scholarly journals miRNA activity inferred from single cell mRNA expression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Morten Muhlig Nielsen ◽  
Jakob Skou Pedersen
Keyword(s):  
Mrna Expression ◽  
Single Cell ◽  
Target Genes ◽  
Expression Profiles ◽  
Single Cells ◽  
Enrichment Analysis ◽  
Sequence Motifs ◽  
Data Set ◽  
Motif Enrichment ◽  
Activity Measures

AbstractHigh throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n = 9679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.

scholarly journals miRNA activity inferred from single cell mRNA expression

2020 ◽  
Author(s):  
Morten Muhlig Nielsen ◽  
Jakob Skou Pedersen
Keyword(s):  
Mrna Expression ◽  
Single Cell ◽  
Target Genes ◽  
Expression Profiles ◽  
Single Cells ◽  
Enrichment Analysis ◽  
Sequence Motifs ◽  
Data Set ◽  
Motif Enrichment ◽  
Activity Measures

AbstractHigh throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data.Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n=9,679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.

scholarly journals Joint analysis of lncRNA m6A methylome and lncRNA/mRNA expression profiles in gastric cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhi Lv ◽  
Liping Sun ◽  
Qian Xu ◽  
Chengzhong Xing ◽  
Yuan Yuan
Keyword(s):  
Gastric Cancer ◽  
Mrna Expression ◽  
Expression Analysis ◽  
Target Genes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Joint Analysis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment

Abstract Background N6-methyladenosine (m6A) modification might be closely associated with the genesis and development of gastric cancer (GC). Currently, the evidence established by high-throughput assay for GC-related m6A patterns based on long non-coding RNAs (lncRNAs) remains limited. Here, a joint analysis of lncRNA m6A methylome and lncRNA/mRNA expression profiles in GC was performed to explore the regulatory roles of m6A modification in lncRNAs. Methods Three subjects with primary GC were enrolled in our study and paired sample was randomly selected from GC tissue and adjacent normal tissue for each case. Methylated RNA Immunoprecipitation NextGeneration Sequencing (MeRIP-Seq) and Microarray Gene Expression Profiling was subsequently performed. Then co-expression analysis and gene enrichment analysis were successively conducted. Results After data analysis, we identified 191 differentially m6A-methylated lncRNAs, 240 differentially expressed lncRNAs and 229 differentially expressed mRNAs in GC. Furthermore, four differentially m6A-methylated and expressed lncRNAs (dme-lncRNAs) were discovered including RASAL2-AS1, LINC00910, SNHG7 and LINC01105. Their potential target genes were explored by co-expression analysis. And gene enrichment analysis suggested that they might influence the cellular processes and biological behaviors involved in mitosis and cell cycle. The potential impacts of these targets on GC cells were further validated by CCLE database and literature review. Conclusions Four novel dme-lncRNAs were identified in GC, which might exert regulatory roles on GC cell proliferation. The present study would provide clues for the lncRNA m6A methylation-based research on GC epigenetic etiology and pathogenesis.

Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA–mRNA network associated with lupus nephritis

Lupus ◽  
2020 ◽  
Vol 29 (8) ◽  
pp. 854-861
Author(s):  
Jianbo Song ◽  
Liqin Zhao ◽  
Yuanping Li
Keyword(s):  
Lupus Nephritis ◽  
Mrna Expression ◽  
Lupus Erythematosus ◽  
Target Genes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network

