scholarly journals Transcriptomic profiling of long- and short-lived mutant mice implicates mitochondrial metabolism in ageing and shows signatures of normal ageing in progeroid mice

2020 ◽  
Author(s):  
Matias Fuentealba ◽  
Daniel K. Fabian ◽  
Handan Melike Dönertaş ◽  
Janet M. Thornton ◽  
Linda Partridge

AbstractGenetically modified mouse models of ageing are the living proof that lifespan and healthspan can be lengthened or shortened, yet the molecular mechanisms behind these opposite phenotypes remain largely unknown. In this study, we analysed and compared gene expression data from 10 long-lived and 8 short-lived mouse models of ageing. Transcriptome-wide correlation analysis revealed that mutations with equivalent effects on lifespan induce more similar transcriptomic changes, especially if they target the same pathway. Using functional enrichment analysis, we identified 58 gene sets with consistent changes in long- and short-lived mice, 55 of which were up-regulated in long-lived mice and down-regulated in short-lived mice. Half of these sets represented genes involved in energy and lipid metabolism, among which Ppargc1a, Mif, Aldh5a1 and Idh1 were frequently observed. Based on the gene sets with consistent changes and also the whole transcriptome, we observed that the gene expression changes during normal ageing resembled the transcriptome of short-lived models, suggesting that accelerated ageing models reproduce partially the molecular changes of ageing. Finally, we identified new genetic interventions that may ameliorate ageing, by comparing the transcriptomes of 51 mouse mutants not previously associated with ageing to expression signatures of long- and short-lived mice and ageing-related changes.HighlightsTranscriptomic changes are more similar within mutant mice that show either lengthened or shortened lifespanThe major transcriptomic differences between long- and short-lived mice are in genes controlling mitochondrial metabolismGene expression changes in short-lived, progeroid, mutant mice resemble those seen during normal ageing

2020 ◽  
Author(s):  
Arnaud Duchon ◽  
Maria del Mar Muñiz Moreno ◽  
Sandra Martin Lorenzo ◽  
Márcia Priscilla Silva de Souza ◽  
Claire Chevalier ◽  
...  

AbstractDown syndrome (DS) is the most common genetic form of intellectual disability caused by the presence of an additional copy of human chromosome 21. To provide novel insights into genotype–phenotype correlations, we screened the in vivo DS mouse library with standardized behavioural tests, magnetic resonance imaging (MRI) and hippocampal gene expression. Altogether this approach brings novel insights into the field. First, we unravelled several genetic interactions between different regions of the chromosome 21 and how they importantly contribute in altering the outcome of the phenotypes in brain function and structure. Then, in depth analysis of misregulated expressed genes involved in synaptic dysfunction highlitghed 6 biological cascades centered around DYRK1A, GSK3β, NPY, SNARE, RHOA and NPAS4. Finally, we provide a novel vision of the existing altered gene-gene crosstalk and molecular mechanisms targeting specific hubs in DS models that should become central to advance in our understanding of DS and therapies development.HighlightsBrain function and morphology changes in DS mouse models result from multiple genetic lociEach combination of loci induces specific alteration of gene expression profile in mouse modelsAltered gene expression converges to a few functional pathwys in DS mouse hippocampiThe synaptic pathway analysis leads to six connected biological cascades and defines a specific DS disease network


Biomolecules ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 139 ◽  
Author(s):  
Cheng Chi ◽  
Sib Giri ◽  
Jin Jun ◽  
Hyoun Kim ◽  
Sang Kim ◽  
...  

