. Although the utility of noninvasive biomarkers based on circulating cell-free DNA (cfDNA) methylation profiling has been widely recognized, limited studies have been reported so far regarding the dynamics of cfDNA methylome in oral cavity squamous cell carcinoma (OCSCC). It is hypothesized in this study that comparison of methylation profiles in pre- and postsurgery plasma samples will reveal OCSCC-specific prognostic and diagnostic biomarkers. As a strategy to further prioritize tumor-specific targets, top differential methylated regions (DMRs) were called by reanalyzing methylation data from paired tumor and normal tissue collected in the the cancer genome atlas head-neck squamous cell carcinoma (TCGA) head and neck cancer cohort. Matched plasma samples from eight patients with OCSCC were collected at Moffitt Cancer Center before and after surgical resection. Plasma-derived cfDNA was analyzed by cfMBD-seq, which is a high-sensitive methylation profiling assay. Differential methylation analysis was then performed based on the matched samples profiled. In the top 200 HNSCC-specific DMRs detected based on the TCGA data set, a total of 23 regions reached significance in the plasma-based DMR test. The top five validated DMR regions (ranked by the significance in the plasma study) are located in the promoter regions of genes PENK, NXPH1, ZIK1, TBXT, and CDO1, respectively. The genome-wide cfDNA DMR analysis further highlighted candidate biomarkers located in genes SFRP4, SOX1, IRF4, and PCDH17. The prognostic relevance of candidate genes was confirmed by survival analysis using the TCGA data. This study supports the utility of cfDNA-based methylome profiling as a promising noninvasive biomarker source for OCSCC and HNSCC.

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

CGPA: multi-context insights from the cancer gene prognosis atlas.

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

Prediction-oriented prognostic biomarker discovery with survival machine learning methods.

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data.

Design, Synthesis, and Biological Evaluation of Novel Derivatives of the Marine Natural Product Laurene.

Plant pathogenic fungi and viruses are seriously threatening agricultural production. There is an urgent need to develop novel fungicides and antiviral agents with low toxicity and high efficiency. In this study, we designed and synthesized 32 thiazole-, hydrazone-, and amide-containing derivatives of laurene and systematically evaluated their antiviral activities and fungicidal activities. Structure-simplified compounds 5a-5c, 5i, 5k, 5l, 11a, 11j, and 12c displayed higher antiviral activities than that of ningnanmycin. Compound 11a with a simple chemical structure, convenient synthetic route, and excellent antiviral activity emerged as a secondary lead compound. The docking results show that compounds 5i, 5k, and 11a have strong interactions with the tobacco mosaic virus coat protein (TMV CP). These compounds also exhibited significant fungicidal activities. Compounds 5g, 5k, 11j, and 11l displayed 9.15-17.45 µg/mL EC50 values against Pyricularia grisea, and compounds 5h (EC50: 8.01 µg/mL) and 11i (EC50: 15.23 µg/mL) exhibited a similar level of EC50 values with chlorothalonil (EC50: 7.33 µg/mL) against Physalospora piricola. Preliminary fungicidal mechanism research indicated that compound 5h has a certain destructive effect on the hyphae of P. piricola. This work lays a foundation for the application of laurene derivatives in plant protection.

A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data.

Cancer prognosis prediction is critical to the clinical decision-making process. Currently, the high availability of transcriptome datasets allows us to extract the gene modules with promising prognostic values. However, the biomarker identification is greatly challenged by tumor and patient heterogeneity. In this study, a framework of three subnetwork-based strategies is presented, incorporating hypothesis-driven, data-driven, and literature-based methods with informative visualization to prioritize candidate genes. By applying the proposed approaches to a head and neck squamous cell cancer (HNSCC) transcriptome dataset, we successfully identified multiple HNSCC-specific gene modules with improved prognostic values and mechanism information compared with the standard gene panel selection methods. The proposed framework is general and can be applied to any type of omics data. Overall, the study demonstrates and supports the use of the subnetwork-based approach for distilling reliable and biologically meaningful prognostic factors.

Unveiling targeted cell-free DNA methylation regions through paired methylome analysis of tumor and normal tissues.

