Abstract
Cisplatin is the first-line chemotherapeutic agent for the treatment of
oral squamous cell carcinoma (OSCC). However, the intrinsic or acquired
resistance against cisplatin remains a major obstacle to treatment
efficacy in OSCC. Recently, mitochondrial DNA (mtDNA) alterations have
been reported in a variety of cancers. However, the role of mtDNA
alterations in OSCC has not been comprehensively studied. In this
study, we evaluated the correlation between mtDNA alterations (mtDNA
content, point mutations, large-scale deletions, and methylation
status) and cisplatin sensitivity using two OSCC cell lines, namely SAS
and H103, and stem cell-like tumour spheres derived from SAS. By
microarray analysis, we found that the tumour spheres profited from
aberrant lipid and glucose metabolism and became resistant to
cisplatin. By qPCR analysis, we found that the cells with less mtDNA
were less responsive to cisplatin (H103 and the tumour spheres). Based
on the findings, we theorised that the metabolic changes in the tumour
spheres probably resulted in mtDNA depletion, as the cells suppressed
mitochondrial respiration and switched to an alternative mode of energy
production, i.e. glycolysis. Then, to ascertain the origin of the
variation in mtDNA content, we used MinION, a nanopore sequencer, to
sequence the mitochondrial genomes of H103, SAS, and the tumour
spheres. We found that the lower cisplatin sensitivity of H103 could
have been caused by a constellation of genetic and epigenetic changes
in its mitochondrial genome. Future work may look into how changes in
mtDNA translate into an impact on cell function and therefore cisplatin
response.
Subject terms: Predictive markers, Cancer genetics
Introduction
Cis-diamminedichloroplatinum (II), or cisplatin, is one of the most
commonly used chemotherapy agents in the treatment of various solid
tumours such as ovarian, colorectal, prostate, lung, and head and neck
tumours^[31]1–[32]5. To date, the intrinsic or acquired resistance of
cancer cells to cisplatin remains a challenge in the chemotherapy of
several cancers including oral squamous cell carcinoma
(OSCC)^[33]3,[34]6. OSCC, which affects the epithelial layer of the
oral cavity, is a common malignant tumour of the head and neck with low
survival rates and high risks of recurrence^[35]7.
The well-characterized mode of action of cisplatin is via causing the
formation of DNA adducts upon its binding to the nucleophilic N7 sites
of purines, which further leads to DNA damage responses and
apoptosis^[36]2,[37]6,[38]8. Cisplatin resistance in general involves
reduced DNA damage due to an increase in DNA adduct repair, reduced
drug uptake, or increased drug inactivation^[39]1,[40]3,[41]4,[42]6.
Activation of these mechanisms depends on multiple factors including
genetic changes, epigenetic alterations at both molecular and cellular
levels, and heterogeneity among cancer cells^[43]4,[44]9,[45]10.
The recently proposed cancer stem cells (CSCs) model highlighted tumour
heterogeneity as an important basis of treatment resistance and relapse
in cancer. According to the model, CSCs comprise a tumourigenic
subpopulation where they exhibit stem cell-like features including the
abilities to self-renew and differentiate into different cell lineages,
thus giving rise to tumour heterogeneity. Conventional chemotherapy
effectively removes rapidly proliferating cancer cells in a bulk tumour
but fails to eliminate CSCs, which are protected by mechanisms of
therapeutic resistance. Subsequently, the surviving CSCs initiate new
populations of cancer cells, which are more drug-resistant and
aggressive^[46]11–[47]13.
Furthermore, cisplatin primarily targets mitochondrial DNA (mtDNA) to
induce apoptosis; its binding to nuclear DNA is limited^[48]14,[49]15.
Interestingly, mtDNA alterations have been implicated in the
development of cancer and chemoresistance^[50]14,[51]16,[52]17.
However, the potential effect of the modifications in the mitochondrial
genome on OSCC treatment has not been comprehensively studied.
High-throughput sequencing has enabled comprehensive surveys of cancer
genomes and helped us to elucidate cisplatin resistance^[53]1,[54]18.
Currently available commercial second-generation sequencing platforms,
such as MiSeq and Ion Torrent, can produce a large volume of sequencing
data at a low cost. Nevertheless, the short read length and the use of
PCR amplification in preparing sequencing libraries are the major
limitations of these sequencers. This has prompted the invention of
third-generation sequencing platforms^[55]19. The latest nanopore
sequencer (MinION) devised by Oxford Nanopore Technologies circumvents
some of the limitations of the older technologies. The device houses a
dense array of buffer-submerged, nanosized pores through which single
DNA strands are allowed to pass. The passage of DNA strands produces
distinctive ionic signals that are converted into DNA sequences.
Because nanopore sequencing needs only minimal pre-sequencing
preparation, it can produce very long reads (sometimes > 50 kb),
minimise potential nucleotide errors introduced by PCR amplification,
and preserve epigenetic modifications such as DNA methylation^[56]20.
In this work, we derived tumour spheres from two OSCC cell lines with
differing cisplatin sensitivity. We first characterized the stem
cell-like features of the tumour spheres using flow cytometry, Western
blotting, and microarray analysis. Then, we used MinION to determine
the influence of a variety of mtDNA alterations on cisplatin
responsiveness in OSCC. We also measured the levels of intracellular
reactive oxygen species (ROS) to gauge the effect of cisplatin exposure
on mitochondrial function. Finally, we pondered the relation between
mitochondria and cisplatin response. Understanding the molecular
markers of cisplatin responsiveness in OSCC may help us to counter
cisplatin resistance in the clinical setting.
Results and discussion
Enhanced tumour sphere-forming capacity of OSCC SAS cells
The tumour spheres formation assay has been reported to successfully
enrich CSCs derived from cell lines of several solid tumours, namely
breast cancer, lung cancer, ovarian cancer, hepatocellular carcinoma,
osteosarcoma, fibrosarcoma, and OSCC^[57]21–[58]27. The assay allows
the enrichment of cells with stem cell traits without prior knowledge
of their surface markers, in contrast to the side population method,
which sorts and isolates CSCs based on specific and predefined surface
markers^[59]28. This could be an advantage since the biomarker profiles
of the CSCs of many cancers are still lacking. Furthermore, spherical
models provide a three-dimensional microenvironment for the cells to
grow, allowing them to mimic the in vivo behaviour of cancer cells more
closely than when they are cultured in monolayers (the conventional
two-dimensional model)^[60]29. We found that SAS formed tumour spheres
more efficiently than H103 (Fig. [61]1a). H103 formed fewer and smaller
spheres, possibly because they were less responsive to growth factors,
their parental cells were innately less active, or they had decreased
self-renewal capacity^[62]30. We could not obtain sufficient H103
tumour spheres for downstream analyses; therefore, they were excluded
from this study.
Figure 1.
