scholarly journals Inferring and analyzing module-specific lncRNA–mRNA causal regulatory networks in human cancer

2018 ◽  
Vol 20 (4) ◽  
pp. 1403-1419 ◽  
Author(s):  
Junpeng Zhang ◽  
Thuc Duy Le ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Target Genes ◽  
Human Cancer ◽  
Noncoding Rnas ◽  
Lung Squamous Cell Carcinoma ◽  
Third Party ◽  
Biological Functions ◽  
Complementary Method ◽  
Statistical Correlations

Abstract It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA–mRNA interactions and the third-party databases of storing lncRNA–mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA–mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA–mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.

scholarly journals Gene Expression Networks in the Drosophila Genetic Reference Panel

2019 ◽  
Author(s):  
Logan J. Everett ◽  
Wen Huang ◽  
Shanshan Zhou ◽  
Mary Anna Carbone ◽  
Richard F. Lyman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transposable Elements ◽  
Dna Sequences ◽  
Quantitative Trait ◽  
Regulatory Networks ◽  
Target Genes ◽  
Reference Panel ◽  
Modern Biology ◽  
Specific Expression ◽  
Drosophila Genetic Reference Panel

SummaryA major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences, and mapped expression quantitative trait loci for annotated genes, novel transcribed regions (most of which are long noncoding RNAs), transposable elements and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, and genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.

scholarly journals Universal attenuators and their interactions with feedback loops in gene regulatory networks

2016 ◽  
Author(s):  
Dianbo Liu ◽  
Luca Albergante ◽  
Timothy J Newman
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Feedback Loops ◽  
Human Cancer Cell ◽  
E Coli ◽  
Gene Regulatory

AbstractUsing a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as “universal attenuators”. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.In briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.HighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variationBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organismsNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

scholarly journals Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
Author(s):  
Mina Fazlollahi ◽  
Ivor Muroff ◽  
Eunjee Lee ◽  
Helen C. Causton ◽  
Harmen J. Bussemaker
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nonsynonymous Mutation ◽  
Gene Regulatory ◽  
Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

Circular RNA as a potential diagnostic and/or therapeutic target for endometriosis

2020 ◽  
Vol 14 (13) ◽  
pp. 1277-1287
Author(s):  
Parisa M Dana ◽  
Mona Taghavipour ◽  
Hamed Mirzaei ◽  
Bahman Yousefi ◽  
Bahram Moazzami ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Therapeutic Target ◽  
Noncoding Rnas ◽  
Circular Rna ◽  
Circular Rnas ◽  
Biological Functions ◽  
Parental Gene

Endometriosis is a pathology form of endometrium that behaves in a similar way to malignancies, such as invasion and resistance to apoptosis. Circular RNAs (CircRNAs) are a class of noncoding RNAs that have several biological functions including, miRNA sponging, sequestering of proteins, enhancing parental gene expression and translation resulting in polypeptides. In this review, we highlighted the roles of circRNAs as potential diagnostic and therapeutic biomarkers in endometriosis. Moreover, we summarized the roles of circRNAs in the pathogenesis of endometriosis via different signaling pathways, such as the miRNA network and apoptosis.

scholarly journals MicroRNAs in Female Malignancies

2019 ◽  
Vol 18 ◽  
pp. 117693511982874 ◽  
Author(s):  
Themis Liolios ◽  
Stavroula Lila Kastora ◽  
Giorgia Colombo
Keyword(s):  
Gene Expression ◽  
Computational Analysis ◽  
Target Genes ◽  
Vulvar Cancer ◽  
Chemotherapeutic Agents ◽  
In Vitro Assays ◽  
Biological Processes ◽  
Potential Mechanism ◽  
Biological Functions

MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs that can play a fundamental regulatory role in the gene expression of various organisms. Current research suggests that miRNAs can assume pivotal roles in carcinogenesis. In this article, through bioinformatics mining and computational analysis, we determine a single miRNA commonly involved in the development of breast, cervical, endometrial, ovarian, and vulvar cancer, whereas we underline the existence of 7 more miRNAs common in all examined malignancies with the exception of vulvar cancer. Furthermore, we identify their target genes and encoded biological functions. We also analyze common biological processes on which all of the identified miRNAs act and we suggest a potential mechanism of action. In addition, we analyze exclusive miRNAs among the examined malignancies and bioinformatically explore their functionality. Collectively, our data can be employed in in vitro assays as a stepping stone in the identification of a universal machinery that is derailed in female malignancies, whereas exclusive miRNAs may be employed as putative targets for future chemotherapeutic agents or cancer-specific biomarkers.

