115 Aged human keratinocytes have protein coding and noncoding RNA signatures indicative of inflammation, defective proliferation, and barrier deficiency

AbstractMolecular-based classifications of gastric cancer (GC) were recently proposed, but few of them robustly predict clinical outcomes. While mutation and expression signature of protein-coding genes were used in previous molecular subtyping methods, the noncoding genome in GC remains largely unexplored. Here, we developed the fast long-noncoding RNA analysis (FLORA) method to study RNA sequencing data of GC cases, and prioritized tumor-specific long-noncoding RNAs (lncRNAs) by integrating clinical and multi-omic data. We uncovered 1235 tumor-specific lncRNAs, based on which three subtypes were identified. The lncRNA-based subtype 3 (L3) represented a subgroup of intestinal GC with worse survival, characterized by prevalent TP53 mutations, chromatin instability, hypomethylation, and over-expression of oncogenic lncRNAs. In contrast, the lncRNA-based subtype 1 (L1) has the best survival outcome, while LINC01614 expression further segregated a subgroup of L1 cases with worse survival and increased chance of developing distal metastasis. We demonstrated that LINC01614 over-expression is an independent prognostic factor in L1 and network-based functional prediction implicated its relevance to cell migration. Over-expression and CRISPR-Cas9-guided knockout experiments further validated the functions of LINC01614 in promoting GC cell growth and migration. Altogether, we proposed a lncRNA-based molecular subtype of GC that robustly predicts patient survival and validated LINC01614 as an oncogenic lncRNA that promotes GC proliferation and migration.

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OP0248 DEVELOPMENT OF A 3D SKIN MICROTISSUE MODEL FOR FIBROTIC DISEASES

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3306 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 151.2-152

Author(s):

E. Pachera ◽

G. Kania ◽

A. Juengel ◽

M. Calcagni ◽

O. Distler

Keyword(s):

Gene Expression ◽

Noncoding Rna ◽

Clinical Investigation ◽

3D Culture ◽

Human Keratinocytes ◽

Dermal Fibroblasts ◽

Proof Of Concept ◽

Research Support ◽

Preclinical Research ◽

Matrix Gene

Background:Traditional preclinical approaches, such as two-dimensional cell culture and animal models, are often inadequate to mimic the pathophysiological features of complex diseases such as systemic sclerosis (SSc). Human specific targets, such as the recently described pro-fibrotic long non coding RNA (lncRNA) H19X1, are becoming increasingly relevant in preclinical research, creating the need of new strategies and tools in translational medicine. The employment of novel three-dimensional (3D) culture systems, where multiple cell types are included, is filling an important gap left by the traditional preclinical methods.Objectives:To develop an easy to produce 3D fibrotic skin microtissues model for translational proof of concept studies.Methods:Two thousand five hundred dermal fibroblasts isolated from skin of SSc patients were seeded in ultra-low attachment 96-well plates. Fibroblast were let to aggregate into spheres for 48h. Two thousand five hundred primary normal human keratinocytes were added to the culture and let to layer onto the fibroblast spheres for 72h. H19X silencing experiments were used as proof of concept studies. H19X silencing with antisense oligonucleotides or transfections with a scrambled control were performed in fibroblasts prior to the sphere formation for 24h. TGFβ (10 ng/ml) was added to microtissue to exacerbate the fibrotic phenotype. Haematoxylin eosin staining as well as immunohistochemistry staining for vimentin and cytokeratin 10 was performed. Skin microtissues were processed for RNA and protein isolation. Pro-collagen Iα1 and fibronectin were quantified in the supernatants with ELISA.Results:The microtissues presented a core of SSc fibroblast as revealed by vimentin staining and an external layer of keratinocytes as revealed by cytokeratin 10 staining, mimicking the human skin architecture. Gene expression analysis following TGFβ stimulation displayed induced expression of extracellular matrix gene COL1A1 (p=0.044) and the myofibroblast marker ACTA2 (p=0.018), indicating that the microtissues were able to develop a fibrotic response. Microtissues, where H19X was silenced, displayed reduced gene expression of COL1A1 and ACTA2 after TGFβ stimulation (COL1A1 p=0.007, ACTA2 p=0.045). Additionally, H19X silencing led to lower levels of αSMA protein expression (p=0.009) and pro-collagen1α1 secretion (p=0.039) in the supernatant of the microtissue cultures as revealed by Western Blot and ELISA, respectively. FN1 expression and fibronectin protein levels were not significantly reduced in the microtissues after H19X silencing.Conclusion:We were able to produce a 3D microtissue resembling skin architecture that can respond to fibrotic stimuli. Knockdown experiments of pro-fibrotic lncRNA H19X confirmed the potential of the model as screening platform for novel pro-fibrotic effectors. A future aim will be to optimize the model for high-throughput automated screening platforms.References:[1]Pachera, E., et al. (2020). “Long noncoding RNA H19X is a key mediator of TGF-β–driven fibrosis.” The Journal of Clinical Investigation 130(9): 4888-4905.Disclosure of Interests:Elena Pachera: None declared, Gabriela Kania: None declared, Astrid Juengel: None declared, Maurizio Calcagni Speakers bureau: Arthrex, Consultant of: Medartis, Arthrex, SilkBiomaterials, Grant/research support from: Medartis, Oliver Distler Speakers bureau: Actelion, Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche, Consultant of: Abbvie, Actelion, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, ChemomAb, Corpuspharma, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, -Kymera, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi, UCB, Grant/research support from: Abbvie, Actelion, Bayer, Boehringer Ingelheim, Kymera Therapeutics, Mitsubishi Tanabe

