STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

MPEMDA: A multi-similarity integration approach with pre-completion and error correction for predicting microbe-drug associations

Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy. To overcome these limitations, we develop MPEMDA, a novel method that pre-completes the microbe-drug association matrix using various similarity combinations and employs a label propagation algorithm with error correction to predict microbe-drug associations. Compared with existing methods, MPEMDA simultaneously utilizes the integrated and individual similarities obtained through the Similarity Network Fusion (SNF) method to pre-complete the known drug-microbe association matrix, followed by error correction to optimize the predictive scores generated by the label propagation algorithm. Experimental results on three benchmark datasets show that MPEMDA outperforms state-of-the-art methods in both the 5-fold cross-validation and de novo test. Additionally, case studies on drugs and microbes highlight the method's strong potential to identify novel microbe-drug associations. The MPEMDA code is available at https://github.com/lyx8527/MPEMDA.

Decoding the therapeutic landscape of alpha-linolenic acid: a network pharmacology and bioinformatics investigation against cancer-related epigenetic modifiers

Omega-3 (n - 3) and omega-6 (n - 6) polyunsaturated fatty acids (PUFAs) are vital for human health, but an imbalance between these types is associated with chronic diseases, including cancer. Alpha-linolenic acid (ALA), a n - 3 PUFA, shows promise as an anticancer agent in both laboratory and animal studies. However, the precise molecular mechanisms underlying ALA's actions against cancer-related epigenetic modifiers (CaEpM) remain unclear. To understand this, we employed network pharmacology (NP) and molecular docking techniques. Our study identified 51 potential ALA targets and GO and KEGG pathway analysis revealed possible molecular targets and signaling pathways of ALA against CaEpM. From PPI analysis, EZH2, KAT2B, SIRT1, KAT2A, KDM6B, EHMT2, WDR5, SETD7, SIRT2, and HDAC3 emerged as the top 10 potential targets. Additionally, GeneMANIA functional association (GMFA) network analysis of these top 10 targets was performed to enhance NP insights and explore ALA's multi-target approach. After an exhaustive analysis of the core FGN subnetwork, it became evident that 9 out of the 15 targets-namely EZH2, SUZ12, EED, PARP1, HDAC3, DNMT1, NCOR2, KAT2B, and TRRAP-manifested evidently strong and abundant interconnections among each other. Molecular docking of both top 10 targets and core FGN targets confirmed strong binding affinity between ALA and SIRT2, WDR5, KDM6B, EHMT2, HDAC3, EZH2, PARP1, and KAT2B, underscoring their roles in ALA's anti-CaEpM mechanism. Our findings suggest that ALA may target key signaling pathways related to transcriptional regulation, microRNA involvement, stem cell pluripotency and cellular senescence in cancer epigenetics. These findings illuminate ALA's potential as a multi-target agent against CaEpM.Communicated by Ramaswamy H. Sarma.

Osteoblast-Derived Mitochondria Formulated with Cationic Liposome Guide Mesenchymal Stem Cells into Osteogenic Differentiation

While mitochondria are known to be essential for intracellular energy production and overall function, emerging evidence highlights their role in influencing cell behavior through mitochondrial transfer. This phenomenon provides a potential basis for the development of treatment strategies for tissue damage and degeneration. This study aims to evaluate whether mitochondria isolated from osteoblasts can promote osteogenic differentiation in mesenchymal stem cells (MSCs). Mitochondria from MSCs, which primarily utilize glycolysis, are compared with those from MG63 cells, which depend on oxidative phosphorylation. Mitochondria from both cell types are then encapsulated in cationic liposomes and transferred to MSCs, and their impact on differentiation is assessed. Mitochondria delivery from MG63 cells to MSCs grown in both two- and three-dimensional cultures results in increased expression of osteogenic markers, including Runt-related transcription factor 2, Osterix, and Osteopontin, and upregulation of genes involved in Bone morphogenetic protein 2 signaling and calcium import. This is accompanied by increased calcium influx and regulated by the Wnt/β-catenin signaling pathway. Transplantation of spheroids containing MSCs with MG63-derived mitochondria in bone defect animal models improves bone regeneration. The results suggest that delivery of MG63-derived mitochondria effectively guides MSCs toward osteogenesis, paving the way for the development of mitochondria-transplantation therapies.

Norwogonin Attenuates Inflammatory Osteolysis and Collagen-Induced Arthritis via Modulating Redox Signalling and Calcium Oscillations

Norwogonin is a flavonoid extraction derived from Scutellaria baicalensis. However, its potential mechanisms in the context of rheumatoid arthritis (RA) are unclear. This study investigates the specific effects and associated targets of Norwogonin in RA-related inflammatory osteolysis. Network pharmacology was conducted to analyse the core targets and signalling pathways of Norwogonin in RA. In vitro experiments were carried out to explore the actual effects of Norwogonin on osteoclast behaviours and related signalling mechanisms. In vivo studies further validated the therapeutic effect of Norwogonin in collagen-induced arthritis (CIA) mice. The network pharmacological analysis identified 18 shared targets between Norwogonin and RA, indicating a connection with inflammatory response and oxidoreductase activity. For biological validations, the results of in vitro experiments revealed 160 μM of Norwogonin inhibited LPS-driven osteoclast differentiation and function. The qPCR assay and Western blot analysis also disclosed consistently diminished changes to osteoclastic marker genes and proteins due to Norwogonin treatment, including those for osteoclast differentiation (Traf6, Tnfrsf11a and Nfatc1), fusion (Atp6v0d2, Dcstamp and Ocstamp) and function (Mmp9, Ctsk and Acp5). Further mechanism study revealed Norwogonin suppressed LPS-driven ROS production and calcium (Ca2+) oscillations. Also, intraperitoneal injection of 30 mg/kg Norwogonin every other day successfully mitigated clinical arthritis progression and attenuated bone destruction in the CIA model. Our study scrutinises Norwogonin's therapeutic prospects in treating RA and illustrates its inhibitory effects and potential mechanism within LPS-induced osteoclastogenesis and CIA mice, providing a basis for further translational research on Norwogonin in the treatment of RA-related inflammatory osteolysis.

Investigation of a set of flavonoid compounds as Helicobacter pylori urease inhibitors: insights from in silico studies

Helicobacter pylori (H. pylori) is a spiral, microaerophilic gram-negative bacterium, which is associated with the destruction of the lining of the stomach, leads to chronic inflammation of the stomach, which can cause stomach and duodenal ulcers. The problems caused by the treatment with antibiotics have caused researchers to use new approaches to treat infections caused by H. pylori, among them specific treatments with flavonoids. Urease enzyme, as one of the most important pathogenic and antigenic factors of this bacterium, is a suitable target for this purpose. In this study, the inhibitory effect of flavonoid compounds compared to acetohydroxamic acid on H. pylori urease enzyme was evaluated using molecular modeling methods. First, the interaction of flavonoids with urease enzyme compared with acetohydroxamic acid was investigated by molecular docking method to produce efficient docking poses. Then the physicochemical properties and toxicity of the best flavonoid compounds were analyzed using the swissadme server. Also, molecular dynamics calculations were performed to precisely understand the interactions between ligands and protein. The results of this study show that all the investigated flavonoid compounds are capable of inhibiting H. pylori urease. Among these compounds, six compounds chrysin, galangin, kaempferol, luteolin, morin and quercetin showed a greater tendency to bind to urease, compared to the acetohydroxamic acid inhibitor. These compounds are desirable in terms of physicochemical properties. This study also revealed that the flavonoids with their hydroxyl groups (-OH) play an important role during bond formation with amino acids Ala278, Ala169, His314, Asp362 and Asn168. Therefore, flavonoid compounds, due to their suitable location in the active site of the urease, create a more effective inhibition than the chemical drug acetohydroxamic acid and can be suitable candidates for the treatment of Helicobacter pylori under in vitro and in vivo investigations.

Integrating network pharmacology to investigate the mechanism of quercetin's action through AKT inhibition in co-expressed genes associated with polycystic ovary syndrome and endometrial cancer

Endometrial cancer (EC) is a common gynecological malignancy for which polycystic ovarian syndrome (PCOS) has been identified as a significant risk factor. Quercetin, a widely distributed natural flavonoid, has demonstrated potential therapeutic effects in managing both PCOS and EC. However, the specific molecular targets of quercetin in the context of PCOS comorbid with EC (PCOS-EC) remain poorly defined. This study aims to elucidate the therapeutic potential of quercetin for treating PCOS-EC using network pharmacology, molecular dynamics simulations, and in vitro assays. The intersection of 379 PCOS-EC-associated targets with 361 quercetin targets identified 47 potential therapeutic targets of quercetin for PCOS-EC. Gene Ontology enrichment analysis revealed the biological functions, while Kyoto Encyclopedia of Genes and Genomes identified the pathways potentially involved in quercetin's effects against PCOS-EC. Protein-protein interaction network analysis highlighted six overlapping targets, namely, ACTB, AKT1, EGFR, ESR1, PTGS2, and TP53. Molecular docking and molecular dynamics simulations indicated that quercetin bound with high affinity to the hub genes, with AKT1 emerging as a central target. In vitro experiments confirmed that quercetin treatment significantly downregulated AKT expression in EC cells. These findings elucidate potential targets and molecular mechanisms through which quercetin exerts its therapeutic effects.

