Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48
https://doi.org/10.1186/s12881-019-0770-6

RESEARCH

Open Access

Novel potential causative genes in carotid
paragangliomas
Anastasiya V. Snezhkina1, Elena N. Lukyanova1, Andrew R. Zaretsky1, Dmitry V. Kalinin2, Anatoly V. Pokrovsky2,
Alexander L. Golovyuk2, George S. Krasnov1, Maria S. Fedorova1, Elena A. Pudova1, Sergey L. Kharitonov1,
Nataliya V. Melnikova1, Boris Y. Alekseev3, Marina V. Kiseleva3, Andrey D. Kaprin3, Alexey A. Dmitriev1 andAnna V. Kudryavtseva1*
From 11th International Multiconference “Bioinformatics of Genome Regulation and Structure\Systems Biology” - BGRS\SB2018
Novosibirsk, Russia. 20-25 August 2018

Abstract
Background: Carotid paragangliomas (CPGLs) are rare neuroendocrine tumors that arise from the paraganglion at thebifurcation of the carotid artery and are responsible for approximately 65% of all head and neck paragangliomas. CPGLs
can occur sporadically or along with different hereditary tumor syndromes. Approximately 30 genes are known to be
associated with CPGLs. However, the genetic basis behind the development of these tumors is not fully elucidated, andthe molecular mechanisms underlying CPGL pathogenesis remain unclear.
Methods: Whole exome and transcriptome high-throughput sequencing of CPGLs was performed on an Illumina
platform. Exome libraries were prepared using a Nextera Rapid Capture Exome Kit (Illumina) and were sequenced
under 75 bp paired-end model. For cDNA library preparation, a TruSeq Stranded Total RNA Library Prep Kit with RiboZero Gold (Illumina) was used; transcriptome sequencing was carried out with 100 bp paired-end read length.
Obtained data were analyzed using xseq which estimates the influence of mutations on gene expression profiles
allowing to identify potential causative genes.
Results: We identified a total of 16 candidate genes (MYH15, CSP1, MYH3, PTGES3L, CSGALNACT2, NMD3, IFI44, GMCL1,
LSP1, PPFIBP2, RBL2, MAGED1, CNIH3, STRA6, SLC6A13, and ATM) whose variants potentially influence their expression (ciseffect). The strongest cis-effect of loss-of-function variants was found in MYH15, CSP1, and MYH3, and several likely
pathogenic variants in these genes associated with CPGLs were predicted.
Conclusions: Using the xseq probabilistic model, three novel potential causative genes, namely MYH15, CSP1, andMYH3, were identified in carotid paragangliomas.
Keywords: Carotid paragangliomas, Tumor-associated genes, Pathogenic variants, High-throughput sequencing, Exome,
Transcriptome, Xseq

* Correspondence: rhizamoeba@mail.ru
1
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences,
Moscow, Russia
Full list of author information is available at the end of the article© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. 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.

Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48

Background
Paragangliomas are rare neuroendocrine neoplasms derived from paraganglionic tissue. In the head and neck,paragangliomas most frequently arise from carotid glomus at the bifurcation of the carotid artery [1]. Carotid
paragangliomas (CPGLs) are highly vascularized tumors
and are anatomically classified on three groups based on
the size and involvement of the carotid artery [2]. The
majority of carotid paragangliomas belong to groups 2
and 3 (primarily large size CPGLs with moderate or higharterial attachment), which are surgically challenging.
CPGLs are typically characterized by slow-growing andnon-aggressive tumors. Up to 10% of CPGLs form regional or distant metastases (malignant CPGLs) [3]. Approximately 10% of CPGLs are hereditary [4]. The new
WHO classification describes paragangliomas as tumors
with variable metastatic potential [5].
Almost all tumors, including paragangliomas, arecharacterized by altered energy metabolism [6–8]. Several genes involved in energy metabolism were foundto be critical for CPGL pathogenesis. Germline variantsin the SDHB [9], SDHC [10], SDHD [11], RET [12], andVHL [13, 14] genes were associated with carotid paragangliomas. Somatic variants in these genes, as well as onesin SDHA, SDHAF2, IDH1, NF1, MEN1, and KIF1 havealso been described in sporadic CPGLs [10, 15, 16].
Germline and somatic variants in SDHx genes areoften found in head and neck paragangliomas [17, 18].
These genes encode the four subunits (SDHA, SDHB,
SDHC, and SDHD) of succinate dehydrogenase (SDH),
also known as the mitochondrial complex II. SDH is acomponent of both the oxidative phosphorylation(OXPHOS) system and the tricarboxylic acid (TCA)
cycle, which are key metabolic pathways in the mitochondria. Dysfunctional SDH resulting from genetic or
epigenetic alterations can contribute to various pathologies, including cancer [19]. The molecular mechanisms underlying tumorigenesis include the increased
generation of reactive oxygen species (ROS) and thedisruption of mitochondrial function during apoptosis[20]. On the other hand, dysfunctional SDH leads tosuccinate accumulation, which in turn affects the activity of the prolyl hydroxylase (PHD) enzyme that catalyzes the hydroxylation of hypoxia inducible factor(HIF). Impaired PHD activity disrupts the binding between HIF and von Hippel-Lindau tumor suppressor
protein (pVHL) and subsequently leads to HIF degradation via the ubiquitin-proteasome pathway [21]. The
development of paragangliomas is often associatedwith the pseudohypoxia state caused by thestabilization and activation of HIF [22, 23]. Variants in
SDHD occur in most carotid paragangliomas, whereas
rarer SDHB variants result in aggressive disease andmetastasis [24–26].

Page 6 of 56

Variants in other known paraganglioma/pheochromocytoma-causative genes (e.g., RET, NF1, and IDH1) areless common in CPGLs and are primarily observed insporadic ones. RET and NF1 are a proto-oncogene and atumor suppressor gene respectively; their protein products participate in the PI3K/AKT and RAS/MAPK pathways and mTOR signaling [27, 28]. Variants in RET and
NF1 lead to deregulation of these pathways and are believed to be critical drivers of tumorigenesis. Several variants of IDH1 but not those of IDH2 were reported in
CPGLs [10, 16]. Inactivation mutations in IDH1 are
known to result in HIF accumulation and tumor development under pseudohypoxia conditions [29]. Two potential pathogenic variants in KIF1B were identified in
CPGLs [16]. KIF1B encodes a motor protein responsible
for organelle transport and cell division [30, 31]. Several
studies have demonstrated the involvement of KIF1B intumorigenesis and its tumor suppressor role [32–34]. A
study using rat pheochromocytoma cells indicated thatthe effects of KIF1B in apoptosis are mediated by the
EglN3 prolyl hydroxylase pathway [35]. Variants
associated with CPGLs were detected in a group of
TP53-related genes [TP53BP1, TP53BP2 (ASPP2), andTP53I13 (DSCP1)] that encode proteins involved in theregulation of cell proliferation, DNA damage response,
and apoptosis. These proteins can act as tumor suppressors by modulating p53 function and promoting apoptosis[36–38]. The mutated ARNT (HIF1B) gene, which encodes an aryl hydrocarbon nuclear translocator, wasidentified in CPGLs [16]. ARNT functions as a
co-factor for various transcription factors, including
HIF1A [39]. Aberrant ARNT expression can contribute to tumor growth via interactions with specifictranscription factors that regulate the expression ofmultiple genes [40]. In addition, likely pathogenic variants have been identified in MEN1, BAP1, BRAF,
BRCA1, BRCA2, CDKN2A, CSDE1, and FGFR3 in individual CPGLs [16]. The above findings indicatedhigh genetic inter-heterogeneity of these tumors.
Although recent studies identified several potential
mechanisms involved in paraganglioma pathogenesis,
further investigations are required to elucidate the molecular genetic basis of the paragangliomas with different
localizations and predispositions to be aggressive. Moreover, most reports focused on mutational status of thegenes associated with CPGLs. The functional effects ofgermline or somatic mutations on gene expression inparagangliomas remain poorly understood. Therefore,
the present study aimed to identify the potential causative genes associated with carotid paragangliomas. Using
exome data of CPGLs from previous study [16] andRNA-Seq data, we evaluated the effects of somatic variants on gene expression profiles using the xseq probabilistic model. This approach allowed the identification

Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48

of novel candidate genes and likely pathogenic variants
involved in the pathogenesis of CPGLs.

