Dysregulation of murine long noncoding single-cell transcriptome in nonalcoholic steatohepatitis and liver fibrosis.

LncRNAs comprise a heterogeneous class of RNA-encoding genes typified by low expression, nuclear enrichment, high tissue-specificity, and functional diversity, but the vast majority remain uncharacterized. Here, we assembled the mouse liver noncoding transcriptome from >2000 bulk RNA-seq samples and discovered 48,261 liver-expressed lncRNAs, a majority novel. Using these lncRNAs as a single-cell transcriptomic reference set, we elucidated lncRNA dysregulation in mouse models of high fat diet-induced nonalcoholic steatohepatitis and carbon tetrachloride-induced liver fibrosis. Trajectory inference analysis revealed lncRNA zonation patterns across the liver lobule in each major liver cell population. Perturbations in lncRNA expression and zonation were common in several disease-associated liver cell types, including nonalcoholic steatohepatitis-associated macrophages, a hallmark of fatty liver disease progression, and collagen-producing myofibroblasts, a central feature of liver fibrosis. Single-cell-based gene regulatory network analysis using bigSCale2 linked individual lncRNAs to specific biological pathways, and network-essential regulatory lncRNAs with disease-associated functions were identified by their high network centrality metrics. For a subset of these lncRNAs, promoter sequences of the network-defined lncRNA target genes were significantly enriched for lncRNA triplex formation, providing independent mechanistic support for the lncRNA-target gene linkages predicted by the gene regulatory networks. These findings elucidate liver lncRNA cell-type specificities, spatial zonation patterns, associated regulatory networks, and temporal patterns of dysregulation during hepatic disease progression. A subset of the liver disease-associated regulatory lncRNAs identified have human orthologs and are promising candidates for biomarkers and therapeutic targets.

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.

Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.

MOTIVATION: Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS: In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.

AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation.

Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots.

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer.

BACKGROUND: There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC. METHODS: Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors. RESULTS: The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1. CONCLUSION: This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

LRcell: detecting the source of differential expression at the sub-cell-type level from bulk RNA-seq data.

Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.

Mapping the cellular landscape of Atlantic salmon head kidney by single cell and single nucleus transcriptomics.

Single-cell transcriptomics is the current gold standard for global gene expression profiling, not only in mammals and model species, but also in non-model fish species. This is a rapidly expanding field, creating a deeper understanding of tissue heterogeneity and the distinct functions of individual cells, making it possible to explore the complexities of immunology and gene expression on a highly resolved level. In this study, we compared two single cell transcriptomic approaches to investigate cellular heterogeneity within the head kidney of healthy farmed Atlantic salmon (Salmo salar). We compared 14,149 cell transcriptomes assayed by single cell RNA-seq (scRNA-seq) with 18,067 nuclei transcriptomes captured by single nucleus RNA-Seq (snRNA-seq). Both approaches detected eight major cell populations in common: granulocytes, heamatopoietic stem cells, erythrocytes, mononuclear phagocytes, thrombocytes, B cells, NK-like cells, and T cells. Four additional cell types, endothelial, epithelial, interrenal, and mesenchymal cells, were detected in the snRNA-seq dataset, but appeared to be lost during preparation of the single cell suspension submitted for scRNA-seq library generation. We identified additional heterogeneity and subpopulations within the B cells, T cells, and endothelial cells, and revealed developmental trajectories of heamatopoietic stem cells into differentiated granulocyte and mononuclear phagocyte populations. Gene expression profiles of B cell subtypes revealed distinct IgM and IgT-skewed resting B cell lineages and provided insights into the regulation of B cell lymphopoiesis. The analysis revealed eleven T cell sub-populations, displaying a level of T cell heterogeneity in salmon head kidney comparable to that observed in mammals, including distinct subsets of cd4/cd8-negative T cells, such as tcrγ positive, progenitor-like, and cytotoxic cells. Although snRNA-seq and scRNA-seq were both useful to resolve cell type-specific expression in the Atlantic salmon head kidney, the snRNA-seq pipeline was overall more robust in identifying several cell types and subpopulations. While scRNA-seq displayed higher levels of ribosomal and mitochondrial genes, snRNA-seq captured more transcription factor genes. However, only scRNA-seq-generated data was useful for cell trajectory inference within the myeloid lineage. In conclusion, this study systematically outlines the relative merits of scRNA-seq and snRNA-seq in Atlantic salmon, enhances understanding of teleost immune cell lineages, and provides a comprehensive list of markers for identifying major cell populations in the head kidney with significant immune relevance.

