affy--analysis of Affymetrix GeneChip data at the probe level

Multiomics Analysis and Machine Learning-based Identification of Molecular Signatures for Diagnostic Classification in Liver Disease Types Along the Microbiota-gut-liver Axis

Background

Liver disease, responsible for around two million deaths annually, remains a pressing global health challenge. Microbial interactions within the microbiota-gut-liver axis play a substantial role in the pathogenesis of various liver conditions, including early chronic liver disease (eCLD), chronic liver disease (CLD), acute liver failure (ALF), acute-on-chronic liver failure (ACLF), non-alcoholic fatty liver disease (NAFLD), steatohepatitis, and cirrhosis. This study aimed to identify key molecular signatures involved in liver disease progression by analyzing transcriptomic and gut microbiome data, and to evaluate their diagnostic utility using machine learning models.

Methods

Transcriptomic analysis identified differentially expressed genes (DEGs) that, when integrated with regulatory elements microRNAs, transcription factors, receptors, and the gut microbiome highlight disease-specific molecular interactions. To assess the diagnostic potential of these molecular signatures, a two-step analysis involving principal component analysis (PCA) and Random Forest classification was conducted, achieving accuracies of 75% for ALF and 89% for NAFLD. Additionally, machine learning algorithms, including K-neighbors, multi-layer perceptron (MLP), decision tree, Random Forest, logistic regression, gradient boosting, CatBoost, Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), were applied to gene expression data for ALF and NAFLD.

Results

Key genes including CLDN14, EGFR, GSK3B, MYC, and TJP2, alongside regulatory miRNAs let-7a-5p, miR-124-3p, and miR-195-5p and transcription factors NFKB1 and SP1 may be suggested as critical to liver disease progression. Additionally, gut microbiota members, Dictyostelium discoideum and Eikenella might be novel candidates associated with liver disease, highlighting the importance of the gut-liver axis. The Random Forest model reached 75% accuracy and 83% area under the curve for ALF, while NAFLD classification achieved 100% accuracy, precision, recall, and area under the curve underscoring robust diagnostic potential.

Conclusion

This study establishes a solid foundation for further research and therapeutic advancement by identifying key biomolecules and pathways critical to liver disease. Additional experimental validation is needed to confirm clinical applicability.

Integrative transcriptomics analysis reveals convergent toxicological effects of perfluorooctanoic acid and perfluorooctane sulfonate on human liver: Evidence from multiple models

Perfluorooctanoic acid and perfluorooctane sulfonate are well-known eight-carbon per- and polyfluoroalkyl substances (8C-PFAS) potentially toxic for the human liver. However, direct experimental evidence demonstrating their toxicity on the human liver remains limited. Consequently, this study aimed to extrapolate the 8C-PFAS liver toxicity mechanisms by leveraging omics data to integrate mouse and human findings. Through integration analyses of nine datasets (one human, six murine, and two rat), we identified 199 genes with known biological functions that are commonly affected by 8C-PFAS across species. We delineated a comprehensive regulatory network of 8C-PFAS toxicity, demonstrating that 8C-PFAS may trigger fatty liver disease by up-regulating CD36 and PPARα pathway; dysregulate xenobiotic metabolism by disrupting CAR and CYP family genes; and induce cancer by dysregulating WNT, TGFβ, FGF21, and P53 pathways. We also identified ATF3, EGR1, ESR1, NFATC4, SNAI2, TP53, and EZH2 as transcriptionally regulated by 8C-PFAS, along with PPARα, RXRα, FGFR1, TCF3, and SMAD3 as potentially functionally impacted. Collectively, these factors account for over 90 % of 8C-PFAS-affected key genes. This study not only developed a novel method for extrapolating human toxicity risks by integrating scattered toxicity evidence based on transcriptomics data, but also proposes new mechanisms by which 8C-PFAS contributes to fatty liver disease and cancer.

Divergent ferroptotic pathways in breast cancer cells: IGFBP6-regulated mitochondrial lipid peroxidation under erastin and omega-3 DHA treatment

Breast cancer remains a major challenge and new therapeutic approaches are needed for its treatment. Ferroptosis is considered a promising alternative cell death mechanism to eliminate resistant cancer cells. In previous works, we identified that lower IGFBP6 gene expression in tumor tissue corresponds to a worse prognosis for breast cancer patients and, at the same, time makes them more sensitive to ferroptosis. In this study, we further investigated the mechanism of ferroptosis induction in IGFBP6 knockdown and control MDA-MB-231 breast cancer cells by the canonical ferroptosis inducer erastin and omega-3 docosahexaenoic acid (DHA). Our results indicate that there is a significant overlap between the mechanisms of action of both of these molecules, as they regulate the same subset of genes, and their action can be inhibited by canonical ferroptosis inhibitors. On the other hand, we also observed significant differences between the effects of erastin and DHA. The most notable of these are the additional activation of apoptosis-related genes by DHA and its minor peroxidation of mitochondrial lipid membranes. Interestingly, our kinetic analysis of ferroptosis induction showed that IGFBP6 knockdown cells began to die earlier and could hardly be rescued from erastin-induced ferroptosis by mitochondrial antioxidant SkQ1, in contrast to control cells. Overall, our data suggest that the action of DHA is less dependent on mitochondrial membrane peroxidation during ferroptosis induction, and this molecule can be a promising candidate for the treatment of breast cancer, especially in the case of reduced IGFBP6 gene expression in cancer cells.

Phosphoribosyl transferase domain containing 1: A prognostic biomarker in testicular germ cell tumors

Due to the heterogeneity and complex classification of testicular germ cell tumors (TGCTs), prognostic evaluation and therapeutic targets remain unclear. Therefore, identifying a novel biomarker to comprehensively assess TGCT prognosis and immunotherapy response is crucial. We collected data from 457 TGCT patient samples and 12 normal testicular samples across six cohorts. Differential expression analysis combined with univariate Cox regression identified prognostic markers for TGCT. Multivariate Cox regression and survival analysis further evaluated the prognostic value of phosphoribosyl transferase domain containing 1 (PRTFDC1). Immunohistochemistry on tissue microarrays validated PRTFDC1's predictive value in clinical samples. We then investigated the relationship between PRTFDC1 and somatic mutations, copy number variations, immune cell infiltration, and immunotherapy response. Through these analyses, we identified PRTFDC1 as an independent risk factor indicating poor prognosis in TGCT. Immunohistochemistry demonstrated high PRTFDC1 expression in TGCT tissues. Gene set enrichment analysis revealed that PRTFDC1 suppresses immune-related pathways. Immune infiltration showed that high PRTFDC1 expression is associated with low CD8+ T cell infiltration. Immunotherapy response analysis indicated that low PRTFDC1 expression predicts better immunotherapy response and favorable prognosis. In conclusion, this study elucidates the biological and clinical significance of PRTFDC1, suggesting it as an effective and reliable biomarker for predicting TGCT prognosis and immunotherapy response.

Identification of VDAC1 as a mitochondria-related target of Duchenne muscular dystrophy based on bioinformatics analysis and in vitro experiments

BACKGROUND

Mitochondrial dysfunction is a well-recognized pathological feature of Duchenne Muscular Dystrophy (DMD). The potential regulatory role of mitochondria-related genes (MRGs) in DMD remains to be further explored.

METHODS

GEO datasets and MRGs were used to analysis mitochondrial scores and evaluate patients' immunological characteristics. Weighted gene co-expression network analysis, differentially expressed genes (DEGs) and MRGs were used to identify hub genes. A specific hub gene was selected, and the effects of this gene overexpression on a horse serum (HS) treated C2C12 cell in vitro model were investigated.

RESULTS

Mitochondrial score was decreased in DMD group. Significant differences were observed in 12 immune cell types in normal/DMD and high/low mitochondrial score groups. 9 hub genes were identified, with 7 validated. Among them, VDAC1 was selected for further study. Overexpression of VDAC1 in HS C2C12 myoblasts promoted cell proliferation, reduced apoptosis rate and the Bax expression (with concurrent Bcl2 upregulation), diminished LDH release to reduce cytotoxicity, decreased intracellular ROS levels to alleviate oxidative stress, inhibited the expression of autophagy (LC3) and atrophy (Atrogin-1 and MuRF-1) markers, and promoted differentiation.

CONCLUSION

In conclusion, VDAC1 may participate in the myoblast proliferation and myotube atrophy by influencing mitochondrial function, which may serve as a new target for DMD treatment.

