UNASSIGNED: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy.
UNASSIGNED: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features.
UNASSIGNED: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients.
UNASSIGNED: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

BACKGROUND: Broilers stand out as one of the fastest-growing livestock globally, making a substantial contribution to animal meat production. However, the molecular and epigenetic mechanisms underlying the rapid growth and development of broiler chickens are still unclear. This study aims to explore muscle development patterns and regulatory networks during the postnatal rapid growth phase of fast-growing broilers. We measured the growth performance of Cornish (CC) and White Plymouth Rock (RR) over a 42-d period. Pectoral muscle samples from both CC and RR were randomly collected at day 21 after hatching (D21) and D42 for RNA-seq and ATAC-seq library construction.
RESULTS: The consistent increase in body weight and pectoral muscle weight across both breeds was observed as they matured, with CC outpacing RR in terms of weight at each stage of development. Differential expression analysis identified 398 and 1,129 genes in the two dimensions of breeds and ages, respectively. A total of 75,149 ATAC-seq peaks were annotated in promoter, exon, intron and intergenic regions, with a higher number of peaks in the promoter and intronic regions. The age-biased genes and breed-biased genes of RNA-seq were combined with the ATAC-seq data for subsequent analysis. The results spotlighted the upregulation of ACTC1 and FDPS at D21, which were primarily associated with muscle structure development by gene cluster enrichment. Additionally, a noteworthy upregulation of MUSTN1, FOS and TGFB3 was spotted in broiler chickens at D42, which were involved in cell differentiation and muscle regeneration after injury, suggesting a regulatory role of muscle growth and repair.
CONCLUSIONS: This work provided a regulatory network of postnatal broiler chickens and revealed ACTC1 and MUSTN1 as the key responsible for muscle development and regeneration. Our findings highlight that rapid growth in broiler chickens triggers ongoing muscle damage and subsequent regeneration. These findings provide a foundation for future research to investigate the functional aspects of muscle development.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Utilizing publicly available RNA-seq data to screen for ideal reference genes is more efficient and accurate than traditional methods. Previous studies have identified optimal reference genes in various chicken tissues, but none have specifically focused on the oviduct (including the infundibulum, magnum, isthmus, uterus, and vagina), which is crucial for egg production. Identifying stable reference genes in the oviduct is essential for improving research on gene expression levels. This study investigated genes with consistent expression patterns in the chicken oviduct, encompassing both individual oviduct tract tissues and the entire oviduct, by utilizing multiple RNA-seq datasets. The screening results revealed the discovery of 100 novel reference genes in each segment of oviduct tissues, primarily associated with cell cycle regulation and RNA binding. Moreover, the majority of housekeeping genes (HKGs) showed inconsistent expression levels across distinct samples, suggesting their lack of stability under varying conditions. The stability of the newly identified reference genes was assessed in comparison to previously validated stable reference genes in chicken oviduct and commonly utilized HKGs, employing traditional reference gene screening methods. HERPUD2, CSDE1, VPS35, PBRM1, LSM14A, and YWHAB were identified to be suitable novel reference gene for different parts of the oviduct. HERPUD2 and YWHAB were reliable for gene expression normalization throughout the oviduct tract. Furthermore, overexpression and interference assays in DF1 cells showed LSM14A and YWHAB play a crucial role in cell proliferation, highlighting the importance of these newly reference genes for further research. Overall, this study has expanded the options for reference genes in RT-qPCR experiments in different segments of the chicken oviduct and the entire oviduct.

