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1.
Genome Biol ; 25(1): 169, 2024 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-38956606

RESUMO

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.


Assuntos
Benchmarking , Análise de Célula Única , Análise de Célula Única/métodos , Humanos , Simulação por Computador , RNA-Seq/métodos , Biologia Computacional/métodos
2.
Sci Adv ; 10(27): eadj7402, 2024 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-38959321

RESUMO

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.


Assuntos
Bactérias , RNA-Seq , Análise de Célula Única , Humanos , Análise de Célula Única/métodos , RNA-Seq/métodos , Bactérias/genética , Microambiente Tumoral , Células Mieloides/metabolismo , Células Mieloides/microbiologia , Análise de Sequência de RNA/métodos , Neoplasias Colorretais/microbiologia , Neoplasias Colorretais/genética , Biologia Computacional/métodos , RNA Bacteriano/genética , Neoplasias Esofágicas/microbiologia , Neoplasias Esofágicas/genética , Microbiota , Análise da Expressão Gênica de Célula Única
3.
Front Immunol ; 15: 1399856, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38962008

RESUMO

Objective: 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. Methods: 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. Results: 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. Conclusion: 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.


Assuntos
Artrite Reumatoide , Análise da Randomização Mendeliana , Locos de Características Quantitativas , RNA-Seq , Análise de Célula Única , Humanos , Artrite Reumatoide/genética , Artrite Reumatoide/imunologia , Artrite Reumatoide/diagnóstico , Análise de Célula Única/métodos , Nomogramas , Aprendizado de Máquina , Linfócitos T/imunologia , Linfócitos T/metabolismo , Perfilação da Expressão Gênica , Análise da Expressão Gênica de Célula Única
4.
Eur J Med Res ; 29(1): 358, 2024 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-38970067

RESUMO

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.


Assuntos
Linfócitos T CD8-Positivos , Neoplasias Ovarianas , Humanos , Feminino , Neoplasias Ovarianas/genética , Linfócitos T CD8-Positivos/imunologia , Linfócitos T CD8-Positivos/metabolismo , Prognóstico , RNA-Seq/métodos , Biomarcadores Tumorais/genética , Análise de Célula Única/métodos , Regulação Neoplásica da Expressão Gênica , Análise da Expressão Gênica de Célula Única
5.
Nat Commun ; 15(1): 5665, 2024 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-38969631

RESUMO

The paradigm for macrophage characterization has evolved from the simple M1/M2 dichotomy to a more complex model that encompasses the broad spectrum of macrophage phenotypic diversity, due to differences in ontogeny and/or local stimuli. We currently lack an in-depth pan-cancer single cell RNA-seq (scRNAseq) atlas of tumour-associated macrophages (TAMs) that fully captures this complexity. In addition, an increased understanding of macrophage diversity could help to explain the variable responses of cancer patients to immunotherapy. Our atlas includes well established macrophage subsets as well as a number of additional ones. We associate macrophage composition with tumour phenotype and show macrophage subsets can vary between primary and metastatic tumours growing in sites like the liver. We also examine macrophage-T cell functional cross talk and identify two subsets of TAMs associated with T cell activation. Analysis of TAM signatures in a large cohort of immune checkpoint inhibitor-treated patients (CPI1000 + ) identify multiple TAM subsets associated with response, including the presence of a subset of TAMs that upregulate collagen-related genes. Finally, we demonstrate the utility of our data as a resource and reference atlas for mapping of novel macrophage datasets using projection. Overall, these advances represent an important step in both macrophage classification and overcoming resistance to immunotherapies in cancer.


Assuntos
Imunoterapia , Neoplasias , Macrófagos Associados a Tumor , Humanos , Imunoterapia/métodos , Macrófagos Associados a Tumor/imunologia , Macrófagos Associados a Tumor/metabolismo , Neoplasias/imunologia , Neoplasias/terapia , Neoplasias/patologia , Neoplasias/genética , Microambiente Tumoral/imunologia , Análise de Célula Única , Linfócitos T/imunologia , RNA-Seq , Inibidores de Checkpoint Imunológico/uso terapêutico , Inibidores de Checkpoint Imunológico/farmacologia , Macrófagos/imunologia , Regulação Neoplásica da Expressão Gênica
6.
Nat Commun ; 15(1): 5600, 2024 Jul 03.
Artigo em Inglês | MEDLINE | ID: mdl-38961061

RESUMO

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell's streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.


