scholarly journals A MicroRNA-Based Network Provides Potential Predictive Signatures and Reveals the Crucial Role of PI3K/AKT Signaling for Hepatic Lineage Maturation

2021 ◽  
Vol 9 ◽  
Author(s):  
Xicheng Wang ◽  
Wencheng Zhang ◽  
Yong Yang ◽  
Jiansong Wang ◽  
Hua Qiu ◽  
...  
Keyword(s):  
Stem Cells ◽  
Regulatory Network ◽  
Mirna Target ◽  
Target Prediction ◽  
Biliary Tree ◽  
Akt Signaling ◽  
Lineage Commitment ◽  
Rna Seq ◽  
Sequencing Data ◽  
Stem Analysis

BackgroundFunctions of miRNAs involved in tumorigenesis are well reported, yet, their roles in normal cell lineage commitment remain ambiguous. Here, we investigated a specific “transcription factor (TF)-miRNA-Target” regulatory network during the lineage maturation of biliary tree stem cells (BTSCs) into adult hepatocytes (hAHeps).MethodBioinformatic analysis was conducted based on our RNA-seq and microRNA-seq datasets with four human hepatic-lineage cell lines, including hBTSCs, hepatic stem cells (hHpSCs), hepatoblasts (hHBs), and hAHeps. Short time-series expression miner (STEM) analysis was performed to reveal the time-dependent dynamically changed miRNAs and mRNAs. GO and KEGG analyses were applied to reveal the potential function of key miRNAs and mRNAs. Then, the miRDB, miRTarBase, TargetScan, miRWalk, and DIANA-microT-CDS databases were adopted to predict the potential targets of miRNAs while the TransmiR v2.0 database was used to obtain the experimentally supported TFs that regulate miRNAs. The TCGA, Kaplan–Meier Plotter, and human protein atlas (HPA) databases and more than 10 sequencing data, including bulk RNA-seq, microRNA-seq, and scRNA-seq data related to hepatic development or lineage reprogramming, were obtained from both our or other published studies for validation.ResultsSTEM analysis showed that during the maturation from hBTSCs to hAHeps, 52 miRNAs were downwardly expressed and 928 mRNA were upwardly expressed. Enrichment analyses revealed that those 52 miRNAs acted as pluripotency regulators for stem cells and participated in various novel signaling pathways, including PI3K/AKT, MAPK, and etc., while 928 mRNAs played important roles in liver-functional metabolism. With an extensive sorting of those key miRNAs and mRNAs based on the target prediction results, 23 genes were obtained which not only functioned as the targets of 17 miRNAs but were considered critical for the hepatic lineage commitment. A “TF-miRNA-Target” regulatory network for hepatic lineage commitment was therefore established and had been well validated by various datasets. The network revealed that the PI3K/AKT pathway was gradually suppressed during the hepatic commitment.ConclusionA total of 17 miRNAs act as suppressors during hepatic maturation mainly by regulating 23 targets and modulating the PI3K/AKT signaling pathway. The regulatory network uncovers possible signatures and guidelines enabling us to identify or obtain the functional hepatocytes for future study.

scholarly journals FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

2020 ◽  
Vol 36 (8) ◽  
pp. 2410-2416
Author(s):  
Thomas Bradley ◽  
Simon Moxon
Keyword(s):  
Programming Languages ◽  
Mirna Target ◽  
Target Prediction ◽  
Supplementary Information ◽  
Mirna Target Prediction ◽  
Rna Seq ◽  
Microrna Target ◽  
R Programming ◽  
High Level ◽  
Transcript Expression Level

Abstract Motivation MicroRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false-positive predictions. Here, we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest. Results We demonstrate that FilTar can be used to: (i) provide sample specific 3′-UTR reannotation; extending or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods. Availability and implementation FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PAREameters: a tool for computational inference of plant miRNA–mRNA targeting rules using small RNA and degradome sequencing data

2020 ◽  
Vol 48 (5) ◽  
pp. 2258-2270 ◽  
Author(s):  
Joshua Thody ◽  
Vincent Moulton ◽  
Irina Mohorianu
Keyword(s):  
Small Rna ◽  
Mirna Target ◽  
Target Prediction ◽  
Model Organisms ◽  
Messenger Rnas ◽  
Mirna Target Prediction ◽  
Sequencing Data ◽  
Translation Rate ◽  
Degradome Sequencing ◽  
Using Data

