RNA-seq Data Research Articles

Disrupting YAP1-mediated glutamine metabolism induces synthetic lethality alongside ODC1 inhibition in osteosarcoma.

Osteosarcoma, a highly malignant primary bone tumor primarily affecting adolescents, frequently develops resistance to initial chemotherapy, leading to metastasis and limited treatment options. Our study aims to uncover novel therapeutic targets for metastatic and recurrent osteosarcoma. In this study, we proved the potential of modulating the YAP1-regulated glutamine metabolic pathway to augment the response of OS to DFMO. We initially employed single-cell transcriptomic data to gauge the activation level of polyamine metabolism in MTAP-deleted OS patients. This was further substantiated by transcriptome sequencing data from recurrent and non-recurrent patient tissues, confirming the activation of polyamine metabolism in progressive OS. Through high-throughput drug screening, we pinpointed CIL56, a YAP1 inhibitor, as a promising candidate for a combined therapeutic strategy with DFMO. In vivo, we utilized PDX and CDX models to validate the therapeutic efficacy of this drug combination. In vitro, we conducted western blot analysis, qPCR analysis, immunofluorescence staining, and PuMA experiments to monitor alterations in molecular expression, distribution, and tumor metastasis capability. We employed CCK-8 and colony formation assays to assess the proliferative capacity of cells in the experimental group. We used flow cytometry and reactive oxygen probes to observe changes in ROS and glutamine metabolism within the cells. Finally, we applied RNA-seq in tandem with metabolomics to identify metabolic alterations in OS cells treated with a DFMO and CIL56 combination. This enabled us to intervene and validate the role of the YAP1-mediated glutamine metabolic pathway in DFMO resistance. Through single-cell RNA-seq data analysis, we pinpointed a subset of late-stage OS cells with significantly upregulated polyamine metabolism. This upregulation was further substantiated by transcriptomic profiling of recurrent and non-recurrent OS tissues. High-throughput drug screening revealed a promising combination strategy involving DFMO and CIL56. DFMO treatment curbs the phosphorylation of YAP1 protein in OS cells, promoting nuclear entry and initiating the YAP1-mediated glutamine metabolic pathway. This reduces intracellular ROS levels, countering DFMO's anticancer effect. The therapeutic efficacy of DFMO can be amplified both in vivo and in vitro by combining it with the YAP1 inhibitor CIL56 or the glutaminase inhibitor CB-839. This underscores the significant potential of targeting the YAP1-mediated glutamine metabolic pathway to enhance efficacy of DFMO. Our findings elucidate YAP1-mediated glutamine metabolism as a crucial bypass mechanism against DFMO, following the inhibition of polyamine metabolism. Our study provides valuable insights into the potential role of DFMO in an "One-two Punch" therapy of metastatic and recurrent osteosarcoma.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BackgroundSingle-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.ResultIn this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.ConclusionsCOMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Microglia specific alternative splicing alterations in multiple sclerosis.

Several aberrant alternative splicing (AS) events and their regulatory mechanisms are widely recognized in multiple sclerosis (MS). Yet the cell-type specific AS events have not been extensively examined. Here we assessed the diversity of AS events using web-based RNA-seq data of sorted CD15-CD11b+ microglia in white matter (WM) region from 10 patients with MS and 11 control subjects. The GSE111972 dataset was downloaded from GEO and ENA databases, aligned to the GRCh38 reference genome from ENSEMBL via STAR. rMATS was used to assess five types of AS events, alternative 3'SS (A3SS), alternative 5'SS (A5SS), skipped exon (SE), retained intron (RI) and mutually exclusive exons (MXE), followed by visualizing with rmats2sashimiplot and maser. Differential genes or transcripts were analyzed using the limma R package. Gene ontology (GO) analysis was performed with the clusterProfiler R package. 42,663 raw counts of AS events were identified and 132 significant AS events were retained based on the filtered criteria: 1) average coverage >10 and 2) delta percent spliced in (ΔPSI) >0.1. SE was the most common AS event (36.36%), followed by MXE events (32.58%), and RI (18.94%). Genes related to telomere maintenance and organization primarily underwent SE splicing, while genes associated with protein folding and mitochondrion organization were predominantly spliced in the MXE pattern. Conversely, genes experiencing RI were enriched in immune response and immunoglobulin production. In conclusion, we identified microglia-specific AS changes in the white matter of MS patients, which may shed light on novel pathological mechanisms underlying MS.