scholarly journals ISMARA: Completely automated inference of gene regulatory networks from high-throughput data

2017 ◽  
Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Regulatory Networks ◽  
Target Genes ◽  
Chromatin State ◽  
Response Analysis ◽  
Rna Seq ◽  
High Throughput Data ◽  
Micro Rnas ◽  
Gene Regulatory

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.

scholarly journals Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tianyuan Lu ◽  
Jessica C. Mar
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Marker Genes ◽  
Biological Functions ◽  
Sex Dimorphism ◽  
Cell Type ◽  
Transcriptional Regulatory ◽  
Single Cell Rna Sequencing

Abstract Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. Methods Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. Results For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. Conclusions We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

CircRNA-WNK2 acts as a ceRNA for miR-328a-3p to promote AANAT expression in the male rat pineal gland

Endocrinology ◽  
2021 ◽  
Author(s):  
Yi Zheng ◽  
Hao-Qi Wang ◽  
Hai-Xiang Guo ◽  
Heng-Li Xie ◽  
Wei-Di Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Circadian Rhythm ◽  
Pineal Gland ◽  
Rat Model ◽  
Noncoding Rnas ◽  
Luciferase Reporter ◽  
Male Rat ◽  
Biological Functions ◽  
Melatonin Secretion ◽  
Rat Pineal Gland

Abstract Noncoding RNAs (ncRNAs), including miRNAs and circRNAs, which are expressed with a daily rhythm in the rat pineal gland, are associated with the regulation of melatonin secretion and other biological functions. However, the mechanisms of these molecules in the rat pineal gland are not yet fully understood. In this study, we found circR-WNK2 was highly expressed at night, which may be involved in the regulation of melatonin secretion through the ceRNA mechanism. By dual luciferase reporter, RNA pulldown, and FISH assays, we found that miR-328a-3p can target circR-WNK2 and the Aa-nat mRNA 3’UTR. Transfection experiments indicated that circR-WNK2 could competitively bind to miR-328a-3p, reduce miR-328a-3p expression, and promote Aa-nat gene expression and melatonin secretion. And by constructing an SCGx rat model, we found that ncRNAs expressed in the pineal gland was regulated by signals from the SCN. This finding supports the hypothesis that these noncoding RNAs may interact to shape the circadian rhythm through transcriptional processing in melatonin synthesis.

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

2021 ◽  
Vol 30 (04) ◽  
pp. 2150022
Author(s):  
Sergio Peignier ◽  
Pauline Schmitt ◽  
Federica Calevro
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Data Driven ◽  
Classification Algorithms ◽  
New Family ◽  
Gene Regulatory ◽  
High Throughput Gene Expression ◽  
Inference Methods

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

scholarly journals Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emily L. Flam ◽  
Ludmila Danilova ◽  
Dylan Z. Kelley ◽  
Elena Stavrovskaya ◽  
Theresa Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Target Genes ◽  
Human Cancer ◽  
Genetic Alterations ◽  
Tumor Type ◽  
Gene Pairs ◽  
Aberrant Gene Expression ◽  
Tumor Types ◽  
Aberrant Gene

Abstract Current literature suggests that epigenetically regulated super-enhancers (SEs) are drivers of aberrant gene expression in cancers. Many tumor types are still missing chromatin data to define cancer-specific SEs and their role in carcinogenesis. In this work, we develop a simple pipeline, which can utilize chromatin data from etiologically similar tumors to discover tissue-specific SEs and their target genes using gene expression and DNA methylation data. As an example, we applied our pipeline to human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV + OPSCC). This tumor type is characterized by abundant gene expression changes, which cannot be explained by genetic alterations alone. Chromatin data are still limited for this disease, so we used 3627 SE elements from public domain data for closely related tissues, including normal and tumor lung, and cervical cancer cell lines. We integrated the available DNA methylation and gene expression data for HPV + OPSCC samples to filter the candidate SEs to identify functional SEs and their affected targets, which are essential for cancer development. Overall, we found 159 differentially methylated SEs, including 87 SEs that actively regulate expression of 150 nearby genes (211 SE-gene pairs) in HPV + OPSCC. Of these, 132 SE-gene pairs were validated in a related TCGA cohort. Pathway analysis revealed that the SE-regulated genes were associated with pathways known to regulate nasopharyngeal, breast, melanoma, and bladder carcinogenesis and are regulated by the epigenetic landscape in those cancers. Thus, we propose that gene expression in HPV + OPSCC may be controlled by epigenetic alterations in SE elements, which are common between related tissues. Our pipeline can utilize a diversity of data inputs and can be further adapted to SE analysis of diseased and non-diseased tissues from different organisms.

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