Abstract MicroRNAs (miRNAs) are short, non-coding RNAs that modulate the translation-rate of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. Degradome sequencing is a high-throughput technique developed to capture cleaved mRNA fragments and thus can be used to support miRNA target prediction. The current criteria used for miRNA target prediction were inferred on a limited number of experimentally validated A. thaliana interactions and were adapted to fit these specific interactions; thus, these fixed criteria may not be optimal across all datasets (organisms, tissues or treatments). We present a new tool, PAREameters, for inferring targeting criteria from small RNA and degradome sequencing datasets. We evaluate its performance using a more extensive set of experimentally validated interactions in multiple A. thaliana datasets. We also perform comprehensive analyses to highlight and quantify the differences between subsets of miRNA–mRNA interactions in model and non-model organisms. Our results show increased sensitivity in A. thaliana when using the PAREameters inferred criteria and that using data-driven criteria enables the identification of additional interactions that further our understanding of the RNA silencing pathway in both model and non-model organisms.

scholarly journals Multitask regression for condition-specific prioritization of miRNA targets in transcripts

2016 ◽  
Author(s):  
Azim Dehghani Amirabad ◽  
Marcel H Schulz
Keyword(s):  
Mirna Target ◽  
Small Sample Size ◽  
Mirna Gene ◽  
Simulated Data ◽  
Target Prediction ◽  
Small Sample ◽  
Specific Information ◽  
Transcript Expression ◽  
Rna Seq ◽  
Mirna Targets

Deregulation of miRNAs is implicated in many diseases in particular cancer, where miRNAs can act as tumour suppressors or oncogenes. As sequence-based miRNA target predictions do not provide condition-specific information, many algorithms combine expression data for miRNAs and genes for prioritization of miRNA targets. However, common strategies prioritize miRNA-gene as- sociations, although a miRNA may only target a subset of the alternative transcripts produced by a gene. Thus, current approaches are suboptimal. Here we address the problem of transcript and not gene based miRNA target prioritization. We show how to leverage methods that were developed for gene expression based miRNA-target prioritization for transcripts. In addition, we introduce a new multitasking based learning (MTL) method that uses structured-sparsity inducing regularization to improve accuracy of the learning. The new MTL approach performs especially favorable in small sample size settings, for genes with many transcripts and with noisy transcript expression level es- timates as shown with simulated data. In an analysis of real liver cancer RNA-seq data we show that the MTL approach better predicts transcript expression and outperforms simpler approaches for miRNA-target prediction.

scholarly journals Single-cell reconstruction of differentiation trajectory reveals a critical role of ETS1 in human cardiac lineage commitment

BMC Biology ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Hang Ruan ◽  
Yingnan Liao ◽  
Zongna Ren ◽  
Lin Mao ◽  
Fang Yao ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Pluripotent Stem Cells ◽  
Heart Development ◽  
Cardiac Differentiation ◽  
Lineage Commitment ◽  
Rna Seq ◽  
Molecular Features ◽  
Molecular Events ◽  
Cardiac Lineage

Abstract Background Cardiac differentiation from human pluripotent stem cells provides a unique opportunity to study human heart development in vitro and offers a potential cell source for cardiac regeneration. Compared to the large body of studies investigating cardiac maturation and cardiomyocyte subtype-specific induction, molecular events underlying cardiac lineage commitment from pluripotent stem cells at early stage remain poorly characterized. Results In order to uncover key molecular events and regulators controlling cardiac lineage commitment from a pluripotent state during differentiation, we performed single-cell RNA-Seq sequencing and obtained high-quality data for 6879 cells collected from 6 stages during cardiac differentiation from human embryonic stem cells and identified multiple cell subpopulations with distinct molecular features. Through constructing developmental trajectory of cardiac differentiation and putative ligand-receptor interactions, we revealed crosstalk between cardiac progenitor cells and endoderm cells, which could potentially provide a cellular microenvironment supporting cardiac lineage commitment at day 5. In addition, computational analyses of single-cell RNA-Seq data unveiled ETS1 (ETS Proto-Oncogene 1) activation as an important downstream event induced by crosstalk between cardiac progenitor cells and endoderm cells. Consistent with the findings from single-cell analysis, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) against ETS1 revealed genomic occupancy of ETS1 at cardiac structural genes at day 9 and day 14, whereas ETS1 depletion dramatically compromised cardiac differentiation. Conclusion Together, our study not only characterized the molecular features of different cell types and identified ETS1 as a crucial factor induced by cell-cell crosstalk contributing to cardiac lineage commitment from a pluripotent state, but may also have important implications for understanding human heart development at early embryonic stage, as well as directed manipulation of cardiac differentiation in regenerative medicine.

scholarly journals Chaperone-Mediated Autophagy Plays an Important Role in Regulating Retinal Progenitor Cell Homeostasis

2021 ◽  
Author(s):  
Caixia Jin ◽  
Qingjian Ou ◽  
Jie Chen ◽  
Tao Wang ◽  
Jieping Zhang ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Viability ◽  
Progenitor Cell ◽  
Time Course ◽  
Rna Seq ◽  
Sequencing Data ◽  
Self Renewal ◽  
Retinal Progenitor ◽  
Significant Difference

