Pine trees, globally distributed and economically vital evergreen conifers, are threatened by pine wilt disease (PWD) attributed to the pine wood nematode (PWN). Many studies have been conducted on phenome and transcriptome profiling in select Pinus species upon PWN infection, but a high-throughput phenotyping of PWD progression and transcriptomic analysis across diverse Pinus species remains lacking. Here, we developed a deep learning-based phenotyping program to quantify PWD symptoms and conducted a pan-transcriptome analysis using PWD-susceptible (Pinus densiflora, Pinus koraiensis, Pinus thunbergii) and -resistant (Pinus parviflora, Pinus strobus, Pinus rigida × Pinus taeda) Pinus species and a hybrid. Our results showed severe wilting of leaves within 14 weeks after PWN infection in susceptible species but not in resistant ones. Pan-transcriptomic analysis revealed the upregulation of genes involved in leaf abscission and abscisic acid responses in PWD-resistant taxa, while PWD-susceptible taxa downregulated genes associated with desiccation response after PWN infection. These findings suggest that activating genes involved in water conservation plays a role in mitigating PWD infection in Pinus trees. Notably, all five Pinus species and one hybrid exhibited upregulation of the elongation factor Tu receptor (EFR) gene and pathogenesis-related (PR)-3 gene upon PWN infection, suggesting a potential role of the EF-Tu receptor in detecting PWN invasion and activating the PR-3 gene. Our study introduces a novel deep learning-based phenotyping program for precise PWD symptom quantification and enhances understanding of the molecular mechanisms underlying PWD resistance. These insights contribute to high-throughput monitoring of PWD progression in Pinus forests for disease prevention and facilitate the development of PWD-resistant pine trees.
Ebola virus (EBOV) infection triggers intense host transcriptional responses that overlap extensively with those induced by other viral and bacterial pathogens. This overlap complicates the identification of EBOV-specific gene expression signatures and limits diagnostic specificity. Defining transcriptional markers that distinguish EBOV from other infections is essential for improving molecular diagnostics and advancing understanding of EBOV-specific host responses. We developed a multi-step filtering framework using blood-derived RNA-Seq data from nonhuman primates and human cohorts organized into independent training and test sets. In the training cohort, differential expression analysis was performed using an edgeR-based GLMQL-MAS approach to identify EBOV-associated genes. Candidates were filtered against non-EBOV comparator datasets, including mpox virus, influenza, bacterial pneumonia, acute HIV-1 infection, and multiple SARS-CoV-2 variants, to remove broadly shared host-response genes. Genes included in the NanoString nCounter® Host Response Panel were additionally excluded. The resulting EBOV-specific signature was evaluated in independent EBOV and non-EBOV test cohorts using principal component analysis and logistic regression. Functional enrichment was assessed using KEGG pathways. Initial analysis identified numerous interferon-stimulated genes that were similarly upregulated across infections. After cross-infection filtering and NanoString exclusion, 281 EBOV-specific genes were identified. Optimization within the training cohort yielded a top-50 gene set that clearly separated EBOV from Non-EBOV samples. In the independent test cohort, classification performance improved substantially, with the F1 score increasing from 37.5% when all genes were used to 95.0% after applying the top-50 gene set. Enrichment analysis of the top-50 EBOV-specific genes revealed significant association with vascular, coagulation, secretory, and metabolic pathways. ADAMTS1 showed consistent upregulation in EBOV while remaining downregulated or inactive in comparator infections. Structured cross-pathogen filtering enables identification of EBOV-specific transcriptional features beyond shared antiviral responses. The validated gene signature generalizes across independent cohorts and highlights biologically distinct pathways, which supports its potential utility for host-based diagnostic development.