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network

Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.

ITGB5 as a Potential Diagnostic Biomarker for Osteoarthritis

OBJECTIVE

Osteoarthritis (OA) is a prevalent degenerative joint disease, especially occur in the elderly. This study aimed to uncover a novel biomarker for early diagnosis and treatment of OA.

METHODS

WGCNA, differential expression analysis and PPI network were used for screening hub genes-related to OA, utilizing the GSE55235 and GSE57218 datasets from GEO database.

RESULTS

Based on the data in the GEO datasets, compared to normal tissues, ITGB5 was obviously elevated in OA cartilage and synovial samples. Additionally, ROC curve results validated the diagnostic value of ITGB5 in OA. Mechanistically, transcription factors KLF4 and KLF11 could modulate ITGB5 gene transcription via binding to its promoter region, thereby affecting ITGB5 gene expression in OA tissues. GSEA results showed that ITGB5 gene was closely related to p53, wnt, TNF and T cell receptor signaling pathways, suggesting that ITGB5 may play potential roles in affecting cell apoptosis and inflammation in OA. Moreover, ITGB5 levels in OA samples was positively correlated to T helper type 1 cells, natural killer T cells, macrophages, memory CD8 T cells, activated dendritic cells.

CONCLUSION

In this study, we found that ITGB5 was obviously elevated in OA samples. Moreover, ITGB5 may function as a diagnostic biomarker in OA.

Function analysis of RNase III in response to oxidative stress in Synechocystis sp. PCC 6803

RNase III, a ubiquitously distributed endonuclease, plays an important role in RNA processing and functions as a global regulator of gene expression. In this study, we explored the role of RNase III in mediating the oxidative stress response in Synechocystis sp. PCC 6803. Phenotypic analysis demonstrated that among the three RNase III-encoding genes (slr0346, slr1646, and slr0954), the deletional mutation of slr0346 significantly impaired the growth of cyanobacteria on BG11 agar plates. However, this growth effect was not observed in liquid culture. In contrast, the deletion of slr1646 and slr0954 did not affect the growth of cyanobacteria under the tested conditions. However, under methyl viologen (MV)-induced oxidative stress, the slr0346 deletion mutant exhibited a slower growth rate compared to the wild-type strain. Transcriptome analysis revealed that five pathways-nitrogen metabolism, ABC transporters, folate biosynthesis, ribosome biogenesis, and oxidative phosphorylation-were implicated in the oxidative stress response. The slr0346 gene suppressed global gene expression, with a particular impact on genes associated with energy metabolism, protein synthesis, and transport. Furthermore, we identified Ssl3432 as an interacting protein that may participate in the oxidative stress response in coordination with Slr0346. Overall, the deletion of slr0346 markedly weakened the ability of Synechocystis sp. PCC 6803 to respond to MV-induced oxidative stress. This study offers valuable insights into the oxidative stress response of Synechocystis sp. PCC 6803 and highlights the role of RNase III in adapting to environmental stress.

In-Silico Identification, Characterization and Expression Analysis of Genes Involved in Resistant Starch Biosynthesis in Potato (Solanum tuberosum L.) Varieties

Potato (Solanum tuberosum L.), an important horticultural crop is a member of the family Solanaceae and is mainly grown for consumption at global level. Starch, the principal component of tubers, is one of the significant elements for food and non-food-based applications. The genes associated with biosynthesis of starch have been investigated extensively over the last few decades. However, a complete regulation pathway of constituent of amylose and amylopectin are still not deeply explored. The current in-silico study of genes related to amylose and amylopectin synthesis and their genomic organization in potato is still lacking. In the current study, the nucleotide and amino acid arrangement in genome and twenty-two genes linked to starch biosynthesis pathway in potato were analysed. The genomic structure analysis was also performed to find out the structural pattern and phylogenetic relationship of genes. The genome mining and structure analysis identified ten specific motifs and phylogenetic analysis of starch biosynthesis genes divided them into three different clades on the basis of their functioning and phylogeny. Quantitative real-time PCR (qRT-PCR) of amylose biosynthesis pathway genes in three contrast genotypes revealed the down-gene expression that leads to identify potential cultivar for functional genomic approaches. These potential lines may help to achieve higher content of resistant starch.

CSSEC: An adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping

Cancer is a complex and heterogeneous disease, and accurate cancer subtyping can significantly improve patient survival rates. The complexity of cancer spans multiple omics levels, and analyzing multi-omics data for cancer subtyping has become a major focus of research. However, extracting complementary information from different omics data sources and adaptively integrating them remains a major challenge. To address this, we proposed an adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping (CSSEC). First, independent self-expressive networks are applied to each omics to calculate coefficient matrices to measure patient similarity. Then, two feature graph convolutional network modules capture consensus and specific similarity features using the topK relevant features. Finally, the multi-omics self-expression coefficient matrix is constructed by consensus and specific similarity features. Furthermore, joint consistency and disparity constraints are applied to regularize the fusion of the self-expressive coefficients. Experimental results demonstrate that CSSEC outperforms existing state-of-the-art methods in survival analysis. Moreover, case studies on kidney cancer confirm that the cancer subtypes identified by CSSEC are biologically significant. The complete code can be available at https://github.com/ykxhs/CSSEC.

Comparative transcriptomics reveals potential regulators of climate adaptation in Santalum album L. (Indian Sandalwood)

Santalum album L. (Indian Sandalwood), a valued tree species known for its fragrant heartwood and essential oil is facing increasing threat due to severe anthropogenic pressures compounded by climate change which has resulted in depletion of its adaptive gene pool. The present study investigates the transcriptome-level responses of nine sandalwood genotypes sourced from diverse climatic zones to identify adaptive genes in the species. Comparative transcriptomics predicted 727, 1141 and 479 differentially expressed transcripts (DETs) across wet vs. dry; monsoon vs. dry and wet vs. monsoon conditions, respectively, and majority of DETs were up-regulated in samples sourced from high rainfall areas. Transcripts including heat shock proteins, Zinc finger binding protein, ribosomal proteins, transcription factors and protein kinase were identified as probable regulators of climate adaptation in S. album. The expression changes of eight selected transcripts were further validated by real-time quantitative PCR. Protein-protein interaction analysis revealed key hub transcripts involved in climate response, while alternative splicing events in transcripts such as SURP and G-patch domain-containing protein 1-like protein, G-type lectin S-receptor-like serine/threonine protein kinase B120, Tetraspanin-3 and ARM repeat superfamily protein indicated the probable role of alternate splicing in increasing the transcript diversity during adaptation. This study presents the first insight into the molecular mechanisms of climate adaptation in the species and can form the basis for specific interventions such as selective breeding, genetic manipulation, and habitat management for conservation and long-term survival of sandalwood.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04218-4.

The proximity interactome of PML isoforms I and II under fatty acid stress

Promyelocytic leukemia (PML) protein forms the scaffold for PML nuclear bodies (PML NB) that reorganize into Lipid-Associated PML Structures (LAPS) under fatty acid stress. We determined how the fatty acid oleate alters the interactome of PMLI or PMLII by expressing fusions with the ascorbate peroxidase APEX2 in U2OS cells. The resultant interactome included ESCRT and COPII transport protein nodes. Proximity ligation assay (PLA) revealed that COPII proteins SEC23B, SEC24A and USO1 preferentially associated with PML NBs. Nuclear localization of USO1, but not SEC23B and SEC24A, was reduced in PML knockout cells and restored by PMLII expression. Thus, proximity-labelling methods identified COPII transport protein interactions with PML NBs that are disrupted by fatty acid stress.

Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity

Despite significant advancements in sample preparation, instrumentation and data analysis, single-cell proteomics is currently limited by proteomic depth and quantitative performance. Here we demonstrate highly improved depth of proteome coverage as well as accuracy and precision for quantification of ultra-low input amounts. Using a tailored library, we identify up to 7,400 protein groups from as little as 250 pg of HeLa cell peptides at a throughput of 50 samples per day. Using a two-proteome mix, we check for optimal parameters of quantification and show that fold change differences of 2 can still be successfully determined at single-cell-level inputs. Eventually, we apply our workflow to A549 cells, yielding a proteome coverage ranging from 1,801 to a maximum of >5,300 protein groups from a single cell depending on cell size and search strategy used, which allows for the study of dependencies between cell size and cell cycle phase. Additionally, our workflow enables us to distinguish between in vitro analogs of two human blastocyst lineages: naive human pluripotent stem cells (epiblast) and trophectoderm-like cells. Our data harmoniously align with transcriptomic data, indicating that single-cell proteomics possesses the capability to identify biologically relevant differences within the blastocyst.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.

