The experience of adversity in childhood can have life-long consequences on health outcomes. In search of mediators of this relationship, alterations of bio-behavioral and cellular regulatory systems came into focus, including those dealing with basic gene regulatory processes. System biology oriented approaches have been proposed to gain a more comprehensive understanding of the complex multiple interrelations between and within layers of analysis. Here, we used co-expression based, supervised and unsupervised single and multi-omics systems approaches to investigate the association between childhood adversity and gene expression, protein expression and DNA methylation in CD14+ monocytes in the context of psychosocial stress exposure, in a sample of healthy adults with (n = 29) or without (n = 27) a history of childhood adversity. Childhood adversity explained some variance at the single analyte level and within gene and protein co-expression structures. A single-omics, post-stress gene expression model differentiated best between participants with a history of childhood adversity and control participants in supervised analyses. In unsupervised analyses, a multi-omics based model showed best performance but separated participants based on sex only. Multi-omics analyses are a promising concept but might yield different results based on the specific approach taken and the omics-datasets supplied. We found that stress associated gene-expression pattern were most strongly associated with childhood adversity, and integrating multiple cellular layers did not results in better discriminatory performance in our rather small sample. The capacity and yield of different omics-profiling methods might currently limit the full potential of integrative approaches.

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

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

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

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

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.

The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments.

Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays.

Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.

Asherman's syndrome (AS) is a significant cause of subfertility in women from developing countries. Over 80% of AS cases in these regions are linked to dilation and curettage (D&C) procedures following pregnancy. The incidence of AS in patients with infertility and recurrent miscarriage can be as high as 10%, while the pregnancy rate in cases of moderate to severe adhesions can be as low as 34%. We aimed to establish a hysteroscopic artificial intelligence system using image-deep-learning algorithms for fertility assessment.

This diagnostic study included 555 cases with 4922 hysteroscopic images from a Chinese intrauterine adhesions cohort clinical database (NCT05381376). The study evaluated two image-deep-learning algorithms' effectiveness in predicting pregnancy within one year, using AUCs and decision curve analysis. The models' performance was evaluated for two-year prediction via concordance index and cumulative time-dependent ROC. A quantifiable visualization panel of the system was established.

The proportional hazard CNN system accurately predicted conception, with AUCs of 0.982, 0.992, and 0.990 in three randomly assigned datasets, superior to the InceptionV3 framework, and achieved a net benefit of 69.4% for subfertility assessment. The system fitted well with c-indexes of 0.920-0.940 and was time-stable. The quantifiable visualization panel displayed four intrauterine pathologies intuitively. The performance was comparable to senior hysteroscopists, with a kappa value of 0.84-0.89.

The CNN based on the proportional hazard approach accurately assesses fertility postoperatively. The quantifiable visualization panel could assist in intrauterine pathologies assessment, optimize treatment strategies, and achieve individualized and cost-efficient practices.

Diabetic Kidney Disease (DKD) is a common complication in patients with diabetes, and its pathogenesis remains incompletely understood. Recent studies have suggested that extracellular vesicles (EVs) may play a significant role in the initiation and progression of DKD. This study aimed to identify biomarkers associated with EVs in DKD through bioinformatics and Mendelian randomization (MR) analysis.

This study utilized two DKD-related datasets, GSE96804 and GSE30528, alongside 121 exosome-related genes (ERGs) and 200 inflammation-related genes (IRGs). Differential analysis, co-expression network construction, and MR analysis were conducted to identify candidate genes. Machine learning techniques and expression validation were then employed to determine biomarkers. Finally, the potential mechanisms of action of these biomarkers were explored through Immunohistochemistry (IHC) staining, enrichment analysis, immune infiltration analysis, and regulatory network construction.

A total of 22 candidate genes were identified as causally linked to DKD. CMAS and RGS10 were identified as biomarkers, with both showing reduced expression in DKD. IHC confirmed low RGS10 expression, providing new insights into DKD management. CMAS was involved primarily in mitochondria-related pathways, while RGS10 was enriched in the extracellular matrix and associated pathways. Significant differences were observed in neutrophils and M2 macrophages between DKD and normal groups, correlating strongly with the biomarkers.

This study identified two EV-associated biomarkers, CMAS and RGS10, linked to DKD and elucidated their potential roles in disease progression. These results offer valuable insights for further exploration of DKD pathogenesis and the development of new therapeutic targets.

Bariatric surgery is highly effective in achieving weight loss in children and adolescents with severe obesity, however the underlying mechanisms are incompletely understood, and gut microbiome changes are unknown. Here, we show that adolescents exhibit significant gut microbiome and metabolome shifts several months after laparoscopic vertical sleeve gastrectomy (VSG), with increased alpha diversity and notably with enrichment of oral-associated taxa. To assess causality of the microbiome/metabolome changes in phenotype, pre-VSG and post-VSG stool was transplanted into germ-free mice. Post-VSG stool was not associated with any beneficial outcomes such as adiposity reduction compared pre-VSG stool. However, post-VSG stool exhibited a potentially inflammatory phenotype with increased intestinal Th17 and decreased regulatory T cells. Concomitantly, we found elevated fecal calprotectin and an enrichment of proinflammatory pathways in a subset of adolescents post-VSG. We show that in some adolescents, microbiome changes post-VSG may have inflammatory potential, which may be of importance considering the increased incidence of inflammatory bowel disease post-VSG.

Juvenile idiopathic arthritis (JIA), superseding juvenile rheumatoid arthritis (JRA), is a chronic autoimmune disease affecting children and characterized by various types of childhood arthritis. JIA manifests clinically with joint inflammation, swelling, pain, and limited mobility, potentially leading to long-term joint damage if untreated. This study aimed to identify genes associated with the progression and prognosis of JIA polyarticular to enhance clinical diagnosis and treatment.