Akt mitigates ER stress-instigated cardiac dysfunction via regulation of ferroptosis and mitochondrial integrity in a DHODH-dependent manner

ER stress evokes various types of cell death and myocardial dysfunction. This study aimed to discern the involvement of ferroptosis in chronic Akt activation-offered benefit, if any, against ER stress-triggered cardiac remodeling and contractile anomalies. Cardiac-selective expression of active mutant of Akt (AktOE) and wild-type (WT) mice were challenged with the ER stress instigator tunicamycin (1 mg/kg, 48 h) prior to assessment of cardiac morphology and function. Tunicamycin insult prompted cardiac remodeling (interstitial fibrosis), deranged echocardiographic (higher LVESD, dropped ejection fraction and fractional shortening), cardiomyocyte mechanical and intracellular Ca2+ features alongside mitochondrial injury (collapsed mitochondrial membrane potential and ultrastructural change), oxidative stress, compromised Akt-GSK3β signaling, ER stress (upregulated GRP78 and Gadd153), carbonyl formation, apoptosis and ferroptosis (decreased GPX4, SLC7A11). Intriguingly, tunicamycin-evoked anomalies (except GRP78 and Gadd153) were abrogated by Akt activation. Chronic Akt activation negated tunicamycin-induced downregulation of ferric flavin enzyme dihydroorotate dehydrogenase (DHODH), which catalyzes the fourth step of pyrimidine ab initio biosynthesis, and conversion of dihydroorotic acid to orotate. ER stress-induced myocardial anomalies were reversed by the newly identified PI3K activator triptolide, DHODH activator menaquinone-4 and pyrimidine booster coenzyme Q. In vitro experiment revealed that Akt activation- or triptolide-evoked beneficial responses against tunicamycin-induced cardiomyocyte anomalies were cancelled off by DHODH inhibitor BAY2402234 or ferroptosis inducer erastin. These findings support that chronic Akt activation rescues ER stress-evoked myocardial derangements through DHODH-dependent control of ferroptosis and mitochondrial homeostasis.

Unraveling the role of HDAC3 as an immunotherapy prognostic biomarker and therapeutic target in advanced non-small cell lung cancer

Background

This study investigates HDAC3 as a potential immunotherapy biomarker in advanced non-small cell lung cancer (aNSCLC), focusing on its association with treatment response to immune checkpoint inhibitors (ICIs).

Methods

We employed a multi-phase approach in 78 aNSCLC patients with 138 plasma samples, starting with a discovery phase that identified differential autoantibodies (AAbs) using proteomic analysis in responders and non-responders. In the validation phase, we assessed AAb levels at multiple time points. Additionally, immunohistochemistry and multiple immunofluorescence (n  = 21) were used to validate HDAC3 expression in FFPE samples, single-cell RNA sequencing (n  = 26) was performed to explore gene expression differences, cell and animal experiments were performed.

Results

We identified 127 differential AAbs, with five key AAbs (HDAC3, METTL21C, HSPB3, SPACA7, and SPPL2B) consistently linked to prognosis pre- and post-treatment (p  < 0.05). A risk score model based on these AAbs effectively predicted progression-free survival. Furthermore, HDAC3 expression correlated with significant pathway enrichments and was associated with higher TGFβ1, PD-L1 infiltration and lower CD8+ T cells infiltration (p  < 0.05). HDAC3 knockdown significantly inhibited cell proliferation, impaired colony formation, and induced G0/G1 phase arrest in lung cancer cells. Preclinical models demonstrated that RGFP966, an HDAC3 inhibitor, combined with anti–PD-1 therapy enhanced CD8+ T cell infiltration (p  < 0.05).

Conclusion

Our findings underscore HDAC3’s role as a biomarker for predicting ICI response in aNSCLC and suggest its potential as a therapeutic target, paving the way for future studies on HDAC3-targeted therapies to improve immunotherapy outcomes.

Clinical trial number

not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-025-03275-w.

The lipidome landscape of amiodarone toxicity: An in vivo lipid-centric multi-omics study

Amiodarone is an effective therapy for arrhythmias, its prolonged management may lead to significant adverse drug reactions. Amiodarone-induced hepatotoxicity is described by phospholipidosis, hepatic steatosis, cholestatic hepatitis, and cirrhosis. However, the systemic and hepatic lipidome disturbances and underlying toxicological mechanisms remain comprehensively elucidated. Untargeted lipidomics were utilized to analyze serum and liver samples from the rats orally administered a daily dose of amiodarone of either 100 or 300 mg/kg for one week. Changes in the expression of hepatic lipid-related genes were also examined utilizing transcriptomics. We found a higher magnitude of lipidome alterations in the 300 mg/kg than those in the 100 mg/kg groups. Treated animals showed elevated abundances of phosphatidylcholines, ether-linked phosphatidylcholines, sphingomyelins, and ceramides, and decreased levels of triacylglycerols, ether-linked triacylglycerols, and fatty acids. We also found 199 lipid-related differentially expressed hepatic genes between the 300 mg/kg group versus controls, implying lipid metabolism and signaling pathways disturbances. Specifically, elevation of serum phosphatidylcholines and ether-linked phosphatidylcholines, as well as hepatic bismonoacylglycerophosphates might be associated with reduced expression of phospholipase genes and elevated expression of glycerophospholipid biosynthesis genes, possibly driving phospholipidosis. Perturbations of sphingolipid metabolism might also be the key events for amiodarone-induced toxicity. Alterations in gene expression levels related to lipid storage and metabolism, mitochondria functions, and energy homeostasis were also found. Collectively, our study characterized the sophisticated perturbations in the lipidome and transcriptome of amiodarone-treated rats and suggested potential mechanisms responsible for amiodarone-induced hepatotoxicity.

CEACAM1 as a mediator of B-cell receptor signaling in mantle cell lymphoma

B-cell receptor (BCR) signaling plays an important role in the pathogenesis of mantle cell lymphoma (MCL), but the detailed mechanisms are not fully understood. In this study, through a genome-wide loss-of-function screen, we identify carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) as an essential factor in a subset of MCL tumors. Our signal transduction studies reveal that CEACAM1 plays a critical role in BCR activation through involvement in two dynamic processes. First, following BCR engagement, CEACAM1 co-localizes to the membrane microdomains (lipid rafts) by anchoring to the F-actin cytoskeleton through the adaptor protein filamin A. Second, CEACAM1 recruits and increases the abundance of SYK in the BCR complex leading to BCR activation. These activities of CEACAM1 require its cytoplasmic tail and the N-terminal ectodomain. Considering that previous studies have extensively characterized CEACAM1 as an ITIM-bearing inhibitory receptor, our findings regarding its activating role are both surprising and context-dependent, which may have implications for BCR-targeting therapies.

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets

Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. Additionally, an independent saliva-derived model was created and evaluated using sensitivity, specificity, accuracy, area under the curve (AUC), and likelihood ratio (LR) metrics. Tissue-derived models demonstrated excellent performance, with AUC values exceeding 0.9, but did not translate effectively to saliva, suggesting distinct molecular landscapes between tissue and saliva in GC. The saliva-specific model using support vector machine (SVM) achieved the highest performance, with an AUC of 0.87 (95% CI 0.72-0.97), a sensitivity of 0.79 (95% CI 0.58-0.95) and a specificity of 0.70 (95% CI 0.40-0.90). While saliva may not mirror tissue gene expression profile, it represents a promising non-invasive predictive tool for the early detection of GC. Further research is warranted to optimize saliva-derived molecular signatures, increasing their sensitivity and specificity for early cancer detection and advance the use of liquid biopsies in personalized medicine for improved screening, diagnostic and prognostic capabilities.

DRaCOon: a novel algorithm for pathway-level differential co-expression analysis in transcriptomics

Understanding the molecular mechanisms underlying diseases is crucial for more precise, personalized medicine. Pathway-level differential co-expression analysis, a powerful approach for transcriptomics, identifies condition-specific changes in gene-gene interaction networks, offering targeted insights. However, a key challenge is the lack of robust methods and benchmarks specifically for evaluating algorithms' ability to identify disrupted gene-gene associations across conditions. We introduce DRaCOoN (Differential Regulatory and Co-expression Networks), a Python package and web tool for pathway-level differential co-expression analysis. DRaCOoN uniquely integrates multiple association and differential metrics, with a novel, computationally efficient permutation test for significance assessment. Crucially, DRaCOoN also provides a benchmarking framework for comprehensive method evaluation. Extensive benchmarking on simulated data and three real-world datasets (bone healing, colorectal cancer, and head/neck carcinoma) showed that DRaCOoN, particularly with an entropy-based association measure and the s differential metric, consistently outperforms eight other methods. It remains highly accurate in balanced datasets, robust to varying gene perturbation levels, and identifies biologically relevant regulatory changes. Furthermore, DRaCOoN serves as both a powerful tool and a benchmarking framework for elucidating disease mechanisms from transcriptomics data, advancing precision medicine by uncovering critical gene regulatory alterations.

Integrative Transcriptomic Profiling Identifies TNF and IL1B as Candidate Key Early-Response Genes in Macrophages Infected with Smooth Brucella Using a Comprehensive Bioinformatic Approach

Smooth Brucella are the main pathogenic bacteria that threaten human health and food safety. The early stage of smooth Brucella and macrophage interaction is an important phase, and smooth Brucella species elicit a dramatic transcriptional response in infected macrophages. However, the key transcriptional events are still obscure. This study aimed to identify key candidate response pathways and genes in macrophages infected with smooth Brucella at the early interaction stage. Three gene expression profiles including GSE21117, GSE5202, and GSE8385 were retrieved from the NCBI GEO database, and were integrated using comprehensive bioinformatics methods including gene set enrichment analysis, differentially expressed gene analysis, protein and protein interaction (PPI) network construction, and transcription factor prediction. The results showed that 16 up-regulated and 22 down-regulated pathways were identified, including six up-regulated immune-related pathways. A total of 41 up-regulated and four down-regulated genes were identified, and a PPI network including 31 nodes and 134 edges was constructed based on the interactive information of 45 dysregulated genes. A highly correlated module comprising 19 nodes and 103 edges was identified based on the topological features of the whole PPI network. Seven centrality analyses revealed that Tnf and Il1b were essential genes in the highly correlated module, and that the two essential genes were simultaneously enriched in eight significantly up-regulated pathways (including two immune-related pathways). Bcl3 was predicted as a transcription factor in the highly correlated module, and may play regulatory roles in the transcription of Tnf and Il1b genes. The present study identified Tnf and IL1b as candidate key response genes in infected macrophages at the early stage of smooth Brucella and macrophage interaction, which contributes to a deeper understanding of the early key transcriptional events in macrophages infected with smooth Brucella species.

Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma

BACKGROUND

Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC). Despite this heterogeneity, EOC is often treated as a homogenous disease, and reliable screening tests are lacking. Although progress has been made, there is a pressing need for biomarkers to refine patient stratification, guide treatment, and improve outcomes. Here, we elucidated the relationship between DNA methylation and gene expression patterns in EOC to identify histotype-specific biomarkers.

METHODS

Differential DNA methylation and gene expression analyses were performed for 86 early-stage EOC samples after histopathological reclassification stratified by histotype. The correlation between DNA methylation and gene expression was examined, and histotype-specific biomarkers were identified. Hierarchical clustering and predictive machine learning modeling were employed to assess the performance of the histotype-specific biomarkers using four external cohorts.

RESULTS

EOC histotypes exhibited distinct epigenetic, transcriptional, and functional profiles, with candidate histotype-specific biomarkers such as CTSE and VCAN effectively distinguishing CCC, HGSC, and MC on the transcriptional level. Gene expression for the candidate biomarkers was found to be reproducible across external cohorts, with histotype-specific differences remaining homogenous.

CONCLUSIONS

This study identified promising histotype-specific biomarkers for EOC using integrative transcriptomic and epigenomic analysis. Furthermore, these findings indicate that additional stratification or potential reclassification of the EC histotype is warranted in future studies.

The micro-RNA expression profile predicts the severity of SARS-CoV-2 infection

Although much is known about the pathophysiology of severe COVID-19, there are still areas that remain to be determined. It is well established that the pivotal molecular event is a hyperinflammatory response also referred to as a cytokine storm. The aim of this retrospective cohort study was to determine miRNAs which might be predictive for the admission to the intensive care unit (ICU). We analyzed blood samples from 210 COVID-19 patients and the control group consisted of 80 healthy individuals. Results revealed the miRNA expression pattern has the potential to predict the severity of COVID-19, reflecting the clinical symptoms of the infection, such as the need for oxygen therapy and concomitant pneumonia. In particular, low expression of miRNAs miR106a-5p, miR17-5p, miR181a-5p, miR191-5p, miR20a-5p and miR451a, especially in the initial phase of the disease, is associated with an unfavorable clinical course of SARS-CoV-2 infection (admission to the ICU).

Profile of Rat Adrenal microRNAs Induced by Gonadectomy and Testosterone or Estradiol Replacement

Sex-related differences in the structure and function of the adrenal cortex in mature rats are well recognized, largely driven by the action of sex hormones on the hypothalamic-pituitary-adrenal axis (HPA). By replacing testosterone or estradiol in gonadectomized rats, we aimed to elucidate the regulation of micro RNA (miRNA) profiles by sex hormones and their role in physiological adrenal function, providing new insights into gene expression modulation in the adrenal gland. This paper focuses on the description of miRNA profiles using the microarray technique. In our study, we observed significant sex differences in miRNA and mRNA expression levels. These differences are as follows: miRNA expression profiles Male C vs. Female C-0 down, 25 up-regulated, while mRNA profiles were 43 down and 27 up-regulated. Moreover, we observed the most significant differences in miRNA profiles between orchiectomized male rats supplemented with testosterone (ORX + T) and ovariectomized female rats treated with estradiol (OVX + E). Furthermore, we described changes in target gene expression and biological processes regulated by miRNAs. The processes most differentially expressed between the ORX + T and OVX + E groups are those related to the metabolism and synthesis of sterol compounds, the positive and negative regulation of metabolic processes in cells, e.g., cholesterol metabolism, response to various external factors, e.g., hormones, regulation of processes related to cell motility. We also identified several miRNAs, such as miR-370, miR-377, and miR-503, that exhibited interesting changes in their expression after testosterone or estradiol replacement. These results contribute to a deeper understanding of adrenal physiology.

Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer

BACKGROUND

Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.

METHODS

We manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.

RESULTS

We depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.

CONCLUSION

Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.

Integrative multi-transcriptomic analysis uncovers core genes and potential defense mechanisms in rice-Magnoporthe oryzae interaction

KEY MESSAGE

Multiple transcriptomic comprehensive analyses highlight key genes and cast new light on multifaceted pathways that may be important arenas in rice innate immunity against Magnoporthe oryzae blast disease. Magnaporthe oryzae (MOR) poses a significant threat to rice production worldwide. However, defense mechanisms in rice against MOR remain inadequately defined. In this study, a multi-transcriptomic integrative analysis on 441 samples from diverse microarrays and RNA-seq sets was conducted to reveal critical factors in rice defense against MOR infection. A robust pattern of 3534 upregulated genes and 2920 repressed genes was commonly identified across all MOR-infected arrays and RNA-seq profiles. Interestingly, enrichment analysis revealed a consistent triggering of endoplasmic reticulum (ER)-related mechanisms and citric acid cycle (TCA) influx in rice response to MOR infection across all the transcriptome profiles, suggesting their critical role in modulating rice immunity against the pathogen. By contrast, chloroplast and photosynthesis pathways were frequently repressed across all the profiles. Among ER-related mechanisms, the phagosome pathway involved in the activation of NADPH oxidase was highly triggered in early response to MOR infection. Moreover, WGCNA analysis highlighted four key co-expressed gene modules and 80 significant hub genes associated with MOR infection. Among the core genes, Sec61 gene involved in the ER-translocation process was identified along with OsMFP (peroxisomal oxidation gene) and OSAHH gene (involved in cyclic-trans-methylation). Furthermore, MPK6, WRKY24, NUP35, and NPR1 genes were observed as core co-expressed genes, suggesting their significance in regulating rice immunity against MOR. Our findings elucidate key genes and multifaceted mechanisms in rice-MOR interaction, proposing new informative clues that can be exploited to improve rice resistance against blast disease.

Systematic review and mega-analysis of the peripheral blood transcriptome in depression implicates dysregulation of lymphoid cells and histones

Background

Depression has been associated with transcriptomic changes in peripheral blood. However, the contribution of specific immune cell subsets or pathways remains unclear, and findings have been variable across previous studies, which have not tended to account for sample cellular composition.

Methods

We performed a systematic review of peripheral blood transcriptome studies in depression. For the five datasets meeting criteria (total N=6,011), we performed harmonized reprocessing and cell-composition-adjusted differential gene and transcript analyses, followed by a bias- and inflation-adjusted weighted Z-score mega-analysis. We investigated the biological pathways and cell subsets implicated by the results. We also performed a sex-stratified gene network mega-analysis using consensus weighted gene co-expression network analysis (WGCNA).

Results

Few genes showed robust differential gene expression (DGE) in depression. Depression was reproducibly associated with decreases in replication-dependent histones, and with a decrease in oxidative phosphorylation pathways in females only. Cell source analyses implicated lymphoid cells (T cells and NK cells) as likely contributors to the depression differential expression signature. WGCNA mega-analysis revealed multiple consensus modules associated with depression, with a PUF60-related module upregulated in both female and male depression in sex-stratified analyses. Two genes predicted to be causally relevant to depression by transcriptome-wide association studies (GPX4 and GYPE) showed significant DGE.

Conclusions

These results are convergent with immunogenetic evidence implicating lymphoid cell dysregulation in depression, while also highlighting histone alterations as a key molecular signature in depression. They also indicate the importance of large-scale datasets for biomarker discovery in the context of heterogeneous disorders like depression.

Maternal COVID-19 infection associated with offspring neurodevelopmental disorders

Maternal COVID-19 infection increases the incidence of neurodevelopmental disorders (NDDs) in offspring, although the underlying mechanisms have not been elucidated. This study demonstrated that COVID-19 infection during pregnancy disrupted the balance of maternal and fetal immune environments, driving alterations in astrocytes, endothelial cells, and excitatory neurons. A risk score was established using 47 unique genes in the single-cell transcriptome of gestational mothers. The high risk score in CD4 proliferating T cell level served as an indicator for increased risk of offspring NDDs. Summary-based Mendelian randomization and phenome-wide association study analyses were conducted to identify the causal association of the transcriptional changes with the increased risk of offspring NDDs. Additionally, 10 drugs were identified as potential therapeutic candidates. Our findings support a model where the maternal COVID-19 infection changed the levels of CD4 proliferating T cells, leading to the alterations of astrocytes, endothelial cells, and excitatory neurons in offspring, contributing to the increased risk of NDDs in these individuals.

The Landscape of Prostate Tumour Methylation

Prostate cancer is characterized by profound clinical and molecular heterogeneity. While its genomic heterogeneity is well-characterized, its epigenomic heterogeneity remains less understood. We therefore created a compendium of 3,001 multi-ancestry prostate methylomes spanning normal tissue through localized disease of all grades to poly-metastatic disease. A subset of 884 samples had multi-omic DNA and/or RNA characterization. We identify four epigenomic subtypes that risk-stratify patients and reflect distinct evolutionary trajectories. We demonstrate extensive regulatory interplay between DNA ploidy and DNA methylation, with transcriptional consequences that vary across genes and disease stages. We define the epigenetic dysregulation signatures of the 15 most important clinico-molecular features, creating predictive models for each. For example, we identify specific epigenetic features that predict patient outcome and that are synergistic with clinico-genomic prognostic features. These results define a complex interplay between tumour genetics and epigenetics that converges to modify gene-expression programs and clinical presentation.

