The transcriptome profile of human trisomy 21 blood cells

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Abstract

Background

Trisomy 21 (T21) is a genetic alteration characterised by the presence of an extra full or partial human chromosome 21 (Hsa21) leading to Down syndrome (DS), the most common form of intellectual disability (ID). It is broadly agreed that the presence of extra genetic material in T21 gives origin to an altered expression of genes located on Hsa21 leading to DS phenotype. The aim of this study was to analyse T21 and normal control blood cell gene expression profiles obtained by total RNA sequencing (RNA-Seq).

Results

The results were elaborated by the TRAM (Transcriptome Mapper) software which generated a differential transcriptome map between human T21 and normal control blood cells providing the gene expression ratios for 17,867 loci. The obtained gene expression profiles were validated through real-time reverse transcription polymerase chain reaction (RT-PCR) assay and compared with previously published data. A post-analysis through transcriptome mapping allowed the identification of the segmental (regional) variation of the expression level across the whole genome (segment-based analysis of expression). Interestingly, the most over-expressed genes encode for interferon-induced proteins, two of them ( MX1 and MX2 genes) mapping on Hsa21 (21q22.3). The altered expression of genes involved in mitochondrial translation and energy production also emerged, followed by the altered expression of genes encoding for the folate cycle enzyme, GART, and the folate transporter, SLC19A1.

Conclusions

The alteration of these pathways might be linked and involved in the manifestation of ID in DS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00325-4.

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Most cited references 61

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Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

Kenneth Livak, Thomas D. Schmittgen (2001)

The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

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/.

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Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

Cole Trapnell, Adam Roberts, Loyal Goff … (2012)

Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.

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Author and article information

Contributors

Maria Caracausi:

ORCID: http://orcid.org/0000-0001-8957-8391

maria.caracausi2@unibo.it

Journal

Journal ID (nlm-ta): Hum Genomics

Journal ID (iso-abbrev): Hum Genomics

Title: Human Genomics

Publisher: BioMed Central (London )

ISSN (Print): 1473-9542

ISSN (Electronic): 1479-7364

Publication date (Electronic): 1 May 2021

Publication date PMC-release: 1 May 2021

Publication date Collection: 2021

Volume: 15

Electronic Location Identifier: 25

Affiliations

[1 ]GRID grid.6292.f, ISNI 0000 0004 1757 1758, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Unit of Histology, Embryology and Applied Biology, , University of Bologna, ; Via Belmeloro 8, 40126 Bologna, BO Italy

[2 ]GRID grid.7637.5, ISNI 0000000417571846, Current Address: Department of Molecular and Translational Medicine (DMMT), , University of Brescia, ; Viale Europa 11, 24123 Brescia, BS Italy

[3 ]MAGI’S Lab, Via delle Maioliche 57/D, 38068 Rovereto, TN Italy

[4 ]Neonatology Unit, St. Orsola-Malpighi Polyclinic, Via Massarenti 9, 40138 Bologna, BO Italy

[5 ]GRID grid.9024.f, ISNI 0000 0004 1757 4641, Current Address: Department of Medical Biotechnologies, , University of Siena, ; Strada delle Scotte, 4, 53100 Siena, SI Italy

[6 ]GRID grid.6292.f, ISNI 0000 0004 1757 1758, Neonatology Unit, St. Orsola-Malpighi Polyclinic, Department of Medical and Surgical Sciences (DIMEC), , University of Bologna, ; Via Massarenti 9, 40138 Bologna, BO Italy

Author information

Maria Caracausi http://orcid.org/0000-0001-8957-8391

Article

Publisher ID: 325

DOI: 10.1186/s40246-021-00325-4

PMC ID: 8088681

PubMed ID: 33933170

SO-VID: bd3187e9-e7ca-4b0e-a127-0b0991ab889e

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 7 January 2021

Date accepted : 14 April 2021

Funding

Funded by: Fondazione Umano Progresso

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: transcriptome,rna sequencing,blood cells,human chromosome 21,trisomy 21,down syndrome

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ScienceOpen disciplines: Genetics

Keywords: transcriptome, rna sequencing, blood cells, human chromosome 21, trisomy 21, down syndrome

The transcriptome profile of human trisomy 21 blood cells

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Most cited references 61

Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

STAR: ultrafast universal RNA-seq aligner.

Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

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