scholarly journals Integrated Analysis of miRNA-mRNA Regulatory Networks Associated with Osteonecrosis of the Femoral Head

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liwei Yu ◽  
Tengfei Yao ◽  
Zhoulei Jiang ◽  
Tong Xu
Keyword(s):  
Gene Expression ◽  
Femoral Head ◽  
Expression Profile ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Differential Expression Analysis ◽  
Integrated Analysis ◽  
Transferase Activity ◽  
Bioinformatics Analyses ◽  
Molecular Abnormalities

Osteonecrosis of the femoral head (ONFH) accounts for as many as 18% of total hip arthroplasties. Knowledge of genetic changes and molecular abnormalities could help identify individuals considered to be at a higher risk of developing ONFH. In this study, we sought to identify differentially expressed miRNAs (DEmiRs) and genes (DEGs) associated with ONFH by integrated bioinformatics analyses as well as to construct the miRNA-mRNA regulatory network involving in the pathogenesis of ONFH. We performed differential expression analysis using a gene expression profile GSE123568 and a miRNA expression profile GSE89587 deposited in the Gene Expression Omnibus and identified 47 DEmiRs (24 upregulated miRNAs and 23 downregulated miRNAs) and 529 DEGs (218 upregulated genes and 311 downregulated genes). Gene Ontology enrichment analyses of DEGs suggested that DEGs were significantly enriched in neutrophil activation, cytosol, and ubiquitin-protein transferase activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs revealed that DEGs were significantly enriched in transcriptional misregulation in cancer. DEGs-based miRNA-mRNA regulatory networks were obtained by searching miRNA-mRNA prediction databases, TargetScan, miTarBase, miRMap, miRDB, and miRanda databases. Then, overlapped miRNAs were selected between these putative miRNAs and DEmiRs between ONFH and non-ONFH, and pairs of the DEmiR-DEG regulatory network were finally depicted. There were 12 nodes and 64 interactions for upDEmiR-downDEG regulatory networks and 6 nodes and 16 interactions for downDEmiR-upDEG regulatory networks. Using the STRING database, we established a protein-protein interaction network based on the overlapped DEGs between ONFH and non-ONFH. C5AR1, CDC27, CDC34, KAT2B, CPPED1, TFDP1, and MX2 were identified as the hub genes. The present study characterizes the miRNA profile, gene profile, and miRNA-mRNA regulatory network in ONFH, which may contribute to the interpretation of the pathogenesis of ONFH and the identification of novel biomarkers and therapeutic targets for ONFH.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

Gene Expression Profile of Acute Myeloid Leukemia (AML) with t(8;16)(p11;p13) and MYST3/CREBBP Rearrangement.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2054-2054
Author(s):  
Mireia Camos ◽  
Jordi Esteve ◽  
Pedro Jares ◽  
Maria Rozman ◽  
Dolors Colomer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
De Novo ◽  
Normal Karyotype ◽  
Genetic Profile ◽  
Transferase Activity ◽  
Internal Tandem Duplication ◽  
Its Gene ◽  
Differential Expression Profile

Abstract Translocation t(8;16)(p11;p13) is an infrequent chromosomal abnormality in de novo and secondary AML cases, leading to the fusion of MYST3 (MOZ) and CREBBP (CBP) genes, both of them harboring histone lysine acetyl-transferase activity. This AML variety displays specific clinical and biological features, although its gene expression profile is currently unknown. In this study, the genetic signature of AML cases with MYST3/CREBBP rearrangement was compared with the genetic profile of other well-defined AML subtypes. Genotypic analyses using oligonucleotide U133A arrays (Affymetrix) were performed on RNA of 19 AML samples, including t(8;16)-AML (n=3), t(15;17) (n=3), t(8;21) (n=2), inv(16)/t(16;16) (n=3), t(9;11) with AF9/MLL rearrangement (n=2), 3 cases with normal karyotype and flt-3 internal tandem duplication (flt-3 ITD), the three remaining samples corresponding to monocytic cases (M4/M5) without MLL rearrangement nor flt-3 ITD. After unsupervised analysis, cases of AML with t(8;16) clustered together, displaying a differential expression profile. Supervised analysis allowed the identification of the top 53 up-regulated and 28 down-regulated genes. Among the set of genes overexpressed, genes involved in chromatin remodelling and transcription (HOXA9, HOXA10, MEIS1, CHD3, SATB1) and protooncogenes (RET, flt-3, LMO2) were identified. In contrast, CREBBP gene and several members of the JAK-STAT pathway (STAT3, STAT5B, JAK2) were underexpressed. Interestingly, overexpression of multiple homeobox genes was detected in flt-3 ITD cases, some of them as a distinctive finding (HOXA2, HOXA3, HOXB6), and others (HOXA9, HOXA10, MEIS1) were found to be highly expressed in MYST3/CREBBP and MLL-rearranged samples. In conclusion, AML with t(8;16) and MYST3/CREBBP rearrangement shows a distinctive gene expression profile, with some similarities with MLL rearranged leukemias and flt-3 ITD AML cases, thus suggesting a partially common leukemogenic pathway.