[63]Figure 1
[64]Open in a new tab
Derivation of cancer stem cells (CSCs) from OSCC cell lines via a
sphere-forming assay and the characterization of their stem cell-like
features. (a) The morphology of the parental SAS and H103 and their
derived tumour spheres. SAS and H103 in normal culture media were
observed as polygonal squamous epithelial cells with the adherent
growth pattern. Within 7 d, tumour spheres, comprised of aggregated and
suspended cells derived from SAS and H103, were formed in the
specialized serum-free medium containing serum substitute, heparin, and
growth factors and in a low attachment plate (100× magnification). The
average diameters of the SAS and H103 tumour spheres were
133.4 ± 34.36 µm and 68.1 ± 13.37 µm, respectively. (b) Assessment of
cell viability of SAS, SAS tumour spheres, and H103 after 72 h exposure
to cisplatin. IC[50] was defined as the concentration of cisplatin
required to reduce cell viability by half. Higher IC[50] values
indicated lower sensitivity of the cells towards cisplatin and possibly
cisplatin resistance. (c) Western blots of Sox2, Oct4 and β-actin and
the relative expression levels of the Sox2 and Oct4 transcription
factors normalized to the β-actin protein in SAS and SAS tumour
spheres. The full-length blots are presented in Supplementary
Figure [65]S2. (d) Expression of CD338, CD117 and CD44 surface markers
in both SAS and SAS tumour spheres, as analyzed by flow cytometry.
Multi-staining flow cytometry was used to analyse the surface
expression of CD338 and CD117 for (I) SAS and (II) SAS tumour spheres.
Single-staining flow cytometry was used to analyse the surface
expression of CD44 for (III) SAS and (IV) SAS tumour spheres. All the
data are presented as mean ± SD. **P < 0.01, n = 3.
SAS tumour spheres exhibited OSCC stemness protein surface marker CD117
By flow cytometry, we investigated the surface expression of several
stemness-related markers that are known to be present on CSCs derived
from OSCC, namely CD117, CD338, and CD44. CD117 or c-Kit, a receptor
tyrosine kinase protein, is a marker for hematopoietic stem and
progenitor cells, ovarian cancer-initiating cells isolated from primary
human tumours, cardiac CD117+ stem cells, and CSCs derived from
OSCC^[66]31. CD338, also known as ABCG2, is a member of a family of
ATP-binding cassette drug transporter proteins that expel drugs from
cells. Overexpression of CD338 has been linked to chemoresistance of
CSCs in OSCC^[67]21,[68]32,[69]33. In cancers, CD44 acts as a cell
surface adhesion receptor and promotes the proliferation, survival, and
metastasis of tumour cells^[70]28,[71]34–[72]37. We found that the
expression of CD117 in SAS tumour spheres was significantly higher than
that in SAS (P = 0.008; Fig. [73]1d); but, CD338 was only weakly
expressed on the surfaces of both SAS and SAS tumour spheres (0.13% and
0.10% respectively), and the surface expression of CD44 did not differ
significantly (P = 0.065) between them (Fig. [74]1d). We suggest that
CD338 may not be a definitive marker for CSCs derived from OSCC. In
breast and prostate cancers, both CD338-positive and -negative cells
isolated by the side population technique were similarly tumourigenic,
and the CD338-negative population also contained primitive stem-like
cancer cells^[75]38. The link between CD44 and OSCC stemness is also
unclear because CD44 exists as several alternatively spliced isoforms
with varied relevance to cancer growth. It has been reported that a
transcript isoform of CD44, CD44v3, is a more specific CSC surface
marker for head and neck cancers, as the isoform is expressed
preferentially on cancer cells for tumourigenesis^[76]39,[77]40. The
importance of CD44 to CSCs may be masked when the different isoforms
are analysed indiscriminately. For instance, both CD44-positive and
-negative cell populations in head and neck squamous carcinoma were
reported in a study to possess CSC traits^[78]41.
SAS tumour spheres demonstrated enhanced cisplatin resistance
We assessed cisplatin sensitivity based on the number of cells that
survived after 72 h exposure to varied doses of cisplatin. From the
cell viability evaluation, we found SAS tumour spheres (IC[50] of
4.45 µM; P = 0.013) and H103 (IC[50] of 20.12 µM; P = 0.0001) to be
less sensitive towards cisplatin than SAS (IC[50] of 3.74 µM;
Fig. [79]1b). Accumulating evidence has shown that CSCs become
resistant to DNA damage-induced cell death by altering their
metabolism, heightening ROS-scavenging activity, and activating DNA
repair and anti-apoptotic pathways that include Wnt, Notch, and PI3K
signalling^[80]42–[81]46.
SAS tumour spheres with stem cell-like features showed increased expressions
of metabolism-associated and pluripotency genes
Through microarray analysis, we found that the SAS tumour spheres
expressed some genes differently than their parental cells. Of the
21488 genes interrogated, 32 were substantially up-regulated (fold
change > 10) and 20 were down-regulated (fold change < −10) in the SAS
tumour spheres (Fig. [82]2a). The list of differentially expressed
genes (DEGs) is provided in Supplementary Table [83]S1. Further pathway
enrichment analysis of the DEGs revealed that the metabolic phenotype
of the SAS tumour spheres was significantly altered (Table [84]1). We
speculated that the metabolic changes were attendant upon the formation
of the tumour spheres, which were rendered metabolically similar to
CSCs. We observed significant increases in the expression of SLC2A3
(P = 0.0012) and SLC2A14 (P = 0.0021), both of which encode glucose
transporters, in SAS tumour spheres compared to SAS. Overexpression of
the genes suggests an increase in the glucose uptake activity in the
tumour spheres and a shift to the glycolytic metabolism for energy
production^[85]47,[86]48. Moreover, the expression levels of some lipid
metabolism-related genes, namely SPTSSB, SCD, ABCG1, INSIG1, HMGCS1,
STARD4, TRIB3, LPIN1, MGLL, RORA, and MSMO1, were also significantly
higher in the SAS tumour spheres than the parental SAS cells
(Table [87]1 and Supplementary Table [88]S1; P < 0.02). CSCs depend on
aberrations in glucose and lipid metabolism for sustenance. They switch
to glycolysis to evade damage that could result from the high levels of
ROS inevitably produced during oxidative phosphorylation (OXPHOS). This
enables CSCs to self-renew infinitely. Furthermore, many studies have
shown that increased lipid synthesis helps to maintain CSCs. It is an
important source of metabolic intermediates and energy needed for cell
growth and stemness-related pathways^[89]47,[90]49–[91]53.
Figure 2.
[92]Figure 2
[93]Open in a new tab
The transcriptomic profiles of SAS cells and their derived tumour
spheres as analysed using the Affymetrix Clariom S arrays. (a) Heat map
generated from the microarray data reflecting log2 normalised gene
expression values using the Robust Multi-array Average method, where
the p-value adjusted for the false discovery rate was less than 0.05
and the positive or negative fold change exceeded 10. Blue represents
lower gene expression and red represents higher gene expression. n = 3.