Liquid biopsy analysis of cell-free DNA (cfDNA) has revolutionized cancer research by enabling non-invasive assessment of tumor-derived genetic and epigenetic changes. In this study, we conducted a comprehensive paired-sample differential methylation analysis (psDMR) on reprocessed methylation data from two large datasets, CPTAC and TCGA, to identify and validate differentially methylated regions (DMRs) as potential cfDNA biomarkers for head and neck squamous cell carcinoma (HNSC). Our hypothesis is that the paired sample test provides a more suitable and powerful approach for the analysis of heterogeneous cancers like HNSC. The psDMR analysis revealed a significant number of overlapped hypermethylated DMRs between two datasets, indicating the reliability and relevance of these regions for cfDNA methylation biomarker discovery. We identified several candidate genes, including CALCA, ALX4, and HOXD9, which have been previously established as liquid biopsy methylation biomarkers in various cancer types. Furthermore, we demonstrated the efficacy of targeted region analysis using cfDNA methylation data from oral cavity squamous cell carcinoma and nasopharyngeal carcinoma patients, further validating the utility of psDMR analysis in prioritizing cfDNA methylation biomarkers. Overall, our study contributes to the development of cfDNA-based approaches for early cancer detection and monitoring, expanding our understanding of the epigenetic landscape of HNSC, and providing valuable insights for liquid biopsy biomarker discovery not only in HNSC and other cancer types.

Genetic components of Escherichia coli involved in its complex prey-predator interaction with Myxococcus xanthus.

Myxococcus xanthus and Escherichia coli represent a well-studied microbial predator-prey pair frequently examined in laboratory settings. While significant progress has been made in comprehending the mechanisms governing M. xanthus predation, various aspects of the response and defensive mechanisms of E. coli as prey remain elusive. In this study, the E. coli MG1655 large-scale chromosome deletion library was screened, and a mutant designated as ME5012 was identified to possess significantly reduced susceptibility to predation by M. xanthus. Within the deleted region of ME5012 encompassing seven genes, the significance of dusB and fis genes in driving the observed phenotype became apparent. Specifically, the deletion of fis resulted in a notable reduction in flagellum production in E. coli, contributing to a certain level of resistance against predation by M. xanthus. Meanwhile, the removal of dusB in E. coli led to diminished inducibility of myxovirescin A production by M. xanthus, accompanied by a slight decrease in susceptibility to myxovirescin A. These findings shed light on the molecular mechanisms underlying the complex interaction between M. xanthus and E. coli in a predatory context.

Jujube witches' broom phytoplasmas inhibit ZjBRC1-mediated abscisic acid metabolism to induce shoot proliferation.

Jujube witches' broom (JWB) phytoplasmas parasitize the sieve tubes of diseased phloem and cause an excessive proliferation of axillary shoots from dormant lateral buds to favour their transmission. In previous research, two JWB effectors, SJP1 and SJP2, were identified to induce lateral bud outgrowth by disrupting ZjBRC1-mediated auxin flux. However, the pathogenesis of JWB disease remains largely unknown. Here, tissue-specific transcriptional reprogramming was examined to gain insight into the genetic mechanisms acting inside jujube lateral buds under JWB phytoplasma infection. JWB phytoplasmas modulated a series of plant signalling networks involved in lateral bud development and defence, including auxin, abscisic acid (ABA), ethylene, jasmonic acid, and salicylic acid. JWB-induced bud outgrowth was accompanied by downregulation of ABA synthesis within lateral buds. ABA application rescued the bushy appearances of transgenic Arabidopsis overexpressing SJP1 and SJP2 in Col-0 and ZjBRC1 in the brc1-2 mutant. Furthermore, the expression of ZjBRC1 and ABA-related genes ZjHB40 and ZjNCED3 was negatively correlated with lateral main bud outgrowth in decapitated healthy jujube. Molecular evidence showed that ZjBRC1 interacted with ZjBRC2 via its N-terminus to activate ZjHB40 and ZjNCED3 expression and ABA accumulation in transgenic jujube calli. In addition, ZjBRC1 widely regulated differentially expressed genes related to ABA homeostasis and ABA signalling, especially by binding to and suppressing ABA receptors. Therefore, these results suggest that JWB phytoplasmas hijack the ZjBRC1-mediated ABA pathways to stimulate lateral bud outgrowth and expansion, providing a strategy to engineer plants resistant to JWB phytoplasma disease and regulate woody plant architecture to promote crop yield and quality.

Systemic administration of urocortin after intracerebral hemorrhage reduces neurological deficits and neuroinflammation in rats.