Bioinformatic identification of monkeypox virus phylogenetic gene trees that are representative of its whole-genome phylogenetic tree.

Phylogenetic analysis based on whole-genome sequences is the gold standard for monkeypox virus (MPXV) phylogeny. However, genomic epidemiology capability and capacity are lacking or limited in resource poor countries of sub-Saharan Africa. Therefore, these make real-time genome surveillance of MPXV virtually impossible. We hypothesized that phylogenetic analysis based on single, conserved genes will produce phylogenetic tree topology consistent with MPXV whole-genome phylogeny, thus serving as a reliable proxy to phylogenomic analysis. In this study, we analyzed 62 conserved MPXV genes and showed that Bayesian phylogenetic analysis based on five genes (OPG 066/E4L, OPG068/E6R, OPG079/I3L, OPG145/A18R, and OPG150/A23R) generated phylogenetic trees with 72.2-96.3% topology similarity index to the reference phylogenomic tree topology. Our results showed that phylogenetic analysis of the identified five genes singly or in combination can serve as surrogate for whole-genome phylogenetic analysis, and thus obviates the need for whole-genome sequencing and phylogenomic analysis in regions where genomic epidemiology competence and capacity are lacking or unavailable. This study is relevant to evolution and genome surveillance of MPXV in resource limited countries.

Time series analysis combined with transcriptome sequencing to explore characteristic genes and potential molecular mechanisms associated with ultrasound-guided microwave ablation of glioma.

OBJECTIVE: This study aimed to explore marker genes and their potential molecular mechanisms involved in US-guided MWA for glioma in mice. METHOD: The differentially expressed genes (DEGs1 and DEGs2) and lncRNAs (DELs1 and DELs2) were obtained between Non (glioma tissues without MWA) and T0 groups (0h after MWA), as well as between Non and T24 groups (24h after MWA). The down-regulation cluster genes (CONDOWNDEGs) and upregulation cluster genes (CONUPDEGs) were identified by time series analysis. Candidate genes were obtained by overlapping CONDOWNDEGs with downregulation DEGs (DOWNDEGs)1 and DOWNDEGs2, as well as CONUPDEGs with up-regulation DEGs (UPDEGs)1 and UPDEGs2. The expressions of immune checkpoints and inflammatory factors, gene set enrichment analysis (GSEA), and protein subcellular localization were performed. The eXpression2Kinases (X2K), GeneMANIA, transcription factor (TF), and competing endogenous (ce) RNA regulatory networks were conducted. The expression of marker genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: Five marker genes (IL32, VCAM1, IL34, NFKB1 and CXCL13) were identified, which were connected with immune-related functions. Two immune checkpoints (CD96 and TIGIT) and six inflammatory factors played key roles in US-guided MWA for glioma. ceRNA regulatory networks revealed that miR-625-5p, miR-625-3p, miR-31-5p and miR-671-5p were associated with target genes. qRT-PCR indicated both IL32, VCAM1, and NFKB1 were potential markers under US-guided MWA-related time series analysis. CONCLUSION: The use of US-guided MWA might be a practical method for influencing the function of target genes, regulating time frames to decrease inflammation, and stimulating immune responses in glioma therapy.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24 h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Oil Palm AP2 Subfamily Gene EgAP2.25 Improves Salt Stress Tolerance in Transgenic Tobacco Plants.