A computational framework for identifying cytoskeletal genes associated with age-related diseases

The cytoskeleton comprises polymers from protein filaments shaped in an organized structure. This structure contributes significantly to the cell's function and viability. Decades of research have implicated that the cytoskeleton's dynamic nature is associated with downstream signaling events that further regulate cellular activity and control aging and neurodegeneration. This study aims to investigate the transcriptional changes of the cytoskeletal genes and their regulators in five age-related diseases: Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), Alzheimer's disease (AD), Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM). An integrative approach of machine learning-based models and differential expression analysis was employed to identify potential biomarkers based on the cytoskeletal genes. Multiple machine-learning algorithms were used, where the Support Vector machines (SVM) classifier achieved the highest accuracy. The study highlighted 17 genes involved in the cytoskeleton's structure and regulation associated with age-related diseases. The results provide a holistic overview of the role of transcriptionally dysregulated cytoskeletal genes in age-related diseases. This study pinpoints cytoskeletal genes and regulators of the cytoskeleton that can be utilized as potential markers and drug targets.

The crucial role of mitochondrial/chloroplast-related genes in viral genome replication and host defense: integrative systems biology analysis in plant-virus interaction

Plant viruses participate as biotrophic parasites in complex interactions with their hosts, resulting in the regulation of a diverse range of chloroplast/mitochondria-related genes that are essential for mediating immune responses. In this study, integrative systems biology approaches were applied to identify chloroplast/mitochondrial genes during viral infections caused by a wide number of viruses in Arabidopsis thaliana, tobacco (Nicotiana tabacum L.), and rice (Oryza sativa L.). These findings indicated that 1.5% of the DEGs were common between Arabidopsis/tobacco and Arabidopsis/rice, whereas 0.1% of the DEGs were shared among all species. Approximately 90% of common DEGs are uniquely associated with chloroplasts and mitochondria in the host defense against viral infection and replication. The functions of WRKY, NAC, and MYB transcription factors in imparting resistance to viral infections can be established. Promoter analysis revealed that AP2/EREBP, DOF, and C2H2 zinc finger factors included the most frequent binding sites and played a more important role in plant-viral interactions. Comparative analysis revealed several miRNAs with defensive functions including miRNA156, miRNA160, and miRNA169. The PPI network revealed several key hub genes mostly related to chloroplasts/mitochondria, including ZAT6, CML37, CHLI, DREB, F27B13.20, and ASP2 with upregulation, also PLGG1, PSBY, APO2, POR, ERF, and CSP with downregulation. Moreover, novel hub genes with unknown functions, such as AT2G41640 and AT3G57380 have been identified. This study represents the first preliminary systems biology approach to elucidate the roles of chloroplast/mitochondria-related genes in Arabidopsis, tobacco, and rice against viral challenges by introducing valuable candidate genes for enhanced genetic engineering programs to develop virus-resistant crop varieties.

Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation

Introduction

Spinal cord injury (SCI) severely affects the central nervous system. Copper homeostasis is closely related to mitochondrial regulation, and cuproptosis is a novel form of cell death associated with mitochondrial metabolism. This study aimed to explore the relationship between SCI and cuproptosis and construct prediction models.

Methods

Gene expression data of SCI patient samples from the GSE151371 dataset were analyzed. The differential expression and correlation of 13 cuproptosis-related genes (CRGs) between SCI and non-SCI samples were identified, and the ssGSEA algorithm was used for immunological infiltration analysis. Unsupervised clustering was performed based on differentially expressed CRGs, followed by weighted gene co-expression network analysis (WGCNA) and enrichment analysis. Three machine learning models (RF, LASSO, and SVM) were constructed to screen candidate genes, and a Nomogram model was used for verification. Animal experiments were carried out on an SCI rat model, including behavioral scoring, histological staining, electron microscopic observation, and qRT-PCR.

Results

Seven CRGs showed differential expression between SCI and non-SCI samples, and there were significant differences in immune cell infiltration levels. Unsupervised clustering divided 38 SCI samples into two clusters (Cluster C1 and Cluster C2). WGCNA identified key modules related to the clusters, and enrichment analysis showed involvement in pathways such as the Ribosome and HIF-1 signaling pathway. Four candidate genes (SLC31A1, DBT, DLST, LIAS) were obtained from the machine learning models, with SLC31A1 performing best (AUC = 0.958). Animal experiments confirmed a significant decrease in the behavioral scores of rats in the SCI group, pathological changes in tissue sections, and differential expression of candidate genes in the SCI rat model.

Discussion

This study revealed a close association between SCI and cuproptosis. Abnormal expression of the four candidate genes affects mitochondrial function, energy metabolism, oxidative stress, and the immune response, which is detrimental to the recovery of neurological function in SCI. However, this study has some limitations, such as unidentified SRGs, a small sample size. Future research requires more in vitro and in vivo experiments to deeply explore regulatory mechanisms and develop intervention methods.

Characterization of lactylation-based phenotypes and molecular biomarkers in sepsis-associated acute respiratory distress syndrome

Sepsis-associated acute respiratory distress syndrome (ARDS) is a heterogeneous disease with high morbidity and mortality. Lactylation plays a crucial role in sepsis and sepsis-induced lung injury. This study aimed to identify distinct lactylation-based phenotypes in patients with sepsis-associated ARDS and determine relevant molecular biomarkers. We analyzed blood transcriptome and clinical data from patients with sepsis-associated ARDS and calculated the lactylation activity. KEGG pathway analysis, drug sensitivity prediction, and immune cell infiltration analysis were performed. Candidate molecular biomarkers were identified by intersecting the feature genes extracted from four machine learning models. Lactylation activity showed significant heterogeneity among patients with sepsis-associated ARDS, which enabled the classification into low- and high-lactylation activity phenotypes. Patients with high-lactylation experienced longer hospital stays and higher mortality rates, as well as distinct signaling pathways, drug responses, and circulating immune cell abundances. Six key markers (ALDOB, CCT5, EP300, PFKP, PPIA, and SIRT1) were identified to differentiate the two lactylation activity phenotypes, all significantly correlated with circulating immune cell populations. This study revealed significant heterogeneity in lactylation activity phenotypes among patients with sepsis-associated ARDS and identified potential biomarkers to facilitate the application of these phenotypes in clinical practice.

Identification of shared important genes associated with ferroptosis across different etiologies of acute lung injury

Acute lung injury (ALI) of different etiologies has shared pathophysiologic process, from which we speculated that ALI of different etiologies may share common molecular features. While the shared genetic characteristics of ALI remain unclear. In this paper, we aimed to identify shared ferroptosis-associated and bottleneck genes from acute lung injury of different etiologies. Firstly, we extracted five groups of gene sets related to three distinct models of ALI from the Gene Expression Omnibus (GEO) database. Then, through the utilization of weighted gene co-expression network analysis (WGCNA), we identified 3 significant gene modules and ascertained 7 shared co-expressed genes affected by these models. Subsequently, through the utilization of differential gene expression analysis and protein-protein interaction network analysis for the 3 gene modules, the shared bottleneck gene Slc7a11 was identified. Moreover, the 7 shared co-expressed genes subjected to these three ALI models were used to identify shared ferroptosis-associated genes via the FerrDb database. Finally, the key gene Slc7a11 was confirmed and validated. In addition, we observed that Slc7a11 is both a driver and a suppressor gene in the FerrDb database. Interestingly, we found the expression level of Slc7a11 was significantly upregulated in the three ALI models. Experimentally, we confirmed the expression of Slc7a11 in rat ALI tissues by using immunofluorescence staining and real-time polymerase chain reaction (qRT-PCR) assays. Collectively, our findings complement the exploration of the shared pathogenesis of ALI. There are genetic features shared by ALI of different etiology and the increased expression of Slc7a11 was identified in the three different etiologies of ALI, which can improve our understanding of the shared molecular mechanisms underlying ALI.

A targetable antioxidant defense mechanism to EZH2 inhibitors enhances tumor cell vulnerability to ferroptosis

Epigenetic changes are present in all human cancers and are responsible for switching on or off genes, thus controlling tumor cell transcriptome. These changes occur through DNA methylation, histone modifiers and readers, chromatin remodelers, and microRNAs. The histone H3 methyl-transferase EZH2 gene is overexpressed in several cancer types, including adrenocortical carcinoma (ACC), a rare cancer still lacking a targeted therapy. EZH2 inhibitors (EZH2i) have been tested in several clinical trials, but their effectiveness was limited by the toxic effects of the therapeutic doses. We tested several EZH2i on ACC cells, and observed a significant reduction in cell growth only with doses much higher than those required to prevent H3 methylation. We found that all tested EZH2i doses affected lipid metabolism genes, ROS, and glutathione production. Transcript changes correlated with metabolic data, which suggested the effects of EZH2i on ferroptosis. We found that EZH2i dose-dependently increased SLC7A11/glutathione axis and glutathione peroxidase-4 (GPX4), required to counteract lipid peroxidation and ferroptosis. A GPX4 inhibitor synergized with EZH2i, making low doses - which otherwise do not affect cell viability - able to significantly reduce ACC cell growth in vitro and in vivo. Importantly, we found that the anti-ferroptosis defense mechanism induced by EZH2i is a common response for several aggressive tumor phenotypes, uncovering a general co-targetable mechanism that could limit EZH2i effectiveness. Correcting this antioxidant response by ferroptosis inducers may be a new combination therapy that will easily find clinical applications.