Assuntos
Análise de Célula Única , Software , Análise de Célula Única/métodos , Humanos , Fluxo de Trabalho , Biologia Computacional/métodos , Interface Usuário-Computador , RNA-Seq/métodos
7.
Brief Bioinform ; 25(4)2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38975891

RESUMO

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.


Assuntos
Análise de Célula Única , Análise de Célula Única/métodos , Análise por Conglomerados , Humanos , Perfilação da Expressão Gênica/métodos , Algoritmos , Biologia Computacional/métodos , Análise de Sequência de RNA/métodos , RNA-Seq/métodos
8.
Brief Bioinform ; 25(4)2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38960404

RESUMO

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.


Assuntos
Aprendizado Profundo , RNA-Seq , Análise de Célula Única , Análise de Célula Única/métodos , Humanos , RNA-Seq/métodos , Biologia Computacional/métodos , Algoritmos , Análise de Sequência de RNA/métodos , Redes Neurais de Computação , Análise da Expressão Gênica de Célula Única
9.
Brief Bioinform ; 25(4)2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38960406

RESUMO

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.


Assuntos
Perfilação da Expressão Gênica , RNA-Seq , Transcriptoma , Humanos , RNA-Seq/métodos , Perfilação da Expressão Gênica/métodos , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Regulação Neoplásica da Expressão Gênica , Biologia Computacional/métodos , Feminino , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo
10.
Function (Oxf) ; 5(4)2024 Jul 11.
Artigo em Inglês | MEDLINE | ID: mdl-38985000

RESUMO

Pancreatic ß-cells are essential for survival, being the only cell type capable of insulin secretion. While they are believed to be vulnerable to damage by inflammatory cytokines such as interleukin-1 beta (IL-1ß) and interferon-gamma, we have recently identified physiological roles for cytokine signaling in rodent ß-cells that include the stimulation of antiviral and antimicrobial gene expression and the inhibition of viral replication. In this study, we examine cytokine-stimulated changes in gene expression in human islets using single-cell RNA sequencing. Surprisingly, the global responses of human islets to cytokine exposure were remarkably blunted compared to our previous observations in the mouse. The small population of human islet cells that were cytokine responsive exhibited increased expression of IL-1ß-stimulated antiviral guanylate-binding proteins, just like in the mouse. Most human islet cells were not responsive to cytokines, and this lack of responsiveness was associated with high expression of genes encoding ribosomal proteins. We further correlated the expression levels of RPL5 with stress response genes, and when expressed at high levels, RPL5 is predictive of failure to respond to cytokines in all endocrine cells. We postulate that donor causes of death and isolation methodologies may contribute to stress of the islet preparation. Our findings indicate that activation of stress responses in human islets limits cytokine-stimulated gene expression, and we urge caution in the evaluation of studies that have examined cytokine-stimulated gene expression in human islets without evaluation of stress-related gene expression.


Assuntos
Citocinas , Ilhotas Pancreáticas , Análise de Célula Única , Humanos , Análise de Célula Única/métodos , Ilhotas Pancreáticas/metabolismo , Ilhotas Pancreáticas/efeitos dos fármacos , Citocinas/metabolismo , Citocinas/genética , Células Secretoras de Insulina/metabolismo , Células Secretoras de Insulina/efeitos dos fármacos , Análise de Sequência de RNA , Estresse Fisiológico/efeitos dos fármacos , Interleucina-1beta/metabolismo , Proteínas Ribossômicas/genética , Proteínas Ribossômicas/metabolismo , Masculino , Camundongos , Animais , RNA-Seq , Feminino , Pessoa de Meia-Idade , Análise da Expressão Gênica de Célula Única
11.
BMC Plant Biol ; 24(1): 649, 2024 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-38977989

RESUMO

BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.