AP2/ERF transcription factor genes play an important role in regulating the responses of plants to various abiotic stresses, such as cold, drought, high salinity, and high temperature. However, less is known about the function of oil palm AP2/ERF genes. We previously obtained 172 AP2/ERF genes of oil palm and found that the expression of EgAP2.25 was significantly up-regulated under salinity, cold, or drought stress conditions. In the present study, the sequence characterization and expression analysis for EgAP2.25 were conducted, showing that it was transiently over-expressed in Nicotiana tabacum L. The results indicated that transgenic tobacco plants over-expressing EgAP2.25 could have a stronger tolerance to salinity stress than wild-type tobacco plants. Compared with wild-type plants, the over-expression lines showed a significantly higher germination rate, better plant growth, and less chlorophyll damage. In addition, the improved salinity tolerance of EgAP2.25 transgenic plants was mainly attributed to higher antioxidant enzyme activities, increased proline and soluble sugar content, reduced H2O2 production, and lower MDA accumulation. Furthermore, several stress-related marker genes, including NtSOD, NtPOD, NtCAT, NtERD10B, NtDREB2B, NtERD10C, and NtP5CS, were significantly up-regulated in EgAP2.25 transgenic tobacco plants subjected to salinity stress. Overall, over-expression of the EgAP2.25 gene significantly enhanced salinity stress tolerance in transgenic tobacco plants. This study lays a foundation for further exploration of the regulatory mechanism of the EgAP2.25 gene in conferring salinity tolerance in oil palm.

The Systematics and Phylogeny of Myxomycetes: Yesterday, Today, and Tomorrow.

Abstract-Myxomycetes are amoeboid fungus-like organisms (Amoebozoa) with a unique life cycle characterized by a great morphological diversity of fruiting bodies. Due to the similarity of these structures to the fruiting bodies of some representatives of Ascomycota and Basidiomycota, myxomycetes have been classified as fungi since the first known scientific description in 1654. Only in the 19th century, when their life cycle was studied, did the difference of this group from fungi become clear. During the same period, microscopic structures of fruiting bodies, as well as ornamentation of the spore surface, began to be considered as diagnostic features. Due to this, in the period from the end of the 19th to the middle of the 20th century, a rather stable system was formed. However, as further studies have shown, both macro- and micromorphological characters are often quite variable, depend on environmental conditions, and often result from convergent evolution, which causes difficulties in defining species and taxonomic units of higher ranks. Since the first decade of the 21st century, due to the development of molecular genetic methods and the accumulation of data on nucleotide sequences of marker genes together with the improvement of microscopic studies, it has been possible to obtain data on the evolutionary relationships of different groups of myxomycetes. A milestone in this process was the publication of the first phylogenetic system of myxomycetes in 2019. This work was the starting point for a number of studies on the relationships between different groups of myxomycetes at a lower taxonomic level. Thus, there has been a surge in the number of studies that bring us closer to constructing a natural system.

A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing.

BACKGROUND: Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed malignancy and the third leading cause of cancer death globally. T cells are significantly correlated with the progression, therapy and prognosis of cancer. Limited systematic studies regarding the role of T-cell-related markers in HCC have been performed. METHODS: T-cell markers were identified with single-cell RNA sequencing (scRNA-seq) data from the GEO database. A prognostic signature was developed with the LASSO algorithm in the TCGA cohort and verified in the GSE14520 cohort. Another three eligible immunotherapy datasets, GSE91061, PRJEB25780 and IMigor210, were used to verify the role of the risk score in the immunotherapy response. RESULTS: With 181 T-cell markers identified by scRNA-seq analysis, a 13 T-cell-related gene-based prognostic signature (TRPS) was developed for prognostic prediction, which divided HCC patients into high-risk and low-risk groups according to overall survival, with AUCs of 1 year, 3 years, and 5 years of 0.807, 0.752, and 0.708, respectively. TRPS had the highest C-index compared with the other 10 established prognostic signatures, suggesting a better performance of TRPS in predicting the prognosis of HCC. More importantly, the TRPS risk score was closely correlated with the TIDE score and immunophenoscore. The high-risk score patients had a higher percentage of SD/PD, and CR/PR occurred more frequently in patients with low TRPS-related risk scores in the IMigor210, PRJEB25780 and GSE91061 cohorts. We also constructed a nomogram based on the TRPS, which had high potential for clinical application. CONCLUSION: Our study proposed a novel TRPS for HCC patients, and the TRPS could effectively indicate the prognosis of HCC. It also served as a predictor for immunotherapy.