Page 7 of 56

abundances of transcripts. Gene expression levels
were normalized using the TPM (transcripts per
million) method.

Methods
Patients and samples

Whole-exome sequencing data

Carotid paraganglioma samples were collected after
obtaining written informed consent from the patients inthe Vishnevsky Institute of Surgery. Pathological evaluation was performed for all samples, and the minimum
percentage of tumor cells was 80%. The study was approved by the ethics committee from Vishnevsky Institute of Surgery and was conducted in accordance withthe principles outlined in the Declaration of Helsinki(1964). Clinicopathologic characteristics of the CPGL
patients are presented in Table 1.

We analyzed exome data of carotid paragangliomas generated from a previous study [16]. In that study, exome
libraries were prepared using a Nextera Rapid Capture
Exome Kit (Illumina). High-throughput sequencing of
75 bp paired-end reads was carried out on a NextSeq500
System (Illumina) at a median sequencing depth of
300×. The raw sequence reads are available at the SRA
under the accession number PRJNA411769.
The quality of raw sequencing data was examined with
FastQC. Trimmomatic was used to trim low-quality
reads and adapter sequences. Reads were then mapped
to the reference human genome (GRCh37/hg19) using
BWA-MEM [43]. SAMtools software [44, 45] was applied for removing of duplicate reads, sorting, merging,etc. Freebayes [46] was used for variant calling; VCF files
were processed with submodules from vcflib (https://github.com/vcflib/vcflib#vcflib). SnpSift from the SnpEff
toolbox [47] was used for annotation of genomic variants; the HGMD, OMIM, ClinVar, dbSNP, dbNSFP,COSMIC, ConsensusPathDB, 1000 Genomes Project,and ExAC databases were used as additional resources. Pathogenicity of all variants was assessed insilico using the SIFT [48], PolyPhen2 [49], MutationTaster [50], and LRT [51] algorithms. PhastCons [52]and PhyloP [53] methods were used to analyze theevolutionary conservation of regions harboring thetarget variants.
Using exome and transcriptome data from the same
carotid paragangliomas (25 samples), we evaluated theimpact of somatic loss-of-function variants on gene
expression profiles using xseq [54]. The xseq model
evaluates the posterior probability of the mutation influence in a certain gene on the expression profiles of anumber of genes, including itself (cis-effect) and associated genes (trans-effect). The output of xseq consists ofP(Fg,m) and P(Dg) values, where P(Fg,m) is the probability that an individual mutation in gene g in an individual patient m influences expression within thatpatient, and P(Dg) is the probability that a recurrently
mutated gene g influences gene expression across the
population of patients [54].

RNA extraction

Total RNA was extracted from 25 formalin-fixedparaffin-embedded (FFPE) tissues of carotid paragangliomas using a RNeasy FFPE Kit (Qiagen, Germany). RNA
concentration was measured on a Qubit 2.0 fluorometer(Thermo Fisher Scientific, USA), and RNA quality wasassessed using an Agilent 2100 Bioanalyzer (Agilent
Technologies, USA).
Library preparation and transcriptome sequencing

The cDNA libraries were prepared using a TruSeq
Stranded Total RNA Library Prep Kit with Ribo-ZeroGold (Illumina, USA) according to the manufacturer’s
instructions. Paired-end (2 × 100 bp) sequencing was
performed on an Illumina HiSeq 2500 Sequencing System (Illumina). RNA-Seq data have been deposited to
the NCBI Sequence Read Archive (SRA) under accession
number PRJNA476709.
Raw reads were subjected to quality control with
FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapter sequences and low-quality reads
were removed using Trimmomatic [41]. Kallisto [42]was used for pseudoalignment and quantifying
Table 1 Clinicopathologic characteristics of the CPGL patients
Characteristic

Total, nGenderMale

5

Female20

Age (years)
≤ 50

13

Validation of variants by Sanger sequencing

> 50

12

Potential pathogenic variants identified using xseq wereselected and validated by Sanger sequencing. Amplification was carried out in 20 μL final reaction volume using
a Tersus PCR Kit (Evrogen, Russia). Nested-PCR was
performed to amplify the two variants (c.2706-7855G >A and c.2706-7798delA) in the CSP1 gene. For the first