We analyzed the gene expression omnibus (GEO) dataset GSE1402 to screen for differentially expressed genes (DEGs) in peripheral blood single nucleated cells (PBMCs) of JIA polyarticular patients. Weighted gene co-expression network analysis (WGCNA) was applied to identify key gene modules, and protein-protein interaction networks (PPIs) were constructed to select hub genes. The random forest model was employed for biomarker gene screening. Functional enrichment analysis was conducted using David's online database, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to annotate and identify potential JIA pathways. Hub genes were validated using the receiver operating characteristic (ROC) curve.

PHLDA1, EGR3, CXCL2, and PF4V1 were identified as significantly associated with the progression and prognosis of JIA polyarticular phenotype, demonstrating high diagnostic and prognostic assessment value.

These genes can be utilized as potential molecular biomarkers, offering valuable insights for the early diagnosis and personalized treatment of JIA polyarticular patients.

Gut microbiota have been shown to influence the social behaviors of their hosts, while variations in host genetics can affect the composition of the microbiome. Nonetheless, the degree to which genetic variations in microbial populations impact host behavior, as well as any potential transgenerational effects, remains inadequately understood. Utilizing C. elegans as a model organism, we identified 77 strains of E. coli from a total of 3,983 mutants that significantly enhanced aggregation behavior through various neurobehavioral pathways. This discovery underscores a collaborative regulatory mechanism between microbial genetics and host behavior. Notably, we observed that some mutant bacteria might affect social behavior via the mitochondrial pathway. Additionally, the modulation of social behavior has been identified as a heritable trait in offspring. Our results provide a novel perspective on the regulatory role of microbial genetic variation in host behavior, which may have significant implications for human studies and the development of genetically engineered probiotics aimed at enhancing well-being across generations.

Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.

The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance. However, unsupervised human protein-based clustering analysis revealed two distinct CVD risk clusters (low-risk and high-risk) with different blood pressure (by 9 mmHg) and sex-dependent dietary potassium and fiber intake. In the human proteome, the low-risk group had lower angiotensin-converting enzymes, inflammatory proteins associated with neutrophil extracellular trap formation and auto-immune diseases. In the microbial proteome, the low-risk group had higher expression of phosphate acetyltransferase that produces SCFAs, particularly in fiber-fermenting bacteria. This model identified severity across phenotypes in heart failure patients and long-term risk of cardiovascular events in a large population-based cohort. These findings underscore multifactorial gut-to-host mechanisms that may underlie risk factors for CVD.

Diabetic kidney disease (DKD) is driven by mitochondrial dysfunction and immune dysregulation, yet the mechanistic interplay remains poorly defined. This study aimed to identify key molecular networks linking mitochondrial and immune pathways to DKD progression, with a focus on uncovering biomarkers and therapeutic targets.

We conducted an integrative analysis of human DKD cohorts (GSE30122, GSE96804) using weighted gene co-expression network analysis (WGCNA) to identify gene modules enriched for immune response genes and mitochondrial pathways (from MitoCarta3.0). Machine learning algorithms were employed to prioritize key biomarkers for further investigation. Experimental validation was performed using a DKD rat model.

WGCNA revealed significant gene modules associated with immune responses and mitochondrial functions. Machine learning analysis highlighted two central biomarkers: aminoadipate-semialdehyde synthase (AASS) and caspase-3 (CASP3). In the DKD rat model, elevated levels of AASS and CASP3 were found to correlate with increased oxidative stress. Mechanistically, AASS was shown to drive mitochondrial damage via lysine metabolism, while CASP3 amplified inflammatory apoptosis pathways.

Our findings establish AASS and CASP3 as dual biomarkers and therapeutic targets, bridging mitochondrial-immune crosstalk to DKD pathogenesis. This multi-omics framework provides actionable insights for targeting kidney damage in diabetes.

Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). However, PCD-relevant biomarkers have not been fully discovered. The aim of this study was to explore the PCD-relevant biomarkers for the treatment and prevention of COVID-19.

Bioinformatic analyses were performed to explore the clinical relevant PCD genes with differential expression (DE) in COVID-19 compared with matched controls. PPI network was used for hub genes screening and machine learning methods were employed for filtering feature genes. The biomarker genes were screened by Venn diagram. The correlations between biomarkers with clinical features and immune microenvironment were further explored. Biomarker validation was performed in clinical samples by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR).

In total, 118 clinically relevant and PCD associated differential expressed genes (DEGs) were screened, which were mainly related with apoptosis related pathways, among which six biomarkers (Cyclin B1 (CCNB1), cyclin-dependent kinase 1 (CDK1), interferon regulatory factor 4 (IRF4), lipoteichoic acid (LTA), matrix metallopeptidase 9 (MMP9) and Oncostatin M (OSM)) were identified. The excellent or good diagnostic performance of biomarkers was determined by receiver operating characteristic (ROC) curve analysis. The biomarkers showed diverse correlations with clinical indicators, such as age, sex and Intensive Care Unit (ICU) admission. Total 14 types of immune cells exerted differential infiltration between COVID-19 and controls. Biomarkers were correlated with immune cells at varying levels. COVID-19 was classified in three clusters, which showed differential expression of biomarker genes and significant associations with clinical information, such as sex, age and ICU admission. The DEGs of biomarkers were determined in COVID-19 patients relative to controls.

The six biomarkers (CCNB1, CDK1, IRF4, LTA, MMP9 and OSM) can be served as the biomarkers for the treatment and prevention of COVID-19.

Ketosis is a common metabolic disease in high-yield dairy cows. Key genes affecting ketosis need to be further explored by new methods. The gene expression profiling and clinical data of GSE92398, GSE104079, and GSE4304 were obtained from the gene expression omnibus (GEO) database. Core modules and genes associated with RFI (residual feed intake) and ADF (alternate day fasting) were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, the key genes related to ketosis and RFI were determined by protein-protein interaction (PPI) networks, ROC curves, functional enrichment, and differential expression analysis, respectively. The results showed that the genes of ACACA, ELOVL6 and XPO7 could be used as regulators of ketosis induced by low feed intake in dairy cows. At the same time, three genes (HRFI, STAT3 and IFNAR1) were retained as additional RFI biomarkers that could be considered. We identified three key factors as candidate genes and biomarkers of ketosis and RFI, respectively. These factors may provide a theoretical basis for targeted therapy of ketosis in dairy cows.