Spermidine antagonizes the anti-cancer effect of cold atmospheric plasma and induces transit G0/G1 cell cycle arrest of triple negative breast cancers

Cancer remains as a global health threat, with the incidence of breast cancers keep increasing. Dis-regulated redox homeostasis has been considered with essential roles for tumor initiation and progression. Using triple negative breast cancers, the most malignant subtype of breast cancers, as the tumor model, we explored the roles of the anti-oxidant spermidine, the pro-oxidative tool cold atmospheric plasma (CAP), and their combined use in cancer growth, anti-oxidative ability and cell cycle. We also characterized the important roles of FTO in driving the redox modulatory functionalities of spermidine and CAP-activated medium (PAM) as well as their demonstrated synergy on breast cancer cells. We found that spermidine reversed the anti-cancer effect of PAM and stimulated outrageous progression of transformed cells to the level exceeding that treated with spermidine alone, and combined launch of spermidine and PAM enabled cancer cells with elevated anti-oxidant ability and enhanced survival in response to instant redox perturbation via transient stalk at the G0/G1 stage. We, in addition, identified the vital role of FTO in mediating the observed effect of spermidine, PAM and their synergy, on triple negative breast cancer cells. Our results reported the antagonism between PAM and anti-oxidants as represented by spermidine for cancer treatment, and implicated the differential responses of healthy and diseased individuals to anti-oxidants for improved design on redox-based anti-cancer regimen.

Effects of ALS-associated 5'tiRNAGly-GCC on the transcriptomic and proteomic profile of primary neurons in vitro

tRNA-derived stress-induced RNAs (tiRNAs) are a new class of small non-coding RNA that have emerged as important regulators of cellular stress responses. tiRNAs are derived from specific tRNA cleavage by the stress-induced ribonuclease angiogenin (ANG). Loss-of-function mutations in the ANG gene are linked to amyotrophic lateral sclerosis (ALS), and elevated levels of specific tiRNAs were recently identified in ALS patient serum samples. However, the biological role of tiRNA production in neuronal stress responses and neurodegeneration remains largely unknown. Here, we investigated the genome-wide regulation of neuronal stress responses by a specific tiRNA, 5'tiRNAGly-GCC, which we found to be upregulated in primary neurons exposed to ALS-relevant stresses and in the spinal cord of three ALS mouse models. Whole-transcript RNA sequencing and label-free mass spectrometry on primary neurons transfected with a synthetic mimic of 5'tiRNAGly-GCC revealed predominantly downregulated RNA and protein levels, with more pronounced changes in the proteome. Over half of the downregulated mRNAs contained predicted 5'tiRNAGly-GCC binding sites, indicating that this tiRNA may silence target genes via complementary binding. On the proteome level, we observed reduction in proteins involved in translation initiation and ribosome assembly, pointing to inhibitory effects on translation. Together, these findings suggest that 5'tiRNAGly-GCC is an ALS-associated tiRNA that functions to fine-tune gene expression and supress protein synthesis as part of an ANG-induced neuronal stress response.

IDENTIFICATION AND VERIFICATION OF FEATURE BIOMARKERS ASSOCIATED WITH CHOLINE METABOLISM IN SEPSIS-INDUCED CARDIOMYOPATHY

ABSTRACT

Background: Sepsis-induced cardiomyopathy ( SIC ), one of the most common complications of sepsis, seriously affects the prognosis of critically ill patients. Choline metabolism is an important biological process in the organism, and the mechanism of its interaction with SIC is unclear. The aim of this study was to reveal the choline metabolism genes (CMGs) associated with SIC and to provide effective targets for the treatment of SIC . Methods: Through a comprehensive analysis of the microarray dataset GSE79962 (comprising 20 SIC patients and 11 healthy controls) from the GEO database, suspected co-expression modules and differentially expressed genes (DEGs) in SIC were identified. Hub CMGs were obtained by intersecting choline metabolism database with DEGs and key model genes. Afterward, hub CMGs most significantly involved in prognosis were further analyzed for the verification of major pathways of enrichment analysis. Finally, the expression of hub CMGs in in vivo and in vitro SIC model was verified by immunohistochemistry staining and quantitative real-time polymerase chain reaction analysis (qPCR). Results: Weighted gene co-expression network analysis identified 1 hub gene panel and 3,867 hub genes, which were intersected with DEGs and CMGs to obtain the same 3 hub CMGs:HIF-1α, DGKD, and PIK3R1. Only HIF-1α shows significant association with mortality ( P = 0.009). Subsequent differential analysis based on the high and low HIF-1α expression yielded 63 DEGs and then they were uploaded into Cytoscape software to construct a protein-protein interaction network and 6 hub genes with the highest priority were obtained (CISH, THBS1, IMP1, MYC, SOCS3, and VCAN). Finally, a multifactorial COX analysis revealed a significant correlation between HIF-1α and survival in SIC patients, which was further validated by in vitro and in vivo experiments. Conclusion: Our findings will provide new insights into the pathogenesis of SIC , and HIF-1α may have important applications as a potential biomarker for early detection and therapeutic intervention in SIC .

Guidance on the assessment of the functionality of biomaterials for periodontal tissue regeneration: Methodologies and testing procedures

Innovative biomaterials and tissue engineering strategies show great promise in regenerating periodontal tissues. This guidance provides an overview and detailed recommendations for evaluating the biological functionality of these new biomaterials in vitro, focusing on mineralization, immunomodulatory effects, cellular differentiation, and angiogenesis. Additionally, it discusses the use of in vivo experimental models that mimic periodontitis and scrutinizes methods such as osteogenic differentiation, immunomodulation, and anti-inflammatory responses to assess the effectiveness of these biomaterials in promoting periodontal tissue reconstruction. The guidance also addresses translating these findings to clinical applications, including using large animal models. This article aims to provide general recommendations for assessing the biological performance of novel materials and scaffold-based strategies using in vitro and in vivo (animal models), examining their advantages, disadvantages, and methodologies to guide effective research and clinical translation of regenerative treatments in periodontology.

The Interplay Between Epilepsy and Parkinson's Disease: Gene Expression Profiling and Functional Analysis

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein-protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor-micro-RNA regulatory and co-regulatory networks, the gene-microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets.

Rapid and cost-effective screening of therapeutic targets for isoquercitrin in insulin resistance using virtual methods and fiber SPR biosensing

The existing screening methods for therapeutic targets of active ingredients in traditional Chinese medicine (TCM) have problems of long detection time and high instrument cost. This article proposes a new target screening method based on virtual screening and fiber surface plasmon resonance (SPR) sensing technology, which has the characteristics of flexibility, speed, and low cost. It also reveals the target mechanism of the active ingredient isoquercitrin in the treatment of insulin resistance (IR). The binding energies of isoquercitrin with target proteins PDPK1, INSR, and PTPN1 were calculated using computer virtual methods to be -8.9, -8.9, -8.8 kcal/mol, indicating strong binding activity with isoquercitrin and predicted as three key targets. Then a fiber optic SPR biosensor functionalized with isoquercitrin molecules was constructed to detect the binding affinity between isoquercitrin and the key targets. The experimental results showed that the binding affinities of isoquercitrin to the targets PDPK1, INSR, and PTPN1 were 1.45, 1.14, and 13.21, respectively, indicating that PTPN1 is the main target of isoquercitrin in the treatment of IR. The proposed sensor has a sensitivity of 0.699 nm/(μg/ml), LOD of 0.515μg/ml, and the experimental detection time of this method is as low as 45 minutes, without the need for large and expensive optical demodulation equipment, and the device volume is 5.50 dm3, providing new ideas for the screening of therapeutic targets of active ingredients in TCM.

Sex-Specific Methylomic and Transcriptomic Responses of the Avian Pineal Gland to Unpredictable Illumination Patterns

In the production environment of chickens, exposure to unpredictable light patterns is a common painless stressor, widely used to influence growth rate and egg production efficiency. The pineal gland, a key regulator of circadian rhythms through melatonin secretion, responds to environmental light cues, and its function is modulated by epigenetic mechanisms. In this study, we investigated how the pineal gland methylome and transcriptome (including micro-RNAs) interact to respond to a rearing exposure to unpredictable illumination patterns, with a particular focus on sex differences. We conducted an integrative multi-omic analysis-including methylomic (MeDIP-seq), transcriptomic (RNA-seq), and miRNA expression profiling-on the pineal gland of Hy-Line White chickens (n = 34, 18 females, 16 males) exposed to either a standard 12:12 light-dark cycle (control) or a randomized, unpredictable light schedule from Days 3 to 24 post-hatch. Our findings reveal that unpredictable light exposure alters the pineal gland methylome and transcriptome in a sex-specific manner. However, while transcriptomic differences between sexes increased due to the stress, methylomic differences decreased, particularly on the Z chromosome. These changes were driven by females (the heterogametic sex in birds), which became more male-like in their pineal methylome after exposure to the illumination stress, leading to reduced epigenetic sexual dimorphism while maintaining differences at the gene expression level. Further, we implemented a fixed sex effect model as a biological proof of concept, identifying a network of 12 key core genes interacting with 102 other genes, all linked to circadian regulation and stress adaptation. This network of genes comprises a core regulatory framework for circadian response. Additionally, tissue-specific expression analysis and cell-type specific expression analysis revealed enrichment in brain regions critical for circadian function, including neuronal populations involved in circadian regulation and the hypothalamic-pituitary-thyroid axis. Together, these findings provide strong evidence of sex-specific epigenetic transcriptomic responses of the pineal gland upon illumination stress and offer valuable insights into the interplay of different omic levels in relation to circadian response.