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network’s top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

Preclinical Efficacy of VTX-0811: A Humanized First-in-Class PSGL-1 mAb Targeting TAMs to Suppress Tumor Growth

Omnipresent suppressive myeloid populations in the tumor microenvironment limit the efficacy of T-cell-directed immunotherapies, become more inhibitory after administration of T-cell checkpoint inhibitors, and are overall associated with worse survival of cancer patients. In early clinical trials, positive outcomes have been demonstrated for therapies aimed at repolarizing suppressive myeloid populations in the tumor microenvironment. We have previously described the key role of P-selectin glycoprotein ligand-1 (PSGL-1) in maintaining an inhibitory state of tumor-associated macrophages (TAMs), most of which express high levels of PSGL-1. Here we describe a novel, first-in-class humanized high-affinity monoclonal antibody VTX-0811 that repolarizes human macrophages from an M2-suppressive phenotype towards an M1 inflammatory phenotype, similar to siRNA-mediated knockdown of PSGL-1. VTX-0811 binds to PSGL-1 of human and cynomolgus macaque origins without inhibiting PSGL-1 interaction with P- and L-Selectins or VISTA. In multi-cellular assays and in patient-derived human tumor cultures, VTX-0811 leads to the induction of pro-inflammatory mediators. RNAseq data from VTX-0811 treated ex vivo tumor cultures and M2c macrophages show similar pathways being modulated, indicating that the mechanism of action translates from isolated macrophages to tumors. A chimeric version of VTX-0811, consisting of the parental murine antibody in a human IgG4 backbone, inhibits tumor growth in a humanized mouse model of cancer. VTX-0811 is exceptionally well tolerated in NHP toxicology assessment and is heading into clinical evaluation after successful IND clearance.

MDK promotes M2 macrophage polarization to remodel the tumour microenvironment in clear cell renal cell carcinoma

The efficacy of immunotherapy for clear cell renal cell carcinoma (ccRCC), especially advanced ccRCC, is limited, presenting a clinical challenge. This limitation is closely tied to the immune regulation network. Understanding the heterogeneity of the tumour microenvironment (TME) is crucial for developing advanced ccRCC therapies. Using publicly available ccRCC data (scRNA-seq, bulk RNA-seq, and somatic mutation data), a multiomics study was performed to explore TME heterogeneity. Three distinct ccRCC immune subtypes were identified through combined scRNA-seq and bulk RNA-seq analysis. A prognostic model based on unique cell signalling molecules in immunosuppressive tumour subtype was validated in the TCGA and CheckMate cohorts. MDK emerged as a critical regulatory gene in the immunosuppressive subtype, predicting a poor ccRCC prognosis and a poor immunotherapy response. MDK promotes M2 macrophage polarization via the MDK-LRP1 interaction, and the inhibition of MDK suppressed M2 polarization. This study revealed the heterogeneity of the ccRCC TME and a reliable prognostic model, shedding light on the vital role of MDK in the immunosuppressive TME and paving the way for optimized ccRCC immunotherapy.

Single-cell multiomics analysis reveals cell/tissue-specific associations in bipolar disorder

This study investigates the cellular origin and tissue heterogeneity in bipolar disorder (BD) by integrating multiomics data. Four distinct datasets were employed, including single-cell RNA sequencing (scRNA-seq) data (embryonic and fetal brain, n = 8, 1,266 cells), BD Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (adult brain, n = 210), BD bulk RNA-seq data (adult brain, n = 314), and BD genome-wide association study (GWAS) summary data (n = 413,466). The integration of scRNA-seq data with multiomics data relevant to BD was accomplished using the single-cell disease relevance score (scDRS) algorithm. We have identified a novel brain cell cluster named ADCY1, which exhibits distinct genetic characteristics. From a high-resolution genetic perspective, glial cells emerge as the primary cytopathology associated with BD. Specifically, astrocytes were significantly related to BD at the RNA-seq level, while microglia showed a strong association with BD across multiple panels, including the transcriptome-wide association study (TWAS), ATAC-seq, and RNA-seq. Additionally, oligodendrocyte precursor cells displayed a significant association with BD in both ATAC-seq and RNA-seq panel. Notably, our investigation of brain regions affected by BD revealed significant associations between BD and all three types of glial cells in the dorsolateral prefrontal cortex (DLPFC). Through comprehensive analyses, we identified several BD-associated genes, including CRMP1, SYT4, UCHL1, and ZBTB18. In conclusion, our findings suggest that glial cells, particularly in specific brain regions such as the DLPFC, may play a significant role in the pathogenesis of BD. The integration of multiomics data has provided valuable insights into the etiology of BD, shedding light on potential mechanisms underlying this complex psychiatric disorder.