The astroglial and stem cell functions of adult rat folliculostellate cells.

The mammalian pituitary gland is a complex organ consisting of hormone-producing cells, anterior lobe folliculostellate cells (FSCs), posterior lobe pituicytes, vascular pericytes and endothelial cells, and Sox2-expressing stem cells. We present single-cell RNA sequencing and immunohistofluorescence analyses of pituitary cells of adult female rats with a focus on the transcriptomic profiles of nonhormonal cell types. Samples obtained from whole pituitaries and separated anterior and posterior lobe cells contained all expected pituitary resident cell types and lobe-specific vascular cell subpopulations. FSCs and pituicytes expressed S100B, ALDOC, EAAT1, ALDH1A1, and VIM genes and proteins, as well as other astroglial marker genes, some common and some cell type-specific. We also found that the SOX2 gene and protein were expressed in ~15% of pituitary cells, including FSCs, pituicytes, and a fraction of hormone-producing cells, arguing against its stem cell specificity. FSCs comprised two Sox2-expressing subclusters; FS1 contained more cells but lower genetic diversity, while FS2 contained proliferative cells, shared genes with hormone-producing cells, and expressed genes consistent with stem cell niche formation, regulation of cell proliferation and stem cell pluripotency, including the Hippo and Wnt pathways. FS1 cells were randomly distributed in the anterior and intermediate lobes, while FS2 cells were localized exclusively in the marginal zone between the anterior and intermediate lobes. These data indicate the identity of the FSCs as anterior pituitary-specific astroglia, with FS1 cells representing differentiated cells equipped for classical FSC roles and FS2 cells exhibiting additional stem cell-like features.

Kidney Development: Recent Insights from Technological Advances.

The kidney is a complex organ, and how it forms is a fascinating process. New technologies, such as single-cell transcriptomics, and enhanced imaging modalities are offering new approaches to understand the complex and intertwined processes during embryonic kidney development.

Integrated analysis of mRNAs and lncRNAs reveals candidate marker genes and potential hub lncRNAs associated with growth regulation of the Pacific Oyster, Crassostrea gigas.

BACKGROUND: The Pacific oyster, Crassostrea gigas, is an economically important shellfish around the world. Great efforts have been made to improve its growth rate through genetic breeding. However, the candidate marker genes, pathways, and potential lncRNAs involved in oyster growth regulation remain largely unknown. To identify genes, lncRNAs, and pathways involved in growth regulation, C. gigas spat was cultured at a low temperature (15 ℃) to yield a growth-inhibited model, which was used to conduct comparative transcriptome analysis with spat cultured at normal temperature (25 ℃). RESULTS: In total, 8627 differentially expressed genes (DEGs) and 1072 differentially expressed lncRNAs (DELs) were identified between the normal-growth oysters (cultured at 25 ℃, hereinafter referred to as NG) and slow-growth oysters (cultured at 15 ℃, hereinafter referred to as SG). Functional enrichment analysis showed that these DEGs were mostly enriched in the AMPK signaling pathway, MAPK signaling pathway, insulin signaling pathway, autophagy, apoptosis, calcium signaling pathway, and endocytosis process. LncRNAs analysis identified 265 cis-acting pairs and 618 trans-acting pairs that might participate in oyster growth regulation. The expression levels of LNC_001270, LNC_003322, LNC_011563, LNC_006260, and LNC_012905 were inducible to the culture temperature and food abundance. These lncRNAs were located at the antisense, upstream, or downstream of the SREBP1/p62, CDC42, CaM, FAS, and PIK3CA genes, respectively. Furthermore, the expression of the trans-acting lncRNAs, including XR_9000022.2, LNC_008019, LNC_015817, LNC_000838, LNC_00839, LNC_011859, LNC_007294, LNC_006429, XR_002198885.1, and XR_902224.2 was also significantly associated with the expression of genes enriched in AMPK signaling pathway, insulin signaling pathway, autophagy, apoptosis, calcium signaling pathway, and endocytosis process. CONCLUSIONS: In this study, we identified the critical growth-related genes and lncRNAs that could be utilized as candidate markers to illustrate the molecular mechanisms underlying the growth regulation of Pacific oysters.