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The Long Noncoding RNA Differential Expression in Peripheral Blood Leukocyte from Schizophrenia Patients by RNA Sequencing

10.21203/rs.3.rs-113426/v1 ◽

2020 ◽

Author(s):

Jie Wu ◽

Zijun Liu ◽

Yunqiao Zhang ◽

Zhaowei Teng ◽

Xu You ◽

...

Keyword(s):

Transcription Factors ◽

Correlation Coefficient ◽

Peripheral Blood ◽

Noncoding Rna ◽

Clinical Symptoms ◽

Function Analysis ◽

Hierarchical Classification ◽

Peripheral Blood Leukocyte ◽

Protein Coding ◽

Functional Sites

Abstract Background: The diagnosis of schizophrenia (SCZ) depends on the evaluation of clinical symptoms, and there is no objective biomarker. Surveys have found that long non-coding RNA (lncRNA) may be affected in the pathogenesis of SCZ. There are also different genes in the expression of peripheral blood (PBL) in SCZ patients.Methods: We profiled transcriptome analysis of PBL in 50 patients with schizophrenia and 50 controls without psychiatric diagnoses, reconstructed PBL transcriptome information using RNA-seq, predicted lncRNA-mRNA interaction via “RNAplex”, a hierarchical classification-Spielman correlation coefficient approach was used to analyze the correlation between lncRNA and protein-coding gene expression among samples, and used systematic bioinformatics methods (Go/Pathway) to perform lncRNA functional annotation and qPCR experimental verification. Predicting functional sites for sequences using the database PROSITE, NCBI, UCSC, JASPAR.Results: We screened 94 lncRNA and 1179 mRNA differential expressions in PBL, of which 46 new lncRNAs were identified for the first time. Enrichment into lncRNA involves biological processes and signaling pathways related to the neutrophil activation involved in immune response. According to Spearman correlation coefficient analysis, 81 lncRNA and 410 mRNA have expression correlation (p<0.01 and |r|≥0.4), QPCR in independent samples verified that the core node of the lncRNA-mRNA co-expression network IL1RAP-TCONS_00138311 variable shear is indeed highly expressed in SCZ patients, 2^-△△Ct is 0.56, the area under the ROC curve is 0.924. The top four ranked transcription factors were predicted to be HSF1, HSF2, HSF4, and FOXA1.Conclusions: Combined with sequence function analysis, it showed that the transcription factors FOXA1, HSF1, HSF2, HSF4, etc. may mediate the activation of IL1B-induced NF-kβ pathway and other inflammatory pathways through the regulation of IL1RAP alternative splicing transcripts TCONS_00138311, thereby participating in the pathogenesis of SCZ. We propose that the frequency of Differential lncRNA in peripheral blood could be used as novel biomarker for distinguishing SCZ from health.