BACKGROUND: Intracerebral hemorrhage (ICH) remains a serious clinical problem lacking effective treatment. Urocortin (UCN), a novel anti-inflammatory neuropeptide, protects injured cardiomyocytes and dopaminergic neurons. Our preliminary studies indicate UCN alleviates ICH-induced brain injury when administered intracerebroventricularly (ICV). The present study examines the therapeutic effect of UCN on ICH-induced neurological deficits and neuroinflammation when administered by the more convenient intraperitoneal (i.p.) route. METHODS: ICH was induced in male Sprague-Dawley rats by intrastriatal infusion of bacterial collagenase VII-S or autologous blood. UCN (2.5 or 25 µg/kg) was administered i.p. at 60 minutes post-ICH. Penetration of i.p. administered fluorescently labeled UCN into the striatum was examined by fluorescence microscopy. Neurological deficits were evaluated by modified neurological severity score (mNSS). Brain edema was assessed using the dry/wet method. Blood-brain barrier (BBB) disruption was assessed using the Evans blue assay. Hemorrhagic volume and lesion volume were assessed by Drabkin's method and morphometric assay, respectively. Pro-inflammatory cytokine (TNF-α, IL-1ß, and IL-6) expression was evaluated by enzyme-linked immunosorbent assay (ELISA). Microglial activation and neuronal loss were evaluated by immunohistochemistry. RESULTS: Administration of UCN reduced neurological deficits from 1 to 7 days post-ICH. Surprisingly, although a higher dose (25 µg/kg, i.p.) also reduced the functional deficits associated with ICH, it is significantly less effective than the lower dose (2.5 µg/kg, i.p.). Beneficial results with the low dose of UCN included a reduction in neurological deficits from 1 to 7 days post-ICH, as well as a reduction in brain edema, BBB disruption, lesion volume, microglial activation and neuronal loss 3 days post-ICH, and suppression of TNF-α, IL-1ß, and IL-6 production 1, 3 and 7 days post-ICH. CONCLUSION: Systemic post-ICH treatment with UCN reduces striatal injury and neurological deficits, likely via suppression of microglial activation and inflammatory cytokine production. The low dose of UCN necessary and the clinically amenable peripheral route make UCN a potential candidate for development into a clinical treatment regimen.

Leader's Implicit Followership and Employees' Innovative Behavior: Chain Mediation Effect of Leader-Member Exchange and Psychological Empowerment.

In the Chinese society, where power distance is high, leaders' attitudes and behavior toward employees determine their career development as well as affect the entire team's performance. Therefore, exploring the kind of employees that leaders expect in China is essential. Based on implicit followership theory perspective, this study considers leaders' positive implicit followership (LPIF) as the main research variable and examines its influence on employees' innovative behavior (EIB). Moreover, it explores the multiple mediation effect of the leader-member exchange (LMX) relationship and psychological empowerment (PE) in this influence mechanism. The study sample comprised 389 leaders and their direct employees at 45 large- and medium-sized enterprises in Shandong, Beijing, Hebei, Shanghai, Shanxi, Zhejiang, and other regions of China. We used the leader-employee 1:1 matching questionnaire, and the longitudinal research design was adopted to avoid homology variance, making the study results more realistic and reliable. This study used the SPSS 26.0 and AMOS 26.0 statistical software to verify the hypotheses. Our findings show that LPIF has a significant positive effect on EIB, and LMX and PE have multiple mediation effects on the relationship between LPIF and EIB. When the level of LPIF is high, LMX and PE are also enhanced, which in turn promotes the increase in EIB. This study provides a new perspective for subsequent research on the psychological mechanism of employees and suggests an important method for understanding leadership and following processes in an organization. It plays a guiding role for the management practice of an enterprise, selection of leaders, and training of employees.

Physicochemical and Biological Insights Into the Molecular Interactions Between Extracellular DNA and Exopolysaccharides in Myxococcus xanthus Biofilms.

Extracellular DNA (eDNA) is a critical component in the extracellular matrix (ECM) of bacterial biofilms, while little is known about the mechanisms underlying how eDNA integrates into the ECM through potential macromolecular interactions. Myxococcus xanthus biofilm was employed as a suitable model for the investigation due to the co-distribution of eDNA and exopolysaccharides (EPS) owing to their direct interactions in the ECM. DNA is able to combine with M. xanthus EPS to form a macromolecular conjugate, which is dominated by the electrostatic forces participating in the polymer-polymer interactions. Without intercalation binding, DNA-EPS interactions exhibit a certain degree of reversibility. Acting as a strong extracellular framework during biofilm formation process, the eDNA-EPS complex not only facilitates the initial cell adhesion and subsequent establishment of ECM architecture, but also renders cells within biofilms stress resistances that are relevant to the survival of M. xanthus in some hostile environments. Furthermore, the EPS protects the conjugated DNA from the degradation by nucleic acid hydrolases, which leads to the continuous and stable existence of eDNA in the native ECM of M. xanthus biofilms. These results will shed light on developing prevention and treatment strategies against biofilm-related risks.

Highly effective copper-catalyzed decarboxylative coupling of aryl halides with alkynyl carboxylic acids.

