Ankylosing spondylitis (AS) is a chronic autoimmune disease that primarily affects the axial joints. Immune cells play a key role in the pathogenesis of AS. This study integrated bioinformatics methods with experimental validation to explore the role of natural killer (NK) cells in AS.

Two microarray datasets, GSE25101 and GSE73754, were selected, and the scRNA-seq data were obtained from GSE194315 and Liu's research. Differentially expressed genes (DEGs) and functional enrichment analysis were performed respectively. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules of co-expressed genes and genes involved in NK cell function. The diagnostic value of the identified key genes was evaluated using ROC curves, logistic regression analysis, and a nomogram. Real-time PCR (RT-PCR) was used to quantified the expression of genes. Statistical analysis was conducted using the R software package, and a p-value of less than 0.05 was considered statistically significant.

Pathways enrichment analysis revealed the involvement of NK cell-mediated immune pathways and regulation of the innate immune response, indicating the crucial role of innate immunity, especially NK cells, in AS pathogenesis. The construction of a co-expression network revealed that the MElightyellow module was most relevant to the NK cell-mediated immune pathway. IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2 from the MElightyellow module were identified as key genes involved in NK cell-mediated immune response and served as potential diagnostic biomarkers for AS, with moderate to high diagnostic values based on AUC values. Further analysis using scRNA-seq profiling revealed the higher expression level of IL2RB, CD247, PLEKHF1, S1PR5, FGFBP2 in NK cells compared to that in other cell types. CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 were reduced expressed in AS patients as compare to control group verified by scRNA-seq data, CD247, EOMES, FGFBP2, IL2RB and S1PR5 were reduced expressed verified by RT-PCR, and PLEKHF1, S1PR5, and FGFBP2 was upregulated after TNF-α blocker therapy.

The study revealed the potential role of NK cells and identified IL2RB, CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 as key genes associated with NK cells in the pathogenesis of AS.

Endometriosis is a chronic inflammatory disease characterized by the invasion of endometrial cells outside the uterine cavity. Current treatments for the disease, whose typical symptoms are pain and infertility, are unsatisfactory, relying on the surgical removal of the lesions and hormonal therapies with high symptom relapse and collateral effects, respectively. The aim of the present study was to exploit the rationale for G protein-coupled receptors (GPCRs) as non-hormonal therapeutic targets for this disease. To this end, human endometriotic epithelial cells 12Z were employed to characterize GPCR-mediated increases in intracellular Ca2+ concentrations ([Ca2+]i) using fluo-4, and cell invasion was measured using Boyden chamber assays. The results showed that the GPCR ligands oxytocin, bradykinin, histamine, lysophosphatidic acid, and sphingosine 1-phosphate (S1P) efficiently increased [Ca2+]i and induced cell invasion in endometriotic cells. In contrast, neuropeptide S, previously identified as a pro-invasive mediator, did not increase [Ca2+]i in 12Z cells. Notably, pretreatment with pertussis toxin significantly reduced S1P-dependent [Ca2+]i increase and cell invasion, highlighting the involvement of Gi-mediated signaling. Employing specific agonists and/or antagonists of S1P receptor isoforms, we demonstrated that S1P1/S1P3/S1P5, but not S1P2/S1P4 mediated the [Ca2+]i increases in this cellular model. Moreover, activation of S1P1/S1P4/S1P5, but not S1P2/S1P3, efficiently stimulated cell invasion. Taken together, we identified several GPCRs that are functionally relevant in human endometriotic epithelial cells and may potentially serve as targets for non-hormonal therapy of endometriosis.

Inflammasomes regulate the host response to intracellular pathogens including mycobacteria. We have previously shown that the course of Mycobacterium marinum infection in adult zebrafish (Danio rerio) mimics the course of tuberculosis in human. To investigate the role of the inflammasome adaptor pycard in zebrafish M. marinum infection, we produced two zebrafish knockout mutant lines for the pycard gene with CRISPR/Cas9 mutagenesis. Although the zebrafish larvae lacking pycard developed normally and had unaltered resistance against M. marinum, the loss of pycard led to impaired survival and increased bacterial burden in the adult zebrafish. Based on histology, immune cell aggregates, granulomas, were larger in pycard-deficient fish than in wild-type controls. Transcriptome analysis with RNA sequencing of a zebrafish haematopoietic tissue, kidney, suggested a role for pycard in neutrophil-mediated defence, haematopoiesis and myelopoiesis during infection. Transcriptome analysis of fluorescently labelled, pycard-deficient kidney neutrophils identified genes that are associated with compromised resistance, supporting the importance of pycard for neutrophil-mediated immunity against M. marinum. Our results indicate that pycard is essential for resistance against mycobacteria in adult zebrafish.

Changes in the three-dimensional (3D) structure of the human genome are associated with various conditions, such as cancer and developmental disorders. Techniques like chromatin conformation capture (Hi-C) have been developed to study these global 3D structures, typically requiring millions of cells and an extremely high sequencing depth (around 1 billion reads per sample) for bulk Hi-C. In contrast, single-cell Hi-C (scHi-C) captures 3D structures at the individual cell level but faces significant data sparsity, characterized by a high proportion of zeros. scHi-C data enable the identification of cell types with distinct 3D structures; consequently, identifying differential chromatin interactions between such groups may offer insights into cell type-specific regulation. While differential analysis methods exist for bulk Hi-C data, they are limited for scHi-C data. To address this, we developed a method for differential scHi-C analysis, extending the HiCcompare R package. Our approach optionally imputes sparse scHi-C data by considering genomic distances and creates pseudo-bulk Hi-C matrices by summing condition-specific data. The data are normalized using locally estimated scatterplot smoothing (LOESS) regression, and differential chromatin interactions are detected via Gaussian Mixture Model (GMM) clustering. Our workflow outperforms existing methods in identifying differential chromatin interactions across various genomic distances, fold changes, resolutions, and sample sizes in both simulated and experimental contexts. This enables the effective detection of cell type-specific differences in chromatin structure and shows expected associations with biological and epigenetic features. Our method is implemented in the scHiCcompare R package, available at https://bioconductor.org/packages/scHiCcompare.