Studies have examined the role of integrin α3 (ITGA3) in papillary thyroid carcinoma (PTC). However, the functional and molecular mechanism by which ITGA3 is involved in the progression of PTC remains poorly understood.

To investigate the role of ITGA3 in PTC, raw PTC transcriptome data underwent comprehensive bioinformatics analyses, including differential expression, co-expression network, and enrichment analyses. ITGA3 expression was validated via immunohistochemistry and western blotting in PTC tissues. Cell functional assays and xenograft models assessed PTC cell behaviour. The potential mechanisms of ITGA3 were elucidated using bioinformatics analyses, western blotting, co-immunoprecipitation, and immunofluorescence. Finally, integration of ITGA3 expression with clinical parameters enabled nomogram construction for precise prediction of cervical lymph node metastasis (CLNM) in PTC.

ITGA3 was upregulated in PTC and associated strongly with CLNM (79.5% vs. 53.84%, p = 0.016). ITGA3 expression enhanced PTC proliferation and migration in vitro and in vivo via cooperating with the MET protein tyrosine kinase, followed by phosphorylation of MET at Tyr1234/1235, and activation of ERK and PI3K/AKT signaling pathways. Furthermore, upregulation ITGA3 reduced phosphorylation at FAK-Tyr397 and Src-Tyr416 in PTC cells. Finally, a nomogram combining ITGA3 expression and clinical parameters for predicting CLNM was constructed and validated, achieving a ROC curve AUC of 0.719, suggesting potential application for PTC diagnosis.

ITGA3 promotes PTC cell proliferation and migration by cooperating with MET to activate MET-ERK and MET-PI3K-AKT signalling. ITGA3-MET cooperation may serve as a potential therapeutic target.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerulonephritis. Oxidative stress plays a critical role in the development and progression of ANCA-associated glomerulonephritis (AAGN), but the underlying mechanisms remain poorly understood. Targeting genes related to oxidative stress may provide novel insights and supplementary therapeutic benefits for AAGN. In the current study, we obtained differentially expressed genes from AAGN-related microarray datasets in the Gene Expression Omnibus database, and oxidative stress-related genes (OSRGs) from the GeneCards and Gene Ontology databases to identify differentially expressed OSRGs. Then, by integrating weighted gene co-expression network analysis, and machine learning algorithms, we identified four upregulated hub OSRGs (all p < 0.01) with strong diagnostic potential (all AUC > 0.9)-CD44, ITGB2, MICB, and RAC2 - in the AAGN glomerular training dataset GSE104948 and validation dataset GSE108109, along with two hub OSRGs (all p < 0.05) with better diagnostic potential (all AUC > 0.7) - upregulated gene VCAM1 and downregulated gene VEGFA-in the AAGN tubulointerstitial training dataset GSE104954 and validation dataset GSE108112. The GSEA analysis suggested that these hub genes may play a role in inflammatory and immune response processes. Moreover, we constructed regulatory networks and identified drugs that potentially target these hub genes. It's to be noted that RAC2 and ITGB2 were associated with cyclophosphamide in the AAGN glomerular compartment, while VCAM1 and VEGFA were associated with dexamethasone in the tubulointerstitial compartment. This study offers novel insights into immune-associated OSRGs within the glomerular and tubulointerstitial compartments of AAGN which may serve as innovative targets for diagnosing and treating AAGN.

Congenital Zika virus (ZIKV) infection significantly affects neurological development in infants and subsequently induces neurodevelopmental abnormality symptoms; however, the potential mechanism is still unknown. Therefore, in order to effectively intervene in neurodevelopmental abnormalities in infected infants, it is necessary to identify the main brain regions affected by congenital infection. In this study, we constructed a congenital ZIKV-infected murine model using immunocompetent human STAT2 knock-in mice, which presented long-term neurodevelopmental abnormalities with abnormal neurodevelopmental symptoms. We found that the hippocampus, which regulates cognitive behaviour and processes spatial information and navigation, was the main brain region affected by congenital infection and that hippocampal cells were more prone to autophagy during the growth period of these mice at the transcriptional and pathological levels. These findings highlighted that congenital ZIKV infection could interrupt hippocampal function by activating autophagy, thus providing a theoretical basis for the clinical treatment of congenital ZIKV-infected infants.

This study investigates the role of Gelatin-Catalase (Gel@CAT)-L hydrogel in mediating reactive oxygen species (ROS) production and maintaining mitochondrial homeostasis through SIRT3-mediated unfolded protein response (UPRmt), while exploring its involvement in the molecular mechanism of osteoarthritis (OA).

Self-assembled Gel@CAT-L hydrogels were fabricated and characterized using transmission electron microscopy, mechanical testing, external release property evaluation, and oxygen production measurement. Biocompatibility was assessed via live/dead cell staining and CCK8 assays. An OA mouse model was established using destabilization of the medial meniscus (DMM) surgery. X-ray and micro-CT imaging were employed to evaluate the structural integrity of the mouse knee joints, while histological staining was used to assess cartilage degeneration. Immunohistochemistry was performed to analyze the expression of proteins including Col2a1, Aggrecan, MMP13, ADAMTS5, SIRT3, PINK1, and Parkin. Multi-omics analyses-encompassing high-throughput sequencing, proteomics, and metabolomics-were conducted to identify key genes and metabolic pathways targeted by Gel@CAT-L hydrogel intervention in OA. Immunofluorescence techniques were utilized to measure ROS levels, mitochondrial membrane potential, and the expression of SIRT3, PINK1, Parkin, LYSO, LC3B, Col2a1, and MMP13 in primary mouse chondrocytes and mouse knee joints. Flow cytometry was applied to quantify ROS-positive cells. RT-qPCR analysis was conducted to determine mRNA levels of Aggrecan, Col2a1, ADAMTS5, MMP13, SIRT3, mtDNA, HSP60, LONP1, CLPP, and Atf5 in primary mouse chondrocytes, mouse knee joints, and human knee joints. Western blotting was performed to measure protein expression levels of SIRT3, HSP60, LONP1, CLPP, and Atf5 in both primary mouse chondrocytes and mouse knee joints. Additionally, 20 samples each from the control (CON) and OA groups were collected for analysis. Hematoxylin and eosin staining was used to evaluate cartilage degeneration in human knee joints. The Mankin histological scoring system quantified the degree of cartilage degradation, while immunofluorescence analyzed SIRT3 protein expression in human knee joints.