Biological Activity of Biomarkers Associated With Metastasis in Osteosarcoma Cell Lines

INTRODUCTION

Osteosarcoma, a highly aggressive bone cancer primarily affecting children and young adults, remains a significant challenge in clinical oncology. Metastasis stands as the primary cause of mortality in osteosarcoma patients. However, the mechanisms driving this process remain incompletely understood. Clarifying the molecular pathways involved in metastasis is essential for enhancing patient prognoses and facilitating the development of targeted therapeutic strategies.

METHODS

RNA sequencing (RNA-Seq) analysis was employed to compare three conditions, hFOB1.19 versus Saos-2, hFOB1.19 versus SJSA-1, and Saos-2 versus SJSA-1, involving non-cancer osteoblasts (hFOB1.19) and highly metastatic osteosarcoma cell lines (Saos-2 and SJSA-1). Additionally, ENA datasets of RNA-Seq from osteosarcoma biopsies were included. Differentially expressed genes (DEGs) were identified and analyzed through enrichment pathway analysis and protein-protein interaction (PPI) networks. Additionally, for gene candidates, a biochemical evaluation was performed.

RESULTS

DEGs associated with biological functions pertinent to migration, invasion, and metastasis in osteosarcoma were identified. Notably, matrix metalloproteinase-2 (MMP-2) emerged as a promising candidate. Both canonical or full-length (FL-mmp-2) and N-terminal truncated (NTT-mmp-2) isoforms were discerned in biopsies. Moreover, MMP-2's activity was characterized in cell lines. Additionally, mRNA expression of voltage-gated sodium channels (NaVs) and voltage-gated potassium channels (KVs) was detected, and their functional expression was validated using patch clamp techniques. Evaluation of cell line migration and invasion capacities revealed their reduction in the presence of ion channel blockers (TTX and TEA) and MMP inhibitor (GM6001).

CONCLUSIONS

The gene functional enrichment analysis of DEGs enabled the identification of interaction networks in osteosarcoma, thereby revealing potential biomarkers. Moreover, the elucidated co-participation of TTX-sensitive NaVs and MMP-2 in facilitating migration and invasion suggests their suitability as novel prognostic biomarkers for osteosarcoma. Additionally, this study introduces a model delineating the potential interaction mechanism among ion channels, MMP-2, and other crucial factors in the metastatic cascade of osteosarcoma.

Orchestrated immune responses to Mycobacterium marinum natural infection in tongue sole (Cynoglossus semilaevis)

Mycobacterium marinum is a major pathogen in aquaculture, posing a substantial threat to the health and sustainability of tongue sole (Cynoglossus semilaevis) farming. This study investigated the genetic basis of immune response in tongue sole by comparing transcriptome profiles of liver and spleen tissues from symptomatic (susceptible) and healthy (resistant) individuals during a natural M. marinum outbreak. Transcriptomic analyses identified differentially expressed genes and enriched pathways related to immune responses. Key genes, including atp6ap1, gpi, and idh3a, were found to be crucial in immune response to M. marinum infection, involved in immune processes such as signal transduction, antigen processing, and metabolic pathways. Protein-protein interaction networks highlighted central hub genes such as nedd8, jun and junb, which play pivotal roles in immune regulation. These findings provide insights into the orchestrated immune responses to mycobacteriosis, which can inform selective breeding strategies for disease-resistant tongue sole strains. This is the first comprehensive transcriptome analysis of M. marinum natural infection in tongue sole, offering valuable data for future research and disease management in aquaculture.

Inhibition of autophagy as a novel treatment for neurofibromatosis type 1 tumors

Neurofibromatosis type 1 (NF1) is a genetic disorder caused by mutation of the NF1 gene that is associated with various symptoms, including the formation of benign tumors, called neurofibromas, within nerves. Drug treatments are currently limited. The mitogen-activated protein kinase kinase (MEK) inhibitor selumetinib is used for a subset of plexiform neurofibromas (PNs) but is not always effective and can cause side effects. Therefore, there is a clear need to discover new drugs to target NF1-deficient tumor cells. Using a Drosophila cell model of NF1, we performed synthetic lethal screens to identify novel drug targets. We identified 54 gene candidates, which were validated with variable dose analysis as a secondary screen. Pathways associated with five candidates could be targeted using existing drugs. Among these, chloroquine (CQ) and bafilomycin A1, known to target the autophagy pathway, showed the greatest potential for selectively killing NF1-deficient Drosophila cells. When further investigating autophagy-related genes, we found that 14 out of 30 genes tested had a synthetic lethal interaction with NF1. These 14 genes are involved in multiple aspects of the autophagy pathway and can be targeted with additional drugs that mediate the autophagy pathway, although CQ was the most effective. The lethal effect of autophagy inhibitors was conserved in a panel of human NF1-deficient Schwann cell lines, highlighting their translational potential. The effect of CQ was also conserved in a Drosophila NF1 in vivo model and in a xenografted NF1-deficient tumor cell line grown in mice, with CQ treatment resulting in a more significant reduction in tumor growth than selumetinib treatment. Furthermore, combined treatment with CQ and selumetinib resulted in a further reduction in NF1-deficient cell viability. In conclusion, NF1-deficient cells are vulnerable to disruption of the autophagy pathway. This pathway represents a promising target for the treatment of NF1-associated tumors, and we identified CQ as a candidate drug for the treatment of NF1 tumors.

Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach

Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases. Furthermore, this model effectively addresses the challenge of balancing large-scale positive and negative samples, leading to improved performance across various evaluation metrics such as an Area under curve (AUC) of 0.9298 and an F1 score of 0.6316. Moreover, the algorithm accurately predicts drug combinations and achieves an F1 score of 0.7746 after fine-tuning. Additionally, it identifies two previously unexplored synergistic drug combinations for distinct cancer types in disease-specific biological network environments. These findings are further validated through in vitro cytotoxicity assays, demonstrating the potential of the model to enhance drug development and identify effective treatment regimens for specific diseases.

ERC 2.0 - evolutionary rate covariation update improves inference of functional interactions across large phylogenies

Evolutionary Rate Covariation (ERC) is an established comparative genomics method that identifies sets of genes sharing patterns of sequence evolution, which suggests shared function. Whereas many functional predictions of ERC have been empirically validated, its predictive power has hitherto been limited by its inability to tackle the large numbers of species in contemporary comparative genomics datasets. This study introduces ERC2.0, an enhanced methodology for studying ERC across phylogenies with hundreds of species and tens of thousands of genes. ERC2.0 improves upon previous iterations of ERC in algorithm speed, normalizing for heteroskedasticity, and normalizing correlations via Fisher transformations. These improvements have resulted in greater statistical power to predict biological function. In exemplar yeast and mammalian datasets, we demonstrate that the predictive power of ERC2.0 is improved relative to the previous method, ERC1.0, and that further improvements are obtained by using larger yeast and mammalian phylogenies. We attribute the improvements to both the larger datasets and improved rate normalization. We demonstrate that ERC2.0 has high predictive accuracy for known annotations and can predict the functions of genes in non-model systems. Our findings underscore the potential for ERC2.0 to be used as a single-pass computational tool in candidate gene screening and functional predictions.

Characterization of immune landscape and prognostic value of IL-17-related signature in invasive breast cancer

Background

Recently, interleukin 17 (IL-17) has been found to play a critical role in the development of breast cancer. However, its prognostic significance in invasive breast cancer (IBC) remains unclear. This study aims to determine the role of IL-17-related signatures in IBC to identify novel therapeutic options.

Methods

IBC data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used to identify IL-17-related prognostic genes. A predictive model was developed using TCGA data and validated using METABRIC data. The relationship between IL-17 scores and immune landscape, chemotherapy drug sensitivity [half maximal inhibitory concentration (IC50)], and immune checkpoint gene expression was analyzed. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to validate key gene expression in breast tumor and normal tissue samples.

Results

The predictive model identified core IL-17-related prognostic genes and successfully estimated the prognosis of IBC patients. The model's validity was confirmed using METABRIC data. Patients with high IL-17 scores had worse overall survival (OS) compared to those with low IL-17 scores. Low IL-17 scores were associated with higher immune checkpoint gene expression and predicted enhanced responses to cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1) therapies. Patients with low IL-17 scores exhibited a higher abundance of immune microenvironment components. Furthermore, qRT-PCR confirmed the lower expression of OR51E1, NDRG2, RGS2, and TSPAN7 in breast tumors compared to normal tissue.

Conclusions

IL-17-related signatures are promising biomarkers for predicting the prognosis of IBC patients. These findings suggest that IL-17-related markers could be used to guide individualized therapeutic strategies, potentially improving outcomes for IBC patients.

IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach

PURPOSE

Colorectal cancer (CRC) is the third most common cancer globally, necessitating novel biomarkers for early diagnosis and treatment. This study proposes an efficient pipeline leveraging an integrated bioinformatics and machine learning framework to enhance the identification of diagnostic and prognostic biomarkers for CRC.