Abstract Purpose: Autophagy is a key regulator of stem cell quiescence and self-renewal, especially in mesenchymal stem cells but related research on neural retinal stem cells is still limited. We are aimed to explore the function and mechanism of autophagy in the neural retinal stem cell.Methods: The published single cell sequencing data was involved to analysis the expression time course of IFITM3 in the mouse neural retinal progenitor cells (mNRPCs). The RNA interference was used to knock down the expression of IFITM3 in the mNRPCs. And the normal mNRPCs and mNRPCs with knockdown of IFITM3 were analysis with the CCK8 for the cell viability, RNA-seq for the mRNA expression, real-time quantitative PCR, immunofluorescence assay for the location of relative proteins, western blot for the levels of relative proteins and autophagy flux assay.Results: This study showed the mNRPCs in vivo and in vitro high expressed IFITM3 which are expressed in the mNRPCs. The proliferation of mNRPCs was greatly inhibited, and cell viability was greatly reduced after IFITM3 knockdown. Moreover, RNA-seq analysis showed that lysosomes were significant changed after IFITM3 knockdown. When cells were treated with rapamycin (RAMP), lysosome activation and agglomeration were evident in all groups. However, there was no significant difference between IFITM3 knockdown groups. The expression of LAMP1 was significantly increased, accompanied by increased lysosome agglomeration, in RAMP-treated cells and especially in IFITM3-knockdown cells. Further detection showed that SQSTM1/p62, HSC70 and LAMP-2A were upregulated, while there was no significant difference in LC3A/B expression, which demonstrated that the MA pathway was not activated but the CMA pathway was activated when knockdown of IFITM3. Conclusion: Our findings indicate that IFITM3 participates in regulating mNRPC viability and proliferation mainly through the CMA pathway, indicating that IFITM3 plays a significant role in maintaining the homeostasis of progenitor cell self-renewal by sustaining low-level activation of the CMA pathway to eliminate factors that are deleterious to cells and acts as a very important protector of RPCs.

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

scholarly journals NKL Homeobox Gene VENTX Is Part of a Regulatory Network in Human Conventional Dendritic Cells

2021 ◽  
Vol 22 (11) ◽  
pp. 5902
Author(s):  
Stefan Nagel ◽  
Claudia Pommerenke ◽  
Corinna Meyer ◽  
Hans G. Drexler
Keyword(s):  
Dendritic Cells ◽  
Transcription Factors ◽  
Cell Lines ◽  
Expression Profiling ◽  
Regulatory Network ◽  
Homeobox Gene ◽  
Leukemia Cell Line ◽  
Specific Gene ◽  
Rna Seq ◽  
And Function

Recently, we documented a hematopoietic NKL-code mapping physiological expression patterns of NKL homeobox genes in human myelopoiesis including monocytes and their derived dendritic cells (DCs). Here, we enlarge this map to include normal NKL homeobox gene expressions in progenitor-derived DCs. Analysis of public gene expression profiling and RNA-seq datasets containing plasmacytoid and conventional dendritic cells (pDC and cDC) demonstrated HHEX activity in both entities while cDCs additionally expressed VENTX. The consequent aim of our study was to examine regulation and function of VENTX in DCs. We compared profiling data of VENTX-positive cDC and monocytes with VENTX-negative pDC and common myeloid progenitor entities and revealed several differentially expressed genes encoding transcription factors and pathway components, representing potential VENTX regulators. Screening of RNA-seq data for 100 leukemia/lymphoma cell lines identified prominent VENTX expression in an acute myelomonocytic leukemia cell line, MUTZ-3 containing inv(3)(q21q26) and t(12;22)(p13;q11) and representing a model for DC differentiation studies. Furthermore, extended gene analyses indicated that MUTZ-3 is associated with the subtype cDC2. In addition to analysis of public chromatin immune-precipitation data, subsequent knockdown experiments and modulations of signaling pathways in MUTZ-3 and control cell lines confirmed identified candidate transcription factors CEBPB, ETV6, EVI1, GATA2, IRF2, MN1, SPIB, and SPI1 and the CSF-, NOTCH-, and TNFa-pathways as VENTX regulators. Live-cell imaging analyses of MUTZ-3 cells treated for VENTX knockdown excluded impacts on apoptosis or induced alteration of differentiation-associated cell morphology. In contrast, target gene analysis performed by expression profiling of knockdown-treated MUTZ-3 cells revealed VENTX-mediated activation of several cDC-specific genes including CSFR1, EGR2, and MIR10A and inhibition of pDC-specific genes like RUNX2. Taken together, we added NKL homeobox gene activities for progenitor-derived DCs to the NKL-code, showing that VENTX is expressed in cDCs but not in pDCs and forms part of a cDC-specific gene regulatory network operating in DC differentiation and function.

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

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