Helicobacter pylori (H. pylori) is associated with various gastric diseases and is one of the pathogenic factors of gastric cancer (GC). We found that H. pylori induce the expression of TRAF1, but its mechanism of action is still unclear. Therefore, we wanted to determine whether TRAF1 is involved in the mechanism of H. pylori-related GC progression.

In this study, we analysed TRAF1 expression and its prognostic significance using clinical specimens, performed functional studies involving TRAF1 overexpression or knockdown in cellular models, identified downstream signalling pathways regulated via RNA-seq, validated these mechanisms through pathway blockade and rescue experiments, and further confirmed the findings in an H. pylori-infected gastritis mouse model.

TRAF1 expression was significantly elevated in GC tissues and served as a poor prognostic biomarker. TRAF1 promoted GC cell proliferation, migration and invasion. RNA-seq analysis revealed that TRAF1 activated the EGFR/STAT/OAS signalling axis, upregulated STAT3 expression and increased the transcription of the OAS gene family. Pharmacological inhibition with ruxolitinib and AG490 effectively blocked EGFR/STAT/OAS signalling. In H. pylori-treated cell models, H. pylori infection activated the EGFR/STAT/OAS signalling axis. In vivo, we established an H. pylori-induced gastritis mouse model to validate the activation of this signalling pathway during the gastritis-carcinoma transition.

TRAF1 may promote the proliferation, migration and invasion of H. pylori-associated GC by activating the EGFR/STAT/OAS signalling axis, suggesting that TRAF1 is a promising novel prognostic biomarker and therapeutic target for this malignancy.

Neuronal heterogeneity is a defining feature of the developing mammalian brain, but the mechanisms regulating the diversification of closely related cell types remain elusive. Here, we investigated granule cell (GC) subtype composition in the dentate gyrus (DG) and the influence of a psychosis-associated V321L mutation in Neuregulin1 (Nrg1). Using morphoelectric characterization, single-nucleus gene expression, and chromatin accessibility profiling, we identified distinctions between typical GCs and a rare subtype known as semilunar granule cells (SGCs). We found that the V321L mutation, which disrupts Nrg1 nuclear back-signaling, results in overabundance of SGC-like cells. Pseudotime analyses suggest a GC-to-SGC transition potential, supported by the accessibility of SGC-enriched genes in non-SGCs. In WT mice, SGC-like gene expression increases during adolescence, coinciding with reduced Nrg1 back-signaling capacity. These results suggest that intact Nrg1 nuclear signaling represses SGC-like fate and that its developmental or pathological loss may permit acquisition of this fate. Our findings reveal a novel role of Nrg1 in maintaining DG cell-type composition and suggest that disrupted subtype regulation may contribute to disease-associated changes in the DG.

Angiotensinogen (AGT) is the precursor of angiotensin II, a potent vasopressor in the renin-angiotensin-aldosterone system. Small interfering RNAs (siRNAs) targeting hepatic AGT can lower blood pressure in hypertension patients by reducing AGT levels, with effects lasting over 6 months. Existing siRNA molecules are effective, but novel ones with better inhibitory activity and longer duration periods may be developed. In this study, we demonstrated an entire development process for a novel siRNA targeting hepatic AGT. Through the proper combination of bioinformatic on-target/off-target screening on sequences, chemical modification patterns optimization, and liver-targeting delivery ligands conjugation, we have successfully developed several promising siRNAs with equivalent or better inhibitory activity, duration of effect, and safety profile compared with previously reported siRNA. Moreover, our comprehensive analysis has elucidated the correlation between the efficacy and free energy of siRNAs. Currently, there exists no reliable model capable of precisely predicting the activity and off-target risk associated with fully modified siRNAs. Therefore, the implementation of efficient screening procedures is of utmost importance during the development of siRNA candidates. This study presents a meticulous and valuable reference for the development of potent and safe siRNAs on other targets.

Although lung myeloid cells provide an intracellular niche for Mycobacterium tuberculosis (Mtb), CD4+ T cells limit Mtb growth in these cells to protect the host. The CD4+ T cell activities including interferon-γ (IFN-γ) production that account for this protection are poorly understood. Using intravenous antibody labeling and lineage-tracing reporter mice, we show that monocyte-derived macrophages (MDMs), rather than phenotypically similar monocytes or dendritic cells, are preferentially infected with Mtb in murine lungs. MDMs were recruited to the lungs by Mtb-specific CD4+ T cells via IFN-γ, which promoted the extravasation of monocyte precursors from the blood. It was possible that CD4+ T cells recruited infectable MDMs because these cells are uniquely poised to receive cognate MHCII-mediated help to control intracellular bacteria. Mice with MHCII deficiency in monocyte-derived cells had normal Mtb-specific CD4+ T cell activation, expansion and differentiation but the CD4+ T cells were unable to attenuate Mtb growth. Using single cell RNA sequencing, we showed that MDMs receiving cognate MHCII-mediated help from CD4+ T cells upregulated glycolytic genes associated with Mtb control. Overall, the results indicate that CD4+ T cells recruit infectable MDMs to the lungs and then trigger glycolysis-dependent bacterial control in the MDMs by engaging their MHCII-bound Mtb peptides. Moreover, the results suggest that cognate MHCII-mediated help to promote MDM glycolysis is an essential, IFN-γ-independent effector function of Mtb-specific CD4+ T cells.

Metabolic dysfunction-associated steatotic liver disease (MASLD) constitutes a global health threat with its ability to develop into liver cirrhosis and hepatocellular carcinoma (HCC). Emerging data suggests that oxidative stress and regulated cell death are major driving forces for liver inflammation in MASH. Febuxostat (Feb.), one of the Xanthine oxidase (XO) inhibitors, has shown promise in significantly improving the prognosis of MASH by reducing inflammatory cytokines and cell death. However, the underlying molecular mechanisms remain unclear. In this study, we evaluated the therapeutic effects of febuxostat on MASH through the modulation of cell death, inflammation, and intestinal permeability, focusing on hepatic mRNAs (HGS, SNF8, TSG101) and their epigenetic regulators (rno-miR-6216, rno-miR-1224). MASH was induced in Wistar rats via a High-sucrose high-fat (HSHF) diet over 14 weeks, followed by febuxostat treatment at doses of 1.5, 3, and 6 mg/kg/day for 4 weeks. Febuxostat treatment significantly improved liver function and lipid profiles, reduced hepatic steatosis, intralobular inflammation, and ballooning, and restored normal expression of the hepatic RNA panel by downregulating HGS, SNF8, and TSG101 mRNAs and their epigenetic regulators. Furthermore, febuxostat decreased serum levels of inflammatory (IL6), fibrosis (TGFB1), and cell death (TSG101) markers while reducing apoptosis and regulated cell death via modulation of Caspase-3 and LC3B expression. Improvements in intestinal permeability were evident via reductions in serum haptoglobin (Hpt) and TMAO and restoration of occludin expression. These findings highlight febuxostat as a promising therapeutic candidate for MASH by targeting key molecular mechanisms of liver inflammation and gut-liver axis dysfunction.