UNASSIGNED: Circular RNAs (circRNAs) have been shown to play critical roles in the initiation and progression of chronic glomerulonephritis (CGN), while their role from mesangial cells in contributing to the pathogenesis of CGN is rarely understood. Our study aims to explore the potential functions of mesangial cell-derived circRNAs using RNA sequencing (RNA-seq) and bioinformatics analysis.
UNASSIGNED: Mouse mesangial cells (MMCs) were stimulated by lipopolysaccharide (LPS) to establish an in vitro model of CGN. Pro-inflammatory cytokines and cell cycle stages were detected by Enzyme-linked immunosorbent assay (ELISA) and Flow Cytometry experiment, respectively. Subsequently, differentially expressed circRNAs (DE-circRNAs) were identified by RNA-seq. GEO microarrays were used to identify differentially expressed mRNAs (DE-mRNAs) between CGN and healthy populations. Weighted co-expression network analysis (WGCNA) was utilized to explore clinically significant modules of CGN. CircRNA-associated CeRNA networks were constructed by bioinformatics analysis. The hub mRNAs from CeRNA network were identified using LASSO algorithms. Furthermore, utilizing protein-protein interaction (PPI), gene ontology (GO), pathway enrichment (KEGG), and GSEA analyses to explore the potential biological function of target genes from CeRNA network. In addition, we investigated the relationships between immune cells and hub mRNAs from CeRNA network using CIBERSORT.
UNASSIGNED: The expression of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α was drastically increased in LPS-induced MMCs. The number of cells decreased significantly in the G1 phase but increased significantly in the S/G2 phase. A total of 6 DE-mRNAs were determined by RNA-seq, including 4 up-regulated circRNAs and 2 down-regulated circRNAs. WGCNA analysis identified 1747 DE-mRNAs of the turquoise module from CGN people in the GEO database. Then, the CeRNA networks, including 6 circRNAs, 38 miRNAs, and 80 mRNAs, were successfully constructed. The results of GO and KEGG analyses revealed that the target mRNAs were mainly enriched in immune, infection, and inflammation-related pathways. Furthermore, three hub mRNAs (BOC, MLST8, and HMGCS2) from the CeRNA network were screened using LASSO algorithms. GSEA analysis revealed that hub mRNAs were implicated in a great deal of immune system responses and inflammatory pathways, including IL-5 production, MAPK signaling pathway, and JAK-STAT signaling pathway. Moreover, according to an evaluation of immune infiltration, hub mRNAs have statistical correlations with neutrophils, plasma cells, monocytes, and follicular helper T cells.
UNASSIGNED: Our findings provide fundamental and novel insights for further investigations into the role of mesangial cell-derived circRNAs in CGN pathogenesis.

Placental hypoxia is hazardous to maternal health as well as fetal growth and development. Preeclampsia and intrauterine growth restriction are common pregnancy problems, and one of the causes is placental hypoxia. Placental hypoxia is linked to a number of pregnancy illnessesv. To investigate their potential function in anoxic circumstances, we mimicked the anoxic environment of HTR-8/Svneo cells and performed lncRNA and circRNA studies on anoxic HTR-8/Svneo cells using high-throughput RNA sequencing. The miRNA target genes were predicted by integrating the aberrant expression of miRNAs in the placenta of preeclampsia and intrauterine growth restriction, and a ceRNA network map was developed to conduct a complete transcriptomic and bioinformatics investigation of circRNAs and lncRNAs. The signaling pathways in which the genes were primarily engaged were predicted using GO and KEGG analyses. To propose a novel explanation for trophoblastic organism failure caused by lncRNAs and circRNAs in an anoxic environment.

UNASSIGNED: Sodium pheophorbide a (SPA) is a natural plant-derived photosensitizer, with high photoactivated antifungal activity against some phytopathogenic fungi. However, its fungicidal effect on Diaporthe mahothocarpus, a novel pathogen that causes Camellia oleifera leaf spot blight, is unclear.
UNASSIGNED: In the present study, we explored its inhibitory effects on spore germination and mycelial growth of D. mahothocarpus. Then we determined its effects on the cell membrane, mycelial morphology, redox homeostasis, and cell death through bioassay. Finally, RNA-seq was used further to elucidate its mode of action at the transcriptional level.
UNASSIGNED: We found that SPA effectively inhibited the growth of D. mahothocarpus, with half-maximal effective concentrations to inhibit mycelial growth and spore germination of 1.059 and 2.287 mg/mL, respectively. After 1.0 mg/mL SPA treatment, the conductivity and malondialdehyde content of D. mahothocarpus were significantly increased. Scanning electron microscopy and transmission electron microscopy indicated that SPA significantly affected the morphology and ultrastructure of D. mahothocarpus hyphae, revealing that SPA can destroy the mycelial morphology and cell structure, especially the cell membrane of D. mahothocarpus. Furthermore, transcriptome analysis revealed that SPA significantly suppressed the expression of genes involved in morphology, cell membrane permeability, and oxidative stress. Then, we also found that SPA significantly promoted the accumulation of reactive oxygen species (ROS) in of D. mahothocarpus, while it decreased the content of reduced glutathione, inhibited the enzyme activities of superoxide dismutase and catalase, and exacerbated DNA damage. Annexin V-FITC/PI staining also confirmed that 1.0 mg/mL SPA could significantly induce apoptosis and necrosis.
UNASSIGNED: Generally, SPA can induce ROS-mediated oxidative stress and cell death, thus destroying the cell membrane and hyphal morphology, and ultimately inhibiting mycelial growth, which indicates that SPA has multiple modes of action, providing a scientific basis for the use of SPA as an alternative plant-derived photoactivated fungicide against C. oleifera leaf spot blight.