Integrating Gene and Mir Expression Profiles and Regulatory Network Structures to Define Aberrent Feed Forward Loops with Functional and Clinical Implications in Myeloma.

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 2386-2386
Author(s):  
Mariateresa Fulciniti ◽  
Yingxiang Li ◽  
Xujun Wang ◽  
Mehmet K Samur ◽  
Zhenyu Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profile ◽  
Regulatory Network ◽  
Gene Networks ◽  
Expression Profiles ◽  
Explanatory Power ◽  
Training Dataset ◽  
Negative Feedback Regulation ◽  
Feed Forward ◽  
Integrated Risk

Abstract Abstract 2386 Multiple myeloma (MM) is a clonal plasma cell malignancy with a heterogeneous genetic background. Extensive gene expression profile analysis have provided interesting insight into the disease biology and its correlation with clinical outcome; however, we have begun to realize the significant limitations of expression profile data alone. Therefore there is a growing understanding that additional genomic correlates need to be incorporated to develop an integrated oncogenomic analysis. We have here developed and defined multi-gene transcriptional and post-transcriptional feed-forward loop (FFL) These conceptual FFLs consist of a master TF which regulates a miR and together with it controls a set of specific common gene/s. These recurrent and important network motifs form functional nodes in the larger regulatory network, and are considered linchpins of disease causing genomic alterations in cancer and MM in particular. We have developed a comprehensive novel integrative analysis method, dChip-GemiNI (Gene and miRNA Network-based Integration), which combines gene and miR expression profiles, and also incorporates regulatory network structure in the form of computationally identified TF–miRNA FFLs. The dChip-GemiNI method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression. We have next applied dChip-GemiNi to a training dataset of 60 MM patients and 5 normal plasma cells (NPCs) with both gene expression (GE) and miR profiles (dataset GSE16558) in order to identify FFLs containing TF-miR-gene networks with loss of negative feedback regulation in MM, supporting the uncontrolled growth, anti-apoptosis and/or other oncogenic effects. We have identified 20 FFLs significantly aberrant between NPC and MM cells. Prominent FFLs involve known MM dysregulated TFs such as MYC, TP53 and Sp1. In addition, we have utilized 3 available myeloma datasets with both miR and GE profiles (GSE16558, GSE17306, GSE17498), and classified MM samples into hyperdiploid MM (HMM) and non-hyperdiploid MM (NHMM) subtype groups by GE profiles at an accuracy >85%. These two groups have different survival outcomes (p-value < 0.01). We have identified 55 FFLs altered between these two MM subtypes. In particular we have observed that the FFL involving CREB1- miR-20a and target genes RRAGD, PIP4K2A, RHOC and CCND2 is altered between HMM and NHMM and is common between the 3 datasets. We have now begun to statistically ranks computationally predicted FFLs and develop a motif score to develop an integrated risk stratification model. In conclusion, FFLs form critical regulatory loops driving the functional behavior of MM cells. Analyzing the molecular impact of FFLs as a unit combining the aggregate impact of TF-miR and the target gene/s in MM will be instrumental in understanding the biology of the disease, developing clinically relevant integrated risk models, and translating basic research into targeted therapy. The ultimate goal is to develop strategies to regulate homeostatic control of these loops and overcome their oncogenic effects that drive the malignant phenotype. Disclosures: Munshi: Celgene: Consultancy; Millenium: Consultancy; Merck: Consultancy; Onyx: Consultancy.

scholarly journals tRNA-Derived Fragments Target the Ribosome and Function as Regulatory Non-Coding RNA inHaloferax volcanii

Archaea ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Jennifer Gebetsberger ◽  
Marek Zywicki ◽  
Andrea Künzi ◽  
Norbert Polacek
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Ribosomal Subunit ◽  
Fine Tuning ◽  
Transferase Activity ◽  
Dependent Manner ◽  
Efficient Manner ◽  
Stress Dependent

Nonprotein coding RNA (ncRNA) molecules have been recognized recently as major contributors to regulatory networks in controlling gene expression in a highly efficient manner. These RNAs either originate from their individual transcription units or are processing products from longer precursor RNAs. For example, tRNA-derived fragments (tRFs) have been identified in all domains of life and represent a growing, yet functionally poorly understood, class of ncRNA candidates. Here we present evidence that tRFs from the halophilic archaeonHaloferax volcaniidirectly bind to ribosomes. In the presented genomic screen of the ribosome-associated RNome, a 26-residue-long fragment originating from the 5′ part of valine tRNA was by far the most abundant tRF. The Val-tRF is processed in a stress-dependent manner and was found to primarily target the small ribosomal subunitin vitroandin vivo. As a consequence of ribosome binding, Val-tRF reduces protein synthesis by interfering with peptidyl transferase activity. Therefore this tRF functions as ribosome-bound small ncRNA capable of regulating gene expression inH. volcaniiunder environmental stress conditions probably by fine tuning the rate of protein production.