RORc-expressing immune cells play important roles in inflammation, autoimmune disease and cancer. They are required for lymphoid organogenesis and have been implicated in tertiary lymphoid structure (TLS) formation. TLSs are formed in many cancer types and have been correlated with better prognosis and response to immunotherapy. In liver cancer, some TLSs are pro-tumorigenic as they harbor tumor progenitor cells and support their growth. The processes involved in TLS development and acquisition of pro- or anti-tumorigenic roles are largely unknown. This study aims to explore the role of RORc-expressing cells in TLS development in the context of inflammation-associated liver cancer.

IKKβ(EE)Hep mice, exhibiting chronic liver inflammation, TLS formation and liver cancer, were crossed with RORc knockout mice to explore RORc's effect on TLS and tumor formation. TLS phenotypes were analyzed using transcriptional, proteomic, and immunohistochemical techniques. CD4, CD8, and B-cell depletions were used to assess their contribution to liver TLS and tumor formation.

RORc-expressing cells are detected within TLSs of both human patients and mice developing intrahepatic cholangiocarcinoma. In mice, these cells negatively regulate TLS formation, as excess TLSs form in their absence. CD4 cells are essential for liver TLS formation, while B cells are required for TLS formation specifically in the absence of RORc-expressing cells. Importantly, in chronically inflamed livers lacking RORc-expressing cells, TLSs become anti-tumorigenic, reducing tumor load. Anti-tumorigenic TLSs revealed enrichment of exhausted CD8 cells with effector functions, germinal center B cells and plasma cells. B cells are key in limiting tumor development, possibly via tumor-directed antibodies.

RORc-expressing cells negatively regulate B-cell responses and facilitate the pro-tumorigenic functions of hepatic TLSs.

RORc-expressing immune cells play critical roles in immune regulation, yet their specific influence on tertiary lymphoid structures (TLSs) in liver pathology and cancer has not been elucidated. Our study reveals that RORc-expressing cells act as negative regulators of TLS formation and shape the immune microenvironment in a manner that promotes tumor development. In the absence of RORc-expressing cells, TLSs not only increase in number but also acquire anti-tumorigenic properties. These findings suggest that RORc-expressing cells serve as key modulators of liver immune dynamics, with potential implications for the use of RORc as a biomarker to differentiate between pro- and anti-tumorigenic immune environments and as a target for manipulating TLS abundance and phenotype in liver cancer.

Spinal cord injury (SCI) triggers intraspinal inflammation through an influx of blood-derived inflammatory cells such as neutrophils and monocyte-derived macrophages. Macrophages play a complex role in SCI pathophysiology ranging from potentiating secondary injury to facilitating recovery and wound healing. In vitro, macrophages have been classified as having a pro-inflammatory, M1 phenotype, or a regenerative, M2 phenotype. In vivo, however, studies suggest that macrophages exist in a spectrum of phenotypes and can shift from one phenotype to another. Single-cell RNA sequencing (scRNA-seq) allows us to assess immune cell heterogeneity in the spinal cord after injury, and several groups have created publicly available datasets containing valuable data for further exploration. In this study, we compared three different scRNA-seq datasets and analyzed macrophage heterogeneity after SCI based on cell clustering according to gene expression profiles. We analyzed data from 7 days post injury (dpi) in young female mice that received a mid-thoracic SCI contusion. Using the Seurat pipeline, we clustered cells, subsetted macrophages from microglia and other myeloid cells, and identified different macrophage populations. Using SingleR as a cross-dataset cluster comparison tool, we identified similarities in macrophage populations across datasets. To confirm and refine this analysis, we analyzed the top 10 differentially expressed genes for each population in each dataset. Most clusters identified in the SingleR analysis were confirmed to have a unique genetic signature and were consistently present in all datasets analyzed. Taken together, four distinct macrophage populations were consistently identified after SCI at 7 dpi in three datasets from independent research teams. Our identification of biologically conserved macrophage populations after SCI using an unbiased approach highlights the power of data sharing and open data in redefining macrophage heterogeneity.

Platinum-based combination chemotherapy remains the backbone of first-line treatment for patients with advanced epithelial ovarian cancer (EOC). While most patients initially respond well to the treatment, patients with relapse ultimately develop platinum resistance. This study identified FLYWCH-type zinc finger-containing protein 1 (FLYWCH1) as an important regulator in the resistance development process. We showed that the loss of FLYWCH1 promotes platinum resistance in EOC cells, and the low FLYWCH1 expression is correlated with poor prognosis of EOC patients. In platinum-sensitive cells, FLYWCH1 colocalizes with H3K9me3, but this association is significantly reduced when cells acquire resistance. The suppression of FLYWCH1 induces gene expression changes resulting in the deregulation of pathways associated with resistance. In line with its connection to H3K9me3, FLYWCH1 induces gene silencing in a synthetic reporter assay and the suppression of FLYWCH1 alters H3K9me3 at promoter regions and repeat elements. The loss of FLYWCH1 leads to the derepression of LTR and Alu repeats, thereby increasing transcriptional plasticity and driving the resistance development process. Our data highlight the importance of FLYWCH1 in chromatin biology and acquisition of platinum resistance through transcriptional plasticity and propose FLYWCH1 as a potential biomarker for predicting treatment responses in EOC patients.