The prevalence and incidence of osteoarthritis (OA) increase significantly in women after menopause, indicating an important role of estrogen in the pathogenesis of OA. This type of OA is termed postmenopausal OA. This study aimed to investigate the feasibility of using bilateral ovariectomy (OVX) in adult SD rats to simulate the human postmenopausal OA model and evaluate the effect of early raloxifene (RAL) intervention on this model. Twenty-four SD rats were randomly divided into 4 groups: Baseline group, Sham + V group, OVX + V group, and OVX + RAL group. Rats in the Baseline group were euthanized for sample collection at the start of the experiment. Rats in the OVX + V and OVX + RAL groups underwent bilateral OVX, while those in the Sham + V group received a sham operation without actual ovarian resection. After surgery, the OVX + RAL group was given RAL (6.2 mg/kg·day) by gavage, and the OVX + V and Sham + V groups received an equal volume of normal saline. Samples were collected 3 months after drug administration. Micro-CT was used to determine the bone histomorphometry of the right proximal tibia. Following Micro-CT analysis, the right knee joints of all animals were decalcified for 8-12 weeks, embedded in paraffin, and sectioned. The sections were subjected to toluidine blue staining and immunohistochemical staining for collagen-II, Caspase-3, and matrix metalloproteinase-13 (MMP-13). The toluidine blue-stained sections were scored using the OARSI histological scoring system, and the positive protein expression in immunohistochemical staining was evaluated using the IOD. The OARSI score revealed that the degree of cartilage degeneration in the OVX + V group was more severe than that in the Sham + V group and the OVX + RAL group. The expression of collagen-II in the OVX + V group was significantly lower than that in the Sham + V group and the OVX + RAL group, while the expressions of MMP-13 and Caspase-3 increased. Micro-CT revealed that the microstructure of subchondral bone in the OVX + V group deteriorated compared with the Sham + V group, while that in the OVX + RAL group improved compared with the OVX + V group. Compared with the Baseline group, the microstructure of subchondral bone and cartilage in the Sham + V group was somewhat degraded. We reached a conclusion that OVX-induced degeneration of subchondral bone and articular cartilage is relatively mild, suggesting that 6-month-old OVX rats are a mild model of postmenopausal OA. RAL can delay OVX-induced postmenopausal subchondral bone and cartilage degeneration. Notably, this study further clarifies the protective effect of RAL on the medial joint capsule and refines the regulatory mechanism of RAL on subchondral bone microstructure in mild postmenopausal OA, which supplements the existing research on RAL in OA intervention.
Feed is the main cost of production in dairy farming. Any improvement in feed efficiency (FE) would increase marginal profit and sustainability and mitigate the environmental impact of dairy farming. In this study, we applied single-step genomic best linear unbiased prediction to different feed-efficiency metrics using records collected from Nordic Red dairy cattle (RDC). The main objective was to compare different metrics in terms of their effectiveness in selecting more feed-efficient animals. Weekly observations (n = 22,071) of dry-matter intake records from 791 RDC cows collected from 1998 to 2021 were used in this study. The pedigree consisted of 5,604 individuals, of which 1,489 animals were genotyped. Different modeling approaches, including conventional residual feed intake (RFI), regression on expected feed intake (ReFI), two multi-trait residual feed efficiency indices (RFIIndex and RZFE), and energy conversion efficiency (ECE) were analyzed. For the ReFI approach, two alternatives for predicting the expected feed intake, namely, a prediction equation tailored to the RDC data and a prediction equation based on Holstein dairy cow data proposed by the National Academies of Sciences, Engineering, and Medicine (NRC 2021), were compared. First, a BLUP model was developed, and the necessary variance components were estimated for each approach. Then, pedigree-based and genomic-enhanced breeding values (PEBV and GEBV, respectively) were estimated using either reduced or full datasets. For model validation, PEBV and GEBV estimated using the full dataset were regressed on PEBV and GEBV estimated using the reduced dataset, respectively, to measure bias, dispersion, and prediction accuracy (PAC). The heritability estimates of different residual metrics ranged from 0.23 for RFI to 0.30 for ReFINRC2021, and the repeatability estimates ranged from 0.48 to 0.52. The estimated heritability and repeatability of ECE were 0.23 and 0.56, respectively. For all metrics, the use of genomic information increased PAC. However, there were discrepancies between the metrics in terms of the magnitude of PAC, with the PAC being the highest for ReFIRDC and the lowest for RFIIndex. Similarly, ReFIRDC had the lowest bias, while the highest bias was estimated for RFIIndex. In addition, RZFE and ReFIRDC showed lower dispersion. The correlations between GEBV of the residual metrics and the GEBV of ECE were lowest for RFINRC2021 and RFI and highest for ReFIRDC. Among the metrics compared, ReFIRDC and RFIIndex showed the highest effectiveness in selecting efficient cows. This indicates that the use of appropriate partial regression coefficients and the type of modeling are vital in breeding programs aimed at enhancing FE.
The tumor microenvironment (TME) represents a complex system comprising various cells and extracellular matrix components that play a crucial role in tumor initiation and progression. While recent therapeutic strategies for predominantly focus on targeting tumor cells, their impact on other cellular components in the TME, such as regulatory T (Treg) cells, remains insufficiently understood. The cellular components of the TME include tumor cells, immune cells, tumor-associated stromal cells, and myeloid-derived suppressor cells. Notably, the role of Treg cells in tumor therapy has emerged as a significant research area of focus in recent years. Regulatory CD4+ T cells, characterized by the expression of the transcription factor Forkhead Box P3 (FOXP3) and the surface marker CD25, are pivotal in mediating immune suppression and maintaining immune tolerance and homeostasis. Current tumor treatments mainly rely on radiation and chemotherapy. Although innovative therapies such as immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T-cell (CAR-T) therapies have demonstrated promising outcomes, their efficacy is limited, benefiting only a small subset of patients. Epigenetic inhibitors are increasingly recognized as pivotal in cancer treatment; however, prior research has predominantly concentrated on their effects on the tumor itself, while overlooking the potential influence of these compounds on regulatory T cells (Tregs) within the tumor microenvironment (TME). The therapeutic viability of modulating Tregs within the TME remains uncertain. The intricate microenvironment of the TME significantly influences the distinct epigenetic landscape of tumor-infiltrating Treg cells, including modifications in DNA methylation, histone modifications, and chromatin remodeling. A comprehensive understanding of these epigenetic modifications and the underlying factors driving them could unveil novel strategies for cancer therapy. This approach would enhance the understanding of the critical role of Tregs in tumor therapy and facilitate the development of more effective targeted therapies by addressing the unique epigenetic characteristics of tumor-infiltrating Tregs.