(b) Microarray validation through qPCR for the top up- or
down-regulated genes in (I) SAS tumour spheres and (II) H103 relative
to SAS. Expression of stemness-associated genes, OCT4 and SOX2, were
also measured by qPCR in SAS and SAS tumour spheres. The amplification
levels of the genes were normalised against two reference genes, ACTB
and GAPDH. Data are presented as mean ± SD. *P < 0.05, **P < 0.01,
***P < 0.001, n = 3.
Table 1.
Lists of the top five up- or down-regulated genes in SAS tumour spheres
compared to SAS, and their associated functional pathways catalogued
from the Reactome database. Genes associated with the regulation of the
pluripotency of stem cells and whose expression was upregulated in SAS
tumour spheres are also included in the table.
Gene Encoded protein Fold change Associated functional pathways
Up-regulated gene
SPTSSB Serine palmitoyltransferase, small subunit B 83.61 Sphingolipids
de novo biosynthesis (metabolism of lipids; metabolism)
SLC2A3 (also known as GLUT3) Solute carrier family 2 (facilitated
glucose transporter), member 3 59.16 Cellular hexose transport
(SLC-mediated transmembrane transport; transport of small
molecules); Vitamin C (ascorbate) metabolism (metabolism of vitamins
and cofactor; metabolism); Neutrophil degranulation (innate immune
system; immune system); Transcriptional regulation by MECP2 (RNA
polymerase II transcription; gene expression (transcription))
ACSS2 Acyl-CoA synthetase short-chain family member 2 50.18
Transcriptional activation of mitochondrial biogenesis (mitochondrial
biogenesis; organelle biogenesis and maintenance); Ethanol oxidation
(biological oxidations; metabolism)
SCD Stearoyl-CoA desaturase (delta-9-desaturase) 38.94 Fatty acyl-CoA
biosynthesis (metabolism of lipids; metabolism); Activation of gene
expression by SREBF (SREBP) (metabolism of lipids; metabolism)
PRSS8 Protease, serine, 8 37.3 Formation of the cornified envelope
(keratinization; developmental biology)
KLF4 Kruppel-like factor 4 2.4 Transcriptional regulation of
pluripotent stem cells (developmental biology)
POU5F1 (OCT4) POU class 5 homeobox 1 1.45
SALL4 Spalt-like transcription factor 4 1.16
SOX2 SRY box 2 1.03
LIN28A Lin-28 homolog A 1.01
ZSCAN10 Zinc finger and SCAN domain containing 10 1.09
Down-regulated genes
CCL2 Chemokine (C-C motif) ligand 2 −151.6 Interleukin- 4, 10 and 13
signalling (cytokine signalling; immune system); ATF4 activates genes
in response to endoplasmic reticulum stress (unfolded protein response;
metabolism of proteins)
KLK10 Kallikrein related peptidase 10 −38.53 Collagen chain
trimerization (collagen formation; extracellular matrix
organization); Macrophage-stimulating protein-Recepteur d’origine
nantais (MSP-RON) kinase signaling (signalling by MST1; receptor
tyrosine kinases signalling; signal transduction)
ID1 Inhibitor of DNA binding 1, dominant negative helix-loop-helix
protein −36.73 Oncogene induced senescence (cellular responses to
external stimuli)
CYP24A1 Cytochrome P450, family 24, subfamily A, polypeptide 1 −31.78
Vitamin D (calciferol) metabolism (metabolism of lipids,
metabolism); Cytochrome P450 - arranged by substrate type (biological
oxidations; metabolism); Defective CYP24A1 causes hypercalcemia,
infantile (HCAI) (disease of metabolism; disease)
KITLG KIT ligand −17.11 Regulation of KIT signalling (SCF-KIT
signalling; receptor tyrosine kinases signalling; signal
transduction); RAF/MAP kinase cascade (FLT3 signalling; cytokine
signalling; immune system); Other interleukin signalling (cytokine
signalling; immune system); Constitutive signalling by aberrant PI3K in
cancer (PI3K/AKT signalling in cancer; diseases of signal
transduction); RAF/MAP kinase cascade (MAPK1/MAPK3 signalling; MAPK
family signalling cascades, signal transduction); PI5P, PP2A and IER3
regulate PI3K/AKT signalling (negative regulation of the PI3K/AKT
network; intracellular signalling by second messengers; signal
transduction)
[94]Open in a new tab
From the microarray data, we evaluated the expression levels of the
genes that regulate the pluripotency or stemness of cancer cells. We
found that the SAS tumour spheres expressed KLF4, OCT4, SALL4, SOX2,
LIN28A, and ZSCAN10 more strongly than SAS (Table [95]1). The increased
expression levels of two of the genes, namely OCT4 (P = 0.016) and SOX2
(P = 0.052), and the proteins they encode (Oct4, P = 0.004; Sox2,
P = 0.17) were confirmed by qPCR (Fig. [96]2b) and Western blotting
(Fig. [97]1c). Klf4, Oct4, Sox2, Sall4, Lin28A, and Zscan10 are
transcription factors that maintain the pluripotency and the
self-renewal capacity of embryonic stem cells^[98]54–[99]58 and CSCs of
breast, laryngeal, gastrointestinal, brain, liver, and oral
cancers^[100]21,[101]31,[102]42,[103]59–[104]64.
MtDNA content was correlated with cisplatin resistance in OSCC
MtDNA content is tightly regulated by the energy requirement of a cell
or tissue, which varies between cell and tissue types or developmental
stages. For instance, low mtDNA content is often observed in cancer
cells and pluripotent cells as they rely on glycolysis instead of
OXPHOS for energy production^[105]65. In addition, some cancer cells
transition into a pseudo-differentiated state that renders them unable
to replicate their mtDNA and establish mtDNA set points^[106]66.
Indeed, prior studies have shown that oxidative stress and several
pathological conditions, including cancer, alter mtDNA
content^[107]67,[108]68. However, the reported relation between mtDNA
content and cancer has been inconsistent. Increased mtDNA content has
been linked to advanced stages of oesophageal squamous cell carcinoma
and head and neck squamous cell carcinoma^[109]69,[110]70. Reduced
mtDNA content has been associated with invasive forms of lung cancer,
ovarian carcinoma, and breast cancer^[111]71–[112]73.
In this study, we estimated mtDNA content by qPCR. Overall, we found
that the cells with lower mtDNA content were less responsive to
cisplatin. As shown in Fig. [113]3, H103, which was more
cisplatin-resistant, had significantly lower mtDNA content than SAS
(P < 0.01). Our observation coincides with that of another study, which
showed that mtDNA content increased in the transition of head and neck
squamous cell carcinoma from low to high histopathological
grades^[114]69. Similarly, SAS tumour spheres had less mtDNA than SAS
and were more cisplatin-resistant, though the difference was not
significant (P > 0.10). Depleted mtDNA content was previously
demonstrated in CSCs and treatment-resistant cancer
cells^[115]74–[116]76.
