Genes最新文献

Background: Peperomia leptostachya is a herbaceous plant with significant medicinal value. To elucidate its mitochondrial genomic characteristics, this study conducted a systematic analysis. Methods: The mitochondrial genome of P. leptostachya was assembled, annotated, and subjected to comparative analysis. Results: (1) The genome exhibits significant structural peculiarities, presenting as an atypical circular structure accompanied by an independent minicircle, forming a multi-branched reticulate configuration spanning a total length of 981,249 bp. Within the mitochondrial genome of P. leptostachya, a total of 52 genes have been identified, including 35 PCGs, 14 tRNAs and 3 rRNAs. (2) A phylogenetic tree was built for 22 species based on the DNA sequences. P. leptostachya belongs to the family Piperaceae within the order Piperales and is closely related to Piper nigrum. (3) Homologous colinear blocks were detected between P. leptostachya and its close relatives, though these blocks exhibited short lengths. Additionally, blank regions were identified that showed no homology with other species. Mitochondrial genomes of P. leptostachya and two close relatives had inconsistent collinear block arrangements. The mitochondrial genome of P. leptostachya had undergone genomic rearrangement relative to closely related species. Conclusions: This study lays the foundation for research into the genetic characteristics and biological traits of P. leptostachya.

Background/objectives: The arrangement and positioning of genes on chromosomes are non-random in plant genomes. Adjacent gene pairs often exhibit similar co-expression patterns and regulatory mechanisms. However, the genomic and epigenetic features influencing such co-expression, particularly in perennial crops like tea (Camellia sinensis), remain largely uncharacterized.

Methods: Firstly, we identified 771 specific neighboring gene pairs (SNGs) in C. sinensis (YK10) and investigated the contributions of intergenic distance and gene length to SNGs' co-expression. Secondly, we integrated multi-omics data including transcriptome, ATAC-seq, Hi-C and histone modification data to explore the factors influencing their co-expression. Thirdly, we employed logistic regression models to individually assess the contributions of nine factors-ATAC-seq, H3K27ac, Hi-C, GO, distance, length, promoter, enhancer, and expression level-to the co-expression of SNGs. Finally, by integrating co-expression networks with metabolic profiles, several transcription factors potentially involved in the regulation of catechin metabolic pathways were identified.

Results: Intergenic distance was significantly negatively correlated with co-expression strength, while gene length showed a positive correlation. Furthermore, these two features exerted synergistic effects with threshold characteristics and functional significance. SNGs marked by either ATAC-seq or H3K27ac peaks displayed significantly higher expression levels, suggesting that epigenetic regulation promotes co-expression. In addition, correlation analysis revealed that the expression of certain SNGs was closely associated with catechin accumulation, particularly epicatechin gallate (EGC) and epigallocatechin gallate (EGCG), highlighting their potential role in modulating tissue-specific catechin levels.

Conclusions: Collectively, this study reveals a multilayered regulatory framework governing SNG co-expression and provides theoretical insights and candidate regulators for understanding metabolic regulation in tea plants.

The gut microbiome is defined as the collective assembly of microbial communities inhabiting the gut, along with their genes and metabolic products. The gut microbiome systematically regulates host metabolism, immunity, and neuroendocrine homeostasis via interspecies interaction networks and inter-organ axes. Given the importance of the gut microbiome to the host, this review integrates the composition, function, and genetic basis of the gut microbiome with host genomics to provide a systematic overview of recent advances in microbiome-host interactions. This encompasses a complete technological pipeline spanning from in vitro to in vivo models to translational medicine. This technological pipeline spans from single-bacterium CRISPR editing, organoid-microbiome co-culture, and sterile/humanized animal models to multi-omics integrated algorithms, machine learning causal inference, and individualized probiotic design. It aims to transform microbiome associations into precision intervention strategies that can be targeted and predicted for clinical application through interdisciplinary research, thereby providing the cornerstone of a new generation of precision treatment strategies for cancer, metabolic, and neurodegenerative diseases.

Background: Black spot disease severely constrains Chinese cabbage production.

Methods: To elucidate the defence mechanisms underlying this response, transcriptomic and metabolomic profiles were analysed in leaves of the Chinese cabbage line 904B at 24 h post-inoculation (hpi) with Alternaria brassicicola. In parallel, gene silencing and overexpression were conducted for BraPBL, an RLCK family member in Chinese cabbage.