Assuntos
Temperatura Baixa , Oryza , Locos de Características Quantitativas , Plântula , Oryza/genética , Oryza/fisiologia , Locos de Características Quantitativas/genética , Plântula/genética , Plântula/fisiologia , Plântula/crescimento & desenvolvimento , Genes de Plantas , RNA-Seq , Mapeamento Cromossômico , Perfilação da Expressão Gênica , Regulação da Expressão Gênica de Plantas , Resposta ao Choque Frio/genética
12.
Brief Bioinform ; 25(4)2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38980373

RESUMO

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.


Assuntos
Aprendizado Profundo , Redes Reguladoras de Genes , Análise de Célula Única , Humanos , Análise de Célula Única/métodos , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Biologia Computacional/métodos , Análise de Sequência de RNA/métodos , RNA-Seq/métodos
13.
BMC Plant Biol ; 24(1): 653, 2024 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-38987678

RESUMO

BACKGROUND: Walnut anthracnose caused by Colletotrichum gloeosporioides seriously endangers the yield and quality of walnut, and has now become a catastrophic disease in the walnut industry. Therefore, understanding both pathogen invasion mechanisms and host response processes is crucial to defense against C. gloeosporioides infection. RESULTS: Here, we investigated the mechanisms of interaction between walnut fruits (anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts) and C. gloeosporioides at three infection time points (24hpi, 48hpi, and 72hpi) using a high-resolution time series dual transcriptomic analysis, characterizing the arms race between walnut and C. gloeosporioides. A total of 20,780 and 6670 differentially expressed genes (DEGs) were identified in walnut and C. gloeosporioides against 24hpi, respectively. Generous DEGs in walnut exhibited opposite expression patterns between F26 and F423, which indicated that different resistant materials exhibited different transcriptional responses to C. gloeosporioides during the infection process. KEGG functional enrichment analysis indicated that F26 displayed a broader response to C. gloeosporioides than F423. Meanwhile, the functional analysis of the C. gloeosporioides transcriptome was conducted and found that PHI, SignalP, CAZy, TCDB genes, the Fungal Zn (2)-Cys (6) binuclear cluster domain (PF00172.19) and the Cytochrome P450 (PF00067.23) were largely prominent in F26 fruit. These results suggested that C. gloeosporioides secreted some type of effector proteins in walnut fruit and appeared a different behavior based on the developmental stage of the walnut. CONCLUSIONS: Our present results shed light on the arms race process by which C. gloeosporioides attacked host and walnut against pathogen infection, laying the foundation for the green prevention of walnut anthracnose.


Assuntos
Colletotrichum , Juglans , Doenças das Plantas , Juglans/microbiologia , Juglans/genética , Colletotrichum/fisiologia , Doenças das Plantas/microbiologia , Doenças das Plantas/genética , RNA-Seq , Frutas/microbiologia , Frutas/genética , Transcriptoma , Regulação da Expressão Gênica de Plantas , Perfilação da Expressão Gênica , Interações Hospedeiro-Patógeno/genética , Resistência à Doença/genética
14.
Nat Methods ; 21(7): 1349-1363, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38849569

RESUMO

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.


Assuntos
Perfilação da Expressão Gênica , RNA-Seq , Humanos , Animais , Camundongos , RNA-Seq/métodos , Perfilação da Expressão Gênica/métodos , Transcriptoma , Análise de Sequência de RNA/métodos , Anotação de Sequência Molecular/métodos
15.
Hum Genomics ; 18(1): 62, 2024 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-38862997