In vitro experiments demonstrated that self-assembled Gel@CAT-L hydrogels exhibited excellent biodegradability and oxygen-releasing capabilities, providing a stable three-dimensional environment conducive to cell viability and proliferation while reducing ROS levels. Multi-omics analysis identified SIRT3 as a key regulatory gene in mitigating OA and revealed its central role in the UPRmt pathway. Furthermore, Gel@CAT-L was confirmed to regulate mitochondrial homeostasis. Both in vitro experiments and in vivo mouse model studies confirmed that Gel@CAT-L significantly reduced ROS levels and regulated mitochondrial autophagy by activating the SIRT3-mediated UPRmt pathway, thereby improving the pathological state of OA. Clinical trials indicated downregulation of SIRT3 and UPRmt-related proteins in OA patients.

Gel@CAT-L hydrogel activates SIRT3-mediated UPRmt to regulate ROS and mitochondrial homeostasis, providing potential therapeutic benefits for OA.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by irreversible scarring of the lungs, predominantly affecting older adults. The limited therapeutic options available are largely due to an insufficient understanding of IPF etiology and pathogenesis. This study investigated potential biomarkers to enhance IPF diagnosis and treatment strategies. Through single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing analyses of public datasets, four hub genes-FTH1, FABP5, DCXR, and IGFBP7-were identified as strongly associated with IPF. Subsequent validation in in vivo and in vitro models confirmed IGFBP7 as a novel biomarker. Double immunofluorescence staining and scRNA-seq analysis revealed that IGFBP7 expression is elevated in IPF epithelial cells. IGFBP7 shows potential for early diagnosis of IPF and can differentiate IPF from other diseases. Gene set enrichment analysis revealed the involvement of IGFBP7 in IPF pathogenesis, particularly through its strong connection to the TGF-β signaling pathway, which drives inflammation and fibrosis. In vitro studies with the TGF-β inhibitor SB431542 showed that inhibition of the TGF-β pathway significantly reduced IGFBP7 expression. Furthermore, IGFBP7 knockdown decreased the expression of markers associated with epithelial-mesenchymal transition and fibrosis while suppressing TGF-β1 expression. These results suggest that IGFBP7 forms a positive feedback loop with TGF-β1. In conclusion, this research identified IGFBP7 as a promising biomarker with significant diagnostic and therapeutic potential for IPF. These insights pave the way for improved diagnostics and the development of targeted antifibrosis therapies, while deepening our understanding of IPF mechanisms.

Aging plays a crucial role in enhancing the flavor of Huangjiu. This study aims to elucidate the changes in taste attributes of aged Huangjiu and explore the correlation between non-volatile compounds. It showed that aging made the sourness, bitterness, and astringency more pronounced. The content of organic acids and amino acids exhibited specific patterns with aging years. The total taste activity value of organic acids showed an increasing trend, peaking at 54.40 in Huangjiu aged 15 years, which served as a key indicator of sourness intensity. A total of 22 potential contributors to umami and 18 to bitterness were screened based on weighted gene coexpression network analysis. The total content of potential umami contributors was significantly higher in fresh Huangjiu and aging stage I, but declined in later periods, while bitterness contributors increased gradually throughout the aging process. This study provided theoretical support for the taste characteristics of aged Huangjiu.

Previous studies have confirmed ambient fine particulate matter (PM2.5) as a major environmental risk factor for cardiovascular diseases (CVDs), yet the specific molecular pathways remain poorly understood. Furthermore, while physical activity benefits cardiovascular health, its protective effects against PM2.5-induced damage need further explored. We aimed to investigate the relationship between long-term PM2.5 exposure, physical activity, and cardiovascular health, and explore the potential molecular mechanisms. This research combined epidemiological and experimental approaches. The epidemiological study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations among long-term PM2.5 exposure, physical activity, and CVDs. For the experimental study, C57BL/6 male mice were assigned to either regular physical activity or sedentary behavior, and were exposed to PM2.5 or filtered air (FA) for 2, 4, and 6 months. We observed that long-term PM2.5 exposure significantly increased cardiovascular disease risk, while physical activity exhibited protective effects and can partially mitigate the adverse impacts of PM2.5 on heart disease and dyslipidemia. In animal study, mice with long-term exposure to PM2.5 demonstrated elevated blood pressure, disrupted adipokine levels, altered lipid profiles, and mitochondrial damage. Regular physical activity partially mitigated these adverse effects. Lipidomics and proteomics analyses revealed that PM2.5 exposure disrupted lipid metabolism networks and protein regulatory pathways, while regular physical activity mitigated these perturbations through the modulation of lipid metabolism, the coagulation cascade, and immune responses. These findings underscore the importance of regular physical activity in public health strategies, while prioritizing PM2.5 reduction measures for cardiovascular disease prevention.

Thyroid cancer (TC) is the most prevalent endocrine malignancy, with a rising incidence necessitating safer treatment strategies to reduce overtreatment and its side effects. Xiaoyao Powder (XYP), a widely used herbal formula, shows promise in treating TC. This study aims to investigate the mechanisms by which XYP may affect TC.