Integrated single-cell and bulk RNA sequencing analyses identify an immunotherapy nonresponse-related fibroblast signature in gastric cancer.

Factors that determine nonresponse to immune checkpoint inhibitor (ICI) remain unclear. The protumor activities of cancer-associated fibroblasts (CAFs) suggest that they are potential therapeutic targets for cancer treatment. There is, however, a lack of CAF-related signature in predicting response to immunotherapy in gastric cancer (GC). Single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data of GC immunotherapy were downloaded from the Gene Expression Omnibus database. Bulk RNA-seq data were obtained from The Cancer Genome Atlas. The R package 'Seurat' was used for scRNA-seq data processing. Cellular infiltration, receptor-ligand interactions, and evolutionary trajectory analysis were further explored. Differentially expressed genes affecting overall survival were obtained using the limma package. Weighted Gene Correlation Network Analysis was used to identify key modules of immunotherapy nonresponder. Prognostic model was constructed by univariate Cox and least absolute contraction and selection operator analysis using the intersection of activated fibroblast genes (AFGs) with key module genes. The differences in clinicopathological features, immune microenvironment, immunotherapy prediction, and sensitivity to small molecule agents between the high- and low-risk groups were further investigated. Based on scRNA-seq, we finally identified 20 AFGs associations with the prognosis of GC patients. AFGs' high expression levels were correlated with both poor prognosis and tumor progression. Three genes (FRZB, SPARC, and FKBP10) were identified as immunotherapy nonresponse-related fibroblast genes and used to construct the prognostic signature. This signature is an independent significant risk factor affecting the clinical outcomes of GC patients. Remarkably, there were more CD4 memory T cells, resting mast cells, and M2 macrophages infiltrating in the high-risk group, which was characterized by higher tumor immune exclusion. Moreover, patients with higher risk scores were more prone to not respond to immunotherapy but were more sensitive to various small molecule agents, such as memantine. In conclusion, this study constructed a fibroblast-associated ICI nonresponse gene signature, which could predict the response to immunotherapy. This study potentially revealed a novel way to overcome immune resistance in GC.

Redirecting B7-H3.CAR T cells to Chemokines Expressed in Osteosarcoma Enhances Homing and Antitumor Activity in Preclinical Models.

Clinical efficacy of CAR T cells against pediatric osteosarcoma (OS) has been limited. One strategy to improve efficacy may be to drive chemokine-mediated homing of CAR T cells to tumors. We investigated the primary chemokines secreted by OS and evaluated efficacy of B7-H3.CAR T cells expressing the cognate receptors. We developed a pipeline to identify chemokines secreted by OS by correlating RNA-seq data with chemokines detected in media from fresh surgical specimens. We identified CXCR2 and CXCR6 as promising receptors for enhancing CAR T cell homing against OS. We evaluated the homing kinetics and efficiency of CXCR2- and CXCR6.T cells and homing, cytokine production, and antitumor activity of CXCR2- and CXCR6.B7-H3.CAR T cells in vitro and in vivo. T cells transgenically expressing CXCR2 or CXCR6 exhibited ligand-specific enhanced migration over T cells modified with nonfunctional receptors. Differential homing kinetics were observed, with CXCR2.T cells homing quickly and plateauing early, while CXCR6.T cells homed more slowly but achieved a similar plateau. When expressed in B7-H3.CAR T cells, CXCR2- and CXCR6 modification conferred enhanced homing towards OS in vitro and in vivo. CXCR2- and CXCR6-B7-H3.CAR treated mice experienced prolonged survival in a metastatic model compared to B7-H3.CAR T cell treated mice. Our patient-based pipeline identified targets for chemokine receptor modification of CAR T cells targeting OS. CXCR2 and CXCR6 expression enhanced homing and anti-OS activity of B7-H3.CAR T cells. These findings support clinical evaluation of CXCR-modified CAR T cells to improve adoptive cell therapy for OS patients.