Environmental and chemical exposures are major yet incompletely characterized drivers of human carcinogenesis. Aflatoxin, a potent food-borne mycotoxin, has been implicated in tumor initiation, proliferation, and immune suppression in intrahepatic cholangiocarcinoma (ICC), but its genomic mechanisms remain poorly defined. We employed extensive literature data mining (LDM) to identify genes associated with both aflatoxin exposure and ICC, enabling construction of a mechanistic genetic pathway linking exposure to disease. These pathways were further evaluated using a meta-analysis of five Gene Expression Omnibus (GEO) expression datasets, followed by functional annotation to characterize their biological roles. LDM identified 1,754 ICC-associated genes, of which 427 were also linked to aflatoxin, with 154 positioned as potential intermediate regulators connecting aflatoxin exposure to ICC. Meta-analysis revealed significant expression alterations in six genes upon aflatoxin exposure, including upregulation of CRP, CDK2, AXL, and MIR221 (overexpression >50%, p < 0.05) and downregulation of F2 and BUB1B (reduced expression >60%, p < 0.014). Co-expression analysis indicated strong interactions among these regulators (Fisher's Z > 0.53, p < 0.05), suggesting coordinated molecular responses associated with ICC progression. Functional annotation further highlighted inflammatory responses, cytokine dysregulation, and kinase-related signaling as key processes potentially linking aflatoxin exposure to ICC development. These findings provide a systems-level view of the genomic mechanisms underlying aflatoxin-associated ICC carcinogenesis and identify candidate molecular mediators linking environmental toxin exposure to tumor development. This integrative framework may facilitate exposure-informed biomarker discovery and potential preventive or therapeutic strategies, particularly in regions where aflatoxin exposure remains prevalent.
Osteoporosis (OP) is characterized by impaired bone homeostasis in which bone resorption exceeds bone formation. Disulfidptosis, a recently described disulfide stress-induced cell death program linked to cytoskeletal collapse, has been suggested to contribute to OP, yet its cell-type-specific relevance within the bone marrow mesenchymal stem cell (BM-MSC) osteogenic lineage-and the upstream upstream transcriptional and epigenetic programs shaping this stress response-remain unclear. This study integrated a peripheral blood monocyte microarray dataset (GSE56815; 20 OP vs. 20 controls) with single-cell RNA sequencing to characterize disulfidptosis-related programs in OP. OP-associated disulfidptosis genes and molecular subtypes were identified using co-expression and differential analyses. Disulfidptosis score and upstream regulators across bone marrow cell populations were inferred from single-cell data using regulon analysis with motif/cis-regulatory evidence. Chromatin remodeling-related gene modules in osteoblasts were additionally scored to assess epigenetic activation/repression programs and their association with BHLHE41. Integrated WGCNA with gene-overlap screening pinpointed 17 OP-linked disulfidptosis signature genes, highlighted by a marked increase of SLC7A11, and unsupervised stratification separated OP into two molecular subtypes. Single-cell analyses showed that disulfidptosis score was enriched in the osteoblast-like subset of BM-MSCs, implicating osteoblasts as a major affected population. Network inference further nominated BHLHE41 as a potential upstream driver of SLC7A11 and connected its expression with a disulfidptosis-tolerant phenotype in OP-derived BM-MSCs. Chromatin remodeling pathway scoring indicated altered epigenetic state programs in OP osteoblasts, and SIRT1 was preferentially upregulated in BHLHE41-high osteoblasts. This study provides a cell-type-resolved map of disulfidptosis-related stress in osteoporosis by integrating bulk and single-cell transcriptomics. We propose a BM-MSC osteogenic-lineage-associated BHLHE41-SLC7A11 axis and link it to chromatin remodeling signatures in osteoblasts, offering a rationale for precision strategies targeting disulfide-stress vulnerability in OP.