Family history of paragangliomas
Yes

1

No

1

N/A

23

Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48

Page 8 of 56

round of amplification, the outer primers (F: 5′-GGAG
GCTGAGGCGGGCAGATC-3′ and R1: 5′-CACC
ATTCTCCTGCCTCAGCCTCT-3′) producing a 181 bp
product were used. PCR products from the firststep were diluted at 1:1000 and subsequently amplified
using the inner primers (F: 5′-GGAGGCTGAGGCGGGCAGATC-3′ and R2: 5′- GTAGCTGGGACTAC
AGGCGCCT -3′) to produce a 154 bp fragment. PCR
and nested-PCR were performed using the following
amplification profile: initial denaturation step at 95 °C
for 3 min; 35 cycles of denaturation at 95 °C for 30 s, annealing at 60–65 °C for 30 s, and extension at 72 °C for
60 s; and a final extension of 72 °C for 5 min. All PCR
products were subjected to 2% agarose gel electrophoresis and visualized using a Gel Doc XR+ Gel
Documentation System (BioRad, USA) with GelRed
DNA stain (Biotium, USA). Primer sequences arelisted in Table 2.

Results
Using the xseq model, we estimated the probability that
potential loss-of-function somatic variants (frameshift,nonsense, and splice-site variants) impact gene expression in individual patients [P(F) value] and across the

population of patients [P(D) value]. The essential firststep was the identification of somatic variants. In the
study, we analyzed previously collected data from carotid
paraganglioma patients. Tumor samples were collected
over a span of more than 20 years in the Vishnevsky
Institute of Surgery; no blood samples were obtained
from the same patients to date. Thus, data from the1000 Genomes Project and ExAC databases were used
to exclude germline variants. Variants with more than
1% frequency in these databases were excluded fromthe analysis. We identified 16 genes (MYH15, CSP1,
MYH3, PTGES3L, CSGALNACT2, NMD3, IFI44,
GMCL1, LSP1, PPFIBP2, RBL2, MAGED1, CNIH3,
STRA6, SLC6A13, and ATM) whose somatic variants
can influence their gene expression. These genes had
maximum P(F) and P(D) values ranging from 0.51 to
0.99 and from 0.32 to 0.99 respectively. Based on thecriteria, MYH15, CSP1, and MYH3 were considered tohave significant cis-effect of somatic variants on gene
expression and were proposed as candidate genes associated with carotid paragangliomas.
To analyze all potential somatic loss-of-function variants in MYH15, CSP1, and MYH3, novel filter conditions
were set for variants derived from the 1000 Genomes

Table 2 List of primers used for validation of the variants
Gene
CPS1

Primer sequence (5′ → 3′)

Product size (bp)

