7
views
0
recommends
+1 Recommend
2 collections
    0
    shares

          The flagship journal of the Society for Endocrinology. Learn more

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A transcriptomic signature of X chromosome overdosage in Saudi Klinefelter syndrome induced pluripotent stem cells

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          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.

          Abstract

          Objective

          The transcriptional landscape of Klinefelter syndromeduring early embryogenesis remains elusive. This study aimed to evaluate the impact of X chromosome overdosage in 47,XXY males induced pluripotent stem cells (iPSCs) obtained from patients with different genomic backgrounds and ethnicities.

          Design and method

          We derived and characterized 15 iPSC lines from four Saudi 47,XXY KS patients and one Saudi 46,XY male. We performed a comparative transcriptional analysis using the Saudi KS-iPSCs and a cohort of European and North American KS-iPSCs.

          Results

          We identified a panel of X-linked and autosomal genes commonly dysregulated in Saudi and European/North American KS-iPSCs vs 46,XY controls. Our findings demonstrate that seven PAR1 and nine non-PAR escape genes are consistently dysregulated and mostly display comparable transcriptional levels in both groups. Finally, we focused on genes commonly dysregulated in both iPSC cohorts and identified several gene-ontology categories highly relevant to KS physiopathology, including aberrant cardiac muscle contractility, skeletal muscle defects, abnormal synaptic transmission, and behavioral alterations.

          Conclusions

          Our results indicate that a transcriptomic signature of X chromosome overdosage in KS is potentially attributable to a subset of X-linked genes sensitive to sex chromosome dosage and escaping X inactivation, regardless of the geographical area of origin, ethnicity, and genetic makeup.

          Related collections

          Most cited references39

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

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              • Record: found
              • Abstract: found
              • Article: not found

              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                27 March 2023
                27 March 2023
                01 May 2023
                : 12
                : 5
                : e220515
                Affiliations
                [1 ]Biological and Environmental Science and Engineering Division , King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
                [2 ]Sequentia Biotech SL , Barcelona, Spain
                [3 ]Department of Genetic Medicine , Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
                [4 ]Department of Medical Laboratory Sciences , Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
                [5 ]Department of Dermatology , Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
                [6 ]Department of Urology , Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
                [7 ]Center of Innovation in Personalized Medicine , King Abdulaziz University, Jeddah, Saudi Arabia
                Author notes
                Correspondence should be addressed to A Adamo: antonio.adamo@ 123456kaust.edu.sa

                *(V Astro and E Fiacco contributed equally to this work)

                Author information
                http://orcid.org/0000-0003-1080-3547
                Article
                EC-22-0515
                10.1530/EC-22-0515
                10160548
                36971776
                02806783-9d09-46d5-a83f-fe9a819a5229
                © the author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 08 March 2023
                : 27 March 2023
                Categories
                Research

                klinefelter syndrome,x chromosome,pseudoautosomal region,escape genes,induced pluripotent stem cells

                Comments

                Comment on this article

                Related Documents Log