The components of XYP were identified through database retrieval, and targets related to TC were collected to construct a target network for key screening. GEO dataset samples analyzed immune cells and identified significantly differentially expressed core genes (SDECGs). Based on SDECG expression and clustering, samples were classified for comparison. WGCNA was employed to identify gene modules linked to clinical characteristics. ML models screened characteristic genes and constructed a nomogram validated using another GEO dataset. MR methods explored causal relationships between genes and TC.

The top ten active components of XYP were identified, along with 27 SDECGs that exhibited significant differences in immune cell infiltration between TC patients and normal controls. The nomogram effectively predicted TC risk, validated through ROC curves. Key characteristic genes included SMIM1, PPP1R16A, KIAA1462, DNAJC22, and EFNA5.

XYP may treat TC by regulating SMIM1, PPP1R16A, KIAA1462, DNAJC22, EFNA5, and associated immune pathways; this provides theoretical support for its potential mechanisms.

Particle-attached (PA) and free-living (FL) microorganisms regulate coastal biogeochemical cycles, yet their roles in nitrogen transformation remain unclear. To address this knowledge gap, we seasonally sampled PA and FL from seawater along salinity gradients in the Yangtze River estuary (YRE) and adjacent regions to investigate the spatio-temporal variability of microbial communities, abundances of nitrogen-cycling genes, and key microbial groups affiliated with the nitrogen cycle in PA and FL. Compared to FL, the composition, structure and diversity of PA exhibited more pronounced variations in response to salinity and [NO3-]. Metagenomic analyses indicated a predominant role of denitrification in both PA and FL, with greater abundances of genes involved in most nitrogen transformation processes observed in the estuarine region. The potential for the nitrogen cycle in PA was relatively lower in May, while greater in FL, potentially due to competition for nitrogen substrates between PA and phytoplankton during spring. PERMANOVA and Mantel tests showed that gene abundances exhibited spatio-temporal dynamics and were associated with species and environmental factors. Gene-affiliated taxa identification and the Weighted Correlation Network Analysis revealed that the differences in environmental factors and taxa responsible for the nitrogen transformation drove spatio-temporal variations of the nitrogen cycle between PA and FL, and implied the significance of their interaction in nitrogen fates in coastal ecosystem. Gammaproteobacteria and Betaproteobacteria were highly affiliated with nitrogen-cycling genes, while Nitrososphaeria played an important role in nitrification and denitrification. This study offered practical insights for mitigating eutrophication through targeted regulation of microbial-mediated nitrogen fluxes.

The intramuscular fat (IMF), fatty acid and amino acid compositions of pork are intricately linked to meat quality, flavor profile, and nutritional composition, and have potential implications for human health. Lipid accumulation in pork is initiated by the biosynthesis of fatty acids and regulated by a complex network of genes. In this study, the IMF content and genotyping of large-scale slaughtered Yorkshire pigs were assessed. Transcriptome sequencing of muscles from 17 individuals and fatty and amino acid analyses of muscles from 28 individuals according to IMF content were conducted. Phenotypic analysis showed a high correlation between IMF and most fatty acids, and the composition ratio of different types of fatty acids varied with IMF content. A negative correlation between the n-6/n-3 polyunsaturated fatty acid (PUFA) ratio and increase in IMF content significantly enhanced the levels of essential fatty acids and ameliorated the n-6/n-3 PUFA ratio in pork, thereby elevating its nutritional value to better align with contemporary health standards. A comprehensive analysis that integrated a genome-wide association study, differential gene expression analysis, and weighted gene co-expression network analysis was employed to identify the regulatory mechanisms of lipids. PRLR, SEC11C, ALPK2, CPLX4, APC, and CREB5 were identified as key candidate genes that affect intramuscular lipids and fatty acids. Through molecular and cellular experiments, our results indicated that high APC and CREB5 gene expression significantly promotes lipogenesis in cells, where these genes play an important role in regulatory pathways related to lipid synthesis in animals, which may affect fat deposition and fatty acid composition in pork. Overall, these results lay the foundation for an in-depth analysis of the genetic regulation of pork lipids and nutrition, and also provide molecular regulatory markers for the primary selection of pigs with better meat quality.

Alzheimer's disease (AD) is a neurodegenerative disease that leads to dementia, but effective treatments are lacking. This study aims to evaluate the therapeutic effects of Gegen Qinlian Decoction (GGQLD) on AD and investigate the underlying mechanisms.

Using network pharmacology and bioinformatics, we identified 376 active ingredients of GGQLD and 427 drug targets. Among these, 7 potential targets (CASP1, MKI67, NFKB1, TLR4, NLRP3, IL1B, and AKT1) were identified as intersecting targets of both GGQLD and AD. Functional enrichment analysis revealed that GGQLD regulates pyroptosis-related pathways. In vivo, GGQLD was administered to AD rat models to assess its effects on spatial learning, memory, and brain tissue injury.

GGQLD significantly reduced latency time by 40 % and increased platform crossings by 60 % in AD rats, demonstrating improved spatial learning and memory abilities. It also reduced hippocampal tissue damage and abnormal Aβ deposition. Mechanistically, GGQLD downregulated pyroptosis-related targets (TLR4, NF-κB, NLRP3, IL-1β, and Caspase-1), which were significantly upregulated in AD. ROC analysis demonstrated strong diagnostic significance for these genes, with AUC values exceeding 0.70. Functional enrichment and KEGG analysis further indicated that GGQLD exerts its therapeutic effects through multiple pathways, particularly the NOD-like receptor pathway, Necroptosis, and NF-kappa B pathway.

This study demonstrates that GGQLD improves spatial learning, reduces brain tissue damage, and alleviates inflammation in AD through the regulation of pyroptosis-related pathways, providing evidence for its potential as a therapeutic agent for AD.