Results: The Chinese cabbage line 904B exhibited marked suppression of cytokinin and auxin signalling, coupled with enhanced expression of genes involved in ethylene and jasmonic acid signalling. Multiple secondary metabolites exhibited differential changes, specifically the sterol compound 4,4-dimethyl-5alpha-cholest-7-en-3beta-ol was significantly upregulated in the treatment group. These metabolites were primarily enriched in the indole alkaloid metabolism and glycerolipid metabolism pathways. Concurrently, BraPBL exhibits increasing expression with prolonged infection. BraPBL overexpression enhances resistance to black spot disease, whereas silencing reduces resistance. Subcellular localization confirmed BraPBL at the plasma membrane. Overexpression of BraPBL upregulates the reactive oxygen species-related gene RBOH and the signal transduction-related gene MEKK1, whilst simultaneously activating the JA pathway.

Conclusions: Overall, 904B activates defence-related hormones while suppressing growth and development-related hormones during early infection. Secondary metabolites, particularly the sterol compound 4,4-dimethyl-5alpha-cholest-7-en-3beta-ol, play key roles in defence, and BraPBL functions as a black spot disease-related defence gene in Chinese cabbage.

Objective: To develop and validate a genetic diagnostic model for colorectal cancer (CRC). Methods: First, differential expression genes (DEGs) between colorectal cancer and normal groups were screened using the TCGA database. Subsequently, a two-sample Mendelian randomization analysis was performed using the eQTL genomic data from the IEU OpenGWAS database and colorectal cancer outcomes from the R12 Finnish database to identify associated genes. The intersecting genes from both methods were selected for the development and validation of the CRC genetic diagnostic model using nine machine learning algorithms: Lasso Regression, XGBoost, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT). Results: A total of 3716 DEGs were identified from the TCGA database, while 121 genes were associated with CRC based on the eQTL Mendelian randomization analysis. The intersection of these two methods yielded 27 genes. Among the nine machine learning methods, XGBoost achieved the highest AUC value of 0.990. The top five genes predicted by the XGBoost method-RIF1, GDPD5, DBNDD1, RCCD1, and CLDN5-along with the five most significantly differentially expressed genes (ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) in the GSE87211 dataset, were selected for the construction of the final colorectal cancer (CRC) genetic diagnostic model. The ROC curve analysis revealed an AUC (95% CI) of 0.9875 (0.9737-0.9875) for the training set, and 0.9601 (0.9145-0.9601) for the validation set, indicating strong predictive performance of the model. SHAP model interpretation further identified IFITM1 and DBNDD1 as the most influential genes in the XGBoost model, with both making positive contributions to the model's predictions. Conclusions: The gene expression profile in colorectal cancer is characterized by enhanced cell proliferation, elevated metabolic activity, and immune evasion. A genetic diagnostic model constructed based on ten genes (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) demonstrates strong predictive performance. This model holds significant potential for the early diagnosis and intervention of colorectal cancer, contributing to the implementation of third-tier prevention strategies.

Background: The striped hyena (Hyaena hyaena) occurs in a wide range from north and east Africa, through southwest Asia to India, but its distribution is increasingly patchy and many of its populations are in decline due to intense human pressure. Its genetic diversity and structure, phylogeography, and evolutionary history, remain poorly understood. Methods: In this study, we investigated mitochondrial DNA variation in Algerian striped hyenas. Moreover, with the aim of contributing to our understanding of the evolutionary history of the species, we also examined samples from other geographic regions and compared our results with those of the only previous study in which individuals from across the range of the species were analyzed. In particular, we performed a wide range of analyses of demographic history and estimation of the age of the extant mitochondrial DNA variation. Results and Conclusions: The Algerian population sample was monomorphic. Overall, the global patterns of genetic diversity and the results of some demographic history analyses support a scenario of population growth in the species, estimated to have occurred in the Late Pleistocene, but many of the analyses did not detect a significant signal of growth, most likely a result of the limited power provided by a small number of segregating sites. The estimates, from three different methods, for the time to the most recent common ancestor (TMRCA) of the mitochondrial DNA variation hovered around 400 ka, coinciding with one of the longest and warmest interglacials of the last 800,000 years, with environmental conditions similar to the Holocene.

Joint acidosis is increasingly recognized as an important determinant of cellular behavior in osteoarthritis (OA). Declines in extracellular pH (pHe) occur across cartilage, meniscus, synovium, and subchondral bone, where they influence inflammation, matrix turnover, and pain. Among proton-sensing G protein-coupled receptors, GPR68 responds to the acidic pH range characteristic of human OA joints. The receptor is activated between pH 6.8 and 7.0, couples to Gq/PLC-MAPK, cAMP-CREB, G12/13-RhoA-ROCK signaling pathways, and is expressed most prominently in articular cartilage, with additional expression reported in synovium, bone, vasculature, and some neuronal populations. These pathways regulate transcriptional programs relevant to cartilage stress responses, inflammation, and matrix turnover. GPR68 expression is increased in human OA cartilage and aligns with regions of active matrix turnover. We previously reported that pharmacologic activation of GPR68 suppresses IL1β-induced MMP13 expression in human chondrocytes under acidic conditions, indicating that increased GPR68 expression may represent a microenvironment-responsive, potentially adaptive signaling response rather than a driver of cartilage degeneration. Evidence from intestinal, stromal, and vascular models demonstrates that GPR68 integrates pH changes with inflammatory and mechanical cues, providing mechanistic context, although these effects have not been directly established in most joint tissues. Small-molecule modulators, including the positive allosteric agonist Ogerin and the inhibitor Ogremorphin, illustrate the tractability of GPR68 as a drug target, although no GPR68-directed therapies have yet been evaluated in preclinical models of OA. Collectively, current data support GPR68 as a functionally relevant proton sensor within the acidic OA joint microenvironment.