Fgl2 regulates FcγRIIB+CD8+ T cell responses during infection

While the inhibitory receptor FcγRIIB has been shown to be upregulated on activated CD8+ T cells in both mice and humans, its effect on T cell fate during infection has not been fully elucidated. We identified an increase in FcγRIIB-expressing CD8+ T cells in patients with COVID-19 relative to healthy controls as well as in mouse models of viral infection. Despite its well-known role as an Fc receptor, FcγRIIB also ligates the immunosuppressive cytokine Fgl2, resulting in CD8+ T cell apoptosis. Both chronic LCMV infection in mice and COVID-19 in humans resulted in a significant increase in plasma Fgl2. Transfer of CD8+ T cells into a Fgl2-replete, but not Fgl2-devoid, environment resulted in elimination of FcγRIIB+, but not FcγRIIB-, CD8+ T cells. Similarly, plasma Fgl2 was directly proportional to CD8+ T cell lymphopenia in patients with COVID-19. RNA-Seq analysis demonstrated that Fgl2 was produced by murine virus-specific CD8+ T cells, with an increase in Fgl2 in CD8+ T cells elicited during chronic versus acute viral infection. Fgl2 was also upregulated in CD8+ T cells from patients with COVID-19 versus healthy controls. In summary, CD8+ T cell production of Fgl2 during viral infection underpinned an FcγRIIB-mediated loss of CD8+ T cell immunity in both mice and humans.

Whole genome transcriptomic profiling reveals distinct sex-specific responses to heat stroke

Heat-related mortality remains health challenges exacerbated by climate change, with sex-based differences in outcomes, yet underlying mechanisms remain poorly understood. This study examined transcriptomic responses to heat exposure in peripheral blood mononuclear cells from 19 patients with heat stroke (HS; 8 males, mean age 64.8 ± 6.6 yr; 11 females, mean age 49.7 ± 11 yr) and 19 controls (11 males, mean age 48.9 ± 9.6 yr; 8 females, mean age 44.9 ± 11.8 yr). At admission, gene expression revealed upregulation of heat shock protein genes, and pathway analysis demonstrated activation of heat shock and unfolded protein responses across both sexes consistent with proteotoxic stress. However, distinct metabolic, oxidative stress, cell cycle control, and immune responses were observed within each sex. Females displayed inhibition of protein synthesis, oxidative phosphorylation, and metabolic pathways, including glucose metabolism, indicative of a hypometabolic state. Males maintained metabolic activity precooling and enhanced adenosine triphosphate production postcooling. Females activated nuclear factor erythroid 2-related factor 2 (NRF2)-mediated oxidative stress responses and inhibited DNA replication and mitosis, potentially mitigating genomic instability, whereas these pathways showed limited regulation in males. Females promoted innate immunity via interleukin (IL)-6, inflammasome, and triggering receptor expressed on myeloid cells 1 (TREM1) signaling, whereas males showed suppression of both innate and adaptive immunity, including IL-12, Th1, and T-cell receptor pathways. Upstream analysis identified over 100 transcription factors in both sexes. Males primarily relied on transcriptional mechanisms, whereas females also exhibited translational regulation via La ribonucleoprotein 1 (LARP1), fragile X messenger ribonucleoprotein 1 (FMR1), insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1), and eukaryotic translation initiation factor 6 (EIF6). These findings suggest distinct, sex-specific molecular adaptations to heat stroke, underscoring the need for targeted therapeutic strategies to mitigate heat-induced morbidity and mortality.NEW & NOTEWORTHY Heat-related mortality continues to rise with climate change. Our transcriptomic analysis reveals distinct sex-specific metabolic strategies to heat stroke: females enter a hypometabolic state, an evolutionary adaptation that conserves energy, whereas males sustain metabolic activity. Transcription factors and a subset of translation regulators in females modulate proteostasis and bioenergetics, driving these sex-specific pathways. These novel findings highlight the critical need to consider sex-specific differences in heat-related illnesses and inform carefully targeted interventions to improve patient outcomes.

Integrative analysis of a novel signature incorporating metabolism and stemness-related genes for risk stratification and assessing clinical outcomes and therapeutic responses in lung adenocarcinoma

BACKGROUND

Metabolism and stemness-related genes (msRGs) are critical in the development and progression of lung adenocarcinoma (LUAD). Nevertheless, reliable prognostic risk signatures derived from msRGs have yet to be established.

METHODS

In this study, we downloaded and analyzed RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed univariate and multivariate Cox regression analyses, along with least absolute shrinkage and selection operator (LASSO) regression analysis, to identify msRGs that are linked to the prognosis of LUAD and to develop the prognostic risk signature. The prognostic value was evaluated using Kaplan-Meier analysis and log-rank tests. We generated receiver operating characteristic (ROC) curves to evaluate the predictive capability of the prognostic signature. To estimate the relative proportions of infiltrating immune cells, we utilized the CIBERSORT algorithm and the MCPCOUNTER method. The prediction of the half-maximal inhibitory concentration (IC50) for commonly used chemotherapy drugs was conducted through ridge regression employing the "pRRophetic" R package. The validation of our analytical findings was performed through both in vivo and in vitro studies.

RESULTS

A novel five-gene prognostic risk signature consisting of S100P, GPX2, PRC1, ARNTL2, and RGS20 was developed based on the msRGs. A risk score derived from this gene signature was utilized to stratify LUAD patients into high- and low-risk groups, with the former exhibiting significantly poorer overall survival (OS). A nomogram was constructed incorporating the risk score and other clinical characteristics, showcasing strong capabilities in estimating the OS rates for LUAD patients. Furthermore, we observed notable differences in the infiltration of various immune cell subtypes, as well as in responses to immunotherapy and chemotherapy, between the low-risk and high-risk groups. Results from gene set enrichment analysis (GSEA) and in vitro studies indicated that the prognostic signature gene ARNTL2 influenced the prognosis of LUAD patients, primarily through the activation of the PI3K/AKT/mTOR signaling pathway.

CONCLUSIONS

Utilizing this gene signature for risk stratification could help with clinical treatment management and improve the prognosis of LUAD patients.

Exposure to a choline-deficient diet during pregnancy and lactation alters the liver transcriptome profile in offspring of dams with fatty liver

BACKGROUND & AIMS

The developmental origin of health and disease hypothesis shows that early adverse exposures can have lifelong health effects. Thus, the aim of this study was to analyze the impact of choline intake during pregnancy and/or lactation on gene expression profiles in the liver of 24-day-old male rat offspring from dams with non-alcoholic fatty liver disease (NAFLD).

METHODS

Phenotypic characteristic, histological examination and global transcriptome pattern of liver tissue specimens obtained from offspring of dams suffering from fatty liver, provided with proper choline intake during pregnancy and lactation (NN), fed a choline-deficient diet during both periods (DD), deprived of choline only during pregnancy (DN), or only during lactation (ND), was performed. The global gene expression profile was analyzed by using microarray approach (Affymetrix® Rat Gene 2.1 ST Array Strip). The relative expression of selected genes was validated by real-time polymerase chain reaction (qPCR).

RESULTS

The histological examination of rat liver sections indicated alternations typical for fatty liver in all analyzed groups with increased progression among groups deprived of choline. Choline deficiency in the maternal diet was associated with changes in body mass and composition but not with biochemical marker levels, except for the high density lipoprotein fraction of cholesterol (HDL). Enhanced expression of genes involved in oxidative stress, cell proliferation, activation of catabolic processes related to hepatocyte dysfunction and cell membrane composition were simultaneously observed in all choline-deficient groups.

CONCLUSIONS

An adequate amount of choline in the diet of a mother with fatty liver during pregnancy and/or lactation can regulate gene expression in the offspring's liver and contribute to a milder stage of the disease in the progeny. Moreover, proper choline supply during the postpartum period is as crucial as during the prenatal period.

Identifying potential prognosis markers in relapsed multiple myeloma via integrated bioinformatics analysis and biological experiments

BACKGROUND

Almost all multiple myeloma (MM) patients will eventually develop disease that has relapsed with or become refractory to current therapeutic regimes. However, the pervious clinical parameters have been proved inaccurate for defining MM relapse, and molecular targets have become the focuses of interests. Prognostic predictions based on molecular targets have been more effective to this day. Our research was performed to demonstrate hub genes involving relapsed MM by bioinformatics and biological experiments.

METHODS AND RESULTS

The integrated bioinformatics analysis in baseline and relapsed MM patients were executed. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to analyze biologic functions of up-regulated differentially expressed genes (DEGs). Four hub genes (CENPE, ASPM, TOP2A and FANCI) were adopted for construction of relapsed gene score model (RGS), and RGS model was evaluated in two testing sets. The CENPE inhibitor GSK923295 had anti-myeloma effect, including promoting cell death, cell cycle arrest and DNA damage of MM cell lines.