RESUMO

BACKGROUND: Diabetic foot ulcers (DFU) is the most serious complication of diabetes mellitus, which has become a global health problem due to its high morbidity and disability rates and the poor efficacy of conventional treatments. Thus, it is urgent to identify novel molecular targets to improve the prognosis and reduce disability rate in DFU patients. RESULTS: In the present study, bulk RNA-seq and scRNA-seq associated with DFU were downloaded from the GEO database. We identified 1393 DFU-related DEGs by differential analysis and WGCNA analysis together, and GO/KEGG analysis showed that these genes were associated with lysosomal and immune/inflammatory responses. Immediately thereafter, we identified CLU, RABGEF1 and ENPEP as DLGs for DFU using three machine learning algorithms (Randomforest, SVM-RFE and LASSO) and validated their diagnostic performance in a validation cohort independent of this study. Subsequently, we constructed a novel artificial neural network model for molecular diagnosis of DFU based on DLGs, and the diagnostic performance in the training and validation cohorts was sound. In single-cell sequencing, the heterogeneous expression of DLGs also provided favorable evidence for them to be potential diagnostic targets. In addition, the results of immune infiltration analysis showed that the abundance of mainstream immune cells, including B/T cells, was down-regulated in DFUs and significantly correlated with the expression of DLGs. Finally, we found latamoxef, parthenolide, meclofenoxate, and lomustine to be promising anti-DFU drugs by targeting DLGs. CONCLUSIONS: CLU, RABGEF1 and ENPEP can be used as novel lysosomal molecular signatures of DFU, and by targeting them, latamoxef, parthenolide, meclofenoxate and lomustine were identified as promising anti-DFU drugs. The present study provides new perspectives for the diagnosis and treatment of DFU and for improving the prognosis of DFU patients.


Assuntos
Pé Diabético , Lisossomos , Humanos , Lisossomos/genética , Lisossomos/metabolismo , Lisossomos/efeitos dos fármacos , Pé Diabético/genética , Pé Diabético/tratamento farmacológico , Pé Diabético/patologia , RNA-Seq , Análise de Célula Única/métodos , Perfilação da Expressão Gênica , Prognóstico , Masculino , Feminino , Aprendizado de Máquina , Análise da Expressão Gênica de Célula Única
16.
Methods Mol Biol ; 2832: 81-98, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38869789

RESUMO

Alternative splicing (AS) of pre-mRNAs is a type of post-transcriptional regulation in eukaryotes that expands the number of mRNA isoforms. Intron retention is the primary form of AS in plants and occurs more frequently when plants are exposed to environmental stresses. Several wet-lab and bioinformatics techniques are used to detect AS events, but these techniques are technically challenging or unsuitable for studying AS in plants. Here, we report a method that combines RNA-sequencing and reverse transcription PCR for visualizing and validating heat stress-induced AS events in plants, using Arabidopsis thaliana and HEAT SHOCK PROTEIN21 (HSP21) as examples.


Assuntos
Processamento Alternativo , Arabidopsis , Resposta ao Choque Térmico , Processamento Alternativo/genética , Resposta ao Choque Térmico/genética , Arabidopsis/genética , Regulação da Expressão Gênica de Plantas , RNA-Seq/métodos , Reação em Cadeia da Polimerase Via Transcriptase Reversa/métodos , RNA de Plantas/genética , Proteínas de Arabidopsis/genética , Proteínas de Arabidopsis/metabolismo , Biologia Computacional/métodos
17.
Pestic Biochem Physiol ; 202: 105912, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38879294

RESUMO

Herbicide resistance is a worldwide concern for weed control. Cucumis melo L. var. agrestis Naud. (C. melo) is an annual trailing vine weed that is commonly controlled by nicosulfuron, acetolactate synthase (ALS)-inhibiting herbicides. However, long-term use of this herbicide has led to the emergence of resistance and several nicosulfuron resistant populations of C. melo have been found. Here we identified a resistant (R) C. melo population exhibiting 7.31-fold resistance to nicosulfuron compared with a reference sensitive (S) population. ALS gene sequencing of the target site revealed no amino acid substitution in R plants, and no difference in enzyme activity, as shown by ALS activity assays in vitro. ALS gene expression was not significantly different before and after the application of nicosulfuron. Pretreatment with the cytochrome P450 monooxygenase (P450) inhibitor malathion reduced nicosulfuron resistance in the R population. RNA-Seq transcriptome analysis was used to identify candidate genes that may confer metabolic resistance to nicosulfuron. We selected genes with annotations related to detoxification functions. A total of 20 candidate genes (7 P450 genes, 1 glutathione S-transferase (GST) gene, 2 ATP-binding cassette (ABC) transporters, and 10 glycosyltransferase (GT)) were identified; 12 of them (7 P450s, 1 GST, 2 ABC transporters, and 2 GTs) were demonstrated significantly differential expression between R and S by quantitative real-time RT-PCR (qRT-PCR). Our findings revealed that the resistance mechanism in C. melo was nontarget-site based. Our results also provide a valuable resource for studying the molecular mechanisms of weed resistance.