Figure 3.
Figure 3
[117]Open in a new tab
qPCR estimation of mtDNA content. The amplification levels of two
mitochondrial genes, tRNA^Leu(UUR) and 16S rRNA, were normalised
against that of a nuclear gene, β2-microglobulin. Data are presented as
mean ± SD. **P < 0.01, n = 3.
MtDNA content was correlated with the extent of ROS production induced by
cisplatin
Other works on hepatoma and small cell lung cancer reported that low
mtDNA content reduced the sensitivity of cancer cells to ROS-induced
cytotoxicity by: 1) causing a compensatory increase in the expression
of antioxidant enzymes; 2) impairing mitochondrial respiration; and 3)
increasing mitochondrial membrane potential (mitochondrial outer
membrane permeability was decreased as a result)^[118]77,[119]78. In
general, apart from directly disrupting the structure of mtDNA,
cisplatin also induces the formation of ROS inside cells, causing
oxidative stress and further DNA damage. We found that the cells with
lower mtDNA content were less sensitive to ROS-induced cytotoxicity,
confirming prior findings that variation in mtDNA content marks the
progress of malignant cells in their transformation into
death-resistant tumours. Both the SAS tumour spheres and H103 had lower
mtDNA content than SAS and produced correspondingly less intracellular
ROS after cisplatin treatment (Fig. [120]4; P < 0.0001). Here, we
suggest that the relation between mtDNA content and the extent of ROS
production induced by cisplatin may indicate the ability of cancer
cells to profit from mitochondrial dysfunction and evade death. Further
investigations should look into how variation in mtDNA content
translates into an impact on cell function and cisplatin sensitivity.
Besides, the influence of mtDNA content variation on cisplatin
sensitivity may also depend on tissue and/or tumour types. A study of
laryngeal, nasopharyngeal, and lung cancers reported a contrasting
observation where increased mtDNA content is a self-protective tactic
used by tumour cells to evade apoptosis. A reduction in mtDNA content
was found to inhibit antioxidant gene expression, increase
intracellular ROS levels, and sensitise tumour cells to
chemotherapy^[121]79.
Figure 4.
Figure 4
[122]Open in a new tab
Measurement of the changes in intracellular ROS levels after treatment
with cisplatin for 72 h. The data are presented in means ± SD of ROS
levels relative to an untreated control group and normalised against
the percentage of viable cells. ****P < 0.0001, against an untreated
control group, n = 3.
Overview of the MinION sequencing data
Six MinION sequencing runs were performed using two MinION SpotOn Flow
Cells as described in Table [123]2. Each MinION sequencing run
generated single-directional (1D) sequencing reads in which a single
DNA strand was ‘read’ only once. The overall read and mapping
statistics for each run are provided in Table [124]3. We observed large
variation in the total sequencing output using the MinION flow cells,
which may be attributed to the quality and performance of the flow
cells. We also observed that the number of active pores declined
substantially after use, decreasing the sequencing output.
Nevertheless, we found that the quality of the raw data produced from a
new or used flow cell was equally satisfactory, as more than 50% of the
base-called reads had a minimum quality score of 5 (Data shown in
Supplementary Table [125]S2).
Table 2.
Six sequencing runs performed successively using two SpotOn Flow Cells.
SpotOn Flow Cell Sequencing Number Sample Sample processing
1 1 SAS Long PCR-amplification and purification
2 SAS Linearisation and purification
3 H103 Long PCR-amplification, purification, and limited barcoding PCR
2 4 SAS tumour spheres Long PCR-amplification, purification, and
limited barcoding PCR
5 SAS tumour spheres Linearisation and purification
6 H103 Linearisation and purification
[126]Open in a new tab
Table 3.
An overview of the MinION sequencing data.
SAS (PCR amplicon) SAS tumour spheres (PCR amplicon) H103 (PCR
amplicon) SAS (Native) SAS tumour spheres (Native) H103 (Native)
Read Statistics
Total reads 25561 110442 3564 7361 16560 5110
Proportion of passed reads (%) 99.7 (25476/ 25561) 99.8 (110229/
110442) 99.0 (3530/ 3564) 99.1 (7297/ 7361) 98.4 (16303/ 16560) 97.3
(4970/ 5110)
Total length (base) 79485220 237092104 11851813 28484148 58638164
17924412
Maximum length (base) 151048 180437 94896 122623 1414982 128173
Median read length 2043 1517 2214.5 2631 2146 2259
Mean read length 3120 2150.9 3357.45 3903.54 3596.77 3606.52
Mapping Statistics
Proportion of reads aligned to ChrM (%) 49.2 (12546/ 25476)
22.9 (25282/ 110229) 10.3 (365/ 3530) 4.3 (314/ 7297) 2.4 (395/ 16303)
1.4 (68/4970)
Total alignment length (base) 234267 114847 27277 50959 57073 34527
Pairwise identity (%) 68.1 72.1 72.4 61.7 55.6 58.7
GC content (%) 45.4 44.5 44.3 46.4 45.6 46.4
Mean read length 2955.2 1192 1815.9 4763.6 3592.4 3994.9
[127]Open in a new tab
The read statistics from the Albacore base-called reads were generated
using NanoStat. The mapping statistics were based on the MinION reads
aligned to the human mitochondrial genome (GRCh38) with a mapping
quality score of at least 20.
When comparing the protocols for library preparation, we noted that
native DNA libraries produced lower proportions of on-target reads than
PCR amplicons (SAS: 4.3% vs. 49.2%; SAS tumour spheres: 2.4% vs. 22.9%;
H103: 1.4% vs. 10.3%; Table [128]3). Nevertheless, using only the
sequence reads from the native DNA libraries, we could still assemble a
complete profile of the mitochondrial genome with adequate coverage
(only for SAS and SAS tumour spheres with average depths of coverage of
60.4 and 55.3, respectively). This suggests that the method we used for
mtDNA extraction^[129]80 was effective at enriching the mtDNA fraction.
Being able to directly sequence mtDNA is important, as it preserves the
methylation status of the samples.
No difference in the mitochondrial genomes of SAS tumour spheres and their
parental cells
We used MinION to sequence the mitochondrial genomes of our samples
(SAS, SAS tumour spheres, and H103) with different sensitivity towards
cisplatin. By cross-checking the variants identified by MinION with
Sanger sequencing, we found that 95.7% of the variants were correctly
called. The accuracy of the variant calling was similar to what was
reported in other studies (Data shown in Supplementary
Table [130]S3)^[131]81–[132]83.