Seasonal song production in canaries is influenced by gonadal hormones, but the molecular mechanisms underlying testosterone-induced song development in adult female canaries, which rarely sing naturally, remain poorly understood. We explored testosterone-induced song development in adult female canaries by comparing gene regulatory networks in the song-controlling brain area HVC at multiple time points (1 h to 14 days) post-treatment with those of placebo-treated controls. Females began vocalizing within 4 days of testosterone treatment, with song complexity and HVC volume increasing progressively over 2 weeks. Rapid transcriptional changes involving 2739 genes preceded song initiation. Over 2 weeks, 9913 genes-approximately 64% of the canary's protein-coding genome-were differentially expressed, with 98% being transiently regulated. These genes are linked to various biological functions, with early changes at the cellular level and later changes affecting the nervous system level after prolonged hormone exposure. Our findings suggest that testosterone-induced song development is accompanied by extensive and dynamic transcriptional changes in the HVC, implicating widespread neuronal involvement. These changes underpin the gradual emergence of singing behavior, providing insights into the neural basis of seasonal behavioral patterns.

Liver Hepatocellular Carcinoma (LIHC) and Kidney Renal Clear Cell Carcinoma (KIRC) are leading causes of cancer death worldwide, but their early detections remain hindered by a lack of genetic markers. Our study aims to find prospective biomarkers that could serve as prognostic indicators for efficient drug candidates for KIRC and LIHC treatment.

To detect differentially expressed genes (DEGs), four datasets were used: GSE66271 and GSE213324 for KIRC, and GSE135631 and GSE202853 for LIHC. Visualization of DEGs was done using heatmaps, volcano plots, and Venn diagrams. Hub genes were identified via PPI analysis and the cytoHubba plugin in Cytoscape. Their expression was evaluated using box plots, stage plots, and survival plots for prognostic assessment via GEPIA2. Molecular docking with PyRx's AutoDock Vina identified optimal binding interactions between compounds and proteins. Pharmacokinetic and toxicity analyses reinforced the drug-likeness and safety of these compounds.

In this study, 47 DEGs were identified, with the top 10 hub genes being TOP2A, BUB1, PTTG1, CCNB2, NUSAP1, KIF20A, BIRC5, RRM2, NDC80 and CDC45, chosen for their high MCC scores. Data mining revealed a correlation between TOP2A expression and clinical survival outcomes in KIRC and LIHC patients. Docking studies of the TOP2A structure identified a promising compound from Andrographis paniculata with high binding energy and interactions with TOP2A. Pharmacokinetic and toxicity assessments support its potential as a drug candidate.

Our study emphasizes TOP2A's prognostic significance in KIRC and LIHC and recognizes Andrographis paniculata compound as potential therapeutics, offering prospective for enhanced treatment and patient outcomes in these cancers.

Cowpea (Vigna unguiculata) is an important protein source in Sub-Saharan Africa. Optimizing resilience and productivity through genetic engineering in cowpea has been slow due in part to a lack of defined species-specific regulatory elements and difficulty testing gene function within the native system. In many plant species, Agrobacterium-mediated transient gene expression is widely used to validate constructs before investing in transgenic lines, but its implementation in legumes has been challenging. In this study, we optimized an in planta agroinfiltration assay in trifoliate cowpea leaves using a betalain reporter. To demonstrate the "intact plant" aspect of this system, we used this assay to characterize drought-inducible promoters by challenging cowpea plants with drought stress. Subsequently, to identify and broaden the pool of native promoters known in cowpea, we developed a user-friendly web application, CowPEAsy, allowing users to interrogate gene expression from our canopy-level, developmental-series RNA-Seq data set. Finally, using CowPEAsy, we identified six promoters that showed constitutive expression across all conditions and verified these promoters with our transient system. This work provides an in vivo platform for preliminary validation of regulatory elements in cowpea and other legumes and enhances current genetic resources by identifying a suite of physiologically relevant promoters of varying strengths.

Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments.

Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients.

The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer.

Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu.

IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

Protein sequence analysis examines the order of amino acids within protein sequences to unlock diverse types of a wealth of knowledge about biological processes and genetic disorders. It helps in forecasting disease susceptibility by finding unique protein signatures, or biomarkers that are linked to particular disease states. Protein Sequence analysis through wet-lab experiments is expensive, time-consuming and error prone. To facilitate large-scale proteomics sequence analysis, the biological community is striving for utilizing AI competence for transitioning from wet-lab to computer aided applications. However, Proteomics and AI are two distinct fields and development of AI-driven protein sequence analysis applications requires knowledge of both domains. To bridge the gap between both fields, various review articles have been written. However, these articles focus revolves around few individual tasks or specific applications rather than providing a comprehensive overview about wide tasks and applications. Following the need of a comprehensive literature that presents a holistic view of wide array of tasks and applications, contributions of this manuscript are manifold: It bridges the gap between Proteomics and AI fields by presenting a comprehensive array of AI-driven applications for 63 distinct protein sequence analysis tasks. It equips AI researchers by facilitating biological foundations of 63 protein sequence analysis tasks. It enhances development of AI-driven protein sequence analysis applications by providing comprehensive details of 68 protein databases. It presents a rich data landscape, encompassing 627 benchmark datasets of 63 diverse protein sequence analysis tasks. It highlights the utilization of 25 unique word embedding methods and 13 language models in AI-driven protein sequence analysis applications. It accelerates the development of AI-driven applications by facilitating current state-of-the-art performances across 63 protein sequence analysis tasks.

