scholarly journals Identification and characterization of key long non-coding RNAs in the mouse cochlea

2020 ◽  
Author(s):  
Tal Koffler-Brill ◽  
Shahar Taiber ◽  
Alejandro Anaya ◽  
Mor Bordeynik-Cohen ◽  
Einat Rosen ◽  
...  
Keyword(s):  
Inner Ear ◽  
Auditory System ◽  
Cellular Localization ◽  
Expression Profiles ◽  
Cell Types ◽  
Tissue Expression ◽  
Distinct Pattern ◽  
Mouse Tissue ◽  
Large Variability ◽  
Non Coding Rnas

AbstractThe auditory system is a complex sensory network with an orchestrated multilayer regulatory program governing its development and maintenance. Accumulating evidence has implicated long non-coding RNAs (lncRNAs) as important regulators in numerous systems, as well as in pathological pathways. However, their function in the auditory system has yet to be explored. Using a set of specific criteria, we selected four lncRNAs expressed in the mouse cochlea, which are conserved in the human transcriptome and are relevant for inner ear function. Bioinformatic characterization demonstrated a lack of coding potential and an absence of evolutionary conservation that represent properties commonly shared by their class members. RNAscope analysis of the spatial and temporal expression profiles revealed specific localization to inner ear cells. Sub-cellular localization analysis presented a distinct pattern for each lncRNA and mouse tissue expression evaluation displayed a large variability in terms of level and location. Our findings establish the expression of specific lncRNAs in different cell types of the auditory system and present a potential pathway by which the lncRNA Gas5 acts in the inner ear. Studying lncRNAs and deciphering their functions may deepen our knowledge of inner ear physiology and morphology and may reveal the basis of as yet unresolved genetic hearing loss-related pathologies. Moreover, our experimental design may be employed as a reference for studying other inner ear-related lncRNAs, as well as lncRNAs expressed in other sensory systems.

scholarly journals Lantern: an integrative repository of functional annotations for lncRNAs in the human genome

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Swapna Vidhur Daulatabad ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga
Keyword(s):  
Human Genome ◽  
Cellular Localization ◽  
Expression Profiles ◽  
Free Text ◽  
Dynamic Visualization ◽  
Specific Expression ◽  
Web Resource ◽  
Functional Dynamics ◽  
Non Coding Rnas ◽  
Retrieval Engine

Abstract Background With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare. Results We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed’s abstract retrieval engine and NCBO’s recommender annotation system. Lantern’s annotations were benchmarked against lncRNAdb’s manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform. Conclusions Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.

scholarly journals Breakdown of multiple sclerosis genetics to identify an integrated disease network and potential variant mechanisms

2019 ◽  
Vol 51 (11) ◽  
pp. 562-577
Author(s):  
C. Joy Shepard ◽  
Sara G. Cline ◽  
David Hinds ◽  
Seyedehameneh Jahanbakhsh ◽  
Jeremy W. Prokop
Keyword(s):  
Multiple Sclerosis ◽  
Immune Cell ◽  
Expression Profiles ◽  
Missense Variant ◽  
Cell Types ◽  
Tissue Expression ◽  
Specific Expression ◽  
Genomic Databases ◽  
Genetic Traits ◽  
Immune System Dysfunction

Genetics of multiple sclerosis (MS) are highly polygenic with few insights into mechanistic associations with pathology. In this study, we assessed MS genetics through linkage disequilibrium and missense variant interpretation to yield a MS gene network. This network of 96 genes was taken through pathway analysis, tissue expression profiles, single cell expression segregation, expression quantitative trait loci (eQTLs), genome annotations, transcription factor (TF) binding profiles, structural genome looping, and overlap with additional associated genetic traits. This work revealed immune system dysfunction, nerve cell myelination, energetic control, transcriptional regulation, and variants that overlap multiple autoimmune disorders. Tissue-specific expression and eQTLs of MS genes implicate multiple immune cell types including macrophages, neutrophils, and T cells, while the genes in neural cell types enrich for oligodendrocyte and myelin sheath biology. There are eQTLs in linkage with lead MS variants in 25 genes including the multitissue eQTL, rs9271640, for HLA-DRB1/ DRB5. Using multiple functional genomic databases, we identified noncoding variants that disrupt TF binding for GABPA, CTCF, EGR1, YY1, SPI1, CLOCK, ARNTL, BACH1, and GFI1. Overall, this paper suggests multiple genetic mechanisms for MS associated variants while highlighting the importance of a systems biology and network approach when elucidating intersections of the immune and nervous system.

scholarly journals Unraveling patterns of disrupted gene expression across a complex tissue

2021 ◽  
Author(s):  
Kelsie E Hunnicutt ◽  
Jeffrey M Good ◽  
Erica L Larson
Keyword(s):  
Gene Expression ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Tissue Expression ◽  
Network Evolution ◽  
F1 Hybrids ◽  
Cellular Composition ◽  
Mus Musculus Musculus