We detected 50 mtDNA variants in total for SAS and SAS tumour spheres,
and 24% of the variants were observed in the displacement loop of the
mitochondrial genome (D-loop). The other variants were found across the
coding regions of the mitochondrial genome. Overall, we found that the
variants observed in SAS were also present in SAS tumour spheres,
though several variants significantly differed in their allele
fractions between the two samples (Table [133]4). However, we could not
determine the functional impact of these variants, as there was limited
data for computational approaches to train the prediction of the
functional impact of mtDNA variants. A recent concept of CSC plasticity
proposes that cancer cells can shift between non-CSC and CSC states,
depending on external signals or their interaction with the
neighbouring cells within a tumour niche. This suggests that DNA
mutation is not a requisite for tumour cells to acquire stem-like
traits^[134]84,[135]85. Our findings seem to support this theory. The
plasticity of SAS cells, rather than DNA mutation, drove their
transformation into tumour spheres with CSC features.
Table 4.
Lists of variants discovered in SAS, SAS tumour spheres, and H103.
Mitochondrial region Base position Reference base Base alteration
Variant allele fraction
SAS SAS tumour spheres H103
D-loop 73 A G 0.22 0.31 0.55^a,c
D-loop 150 C T 0.52
D-loop 260 G A 0.30^a 0.24
D-loop 263 A G 0.58 0.51^a 0.57^a
D-loop 282 T C 0.45
D-loop 309 C CCC 0.10^a 0.12^a
D-loop 315 C CC 0.40^a
D-loop 489 T C 0.41 0.36
MT-RNR1 709 G A 0.75 0.56^b
MT-RNR1 750 A G 0.64 0.52 0.79^a
MT-RNR1 1438 A G 0.51 0.39 0.53^a
MT-RNR2 1811 A G 0.44^a
MT-RNR2 2706 A G 0.64 0.51 0.73^a
MT-ND1 3738 C T 0.91^a
MT-ND1 4107 C T 0.23^a 0.16^a
MT-ND2 4505 C T 0.67 0.54
MT-ND2 4769 A G 0.37 0.38 0.50^a
MT-ND2 4833 A G 0.42 0.35
MT-ND2 5108 T C 0.59 0.31^b
MT-ND2 5240 A G 0.54^a
MT-TA 5601 C T 0.34 0.39
MT-CO1 6392 T C 0.17^a
MT-CO1 6455 C T 0.55^a
MT-CO1 6737 A G 0.62 0.54
MT-CO1 7028 C T 0.68 0.51 0.46^a
MT-CO1 7055 A G 0.46^a
MT-CO2 7600 G A 0.63 0.25^b
MT-ATP6 8701 A G 0.63 0.57
MT-ATP6 8860 A G 0.58 0.48 0.68^a
MT-ATP6 9165 T C 0.75 0.71
MT-CO3 9365 C T 0.33^a
MT-CO3 9377 A G 0.58 0.63
MT-CO3 9540 T C 0.69 0.48^b
MT-CO3 9575 G A 0.62 0.56
MT-CO3 9698 T C 0.67^a
MT-ND3 10398 A G 0.58 0.53
MT-ND3 10400 C T 0.63 0.62
MT-ND4L 10733 C T 0.60^a
MT-ND4 10873 T C 0.26^a 0.09^a
MT-ND4 11465 T C 0.55^a
MT-ND4 11467 A G 0.64^a
MT-ND4 11719 G A 0.73 0.72 0.67^a
MT-ND4 11809 T C 0.65 0.50
MT-TL2 12308 A G 0.50^a
MT-TL2 12311 T C 0.58 0.42
MT-ND5 12372 G A 0.30^a
MT-ND5 12705 C T 0.70 0.47^b
MT-ND5 13145 G A 0.38^a
MT-ND5 13247 T C 0.31^a
MT-ND5 13563 A G 0.64 0.47
MT-ND5 13677 C T 0.36 0.39
MT-ND6 14200 T C 0.54 0.56
MT-ND6 14281 C T 0.31 0.54^a,b
MT-ND6 14569 G A 0.59 0.59
MT-CYB 14766 C T 0.69 0.53 0.64^a
MT-CYB 14783 T C 0.59 0.42^b
MT-CYB 14798 T C 0.51 0.31^b
MT-CYB 15043 G A 0.67 0.57
MT-CYB 15301 G A 0.42 0.32
MT-CYB 15326 A G 0.76 0.57^b 0.70
MT-TT 15924 A G 0.60 0.37^b
D-loop 16146 A G 0.27^a
D-loop 16184 C A 0.21^a 0.26a
D-loop 16186 C T 0.13^a 0.26^a,b
D-loop 16189 T C 0.15^a 0.14^a
D-loop 16223 C T 0.67 0.60
D-loop 16260 C T 0.29^a
D-loop 16269 A G 0.64 0.37^b
D-loop 16278 C T 0.74 0.65
D-loop 16342 T C 0.37
D-loop 16362 T C 0.44 0.33
[136]Open in a new tab
The variant allele fraction was computed based on the fraction of the
base-called reads that supported the variant, generated by Nanopolish,
and the base statistics from Integrative Genomics Viewer version
2.3.97.
^aVariant allele fraction was calculated from the base statistics from
Integrative Genomics Viewer version 2.3.97, where the minimum allele
coverage was set to nine and the minimum number of variant reads was
set to three.
^bFisher’s exact test for the differences in the variant allele
fractions between SAS and SAS tumour spheres. P < 0.05.
^cFisher’s exact test for the differences in the variant allele
fractions between H103 and SAS. P < 0.05.
MtDNA D-loop alteration was associated with the mtDNA content and cisplatin
response
H103 harboured 32 variants, 24 of which were found in the coding region
of the mitochondrial genome, and 8 mutations were found in the D-loop
(Table [137]4). Comparing the mtDNA profiles of SAS and H103, we found
a D-loop mutation that was only present in SAS. The mutation involved
1 C or 2 C insertions (C7→C8/C9) in the D310 mononucleotide sequences
(position 303–309) of the D-loop. The D-loop is a non-coding region
that contains the leading strands for the origin of mtDNA replication,
and the promoters for the transcription of mitochondrial genes^[138]86.
Therefore, we deduced that the D310 mutation could alter mtDNA content
and thus cisplatin response. Lacking the D310 mutation, H103 was less
efficient at replicating its mitochondrial genome than SAS, hence
lowering its mtDNA content as shown by our qPCR analysis (Fig. [139]3).
The D310 mutation was previously correlated with an increase in the
mtDNA copy number in human laryngeal squamous cell carcinoma^[140]87.
Other works have also reported the importance of the D310 mutation in
breast, gallbladder, and colorectal tumourigenesis^[141]88–[142]90.