METHODS

A selection of methylated differentially expressed genes (MeDEGs) and features (genes) was made using both statistical and Machine learning (ML) approaches from publically available datasets. These genes were subjected to STRING network construction and hub genes estimation, separately. Also, essential miRNAs (micro-RNAs) and TFs (Transcription factors) as regulatory elements were revealed and findings were validated through scRNA-seq analysis, promoter methylation, gene expression levels correlated with pathological stage, and interaction with tumor-infiltrating immune cells.

RESULTS

Through an integrated analysis pipeline, we identified 27 hub genes, among which CTNNB1, GSK3B, IL-1β, MYC, PXDN, TP53, EGFR, SRC, COL1A1, and TGBF1 showed better diagnostic behaviour. Machine learning approach includes the development of K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Random Forest (RF) models using TCGA datasets, achieving an accuracy range between 99 and 100%. The Area Under the Curve (AUC) value for each model is 1.00, signifying good classification performance. The high expression of some diagnostic genes was associated with poor prognosis, concluding IL-1β as both a prognostic and diagnostic biomarker. Additionally, the NF-κB and microRNAs (miR-548d-3p, miR-548-ac) and TFs (NFκB and STAT5A) play a major role in the comprehensive regulatory network for CRC. Furthermore, hub genes such as IL-1β, TGFB1, and COL1A1 were significantly correlated with immune infiltrates, suggesting their potential role in CRC progression.

CONCLUSION

Overall, the elevated expression of IL-1β coupled with abnormal DNA methylation, and its consequent effect on the PI3K/Akt signaling pathway are relevant prognostic and therapeutic marker in CRC. Additional molecular candidates reveal insights into the epigenetic regulatory targets of CRC and their association with immune cell infiltration.

Targeting Matrix Metalloproteinase-1, Matrix Metalloproteinase-7, and Serine Protease Inhibitor E1: Implications in preserving lung vascular endothelial integrity and immune modulation in COVID-19

BACKGROUND

SARS-CoV-2 disrupts lung vascular endothelial integrity, contributing to severe COVID-19 complications. However, the molecular mechanisms driving endothelial dysfunction remain underexplored, and targeted therapeutic strategies are lacking.

OBJECTIVE

This study investigates Naringenin-7-O-glucoside (N7G) as a multi-target therapeutic candidate for modulating vascular integrity and immune response by inhibiting MMP1, MMP7, and SERPINE1-key regulators of extracellular matrix (ECM) remodeling and inflammation.

METHODS & RESULTS

RNA-seq analysis of COVID-19 lung tissues identified 17 upregulated N7G targets, including MMP1, MMP7, and SERPINE1, with the latter exhibiting the highest expression. PPI network analysis linked these targets to ECM degradation, IL-17, HIF-1, and AGE-RAGE signaling pathways, and endothelial dysfunction. Disease enrichment associated these genes with idiopathic pulmonary fibrosis and asthma. Molecular docking, 200 ns MD simulations (triplicate), and MMGBSA calculations confirmed N7G's stable binding affinity to MMP1, MMP7, and SERPINE1. Immune profiling revealed increased neutrophils and activated CD4+ T cells, alongside reduced mast cells, NK cells, and naïve B cells, indicating immune dysregulation. Correlation analysis linked MMP1, MMP7, and SERPINE1 to distinct immune cell populations, supporting N7G's immunomodulatory role.

CONCLUSION

These findings suggest that N7G exhibits multi-target therapeutic potential by modulating vascular integrity, ECM remodeling, and immune dysregulation, positioning it as a promising candidate for mitigating COVID-19-associated endothelial dysfunction.

Machine learning analysis of gene expression profiles of pyroptosis-related differentially expressed genes in ischemic stroke revealed potential targets for drug repurposing

The relationship between ischemic stroke (IS) and pyroptosis centers on the inflammatory response elicited by cerebral tissue damage during an ischemic stroke event. However, an in-depth mechanistic understanding of their connection remains limited. This study aims to comprehensively analyze the gene expression patterns of pyroptosis-related differentially expressed genes (PRDEGs) by employing integrated IS datasets and machine learning techniques. The primary objective was to develop classification models to identify crucial PRDEGs integral to the ischemic stroke process. Leveraging three distinct machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), models were developed to differentiate between the Control and the IS patient samples. Through this approach, a core set of 10 PRDEGs consistently emerged as significant across all three machine learning models. Subsequent analysis of these genes yielded significant insights into their functional relevance and potential therapeutic approaches. In conclusion, this investigation underscores the pivotal role of pyroptosis pathways in ischemic stroke and identifies pertinent targets for therapeutic development and drug repurposing.

Molecular mechanism of Gancao Xiexin Decoction regulating EMT and suppressing hepatic metastasis of gastric cancer via the TGF-β1/SMAD pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Gastric cancer (GC) is a highly malignant tumor of the digestive tract, posing a significant menace to human health. Gancao Xiexin Decoction (GCXXD), being a traditional Chinese medicine (TCM), has a good effect on inhibiting the proliferation and metastasis of GC. However, its mechanisms still need further investigation.

AIM OF STUDY

To investigate the mechanism by which GCXXD inhibits GC metastasis through network pharmacology, and to verify through in vivo and in vitro experiments.

MATERIALS AND METHODS

The TCMSP and GEO databases, in combination with UPLC-MS/MS techniques, were employed to identify the hub genes, active ingredients, and critical pathways of GCXXD in the treatment of GC. Subsequently, molecular docking was conducted on both the hub genes and the core components. Finally, based on the results of the bioinformatics analysis, the role of GCXXD in inhibiting liver metastasis of GC was elucidated through in vivo and in vitro experiments, including scratch assays, Transwell assays, HE staining, immunohistochemistry, in vivo live imaging, qRT-PCR, and Western blotting.

RESULTS

Utilizing UPLC-MS/MS and network pharmacology, we identified 20 active ingredients and 5 hub targets in the treatment of GC by GCXXD. Through KEGG analyses, GCXXD treatment of GC could through the TGF-beta pathway. In vivo and in vitro experiments, GCXXD downregulated the mRNA and protein expression level of hub genes involved in the TGF-β1/SMAD pathway and the EMT process. Additionally, GCXXD significantly reduced the incidence of liver metastases in GC.

CONCLUSION

GCXXD inhibited EMT via blocking the TGF-β1/SMAD pathway, which suppressed GC cell growth and liver metastasis. This study provides data to support the treatment of liver metastasis in GC with TCM and holds significant importance for the research and development of new anticancer drugs.

Pan-cancer analysis of Methyltransferase-like 16 (METTL16) and validated in colorectal cancer

Human Methyltransferase-like 16(METTL16) is an independent N6-methyladenosine (m6A) methyltransferase. Previous studies have proven METTL16 been linked with some types of cancers. However, comparative studies of the relevance of METTL16 across diverse tumors remain sparse. We comprehensively investigated the effect of METTL16 expression on tumor prognosis across human malignancies by analyzing multiple cancer-related databases like Tumor Immune Estimation Resource (TIMER) and human protein atlas (HPA). Bioinformatics data indicated that METTL16 was overexpressed in most of these human malignancies and was significantly associated with the prognosis of patients with cancer, especially in colorectal cancer (CRC). Subsequently, In vitro experiments, the utility of METTL16 that downregulation of its expression could result in reduced proliferation and migration of CRC cells. Our findings reveal novel insights into METTL16 expression and its biological functions in diverse cancer types, indicating that METTL16 could serve as a prognostic biomarker and plays an important role in colorectal cancer.

Therapeutic Mechanisms of Medicine Food Homology Plants in Alzheimer's Disease: Insights from Network Pharmacology, Machine Learning, and Molecular Docking

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. Currently, there are no effective treatments for this condition. Medicine food homology plants have gained increasing attention as potential natural treatments for AD because of their nutritional value and therapeutic benefits. In this work, we aimed to provide a deeper understanding of how medicine food homology plants may help alleviate or potentially treat AD by identifying key targets, pathways, and small molecule compounds from 10 medicine food homology plants that play an important role in this process. Using network pharmacology, we identified 623 common targets between AD and the compounds from the selected 10 plants, including crucial proteins such as STAT3, IL6, TNF, and IL1B. Additionally, the small molecules from the selected plants were grouped into four clusters using hierarchical clustering. The ConPlex algorithm was then applied to predict the binding capabilities of these small molecules to the key protein targets. Cluster 3 showed superior predicted binding capabilities to STAT3, TNF, and IL1B, which was further validated by molecular docking. Scaffold analysis of small molecules in Cluster 3 revealed that those with a steroid-like core-comprising three fused six-membered rings and one five-membered ring with a carbon-carbon double bond-exhibited better predicted binding affinities and were potential triple-target inhibitors. Among them, MOL005439, MOL000953, and MOL005438 were identified as the top-performing compounds. This study highlights the potential of medicine food homology plants as a source of active compounds that could be developed into new drugs for AD treatment. However, further pharmacokinetic studies are essential to assess their efficacy and minimize side effects.