BACKGROUND: In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell data. And the integration and refinement of annotated data are essential for building comprehensive databases. However, prevailing annotation techniques often overlook the hierarchical organization of cell types, resulting in inconsistent annotations. Meanwhile, most existing integration approaches fail to integrate datasets with different annotation depths and none of them can enhance the labels of outdated data with lower annotation resolutions using more intricately annotated datasets or novel biological findings.
RESULTS: Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels.
BACKGROUND: https://github.com/michaelGuo1204/scPLAN.

Brassica napus is one of the most important oil crops in the world. Breeding oilseed rape with colorful flowers can greatly enhance the ornamental value of B. napus and thus improve the economic benefits of planting. As water-soluble flavonoid secondary metabolites, anthocyanins are very important for the synthesis and accumulation of pigments in the petals of plants, giving them a wide range of bright colors. Despite the documentation of over 60 distinct flower shades in B. napus, the intricacies underlying flower color variation remain elusive. Particularly, the mechanisms driving color development across varying flower color backgrounds necessitate further comprehensive investigation. This research undertook a comprehensive exploration through the integration of transcriptome and metabolome analyses to pinpoint pivotal genes and metabolites underpinning an array of flower colors, including beige, beige-red, yellow, orange-red, deep orange-red, white, light-purple, and purple. First, we used a two-way BLAST search to find 275 genes in the reference genome of B. napus Darmor v10 that were involved in making anthocyanins. The subsequent scrutiny of RNA-seq outcomes underscored notable upregulation in the structural genes F3H and UGT, alongside the MYB75, GL3, and TTG1 transcriptional regulators within petals, showing anthocyanin accumulation. By synergizing this data with a weighted gene co-expression network analysis, we identified CHS, F3H, MYB75, MYB12, and MYB111 as the key players driving anthocyanin synthesis in beige-red, orange-red, deep orange-red, light-purple, and purple petals. By integrating transcriptome and weighted gene co-expression network analysis findings with anthocyanin metabolism data, it is hypothesized that the upregulation of MYB75, which, in turn, enhances F3H expression, plays a pivotal role in the development of pigmented oilseed rape flowers. These findings help to understand the transcriptional regulation of anthocyanin biosynthesis in B. napus and provide valuable genetic resources for breeding B. napus varieties with novel flower colors.

Pinus yunnanensis is an important component of China\'s economic development and forest ecosystems. The growth of P. yunnanensis seedlings experienced a slow growth phase, which led to a long seedling cultivation period. However, asexual reproduction can ensure the stable inheritance of the superior traits of the mother tree and also shorten the breeding cycle. The quantity and quality of branching significantly impact the cutting reproduction of P. yunnanensis, and a shaded environment affects lateral branching growth, development, and photosynthesis. Nonetheless, the physiological characteristics and the level of the transcriptome that underlie the growth of lateral branches of P. yunnanensis under shade conditions are still unclear. In our experiment, we subjected annual P. yunnanensis seedlings to varying shade intensities (0%, 25%, 50%, 75%) and studied the effects of shading on growth, physiological and biochemical changes, and gene expression in branching. Results from this study show that shading reduces biomass production by inhibiting the branching ability of P. yunnanensis seedlings. Due to the regulatory and protective roles of osmotically active substances against environmental stress, the contents of soluble sugars, soluble proteins, photosynthetic pigments, and enzyme activities exhibit varying responses to different shading treatments. Under shading treatment, the contents of phytohormones were altered. Additionally, genes associated with phytohormone signaling and photosynthetic pathways exhibited differential expression. This study established a theoretical foundation for shading regulation of P. yunnanensis lateral branch growth and provides scientific evidence for the management of cutting orchards.