CONCLUSION

Through bioinformatics analysis, we found that the four hub genes (CENPE, ASPM, TOP2A and FANCI) were associated to cell cycle, nuclear division, mitosis and spindle. Our research provided proof-of-concept that RGS model could be utilized to estimate recurrence risk and prognosis for patients, and targeting CENPE contributed to developing novel therapeutic pattern for MM.

Lactate controls cancer stemness and plasticity through epigenetic regulation

Tumors arise from uncontrolled cell proliferation driven by mutations in genes that regulate stem cell renewal and differentiation. Intestinal tumors, however, retain some hierarchical organization, maintaining both cancer stem cells (CSCs) and cancer differentiated cells (CDCs). This heterogeneity, coupled with cellular plasticity enabling CDCs to revert to CSCs, contributes to therapy resistance and relapse. Using genetically encoded fluorescent reporters in human tumor organoids, combined with our machine-learning-based cell tracker, CellPhenTracker, we simultaneously traced cell-type specification, metabolic changes, and reconstructed cell lineage trajectories during tumor organoid development. Our findings reveal distinctive metabolic phenotypes in CSCs and CDCs. We find that lactate regulates tumor dynamics, suppressing CSC differentiation and inducing dedifferentiation into a proliferative CSC state. Mechanistically, lactate increases histone acetylation, epigenetically activating MYC. Given that lactate's regulation of MYC depends on the bromodomain-containing protein 4 (BRD4), targeting cancer metabolism and BRD4 inhibitors emerge as a promising strategy to prevent tumor relapse.

AtSRGA: A shiny application for retrieving and visualizing stress-responsive genes in Arabidopsis thaliana

Abiotic and biotic stresses pose serious threats to plant productivity. Elucidating the gene regulatory networks involved in plant stress responses is essential for developing future breeding programs and innovative agricultural products. Here, we introduce the AtSRGA (Arabidopsis thaliana Stress-Responsive Gene Atlas), a user-friendly application facilitating the retrieval of stress-responsive genes in Arabidopsis (Arabidopsis thaliana). The application was developed using 1,131 microarrays and 1,050 RNA sequencing datasets obtained from public databases. These datasets correspond to 11 stress-related conditions, namely abscisic acid, cold, drought, heat, high-light, hypoxia, osmotic stress, oxidative stress, salt, wounding, and Pseudomonas syringae pv. tomato DC3000. Using a modified meta-analysis technique known as the vote-counting method, we computed integrated scores to evaluate stress responsiveness for each condition across multiple studies. AtSRGA visualizes gene behavior under 11 stress conditions and offers an interactive, user-friendly interface accessible to all researchers. It presents a comprehensive heatmap of stress-responsive genes, facilitating the comparative analysis of individual stress responses and groups of genes responding to multiple stresses. We validated the expression patterns of several high-scoring genes of unknown function under cold and heat stress using RT-qPCR, thus demonstrating that our application helps select targets to understand stress-responsive gene networks in Arabidopsis. AtSRGA will improve the screening of stress-responsive genes in Arabidopsis, thereby supporting the advancement of plant science toward a sustainable society.

Identification of key hub genes in pancreatic ductal adenocarcinoma: an integrative bioinformatics study

Pancreatic Ductal Adenocarcinoma (PDAC) poses a significant health threat characterized by poor clinical outcomes, largely attributable to late detection, chemotherapy resistance, and the absence of tailored therapies. Despite progress in surgical, radiation, and chemotherapy treatments, 80% of PDAC patients do not benefit optimally from systemic therapy, often due to asymptomatic presentation or disease regression upon diagnosis. The disease's progression is influenced by complex interactions involving immunological, genetic, and environmental factors, among others. However, the precise molecular mechanisms underlying PDAC remain incompletely understood. A major challenge in elucidating PDAC's origins lies in deciphering the genetic variations governing its network. PDAC exhibits heterogeneity, manifesting diverse genetic compositions, cellular attributes, and behaviors across patients and within tumors. This diversity complicates diagnosis, treatment strategies, and prognostication. Identification of "Differentially Expressed Genes" (DEGs) between PDAC and healthy controls is vital for addressing these challenges. These DEGs serve as the foundation for constructing the PDAC protein interaction network, with their network properties being assessed for further insights. Our analysis revealed five key hub genes (KHGs): EGF, SRC, SDC1, ICAM1 and CEACAM5. The KHGs were predominantly enriched in pathways such as: ErbB signaling pathway, Rap1 signaling pathway, etc. Acknowledging the therapeutic promise and biomarker importance of PDAC KHGs, we have also pinpointed approved medications for the identified key genes. Nevertheless, it is crucial to conduct experimental validation on KHGs to confirm their effectiveness within the PDAC context. Overall, this study identified potential key hub genes implicated in the progression of PDAC, offering significant guidance for personalized clinical decision-making and molecular-targeted therapy for PDAC patients.

Construction and evaluation of a diagnostic model for Alzheimer's disease based on mitophagy-related genes

Alzheimer's disease (AD) is the most common cause of dementia. Mitophagy fulfills crucial functions in neurodegenerative disorders and neuronal survival but the relationship between mitophagy and AD is unclear. Mitophagy correlation scores between AD samples and control samples were calculated using single-sample GSEA (ssGSEA) based on two datasets from gene expression omnibus (GEO) database. Mitophagy-related genes (MRGs) and differentially expressed genes (DEGs) in AD screened by WGCNA and "limma" package were intersected to take common genes. These overlapping genes were further compressed and used for diagnostic modeling by adopting the recursive feature elimination (RFE) and LASSO analysis. The reliability of the diagnostic model was verified based on the receiver operating characteristic (ROC) curve. Then, a transcription factor (TF)-mRNA regulatory network of these key genes was established. Lastly, ssGSEA was employed to examine the relationship between the identified genes and cellular pathways and immune cell infiltration. AD samples had notably lower mitophagy correlation scores than control samples. A total of 12 MRGs in the module with the greatest mitophagy connection with AD patients were identified. Functional enrichment analysis revealed that the DEGs were significantly enriched in synaptic function-related pathways. Based on GSE122063, a diagnostic prediction model was created and validated using two mitophagy-related genes (YWHAZ and NDE1), showing an area under ROC curve (AUC) greater than 0.7. This confirmed that the diagnostic model had a high predictive value. The TF-mRNA network showed that four TFs, namely, FOXC1, FOXL1, HOXA5 and GATA2, were regulated by both YWHAZ and NDE1 genes. Immune infiltration analysis revealed that NDE1 promoted the infiltration of most immune cells, while YWHAZ mainly inhibited the infiltration of most immune cells. The current findings improved our understanding of mitophagy in AD, contributing to future research and treatment development in AD.

Combined analysis of single-cell and bulk transcriptome sequencing data identifies critical glycolysis genes in idiopathic pulmonary arterial hypertension

BACKGROUND

Abnormal glycolytic metabolism plays a significant role in pulmonary vascular remodeling in idiopathic pulmonary arterial hypertension (IPAH), yet the specific mechanisms remain unclear. The primary objective of this study is to investigate the key regulatory mechanisms of glycolysis in IPAH.

METHODS

Bulk and single-cell sequencing data obtained from IPAH patient tissue samples were downloaded from the GEO database. scMetabolism and AUCcell analyses of the IPAH single-cell sequencing data were carried out to quantify the glycolytic metabolic activity and identify the main cell types regulating glycolysis, respectively. The ssGSEA method was used to assess the glycolytic activity in each bulk sample within the bulk sequencing data. Differential analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted to identify key genes associated with glycolysis in IPAH samples. Single-cell sequencing and a monocrotaline (MCT)-induced model of PH in rats were utilized to validate the expression of these key genes.

RESULTS

Single-cell sequencing data indicated that IPAH patients displayed increased glycolytic activity, which was primarily regulated by fibroblasts. Similarly, bulk transcriptomic data revealed a significant increase in glycolytic activity in IPAH patients. Differential analysis, WGCNA, PPI network analysis, and integrated single-cell analysis further identified insulin-like growth factor-1 (IGF1), lysyl-tRNA synthetase (KARS), caspase-3 (CASP3), and cyclin-dependent kinase inhibitor 2 A (CDKN2A) as key genes associated with fibroblast-mediated glycolysis in IPAH patients. Differential expression of IGF1, KARS, CASP3, and CDKN2A was also observed in our in vivo model of PH.

CONCLUSION

Our study identifies IGF1, KARS, CASP3, and CDKN2A as key regulatory genes in glycolysis in IPAH, which provides the basis for the development of targeted therapies.