Protein complexes play a crucial role in cellular biological processes. Identifying these complexes is essential for understanding cellular functions and biological mechanisms. Graph clustering approaches to identify protein complexes in protein-protein interaction (PPI) networks have become a significant research hotspot in data mining and bioinformatics. Many graph clustering methods have been developed for protein complex identification. However, most existing methods only utilize original networks to discover dense subgraphs and ignore higher-order topological characteristics. Considering the prevalent multi-relational and complex interactions in biological networks, a graph clustering algorithm based on hypergraph learning and a core-attachment strategy is proposed for protein complex identification, called HLCA. Hypergraph networks are employed to directly model multi-relational interactions. Based on this method, a multi-level hypergraph is used as higher-order topology and a core-attachment strategy are adopted to identify protein complexes. Firstly, the original PPI network is transformed into a hypergraph network. Secondly, a hierarchical compression strategy is applied to recursively compress the hypergraph into smaller hypergraphs at various levels, forming a multi-level analytical framework. Thirdly, hypergraph convolution is performed across different hierarchical levels to obtain node representations at each level. These node representations are then combined to produce complete node embeddings. Based on these node embeddings, a weighted PPI network is constructed by cosine similarity from the original PPI network. Core clusters are obtained in this weighted network by cluster density. Finally, remaining protein nodes are added to the core clusters using a core-attachment strategy combining hyperedge density and overlap. The effectiveness of HLCA is evaluated by comparing it with other protein complex identification methods on multiple datasets. Experimental results show that the proposed method outperforms comparison methods regarding F-measure and Accuracy.
To perform a genetic analysis of a rare complex chimeric fetus with a 45,X/46,X,dic r(Y; Y)/46,X,r(Y) karyotype, indicated by NIPT as having sex chromosome abnormalities but with normal ultrasound findings. This study underscores the critical role of integrating multiple molecular cytogenetic techniques in deciphering such complex cases, which is essential for accurate prognosis and personalized genetic counseling. The findings aim to deepen the understanding of genotype-phenotype correlations in rare chromosomal mosaicism and to guide clinical management. Amniotic fluid was collected from a pregnant woman with an abnormal sex chromosome indicated by NIPT. Combined detection using G-banding karyotype analysis, fluorescence in situ hybridization (FISH), and low-depth whole-genome copy number variation sequencing (CNV-seq) techniques was performed. Simultaneously collect peripheral blood samples from the fetus's parents for CNV-seq detection and paternal chromosomal karyotype analysis. The infant underwent comprehensive postnatal follow-up, including physical examination, growth assessment, developmental screening, sex hormone profiling, Y chromosome microdeletion testing, and scrotal ultrasound at 19 months of age. The male fetus was confirmed to have a complex karyotype through combined analysis of chromosomal G-band technology, FISH, and CNV-seq. The findings included a dicentric ring Y chromosome with mosaicism for Yp and Yq deletions, as well as a 1.40 Mb duplication in the 7q11.23 region, resulting in the karyotype: 45,X[82]/46,X,dic r(Y; Y)(p11.31q11.23; p11.31q11.23)[13]/46,X,r(Y)(p11.31q11.23) [5]dn. The father's karyotype was normal, suggesting a de novo mutation. Maternal CNV-seq was normal, while paternal CNV-seq identified the same 1.40 Mb 7q11.23 duplication, indicating paternal inheritance of this pathogenic variant. After genetic counseling, the parents proceeded with the pregnancy. On 27 June 2024, at 35+5 weeks of gestation, they gave birth to a live male infant naturally, with a length of 48 cm and a weight of 2800 g. No obvious abnormalities were observed in the appearance. The integration of G-banding, FISH, and CNV-seq enables accurate diagnosis of complex ring Y chromosome mosaicism, providing crucial information for genetic counseling and clinical management. The clinical phenotype depends on the ring chromosome's structure, breakpoints, and the degree of mosaicism.