Microbial carbon use efficiency (CUE) typically promotes soil organic carbon (SOC) storage in terrestrial ecosystems. However, this relationship remains poorly understood in coastal wetlands, where tidal flooding creates unique environmental conditions, facilitates lateral transfer and SOC loss, and mediates organic matter exchange between terrestrial and marine systems. Here we examined the CUE-SOC relationship across a tidal flooding gradient (4-25 % frequency) in a subtropical coastal wetland. Along this gradient, SOC decreased by 65 % while microbial CUE increased from 0.24 to 0.32. This inverse relationship coincided with marked compositional shifts: plant debris declined from 57 % to 18 %, while microbial necromass increased from 21 % to 35 %. The enhanced CUE was accompanied by increased turnover times alongside decreased metabolic quotient (qCO2), C-acquiring enzyme activities, soil basal respiration, and microbial biomass carbon (MBC). This enhanced efficiency stemmed from substrate-microbe interactions rather than environmental stresses, as communities transitioned from oligotrophic taxa (α-proteobacteria, Basidiomycota) specializing in recalcitrant terrestrial substrates to copiotrophic microorganisms (γ-proteobacteria, Bacteroidota, Ascomycota) efficiently metabolizing labile marine compounds. Contrary to terrestrial patterns, enhanced CUE did not promote SOC storage due to three key mechanisms: (i) enhanced CUE from marine substrates could not compensate for declining plant debris accumulation; (ii) reduced microbial biomass limited necromass formation despite higher CUE; and (iii) metabolic benefits from high CUE (reduced enzyme activities and respiration rates) could not offset the substantial decrease in SOC inputs. Our findings reveal distinct CUE-SOC relationships in coastal wetlands compared to terrestrial ecosystems, highlighting the importance of considering both terrestrial and marine processes in understanding carbon cycling in these transitional environments.

Silicon is widely used as a "quality element" and "stress resistance element" in crop production and the remediation of heavy metal-contamination soils. Compared to inorganic silicon, organosilicon has unique properties such as amphiphilicity, low surface energy and high biocompatibility. Our previous research has confirmed the effectiveness of organosilicon-modified fertilizers in inhibiting Cadmium (Cd) absorption in wheat. Therefore, it is of great importance to further explore the potential mechanisms and comprehensive benefits of organosilicon. In this study, the microbiological and molecular mechanisms by which organosilicon reduces Cd concentration in wheat compared to inorganic silicon were investigated in depth. The findings indicated that, in comparison with inorganic silicon, organosilicon exhibited a more remarkable efficacy. Specifically, it was more effective in reducing the Cd concentration in wheat grains, achieving a reduction range of 35-39 % as opposed to the 23-28 % reduction achieved by inorganic silicon. Moreover, it manifested a greater ability to mitigate health risks, with a reduction range of 33-42 % compared to the 25-30 % reduction of inorganic silicon. Furthermore, organosilicon contributed to a significant increase in wheat yield, with a growth range of 11-14 % in contrast to the 8-11 % increase from inorganic silicon. Additionally, it enhanced the quality of the grains, substantially improving the protein content and amino acid content. The comparative advantages of organosilicon over inorganic silicon would be firstly due to the reduction of the bioavailability of soil Cd by increasing the available silicon content in the soil and improving the soil microbial ecology (increasing the abundance of Bacillus, Pseudomonas, Massilia and Talaromyces and reducing the enrichment of Fusarium). Secondly, organosilicon achieved vacuolar compartmentalization of Cd by upregulating the expression of the ABC transporter gene (TaABCB7), thereby alleviating Cd toxicity and restricting Cd transport from leaves to grains. Meanwhile, organosilicon increased the wheat yield by optimizing the availability of soil nutrients and enhancing photosynthesis. These results demonstrate the immense potential of organosilicon in mitigating heavy metal contamination in crops.

Scalp seborrheic dermatitis (SD) and psoriasis (PSO) are prevalent chronic skin conditions with overlapping clinical manifestations, especially when confined to the scalp, which complicates differential diagnosis. This study integrates transcriptomic profiling and genetic association analyses to investigate potential causal links between SD and scalp PSO. Through RNA sequencing on lesional and non-lesional scalp samples from patients with SD and scalp PSO, as well as healthy controls, we found that seborrheic dermatitis may represent a risk factor for the progression to scalp PSO. Furthermore, TGM1 and IL36RN were identified as key potential molecular targets that could differentiate seborrheic dermatitis from scalp PSO.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disorder that is characterized by the disruption of lung architecture and respiratory failure. Notwithstanding the advent of novel therapeutic agents such as pirfenidone and nintedanib, there remains a pressing need for the development of innovative diagnostic and therapeutic strategies. Next-generation sequencing allows for the analysis of gene expression and the discovery of biomarkers. The objective of our study was to identify IPF-specific gene signatures, construct a diagnostic nomogram, and explore the role of the extracellular matrix (ECM) and epithelial-to-mesenchymal transition (EMT) in IPF pathogenesis. Utilizing data from the Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs), performed weighted correlation network analysis (WGCNA), and constructed a nomogram. The present study has identified a group of key genes that are associated with IPF. The identified genes include GREM1, ITLN2, MAP3K15, RGS9BP, and SLCO1A2. The results of the immunohistochemical analysis indicated a significant correlation between these central genes and immune cell infiltration. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed that these genes play a critical role in the pathogenesis of IPF. To validate the diagnostic potential of these core genes, we performed confirmatory analyses in independent Gene Expression Omnibus (GEO) datasets. We observed a significant upregulation of GREM1 expression in IPF animal and cellular models. These findings provide new insights into the molecular mechanisms of IPF and suggest potential targets for future diagnostic and therapeutic strategies.