Integrated analysis of single-cell and bulk RNA sequencing identifies a signature based on macrophage marker genes involved in prostate cancer prognosis and treatment responsiveness.

In the tumor microenvironment, tumor-associated macrophages (TAMs) interact with cancer cells and contribute to the progression of solid tumors. Nonetheless, the clinical significance of TAM-related biomarkers in prostate cancer (PCa) is largely unexplored. The present study aimed to construct a macrophage-related signature (MRS) for predicting PCa patient prognosis based on macrophage marker genes. Six cohorts comprising 1056 PCa patients with RNA-Seq and follow-up data were enrolled. Based on macrophage marker genes identified by single-cell RNA-sequencing (scRNA-seq) analysis, univariate analysis, least absolute shrinkage and selection operator (Lasso)-Cox regression, and machine learning procedures were performed to derive a consensus MRS. Receiver operating characteristic curve (ROC), concordance index, and decision curve analyses were used to confirm the predictive capacity of the MRS. The predictive performance of the MRS for recurrence-free survival (RFS) was stable and robust, and the MRS outperformed traditional clinical variables. Furthermore, high-MRS-score patients presented abundant macrophage infiltration and high-expression levels of immune checkpoints (CTLA4, HAVCR2, and CD86). The frequency of mutations was relatively high in the high-MRS-score subgroup. However, the low-MRS-score patients had a better response to immune checkpoint blockade (ICB) and leuprolide-based adjuvant chemotherapy. Notably, abnormal ATF3 expression may be associated with docetaxel and cabazitaxel resistance in PCa cells, T stage, and the Gleason score. In this study, a novel MRS was first developed and validated to accurately predict patient survival outcomes, evaluate immune characteristics, infer therapeutic benefits, and provide an auxiliary tool for personalized therapy.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.

BACKGROUND: Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. METHODS: We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. RESULTS: We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. CONCLUSIONS: By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Fruit Characteristics of Transgenic pear (Pyrus communis L.) Trees During Long-Term Field Trials.

The quality of transgenic fruits was studied only for apple, plum and citrus. We first evaluated the transgenic fruit characteristics of pear, which is one of the most consumed fruit crops. The size, shape and biochemical composition of fruits from field-grown pear trees with marker genes were analyzed for 5 years. Soluble solids, vitamin C, and phenolic compounds varied significantly between transgenic lines, but these deviations were inconsistent. Arbutin content and sugar:acidity ratio were the most stable parameters. One transgenic line showed a stable increase in fruit weight (by 12.2-21.2%). The extremely dry and hot season increased the total phenolics (2.6-3.6 times) and tannin (3.2-3.6 times) levels, but not flavonoids. The harvest year had a stronger effect on analyzed fruit parameters than the genotype. Our study found no unintended effects of genetic transformation on pear fruit quality and confirms the importance of long-term field tests for perennial transgenic plants.