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Long noncoding RNA: a dazzling dancer in tumor immune microenvironment

Journal of Experimental & Clinical Cancer Research ◽

10.1186/s13046-020-01727-3 ◽

2020 ◽

Vol 39 (1) ◽

Author(s):

Yalu Zhang ◽

Qiaofei Liu ◽

Quan Liao

Keyword(s):

Noncoding Rna ◽

Molecular Mechanisms ◽

Immune Escape ◽

Noncoding Rnas ◽

Pathological Process ◽

Cellular Functions ◽

Immune Microenvironment ◽

Protein Coding ◽

Functional Roles ◽

Tumor Immune Microenvironment

Abstract Long noncoding RNAs (lncRNAs) are a class of endogenous, non-protein coding RNAs that are highly linked to various cellular functions and pathological process. Emerging evidence indicates that lncRNAs participate in crosstalk between tumor and stroma, and reprogramming of tumor immune microenvironment (TIME). TIME possesses distinct populations of myeloid cells and lymphocytes to influence the immune escape of cancer, the response to immunotherapy, and the survival of patients. However, hitherto, a comprehensive review aiming at relationship between lncRNAs and TIME is missing. In this review, we focus on the functional roles and molecular mechanisms of lncRNAs within the TIME. Furthermore, we discussed the potential immunotherapeutic strategies based on lncRNAs and their limitations.

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Protein Coding and Long Noncoding RNA (lncRNA) Transcriptional Landscape in SARS-CoV-2 Infected Bronchial Epithelial Cells Highlight a Role for Interferon and Inflammatory Response

Genes ◽

10.3390/genes11070760 ◽

2020 ◽

Vol 11 (7) ◽

pp. 760 ◽

Cited By ~ 10

Author(s):

Radhakrishnan Vishnubalaji ◽

Hibah Shaath ◽

Nehad M. Alajez

Keyword(s):

Epithelial Cells ◽

Inflammatory Response ◽

Noncoding Rna ◽

Cell Model ◽

Bronchial Epithelial Cells ◽

Host Cells ◽

Protein Coding ◽

Bronchial Epithelial ◽

Upstream Regulator

The global spread of COVID-19, caused by pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need for an imminent response from medical research communities to better understand this rapidly spreading infection. Employing multiple bioinformatics and computational pipelines on transcriptome data from primary normal human bronchial epithelial cells (NHBE) during SARS-CoV-2 infection revealed activation of several mechanistic networks, including those involved in immunoglobulin G (IgG) and interferon lambda (IFNL) in host cells. Induction of acute inflammatory response and activation of tumor necrosis factor (TNF) was prominent in SARS-CoV-2 infected NHBE cells. Additionally, disease and functional analysis employing ingenuity pathway analysis (IPA) revealed activation of functional categories related to cell death, while those associated with viral infection and replication were suppressed. Several interferon (IFN) responsive gene targets (IRF9, IFIT1, IFIT2, IFIT3, IFITM1, MX1, OAS2, OAS3, IFI44 and IFI44L) were highly upregulated in SARS-CoV-2 infected NBHE cell, implying activation of antiviral IFN innate response. Gene ontology and functional annotation of differently expressed genes in patient lung tissues with COVID-19 revealed activation of antiviral response as the hallmark. Mechanistic network analysis in IPA identified 14 common activated, and 9 common suppressed networks in patient tissue, as well as in the NHBE cell model, suggesting a plausible role for these upstream regulator networks in the pathogenesis of COVID-19. Our data revealed expression of several viral proteins in vitro and in patient-derived tissue, while several host-derived long noncoding RNAs (lncRNAs) were identified. Our data highlights activation of IFN response as the main hallmark associated with SARS-CoV-2 infection in vitro and in human, and identified several differentially expressed lncRNAs during the course of infection, which could serve as disease biomarkers, while their precise role in the host response to SARS-CoV-2 remains to be investigated.

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Long-Noncoding RNA (lncRNA) in the Regulation of Hypoxia-Inducible Factor (HIF) in Cancer

Non-Coding RNA ◽

10.3390/ncrna6030027 ◽

2020 ◽

Vol 6 (3) ◽

pp. 27 ◽

Cited By ~ 3

Author(s):

Dominik A. Barth ◽

Felix Prinz ◽

Julia Teppan ◽

Katharina Jonas ◽

Christiane Klec ◽

...