F: GGAGGCTGAGGCGGGCAGATC
R1: CACCATTCTCCTGCCTCAGCCTCT
R2: GTAGCTGGGACTACAGGCGCCT

181
154

c.2706-7745C > G

F1: GAG ACC ATC CTG GCT AAC ACG GTG AAA C
R1: GTC TCG CTC TCT CTC CCA GGC TG

195

p.Gly1382Ser/c.4144G > A

F: CTAAAACTCACACTCACCATAGAAGG
R: AGATTCCATGCTGACAGAAAACAAC

276

p.Arg1748Gln/c.5243G > A

F: TCTCTTCTGCATTTTGACACTCCTG
R: TTTCTTTAGAACACAAGCCTCCTCAG

113

p.Glu1811Lys/c.5431G > A

F: ACTGTCAAGCCCTCATGATTACC
R: AAGAACTGAAGAAGAAGCAAGACAC

190

p.Ala1003Val/c.3008C > T

F: TTCTTCTTAGGTTAAAAACCTTACTGAGG
R: GTTTGCTCTTGGTTTTGTTCAAAG

167

p.Ala1198Thr/c.3592G > A

F: TGACCCGCTGCAGGTTG
R: CGTCACCTCCACGCAGATAG

176

p.Ala1604Thr/c.4810G > A

F: TCAGGTCCCCCTCCATCTTC
R: GAGCAGCTGAAGAGGAACTACC

118

p.Ala1752Thr/c.5254G > A

F: CTCATGCAGCTCCAGAGTGAGG
R: GGCTTTAATGCAATGCAATCCCG

141

p.Ile264Thr/c.791 T > C

F: CAAACACAGGACTCTCTTTCAAACTG
R: GCCATGGTAGAATCTGGTCTCC

182

p.Met798Val/c.2392A > G

F: GAGCTCATGTCTGAACAAAGACCC
R: CTTCTGCCCCATAAGGTGTTCTTC

184

p.Ser1240Leu/c.3719C > T

F: CAGGTTTCAATGACCACGGAGT
R: GGAGAAGGAGAAGAGCGAGTTC

142

p.Thr442Ile/c.1325C > T

F: GATCTGGAACACAAACACAGGACTC
R: GGGCCATGGTAGAATCTGGTC

195

Variantac.2706-7855G > A
c.2706-7798delAa

MYH15

MYH3

a

Amplification of fragments carrying two variants (c.2706-7855G > A and c.2706-7798delA) in the CSP1 gene was performed using nested-PCR

Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48

Page 9 of 56

Project and ExAC; in particular, variants with frequencies of 2% or less were used in the analysis. Predictions
using the SIFT, PolyPhen2, MutationTaster, or LRT algorithms indicated that the MYH3 gene was predominantly enriched in likely pathogenic variants (Table 3).
The PhastCons and PhyloP values for these variants
were also high, indicating strong evolutionary conservation. However, several variants were frequently identified in the carotid paraganglioma samples and appear
to be germline ones. These variants were excluded fromthe sample set, and the cis-effects were measured
again using xseq. In this case, only MYH15 was characterized by variants that can result in gene expression alterations [average P(F) value – 0.66; and P(D)value – 0.68].

All identified variants were validated by Sanger
sequencing.

Discussion
Using integrative analysis of whole exome and RNA-Seq
data from a representative sample set, we identified
novel potential causative genes in carotid paragangliomas. We employed methods based on the calculation of
the posterior probability of a mutation to influence gene
expression. From the set of 16 candidate genes, somatic
loss-of-function variants in three genes, namely CPS1,
MYH15, and MYH3, were found to significantly alter
their gene expression levels.
CPS1 encodes carbamoyl-phosphate synthase 1, a
mitochondrial enzyme that mediates the first step of theTable 3 Likely pathogenic variants in MYH15, CSP1, and MYH3
Gene

CPS1

Coordinate NucleotidechangeAmino acidchangeGenotype/ Predictions
number
SIFT
PolyPhen2
ofsamples