Systemic lupus erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and lupus nephritis (LN) is a severe manifestation. Long non-coding RNAs (lncRNAs) have been implicated in regulating immune responses in autoimmune diseases. LOC100130476, a lncRNA located on chromosome 6q23.3, has been linked to inflammation and cancer progression, but its role in SLE and LN remains unclear. We studied the association between the rs80213143 variant at LOC100130476 and SLE susceptibility in a Chinese Han cohort, using SNP genotyping and Bonferroni correction for multiple comparisons. Functional annotations were conducted to explore the effects of rs80213143 on transcription factor binding and gene expression. eQTL analysis was performed to assess the variant's impact on immune cell gene expression. Within LOC100130476, the strongest association was observed at rs80213143 (p = 2.5 × 10-7), which was successfully replicated (p = 2.64 × 10-9) in an independent cohort. The combined analysis of both discovery and replication cohorts reinforced the genetic association (pmeta = 2.04 × 10-14). The risk C allele was linked to more severe renal involvement, including higher 24-h proteinuria and serum creatinine levels. Functional annotations indicated that rs80213143 potentially influences immune cell functionality through regulatory motif alterations. The expression of LOC100130476 was abnormally upregulated in the whole blood of SLE patients, particularly in lupus nephritis patients. Moreover, the expression of LOC100130476 was significantly upregulated in the biopsy samples of lupus nephritis patients. Differentially expressed genes in whole blood between SLE patients and healthy donors, positively associated with LOC100130476 expression, were significantly enriched in pathways involving T cell receptor signaling, antigen presentation, interferon response, and apoptosis. Furthermore, LOC100130476 showed positive associations with genes differentially expressed between LN patients' renal biopsy tissues and adjacent normal renal tissues, enriched in leukocyte-mediated immunity, inflammatory responses, extracellular matrix and tissue repair pathways, and the PI3K signaling network. The rs80213143 variant in LOC100130476 is associated with SLE susceptibility and renal involvement. Its elevated expression in lupus nephritis suggests it may be an important factor in disease pathogenesis and a potential biomarker for lupus nephritis.
Pre-eclampsia (PE) is a specific type of gestational hypertension associated with high morbidity and mortality. This study aims to identify mitochondria-related regulatory molecules in PE through bioinformatics analysis, which will help pinpoint potential therapeutic targets and elucidate potential mechanisms of action in PE. This study integrated three PE placental transcriptome datasets (n = 103/157) to screen for mitochondrial-related hub genes. Key gene screening was performed by combining three machine learning algorithms-Random Forest, LASSO, and SVM-followed by the construction of a diagnostic neural network model. Additionally, single-cell sequencing data were utilized to analyze the cellular expression patterns of candidate genes in the placenta. To further elucidate the underlying mechanisms, functional validation was conducted both in PE rat model and in vitro using HTR-8 cells, supplemented by multi-omics correlation analysis. Machine learning analysis identified three key genes (GCLM, SNAP23, RHOT2), and the diagnostic model built upon them demonstrated excellent performance (training set AUC = 0.907; validation set AUC = 0.875). Single-cell analysis revealed the expression patterns of these genes within specific cell subtypes, consistent with the transcriptional features of trophoblast cell populations. In the PE rat model, downregulation of GCLM and SNAP23 and upregulation of RHOT2 were significantly correlated with clinical phenotypes such as hypertension and proteinuria, as well as changes in placental inflammatory factor levels (TNF-α, IL-1β, IL-6). Specifically, SNAP23 and GCLM showed negative correlations with inflammatory cytokines but positive correlations with fetal weight, while RHOT2 expression positively correlated with disease severity. In vitro experiments confirmed that overexpression of SNAP23 restored mitochondrial membrane potential, reduced reactive oxygen species levels, and suppressed cytokine release in lipopolysaccharide (LPS)-treated HTR-8 cells. Multi-omics analysis further indicated that these genes are involved in immune dysregulation and mitochondrial dysfunction during PE progression. This study establishes GCLM, SNAP23, and RHOT2 as mechanistically important biomarkers for preeclampsia. Among them, modulation of SNAP23 shows therapeutic potential in alleviating mitochondrial damage and inflammatory responses in PE, providing a new direction for intervention strategies.
Fertility is a multifactorial trait and a key determinant of productivity and sustainability in beef cattle production. Identifying molecular mechanisms and biomarkers associated with fertility could improve the prediction of reproductive potential in beef heifers. Herein, by combining transcriptomic and proteomic data from peripheral white blood cells (PWBCs) collected before the time of artificial insemination (AI), we investigated molecular differences between fertile and subfertile beef heifers (n = 6 per group) classified based on their reproductive outcomes. RNA-Sequencing and untargeted proteomics identified 230 differentially expressed genes (DEGs; P ≤ 0.05 and |log2FC| ≥ 0.5) and 70 differentially abundant proteins (DAPs; P ≤ 0.05) between groups. Over-representation analyses revealed that these molecules were associated with cell cycle regulation, metabolism, and immune-related pathways, including chemokine and JAK-STAT signaling (P ≤ 0.01). Data integration revealed limited overlap between DEGs and DAPs (UROS, KIFC3, DHRSX, and NPL). Among these, NPL expression was previously reported to be progesterone-responsive, supporting its potential role in early pregnancy establishment. Network analyses revealed distinct regulatory patterns between groups (|r ≥ 0.95| and P ≤ 0.05). At the transcript level, subfertile heifers exhibited increased connectivity, indicating potential compensatory transcriptional rewiring. We identified 92 regulatory impact factor (RIF) genes with potential modulatory roles, including ESR1. Epigenetic transcription factors, including MBD1, MBD2, and SMARCE1, were also rewired, suggesting an interplay between hormone signaling and chromatin regulation that modulates transcript expression and consequently fertility outcomes. Our results show that PWBCs reflect systemic molecular changes associated with fertility status and represent a promising, non-invasive source for biomarker discovery. This integrative multi-omics approach provided novel insights into the regulatory networks underlying fertility in beef heifers, highlighting the value of integrating multi-omics to identify key pathways and molecular targets to improve reproductive efficiency in beef production systems.