A variety of techniques for DNA sequencing, such as specific gene sequencing, whole genome sequencing, or exome sequencing, are currently used to detect single nucleotide variations (SNVs). Although RNA-seq can be used to identify SNVs, studies that employ this approach are uncommon, and those that do often rely on outdated mapping methods or methods that are more suitable for genomic and exomic alignment. In this work, our aim is to apply modern RNA-seq specific alignment method in order to identify SNV in a cohort of HCMP patients, and characterize those SNV to gain insight into possible mechanisms of HCMP pathogenesis. The algorithm of identification of SNV based on transcriptomic sequencing data has been developed and evaluated. The algorithm was evaluated and the optimal quality threshold was determined based on allelic discrimination for the rs397516037 mutation (MYBPC3 c.3697 C > T) among patients. A total of 42,809 SNVs with a quality of 75 or higher were identified in 48 transcriptomes of hypertrophic cardiomyopathy (HCMP) myocardial tissue. Verification of missense and nonsense variants in key HCMP genes using Sanger sequencing confirmed the accuracy of the pipeline results. To identify variants potentially associated with HCMP pathogenesis, a filtration process was conducted based on minor allele frequency, substitution prediction score and ClinVar outcome. 214 missense mutations and 6 nonsense mutations were selected. Together with nonsense mutations, 19 mutations meeting the strictest SIFT and PolypPhen criteria were identified as potential factors influencing HCMP pathogenesis. We have developed and validated a method for identifying SNVs based on transcriptomic data, which can be used to identify putative pathogenic variants. We identified mutations in key HCMP genes MYBPC3 and MYH7 in a cohort of patients. We also found potentially pathologic mutations in genes ANXA6 and FEM1 A and obtained data supporting the role of NEBL in myocardial diseases. This method would be useful in analyzing transcriptomic data available in the Gene Expression Omnibus, but should be used with caution as we have tested it on a specific disease.

Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for biomarker genes related to the inflammatory response in PD. First, differentially expressed genes associated with inflammatory response were screened, PPI networks were constructed and enriched for analysis. LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. Finally, drug sensitivity analysis, mRNA-miRNA network construction and single-cell transcriptome data analysis were performed. The experimental results showed that we screened 31 differentially expressed genes related to inflammatory response. Signaling pathways such as cytokine activity were associated with these genes. Three biomarkers were identified using machine learning algorithms: IL18R1, NMUR1 and RELA. Seventeen co-associated miRNAs were identified by the mRNA-miRNA network as possible regulatory nodes in PD. Finally, these three biomarkers were found to be closely associated with T cells, Endothelial cells, excitatory neurons, inhibitory neurons, and other cells in single-cell transcriptomic analysis. In conclusion, IL18R1, NMUR1 and RELA could be potential therapeutic targets for PD in inflammatory response and new biomarkers for PD diagnosis.

Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy.

This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge.

Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.

Verticillium longisporum is a soil-borne fungal pathogen that causes vascular disease predominantly in Brassicaceae. We have previously reported that the receptor of the plant defense hormone jasmonoyl-isoleucine (JA-Ile), CORONATINE INSENSITIVE 1 (COI1), is required in roots for the fungus to proliferate efficiently in the shoot, suggesting the presence of a mobile root-borne signal that influences the outcome of the disease in shoots. This function of COI1 in promoting susceptibility is independent of JA-Ile. To explore the underlying mechanisms, in this study we compared the root transcriptome of the Arabidopsis coi1 mutant with those of the susceptible JA-Ile-deficient allene oxide synthase (aos) mutant and the susceptible wild-type (WT). The biggest difference between the transcriptomes was due to 316 immunity-related genes that were constitutively higher expressed in coi1 as compared to the susceptible genotypes. Interfering with the expression of a sub-group of these genes partially suppressed the coi1-mediated tolerance phenotype. We therefore hypothesize that secreted defense compounds encoded by genes constitutively expressed in coi1 are transported to the shoot with the transpiration stream where they accumulate and interfere with fungal growth. In addition, we found that 149 of the 316 COI1-repressed genes were induced in the WT and aos upon infection, reaching similar expression levels as in mock-treated coi1. These were not further induced in coi1 upon infection. Thus, the repressive effect of COI1 is either lifted or overridden upon infection with V. longisporum.

Vascular dementia (VaD) is the second most common cause of dementia. Few bioinformatic analysis has been done to explore its biomarkers. This study aimed to excavate potential biomarkers for VaD using bioinformatic analysis and validate them at both animal and patient levels. Based on microarray data of GSE122063, bioinformatic analysis revealed 502 DEGs in the frontal and 674 DEGs in the temporal cortex of VaD patients. Afterward, the hub genes between two regions, including C1QA, C1QB, C1QC, and C5AR1, were dugout. Interestingly, compared with sham mice or controls, the above four complements were highly expressed in the cortices of VaD animals and in the peripheral serum of VaD patients. Moreover, receiver operating characteristic curve analysis conformed to good diagnostic powers of these complements, with C1QB having the most prominent capacity (AUC = 0.799, 95%CI 0.722-0.875). That means the complements, especially subunits of C1Q, might be used as specific early VaD diagnostic biomarkers.

Pancreatic cancer remains one of the leading causes of mortality worldwide, largely due to the limitations of current clinical strategies for its treatment. As a result, identifying genetic alterations and potential therapeutic targets could offer new opportunities for improving the diagnosis and treatment of pancreatic cancer. The identification of differentially expressed genes (DEGs) and subsequent analyses, including signaling pathway enrichment, functional classification, and protein-protein interaction (PPI) network construction, were conducted using three public datasets: GSE32676, GSE71989, and GSE16515. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves were employed to investigate the correlation between hub genes and clinicopathological features in pancreatic cancer patients. Genetic alterations were analyzed using the CBioPortal web tool. Cell proliferation was assessed through CCK-8, colony formation, and EdU assays. Tumor migration, invasion, and angiogenesis were evaluated using transwell and tube formation assays, respectively. Protein and mRNA expression levels were measured via western blot analysis and qPCR assays. The subcutaneous xenografted nude mice models were generated to evaluate the potential effect of miR-485-3p/MELK cascade on tumor growth in vivo. Our analysis revealed that MELK expression is positively correlated with poor prognosis in patients with pancreatic cancer. The overexpression or knockdown of MELK significantly influences cell proliferation, tumor metastasis, and angiogenesis across various pancreatic cancer cell lines. Furthermore, we identified that miR-485-3p regulates MELK expression by directly targeting the MELK 3'UTR binding site in pancreatic cancer cells, which subsequently impacts tumor progression. Additionally, our findings demonstrate that the miR-485-3p/MELK cascade is closely associated with tumor progression in pancreatic cancer cells. Mechanistically, the miR-485-3p/MELK cascade promotes the phosphorylation of Akt to regulate pancreatic cancer cell progression, metastasis, and angiogenesis. Furthermore, overexpression of miR-485-3p inhibits the tumor growth induced by MELK overexpression in subcutaneous xenograft model. MiR-485-3p/MELK cascade may serve as a promising biomarker and therapeutic target for the diagnosis and treatment of pancreatic cancer.