Common deletion of the mitochondrial genomes was not associated in cisplatin
responsiveness in OSCC
Aside from point mutations, mtDNA alterations may also involve mtDNA
deletions. To date, many mtDNA deletions have been catalogued in
MITOMAP (https://www.mitomap.org), a human mitochondrial genome
database, and associated with various pathological conditions. Among
the reported mtDNA deletions, a common mtDNA deletion (4977-bp deletion
between 8470–13459 bp) has been shown to promote the carcinogenesis of
hepatocellular carcinoma and colorectal cancer^[143]91,[144]92. The
mtDNA deletion causes the loss of several genes that encode the OXPHOS
proteins, namely ATP synthase F[o] subunits 6 and 8, cytochrome c
oxidase III, and NADH dehydrogenase subunits 3, 4, 4 L and 5. This will
in turn lead to dysfunction of the cellular energy metabolism^[145]93.
Here, we ascertained the presence of mtDNA deletions in our samples by
analysing the nanopore sequences, whose length allows the detection of
structural DNA variants. From the analysis, all the samples were found
to have intact mtDNA. To confirm this, we then used a PCR-based assay
to detect the 4977-bp mtDNA deletion in the SAS and H103 cells, after
they had been treated repeatedly with cisplatin. Consistently, the
cisplatin treatments were not found to induce mtDNA deletions
(Supplementary Figure [146]S1). Prior work has similarly shown that
mtDNA was resistant to chemically induced deletions, as damaged mtDNA
was presumably excluded during replication^[147]94. In addition, it has
been reported that mtDNA deletions were less common in cancerous
tissues than their benign counterparts in breast, gastric, and head and
neck cancers^[148]95–[149]97. Hence, we suggest that large-scale mtDNA
deletion is not required for oral cancer development and therefore is
not a crucial factor affecting the cisplatin response.
Enhanced cisplatin resistance in stem cell-like tumour spheres was not
influenced by methylation status of the mitochondrial genome
One of the most studied epigenetic modifications in cancer is DNA
methylation, which involves the addition of a methyl group to a
cytosine base (commonly at cytosine-guanine dinucleotide, CpG) and
promotes changes in gene expression without changing the DNA sequences.
Much of prior research has focused on the relation between epigenetic
changes in nuclear DNA and the development of cancer^[150]98.
Methylation of mtDNA was thought to be absent. However, this has been
disproved; methylation of the mitochondrial genomes actually exists,
albeit at a low level as compared to the nuclear
genomes^[151]99–[152]102, and it regulates mtDNA replication and
transcription^[153]103–[154]105. Thus, we hypothesised that the
epigenetic modifications of mtDNA may influence mtDNA replication,
altering mitochondrial function and the cellular response of OSCC to
cisplatin. In this study, we used MinION to evaluate the methylation
status of the CpG sites in the mitochondrial genomes of SAS, SAS tumour
spheres, and H103. The methylation-calling pipeline is described in
detail in the Supplementary Notes. In brief, the CpG methylation
frequency was computed by Nanopolish and the CpG sites that were
differentially methylated between the samples were obtained from MOABS
(Model-based Analysis of Bisulphite Sequencing Data). We found that no
CpG sites were differentially methylated between SAS and SAS tumour
spheres.
The relation between mtDNA alterations, gene expression profiles, and
cisplatin sensitivity
Overall, cisplatin sensitivity exists on a continuum, and a melange of
factors may contribute to its variation between tumours. H103 was
substantially more cisplatin-resistant than SAS and the tumour spheres.
By a series of analyses, we found that the difference probably arose
from a constellation of genetic or epigenetic changes in their nuclear
and mitochondrial genomes.
Cancer cells may alter mtDNA content to fit their energy
demand^[155]66,[156]106, shifting the expression of mitochondrial genes
and thus mitochondrial activities^[157]67,[158]107. We found that SAS
tumour spheres, with considerably less mtDNA, had a lower expression
level of MT-CO2 than SAS (P = 0.0323). Similarly, the expression levels
of most of the other mitochondrial genes were slightly lower in SAS
tumour spheres than the parent SAS cells (Supplementary Table [159]S4).
As described earlier, the microarray analysis revealed that the tumour
spheres underwent metabolic reprogramming and preferentially used
glycolytic metabolism as an energy source. In cancer, glycolysis is
augmented at the expense of mitochondrial activity, resulting in low
mtDNA content^[160]67,[161]107. Such precise control of mitochondrial
activity by the nucleus is well known and serves to maintain cellular
homeostasis^[162]100,[163]108. The replication and the transcription of
mtDNA are regulated by an assembly of nucleus-encoded proteins at the
mitochondrial D-loop, namely DNA polymerase γ, Twinkle DNA helicase,
mitochondrial single-stranded DNA binding protein (mtSSB),
mitochondrial RNA polymerase (POLRMT), mitochondrial transcription
factor A (TFAM), and mitochondrial transcription factors B1 (TFB1M) and
B2 (TFB2M)^[164]105,[165]109,[166]110. The link between metabolic
reprogramming of cancer cells and drug resistance has been described in
several prior studies. Increased glycolysis was observed in
drug-resistant lung cancer, multiple myeloma, and ovarian cancer.
Interestingly, blockade of glycolysis killed the drug-resistant cancer
cells^[167]111–[168]114. In this study, it is likely that the metabolic
reprogramming of the tumour spheres, in tandem with the resultant
effect on the mitochondrial genome, reduced their cisplatin
responsiveness.
The cisplatin resistance we observed for H103 was likely caused by a
similar relationship between reduced mitochondrial function and
aberrant cellular adaptation. We expected H103 to have uniformly lower
expression levels of its mitochondrial genes when compared with SAS.
However, the microarray analysis showed that only the expression of
MT-ND2 was significantly lower in H103 than SAS (Supplementary
Tables [169]S5; P = 0.0163). This suggests that the mitochondrial genes
contribute unequally to cisplatin resistance, or MT-ND2 is most
responsive to external signals. The first hypothesis seems most
plausible. MT-ND2 encodes NADH dehydrogenase 2, which is a subunit of
mitochondrial complex I and a major gatekeeper of ROS
production^[170]115. Hence, the lower expression level of MT-ND2 in the
H103 cells may explain their reduced capacity to produce ROS, rendering
them less sensitive to cisplatin.
Then, we tried to reconcile our findings with the large body of
evidence for the complex interplay between DNA methylation, mtDNA
replication and transcription, and cisplatin
response^[171]116–[172]118. Recent studies have shown that mtDNA
replication and transcription are regulated by methylation at the
mitochondrial D-loop^[173]105,[174]109. However, in this study, we
found no differentially methylated CpGs in the D-loops of SAS tumour
spheres and H103, when we compared them with SAS. This suggests that
the mitochondrial D-loop methylation was not responsible for the
variation of mtDNA content and mitochondrial gene expression, and other
regulatory mechanisms might be at play. Prior studies have shown that
mtDNA replication is epigenetically controlled by the nuclear genome.
In particular, global DNA methylation reduces mtDNA content by
suppressing the expression of the nucleus-encoded proteins that drive
mtDNA replication and transcription^[175]65,[176]116,[177]119.