MutationTaster LRT

–

Chr2:
c.2706211494571 7855G > A

–

Het/2

N/A

N/A

N/A

N/A

–

–

Chr2:
c.2706211494681 7745C > G

–

Het/1

N/A

N/A

N/A

N/A

–

–

Chr2:
c.2706211494618 7798delA

–

Het/1

N/A

N/A

N/A

N/A

–

NM_001122633.2 Chr2:
c.4144G >211539650 A

p.Gly1382Ser

Het/2

Tolerated

BenignDiseasecausing

Deleterious

NM_014981.1

Chr3:
c.5243G >108112954 A

p.Arg1748Gln Het/4

Tolerated

BenignDiseasecausing

N/A

Chr3:
c.5431G >108110666 A

p.Glu1811Lys Het/1

Deleterious Probably
damaging

Diseasecausing

N/A

Het/2

Tolerated

BenignDiseasecausing

N/A

rs ID
number

GenBank–

MYH15 rs56118396–
MYH3

rs34088014

NM_002470.3

Chr17:
10542709

c.3008C > T p.Ala1003Val

rs61735358

Chr17:
10541497

c.3592G >A

p.Ala1198Thr Het/1

Tolerated

Possiblydamaging

Diseasecausing

N/A

rs201488879

Chr17:
10535939

c.4810G >A

p.Ala1604Thr Het/1

Deleterious Possibly
damaging

Diseasecausing

N/A

rs34393601

Chr17:
10534960

c.5254G >A

p.Ala1752Thr Het/3

Deleterious Possibly
damaging

Diseasecausing

N/A

rs763347751

Chr17:
10550688

c.791 T > C

p.Ile264Thr

Het/1

Deleterious Probably
damaging

Diseasecausing

N/A

rs746986821

Chr17:
10543684

c.2392A >G

p.Met798Val

Het/1

Deleterious Benign

Possibly
diseasecausing

N/A

rs551363957

Chr17:
10541370

c.3719C > T p.Ser1240Leu Het/1

Deleterious Benign

Diseasecausing

N/A

rs769788909

Chr17:
10547753

c.1325C > T p.Thr442Ile

Tolerated

Possibly
diseasecausing

N/A

Het/1

BenignSIFT scores range from 0 to 1, and variants with SIFT scores less than 0.05 are considered deleterious. SIFT scores ranging from 0.05 to 1 are predicted to be
tolerated variants
The PolyPhen-2 scores range from 0 to 1. Variants with scores ranging from 0 to 0.15 in this range are predicted to be benign. Variants with scores ranging from
0.15 to 0.85 are potential damaging, whereas values above 0.85 have higher probability of being damaging variants
MutationTaster p-values (probability) range from 0 to 1. P-values close to 1 indicate pathogenicity (disease-causing)
LRT is a likelihood ratio test. LRT scores range from 0 (neutral) to 1 (deleterious)
Het heterozygous genotype

Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48

urea cycle [55]. Germline variants in CPS1 cause rareautosomal
recessive
disorderscalledcarbamoyl-phosphate synthetase I deficiency (CPSID)[56]. Recent studies have indicated that CPS1 is primarily expressed in liver tissues and participates in the conversion of ammonia to urea in hepatocytes in humans.
However, several studies have demonstrated the involvement of CPS1 in carcinogenesis. Alterations in CPS1
were identified in malignant glioma [57]. CPS1 expression has been associated with the initiation and progression of colorectal cancer [58]. Hypermethylation of
CPS1 accompanied by its downregulated expression was
detected in hepatocellular carcinoma [59]. On the other
hand, CPS1 overexpression was observed in rectal, lung,and cervical cancers and was correlated with poor prognosis [60, 61]. In lung adenocarcinoma cells, inhibition of
CPS1 expression was found to reduce tumor growth [61,62]. Our current findings revealed that CPS1 is a potential
causative gene in carotid paragangliomas. The variants in
CPS1 can influence its expression level. Both genetic andepigenetic factors influence the expression of CPS1, consistent with previous studies and our current findings.
Similar to the development of inherited CPSID, variants
identified in CPS1 can be assumed to cause CPS1 inactivation, thereby leading to CPGLs. In turn, CPS1 variants
could lead to alterations in the urea cycle and accumulation of toxic ammonia, which is normally converted tourea. The brain is known to be highly sensitive to the effects of excess ammonia. Ammonia can be transported
through the cell membranes in many cell types, including
cells of the nervous tissue. In vitro studies have demonstrated that the presence of glutamate and ammonium isnecessary for lactate release in astrocytes and glial cells[63]. In turn, extracellular lactate is associated with malignant transformation and can regulate cancer-related signaling pathways (e.g., NF-kB/IL-8, HIF1, and PI3K/Akt/mTOR/Bcl-2), angiogenesis, and ATP production, which
in turn influence cell proliferation and migration [64–66].
Such molecular mechanism may be responsible for thedevelopment of CPGLs in some cases.
Variants in MYH3 and MYH15 were additionally demonstrated to exert cis-effects on gene expression in carotid
paragangliomas. MYH3 and MYH15 belong to the highly
conserved myosin heavy chain (MYH) family that encode
heavy chains of myosins [67]. Myosins are motor proteinsconsisting of heavy and light chains and are involved invarious types of cellular movements resulting from actinmyosin interactions. In muscle cells, myosin and actin filaments form myofibrils; in non-muscle cells, myosins were
demonstrated to participate in various biological processes, such as transport of cellular organelles, chromosomal segregation, actin organization, plasma membranetension, endocytosis, cell division, and cell motility [68, 69].
Expression of MYH genes varies in human tissues. MYH3