RNA metabolism involves coordinating RNA synthesis with RNA processing and degradation. Ribonucleases play fundamental roles within the cell, contributing to the cleavage, modification, and degradation of RNA molecules, with these actions ensuring appropriate gene regulation and cellular homeostasis. Here, we employed RNA sequencing to explore the impact of RNase III and RNase J on the transcriptome of Streptomyces venezuelae. Differential expression analysis comparing wild-type and RNase mutant strains at distinct developmental stages revealed significant changes in transcript abundance, particularly in pathways related to multicellular development, nutrient acquisition, and specialized metabolism. Both RNase mutants exhibited dysregulation of the BldD regulon, including altered expression of many cyclic-di-GMP-associated enzymes. We also observed precocious chloramphenicol production in these RNase mutants and found that in the RNase III mutant, this was associated with PhoP-mediated regulation. We further found that RNase III directly targeted members of the PhoP regulon, suggesting a link between RNA metabolism and a regulator that bridges primary and specialized metabolism. We connected RNase J function with translation through the observation that RNase J directly targets multiple ribosomal protein transcripts for degradation. These findings establish distinct but complementary roles for RNase III and RNase J in coordinating the gene expression dynamics critical for S. venezuelae development and specialized metabolism.

RNA processing and metabolism are mediated by ribonucleases and are fundamental processes in all cells. In the morphologically complex and metabolically sophisticated Streptomyces bacteria, RNase III and RNase J influence both development and metabolism through poorly understood mechanisms. Here, we show that both ribonucleases are required for the proper expression of the BldD developmental pathway and contribute to the control of chloramphenicol production, with an interesting connection to phosphate regulation for RNase III. Additionally, we show that both RNases have the potential to impact translation through distinct mechanisms and can function cooperatively in degrading specific transcripts. This study advances our understanding of RNases in Streptomyces biology by providing insight into distinct contributions made by these enzymes and the intriguing interplay between them.

Heterologous expression in well-studied model strains is a routinely applied method to investigate biosynthetic pathways. Here, we pursue a comparative approach of large-scale DNA-affinity-capturing assays (DACAs) coupled with semi-quantitative mass spectrometry (MS) to identify putative regulatory proteins from Streptomyces coelicolor M512, which bind to the heterologously expressed biosynthetic gene clusters (BGCs) of the liponucleoside antibiotics caprazamycin and liposidomycin. Both gene clusters share an almost identical genetic arrangement, including the location of promoter regions, as detected by RNA sequencing. A total of 2,214 proteins were trapped at the predicted promoter regions, with only three binding to corresponding promoters in both gene clusters. Among these, the overexpression of a yet uncharacterized TetR-family regulator (TFR), Sco4385, increased caprazamycin but not liposidomycin production. Protein-DNA interaction experiments using biolayer interferometry confirmed the binding of Sco4385 to Pcpz10 and PlpmH at different locations within both promoter regions, which might explain its functional variance. Sequence alignment allowed the determination of a consensus sequence present in both promoter regions, to which Sco4385 was experimentally shown to bind. Furthermore, we found that the overexpression of the Crp regulator, Sco3571, leads to a threefold increase in caprazamycin and liposidomycin production yields, possibly due to an increased expression of a precursor pathway.IMPORTANCEStreptomycetes are well-studied model organisms for the biosynthesis of pharmaceutically, industrially, and biotechnologically valuable metabolites. Their naturally broad repertoire of natural products can be further exploited by heterologous expression of biosynthetic gene clusters (BGCs) in non-native host strains. This approach forces the host to adapt to a new regulatory and metabolic environment. In our study, we demonstrate that a host regulator not only interacts with newly incorporated gene clusters but also regulates precursor supply for the produced compounds. We present a comprehensive study of regulatory proteins that interact with two genetically similar gene clusters for the biosynthesis of liponucleoside antibiotics. Thereby, we identified regulators of the heterologous host that influence the production of the corresponding antibiotic. Surprisingly, the regulatory interaction is highly specific for each biosynthetic gene cluster, even though they encode largely structurally similar metabolites.

Despite not proliferating, senescent cells remain metabolically active to maintain the senescence program. However, the mechanisms behind this metabolic reprogramming are not well understood. We identify senescence-induced long noncoding RNA (sin-lncRNA), a previously uncharacterized long noncoding RNA (lncRNA), a key player in this response. While strongly activated in senescence by C/EBPβ, sin-lncRNA loss reinforces the senescence program by altering oxidative phosphorylation and rewiring mitochondrial metabolism. By interacting with dihydrolipoamide S-succinyltransferase (DLST), it facilitates its mitochondrial localization. Depletion of sin-lncRNA causes DLST nuclear translocation, leading to transcriptional changes in oxidative phosphorylation (OXPHOS) genes. While not expressed in highly proliferative cancer cells, it is strongly induced upon cisplatin-induced senescence. Depletion of sin-lncRNA in ovarian cancer cells reduces oxygen consumption and increases extracellular acidification, sensitizing cells to cisplatin treatment. Altogether, these results indicate that sin-lncRNA is specifically induced in senescence to maintain metabolic homeostasis, unveiling an RNA-dependent metabolic rewiring specific to senescent cells.

Information on global transcriptomic changes in the porcine ampulla after ovulation is crucial for understanding of oviductal physiology at the molecular level. The objective of the present study was to investigate the differentially expressed genes (DEGs) and signalling pathways regulating the functionality of ampulla in pigs post-ovulation.