Genome-Wide Identification and Evolution-Profiling Analysis of TPS Gene Family in Triticum Plants

Terpenoids play a crucial role in plant growth and development, as well as in regulating resistance mechanisms. Terpene synthase (TPS) serves as the final step in the synthesis process of terpenoids. However, a comprehensive bioinformatics analysis of the TPS gene family in Triticum plants had not previously been systematically undertaken. In this study, a total of 531 TPS members were identified in Triticum plants. The evolutionary tree divided the TPS proteins into five subfamilies: Group1, Group2, Group3, Group4, and Group5. The results of the duplication events analysis showed that TD and WGD were major driving forces during the evolution of the TPS family. The cis-element analysis showed that the TPS genes were related to plant growth and development and environmental stress. Moreover, the GO annotation displayed that the biological function of TPS was relatively conserved in wheat plants. The RNA-seq data showed that the rice and wheat TPS genes responded to low-temperature stress and exhibited significantly different expression patterns. This research shed light on the functions of TPSs in responding to abiotic stress and demonstrated their modulatory potential during root development. These findings provide a foundation for further and deeper investigation of the TPSs’ functions in Triticum plants.

Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package “ConsensusClusterPlus” on the bulk RNA-seq data. Hub genes were discerned through the application of the R package “limma” and univariate regression analysis, with the calculation of risk scores carried out using the R package “survminer”. To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan–Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages “ESTIMATE” and “GSVA” were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

ScTab: Scaling cross-tissue single-cell annotation models

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.

CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data

RNA sequencing (RNA-seq) has become a standard method for profiling gene expression, yet genomic DNA (gDNA) contamination carried over to the sequencing library poses a significant challenge to data integrity. Detecting and correcting this contamination is vital for accurate downstream analyses. Particularly, when RNA samples are scarce and invaluable, it becomes essential not only to identify but also to correct gDNA contamination to maximize the data’s utility. However, existing tools capable of correcting gDNA contamination are limited and lack thorough evaluation. To fill the gap, we developed CleanUpRNAseq, which offers a comprehensive set of functionalities for identifying and correcting gDNA-contaminated RNA-seq data. Our package offers three correction methods for unstranded RNA-seq data and a dedicated approach for stranded data. Through rigorous validation on published RNA-seq datasets with known levels of gDNA contamination and real-world RNA-seq data, we demonstrate CleanUpRNAseq’s efficacy in detecting and correcting detrimental levels of gDNA contamination across diverse library protocols. CleanUpRNAseq thus serves as a valuable tool for post-alignment quality assessment of RNA-seq data and should be integrated into routine workflows for RNA-seq data analysis. Its incorporation into OneStopRNAseq should significantly bolster the accuracy of gene expression quantification and differential expression analysis of RNA-seq data.

Overexpression of protection of telomeres 1 (POT1), a single-stranded DNA-binding proteins in alfalfa (Medicago sativa), enhances seed vigor

Extensive bodies of research are dedicated to the study of seed aging with a particular focus on the roles of reactive oxygen species (ROS), and the ensuing oxidative damage during storage, as a primary cause of seed vigor decreasing. ROS diffuse to the nucleus and damage the telomeres, resulting in a loss of genetic integrity. Protection of telomeres 1 (POT1) is a telomeric protein that binds to the telomere region, and plays an essential role in maintaining genomic stability in plants. In this study, there were totally four MsPOT1 genes obtained from alfalfa genome. Expression analysis of four MsPOT1 genes in germinated seed presented the different expressions. Four MsPOT1 genes displayed high expression levels at the early stage of seed germination, Among the four POT1 genes, it was found that MS. gene040108 was significantly up-regulated in the early germination stage of CK seeds, but down-regulated in aged seeds. RT-qPCR assays and RNA-seq data revealed that MsPOT1-X gene was significantly induced by seed aging treatment. Transgenic seeds overexpressing MsPOT1-X gene in Arabidopsis thaliana and Medicago trunctula exhibited enhanced seed vigor, telomere length, telomerase activity associated with reduced H2O2 content. These results would provide a new way to understand aging stress-responsive MsPOT1 genes for genetic improvement of seed vigor. Although a key gene regulating seed vigor was identified in this study, the specific mechanism of MsPOT1-X gene regulating seed vigor needs to be further explored.

Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits

Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice (Oryza sativa), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for ~15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.

H3K36 methyltransferase GhKMT3;1a and GhKMT3;2a promote flowering in upland cotton

BackgroundThe SET domain group (SDG) genes encode histone lysine methyltransferases, which regulate gene transcription by altering chromatin structure and play pivotal roles in plant flowering determination. However, few studies have investigated their role in the regulation of flowering in upland cotton.ResultsA total of 86 SDG genes were identified through genome-wide analysis in upland cotton (Gossypium hirsutum). These genes were unevenly distributed across 25 chromosomes. Cluster analysis revealed that the 86 GhSDGs were divided into seven main branches. RNA-seq data and qRT‒PCR analysis revealed that lysine methyltransferase 3 (KMT3) genes were expressed at high levels in stamens, pistils and other floral organs. Using virus-induced gene silencing (VIGS), functional characterization of GhKMT3;1a and GhKMT3;2a revealed that, compared with those of the controls, the GhKMT3;1a- and GhKMT3;2a-silenced plants exhibited later budding and flowering and lower plant heightwere shorter. In addition, the expression of flowering-related genes (GhAP1, GhSOC1 and GhFT) significantly decreased and the expression level of GhSVP significantly increased in the GhKMT3;1a- and GhKMT3;2a-silenced plants compared with the control plants.ConclusionA total of 86 SDG genes were identified in upland cotton, among which GhKMT3;1a and GhKMT3;2a might regulate flowering by affecting the expression of GhAP1, GhSOC1, GhFT and GhSVP. These findings will provide genetic resources for advanced molecular breeding in the future.

RNA splicing as a biomarker and phenotypic driver of meningioma DNA methylation groups.

Advances in our understanding of the molecular biology of meningiomas have led to significant gains in the ability to predict patient prognosis and tumor recurrence and to identify novel targets for therapeutic design. Specifically, classification of meningiomas based on DNA methylation has greatly improved our ability to risk stratify patients, however new questions have arisen in terms of the underlying impact these DNA methylation signatures have on meningioma biology. This study utilizes RNA-seq data from 486 meningioma samples corresponding to three meningioma DNA methylation groups (Merlin-intact, Immune-enriched, and Hypermitotic), followed by in vitro experiments utilizing human meningioma cell lines. We identify alterations in RNA splicing between meningioma DNA methylation groups including individual splicing events that correlate with Hypermitotic meningiomas and predict tumor recurrence and overall patient prognosis and compile a set of splicing events that can accurately predict DNA methylation classification based on RNA-seq data. Furthermore, we validate these events using RT-PCR in patient samples and meningioma cell lines. Additionally, we identify alterations in RNA binding proteins and splicing factors that lie upstream of RNA splicing events, including upregulation of SRSF1 in Hypermitotic meningiomas which we show drives alternative RNA splicing changes. Finally, we design splice switching antisense oligonucleotides to target RNA splicing changes in NASP and MFF observed in Hypermitotic meningiomas, providing a rationale for RNA-based therapeutic design. RNA splicing is an important driver of meningioma phenotypes that can be useful in prognosticating patients and as a potential exploit for therapeutic vulnerabilities.

A comprehensive atlas of pig RNA editome across 23 tissues reveals RNA editing affecting interaction mRNA-miRNAs.

RNA editing is a co-transcriptional/post-transcriptional modification that is mediated by the ADAR enzyme family. Profiling of RNA editing is very limited in pigs. In this study, we collated 3813 RNA-seq data from the public repositories across 23 tissues and carried out comprehensive profiling of RNA editing in pigs. In total, 127,927 A-to-I RNA editing sites were detected. Our analysis showed that 98.2% of RNA editing sites were located within repeat regions, primarily within the pig-specific SINE retrotransposon PRE-1/Pre0_SS elements. Subsequently, we focused on analyzing specific editing sites (SESs) in skeletal muscle tissues. Functional enrichment analyses suggested that they were enriched in signaling pathways associated with muscle cell differentiation, including DMD, MYOD1 and CAV1 genes. Furthermore, we discovered that RNA editing event in the 3`UTR of CFLAR mRNA influenced miR-708-5p binding in this region. In this study, the panoramic RNA editing landscape of different tissues of pigs was systematically mapped, and RNA editing sites and genes involved in muscle cell differentiation were identified. In summary, we identified modifications to pig RNA editing sites and provided candidate targets for further validation.

Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune checkpoint inhibitors have demonstrated promising therapeutic efficacy in HCC, not all patients exhibit a favorable response to these treatments. Glutamine is a crucial immune cell regulatory factor, and tumor cells exhibit glutamine dependence. In this study, HCC patients were divided into two subtypes (C1 and C2) based on glutamine metabolism-related genes via consensus clustering. The C1 pattern, in contrast to C2, was associated with a lower survival probability among HCC patients. Additionally, the C1 pattern exhibited higher proportions of patients with advanced tumor stages. The activity of C1 in glutamine metabolism and transport is significantly enhanced, while its oxidative phosphorylation activity is reduced. And, C1 was mainly involved in the progression-related pathway of HCC. Furthermore, C1 exhibited high levels of immunosuppressive cells, cytokine-receptor interactions and immune checkpoint genes, suggesting C1 as an immunosuppressive subtype. After stepwise selection based on integrated four machine learning methods, SLC1A5 was finally identified as the pivotal gene that distinguishes the subtypes. The expression of SLC1A5 was significantly positively correlated with immunosuppressive status. SLC1A5 showed the most significant correlation with macrophage infiltration, and this correlation was confirmed through the RNA-seq data of CLCA project and our cohort. Low-SLC1A5-expression samples had better immunogenicity and responsiveness to immunotherapy. As expected, SubMap and survival analysis indicated that individuals with low SLC1A5 expression were more responsive to anti-PD1 therapy. Collectively, this study categorized HCC patients based on glutamine metabolism-related genes and proposed two subclasses with different clinical traits, biological behavior, and immune status. Machine learning was utilized to identify the hub gene SLC1A5 for HCC classification, which also could predict immunotherapy response.

Association mapping with a diverse population of Puccinia graminis f. sp. tritici identified avirulence loci interacting with the barley Rpg1 stem rust resistance gene

BackgroundWheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is an important disease of barley and wheat. A diverse sexual Pgt population from the Pacific Northwest (PNW) region of the US contains a high proportion of individuals with virulence on the barley stem rust resistance (R) gene, Rpg1. However, the evolutionary mechanisms of this virulence on Rpg1 are mysterious considering that Rpg1 had not been deployed in the region and the gene had remained remarkably durable in the Midwestern US and prairie provinces of Canada.Methods and resultsTo identify AvrRpg1 effectors, genome wide association studies (GWAS) were performed using 113 Pgt isolates collected from the PNW (n = 89 isolates) and Midwest (n = 24 isolates) regions of the US. Disease phenotype data were generated on two barley lines Morex and the Golden Promise transgenic (H228.2c) that carry the Rpg1 gene. Genotype data was generated by whole genome sequencing (WGS) of 96 isolates (PNW = 89 isolates and Midwest = 7 isolates) and RNA sequencing (RNAseq) data from 17 Midwestern isolates. Utilizing ~1.2 million SNPs generated from WGS and phenotype data (n = 96 isolates) on the transgenic line H228.2c, 53 marker trait associations (MTAs) were identified. Utilizing ~140 K common SNPs generated from combined analysis of WGS and RNAseq data, two significant MTAs were identified using the cv Morex phenotyping data. The 55 MTAs defined two distinct avirulence loci, on supercontig 2.30 and supercontig 2.11 of the Pgt reference genome of Pgt isolate CRL 75-36-700-3. The major avirulence locus designated AvrRpg1A was identified with the GWAS using both barley lines and was delimited to a 35 kb interval on supercontig 2.30 containing four candidate genes (PGTG_10878, PGTG_10884, PGTG_10885, and PGTG_10886). The minor avirulence locus designated AvrRpg1B identified with cv Morex contained a single candidate gene (PGTG_05433). AvrRpg1A haplotype analysis provided strong evidence that a dominant avirulence gene underlies the locus.ConclusionsThe association analysis identified strong candidate AvrRpg1 genes. Further analysis to validate the AvrRpg1 genes will fill knowledge gaps in our understanding of rust effector biology and the evolution and mechanism/s of Pgt virulence on Rpg1.