Background/Objectives: Emerging evidence suggests that long non-coding RNA (lncRNA) molecules influence the adaptive response to exercise, but how lncRNA responses differ between endurance and resistance exercise (RE) modalities is poorly understood. The purpose of this study was to bioinformatically infer the expression of lncRNA in skeletal muscle following acute aerobic exercise (AE) and RE. Methods: We downloaded publicly available RNA-seq data, performed a differential expression (DE) analysis, and compared lncRNA expression profiles between different exercise types (AE vs. RE) at three timepoints: baseline, 1 h post-exercise, and 4 h post-exercise. Results: We observed distinct lncRNA profiles between acute AE and RE at different timepoints, suggesting that lncRNA perform distinct roles in controlling the response to different exercise modalities in skeletal muscle. Conclusions: Future studies should investigate the specific roles of these lncRNAs in the response to acute exercise in skeletal muscle.

Background: Eriobotrya seguinii (Lév.) Cardot ex Guillaumin (Rosaceae, Maleae) is native to China and inhabits various altitudes within the subtropical biome of the Yunnan-Guizhou Plateau. The complexity of the plant mitogenome has impeded a systematic description of this species, leading to a limited understanding of its evolutionary position.

Methods: In this study, we constructed, annotated, characterized, and compared the complete E. seguinii mitogenome with previously reported Eriobotrya japonica.

Results: The E. seguinii mitogenome exhibited a typical circular architecture, spanning 372,899 bp in length, with a GC content of 46%, making it the smallest and highest GC content of any known Eriobotrya species. It encodes 71 unique genes, comprising 47 protein-coding genes, 20 transfer RNA (tRNA) genes, and 4 ribosomal RNA (rRNA) genes. The genome contains rich repetitive sequences, with mononucleotides, A/T bias, and forward and palindromic repeats being the most prevalent. The predominant codons were GCU (Ala) and UAU (Tyr), with frequencies of 1.54 and 1.53, respectively. Thirteen genes (atp9, atp6, atp1, rps14, sdh4, sdh3, rps12, rnaseH, nad1, nad6, nad7, rpl16, and mttB) demonstrated high Pi values, ranging from 0.84 to 1. The evolutionary lineage of E. seguinii was explored using mitogenome data from 19 genera within the Rosaceae family, revealing that Eriobotrya species are monophyletic and closely related to E. japonica (MN481990).

Conclusions: Understanding the mitogenome characteristics of E. seguinii enhances our understanding of its genesis and classification based on mitochondrial genome data. This study provides additional evidence for future research on the evolutionary relationships among species in the Rosaceae family.

Mesenchymal stromal cells (MSCs) are intensively investigated in oncology owing to their intrinsic tumor-homing ability and capacity to deliver therapeutic agents directly into the tumor microenvironment (TME). Recent advances in genetic engineering have enabled precise modification of MSCs, allowing controlled expression of therapeutic genes and other cargo delivery, thus improving targeting efficiency. As cellular carriers, MSCs have been engineered to transport oncolytic viruses, suicide genes in gene-directed enzyme prodrug therapy (GDEPT), multifunctional nanoparticles, and therapeutic factors such as IFN-β or TRAIL, while engineered MSC-derived extracellular vesicles (MSC-EVs) offer a promising cell-free alternative. These strategies increase intratumoral drug concentration, amplify bystander effects, and synergize with standard therapies while reducing systemic toxicity. Conversely, accumulating evidence highlights the tumor-promoting properties of MSCs: once recruited by inflammatory and hypoxic cues, they remodel the tumor microenvironment by stimulating angiogenesis, suppressing immune responses, differentiating into cancer-associated fibroblasts, and promoting epithelial-to-mesenchymal transition (EMT), ultimately enhancing invasion, metastasis, and therapy resistance. This duality has sparked both enthusiasm and concern in the oncology field. The present review outlines the paradoxical role of MSCs in oncology-ranging from their potential to promote tumor growth to their emerging utility as vehicles for targeted drug delivery. By highlighting both therapeutic opportunities and biological risks, we aim to provide a balanced perspective on how MSC-based strategies might be refined, optimized, and safely integrated into future cancer therapies.