Palmitoleic acid (PA) is an effective algicide against Alexandrium tamarense. However, the toxicological mechanism of PA exposure is unclear. The transcript abundance and differentially expressed genes (DEGs) in gills of bay scallop were investigated following 80 mg/L PA exposure up to 48 h using the Illumina HiSeq 4000 deep-sequencing platform with the recommended read length of 100 bp. De novo assembly of paired-end reads yielded 62,099 unigenes; 5414 genes were identified as being significantly increased, and 4452 were decreased. Based on gene ontology classification and enrichment analysis, the ‘cellular process’, ‘metabolic process’, ‘response to stimulus’, and ‘catalytic process’ with particularly high functional enrichment were revealed. The DEGs, which are related to detoxification and immune responses, revealed that acid phosphatase, fibrinogen C domain-containing protein, cyclic AMP-responsive element-binding protein, glutathione reductase, ATP-binding cassette, nuclear factor erythroid 2-related factor, NADPH2:quinone reductase, and cytochrome P450 4F22, 4B1, and 2C8-related gene expression decreased. In contrast, some genes related to glutathione S-transferase, C-type lectin, superoxide dismutase, toll-like receptors, and cytochrome P450 2C14, 2U1, 3A24 and 4A2 increased. The results of current research will be a valuable resource for the investigation of gene expression stimulated by PA, and will help understanding of the molecular mechanisms underlying the scallops’ response to PA exposure.


2020 ◽  
Author(s):  
Shen Pan ◽  
Yunhong Zhan ◽  
Xiaonan Chen ◽  
Bin Wu ◽  
Bitian Liu

Abstract Background T1G3 shows a higher chance of recurrence and progression among early bladder cancer types and the available treatment option is controversial. High recurrence and progression are the problems that need to be explored and solved. Changes in the internal signals of bladder cancer cells and differential genes may be the root cause of these problems. Methods GSE120736, GSE19915, GSE19423, GSE32548 and GSE37815 datasets were obtained from Gene Expression Omnibus (GEO ) to identify differentially expressed genes (DEGs). Bladder cancer transcript data from The Cancer Genome Atlas (TCGA) were clustered into different cell-specific gene sets according to weighted gene co-expression network analysis (WGCNA). Multiple sets of databases were used for gene expression comparison, functional enrichment, and protein interaction analysis, including The Human Protein Atlas, Cancer Dependency Map, Metascape, Gene set enrichment analysis, and DisNor. Results DEGs were obtained through GEO data comparison and intersection. After WGCNA was proven to recognise cell-specific gene sets, candidate DEGs were selected and shown to be specifically expressed in cancer cells. Candidate DEGs were related to mitosis and cell cycle. Further, 12 functional candidate markers were identified from the sequencing data of 30 bladder cancer cell lines. These genes were all up-regulated and previously shown to be closely related to bladder cancer progression. Conclusions Twelve functional genes with specific differential expression in bladder cancer cells were identified. WGCNA can identify the relatively specific expression sets of different cells in bladder cancer with greater tumour heterogeneity, which provides new perspectives for future cancer research.


2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257343
Author(s):  
Shaoshuo Li ◽  
Baixing Chen ◽  
Hao Chen ◽  
Zhen Hua ◽  
Yang Shao ◽  
...  

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.


2020 ◽  
Author(s):  
Xiaomei Lei ◽  
Zhijun Feng ◽  
Xiaojun Wang ◽  
Xiaodong He

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identified from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.


Author(s):  
Qingchun Liang ◽  
Qin Zhou ◽  
Jinhe Li ◽  
Zhugui Chen ◽  
Zhihao Zhang ◽  
...  

Abstract Acute lung injury (ALI) is an inflammatory pulmonary disease that can easily develop into serious acute respiratory distress syndrome, which has high morbidity and mortality. However, the molecular mechanism of ALI remains unclear, and few molecular biomarkers for diagnosis and treatment have been identified. In this study, we aimed to identify novel molecular biomarkers using a bioinformatics approach. Gene expression data were obtained from the Gene Expression Omnibus database, co-expressed differentially expressed genes (CoDEGs) were identified using R software, and further functional enrichment analyses were conducted using the online tool Database for Annotation, Visualization, and Integrated Discovery. A protein–protein interaction network was established using the STRING database and Cytoscape software. Lipopolysaccharide (LPS)-induced ALI mouse model was constructed and verified. The hub genes were screened and validated in vivo. The transcription factors (TFs) and miRNAs associated with the hub genes were predicted using the NetworkAnalyst database. In total, 71 CoDEGs were screened and found to be mainly involved in the cytokine–cytokine receptor interactions, and the tumor necrosis factor and malaria signaling pathways. Animal experiments showed that the lung injury score, bronchoalveolar lavage fluid protein concentration, and wet-to-dry weight ratio were higher in the LPS group than those in the control group. Real-time polymerase chain reaction analysis indicated that most of the hub genes such as colony-stimulating factor 2 (Csf2) were overexpressed in the LPS group. A total of 20 TFs including nuclear respiratory factor 1 (NRF1) and two miRNAs were predicted to be regulators of the hub genes. In summary, Csf2 may serve as a novel diagnostic and therapeutic target for ALI. NRF1 and mmu-mir-122-5p may be key regulators in the development of ALI.


