1
|
De la Fuente-Hernandez MA, Sarabia-Sanchez MA, Melendez-Zajgla J, Maldonado-Lagunas V. Role of lncRNAs into Mesenchymal Stromal Cell Differentiation. Am J Physiol Cell Physiol 2022; 322:C421-C460. [PMID: 35080923 DOI: 10.1152/ajpcell.00364.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Currently, findings support that 75% of the human genome is actively transcribed, but only 2% is translated into a protein, according to databases such as ENCODE (Encyclopedia of DNA Elements) [1]. The development of high-throughput sequencing technologies, computational methods for genome assembly and biological models have led to the realization of the importance of the previously unconsidered non-coding fraction of the genome. Along with this, noncoding RNAs have been shown to be epigenetic, transcriptional and post-transcriptional regulators in a large number of cellular processes [2]. Within the group of non-coding RNAs, lncRNAs represent a fascinating field of study, given the functional versatility in their mode of action on their molecular targets. In recent years, there has been an interest in learning about lncRNAs in MSC differentiation. The aim of this review is to address the signaling mechanisms where lncRNAs are involved, emphasizing their role in either stimulating or inhibiting the transition to differentiated cell. Specifically, the main types of MSC differentiation are discussed: myogenesis, osteogenesis, adipogenesis and chondrogenesis. The description of increasingly new lncRNAs reinforces their role as players in the well-studied field of MSC differentiation, allowing a step towards a better understanding of their biology and their potential application in the clinic.
Collapse
Affiliation(s)
- Marcela Angelica De la Fuente-Hernandez
- Facultad de Medicina, Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Mexico City, Mexico.,Laboratorio de Epigenética, Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Miguel Angel Sarabia-Sanchez
- Facultad de Medicina, Posgrado en Ciencias Bioquímicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Jorge Melendez-Zajgla
- Laboratorio de Genómica Funcional del Cáncer, Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | | |
Collapse
|
2
|
Gene Expression Signature Associated with Clinical Outcome in ALK-Positive Anaplastic Large Cell Lymphoma. Cancers (Basel) 2021; 13:cancers13215523. [PMID: 34771686 PMCID: PMC8582782 DOI: 10.3390/cancers13215523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/15/2021] [Accepted: 10/28/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Anaplastic large cell lymphomas associated with ALK translocation have a good outcome after CHOP treatment; however, the 2-year relapse rate remains at 30%. Microarray gene-expression profiling, high throughput RT-qPCR, and RNA sequencing of 48 ALK-positive anaplastic large cell lymphoma (ALK+ ALCL) samples obtained at diagnosis enable the identification of genes associated with clinical outcome. More particularly, our molecular signatures indicate that the FN1 gene, a matrix key regulator, might also be involved in the prognosis and the therapeutic response in anaplastic lymphomas. Abstract Anaplastic large cell lymphomas associated with ALK translocation have a good outcome after CHOP treatment; however, the 2-year relapse rate remains at 30%. Microarray gene-expression profiling of 48 samples obtained at diagnosis was used to identify 47 genes that were differentially expressed between patients with early relapse/progression and no relapse. In the relapsing group, the most significant overrepresented genes were related to the regulation of the immune response and T-cell activation while those in the non-relapsing group were involved in the extracellular matrix. Fluidigm technology gave concordant results for 29 genes, of which FN1, FAM179A, and SLC40A1 had the strongest predictive power after logistic regression and two classification algorithms. In parallel with 39 samples, we used a Kallisto/Sleuth pipeline to analyze RNA sequencing data and identified 20 genes common to the 28 genes validated by Fluidigm technology—notably, the FAM179A and FN1 genes. Interestingly, FN1 also belongs to the gene signature predicting longer survival in diffuse large B-cell lymphomas treated with CHOP. Thus, our molecular signatures indicate that the FN1 gene, a matrix key regulator, might also be involved in the prognosis and the therapeutic response in anaplastic lymphomas.
Collapse
|
3
|
Riquier S, Bessiere C, Guibert B, Bouge AL, Boureux A, Ruffle F, Audoux J, Gilbert N, Xue H, Gautheret D, Commes T. Kmerator Suite: design of specific k-mer signatures and automatic metadata discovery in large RNA-seq datasets. NAR Genom Bioinform 2021; 3:lqab058. [PMID: 34179780 PMCID: PMC8221386 DOI: 10.1093/nargab/lqab058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 05/10/2021] [Accepted: 06/17/2021] [Indexed: 11/12/2022] Open
Abstract
The huge body of publicly available RNA-sequencing (RNA-seq) libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large dataset characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor gene-specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualized through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non-coding RNAs for human health applications.
Collapse
Affiliation(s)
- Sébastien Riquier
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| | - Chloé Bessiere
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| | - Benoit Guibert
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| | | | - Anthony Boureux
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| | - Florence Ruffle
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| | | | - Nicolas Gilbert
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| | - Haoliang Xue
- Institute for Integrative Biology of the Cell, CEA, CNRS, Université Paris-Saclay, 91198, Gif sur Yvette, France
| | - Daniel Gautheret
- Institute for Integrative Biology of the Cell, CEA, CNRS, Université Paris-Saclay, 91198, Gif sur Yvette, France
| | - Thérèse Commes
- IRMB, University of Montpellier, INSERM, 80 rue Augustin Fliche, 34295, Montpellier, France
| |
Collapse
|