scholarly journals Histone demethylome map reveals combinatorial gene regulatory functions in embryonic stem cells

2020 ◽  
Author(s):  
Yogesh Kumar ◽  
Pratibha Tripathi ◽  
Majid Mehravar ◽  
Michael J. Bullen ◽  
Varun K. Pandey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Regulation ◽  
Embryonic Stem Cells ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Gene ◽  
Embryonic Stem ◽  
Epigenetic Regulators ◽  
Regulatory Functions

SUMMARYEpigenetic regulators and transcription factors establish distinct regulatory networks for gene regulation to maintain the embryonic stem cells (ESC) state. Although much has been learned regarding individual epigenetic regulators, their combinatorial functions remain elusive. Here, we report combinatorial functions of histone demethylases (HDMs) in gene regulation of mouse ESCs that are currently unknown. We generated a histone demethylome (HDMome) map of 20 well-characterized HDMs based on their genome-wide binding. This revealed co-occupancy of HDMs in different combinations; predominantly, KDM1A-KDM4B-KDM6A and JARID2-KDM4A-KDM4C-KDM5B co-occupy at enhancers and promoters, respectively. Comprehensive mechanistic studies uncover that KDM1A-KDM6A combinatorially modulates P300/H3K27ac, H3K4me1, H3K4me2 deposition and OCT4 recruitment that eventually directs the OCT4/CORE regulatory network for target gene expression; while co-operative actions of JARID2-KDM4A-KDM4C-KDM5B control H2AK119ub1 and bivalent marks of polycomb-repressive complexes that facilitates the PRC regulatory network for target gene repression. Thus, combinatorial functions of HDMs impact gene expression programs to maintain the ESC state.

scholarly journals CircHIPK3 dysregulation of the miR-30c/DLL4 axis is essential for KSHV lytic replication

2021 ◽  
Author(s):  
Katherine L Harper ◽  
Timothy J Mottram ◽  
Chinedu A Arene ◽  
Becky Foster ◽  
Molly R Patterson ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cell Cycle Control ◽  
Lytic Replication ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Novel Host ◽  
Cellular Pathways

Non coding RNA (ncRNA) regulatory networks are emerging as critical regulators of gene expression. These intricate networks of ncRNA-ncRNA interactions modulate multiple cellular pathways and impact the development and progression of multiple diseases. Herpesviruses, including Kaposi's sarcoma-associated herpesvirus, are adept at utilising ncRNAs, encoding their own as well as dysregulating host ncRNAs to modulate virus gene expression and the host response to infection. Research has mainly focused on unidirectional ncRNA-mediated regulation of target protein-coding transcripts; however, we have identified a novel host ncRNA regulatory network essential for KSHV lytic replication in B cells. KSHV-mediated upregulation of the host cell circRNA, circHIPK3, is a key component of this network, functioning as a competing endogenous RNA of miR-30c, leading to increased levels of the miR-30c target, DLL4. Dysregulation of this network highlights a novel mechanism of cell cycle control during KSHV lytic replication in B cells. Importantly, disruption at any point within this novel ncRNA regulatory network has a detrimental effect on KSHV lytic replication, highlighting the essential nature of this network and potential for therapeutic intervention.

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

scholarly journals VariED: the first integrated database of gene annotation and expression profiles for variants related to human diseases

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Chien-Yueh Lee ◽  
Amrita Chattopadhyay ◽  
Li-Mei Chiang ◽  
Jyh-Ming Jimmy Juang ◽  
Liang-Chuan Lai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Gene Annotation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Integrated Analysis ◽  
Free Text ◽  
Functional Prediction ◽  
Annotation Database ◽  
Dna Variants

Abstract Integrated analysis of DNA variants and gene expression profiles may facilitate precise identification of gene regulatory networks involved in disease mechanisms. Despite the widespread availability of public resources, we lack databases that are capable of simultaneously providing gene expression profiles, variant annotations, functional prediction scores and pathogenic analyses. VariED is the first web-based querying system that integrates an annotation database and expression profiles for genetic variants. The database offers a user-friendly platform and locates gene/variant names in the literature by connecting to established online querying tools, biological annotation tools and records from free-text literature. VariED acts as a central hub for organized genome information consisting of gene annotation, variant allele frequency, functional prediction, clinical interpretation and gene expression profiles in three species: human, mouse and zebrafish. VariED also provides a novel scoring scheme to predict the functional impact of a DNA variant. With one single entry, all results regarding queried DNA variants can be downloaded. VariED can potentially serve as an efficient way to obtain comprehensive variant knowledge for clinicians and scientists around the world working on important drug discoveries and precision treatments.

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