We have developed a highly effective copper-catalyzed decarboxylative coupling of alkynylcarboxylic acids with various aryl and alkyl halides at 2 mol% loading of copper. This method is simple, economical and practical for the synthesis of disubstituted alkyne compounds.

Copper(I)-catalyzed synthesis of 1,3-enynes via coupling between vinyl halides and alkynes or domino coupling of vinyl halides.

1,3-Enynes were easily prepared from coupling between vinyl halides and alkynes or domino coupling of vinyl halides in the presence of copper iodide. It is noteworthy that the double-bond geometry of the vinyl halides was retained during the reaction. This ligand-free protocol is potentially useful and practical.

Ligand-free highly effective iron/copper co-catalyzed formation of dimeric aryl ethers or sulfides.

Highly selective coupling of diiodoarenes with phenols or phenthiols can be performed by using a low-cost, benign character and readily available Fe/Cu catalytic system in the absence of ligands. It is noteworthy that the desired dimeric aryl ethers or sulfides could be obtained in high yields by coupling between diiodoarenes and phenols, or diphenols with aryl iodides.

Analysis of the Influence of Entrepreneur's Psychological Capital on Employee's Innovation Behavior Under Leader-Member Exchange Relationship.

How to make use of leaders' psychological capital to improve the innovation behavior of employees has become an important issue for the talent management of enterprises today, and it is also the goal that enterprises must pursue if they want to stand out in fierce competition. Therefore, a total of 154 enterprises in a high-tech area were selected for questionnaire survey in this study. The correlation between leader-member exchange (LMX) relationship (emotion, loyalty, contribution, and professional respect), leaders' psychological capital (confidence, hope, optimism, and tenacity), and employees' innovation behaviors were analyzed based on multivariate regression. Hierarchical regression method was used to examine the mediating effect of the LMX. It was found that confidence, toughness, and contribution were significantly positively correlated with employee innovation behavior (p < 0.001). The positive correlation between hope, optimism, emotion, and loyalty with employees' innovation behavior was significant (p < 0.05). Besides, emotion, loyalty, and contribution had mediating effects on the leaders' psychological capital and the innovation behavior of employees. In conclusion, leaders' psychological capital can have a significant positive effect on the innovation behavior of employees directly, and it can also have an indirect positive effect on the innovation behavior of employees by maintaining high quality LMX.

Cdc13 is predominant over Stn1 and Ten1 in preventing chromosome end fusions.

Telomeres define the natural ends of eukaryotic chromosomes and are crucial for chromosomal stability. The budding yeast Cdc13, Stn1 and Ten1 proteins form a heterotrimeric complex, and the inactivation of any of its subunits leads to a uniformly lethal phenotype due to telomere deprotection. Although Cdc13, Stn1 and Ten1 seem to belong to an epistasis group, it remains unclear whether they function differently in telomere protection. Here, we employed the single-linear-chromosome yeast SY14, and surprisingly found that the deletion of CDC13 leads to telomere erosion and intrachromosome end-to-end fusion, which depends on Rad52 but not Yku. Interestingly, the emergence frequency of survivors in the SY14 cdc13Δ mutant was ~29 fold higher than that in either the stn1Δ or ten1Δ mutant, demonstrating a predominant role of Cdc13 in inhibiting telomere fusion. Chromosomal fusion readily occurred in the telomerase-null SY14 strain, further verifying the default role of intact telomeres in inhibiting chromosome fusion.

4-Acetylantroquinonol B from antrodia cinnamomea enhances immune function of dendritic cells against liver cancer stem cells.

The functions of 4-acetylantroquinonol B (4-AAQB), a ubiquinone derivative isolated from the mycelium of Antrodia cinnamomea, in immunotherapy for liver cancer were investigated. We found that 4-AAQB could inhibit liver cancer stem cell related manifestations and activate the antitumor ability of dendritic cells. Specifically, 4-AAQB can inhibit EpCAM, AFP and related pathways of HepG2 cells. It also significantly decreases the expression of ß-catenin, inhibits the tumorigenicity and decreases the secretion of immune escape related cytokines. Moreover, 4-AAQB can stimulate the proliferation of immune cells and promote the endocytosis of immature dendritic cells. When co-cultured immature dendritic cells with EpCAM+ HepG2 cells, 4-AAQB enhanced the expression of MHC class I and II on the surface of liver cancer stem cells and dendritic cells, increased the expression of costimulatory molecules CD80 of dendritic cells and cytokines related to immune activation. In conclusion, 4-AAQB from Antrodia cinnamomea can enhance immune function of dendritic cells against liver cancer stem cells, and may have the potential to be used for liver cancer prevention and immunotherapy.