Machine Learning and Weighted Gene Coexpression Network-Based Identification of Biomarkers Predicting Immune Profiling and Drug Resistance in Lung Adenocarcinoma

Background: The prognosis for lung adenocarcinoma (LUAD) is poor, and the recurrence rate is high. Thus, to evaluate patients' prognoses and direct therapy choices, new prognostic markers are desperately needed. Methods: First, gene modules associated with LUAD were identified by weighted gene coexpression network analysis (WGCNA) analysis. The expression profiles obtained were intersected with the differential expressed genes taken between LUAD samples and paracancerous samples. Afterward, stepwise regression analysis and the LASSO were used to compress the genes further, and a risk model was created. Furthermore, a nomogram based on risk scores and clinical features was created to validate the model. After that, the distinctions between the pertinent biological processes and signaling pathways among the various subgroups were investigated. Additionally, drug sensitivity testing, immunotherapy, immune infiltration analysis, and enrichment analysis were carried out. Finally, the biological role of ANLN in LUAD was explored by qPCR, cell scratch assay, and transwell. Results: A total of 257 intersected genes were obtained by taking the intersection of the differential genes between 2866 LUAD samples and paraneoplastic samples with the module genes after we screened two particular modules that had the strongest link with LUAD by WGCNA. ANLN, CASS4, and NMUR1 were found to be distinctive genes for the development of risk models after the intersecting genes were screened to find 176 genes linked to the prognosis for LUAD. Based on risk assessments, high- and low-risk groups of LUAD patients were divided. Low-risk patients exhibited a significantly higher overall survival (OS) than those in the high-risk group. Expression of model genes correlates with infiltration of the vast majority of immune cells. Significant differences in the biological pathways, immune microenvironment, and abundance of immune cell infiltration were found between the two groups. The drug sensitivity study showed that patients in the high-risk group had higher IC50 values for BMS-754807_2171 and Doramapimod_10424. Finally, in vitro experiments demonstrated that knocking down ANLN noticeably inhibited the viability, migration, and invasion of A549 cells. Conclusion: This study may provide a theoretical reference for future exploration of potential diagnostic and prognostic biomarkers for LUAD.

Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing

BACKGROUND

There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS

This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS

The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION

Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

Integrated analysis and experimental validation reveal the prognostic and immunological features associated with coagulation in hepatocellular carcinoma

Coagulation is intensively related to various tumors, which affects their progression and prognosis. However, research on the impact of coagulation-associated genes (CAGs) on hepatocellular carcinoma (HCC) occurrence, prognosis, and immune microenvironment is limited. Consequently, our research aims to uncover how CAGs affect the prognosis and immune microenvironments of HCC. We integrated gene expression data and clinical information from three datasets (GSE14520, GSE76427, and TCGA-LIHC). 281 CAGs were obtained from the coagulation-related pathway (hsa04610). We obtained three CAG patterns through a consensus clustering algorithm. Afterward, differential analyses of prognosis, biological processes, immune infiltration, and functional and pathway enrichment were conducted on the three CAG patterns. We intersected CAGs with differentially expressed genes in GSE76427 and then conducted Cox regression analysis to obtain the prognostic genes in HCC. Glycerol-3-phosphate dehydrogenase 2 (GPD2) was selected for further analyses. TCGA-LIHC samples with different GPD2 expression levels were analyzed for prognosis, DNA methylation, immune infiltration, and drug sensitivity. The expression level of GPD2 was verified through quantitative real-time PCR (qPCR) and immunohistochemistry. The wound-healing and Transwell assays were used to analyze the tumor cell migration and the Matrigel invasion and apoptosis assays were performed to determine cell invasion and apoptosis. Three CAG patterns were obtained through an unsupervised consensus clustering algorithm. CAGclusterA held the best prognosis compared to the other two clusters. The CAGclusterC was characterized by poor prognosis and abundant immune cell infiltration. The TCGA-LIHC dataset, as an internal validation, also yielded similar subtype classifications. Afterward, we identified the GPD2 gene, which significantly affected the prognosis of HCC and was positively correlated with the tumor progression. The upregulation of GPD2 expression was closely related to tumorigenic signatures and immune escape. The qPCR confirmed the upregulation of GPD2 expression in HCC tumor cell lines, compared to normal liver cell lines. Immunohistochemical staining confirmed the high expression of GPD2 in HCC tumor tissues compared to normal tissues. Regulating the expression level of GPD2 can inhibit the proliferation, migration, invasion, and induce apoptosis of HCC cells. Our study comprehensively elucidated the coagulation characteristics in HCC and identified a promising oncogenic gene GPD2. Exploring targeted strategies based on coagulation-related characteristics and biomarkers may shed light on HCC treatment.

Exploring The Role of TOP2A in the Intersection of Pathogenic Mechanisms Between Rheumatoid Arthritis and Idiopathic Pulmonary Fibrosis Based on Bioinformatics

Background

Rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF) share a common pathogenic mechanism, but the underlying mechanisms remain ambiguous. Our study aims at exploring the genetic-level pathogenic mechanism of these two diseases.

Methods

We carried out bioinformatics analysis on the GSE55235 and GSE213001 datasets. Machine learning was employed to identify candidate genes, which were further verified using the GSE92592 and GSE89408 datasets, as well as quantitative real-time PCR (qRT-PCR). The expression levels of TOP2A in RA and IPF in vitro models were confirmed using Western blotting and qRT-PCR. Furthermore, we explored the influence of TOP2A on the occurrence and development of RA and IPF by using the selective inhibitor PluriSIn #2 in an in vitro model. Finally, an in vivo model of RA and IPF was constructed to assess TOP2A expression levels via immunohistochemistry.

Results

Our bioinformatics analysis suggests a potential intersection in the pathogenic mechanisms of RA and IPF. We have identified 7 candidate genes: CXCL13, TOP2A, MMP13, MMP1, LY9, TENM4, and SEMA3E. Our findings reveal that the expression level of TOP2A is significantly elevated in both in vivo and in vitro models of RA and IPF. Additionally, our research indicates that PluriSIn #2 can effectively restrain inflammatory factors, extracellular matrix deposition, migration, invasion, the expression and nuclear uptake of p-smad2/3 protein in RA and IPF in vitro models.

Conclusion

There is a certain correlation between RA and IPF at the genetic level, and the molecular mechanisms of their pathogenesis overlap, which might be the reason for the progression of RA. Among the candidate genes we identified, TOP2A may influence the occurrence and development of RA and IPF through the TGF-β/Smad signal pathway. This could be beneficial to the study of the pathogenesis and treatment of RA and IPF.

Molecular characterization underlying IFN-α2 treatment in polycythemia vera: a transcriptomic overview

Polycythemia vera (PV) is the most common chronic myeloproliferative neoplasm (MPN) in adults. Pegylated interferon-α2 (IFN-α2) is an effective and safe drug for the treatment of PV. However, the mechanisms of its action in PV are still not fully understood. Using the WGCNA and Limma algorithm, we found a subset of IFN-α2 sensitive genes and four gene co-expression modules. Meanwhile, we also found 820 genes were differentially expressed in PV compared with healthy controls. By integrating the above results, several differentially expressed genes (DEGs) that were up- or down-regulated in PV but showed opposite alterations in the IFN-α2-treated group were found. These genes were mainly related to three types of biological processes (metal ion homeostasis, metabolic/catabolic process, and Jak-STAT signaling pathway), the dysfunctions of which were prevalent in PV. Moreover, we applied another threshold-free analysis method to compare global gene expression between IFN-α2 treated PV, PV, and control groups. Results showed the transcriptome changes of PV versus controls were negatively correlated with that of IFN-α2 treated versus untreated PV, indicating IFN-α2 treatment could partially reverse the dysregulated gene expression profile due to PV pathology. In summary, interferon may alleviate the progression of PV through multiple pathways. The findings may be of assistance in understanding the molecular basis underlying this treatment.

Tonabersat enhances temozolomide-mediated cytotoxicity in glioblastoma by disrupting intercellular connectivity through connexin 43 inhibition

Glioblastoma cells rely on connexin 43 (Cx43)-based gap junctions (GJs) for intercellular communication, enabling them to integrate into a widely branched malignant network. Although there are promising prospects for new targeted therapies, the lack of clinically feasible GJ inhibitors has impeded their adoption in clinical practice. In the present study, we investigated tonabersat (TO), a blood-brain-barrier-penetrating drug with GJ-inhibitory properties, in regard to its potential to disassemble intercellular connectivity in glioblastoma networks. Fluorescence-guided measurements of calcein cell-to-cell transfer were used to study functional intercellular connectivity. Specific DNA fragmentation rates of propidium iodide-stained nuclei were measured as a surrogate readout for cell death using flow cytometry. CRISPR/Cas9-mediated gene editing of Cx43 served as a validation tool of cellular effects related to Cx43 GJ inhibition. 3' mRNA sequencing was performed for molecular downstream analysis. We found that TO reduced intercellular GJ-mediated cytosolic traffic and yielded a significant reduction of tumor microtube (TM) length. TO-mediated inhibition of cellular tumor networks was accompanied by a synergistic effect for temozolomide-induced cell death. CRISPR/Cas9 Cx43-knockout revealed similar results, indicating that TO-mediated inhibitory effects rely on the inhibition of Cx43-based GJs. Gene set enrichment analyses found that GJ-mediated synergistic cytotoxic effects were linked to a significant upregulation of cell death signaling pathways. In conclusion, TO disrupts TM-based network connectivity via GJ inhibition and renders glioblastoma cells more susceptible to cytotoxic therapy. Given its previous use in clinical trials for migraine therapy, TO might harbor the potential of bridging the idea of a GJ-targeted therapeutic approach from bench to bedside.