2020 ◽  
Vol 8 (Suppl 1) ◽  
pp. A5.2-A6
Author(s):  
Nils-Petter Rudqvist ◽  
Roberta Zappasodi ◽  
Daniel Wells ◽  
Vésteinn Thorsson ◽  
Alexandria Cogdill ◽  
...  

BackgroundImmune checkpoint blockade (ICB) has revolutionized cancer treatment. However, long-term benefits are only achieved in a small fraction of patients. Understanding the mechanisms underlying ICB activity is key to improving the efficacy of immunotherapy. A major limitation to uncovering these mechanisms is the limited number of responders within each ICB trial. Integrating data from multiple studies of ICB would help overcome this issue and more reliably define the immune landscape of durable responses. Towards this goal, we formed the TimIOs consortium, comprising researchers from the Society for Immunotherapy of Cancer Sparkathon TimIOs Initiative, the Parker Institute of Cancer Immunotherapy, the University of North Carolina-Chapel Hill, and the Institute for Systems Biology. Together, we aim to improve the understanding of the molecular mechanisms associated with defined outcomes to ICB, by building on our joint and multifaceted expertise in the field of immuno-oncology. To determine the feasibility and relevance of our approach, we have assembled a compendium of publicly available gene expression datasets from clinical trials of ICB. We plan to analyze this data using a previously reported pipeline that successfully determined main cancer immune-subtypes associated with survival across multiple cancer types in TCGA.1MethodsRNA sequencing data from 1092 patients were uniformly reprocessed harmonized, and annotated with predefined clinical parameters. We defined a comprehensive set of immunogenomics features, including immune gene expression signatures associated with treatment outcome,1,2 estimates of immune cell proportions, metabolic profiles, and T and B cell receptor repertoire, and scored all compendium samples for these features. Elastic net regression models with parameter optimization done via Monte Carlo cross-validation and leave-one-out cross-validation were used to analyze the capacity of an integrated immunogenomics model to predict durable clinical benefit following ICB treatment.ResultsOur preliminary analyses confirmed an association between the expression of an IFN-gamma signature in tumor (1) and better outcomes of ICB, highlighting the feasibility of our approach.ConclusionsIn line with analysis of pan-cancer TCGA datasets using this strategy (1), we expect to identify analogous immune subtypes characterizing baseline tumors from patients responding to ICB. Furthermore, we expect to find that these immune subtypes will have different importance in the model predicting response and survival. Results of this study will be incorporated into the Cancer Research Institute iAtlas Portal, to facilitate interactive exploration and hypothesis testing.ReferencesThorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Yang T-H O, Porta-Pardo E. Gao GF, Plaisier CL, Eddy JA, et al. The Immune Landscape of Cancer. Immunity 2018; 48(4): 812–830.e14. https://doi.org/10.1016/j.immuni.2018.03.023.Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, et al. Robust Prediction of Response to Immune Checkpoint Blockade Therapy in Metastatic Melanoma. Nat. Med 2018; 24(10): 1545. https://doi.org/10.1038/s41591-018-0157-9.