Page 10 of 56

is expressed during the early stages of development in skeletal muscles, and its expression decreases after birth,whereas MYH15 expression is exclusively detected in adultextraocular muscles [70, 71]. Data from the Human Proteome Atlas (HPA, https://www.proteinatlas.org/) indicatethat the peak expression of the MYH3 protein is observedin the heart and skeletal muscles, esophagus, prostate, andseminal vesicle. MYH15 is expressed in the brain, lungs,
small intestines, and testes. The Cancer Genome Atlas(TCGA, https://cancergenome.nih.gov/) project, whichcontains RNA-Seq data from various types of cancer, indicate that MYH3 and MYH15 are expressed in almost all tumors at the lowest levels. Slightly elevated mRNA level of
MYH3 gene was found in head and neck cancer with average fragments per kilobase million (FPKM) 3.3 in a set of
499 samples. However, MYH15 proteins are expressed inglioma, urothelial and colorectal cancers (HPA data). In
addition, proteome studies revealed deferential expressionof MYH15 in early onset dementia, an atypical frontotemporal lobar degeneration (aFTLD) [72].
Variants in these genes were observed in different
diseases. Variants in MYH3 were detected as benzo(a)pyrene exposure-genomic alterations in lung cancer
patients [73]. Germline variants in MYH3 are associatedwith distal arthrogryposis syndromes [distal arthrogryposis type 1, Freeman-Sheldon syndrome (DA2A), andSheldon-Hall syndrome (DA2B)], multiple pterygium
syndrome (MPS), and spondylocarpotarsal synostosis
syndrome (SCT) [74–77]. These disorders are characterized by skeletal anomalies and multiple congenital contractures in the limbs, thereby indicating an essential
role of MYH3 in bone development. Variants in MYH15
were reported to be associated with higher risk for
non-cardioembolic stroke and ischemic stroke [78, 79].
In the present study, we identified several likely pathogenic variants in MYH3 and MYH15 in carotid paragangliomas, and most of these variants were found to belocated in MYH3. Currently, the direct roles of MYH3
and MYH15 in normal cellular processes and the development of various pathologies, including cancer, remain
unclear. Moreover, only few studies in literature havereported the involvement of MYH3 and MYH15 intumorigenesis. Therefore, variants identified in MYH3
and MYH15 are likely to affect the function of MYH
proteins and are potentially associated with carotid paragangliomas. However, further studies are required toelucidate the mechanisms by which MYH3 and MYH15
can promote tumor pathogenesis.

Conclusions
In the present study, we identified several novel genes,
namely CPS1, MYH15, and MYH3, associated with carotid paragangliomas. These results contribute to a better

Snezhkina et al. BMC Medical Genetics 2019, 20(Suppl 1):48

understanding of the molecular mechanisms behind thepathogenesis of CPGLs.
Acknowledgments
The authors thank the Vishnevsky Institute of Surgery for providing thesamples and their morphological characterization and the National Medical
Research Radiological Center for the assistance in data analysis. This work
was performed using the equipment of the EIMB RAS “Genome” center(http://www.eimb.ru/rus/ckp/ccu_genome_c.php).
Funding
This work and publication costs were funded by the Russian Science
Foundation, grant 17-75-20105.
Availability of data and materials
All data generated or analyzed in this study are included in the publishedarticle. Whole-exome sequence data are available at the NCBI Sequence Read
Archive (SRA) under the accession number PRJNA411769. RNA-Seq data are
available at the NCBI SRA under accession number PRJNA476709.
About this supplement
This article has been published as part of BMC Medical Genetics Volume 20
Supplement 1, 2019: Selected articles from BGRS\SB-2018: medical genetics. The
full contents of the supplement are available online at https://
bmcmedgenet.biomedcentral.com/articles/supplements/volume-20supplement-1.
Authors’ contributions
AVK, AVS, and ENL conceived and designed the work; MSF, EAP, SLK, DVK,
ARZ, ALG, NVM, AVP, and AVS performed the experiments; ENL, GSK, MVK,
and AAD analyzed the data; AVS, AVK, BYA, ADK, and ENL wrote themanuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study was approved by The Ethics committee of Vishnevsky Institute ofSurgery, Ministry of Health of the Russian Federation. The study was
conducted in accordance with the principles outlined in the Declaration of
Helsinki (1964).

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Consent for publication
Not applicable.
17.
Competing interests
The authors declare that they have no competing interests.
18.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Engelhardt Institute of Molecular Biology, Russian Academy of Sciences,
Moscow, Russia. 2Vishnevsky Institute of Surgery, Ministry of Health of theRussian Federation, Moscow, Russia. 3National Medical Research RadiologicalCenter, Ministry of Health of the Russian Federation, Moscow, Russia.

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Published: 9 April 201922.
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