Multi-omics analysis identifies a liquid-liquid phase separation-related subtypes in head and neck squamous cell carcinoma

Background

Growing evidence indicates that abnormal liquid-liquid phase separation (LLPS) can disrupt biomolecular condensates, contributing to cancer development and progression. However, the influence of LLPS on the prognosis of head and neck squamous cell carcinoma (HNSCC) patients and its effects on the tumor immune microenvironment (TIME) are not yet fully understood. Therefore, we aimed to categorize patients with HNSCC based on LLPS-related genes and explored their multidimensional heterogeneity.

Methods

We integrated the transcriptomic data of 3,541 LLPS-related genes to assess the LLPS patterns in 501 patients with HNSCC within The Cancer Genome Atlas cohort. Subsequently, we explored the differences among the three LLPS subtypes using multi-omics analysis. We also developed an LLPS-related prognostic risk signature (LPRS) to facilitate personalized and integrative assessments and then screened and validated potential therapeutic small molecule compounds targeting HNSCC via experimental analyses.

Result

By analyzing the expression profiles of 85 scaffolds, 355 regulators, and 3,101 clients of LLPS in HNSCC, we identified three distinct LLPS subtypes: LS1, LS2, and LS3. We confirmed notable differences among these subtypes in terms of prognosis, functional enrichment, genomic alterations, TIME patterns, and responses to immunotherapy. Additionally, we developed the LPRS, a prognostic signature for personalized integrative assessments, which demonstrated strong predictive capability for HNSCC prognosis across multiple cohorts. The LPRS also showed significant correlations with the clinicopathological features and TIME patterns in HNSCC patients. Furthermore, the LPRS effectively predicted responses to immune checkpoint inhibitor therapy and facilitated the screening of potential small-molecule compounds for treating HNSCC patients.

Conclusion

This study presents a new classification system for HNSCC patients grounded in LLPS. The LPRS developed in this research offers improved personalized prognosis and could optimize immunotherapy strategies for HNSCC.

Single-cell Analysis Highlights Anti-apoptotic Subpopulation Promoting Malignant Progression and Predicting Prognosis in Bladder Cancer

Backgrounds

Bladder cancer (BLCA) has a high degree of intratumor heterogeneity, which significantly affects patient prognosis. We performed single-cell analysis of BLCA tumors and organoids to elucidate the underlying mechanisms.

Methods

Single-cell RNA sequencing (scRNA-seq) data of BLCA samples were analyzed using Seurat, harmony, and infercnv for quality control, batch correction, and identification of malignant epithelial cells. Gene set enrichment analysis (GSEA), cell trajectory analysis, cell cycle analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis explored the functional heterogeneity between malignant epithelial cell subpopulations. Cellchat was used to infer intercellular communication patterns. Co-expression analysis identified co-expression modules of the anti-apoptotic subpopulation. A prognostic model was constructed using hub genes and Cox regression, and nomogram analysis was performed. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to predict immunotherapy response.

Results

Organoids recapitulated the cellular and mutational landscape of the parent tumor. BLCA progression was characterized by mesenchymal features, epithelial-mesenchymal transition (EMT), immune microenvironment remodeling, and metabolic reprograming. An anti-apoptotic tumor subpopulation was identified, characterized by aberrant gene expression, transcriptional instability, and a high mutational burden. Key regulators of this subpopulation included CEBPB, EGR1, ELF3, and EZH2. This subpopulation interacted with immune and stromal cells through signaling pathways such as FGF, CXCL, and VEGF to promote tumor progression. Myofibroblast cancer-associated fibroblasts (mCAFs) and inflammatory cancer-associated fibroblasts (iCAFs) differentially contributed to metastasis. Protein-protein interaction (PPI) network analysis identified functional modules related to apoptosis, proliferation, and metabolism in the anti-apoptotic subpopulation. A 5-gene risk model was developed to predict patient prognosis, which was significantly associated with immune checkpoint gene expression, suggesting potential implications for immunotherapy.

Conclusions

We identified a distinct anti-apoptotic tumor subpopulation as a key driver of tumor progression with prognostic significance, laying the foundation for the development of new therapeutic strategies to improve patient outcomes.

Transcriptome analysis of Aedes aegypti midgut and salivary gland post-Zika virus infection

This study aimed to investigate the transcriptomic changes in the midgut and salivary glands of Aedes aegypti mosquitoes infected with Zika virus (ZIKV), in order to explore the molecular mechanisms underlying the interaction between the virus and the mosquito vector. Aedes aegypti from Jiegao (JG) and Mengding (MD) in China were experimentally infected with ZIKV, and the midgut and salivary gland tissues were collected at 2-, 4- and 6 days post-infection (dpi). High-throughput sequencing was performed to analyze the transcriptomic changes between ZIKV-infected and non-infected Ae. aegypti midgut and salivary gland tissues. Bioinformatics tools were employed for further analysis of the transcriptomic data. The expression levels of 8 significantly differentially expressed genes (DEGs) were validated using RT-qPCR. A conjoint analysis of small RNA-seq and mRNA-seq was performed to screen interactional miRNA-mRNA pairs during ZIKV infection. Using the Search Tool for the Retrieval of Interacting Genes, we constructed a protein-protein interaction network of genes and subsequently identified hub genes. The most significant transcriptional changes in Ae. aegypti occurred at 2 dpi. On 2, 4 and 6 dpi, 11 genes showed significant changes in both the midgut and salivary glands of the same mosquito strain, while 25 genes exhibited significant changes in the same tissue between the JG and MD strains. The expression tendencies of 8 DEGs obtained by RNA-Seq were similar to those detected by RT-qPCR. Furthermore, we individually identified 10 hub genes in the midgut and salivary glands. Based on previous miRNA research, we discovered the involvement of 9 miRNAs in the regulation of these hub genes. Our findings demonstrate that Ae. aegypti exhibit distinct transcriptomic changes in response to ZIKV infection. The identification of the hub genes and their regulatory miRNAs provides valuable insights into the molecular mechanisms underlying ZIKV infection in mosquitoes. This study contributes to a better understanding of the pathogen-vector interactions and may aid in the development of targeted strategies for ZIKV control.

Profiling hippocampal neuronal populations reveals unique gene expression mosaics reflective of connectivity-based degeneration in the Ts65Dn mouse model of Down syndrome and Alzheimer's disease

Introduction

Individuals with Down syndrome (DS) exhibit neurological deficits throughout life including the development of in Alzheimer's disease (AD) pathology and cognitive impairment. At the cellular level, dysregulation in neuronal gene expression is observed in postmortem human brain and mouse models of DS/AD. To date, RNA-sequencing (RNA-seq) analysis of hippocampal neuronal gene expression including the characterization of discrete circuit-based connectivity in DS remains a major knowledge gap. We postulate that spatially characterized hippocampal neurons display unique gene expression patterns due, in part, to dysfunction of the integrity of intrinsic circuitry.

Methods

We combined laser capture microdissection to microisolate individual neuron populations with single population RNA-seq analysis to determine gene expression analysis of CA1 and CA3 pyramidal neurons and dentate gyrus granule cells located in the hippocampus, a region critical for learning, memory, and synaptic activity.

Results

The hippocampus exhibits age-dependent neurodegeneration beginning at ~6 months of age in the Ts65Dn mouse model of DS/AD. Each population of excitatory hippocampal neurons exhibited unique gene expression alterations in Ts65Dn mice. Bioinformatic inquiry revealed unique vulnerabilities and differences with mechanistic implications coinciding with onset of degeneration in this model of DS/AD.

Conclusions

These cell-type specific vulnerabilities may underlie degenerative endophenotypes suggesting precision medicine targeting of individual populations of neurons for rational therapeutic development.

Comprehensive SHAP Values and Single-Cell Sequencing Technology Reveal Key Cell Clusters in Bovine Skeletal Muscle

The skeletal muscle of cattle is the main component of their muscular system, responsible for supporting and movement functions. However, there are still many unknown areas regarding the ranking of the importance of different types of cell populations within it. This study conducted in-depth research and made a series of significant findings. First, we trained 15 bovine skeletal muscle models and selected the best-performing model as the initial model. Based on the SHAP (Shapley Additive exPlanations) analysis of this initial model, we obtained the SHAP values of 476 important genes. Using the contributions of these 476 genes, we reconstructed a 476-gene SHAP value matrix, and relying solely on the interactions among these 476 genes, successfully mapped the single-cell atlas of bovine skeletal muscle. After retraining the model and further interpretation, we found that Myofiber cells are the most representative cell type in bovine skeletal muscle, followed by neutrophils. By determining the key genes of each cell type through SHAP values, we conducted analyses on the correlations among key genes and between cells for Myofiber cells, revealing the critical role these genes play in muscle growth and development. Further, by using protein language models, we performed cross-species comparisons between cattle and pigs, deepening our understanding of Myofiber cells as key cells in skeletal muscle, and exploring the common regulatory mechanisms of muscle development across species.

Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential interventions. Leveraging data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline, we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident depression. Linear regression was then used to assess associations between depression-related proteins and brain structures, genetic factors, and stress-related events. Our analysis identified 157 proteins associated with incident depression (P <1.71 × 10-5), including novel associations with proteins such as GAST, PLAUR, LRRN1, BCAN, and ITGA11. Notably, higher expression levels of GDF15 (P = 6.18 × 10-26) and PLAUR (P = 2.88 × 10-14) were linked to an increased risk of depression, whereas higher levels of LRRN1 (P = 4.28 × 10-11) and ITGA11 (P = 3.68 × 10-9) were associated with a decreased risk. Dysregulation of the 157 proteins is correlated with brain regions implicated in depression, including the hippocampus and middle temporal gyrus. Additionally, these protein alterations were strongly correlated with stress-related events, including self-harm events, adult, and childhood trauma. Biological pathway enrichment analysis highlighted the critical roles of the immune response. EGFR and TNF emerged as key proteins in the protein-protein interaction network. BTN3A2, newly linked to incident depression (P = 4.35 × 10-10), was confirmed as a causal factor through Mendelian randomization analysis. In summary, our research identified the proteomic signatures associated with the onset of depression, highlighting its potential for early intervention and tailored therapeutic avenues.

Proteomic profiling of plasma extracellular vesicles identifies signatures of innate immunity, coagulation, and endothelial activation in septic patients

Plasma extracellular vesicles (EVs) are cell-derived lipid particles and reportedly play a role in sepsis pathogenesis. This study aimed to identify EV cargo proteins in septic patients and explore their association with key sepsis pathophysiology. Plasma EVs were subjected to Tandem Mass Tag (TMT)-based quantitative proteomic analysis. We identified 522 differentially expressed (DE) EV proteins in septic patients (n=15) compared to the healthy controls (n=10). The KEGG analysis of the DE proteins revealed multiple functional pathways linked to sepsis, e.g., complement/coagulation, platelet activation, phagosome, inflammation, and neutrophil extracellular trap formation. Weighted Gene Coexpression Network Analysis of 1,642 EV proteins identified nine unique protein modules, some of which were highly correlated with the sepsis diagnosis and diverse plasma markers, including organ injury, inflammation, coagulopathy, and endothelial activation. Cell type-specific enrichment analysis revealed the cellular origins of EVs, including immune and epithelial cells, neurons, and glial cells. Thus, the current study discovered complex proteomic signatures in plasma EVs that are closely associated with key pathophysiological responses in sepsis. These findings support the importance of EV cargo proteins in the patients' immune responses, coagulation, and endothelial activation and lay the foundation for future mechanistic study of plasma EVs in sepsis pathogenesis.

UHPLC-Q Exactive-Orbitrap-MS and network pharmacology analyses to investigate the mechanism by which Danggui-Shaoyao-San affects 27-OHC-induced cell damage in SH-SY5Y/C6 coculture

BACKGROUND

Danggui-Shaoyao-San (DSS) is a classic Chinese medicine formula that has been extensively studied for its efficacy in treating Alzheimer's disease (AD). However, its mechanism of action is still unclear.

METHODS

In this study, UHPLC-Q Exactive-Orbitrap-MS was used to analyze and identify the compounds in DSS. Network pharmacology was used to analyze the common targets of drug-containing serum chemistries and AD, as well as the AD pathways in which drug-containing serum chemistries may be involved. The 27-OHC-induced SH-SY5Y/C6 coculture cell injury model was used to explore the mechanism of action of DSS in the treatment of AD.

RESULTS

UHPLC-Q Exactive-Orbitrap-MS analysis identified 73 chemical constituents in DSS aqueous extract and 39 compounds in drug-containing serum. According to network pharmacology analysis, DSS and AD share 181 common targets, with interleukin-6 (IL-6) and tumor necrosis factor (TNF) being the main effective targets. Furthermore, DSS may treat AD through the modulation of lipid metabolism-related pathways and the interleukin-17 (IL-17) signaling pathway. 27-hydroxycholesterol acid (27-OHC) significantly reduced the viability of SH-SY5Y cells and C6 cells in vitro, while DSS administration upregulated the expression of cytochrome P450 46A1 (CYP46A1) and cytochrome P450 7B1 (CYP7B1) enzymes and reduced cholesterol levels in SH-SY5Y cells. Additionally, DSS decreased reactive oxygen species (ROS) levels and increased glutathione (GSH) levels in coculture systems. DSS downregulated the expression of IL-17 in 27-OHC-injured SH-SY5Y cells and downregulated the expression of TNF-α, IL-6 and transforming growth factor-β1 (TGF-β1) in 27-OHC-injured C6 cells.

CONCLUSION

This study revealed the effective components, targets and mechanisms of DSS in the treatment of AD, highlighting the significant potential of DSS in treating this disease.

The mutational landscape and its longitudinal dynamics in relapsed and refractory classic Hodgkin lymphoma

In classic Hodgkin-lymphoma (cHL), only a few cases recur, and only a limited fraction of patients is primary-refractory to standard-polychemotherapy. Underlying genomic features of unfavorable clinical courses remain sparsely characterized. Here, we investigated the genomic characteristics of primary-refractory/relapsed cHL in contrast with responders. Therefore, ultra-deep next-generation panel-sequencing was performed on a total of 59 FFPE-samples (20 responders, 26 relapsed (rHL: 11 initial-diagnosis, 15 relapse) and 13 primary-refractory (prHL: 8 initial-diagnosis, 5 progression) from 44 cHL-patients applying a hybrid-capture approach. We compared samples associated with distinct disease courses concerning their oncogenic drivers, mutational signatures, and perturbed pathways. Compared to responders, mutations in genes such as PMS2, PDGFRB, KAT6A, EPHB1, and HGF were detected more frequently in prHL/rHL. Additionally, we observed that in rHL or prHL, BARD1-mutations occur, whereas ETV1, NF1, and MET-mutations were eliminated through clonal selection. A significant enrichment of non-synonymous variants was detected in prHL compared to responders and a significant selection process in favor of NOTCH-pathway mutations driving rHL or prHL was observed. However, our analysis revealed a negative selection process for non-synonymous variants affecting the hippo-pathway. This study delineates distinct mutational signatures between responders and rHL/prHL, whilst illustrating longitudinal dynamics in mutational profiles using paired samples. Further, several exploitable therapeutic vulnerabilities for rHL and prHL were identified.

Gene module reconstruction identifies cellular differentiation processes and the regulatory logic of specialized secretion in zebrafish

During differentiation, cells become structurally and functionally specialized, but comprehensive views of the underlying remodeling processes are elusive. Here, we leverage single-cell RNA sequencing (scRNA-seq) developmental trajectories to reconstruct differentiation using two secretory tissues as models-the zebrafish notochord and hatching gland. First, we integrated expression and functional similarities to identify gene modules, revealing dozens of modules representing known and newly associated differentiation processes and their dynamics. Second, we focused on the unfolded protein response (UPR) transducer module to study how general versus cell-type-specific secretory functions are regulated. Profiling loss- and gain-of-function embryos identified that the UPR transcription factors creb3l1, creb3l2, and xbp1 are master regulators of a general secretion program. creb3l1/creb3l2 additionally activate an extracellular matrix secretion program, while xbp1 partners with bhlha15 to activate a gland-like secretion program. Our study presents module identification via multi-source integration for reconstructing differentiation (MIMIR) and illustrates how transcription factors confer general and specialized cellular functions.

Hirschsprung's disease may increase the incidence of inflammatory bowel disease through alterations in CA1

BACKGROUND

The role of Hirschsprung's disease (HSCR) for the development of inflammatory bowel disease (IBD) and the common pathogenesis of the diseases remains unclear. The objective is to investigate the relationship between HSCR and IBD.

METHODS

In our study, the Mendelian randomization approach was employed to analyze the causal relationships. A further search was conducted for differentially expressed genes (DEGs) between disease and control tissues in HSCR and IBD. Subsequently, the potential pathway mechanisms were subjected to an enrichment analysis. Furthermore, the molecular docking was employed to investigate the binding relationship between potential therapeutic targets and drugs.

RESULTS

The results show HSCR have an increased risk of developing IBD (IVW: OR = 1.048, P < 0.05; weighted median: OR = 1.065, P < 0.05). A total of 111 DEGs were identified in IBD, while 471 DEGs were observed in HSCR. CA1 was identified as core gene and exhibited lower expression levels in IBD (P < 0.05). Concomitantly, CA1 exhibited reduced expression levels in inflamed tissues. And the TNF and IL17 signaling pathway were found closely related to CA1 expression.

CONCLUSION

In total, our study shows HSCR promote the occurrence of IBD and reveals pathogenesis. Our results suggest CA1 may provide novel insight for the treatment of HSCR complicated with IBD.

IMPACT

Individuals with HSCR are at a higher risk of developing IBD (IVW: OR = 1.048, P < 0.05; Weighted median: OR = 1.065, P < 0.05). Patients with IBD exhibited lower expression levels of CA1 (P < 0.05). Furthermore, CA1 expression was found to be lower in inflamed tissues (P < 0.05). CA1 may provide novel insight for the treatment of HSCR complicated with IBD.

Genome-Wide Exploration and Characterization of the TCP Gene Family's Expression Patterns in Response to Abiotic Stresses in Siberian Wildrye (Elymus sibiricus L.)