Cutaneous melanoma is a highly aggressive malignancy characterized by significant heterogeneity, rapid progression, and variable treatment responses. Understanding the functional diversity of melanoma cells and their interactions with the tumor microenvironment (TME) is crucial for developing effective therapeutic strategies and identifying prognostic biomarkers. We performed comprehensive single-cell RNA sequencing (scRNA-seq) analysis of 70,760 cells from 11 melanoma samples. Data processing was conducted using Seurat v4.3.0 with Harmony integration. Cell-cell communication was inferred using CellChat, and pseudotime trajectory analysis was performed using Monocle 2. A prognostic model was constructed by integrating 10 machine learning algorithms within a leave-one-out cross-validation (LOOCV) framework using the TCGA-SKCM cohort. Experimental validation was performed using immunofluorescence analysis on clinical specimens from seven melanoma patients. We identified seven major cell types and characterized nine distinct melanoma cell subpopulations with unique molecular signatures. Notably, subpopulations Mela4, Mela6, and Mela9 demonstrated significant associations with favorable patient prognosis and exhibited the highest interaction strength with immune cells in the TME. Cell communication analysis revealed that these subpopulations primarily engaged in signaling through MIF-CD74/CD44/CXCR4 and MHC-I pathways, with CD8+ T cells being the predominant signal recipients. Pseudotime trajectory analysis identified critical genes (CYR61, JUN, RHOC) involved in melanoma cell state transitions. Using an integrative machine learning approach, we developed a melanoma cell-associated signature (MRS) comprising 15 genes that achieved a mean C-index of 0.675 across validation cohorts. Furthermore, High EIF5A expression was significantly associated with poor patient outcomes (p < 0.001), Immunofluorescence analysis showing significantly elevated EIF5A expression in melanoma tissues compared to controls (p < 0.01). This study reveals the functional heterogeneity of melanoma cells and their interactions with the immune microenvironment, identifies key subpopulations, prognostic signatures, and EIF5A as a plausible prognostic biomarker candidate and potential therapeutic target that warrants mechanistic validation in melanoma.
Sponge gourd (L. cylindrica Mill.), a crucial dual-purpose crop in the Cucurbitaceae family, faces severe yield losses due to diseases like Fusarium wilt and Tomato leaf curl New Delhi virus (ToLCNDV) infection. The NBS-LRR (Nucleotide-Binding Site-Leucine-Rich Repeat) gene family, core components of plant Effector-Triggered Immunity (ETI), plays a vital role in pathogen defense. This study conducted a comprehensive genome-wide analysis of the NBS-LRR gene family in Luffa cylindrica using bioinformatics tools and transcriptome data. A total of 89 NBS-LRR genes were identified and classified into seven subfamilies: TIR(Toll/Interleukin-1 Receptor)-NBS-LRR(TNL) (15), TIR-NBS(TN)(16), CC (Coiled-Coil))-NBS-LRR(CNL)(8), CC-NBS (CN) (14), NBS(N)(23), NBS-LRR (NL) (10), and RN(RPW8-NBS) (3). These genes showed irregular distribution across 12 chromosomes, with the highest density on Chr08. Phylogenetic analysis revealed five primary clades, reflecting evolutionary relationships among subfamilies. Conserved domain and motif analysis indicated intra-subfamily conservation and inter-subfamily divergence, with all members containing the core NB-ARC domain. Promoter cis-acting element analysis identified 65 elements, with light-responsive and hormone/defense-stress-responsive elements being predominant, suggesting involvement in multiple biological processes. Intra-species synteny analysis found two homologous gene pairs between Chr02 and Chr06, while inter-species analysis showed closer evolutionary ties with cucumber (18 orthologous pairs) than Arabidopsis (1 ortholog) and no orthologs with rice. Tissue-specific expression analysis revealed highest expression in roots, and disease response analysis identified six genes associated with ToLCNDV resistance and nine genes linked to Fusarium wilt resistance. These findings provide valuable resources for understanding the molecular basis of disease resistance in L. cylindrica and accelerating disease-resistant breeding.