2021 ◽  
Vol 12 ◽  
Author(s):  
Andressa O. de Lima ◽  
Juliana Afonso ◽  
Janette Edson ◽  
Esteban Marcellin ◽  
Robin Palfreyman ◽  
...  

Spermatogenesis relies on complex molecular mechanisms, essential for the genesis and differentiation of the male gamete. Germ cell differentiation starts at the testicular parenchyma and finishes in the epididymis, which has three main regions: head, body, and tail. RNA-sequencing data of the testicular parenchyma (TP), head epididymis (HE), and tail epididymis (TE) from four bulls (three biopsies per bull: 12 samples) were subjected to differential expression analyses, functional enrichment analyses, and co-expression analyses. The aim was to investigate the co-expression and infer possible regulatory roles for transcripts involved in the spermatogenesis of Bos indicus bulls. Across the three pairwise comparisons, 3,826 differentially expressed (DE) transcripts were identified, of which 384 are small RNAs. Functional enrichment analysis pointed to gene ontology (GO) terms related to ion channel activity, detoxification of copper, neuroactive receptors, and spermatogenesis. Using the regulatory impact factor (RIF) algorithm, we detected 70 DE small RNAs likely to regulate the DE transcripts considering all pairwise comparisons among tissues. The pattern of small RNA co-expression suggested that these elements are involved in spermatogenesis regulation. The 3,826 DE transcripts (mRNAs and small RNAs) were further subjected to co-expression analyses using the partial correlation and information theory (PCIT) algorithm for network prediction. Significant correlations underpinned the co-expression network, which had 2,216 transcripts connected by 158,807 predicted interactions. The larger network cluster was enriched for male gamete generation and had 15 miRNAs with significant RIF. The miRNA bta-mir-2886 showed the highest number of connections (601) and was predicted to down-regulate ELOVL3, FEZF2, and HOXA13 (negative co-expression correlations and confirmed with TargetScan). In short, we suggest that bta-mir-2886 and other small RNAs might modulate gene expression in the testis and epididymis, in Bos indicus cattle.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ying Mao ◽  
Peng Huang ◽  
Yan Wang ◽  
Maiqiu Wang ◽  
Ming D. Li ◽  
...  

Abstract Background Smoking is a major causal risk factor for lung cancer, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and is the main preventable cause of deaths in the world. The components of cigarette smoke are involved in immune and inflammatory processes, which may increase the prevalence of cigarette smoke-related diseases. However, the underlying molecular mechanisms linking smoking and diseases have not been well explored. This study was aimed to depict a global map of DNA methylation and gene expression changes induced by tobacco smoking and to explore the molecular mechanisms between smoking and human diseases through whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq). Results We performed WGBS on 72 samples (36 smokers and 36 nonsmokers) and RNA-seq on 75 samples (38 smokers and 37 nonsmokers), and cytokine immunoassay on plasma from 22 males (9 smokers and 13 nonsmokers) who were recruited from the city of Jincheng in China. By comparing the data of the two groups, we discovered a genome-wide methylation landscape of differentially methylated regions (DMRs) associated with smoking. Functional enrichment analyses revealed that both smoking-related hyper-DMR genes (DMGs) and hypo-DMGs were related to synapse-related pathways, whereas the hypo-DMGs were specifically related to cancer and addiction. The differentially expressed genes (DEGs) revealed by RNA-seq analysis were significantly enriched in the “immunosuppression” pathway. Correlation analysis of DMRs with their corresponding gene expression showed that genes affected by tobacco smoking were mostly related to immune system diseases. Finally, by comparing cytokine concentrations between smokers and nonsmokers, we found that vascular endothelial growth factor (VEGF) was significantly upregulated in smokers. Conclusions In sum, we found that smoking-induced DMRs have different distribution patterns in hypermethylated and hypomethylated areas between smokers and nonsmokers. We further identified and verified smoking-related DMGs and DEGs through multi-omics integration analysis of DNA methylome and transcriptome data. These findings provide us a comprehensive genomic map of the molecular changes induced by smoking which would enhance our understanding of the harms of smoking and its relationship with diseases.


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