Siberian wildrye (Elymus sibiricus L.), a model Elymus Gramineae plant, has high eco-economic value but limited seed and forage yield. TCP transcription factors are widely regarded as influencing yield and quality and being crucial for growth and development; still, this gene family in Siberian wildrye remains unexplored. Therefore, this study looked at the Siberian wildrye TCP gene family's reaction to several abiotic stresses, its expression pattern, and its potential evolutionary path. Fifty-four members of the EsTCP gene family were discovered. There are two major subfamilies based on the phylogenetic tree: 27 of Class I (PCF) and 27 of Class II (12 CIN-type and 15 TB1/CYC-type). Gene structure, conserved motif, and sequence alignment analyses further validated this classification. Cis-elements found in the promoter region of EsTCPs are associated with lots of plant hormones and stress-related reactions, covering drought induction and cold tolerance. EsCYC5, EsCYC6, and EsCYC7 may regulate tillering and lateral branch development. EsPCF10's relative expression was significant under five stresses. Additionally, eight EsTCP genes are potential miR319 targets. These findings highlight the critical significance of the TCP gene family in Siberian wildrye, laying the groundwork for understanding the function of the EsTCP protein in abiotic stress studies and high-yield breeding.

Network pharmacology unveils the intricate molecular landscape of Chrysin in breast cancer therapeutics

Chrysin is one of the natural flavonoid compounds Sourced from various plant source, mainly in propolis and honey, demonstrates effective Cancer-suppressing properties, particularly in Breast cancer (BC). However, the specific molecular mechanisms underlying its efficacy in breast cancer treatment have remained elusive. This study employed network pharmacology combined with a molecular docking approach to uncover the intricate details of Chrysin's impact on breast cancer. Utilizing databases such as GeneCards, and disgenet, Pharmmapper, ctd database, Chrysin and potential breast cancer targets were meticulously curated. Through a strategic process of mapping and screening, core targets essential for Chrysin's efficacy in breast cancer treatment were identified. Further refinement through Venn diagram analysis, considering 1350 breast cancer target genes and 433 Chrysin-related targets, identified 140 intersection targets. Subsequent construction of protein-protein interaction networks of 140 intersecting using the STRING and Cytoscape software highlighted these ten targets as core candidates. Functional annotation and pathway analysis, performed using the ShinyGO database, unveiled that the key targets were significantly associated with the Prostate cancer pathways and IL17 signaling pathways. Molecular docking results underscored Chrysin's effective binding to these ten key targets, forming stable protein-ligand complexes. Molecular docking analyses were then conducted to evaluate the impact of Chrysin in the key targets, revealing TP53, JUN, HIF1A, ALB, CASP3, STAT3, BCL2, TNF, AKT1, and IL6 as pivotal players. In summary, this investigation provides valuable revelations into the essential targets and molecular processes through which Chrysin exerts its anti-breast cancer effects. These findings not only enhance our understanding of Chrysin's pharmacological actions in breast cancer but also lay a theoretical groundwork for future investigations into the therapeutic mechanisms of Chrysin in this context.

Correlation Analysis of Pyroptosis-Related Genes CASP1, NLRP3, AIM2, and NLRP1 With Lung Adenocarcinoma

Purpose: This study is aimed at exploring the role of pyroptosis-related genes in the development, immune infiltration, and clinical features of lung adenocarcinoma. Method: Pyroptosis-related genes were searched using online databases, including MSigDB, Gene, and GeneCards. We explored pyroptosis-related gene expression patterns in lung adenocarcinoma using the UALCAN database. Functional enrichment analysis of pyroptosis-related genes in lung adenocarcinoma was performed using the Metascape database. A protein-protein interaction network was constructed using the STRING database, and the outcomes were visualized using Cytoscape. The top five core genes were screened utilizing the MCC algorithm with its cytoHubba plugin. The correlation between immune cell infiltration, diagnosis, and prognosis of core genes in lung adenocarcinoma was explored using the TIMER 2.0, TCGA, and Kaplan-Meier plotter databases. A nomogram was constructed to predict the survival of patients with lung adenocarcinoma using Cox regression analysis, and its clinical value was validated. Samples of paraffin-embedded lung adenocarcinoma tissues were collected and subjected to immunohistochemical tests to verify the expression of core genes in lung adenocarcinoma and adjacent tissues. Results: Overall, 202 genes related to pyroptosis were identified, with 67 upregulated and 60 downregulated in lung adenocarcinomas. The top five core genes-namely, CASP1 (caspase1), PYCARD (PYD and CARD domain-containing protein), NLRP3 (NOD-like receptor protein 3), AIM2 (absent in melanoma 2), and NLRP1 (NOD-like receptor protein 1)-related to lung adenocarcinoma pyroptosis were selected. The correlation analysis of immune cell infiltration showed that CASP1, NLRP3, and AIM2, which showed that pyroptosis was involved in the infiltration of immune cells in the tumor microenvironment and NLRP1 exhibited high diagnostic efficacy, while PYCARD demonstrated poor diagnostic efficacy. High expression of CASP1, NLRP3, and NLRP1 correlated with a better prognosis (p < 0.05), while elevated AIM2 expression was associated with a poor prognosis (p < 0.05). However, PYCARD exhibited no significant correlation with prognosis (p > 0.05). The immunohistochemistry results showed that positive rates of CASP1, NLRP3, AIM2, and NLRP1 were 20%, 15%, 70%, and 10%, respectively, while in adjacent tissues, the positive rates were 60%, 60%, 20%, and70%, indicating high expression of AIM2 and low expression of CASP1, NLRP3, and NLRP1 in lung adenocarcinoma. Conclusion: CASP1, NLRP3, AIM2, and NLRP1 are core pyroptotic genes in lung adenocarcinoma and exhibit a strong correlation with immune cell infiltration, diagnosis, and prognosis of this condition. These genes may be useful in the clinical diagnosis and treatment of patients with lung adenocarcinoma.

Ferroptosis-related genes in preeclampsia: integrative bioinformatics analysis, experimental validation and drug prediction

INTRODUCTION

Preeclampsia (PE) is a severe pregnancy complication with limited early diagnostic and therapeutic options. Ferroptosis, an iron-dependent cell death pathway, has emerged as a potential mechanism in PE pathogenesis. This study investigated ferroptosis-related genes (FRGs) in PE to identify diagnostic biomarkers and therapeutic targets.

METHODS

Differentially expressed genes were identified from GEO databases and intersected with FRGs. Hub genes were selected using RandomForest and LASSO algorithms. Their diagnostic potential was evaluated through ROC analysis. Regulatory networks were constructed using transcription factors, microRNAs and potential drug targets. Hub gene expression was validated through immunohistochemistry, Western blot, and RT-qPCR in placental tissues and hypoxic trophoblasts.

RESULTS

We identified 25 ferroptosis-related differentially expressed genes enriched in ferroptosis and HIF-1 pathways. Four hub genes (NDRG1, P4HA1, LDHA, and IDO1) showed high diagnostic efficiency (AUC=0.9182). Immune cell analysis revealed altered levels of plasma cells, CD8+ T cells, Tregs, monocytes, and M2 macrophages in PE, correlating significantly with hub gene expression. We identified 84 mRNA-miRNA and 119 mRNA-TF interactions. Among 19 potential drugs, Tetrahydro-NAD showed promising targeting potential. Experimental validation confirmed elevated expression of NDRG1, P4HA1, and LDHA, and decreased IDO1 in PE tissues and hypoxic conditions.

DISCUSSION

This study identified four FRGs as potential PE biomarkers and therapeutic targets, providing new insights into PE pathogenesis through integrated bioinformatics and experimental validation. These findings may facilitate early PE diagnosis and treatment development.

Exploring the dynamic responses of group 3 innate lymphoid cells at different times in response to LPS challenge

Group 3 innate lymphoid cells (ILC3s) have clear roles in regulating mucosal immunity and tissue homeostasis in the intestine, though the immunological functions in lungs remain unclear. This study aimed to demonstrate the dynamic responses of ILC3s to acute inflammation upon LPS challenge. Microarray data and single-cell RNA sequencing (scRNA-seq) data obtained from the GEO database were combined to analyze the function of ILC3 subset, confirmed by flow cytometry assay and qRT-PCR. The gene enrichment analysis of intersected genes identified between microarray data in bacterial pneumonia and single-cell RNA sequencing of intestinal ILC3s were closely related to TNF-alpha effects on cytokine activity, cell motility and apoptosis pathway, indicating the possibility of intestinal ILC3s migration to the lung. Furthermore, the cellular landscapes of ILC3s in lung and intestine at different times after pulmonary infection exhibited varied ILC3 statuses. ILC3s in lung expanded a lot at 48 h while intestinal ILC3s decreased at 72 h response to LPS challenge, with higher expression of marked genes related to TNF-alpha effects on cytokine activity, cell motility and apoptosis pathway. The main findings in our study may serve as valuable resources for understanding the roles that ILC3s play upon LPS challenge, which may offer opportunities for translating ILC3s as therapeutic targets to regulate LPS-induced pulmonary inflammation.