Keyword(s):

Noncoding Rna ◽

Molecular Mechanisms ◽

Hypoxia Inducible Factor ◽

Protein Coding ◽

Rna Molecules ◽

Von Hippel Lindau ◽

Hypoxic Conditions ◽

Main Regulator ◽

Upstream Regulators

Hypoxia is dangerous for oxygen-dependent cells, therefore, physiological adaption to cellular hypoxic conditions is essential. The transcription factor hypoxia-inducible factor (HIF) is the main regulator of hypoxic metabolic adaption reducing oxygen consumption and is regulated by gradual von Hippel-Lindau (VHL)-dependent proteasomal degradation. Beyond physiology, hypoxia is frequently encountered within solid tumors and first drugs are in clinical trials to tackle this pathway in cancer. Besides hypoxia, cancer cells may promote HIF expression under normoxic conditions by altering various upstream regulators, cumulating in HIF upregulation and enhanced glycolysis and angiogenesis, altogether promoting tumor proliferation and progression. Therefore, understanding the underlying molecular mechanisms is crucial to discover potential future therapeutic targets to evolve cancer therapy. Long non-coding RNAs (lncRNA) are a class of non-protein coding RNA molecules with a length of over 200 nucleotides. They participate in cancer development and progression and might act as either oncogenic or tumor suppressive factors. Additionally, a growing body of evidence supports the role of lncRNAs in the hypoxic and normoxic regulation of HIF and its subunits HIF-1α and HIF-2α in cancer. This review provides a comprehensive update and overview of lncRNAs as regulators of HIFs expression and activation and discusses and highlights potential involved pathways.

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Angiogenic patterning by STEEL, an endothelial-enriched long noncoding RNA

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1715182115 ◽

2018 ◽

Vol 115 (10) ◽

pp. 2401-2406 ◽

Cited By ~ 30

Author(s):

H. S. Jeffrey Man ◽

Aravin N. Sukumar ◽

Gabrielle C. Lam ◽

Paul J. Turgeon ◽

Matthew S. Yan ◽

...

Keyword(s):

Noncoding Rna ◽

De Novo ◽

Feedback Inhibition ◽

Body Position ◽

In Vivo Model ◽

Protein Coding ◽

Angiogenic Potential ◽

Hemodynamic Forces

Endothelial cell (EC)-enriched protein coding genes, such as endothelial nitric oxide synthase (eNOS), define quintessential EC-specific physiologic functions. It is not clear whether long noncoding RNAs (lncRNAs) also define cardiovascular cell type-specific phenotypes, especially in the vascular endothelium. Here, we report the existence of a set of EC-enriched lncRNAs and define a role for spliced-transcript endothelial-enriched lncRNA (STEEL) in angiogenic potential, macrovascular/microvascular identity, and shear stress responsiveness. STEEL is expressed from the terminus of the HOXD locus and is transcribed antisense to HOXD transcription factors. STEEL RNA increases the number and integrity of de novo perfused microvessels in an in vivo model and augments angiogenesis in vitro. The STEEL RNA is polyadenylated, nuclear enriched, and has microvascular predominance. Functionally, STEEL regulates a number of genes in diverse ECs. Of interest, STEEL up-regulates both eNOS and the transcription factor Kruppel-like factor 2 (KLF2), and is subject to feedback inhibition by both eNOS and shear-augmented KLF2. Mechanistically, STEEL up-regulation of eNOS and KLF2 is transcriptionally mediated, in part, via interaction of chromatin-associated STEEL with the poly-ADP ribosylase, PARP1. For instance, STEEL recruits PARP1 to the KLF2 promoter. This work identifies a role for EC-enriched lncRNAs in the phenotypic adaptation of ECs to both body position and hemodynamic forces and establishes a newer role for lncRNAs in the transcriptional regulation of EC identity.

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Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1909998116 ◽

2019 ◽

Vol 116 (44) ◽

pp. 22020-22029 ◽

Cited By ~ 9

Author(s):

Aritro Nath ◽

Eunice Y. T. Lau ◽

Adam M. Lee ◽

Paul Geeleher ◽

William C. S. Cho ◽

...

Keyword(s):

Anticancer Drug ◽

Noncoding Rna ◽

Large Scale ◽

Drug Response ◽

Cancer Cell Line ◽

Systematic Evaluation ◽

Protein Coding ◽

Protein Coding Genes ◽

Clinical Biomarkers ◽

Response Predictors

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

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