Some studies have pointed to a potential role of gene body methylation
within mtDNA in regulating the expression of mitochondrial genes,
though this is still incompletely elucidated^[178]99,[179]116. In
general, the methylation at promoter sites silences gene expression,
but gene body methylation produces an opposite effect and activates
gene transcription^[180]98,[181]120–[182]122. A study on glioblastoma
found no direct correlation between gene body mtDNA methylation and
gene transcription and suggested that the mtDNA methylation may have an
indirect and widespread effect on post-transcriptional events. In the
study, the overall mtDNA gene expression was down-regulated although
only certain mtDNA gene regions became less methylated following
treatments with DNA demethylating agents^[183]116. In this study, we
found that three CpG sites in the mitochondrial COX1 and CYTB genes
(MT-CO1 and MT-CYB respectively) of H103 were hypermethylated when
compared with both SAS and SAS tumour spheres (Table [184]5). Through
the microarray analysis, we found that H103 had marginally higher
expression levels of most of the mitochondrial genes, including MT-CO1
and MT-CYB, than SAS, though the differences were not statistically
meaningful (Supplementary Table [185]S5). Hence, we suggest that the
mtDNA methylation in the gene bodies promoted the expression of the
genes, presumably by affecting the post-transcriptional modifications
of polycistronic mitochondrial mRNAs^[186]116,[187]123. However, the
transcription-enhancing effect of the hypermethylation could have been
offset by low mtDNA content, curbing any potential increases in gene
expression. In addition, we may suggest that the mtDNA methylation
differences between H103 and SAS could have arisen from their distinct
mitochondrial and nuclear genotypes. The findings of a prior study
support this possibility. Changes in mtDNA methylation patterns were
observed in the tumour models of glioblastoma and osteosarcoma when
their mtDNA or nuclear genotypes were varied^[188]105.
Table 5.
Differences in the methylation of the CpG sites in the mitochondrial
genomes of SAS and H103, as analysed by MOABS.
CpG position Gene region SAS H103 Credible methylation difference
(CDIF)
Total called sites Methylated frequency Total called sites Methylated
frequency
7160 COX1 16 0.125 8 0.75 0.264
7332 COX1 33 0.0909 9 0.667 0.246
15698 CYTB 30 0.233 6 0.833 0.204
[189]Open in a new tab
A CpG site was considered differentially methylated between two samples
when the credible methylation difference exceeded 0.2.
Conclusions
In this study, we derived CSCs from SAS OSCC cells using a
sphere-forming assay. A combination of flow cytometry, qPCR, Western
blot, and microarray analyses showed that the tumour spheres exhibited
marked stemness features, namely increased expression levels of
stemness genes and proteins, common CSCs surface markers, and genes
involved in glucose and lipid metabolism. We found that SAS tumour
spheres were more cisplatin-resistant than their parental cells and
that they had less mtDNA, which is the therapeutic target of cisplatin.
Consistently, we found that mtDNA content was also reduced in another
cell line that was similarly cisplatin-resistant to the tumour spheres,
namely H103.
Using a novel nanopore sequencer, MinION, we then sequenced their
mtDNA. We found that SAS and SAS tumour spheres harboured a D-loop
mutation that was absent in H103. The mutation could have altered mtDNA
content and therefore cisplatin response. We also found that all the
cells had intact mtDNA, suggesting that mtDNA deletion is not one of
the factors affecting cisplatin sensitivity. An analysis of mtDNA
methylation detected three hypermethylated CpG sites in the COX1 and
CYTB genes of H103. We inferred that the reduced cisplatin sensitivity
in H103 could have been caused by a variety of converging genetic
mechanisms, of which mtDNA alterations are key drivers (low mtDNA
content, point mutations and methylation changes), that disrupt
mitochondrial function, apoptosis, and cisplatin response. However, how
the differences in mtDNA variants between H103 and SAS could
have altered protein functions and cisplatin sensitivity has yet to be
confirmed. The recent approaches to precise genome editing, using
transcription activator-like effector nucleases (TALENs), zinc-finger
nucleases (ZFNs), or the CRISPR-Cas9 system, provide new opportunities
for understanding the effects of the changes in a cancer
genome^[190]124.
We did not find significant differences in the mtDNA profiles of SAS
and the tumour spheres that could have been culpable of the difference
in their responses to cisplatin. A possible explanation may lie in the
metabolic reprogramming exhibited by CSCs and the interaction between
the mitochondria and the nucleus in regulating mtDNA content.
Overall, we suggest that mtDNA alterations might serve as markers of
cisplatin responsiveness in OSCC. Future work may aim to investigate
the mechanisms that underpin variation in mtDNA and therefore cisplatin
response.
Methods
Cell lines
Human OSCC cell lines used in this study, namely SAS (poorly
differentiated; stage II; Japanese Cell Bank Research) and H103 (well
differentiated; stage I; European Collection of Authenticated Cell
Cultures), were generous gifts from Professor Leong Chee-Onn,
(International Medical University, Malaysia) and Professor Ian Charles
Paterson (University of Malaya, Malaysia). SAS was cultured in
Dulbecco’s Modified Eagle’s Medium/Ham’s Nutrient Mixture F12
(DMEM/Ham’s F12; Nacalai Tesque Inc., Japan), supplemented with 10%
fetal calf serum (FCS; GE Healthcare Life Sciences, USA) and 1%
penicillin/streptomycin (Nacalai Tesque Inc., Japan). H103 was cultured
in DMEM/Ham’s F12, supplemented with 10% FCS, 1%
penicillin/streptomycin, and 0.5 µg/ml sodium hydrocortisone succinate
(Sigma-Aldrich, USA). The cells were maintained at 37 °C in 5% CO[2]
humidified air.
Tumour sphere-forming assay
CSCs were derived from SAS and H103 using a sphere-forming assay. Cells
were cultured at a density of 2.5 × 10^4 cells/ml in a low-attachment
6-well plate (Corning Inc., USA) as tumour spheres in serum-free
DMEM/Ham’s F12, supplemented with 1× N-2 supplement (Thermo Fisher
Scientific, USA), 1% penicillin/streptomycin, 10 µg/ml heparin sodium
salt (Sigma-Aldrich, USA), 20 ng/ml human recombinant basic fibroblast
growth factor, and 20 ng/ml epidermal growth factor (Gold Biotechnology
Inc., USA) for 7 d. The medium was replenished every other day. The
microscopic images of the tumour spheres were taken, and the diameter
of each sphere was measured using ImageJ version 1.50i (National
Institutes of Health, USA).
Flow cytometry
The expression of common surface markers for CSCs, namely CD338, CD117,
and CD44, was measured via flow cytometry. For the multi-staining flow
cytometric assay, single-cell suspensions (1 × 10^6 cells/100 µl) were
incubated with 5 µl of monoclonal phycoerythrin (PE)-conjugated mouse
anti-human CD338 (Catalog No. 561180; BD Biosciences, USA) and
BB515-conjugated mouse anti-human CD117 (Catalog No. 559925; BD
Biosciences, USA) for 30 min on ice. A single-staining flow cytometric
assay was performed to analyse the surface expression of CD44, after
the cells were incubated with 5 µl of monoclonal PE-Cy7-conjugated
mouse anti-human CD44 (Catalog No. 560533; BD Biosciences, USA).