Whole tissue RNASeq is the standard approach for studying gene expression divergence in evolutionary biology and provides a snapshot of the comprehensive transcriptome for a given tissue. However, whole tissues consist of diverse cell types differing in expression profiles, and the cellular composition of these tissues can evolve across species. Here, we investigate the effects of different cellular composition on whole tissue expression profiles. We compared gene expression from whole testes and enriched spermatogenesis populations in two species of house mice, Mus musculus musculus and M. m. domesticus, and their sterile and fertile F1 hybrids, which differ in both cellular composition and regulatory dynamics. We found that cellular composition differences skewed expression profiles and differential gene expression in whole testes samples. Importantly, both approaches were able to detect large-scale patterns such as disrupted X chromosome expression although whole testes sampling resulted in decreased power to detect differentially expressed genes. We encourage researchers to account for histology in RNASeq and consider methods that reduce sample complexity whenever feasible. Ultimately, we show that differences in cellular composition between tissues can modify expression profiles, potentially altering inferred gene ontological processes, insights into gene network evolution, and processes governing gene expression evolution.

scholarly journals Heterozygous transcriptional and nonsense decay signatures in blood outgrowth endothelial cells from patients with hereditary haemorrhagic telangiectasia

2021 ◽  
Author(s):  
Maria E Bernabeu-Herrero ◽  
Dilip Patel ◽  
Adrianna Bielowka ◽  
Sindu Srikaran ◽  
Patricia Chaves Guerrero ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Protein Production ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Transforming Growth Factor Β ◽  
Premature Termination Codon ◽  
Cell Types ◽  
Tissue Expression ◽  
Additional Stress ◽  
Hereditary Haemorrhagic Telangiectasia

ABSTRACTIn order to identify cellular phenotypes resulting from nonsense (gain of stop/premature termination codon) variants, we devised a framework of analytic methods that minimised confounder contributions, and applied to blood outgrowth endothelial cells (BOECs) derived from controls and patients with heterozygous nonsense variants in ACVRL1, ENG or SMAD4 causing hereditary haemorrhagic telangiectasia (HHT). Following validation of 48 pre-selected genes by single cell qRT-PCR, discovery RNASeq ranked HHT-differential alignments of 16,807 Ensembl transcripts. Consistent gene ontology (GO) processes enriched compared to randomly-selected gene lists included bone morphogenetic protein, transforming growth factor-β and angiogenesis GO processes already implicated in HHT, further validating methodologies. Additional terms/genes including for endoplasmic reticulum stress could be attributed to a generic process of inefficient nonsense mediated decay (NMD). NMD efficiency ranged from 78-92% (mean 87%) in different BOEC cultures, with misprocessed mutant protein production confirmed by pulse chase experiments. Genes in HHT-specific and generic nonsense decay (ND) lists displayed differing expression profiles in normal endothelial cells exposed to an additional stress of exogenous 10μmol/L iron which acutely upregulates multiple mRNAs: Despite differing donors and endothelial cell types, >50% of iron-induced variability could be explained by the magnitude of transcript downregulation in HHT BOECs with less efficient NMD. The Genotype Tissue Expression (GTEx) Project indicated ND list genes were usually most highly expressed in non-endothelial tissues. However, across 5 major tissues, although 18/486 nonsense and frameshift variants in highly expressed genes were captured in GTEx, none were sufficiently prevalent to obtain genome-wide significant p values for expression quantitative trait loci (GnomAD allele frequencies <0.0005). In conclusion, RNASeq analytics of rare genotype-selected, patient-derived endothelial cells facilitated identification of natural disease-specific and more generic transcriptional signatures. Future studies should evaluate wider relevance and whether injury from external agents is augmented in cells with already high burdens of defective protein production.

scholarly journals SC3 - consensus clustering of single-cell RNA-Seq data

2016 ◽  
Author(s):  
Vladimir Yu. Kiselev ◽  
Kristina Kirschner ◽  
Michael T. Schaub ◽  
Tallulah Andrews ◽  
Andrew Yiu ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Marker Genes ◽  
Consensus Clustering ◽  
Rna Seq ◽  
Large Dataset ◽  
Wide Audience ◽  
Large Variability ◽  
Biological Interpretation

AbstractUsing single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.

Regulation of cell migration by α4 and α9 integrins

2019 ◽  
Vol 476 (4) ◽  
pp. 705-718 ◽  
Author(s):  
Willow Hight-Warburton ◽  
Maddy Parsons
Keyword(s):  
Cell Migration ◽  
Expression Profiles ◽  
Focal Adhesions ◽  
Cell Types ◽  
Tissue Expression ◽  
Signalling Pathways ◽  
Adherent Cell ◽  
Transmembrane Receptors ◽  
Binding Partners ◽  
And Migration