The recent rise in global food insecurity has renewed scientific interest in understanding the long-term health consequences of early-life nutritional deprivation. This study critically evaluates the experimental designs and methodological approaches of key publications examining the epigenetic and phenotypic effects of the Dutch and Chinese famines. Specifically, these studies were assessed for sample size, control group selection, relevance of tissue sampling, timing of famine exposure, and the quality of statistical reporting. Research on both famines has centered on prenatal exposure and subsequent health outcomes, providing important insights into how in utero nutritional deprivation may lead to long-lasting epigenetic modifications. These changes have been linked to elevated risks for metabolic, cardiovascular, and neuropsychiatric disorders. Despite these contributions, many studies exhibited notable limitations, including small sample sizes, questionable accuracy in reporting health outcomes, and issues with the selection of control groups. Such methodological shortcomings may have led to the misinterpretation of some findings. Ongoing and recent famines in regions such as Sudan, Somalia, and Gaza-driven by conflict and environmental disasters, including droughts and floods-represent some of the most pressing humanitarian crises of our time. Lessons from studies of the 20th-century Dutch and Chinese famines can inform the design of future research on the biological and intergenerational consequences of famine and trauma. Improved study designs will enhance the ability to generate reliable evidence and guide global health strategies for populations at risk of transgenerational effects from nutritional deprivation.
Nijmegen Breakage Syndrome (NBS) is a rare autosomal recessive disorder characterized by chromosomal instability, immunodeficiency, radiosensitivity, and a strong predisposition to lymphoid malignancies. It is caused by mutations in the NBN gene encoding nibrin (NBS1) protein, a core component of the MRE11-RAD50-NBS1 (MRN) complex that senses DNA double-strand breaks (DSBs) and coordinates DNA damage response, including ATM activation. Despite the importance of NBS1, in the Chinese hamster system, which offers significant advantages in radiation biology and toxicology, no mutant lines deficient in the NBS1 gene have been isolated. In this study, we generated two novel NBS1 mutant Chinese hamster cell lines using CRISPR/Cas9, each carrying distinct NBN mutations leading to either null or hypomorphic mutations. These mutants exhibited growth retardation, marked sensitivity to ionizing radiation and various DNA damaging agents and elevated radiation induced chromosomal aberrations, recapitulating key NBS phenotypes. Notably, NBS1 mutant cells displayed pronounced hypersensitivity to ionizing radiation when co-treated with an ATR inhibitor, but not with a DNA-PK inhibitor. The ATR inhibitor also markedly sensitized NBS1 mutants to Etoposide, suggesting that ATR functions as a compensatory pathway in the absence of functional NBS1 during specific types of DNA damage. Collectively, our findings establish valuable NBS1-deficient Chinese hamster cell models that expand understanding of NBS1 function and highlight their utility for investigating DNA repair deficiencies and developing targeted therapeutic approaches for chromosomal instability disorders and cancers with NBS1 mutations.
Cancer remains one of the leading causes of morbidity and mortality worldwide and poses a major threat to global public health. Despite substantial advances in early diagnosis and therapeutic strategies, patient outcomes vary widely due to the pronounced molecular and clinical heterogeneity of tumors. Accurate identification of cancer subtypes is therefore essential for elucidating tumor heterogeneity, improving prognostic assessment, and enabling precision medicine. In recent years, multi-omics technologies have provided unprecedented opportunities to characterize cancer at multiple molecular layers, including genomic, epigenomic, transcriptomic, and proteomic levels. However, effectively integrating high-dimensional and heterogeneous multi-omics data remains a major challenge. Moreover, many existing graph convolutional network-based integration methods suffer from over-smoothing and limited utilization of deep feature representations, which restrict their ability to capture complex multi-scale relationships inherent in cancer biology. To address these challenges, we propose MoJKNet, a novel multi-omics integration framework for cancer subtype classification. MoJKNet incorporates a jumping knowledge network (JK-Net) to adaptively aggregate node representations across multiple propagation depths, thereby alleviating over-smoothing and enhancing feature extraction within each omics modality. Subsequently, a multimodal autoencoder combined with similarity network fusion (SNF) is employed to capture complementary information across different omics layers. Finally, a graph attention network (GAT) assigns adaptive feature weights to enable accurate cancer subtype prediction. We evaluated MoJKNet on seven cancer types from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that MoJKNet consistently outperforms state-of-the-art methods, including MOGCAN, MOGONET, and MoGCN, in terms of precision, recall, and F1-score, achieving nearly a 10% performance improvement on the COADREAD dataset. Ablation studies further confirm the critical contribution of the jumping knowledge mechanism to improved representation learning. Overall, MoJKNet provides an effective and generalizable solution for multi-omics data integration and cancer subtype classification, with strong potential for downstream biological interpretation and translational applications.