The RNA-sequencing of the post-ovulatory ampulla (POA) and early luteal ampulla (ELA) tissues was conducted using Illumina NextSeq2000. The R package NOISeq was used to obtain significantly differentially expressed genes (DEGs) with the probability of differential expression (1-FDR) value ≥ 0.95 and log2 fold change (log2FC) ≥ 1, which revealed 817 DEGs (657 up- and 160 down-regulated) in the POA vs. ELA group comparison. These DEGs were functionally annotated with various gene ontology terms like sterol biosynthetic process, growth, cell migration, and Reactome pathways like signal transduction, metabolism, and cell cycle, indicating key role of these molecular events in POA. The WNT, TNFR2 non-canonical NF-kB, and hedgehog signalling pathways along with the activation of the immune system process, were enriched in the POA vs. ELA group, which indicates their role in cell-cell interactions and cell fate determination in remodelling the oviductal microenvironment during transition from estrogen to progesterone domination. The highly connected upregulated hub genes ESR1, RAD51, YARS1, TYMS and CDK2 can be regarded as key regulatory factors in synchronizing the changes in POA at the molecular level in the oviduct.

The present study revealed several DEGs, signalling pathways and novel modulatory factors associated with the ampullary physiology during early embryonic development in the POA, which may influence fertility and litter size in pigs.

Mitochondria are key to cellular energetics, metabolism, and signaling. Their dysfunction is linked to devastating diseases, including mitochondrial disorders, diabetes, neurodegenerative diseases, cardiac disorders, and cancer. Here, we present a knockout mouse model lacking the complex IV assembly factor SMIM20/MITRAC7. SMIM20-/- mice display cardiac pathology with reduced heart weight and cardiac output. Heart mitochondria present with reduced levels of complex IV associated with increased complex I activity, have altered fatty acid oxidation, and display elevated levels of ROS production. Interestingly, mutant mouse ventricular myocytes show unphysiological Ca2+ handling, which can be attributed to the increase in mitochondrial ROS production. Our study presents an example of a tissue-specific phenotype in the context of OXPHOS dysfunction. Moreover, our data suggest a link between complex IV dysfunction and Ca2+ handling at the endoplasmic reticulum through ROS signaling.

PAX5 acts as a master regulator of B-cell proliferation and differentiation. Its germline and somatic deregulation have both been implicated in the development of B-cell precursor acute lymphoblastic leukemia (BCP-ALL). However, the process how reduced PAX5 transcriptional activity mediates progression to BCP-ALL, is still poorly understood. Here, we characterized the longitudinal effects of PAX5 reduction on healthy, pre-leukemic and BCP-ALL cells at the single-cell level. Cell-surface marker analysis revealed a genotype-driven enrichment of the pre-BII population in healthy Pax5± mice. This population showed downregulated B-cell receptor signaling, while DNA replication/repair and cell-cycle signaling pathways were upregulated. Moreover, we observed a shift in the kappa/lambda light chain ratio toward lambda rearranged B-cells. Transplantation experiments further validated a delay of Pax5± pre-BII cells in maturation and transition to IgM-positivity. Additionally, single-cell RNA-Sequencing and bulk ATAC-Sequencing of different stages of BCP-ALL evolution showed that Pax5± pre-leukemic cells lose their B-cell identity and display Myc activation. Subsequently, BCP-ALLs acquired additional RAG-mediated aberrations and driver mutations in JAK-STAT and RAS-signaling pathways. Together, this study elucidates molecular and functional checkpoints in PAX5-mediated pre-leukemic cell progression exploitable for therapeutic intervention and demonstrates that PAX5 reduction is sufficient to initiate clonal evolution to BCP-ALL through activation of MYC.

Despite recent advancements, the pathogenesis of multiple myeloma (MM) remains incompletely elucidated, with relapse and therapy resistance persisting as major clinical challenges, underscoring the imperative to identify novel therapeutic targets.

Differentially expressed genes were initially screened from the GSE6477 and GSE6691 datasets. Subsequent functional annotation and pathway enrichment analyses were conducted utilizing the DAVID bioinformatics platform. A protein-protein interaction network was constructed via the STRING database, followed by module analysis and hub genes identification through CytoHubba plugin. The biological significance of candidate genes was ultimately validated through ex vivo cellular functional assays and in vivo xenograft tumorigenesis experiments in murine models.

Bioinformatics analysis identified spleen tyrosine kinase (SYK) as the most prognostically significant candidate gene (p = 0.027). The SYK-specific inhibitor BAY61-3606 demonstrated time- (p < 0.05) and dose- (p < 0.01) dependent inhibition of MM cell viability, concomitant induction of G2/M phase cell cycle arrest (p < 0.001), and significant promotion of apoptosis (p < 0.05). In vivo experiments utilizing MM xenograft models demonstrated that BAY61-3606 administration significantly attenuated tumor growth kinetics (p < 0.05).

Our findings establish SYK as a therapeutic target in MM, thereby facilitating the development of innovative treatment strategies.

Since Waddington proposed the concept of the "epigenetic landscape" in 1957, researchers have developed various methodologies to represent it in diverse processes. Studying the epigenetic landscape provides valuable qualitative information regarding cell development and the stability of phenotypic and morphogenetic patterns. Although Waddington's original idea was a visual metaphor, a contemporary perspective relates it to the landscape formed by the basins of attraction of a dynamical system describing the temporal evolution of protein concentrations driven by a gene regulatory network. Transitions among these attractors can be driven by stochastic perturbations, with the cell state more likely to transition to the nearest attractor or to the one that presents the path of least resistance. In this study, we define the epigenetic landscape using the free energy potential obtained from the solution of the Fokker-Planck equation on the regulatory network. Specifically, we obtained a numerical approximate solution of the Fokker-Planck equation describing the Arabidopsis thaliana flower morphogenesis process. We observed good agreement between the coexpression matrix obtained from the Fokker-Planck equation and the experimental coexpression matrix. This paper proposes a method for obtaining this landscape by solving the Fokker-Planck equation (FPE) associated with a dynamical system describing the temporal evolution of protein concentrations involved in the process of interest. As these systems are high-dimensional and analytical solutions are often unfeasible, we propose a gamma mixture model to solve the FPE, transforming this problem into an optimization problem. This methodology can enhance the analysis of gene regulatory networks by directly relating theoretical mathematical models with experimental observations of coexpression matrices, thus providing a discriminating technique for competing models.

