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07/11/2019 RNA Sequencing | AACC.org
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RNA Sequencing
A complementary tool kit for the diagnosis of Mendelian disorders
Author: Catarina Silveira, MSc, Marcia Triunfol, PhD, and
Maria Carmo-Fonseca, MD, PhD // Date: OCT.1.2019 //
Source: Clinical Laboratory News
Topics: Sequencing, Nucleic Acids, Genetic disorders
The fact that our genome encompasses approximately 20,000
genes indicates that the huge variety of existing proteins in the
human body cannot be explained by our species’ gene
repertory alone. This means that if we want to understand the
work of the many players acting in a given dynamic biological
system, we should look beyond mere DNA sequencing.
The term transcriptome refers to the entire collection of
sequences that are transcribed from DNA into RNA. For
protein coding genes, transcriptome analysis provides
information on how often a gene is transcribed, how the
nascent transcript is processed into mature mRNA, and how
stable the messenger (mRNA) is in a cell. The transcriptome is
unique to cells, tissues, conditions, and physiological states.
The transcriptome of diseased cells has features not found in
the transcriptome of their healthy counterparts.
Thus, by analyzing the transcriptome of affected cells or
tissues it is possible to identify differences that reveal the many
paths leading to disease, offering new opportunities for
diagnosing diseases and monitoring their progression.
RNA ANALYSIS BY HYBRIDIZATION-BASED
METHODS
Northern blot was the first laboratory method created to identify
specific RNA molecules within a mixture of RNA. Northern blot
involves denaturing RNA and separating it by gel
electrophoresis, transferring RNA onto a blotting membrane,
and hybridizing it with a nucleic acid probe labeled with either a
radioactive atom or a fluorescent dye. Although laborious and
Clinical
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time-consuming, Northern blot remains a reliable method to
study any type of RNA.
Reverse transcription-polymerase chain reaction (RT-PCR)
came on the scene after PCR. RT-PCR converts RNA into
complementary DNA (cDNA), which then serves as a template
for PCR. RT-PCR provides results much faster than Northern
blot but quantification can be difficult. Real-time quantitative
reverse transcription PCR (qRT-PCR) overcame this limitation,
by enabling reliable detection and measurement of products
generated during each cycle of the PCR process. Compared to
Northern blot, qRT-PCR is easier to perform and requires less
time because it’s possible to run the PCR simultaneously at
different melting temperatures using a gradient thermocycler.
More recently, PCR combined with microfluidic devices has
enabled parallel quantification of multiple distinct RNAs.
Despite these advances, the first molecular biology tool
capable of quantitating hundreds or thousands of RNAs from a
given cell or tissue sample simultaneously was microarray
technology. A microarray has thousands of oligonucleotides of
known sequences arrayed on a chip, and quantitation relies
upon hybridization of sample RNA that has been reverse-
transcribed and labeled.
Although microarray technology flourished to become the main
platform for high-throughput analysis of RNA in biological
systems, it shares a major limitation with all other hybridization-
based methods in that it requires previous knowledge of the
RNA molecules to be analyzed, thus limiting the potential for
discovery.
Additional drawbacks of hybridization-based approaches
include high background levels resulting from cross-
hybridization and a limited dynamic range of detection due to
both background and saturation of signals. For these reasons,
transcriptomic analysis based on microarrays typically failed to
provide relevant information that could be transferred directly
into clinical applications.
THE ADVENT OF RNA-SEQ
In contrast to hybridization methods, sequence-based
approaches directly determine the cDNA sequence. Initially,
Sanger sequencing of cDNA was used, but following the
development of next-generation sequencing (NGS) and deep-
sequencing technologies, RNA sequencing (RNA-Seq)
emerged and revolutionized the way entire transcriptomes
would be analyzed.
NGS differs from conventional capillary-based (Sanger)
sequencing in being able to process millions of sequence
reads in parallel. Deep sequencing is an NGS approach that
sequences the same cDNA region hundreds or even
thousands of times.
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The RNA-Seq workflow (Fig. 1) starts with selecting a method
for RNA purification best adapted to the biological sample
being analyzed. After assessing the quantity and quality of the
purified RNA, the next step consists of converting the
population of RNA molecules into a library of cDNA fragments.
This involves a reverse transcriptase-mediated first strand
synthesis followed by a DNA polymerase-mediated second
strand synthesis.