Cotton (Gossypium spp.) originated in tropical and subtropical regions, spreading to higher latitudes through domestication while retaining thermophilic characteristics. Xinjiang, a major cotton-producing area in China, frequently experiences 'late spring cold snaps' due to its location, causing chilling injury during critical sowing periods. Current research on cotton chilling stress primarily focuses on physiological studies such as evaluations of chilling stress and biochemical indices but lacks systematic investigation into the difference among varieties. Phenotypic screening across seed germination, cotyledon, and seedling stages identified upland cotton (Gossypium hirsutum) cultivar, Junmian1 exhibits superior cold tolerance relative to the sensitive genotype C1470. Under chilling stress, Junmian1 protects chloroplasts and other cellular structures in its first true leaf to survive the chilling stress. Weighted gene co-expression network analysis (WGCNA) analysis pinpointed Module Brown as a chilling-tolerance responsive hub, with subsequent validation via virus-induced gene silencing (VIGS) confirming the regulatory roles of GhRBL (Ribulose-bisphosphate carboxylase), GhGI (GIGANTEA), and lncRNA MSTR.1631 in cold tolerance. Additionally, integrated metabolomic and transcriptomic analyses demonstrated that jasmonic acid plays a crucial role in enhancing cotton's chilling tolerance at seedling stage. The primary difference in chilling tolerance between Junmian1 and C1470 is attributed to the signaling efficiency of the jasmonic acid synthesis and metabolism pathways. These findings establish JA metabolic engineering as a viable approach for enhancing cold resilience in early-stage cotton seedlings.

Sepsis is a non-discriminatory inflammatory reaction that can result in a diverse array of organ dysfunctions, which can be fatal. Pyroptosis is a programmed mechanism of cell death that is distinguishable from apoptosis and other forms of cellular demise. However, the role of pyroptosis in sepsis remains to be further explored. In this study, by employing a combination of the difference analysis, WGCNA, Friends' analysis, and machine learning, the central gene S100A12 was successfully identified. S100A12 demonstrated superb diagnostic capabilities in both the integrated and external validation datasets. Furthermore, significant disparities were observed in the levels of monocytes, eosinophils, and neutrophils between sepsis patients and the control group, as per the findings of immune infiltration analysis. The aforementioned immune infiltrating cells exhibited an increase in expression levels among patients diagnosed with sepsis and were found to be significantly and positively associated with S100A12 expression. The results of the single-cell analysis indicated a significant expression of S100A12 in both neutrophils and monocytes, which was in complete alignment with the outcomes of immune infiltration. In summary, the pyroptosis-related gene S100A12 represents a potential biomarker for the diagnosis and treatment of sepsis.

The efficacy of clinical drugs for acute lung injury/acute respiratory distress syndrome (ALI/ARDS) remains suboptimal. Phillyrin (PHN), a compound derived from Forsythia, is believed to alleviate sepsis-related ALI/ARDS; however, its mechanisms are not fully elucidated. In this study, we screened 8331 target genes associated with ALI/ARDS from public databases and identified six hub genes relevant to PHN treatment: AKT1, GSK-3β, PPP2CA, PPP2CB, PPP2R1A, and AR. Receiver operating characteristic analysis and single-cell sequencing analysis revealed the expression of AKT1, GSK-3β, PPP2CA, PPP2CB, and PPP2R1A were markedly elevated. Molecular docking and dynamics simulations indicated that PHN forms a structurally stable complex with glycogen synthase kinase-3β (GSK-3β). Mendelian randomization analyses suggested that PHN, as a potent GSK-3β inhibitor, may promote M2 macrophage polarization and reduce neutrophil recruitment. We validated these findings through in vivo and in vitro experiments, demonstrating that PHN lowers iNOS levels and raises MMR levels by downregulating GSK-3β mRNA expression and protein activity during lipopolysaccharide (LPS)-induced macrophage inflammation. Additionally, PHN inhibited GSK-3β mRNA expression and protein activity, reducing NF-κB-p65 nuclear translocation in LPS-induced zebrafish inflammation and mice ALI. This inhibition decreased levels of TNF-α and IL-6, increased IL-10 levels, promoted M2 macrophage polarization, suppressed neutrophil recruitment, and ultimately ameliorated ALI/ARDS. In conclusion, our results indicate that PHN effectively alleviates LPS-induced ALI/ARDS by suppressing GSK-3β signaling.

JOURNAL/nrgr/04.03/01300535-202507000-00024/figure1/v/2024-09-09T124005Z/r/image-tiff Differentiation of oligodendrocyte progenitor cells into mature myelin-forming oligodendrocytes contributes to remyelination. Failure of remyelination due to oligodendrocyte progenitor cell death can result in severe nerve damage. Ferroptosis is an iron-dependent form of regulated cell death caused by membrane rupture induced by lipid peroxidation, and plays an important role in the pathological process of ischemic stroke. However, there are few studies on oligodendrocyte progenitor cell ferroptosis. We analyzed transcriptome sequencing data from GEO databases and identified a role of ferroptosis in oligodendrocyte progenitor cell death and myelin injury after cerebral ischemia. Bioinformatics analysis suggested that perilipin-2 (PLIN2) was involved in oligodendrocyte progenitor cell ferroptosis. PLIN2 is a lipid storage protein and a marker of hypoxia-sensitive lipid droplet accumulation. For further investigation, we established a mouse model of cerebral ischemia/reperfusion. We found significant myelin damage after cerebral ischemia, as well as oligodendrocyte progenitor cell death and increased lipid peroxidation levels around the infarct area. The ferroptosis inhibitor, ferrostatin-1, rescued oligodendrocyte progenitor cell death and subsequent myelin injury. We also found increased PLIN2 levels in the peri-infarct area that co-localized with oligodendrocyte progenitor cells. Plin2 knockdown rescued demyelination and improved neurological deficits. Our findings suggest that targeting PLIN2 to regulate oligodendrocyte progenitor cell ferroptosis may be a potential therapeutic strategy for rescuing myelin damage after cerebral ischemia.