Abstract Integrins are heterodimeric transmembrane receptors that play an essential role in enabling cells to sense and bind to extracellular ligands. Activation and clustering of integrins leads to the formation of focal adhesions at the plasma membrane that subsequently initiate signalling pathways to control a broad range of functional endpoints including cell migration, proliferation and survival. The α4 and α9 integrins form a small sub-family of receptors that share some specific ligands and binding partners. Although relatively poorly studied compared with other integrin family members, emerging evidence suggests that despite restricted cell and tissue expression profiles, these integrins play a key role in the regulation of signalling pathways controlling cytoskeletal remodelling and migration in both adherent and non-adherent cell types. This review summarises the known shared and specific roles for α4 and α9 integrins and highlights the importance of these receptors in controlling cell migration within both homeostatic and disease settings.

scholarly journals NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants

2020 ◽  
Vol 49 (D1) ◽  
pp. D165-D171
Author(s):  
Lianhe Zhao ◽  
Jiajia Wang ◽  
Yanyan Li ◽  
Tingrui Song ◽  
Yang Wu ◽  
...  
Keyword(s):  
Transcriptome Sequencing ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Transcript Level ◽  
Tissue Expression ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
The Relationship ◽  
Long Non Coding Rna ◽  
Predicted Function

Abstract NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

Preprocessing implementation for microarray (PRIM): an efficient method for processing cDNA microarray data

2001 ◽  
Vol 4 (3) ◽  
pp. 183-188 ◽  
Author(s):  
KOJI KADOTA ◽  
RIKA MIKI ◽  
HIDEMASA BONO ◽  
KENTARO SHIMIZU ◽  
YASUSHI OKAZAKI ◽  
...  
Keyword(s):  
Cdna Microarray ◽  
Expression Profiles ◽  
Threshold Value ◽  
Tissue Expression ◽  
Mouse Tissue ◽  
Analysis Software ◽  
Profile Data ◽  
Microarray Image ◽  
Data Processing Method ◽  
Tissue Expression Profile

cDNA microarray technology is useful for systematically analyzing the expression profiles of thousands of genes at once. Although many useful results inferred by using this technology and a hierarchical clustering method for statistical analysis have been confirmed using other methods, there are still questions about the reproducibility of the data. We have therefore developed a data processing method that very efficiently extracts reproducible data from the result of duplicate experiments. It is designed to automatically filter the raw results obtained from cDNA microarray image-analysis software. We optimize the threshold value for filtering the data by using the product of N and R, where N is the ratio of the number of spots that passed the filtering vs. the total number of spots, and R is the correlation coefficient for results obtained in the duplicate experiments. Using this method to process mouse tissue expression profile data that contain 1,881,600 points of analysis, we obtained clustered results more reasonable than those obtained using previously reported filtering methods.

scholarly journals Deep-learning-based cell composition analysis from tissue expression profiles

2019 ◽  
Author(s):  
Kevin Menden ◽  
Mohamed Marouf ◽  
Sergio Oller ◽  
Anupriya Dalmia ◽  
Karin Kloiber ◽  
...  
Keyword(s):  
Deep Learning ◽  
Expression Profiles ◽  
Tissue Expression ◽  
Mouse Tissue ◽  
Composition Analysis ◽  
Complex Data ◽  
Rna Seq ◽  
Data Types ◽  
Multiple Data ◽  
Multiple Data Sets

AbstractWe present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

scholarly journals HumiR: Web Services, Tools and Databases for Exploring Human microRNA Data

Biomolecules ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1576
Author(s):  
Jeffrey Solomon ◽  
Fabian Kern ◽  
Tobias Fehlmann ◽  
Eckart Meese ◽  
Andreas Keller
Keyword(s):  
Expression Profiles ◽  
Cell Types ◽  
Independent Manner ◽  
Fully Integrated ◽  
Web Presence ◽  
Non Coding Rna ◽  
Pathway Prediction ◽  
Non Coding Rnas ◽  
Mirna Pathway ◽  
The Right

For many research aspects on small non-coding RNAs, especially microRNAs, computational tools and databases are developed. This includes quantification of miRNAs, piRNAs, tRNAs and tRNA fragments, circRNAs and others. Furthermore, the prediction of new miRNAs, isomiRs, arm switch events, target and target pathway prediction and miRNA pathway enrichment are common tasks. Additionally, databases and resources containing expression profiles, e.g., from different tissues, organs or cell types, are generated. This information in turn leads to improved miRNA repositories. While most of the respective tools are implemented in a species-independent manner, we focused on tools for human small non-coding RNAs. This includes four aspects: (1) miRNA analysis tools (2) databases on miRNAs and variations thereof (3) databases on expression profiles (4) miRNA helper tools facilitating frequent tasks such as naming conversion or reporter assay design. Although dependencies between the tools exist and several tools are jointly used in studies, the interoperability is limited. We present HumiR, a joint web presence for our tools. HumiR facilitates an entry in the world of miRNA research, supports the selection of the right tool for a research task and represents the very first step towards a fully integrated knowledge-base for human small non-coding RNA research. We demonstrate the utility of HumiR by performing a very comprehensive analysis of Alzheimer’s miRNAs.

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