Objective Lupus nephritis (LN) is one of the serious complications of systemic lupus erythematosus. The aim of this study was to identify core genes and pathways involved in the pathogenesis of LN. Methods We screened differentially expressed genes (DEGs) in LN patients using mRNA expression profile data from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DEGs was performed utilizing the Database for annotation, Visualization and Integrated Discovery. Target genes with differentially expressed miRNAs (DEMIs) were predicted using the miRTarBase database, and the intersection between these target genes and DEGs was selected to be studied further. Results In total, 107 common DEGs (CDEGs) were identified from the Tub_LN group and Glom_LN group, and 66 DEMIs were identified. Fifty-three hub genes and two significant modules were identified from the protein–protein interaction (PPI) network, and a miRNA–mRNA network was constructed. The CDEGs, module genes in the PPI network and genes intersecting with the CDEGs and target genes of DEMIs were all associated with the PI3K-Akt signalling pathway. Conclusion In summary, this study reveals some crucial genes and pathways potentially involving in the pathogenesis of LN. These findings provide a new insight for the research and treatment of LN.

scholarly journals miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11458
Author(s):  
Malik Yousef ◽  
Gokhan Goy ◽  
Ramkrishna Mitra ◽  
Christine M. Eischen ◽  
Amhar Jabeer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mrna Expression ◽  
Mirna Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mrna Gene

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals Dynamic characteristics and functional analysis provide new insights into long non-coding RNA responsive to Verticillium dahliae infection in Gossypium hirsutum

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guoning Wang ◽  
Xingfen Wang ◽  
Yan Zhang ◽  
Jun Yang ◽  
Zhikun Li ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Verticillium Wilt ◽  
Dynamic Characteristics ◽  
Target Genes ◽  
Acid Metabolism ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Alpha Linolenic Acid ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Background Verticillium wilt is a widespread and destructive disease, which causes serious loss of cotton yield and quality. Long non-coding RNA (lncRNA) is involved in many biological processes, such as plant disease resistance response, through a variety of regulatory mechanisms, but their possible roles in cotton against Verticillium dahliae infection remain largely unclear. Results Here, we measured the transcriptome of resistant G. hirsutum following infection by V. dahliae and 4277 differentially expressed lncRNAs (delncRNAs) were identified. Localization and abundance analysis revealed that delncRNAs were biased distribution on chromosomes. We explored the dynamic characteristics of disease resistance related lncRNAs in chromosome distribution, induced expression profiles, biological function, and these lncRNAs were divided into three categories according to their induced expression profiles. For the delncRNAs, 687 cis-acting pairs and 14,600 trans-acting pairs of lncRNA-mRNA were identified, which indicated that trans-acting was the main way of Verticillium wilt resistance-associated lncRNAs regulating target mRNAs in cotton. Analyzing the regulation pattern of delncRNAs revealed that cis-acting and trans-acting lncRNAs had different ways to influence target genes. Gene Ontology (GO) enrichment analysis revealed that the regulatory function of delncRNAs participated significantly in stimulus response process, kinase activity and plasma membrane components. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that delncRNAs participated in some important disease resistance pathways, such as plant-pathogen interaction, alpha-linolenic acid metabolism and plant hormone signal transduction. Additionally, 21 delncRNAs and 10 target genes were identified as being involved in alpha-linolenic acid metabolism associated with the biosynthesis of jasmonic acid (JA). Subsequently, we found that GhlncLOX3 might regulate resistance to V. dahliae through modulating the expression of GhLOX3 implicated in JA biosynthesis. Further functional analysis showed that GhlncLOX3-silenced seedlings displayed a reduced resistance to V. dahliae, with down-regulated expression of GhLOX3 and decreased content of JA. Conclusion This study shows the dynamic characteristics of delncRNAs in multiaspect, and suggests that GhlncLOX3-GhLOX3-JA network participates in response to V. dahliae invasion. Our results provide novel insights for genetic improvement of Verticillium wilt resistance in cotton using lncRNAs.

scholarly journals Pineal gland transcriptomic profiling reveals the differential regulation of lncRNA and mRNA related to prolificacy in STH sheep with two FecB genotypes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chunyan Li ◽  
Xiaoyun He ◽  
Zijun Zhang ◽  
Chunhuan Ren ◽  
Mingxing Chu
Keyword(s):  
Pineal Gland ◽  
Expression Pattern ◽  
Noncoding Rna ◽  
Target Genes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Rna Seq ◽  
Important Regulator ◽  
Gsh Metabolism ◽  
Transcriptome Expression