Identification of the Hub Gene LDB3 in Stanford Type A Aortic Dissection Based on Comprehensive Bioinformatics Analysis

Stanford type A aortic dissection (TAAD) is a life-threatening disease. This study explored the role of LIM domain binding 3 (LDB3) in TAAD progression. Four datasets from the Gene Expression Omnibus were analyzed to identify TAAD-related hub genes. LDB3 single nucleotide polymorphisms (SNPs) were assessed in the UK Biobank. Western blotting and immunofluorescence detected LDB3 expression in angiotensin II (Ang II) stimulated human aortic vascular smooth muscle cells (HA-VSMC), human samples, and a murine model. Bioinformatics identified tissue inhibitor of metalloproteinase-1 (TIMP1) and LDB3 as TAAD hub genes. TIMP1 was expressed in macrophages, mesenchymal cells, and smooth muscle cells, while LDB3 was mostly expressed in smooth muscle cells. Validation showed TIMP1 was upregulated and LDB3 downregulated in TAAD. Six LDB3 SNPs were associated with aortic aneurysm and dissection in the UK Biobank. In human and murine samples, LDB3 expression was reduced in diseased tissues and co-localized with smooth muscle. Ang II-stimulated HA-VSMC exhibited LDB3 reduction and altered intercellular connections. The aforementioned findings suggest that the newly identified gene LDB3 is crucial in the progression of TAAD.

Identification of immune subtypes associated with CD8+ T cell-related genes providing new treatment strategies of esophageal carcinoma

Background

CD8+ T lymphocytes greatly affect the efficacy of immunotherapy, displaying promising potential in various tumors. Here, we aimed to identify immune subtypes associated with CD8+ T cell-related genes to predict the efficacy of treatment in esophageal cancer (ESCA).

Methods

We obtained 13 immune cell-related datasets from the Gene Expression Omnibus (GEO) database and removed batch effects. Weighted correlation network analysis (WGCNA) and co-expression analysis were performed to identify highly correlated CD8+ T cell genes. Cox analysis was used to process ESCA clinical information, and the immune clusters (ICs) were constructed through consensus cluster analysis. Furthermore, we constructed an immune risk score model to predict the prognosis of ESCA based on these CD8+ T cell genes. This model was verified using the IMvigor210 dataset, and we functionally validated the immune risk score model in vitro.

Results

The results revealed significant correlations between CD8+ T cell-related genes and immune-related pathways. Three ICs were identified in ESCA, with IC3 demonstrating the most favorable prognosis. The final 6-gene prognostic risk model exhibited stable predictive performance in datasets across different platforms. Compared with that in normal esophageal epithelial (HEEC cells), CHMP7 in the 6-gene prognostic risk model was upregulated in KYSE150 and TE-1 cells. Si-CHMP7 transfection led to a decrease in tumor cell migration, invasion, and proliferation, accompanied by an accelerated apoptotic process.

Conclusions

Collectively, we identified the immune subtypes of CD8+ T cell-related genes with different prognostic significance. We designated CHMP7 in the 6-gene prognostic risk model as a potential target to improve tumor cell prognosis. These insights provide a strong basis for improving prognosis and facilitating more personalized and accurate treatment decisions for the immunotherapy of ESCA.

Revolutionizing the treatment of intervertebral disc degeneration: an approach based on molecular typing

BACKGROUND

Intervertebral disc degeneration (IVDD) is a significant cause of global disability, reducing labor productivity, increasing the burden on public health, and affecting socio-economic well-being. Currently, there is a lack of recognized clinical approaches for molecular classification and precision therapy.

METHODS

Chondrocyte differentiation and prognosis-related genes were extracted from single-cell RNA sequencing and multi-omics data in the Gene Expression Omnibus (GEO) database through chondrocyte trajectory analysis and non-parametric tests. Subsequently, a precise IVDD risk stratification system was developed using ConsensusClusterPlus analysis. The clinical significance of molecular typing was demonstrated through case-control trials involving IVDD patients. Specific inhibitors of molecular typing were predicted using the pRRophetic package in R language and then validated in vitro.

RESULTS

A stratified model for IVDD, considering chondrocyte differentiation and demonstrating high clinical relevance, was developed using a set of 44 chondrocyte fate genes. Extensive analyses of multi-omics data confirmed the clinical relevance of this model, indicating that cases in the High Chondrocyte Scoring Classification (HCSC) group had the most favorable prognosis, whereas those in the Low Chondrocyte Scoring Classification (LCSC) group had the worst prognosis. Additionally, clinical case-control studies provided evidence of the utility of IVDD molecular typing in translational medicine. A gene expression-based molecular typing approach was used to create a matrix identifying potential inhibitors specific to each IVDD subtype. In vitro experiments revealed that gefitinib, a drug designed for LCSC, not only had protective effects on chondrocytes but also could induce the conversion of LCSC into the HCSC subgroup. Therefore, IVDD molecular typing played a critical role in assisting clinicians with risk stratification and enabling personalized treatment decisions.

CONCLUSION

The results of the study have provided a comprehensive and clinically relevant molecular typing for IVDD, involving a precise stratification system that offers a new opportunity for customizing personalized treatments for IVDD.

Refined culture conditions with increased physiological relevance uncover oncogene-dependent metabolic signatures in Ewing sarcoma spheroids

Ewing sarcoma (EwS) cell line culture largely relies on standard techniques, which do not recapitulate physiological conditions. Here, we report on a feasible and cost-efficient EwS cell culture technique with increased physiological relevance employing an advanced medium composition, reduced fetal calf serum, and spheroidal growth. Improved reflection of the transcriptional activity related to proliferation, hypoxia, and differentiation in EwS patient tumors was detected in EwS cells grown in this refined in vitro condition. Moreover, transcriptional signatures associated with the oncogenic activity of the EwS-specific FET::ETS fusion transcription factors in the refined culture condition were shifted from proliferative toward metabolic gene signatures. The herein-presented EwS cell culture technique with increased physiological relevance provides a broadly applicable approach for enhanced in vitro modeling relevant to advancing EwS research and the validity of experimental results.

Increased ectodysplasin-A2-receptor EDA2R is a ubiquitous hallmark of aging and mediates parainflammatory responses

Intensive efforts have been made to identify features that could serve as biomarkers of aging. Yet, drug-based interventions aimed at lessening the detrimental effects of getting older are lacking. This is largely attributable to tissue-specificity, sex-related differences, and to the difficulty of identifying actionable targets, which continues to pose a significant challenge. Here, we implement a bioinformatics approach revealing that aging-associated increase of the transmembrane Ectodysplasin-A2-Receptor is a prominent tissue-independent alteration occurring in humans and other species, and is particularly pronounced in models of accelerated aging. We show that strengthening of the Ectodysplasin-A2-Receptor signalling axis in myogenic precursors and differentiated myotubes suffices to trigger potent parainflammatory responses, mirroring aspects of aging-driven sarcopenia. Intriguingly, obesity, insulin-resistance, and aging-related comorbidities, such as type-2-diabetes, result in heightened levels of the Ectodysplasin-A2 ligand. Our findings suggest that targeting the Ectodysplasin-A2 surface receptor represents a promising pharmacological strategy to mitigate the development of aging-associated phenotypes.

Bioinformatics analysis reveals key mechanisms of oligodendrocytes and oligodendrocyte precursor cells regulation in spinal cord Injury

Despite extensive research, spinal cord injuries (SCI), which could cause severe sensory, motor and autonomic dysfunction, remain largely incurable. Oligodendrocytes and oligodendrocyte precursor cells (ODC/OPC) play a crucial role in neural morphological repair and functional recovery following SCI. We performed single-cell sequencing (scRNA-seq) on 59,558 cells from 39 mouse samples, combined with microarray data from 164 SCI samples and 3 uninjured samples. We further validated our findings using a large clinical cohort consisting of 38 SCI patients, 10 healthy controls, and 10 trauma controls, assessed with the American Spinal Cord Injury Association (ASIA) scale. We proposed a novel SCI classification model based on the expression of prognostic differentially expressed ODC/OPC differentiation-related genes (PDEODGs). This model includes three types: Low ODC/OPC Score Classification (LOSC), Median ODC/OPC Score Classification (MOSC), and High ODC/OPC Score Classification (HOSC). Considering the relationship between these subtypes and prognosis, we speculated that enhancing ODC/OPC differentiation and inhibiting inflammatory infiltration may improve outcomes. Additionally, we identified potential treatments for SCI that target key genes within these subtypes, offering promising implications for therapy.

Comparison Between Signal Transduction Pathway Activity in Blood Cells of Sepsis Patients and Laboratory Models

Sepsis represents a serious disease burden that lacks effective treatment. Drug development for sepsis requires laboratory models that adequately represent sepsis patients. Simultaneous Transcriptome-based Activity Profiling of Signal Transduction Pathway (STAP-STP) technology quantitatively infers STP activity from mRNA levels of target genes of the STP-associated transcription factor. Here, we used STAP-STP technology to compare STP activities between sepsis patients and lipopolysaccharide (LPS)-based models. Activity scores of Androgen Receptor (AR), TGFβ, NFκB, JAK-STAT1/2, and JAK-STAT3 STPs were calculated based on publicly available transcriptome data. Peripheral blood mononuclear cells (PBMCs) from patients with Gram-negative sepsis, nor PBMCs stimulated with LPS in vitro, showed altered STP activity. Increased NFκB, JAK-STAT1/2, and JAK-STAT3 STP activity was found in whole blood stimulated with LPS in vitro, and in whole blood obtained after intravenous injection of LPS in humans in vivo; AR and TGFβ STP activity only increased in the in vivo LPS model. These results resembled previously reported STP activity in whole blood of sepsis patients. We provide the first comparison of STP activity between patients with sepsis and laboratory model systems. Results are of use for the refinement of sepsis model systems for rational drug development.