In a typical RNA-Seq experiment, RNAs are converted into a
library of cDNA fragments of an appropriate size for
sequencing through either RNA fragmentation or
complementary DNA (cDNA) fragmentation. Sequencing
adaptors are added to each fragment. Each fragment is further
tagged with a unique molecular identif¬ier (UMI) sequence
chosen from approximately 10,000 combinations, so that two
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identical molecules become distinguishable; this provides a
digital measurement of absolute numbers of each RNA
species, irrespective of PCR amplification biases. Index
sequences are also introduced, enabling sequencing up to 96
different samples on a single run. In the Illumina sequencing
platform, oligonucleotides complementary to the adaptor
sequences are immobilized on the surface of a flow cell.
Attached cDNA fragments anneal to a nearby read primer for
polymerase chain reaction (PCR) amplification. Repeated
cycles form colony-like clusters, each containing approximately
1,000 copies. The sequencing reaction (sequencing-by-
synthesis) is then carried out with fluorescently labeled
modified nucleotides that act as reversible terminators. Thus,
only a single fluorescent nucleotide can be added by a
polymerase to each growing DNA copy. Images are recorded
to identify the fluorescent base incorporated in each cluster.
Next, the fluorophores are cleaved off and the terminators are
removed, allowing another round of nucleotide incorporation.
Depending on the NGS sequencing platform, specific adapter
sequences are attached to one or both ends of each cDNA
fragment. Short (typically 50-150bp long) sequence reads are
then obtained from either one end (single-end sequencing) or
both ends (pair-end sequencing) of each cDNA. Finally,
bioinformatics methods are used to align the individual reads to
the reference genome or transcriptome.
RNA-Seq provides more precise qualitative and quantitative
information than previous technologies and has already
reshaped our view of several organisms’ transcriptomes. RNA-
Seq has revealed many novel transcribed regions in every
genome analyzed, from yeast to human, as well as many novel
human mRNA isoforms resulting from alternative splicing,
alternative polyadenylation, and/or alternative promoter use.
Moreover, profiling the transcriptome of thousands of human
cancer samples has unraveled specific gene fusions and gene
expression signatures with demonstrated prognostic and
predictive value.
RNA-SEQ: MATURING METHOD FOR
DIAGNOSIS
The current rate of molecular diagnosis of Mendelian diseases
is low and between 25% and 50%. The vast majority of known
genetic alterations associated with Mendelian disorders have
been identified by sequencing the protein-coding regions of
genes (exome sequencing). However, evidence is mounting
that hereditary diseases can also be caused by mutations
located within non-coding regions of the genome, such as
introns, transcriptional regulatory sequences of protein-coding
genes, and non-coding regulatory RNA genes (1).
Mutations leading to down-regulation of gene expression can
be better identified by combining DNA and RNA sequencing
(2). The definitive functional identification of mutations that
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interfere with splicing also requires RNA analysis. Indeed, the
correct removal of introns from the nascent precursor mRNAs
is a tightly controlled process that depends upon combinatorial
cross-talk between the splice site sequences and regulatory
sequences located within exons and introns. Thus, not only
mutations located deep within introns (that are not detected by
exome sequencing), but also exonic mutations may alter
splicing. Identification of both intronic and exonic mutations
affecting splicing will increase the overall mutation-detection
rates, which ultimately will facilitate more patients carrying
disease-causing mutations to receive the right diagnosis.
However, a major downside of using RNA-Seq for human
studies is that gene expression is cell-type specific. Thus, it
may be necessary to extract RNA from the tissue or organ that
is specifically affected in a particular disease, and many tissues
in the human body are not easy to access.
Despite this drawback, RNA-Seq is starting to offer new hopes
for many patients and families carrying a hereditary disease for
which DNA sequencing failed to provide a diagnosis (3). For
instance, a recent study (4) used RNA-Seq to analyze RNA
extracted from muscles obtained from biopsy samples of 63
patients with a range of muscle disorders. While some patients
already had a defined diagnosis and were included in the study
to validate the findings obtained with RNA-Seq, others had not
yet received a diagnosis. RNA-Seq was able to correctly
determine the molecular diagnosis for 66% of patients whose
samples already had undergone DNA sequencing and for
which strong gene candidates were indicated.