Trisomy of chromosome 21, the cause of Down syndrome (DS), is the most commonly occurring genetic cause of Alzheimer's disease (AD). Here, we compare the frontal cortex proteome of people with Down syndrome-Alzheimer's disease (DSAD) to demographically matched cases of early onset AD and healthy ageing controls. We find dysregulation of the proteome, beyond proteins encoded by chromosome 21, including an increase in the abundance of the key AD-associated protein, APOE, in people with DSAD compared to matched cases of AD. To understand the cell types that may contribute to changes in protein abundance, we undertook a matched single-nuclei RNA-sequencing study, which demonstrated that APOE expression was elevated in subtypes of astrocytes, endothelial cells, and pericytes in DSAD. We further investigate how trisomy 21 may cause increased APOE. Increased abundance of APOE may impact the development of, or response to, AD pathology in the brain of people with DSAD, altering disease mechanisms with clinical implications. Overall, these data highlight that trisomy 21 alters both the transcriptome and proteome of people with DS in the context of AD, and that these differences should be considered when selecting therapeutic strategies for this vulnerable group of individuals who have high risk of early onset dementia.

Phytoplankton are responsible for half of the global photosynthesis and form vast blooms in aquatic ecosystems. Bloom demise fuels marine microbial life and is suggested to be mediated by programmed cell death (PCD) induced by diverse environmental stressors. Despite its importance, the molecular basis for algal PCD remains elusive. Here, we reveal novel PCD genes conserved across distant algal lineages using cell-to-cell heterogeneity in the response of the diatom Phaeodactylum tricornutum to oxidative stress. Comparative transcriptomics of sorted sensitive and resilient subpopulations following oxidative stress revealed genes directly linked to their contrasting fates of cell death and survival. Comparing these genes with those found in a large-scale mutant screen in the green alga Chlamydomonas reinhardtii identified functionally relevant conserved PCD gene candidates, including the cysteine protease cathepsin X/Z (CPX). CPX mutants in P. tricornutum CPX1 and C. reinhardtii CYSTEINE ENDOPEPTIDASE 12 (CEP12) exhibited resilience to oxidative stress and infochemicals that induce PCD, supporting a conserved function of these genes in algal PCD. Phylogenetic and predictive structural analyses show that CPX is highly conserved in eukaryotes, and algae exhibit strong structural similarity to human Cathepsin X/Z (CTSZ), a protein linked to various diseases. CPX is expressed by diverse algae across the oceans and correlates with upcoming demise events during toxic Pseudo-nitzschia blooms, providing support for its ecological significance. Elucidating PCD components in algae sheds light on the evolutionary origin of PCD in unicellular organisms and on the cellular strategies employed by the population to cope with stressful conditions.

The increasing prevalence of age-related macular degeneration (AMD), a disease that can result in the loss of central vision, is an emerging problem worldwide due to aging societies. Growing patient numbers create a challenge for the healthcare system. Understanding the mechanisms of AMD pathogenesis will aid in early, personalized, and efficient intervention, helping to mitigate this issue. Current diagnostic methods rely on optical coherence tomography and angiography imaging, which identify existing damages, but do not provide information on the mechanisms behind them. In the present work, we demonstrate a difference in the serum RNA profile between neovascular AMD (nAMD) patients and controls. Moreover, the RNA profile of nAMD patients corresponded with anatomical changes in the retinal fluid compartments as well as atrophic changes of the retina. We followed two independent ways to control false positive leads, and when these approaches were combined, thioredoxin-related transmembrane protein 4 (TMX4) was observed to be differentially expressed by both approaches. This finding opens a new pathway in AMD studies, which are limited due to restricted access to live human target material and the limited value of animal models of human AMD.

Esophageal cancer (EC) is a leading cause of cancer-related mortality globally, with alcohol consumption being a significant risk factor. However, the genetic and molecular mechanisms linking alcohol intake to EC remain unclear. This study utilized Mendelian randomization analysis to establish a causal relationship between alcohol consumption and EC (OR: 4.11 [95% CI 1.83-9.23]). Transcriptomic analysis identified 83 differentially expressed genes (log₂ fold change > 1, false discovery rate [FDR] < 0.05), among which DLEU2 was uniquely transcribed into a long non-coding RNA (lncRNA). Pan-cancer analysis revealed its association with the tumor immune microenvironment and cancer progression. Single-cell RNA sequencing localized DLEU2 expression predominantly to T cells, particularly exhausted subpopulations, and pseudo-temporal analysis demonstrated increased DLEU2 expression during late T cell differentiation stages, co-expressing immune suppression markers, with consistent expression patterns observed across multiple patient-derived samples. Additionally, cell communication analysis suggested that DLEU2 modulates TNF signaling through TNFRSF1A/B pathways, contributing to immune evasion and poor prognosis. These findings position DLEU2 as a pivotal regulator of the immune landscape in EC and a potential prognostic biomarker and therapeutic target.

To examine the impact of Piperlongumine (PL) on the proliferation, migration, invasion, cell cycle progression, and apoptosis in esophageal squamous cell carcinoma (ESCC) cells, as well as to elucidate the underlying molecular mechanisms. The suppressive effects of PL on the viability of ESCC cells were assessed using the CCK-8 assay, bright field imaging, and colony formation assays. Apoptosis induction and cell cycle disruption by PL were evaluated using flow cytometry. The impact of PL on ESCC cell migration and invasion was examined through scratch healing and Transwell assays. Differential gene expression analysis of ESCC tumor and normal tissues from the GSE29886 dataset, integrated with network pharmacology predictions, was conducted to identify core genes and molecular mechanisms involved in PL action. Key protein expression levels in the apoptosis, epithelial-mesenchymal transition (EMT), and PI3K/AKT signaling pathways were quantified by Western blotting. The CCK-8 and colony formation assays demonstrated that PL effectively suppressed cell viability and proliferation in ESCC. Flow cytometry revealed that PL down-regulated CDK1 expression, resulting in G2/M phase arrest, and promoted apoptosis by decreasing Bcl-2 levels and increasing cleaved caspase-3 and PARP. The scratch and Transwell assays indicated that PL inhibited ESCC cell migration and invasion, down-regulated the EMT-associated proteins Vimentin and N-cadherin, and up-regulated E-cadherin. Western blotting confirmed the down-regulation of P-PI3K and P-AKT, indicating the inhibition of the PI3K/AKT pathway by PL. These findings offer a pharmacological foundation for the development of PL as a potential phytotherapeutic agent for the clinical management of ESCC.