Assuntos
Acetolactato Sintase , Cucumis melo , Resistência a Herbicidas , Herbicidas , Piridinas , Compostos de Sulfonilureia , Resistência a Herbicidas/genética , Compostos de Sulfonilureia/farmacologia , Herbicidas/farmacologia , Herbicidas/toxicidade , Acetolactato Sintase/genética , Acetolactato Sintase/metabolismo , Cucumis melo/genética , Cucumis melo/efeitos dos fármacos , Piridinas/farmacologia , RNA-Seq , Perfilação da Expressão Gênica , Malation/farmacologia , Regulação da Expressão Gênica de Plantas/efeitos dos fármacos , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo
18.
Sci Data ; 11(1): 629, 2024 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-38877023

RESUMO

The Chungtien schizothoracin (Ptychobarbus chungtienensis), an endangered fish species endemic to the Zhongdian Plateau, remains underexplored in terms of transcriptomic sequencing. This investigation used tissues from five distinct organs (heart, liver, spleen, kidney, and brain) of the Chungtien schizothoracin for PacBio Iso-seq and RNA-seq analyses, yielding a repertoire of 16,598 full-length transcripts spanning lengths from 363 bp to 7,157 bp. Gene family clustering and phylogenetic analysis encompassed a comprehensive set of 13 fish species, all of which were cyprinids, including the zebrafish and the examined species Ptychobarbus chungtienensis. Moreover, the identification of long non-coding RNAs (lncRNAs) and coding sequences was accomplished across all five tissues. Comprehensive analyses of gene expression profiles and differentially expressed genes among the above five tissues were performed. In summary, the obtained full-length transcripts and detailed gene expression profiles of the Chungtien schizothoracin tissues furnish crucial expression data and genetic sequences, laying the groundwork for future investigations and fostering a holistic comprehension of the adaptive mechanisms inherent in the Chungtien schizothoracin under various conditions.


Assuntos
Cyprinidae , Filogenia , Transcriptoma , Animais , Cyprinidae/genética , RNA-Seq , RNA Longo não Codificante/genética , Espécies em Perigo de Extinção
19.
PLoS One ; 19(6): e0293688, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38843139

RESUMO

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: a. human pituitary FSH18/21 (hypo-glycosylated); b. human pituitary FSH24 (fully glycosylated); c. Equine FSH (eqFSH) (hypo-glycosylated); and d. Chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10-15 x 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent distinctly different regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.


Assuntos
Hormônio Foliculoestimulante , Células da Granulosa , Transcriptoma , Animais , Feminino , Células da Granulosa/metabolismo , Células da Granulosa/efeitos dos fármacos , Hormônio Foliculoestimulante/farmacologia , Hormônio Foliculoestimulante/metabolismo , Ratos , Glicosilação , Transcriptoma/efeitos dos fármacos , Humanos , Células Cultivadas , RNA-Seq/métodos , Células CHO , Cricetulus
20.
Nat Commun ; 15(1): 4710, 2024 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-38844475

RESUMO

Alzheimer's Disease (AD) pathology has been increasingly explored through single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST). However, the surge in data demands a comprehensive, user-friendly repository. Addressing this, we introduce a single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD). It offers a broader spectrum of AD-related datasets, an optimized analytical pipeline, and improved usability. The database encompasses 1,053 samples (277 integrated datasets) from 67 AD-related scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells. Additionally, it archives 381 ST datasets from 18 human and mouse brain studies. Each dataset is annotated with details such as species, gender, brain region, disease/control status, age, and AD Braak stages. ssREAD also provides an analysis suite for cell clustering, identification of differentially expressed and spatially variable genes, cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis. ssREAD is freely available at https://bmblx.bmi.osumc.edu/ssread/ .


Assuntos
Doença de Alzheimer , RNA-Seq , Análise de Célula Única , Doença de Alzheimer/genética , Humanos , Análise de Célula Única/métodos , Animais , Camundongos , RNA-Seq/métodos , Encéfalo/metabolismo , Encéfalo/patologia , Bases de Dados Genéticas , Transcriptoma , Análise de Sequência de RNA/métodos , Perfilação da Expressão Gênica/métodos , Masculino
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