2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Transcriptomics for child and adolescent tuberculosis*

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Tuberculosis (TB) in humans is caused by Mycobacterium tuberculosis ( Mtb). It is estimated that 70 million children (<15 years) are currently infected with Mtb, with 1.2 million each year progressing to disease. Of these, a quarter die. The risk of progression from Mtb infection to disease and from disease to death is dependent on multiple pathogen and host factors. Age is a central component in all these transitions. The natural history of TB in children and adolescents is different to adults, leading to unique challenges in the development of diagnostics, therapeutics, and vaccines. The quantification of RNA transcripts in specific cells or in the peripheral blood, using high‐throughput methods, such as microarray analysis or RNA‐Sequencing, can shed light into the host immune response to Mtb during infection and disease, as well as understanding treatment response, disease severity, and vaccination, in a global hypothesis‐free manner. Additionally, gene expression profiling can be used for biomarker discovery, to diagnose disease, predict future disease progression and to monitor response to treatment. Here, we review the role of transcriptomics in children and adolescents, focused mainly on work done in blood, to understand disease biology, and to discriminate disease states to assist clinical decision‐making. In recent years, studies with a specific pediatric and adolescent focus have identified blood gene expression markers with diagnostic or prognostic potential that meet or exceed the current sensitivity and specificity targets for diagnostic tools. Diagnostic and prognostic gene expression signatures identified through high‐throughput methods are currently being translated into diagnostic tests.

          Related collections

          Most cited references137

          • Record: found
          • Abstract: found
          • Article: not found

          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
                Bookmark

                Author and article information

                Contributors
                m.kaforou@imperial.ac.uk
                o.vito@imperial.ac.uk
                m.levin@imperial.ac.uk
                james.seddon@imperial.ac.uk
                Journal
                Immunol Rev
                Immunol Rev
                10.1111/(ISSN)1600-065X
                IMR
                Immunological Reviews
                John Wiley and Sons Inc. (Hoboken )
                0105-2896
                1600-065X
                12 July 2022
                August 2022
                : 309
                : 1 , SARS‐CoV‐2 Immunity ( doiID: 10.1111/imr.v309.1 )
                : 97-122
                Affiliations
                [ 1 ] Department of Infectious Disease Imperial College London London UK
                [ 2 ] South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology University of Cape Town Cape Town South Africa
                [ 3 ] Desmond Tutu TB Centre, Department of Paediatrics and Child Health Stellenbosch University Cape Town South Africa
                Author notes
                [*] [* ] Correspondence

                Myrsini Kaforou, Department of Infectious Disease, Imperial College London, Norfolk Place, London W2 1PG, UK.

                Email: m.kaforou@ 123456imperial.ac.uk

                Author information
                https://orcid.org/0000-0001-9878-4007
                https://orcid.org/0000-0003-2582-4527
                https://orcid.org/0000-0001-5509-6351
                https://orcid.org/0000-0003-2767-6919
                https://orcid.org/0000-0002-0641-1359
                https://orcid.org/0000-0002-2296-2302
                Article
                IMR13116 IMR-2022-016.R1
                10.1111/imr.13116
                9540430
                35818983
                98d91d7d-3b5b-4316-b8a5-f224be6941c0
                © 2022 The Authors. Immunological Reviews published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 12, Tables: 1, Pages: 26, Words: 16775
                Funding
                Funded by: Medical Research Foundation , doi 10.13039/501100009187;
                Award ID: MRF‐160‐0008‐ELP‐KAFO‐C0801
                Funded by: NIHR Imperial BRC , doi 10.13039/501100013342;
                Award ID: RDA02
                Funded by: UK Department for International Development (DFID)
                Award ID: MR/R007942/1
                Funded by: UK Medical Research Council (MRC)
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Award ID: 206508/Z/17/Z
                Categories
                Invited Review
                Invited Reviews
                Custom metadata
                2.0
                August 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.0 mode:remove_FC converted:07.10.2022

                children,diagnosis,differential expression,transcriptomics,tuberculosis

                Comments

                Comment on this article