Lung adenocarcinoma (LUAD) is a prevalent and aggressive subtype of lung cancer, with a 5-year survival rate below 20% due to late-stage diagnosis and drug resistance. Endoplasmic reticulum stress (ERS) and butyrate metabolism (BM) play critical roles in tumor progression, but their co-regulatory features in LUAD remain unclear. This study integrated single-cell transcriptome analysis and Mendelian randomization (MR) to identify prognostic genes associated with ERS and BM in LUAD. Public datasets were analyzed using weighted gene co-expression network analysis, differential expression analysis, and MR. A risk model and nomogram were constructed, and immune microenvironment, gene set enrichment, and single-cell analyses were performed to validate findings. Moreover, the expression of prognostic genes was validated in different Non-small cell lung cancer (NSCLC) cell lines through reverse transcription quantitative polymerase chain reaction (RT-qPCR). Seven prognostic genes (VDAC1, TXNRD1, GDF15, TRIB3, LPL, KCNQ1, PKP2) were identified, RT-qPCR assays confirmed that these genes exhibited significant expression differences in different NSCLC cell lines. The risk model demonstrated that low-risk patients had significantly better survival outcomes. The nomogram exhibited strong predictive accuracy for 1-, 3-, and 5-year survival. Enriched pathways in high-risk patients included olfactory transduction, while low-risk patients showed enrichment in ribosome and complement-coagulation cascades. Immune profiling revealed 13 differentially abundant immune cell types, including M1 macrophages. Single-cell analysis identified macrophages as key players in LUAD. Notably, VDAC1, TXNRD1, and LPL were highly expressed during early macrophage differentiation. This study identifies seven ERS- and BM-related prognostic genes and highlights macrophages as pivotal in LUAD progression, the expression differences of candidate genes were verified by RT-qPCR assay. These findings provide novel insights into LUAD diagnosis, prognosis, and potential therapeutic targets, offering a foundation for precision medicine strategies. Further validation in clinical cohorts and functional studies is warranted to translate these discoveries into clinical applications.
Familial lecithin-cholesterol acyltransferase (LCAT) deficiency and α0-thalassemia are rare autosomal recessive disorders. Although both disease-causing genes reside on chromosome 16, their physical distance typically results in independent inheritance in non-consanguineous populations. Co-inheritance of both conditions has not been previously reported. A 50-year-old Chinese man with childhood-onset corneal opacity and long-standing anemia presented with 2 months of progressive lower limb edema. Laboratory evaluation revealed nephrotic syndrome and markedly reduced high-density lipoprotein cholesterol (HDL-C). Renal biopsy showed characteristic glomerular lipid deposition, confirming LCAT deficiency. Genetic testing identified a homozygous LCAT mutation (c.355G>C, p.Gly119Arg), with both parents confirmed as heterozygous carriers. The patient had severe microcytic hypochromic anemia that did not fully align with the mild hemolytic anemia typical of LCAT deficiency. Given parental consanguinity, expanded genetic testing revealed co-inheritance of α0-thalassemia (HBA: -SEA/αα), explaining the hematological phenotype. No specific treatment exists for LCAT deficiency. Symptomatic management with angiotensin-converting enzyme inhibitors and diuretics improved edema. The α0-thalassemia trait is asymptomatic and requires no intervention; its diagnosis avoided unnecessary iron therapy and the associated risk of iron overload. Long-term follow-up will focus on renal function, proteinuria, lipid profile, and ocular findings. Genetic counseling will also be provided to the patient and their family. To our knowledge, this is the first reported case of co-inherited LCAT deficiency and α0-thalassemia confirmed by both renal pathology and comprehensive genetic testing. The consanguineous background suggests possible co-transmission of distant recessive variants on the same chromosome. This case highlights the importance of considering coexisting genetic disorders in patients with consanguinity or unexplained multisystem involvement.
The ancestral recombination graph (ARG) is the model of choice in statistical genetics to model population ancestries. Software capable of inferring ARGs on a genome scale within a reasonable amount of time are now widely available for most practical use cases. While the inverse problem of inferring ancestries from a sample of haplotypes has seen major progress in the last decade, it does not enjoy the same level of advancement as its counterpart. Up until recently, even moderately sized samples could only be handled using heuristics. In recent years, the possibility of model-based inference for datasets closer to "real world" scenarios has become a reality, largely due to the development of threading-based algorithms. This article introduces Moonshine.jl, a Julia package that has the ability, among other things, to infer ARGs for samples of thousands of human haplotypes of sizes on the order of hundreds of megabases within a reasonable amount of time. On recent hardware, our package is able to infer an ARG for samples of densely haplotyped (over one marker/kilobase) human chromosomes of sizes up to 10,000 in well under a day on data simulated by msprime. Scaling up simulation on a compute cluster is straightforward since each ARG is inferred independently using a single thread. While model-based, it does not resort to threading but rather places restrictions on probability distributions typically used in simulation software in order to enforce sample consistency. In addition to being efficient, a strong emphasis is placed on ease of use and integration into the biostatistical software ecosystem.