Abstract Background Long noncoding RNA (lncRNA) has been identified as important regulator in hypothalamic-pituitary-ovarian axis associated with sheep prolificacy. However, little is known of their expression pattern and potential roles in the pineal gland of sheep. Herein, RNA-Seq was used to detect transcriptome expression pattern in pineal gland between follicular phase (FP) and luteal phase (LP) in FecBBB (MM) and FecB++ (ww) STH sheep, respectively, and differentially expressed (DE) lncRNAs and mRNAs associated with reproduction were identified. Results Overall, 135 DE lncRNAs and 1360 DE mRNAs in pineal gland between MM and ww sheep were screened. Wherein, 39 DE lncRNAs and 764 DE mRNAs were identified (FP vs LP) in MM sheep, 96 DE lncRNAs and 596 DE mRNAs were identified (FP vs LP) in ww sheep. Moreover, GO and KEGG enrichment analysis indicated that the targets of DE lncRNAs and DE mRNAs were annotated to multiple biological processes such as phototransduction, circadian rhythm, melanogenesis, GSH metabolism and steroid biosynthesis, which directly or indirectly participate in hormone activities to affect sheep reproductive performance. Additionally, co-expression of lncRNAs-mRNAs and the network construction were performed based on correlation analysis, DE lncRNAs can modulate target genes involved in related pathways to affect sheep fecundity. Specifically, XLOC_466330, XLOC_532771, XLOC_028449 targeting RRM2B and GSTK1, XLOC_391199 targeting STMN1, XLOC_503926 targeting RAG2, XLOC_187711 targeting DLG4 were included. Conclusion All of these differential lncRNAs and mRNAs expression profiles in pineal gland provide a novel resource for elucidating regulatory mechanism underlying STH sheep prolificacy.

scholarly journals Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuntao Shi ◽  
Yingying Zhuang ◽  
Jialing Zhang ◽  
Mengxue Chen ◽  
Shangnong Wu
Keyword(s):  
Target Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Risk Groups ◽  
Normal Mucosa ◽  
Microrna Expression ◽  
Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

scholarly journals miRNA Mediated Noise Making of 3′UTR Mutations in Cancer

Genes ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 545 ◽  
Author(s):  
Wei Wu ◽  
Lingxiang Wu ◽  
Mengyan Zhu ◽  
Ziyu Wang ◽  
Min Wu ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Target Genes ◽  
Expression Profiles ◽  
Tumor Development ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Messenger Rnas ◽  
Cellular Functions ◽  
Mrna Binding

Somatic mutations in 3′-untranslated regions (3′UTR) do not alter amino acids and are considered to be silent in cancers. We found that such mutations can promote tumor progression by altering microRNA (miRNA) targeting efficiency and consequently affecting miRNA–mRNA interactions. We identified 67,159 somatic mutations located in the 3′UTRs of messenger RNAs (mRNAs) which can alter miRNA–mRNA interactions (functional somatic mutations, funcMutations), and 69.3% of these funcMutations (the degree of energy change > 12 kcal/mol) were identified to significantly promote loss of miRNA-mRNA binding. By integrating mRNA expression profiles of 21 cancer types, we found that the expression of target genes was positively correlated with the loss of absolute affinity level and negatively correlated with the gain of absolute affinity level. Functional enrichment analysis revealed that genes carrying funcMutations were significantly enriched in the MAPK and WNT signaling pathways, and analysis of regulatory modules identified eighteen miRNA modules involved with similar cellular functions. Our findings elucidate a complex relationship between miRNA, mRNA, and mutations, and suggest that 3′UTR mutations may play an important role in tumor development.

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

Sign in / Sign up

Export Citation Format

Share Document