The partitioning and metabolism of carbohydrates and lignin in leaves are essential for numerous physiological functions, growth and development of plants. This study was aimed to characterize these processes in four leaf types (i.e., autumn-, summer-, spring- and current-year spring shoots) of two citrus hybrids (loose-skin mandarin cultivars OP (i.e., cultivars 'Orah' (OR) Citrus reticulata Blanco and 'Ponkan' (PO) Citrus reticulata Blanco and the sweet orange cultivars NT 'Newhall navel orange' (NO) Citrus sinensis (L.) Osbeck and 'Tarocco' (TA) Citrus sinensis (L.) Osbeck) differing in fruit maturation under field conditions. For this purpose, we analyzed the levels of foliar structural, non-structural carbohydrates and lignin and the expression of related genes. Our results showed that the contents of structural, non-structural carbohydrates and lignin measured in the two hybrids and its partitioning were mostly determined by differences in gene expression recorded in hybrids, cultivars and leaf type. Particularly, differences between leaf types were largely attributed to up- and down-regulation of the expression of genes of cellulose synthesis, lignin precursor synthesis, the Calvin cycle, glycolysis, the tricarbonic acid and starch synthesis and degradation pathways. These differences between leaf types required more complex transcriptional regulation than differences between hybrids and cultivars. The present results indicated that the two citrus hybrids studied differed in the expression of structural, non-structural carbohydrates and lignin-related genes. Future studies have to show if the differences observed in foliar partitioning and metabolism of carbohydrates and lignin are translated into partitioning and metabolism of carbohydrates and lignin in the roots.

Polycystic ovary syndrome (PCOS) is strongly associated with major depressive disorder (MDD), but the shared pathophysiological mechanisms between them are ambiguous, and the aim of this study was to explore the shared genetic features and associated pathways between these two disorders. MDD-related genes and mitochondrial function genes were downloaded from the GeneCards database. Weighted gene co-expression network analysis of Merge Cohort (GSE80432 and GSE34526) was performed to identify PCOS-related genes. Overlaps between PCOS-related genes, MDD-related genes, and mitochondrial function genes were defined as mitochondrial function-related shared genes. Functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed on the shared genes. Functional genes were then identified using Last Absolute Shrinkage and Selection Operator Regression (LASSO), and a support vector machine (SVM-RFE) was constructed to measure the accuracy of the calculations. Finally, the results were tested using the whole blood datasets GSE54250 (for PCOS) and GSE98793 (for MDD) as external validation sets. A total of 498 PCOS-related genes, 5909 MDD-related genes, and 7232 mitochondrial function genes were acquired, and totally, 40 shared genes were obtained from the overlap of the above three. The shared mitochondrial function genes were enriched for biological processes mainly involving cholesterol biosynthetic process, lipid metabolic process, triglyceride biosynthetic process, response to drug phosphatidic acid biosynthetic process, and endoplasmic reticulum membrane. Based on LASSO regression and SVM-RFE model, NPAS2 and NTS were identified as characteristic genes shared by two disorders. According to two external validation sets for PCOS and MDD, NPAS2 was finally identified as a key shared gene. Our analysis identified a mitochondrial functional gene-NPAS2-as the most critical candidate for linking PCOS and MDD. The present findings may provide new insights into the diagnosis and treatment of PCOS and MDD comorbidities.

Euphorbiaceae species are renowned not only for horticultural significance but for their production of numerous bicyclic diterpenes with antitumor and antiviral activities. However, the gene clusters responsible for the biosynthesis of these terpenes remain largely unidentified. We here initiated the construction of a comprehensive procedure for terpene gene clusters in Euphorbiaceae species. A total of 1824 candidate gene clusters with the range of 30-800 kb were identified across seven representative species including Ricinus communis, Hevea brasiliensis, Euphorbia peplus, Jatropha curcas, Manihot esculenta, Vernicia montana, and Vernicia fordii in Euphorbiaceae. The 16 high-confidence terpene gene clusters were ultimately pinpointed in Euphorbiaceae after satisfied the three stringent screening criteria: TPS/CYP pairwise relationship, copathway and coexpression patterns. Notably, the well-known casbene and casbene-derived diterpenoid gene cluster, involved in the biosynthesis of casbene, neocembrene, ingenanes, and jatrophanes, were identified. It was observed that casbene gene clusters were universally presented in Euphorbiaceae species, except M. esculenta. Among the casbene gene cluster, the alcohol dehydrogenase (ADH) was initially appeared, and neocembrene synthase is exclusively present in R. communis while absent in all the other species. These findings represent a significant step toward understanding the genetic basis of terpene biosynthesis in Euphorbiaceae species. Moreover, this knowledge on gene clusters responsible for the biosynthesis of pharmacologically relevant terpenes can serve as a theoretical foundation for future applications.

This study aimed to explore the key efferocytosis-related genes in diabetic retinopathy (DR) and their regulatory mechanisms. Public DR-related gene expression datasets, GSE160306 (training) and GSE60436 (validation), were downloaded. Differentially expressed efferocytosis-related genes (DEERGs) were analyzed using differential expression analysis and weighted gene co-expression network analysis. Functional enrichment analysis was conducted. Moreover, efferocytosis-related signature genes were identified using machine learning analysis, and their expression levels and diagnostic value were analyzed. Furthermore, nomograms were constructed; immune cell infiltration was analyzed; and gene set enrichment analysis, transcriptional regulation analysis, and small-molecule drug (SMD) prediction of efferocytosis-related signature genes were performed. In total, 36 DEERGs were identified in DR, and were markedly enriched in multiple functions, such as visual system development. Through further machine learning analysis, two efferocytosis-related signature genes, Ferritin Light Chain (FTL) and Fc Gamma Binding Protein (FCGBP), were identified, and were found to be upregulated in DR samples and showed high diagnostic performance for DR. A nomogram constructed using FTL and FCGBP accurately predicted the risk of DR. Moreover, the level of infiltration of immature B cells was positively correlated with FTL and FCGBP expression levels. Multiple transcription factors (TFs), such as CCCTC-Binding Factor (CTCF) and KLF Transcription Factor 9 (KLF9), were found to interact with both FTL and FCGBP. In addition, FTL can be targeted by miRNAs, such as miR-22-3p, and FCGBP can be targeted by miR-7973. In addition, both FTL and FCGBP can be targeted by SMDs, such as bisphenol A. Key efferocytosis-related genes, such as FTL and FCGBP, may promote DR development. Detecting or targeting FTL and FCGBP may aid in the prevention, diagnosis, and treatment of DR.