In contrast, RNA-Seq identified aberrant splicing isoforms and
provided a diagnosis in 21% of cases lacking candidate
mutations. Overall, the researchers made 17 new diagnoses
and associated the splicing abnormalities with mutations
located either at splice sites or within introns that caused exon
extension (when the exon extends beyond its normal limits),
intronic splice gain (gain of intronic sequence), exon skipping
(when an exon is missing in the transcript), and other splice
disruptions. Of note, without the RNA-Seq analysis these
genetic variants would be reported as variants of unknown
significance (VUS).
Another study (5) analyzed fibroblasts from 105 patients with
suspected mitochondrial disease. Almost half of the patients
(48) submitted samples for whole-exome sequencing but no
molecular diagnosis could be established. RNA-Seq analysis
revealed aberrant splicing isoforms by comparing the pattern of
each patient against the others.
One patient showed a splice defect that resulted in a truncated
CLPP, a mitochondrial ATP-dependent endopeptidase, and
Western blot analysis confirmed the loss of the full-length
CLPP. The variant, detected as a homozygous change in the
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very last nucleotide of exon 5 of the CLPP gene, had been
previously reported as VUS.
Based on the results obtained by RNA-Seq, this variant was
reclassified as disease-causing. Knowing that CLPP was the
implicated gene allowed clinicians to associate the patient’s
manifestations with Perrault syndrome (OMIM #601119), which
is caused by a deficiency of the CLPP protein.
In another patient, whole-exome sequencing revealed a mono-
allelic expressed VUS in the ALDH18A1 gene; this variant was
a compound heterozygous with a nonsense variant in the same
gene. RNA-Seq showed very low levels of ALDH18A1 mRNA.
Quantitative proteomics confirmed that the levels of the
ALDH18A1 protein were almost zero, and functional studies
supported the reclassification of the two detected variants as
disease-causing.
Hamanaka et al. recently proposed a workflow strategy to
combine exome sequencing and RNA-Seq findings to solve
undiagnosed cases of patients with nemaline myopathy (NM)
(6). Skin samples from six patients with incomplete molecular
diagnosis were submitted to the workflow. In these cases,
exome sequencing had revealed only one of the pathogenic
variants between the two required to cause NM, but RNA-Seq
solved four cases by identifying the second missing pathogenic
allele. In all four cases the second hit involved abnormal
splicing events.
CONCLUSION
RNA-Seq is solving several undiagnosed cases for which DNA
sequencing alone was inconclusive. However, a major
downside of using RNA-Seq to diagnose hereditary diseases is
that in many cases RNA needs to be extracted from the
affected tissue or organ because expression of the mutated
gene is tissue-specific.
Nevertheless, evidence is mounting that combined DNA and
RNA analysis may greatly increase the success rate of
molecular diagnosis of hereditary diseases, offering new
opportunities for discovery and new hopes for families affected
by hereditary genetic disorders.
Catarina Silveira, MSc,is a senior molecular genetic laboratory
technologist at GenoMed Diagnostics, in Lisbon, and aPhD
candidate at the University of Lisbon in Portugal. +Email:
csilveira@medicina.ulisboa.pt Lobo Antunes, University of
Lisbon Medical School, Portugal. +Email:
marcia.triunfol@medicina.ulisboa.pt
Maria Carmo-Fonseca, MD, PhD, is a principal investigator at
Instituto de Medicina Molecular João Lobo Antunes, University
of Lisbon Medical School, Portugal. +Email:
carmo.fonseca@medicina.ulisboa.pt
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REFERENCES
1. Vaz-Drago R, Custódio N, Carmo-Fonseca M. Deep
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2. Byron SA, Keuren-jensen KR Van, Engelthaler DM, et
al. Translating RNA sequencing into clinical
diagnostics: Opportunities and challenges. Nat Rev
Genet 2016;17:257-71.
3. Chakravorty S, Hegde M. Clinical utility of transcriptome
sequencing: Toward a better diagnosis for Mendelian
disorders. Clin Chem 2018;64:882-4.
4. Cummings BB, Marshall JL, Tukiainen T, et al.
Improving genetic diagnosis in Mendelian disease with
transcriptome sequencing. Sci Transl Med
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5. Kremer LS, Wortmann SB, Prokisch H.
Transcriptomics: Molecular diagnosis of inborn errors of
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6. Hamanaka K, Miyatake S, Koshimizu E, et al. RNA
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