7-amino-actinomycin D (7-AAD; Catalog No. 561180; BD Biosciences, USA)
was added to exclude non-viable cells in both assessments. Flow
cytometric analyses were carried out using BD FACSCanto II Cell
Analyzer and BD FACSDiva Software version 6.1.3 (BD Biosciences, USA).
Western blotting
The expression levels of stemness proteins were measured by Western
blotting. Total proteins were extracted on ice using a RIPA lysis
buffer (EMD Millipore, USA), which contained a cocktail of protease
inhibitors (Thermo Fisher Scientific, USA), for 30 min. 30 µg of
proteins were separated electrophoretically in a 10% sodium dodecyl
sulphate–polyacrylamide gel at 150 V for 100 min and were transferred
to a nitrocellulose membrane (GE Healthcare Life Sciences, USA) at
100 V for 75 min. The membrane was blocked with 5% non-fat skim milk
(Nacalai Tesque Inc., Japan) and incubated overnight at room
temperature with primary antibodies against the mouse monoclonal Sox2
antibody (1:1000; Catalog No. GTX627404; GeneTex Inc., USA), the mouse
monoclonal Oct4 antibody (1:1000; Catalog No. GTX627419; GeneTex Inc.,
USA), and the mouse monoclonal beta-actin loading control antibody
(1:3000; Catalog No. MA5–15793; Thermo Fisher Scientific, USA). On the
next day, the membrane was further incubated with a corresponding
horseradish peroxidase-conjugated goat anti-mouse IgG polyclonal
secondary antibody (1:3000; Catalog No. GTX213111-01; GeneTex Inc.,
USA; and Catalog No. 31430; Thermo Fisher Scientific, USA) for 2 h at
room temperature. The protein bands were visualised via ChemiDoc
XRS + System (Bio-Rad Laboratories Inc., USA) after the addition of an
enhanced chemiluminescence substrate containing 0.1 M Tris pH 8.5
(First Base Laboratories Sdn Bhd, Malaysia), 1.25 mM luminol
(Sigma-Aldrich, USA), 1.15 mM coumeric acid (Sigma-Aldrich, USA), and
0.192% hydrogen peroxide (Bio Basic Inc., Canada). The data were
analysed using Image Lab Software version 5.2.1 (Bio-rad Laboratories
Inc., USA). The background-subtracted intensities of the bands were
used to quantify the expression levels of the target proteins.
Cisplatin sensitivity testing
Single-cell suspensions of SAS and H103 were plated in a 96-well
normal-attachment plate while SAS tumour spheres were plated in a
96-well low-attachment plate, at a density of 5×10^3 cells/well and
incubated overnight at 37 °C in 5% CO[2] humidified air. The cells were
treated with varied concentrations of cisplatin (5, 10, 20, 30, 60 and
100 µM; TCI America, USA) for 72 h. The viability of the cells
following the drug treatment was assessed using CellTiter 96 AQueous
Non-Radioactive Cell Proliferation Assay (MTS assay; Promega Inc.,
USA), according to the manufacturer’s instructions. The absorbance was
measured at a wavelength of 490 nm with Infinite 200 PRO microplate
reader (Tecan Group Ltd., Switzerland). The control group consisted of
untreated cells. The results were expressed as percentages of cell
viability compared to the control group. IC[50] was defined as the
concentration of cisplatin required to inhibit cell viability by half.
Microarray analysis
Total RNA was isolated using innuPREP RNA Mini Kit (Analytik Jena,
Germany). The contaminating genomic DNA in the RNA samples was removed
by DNase 1 treatment using RapidOut DNA Removal Kit (Thermo Scientific
Inc., USA). The concentrations of the RNA samples and their quality,
assessed based on the A[260]/A[280] ratio, were determined via an
OPTIZEN NanoQ Microvolume UV/Visible Spectrophotometer (Mecasys Co.
Ltd, Korea). The integrity of RNA was evaluated using Agilent 2100
Bioanalyzer (Agilent Technologies Inc., USA). An A[260]/A[280] ratio of
1.8–2.1 and an RNA integrity number (RIN) greater than 7.0 indicated
RNA of acceptable quality for microarray analysis. The purified RNA
samples were then submitted to Research Instruments Sdn. Bhd. Malaysia
for microarray analysis. In brief, 100 ng of purified RNA from each
sample was used to generate amplified and biotinylated sense strand
cDNA using GeneChip WT PLUS Reagent Kit (Thermo Fisher Scientific Inc.,
USA). Hybridization-ready targets also were prepared using the same kit
prior to insertion into Clariom S arrays (Affymetric Inc., USA), which
contained over 211300 probes for more than 337100 transcripts of >20000
well-annotated human genes. Hybridization, washing, staining and
scanning were performed as described by the manufacturer’s protocol
using GeneChip Hybridization Oven 645, GeneChip Fluidic Station 450,
and GeneChip Scanner 3000 7 G (Thermo Fisher Scientific Inc., USA).
Quality control checks and normalization of the raw gene expression
data with the Robust Multi-array Average (RMA) algorithm were performed
by using a set of R and Bioconductor modules provided in Transcriptome
Analysis Console 4.0 software (Affymetric Inc., USA). Differences in
gene expression between paired samples were determined by one-way
analysis of variance (ANOVA). A gene was considered to be
differentially expressed between two samples when the positive or
negative fold change exceeded 2 and the p-value adjusted for the false
discovery rate was less than 0.05. The lists of differentially
expressed genes with a higher fold change cut-off < −10 or >10 were
selected for pathway enrichment analysis using Reactome^[191]125.
Real-time quantitative polymerase chain reaction (qPCR)
qPCR was performed to validate the microarray data and evaluate the
expression of stemness genes in the derived tumour spheres. The same
RNA samples used in the microarray assay were converted into cDNA using
SensiFAST cDNA Synthesis Kit (Bioline, Australia). qPCR was performed
on a CFX Connect Real-Time PCR Detection System (Bio-rad Laboratories
Inc., USA) using SensiFAST SYBR No-ROX Kit (Bioline, Australia) with a
three-step thermal cycling protocol, which consisted of an initial
denaturation step of 95 °C for 2 min, followed by 40 cycles of 95 °C
for 5 sec, 59 °C for 10 sec, and 72 °C for 20 sec. Post-amplification
melting curves were analysed to evaluate the reaction specificity and
the presence of primer-dimers. The gene expression levels of six genes,
namely serine palmitoyltransferase small subunit B (SPTSSB), C-C motif
chemokine ligand 2 (CCL2), microsomal glutathione S-transferase 1
(MGST1), Dickkopf WNT signaling pathway inhibitor 1 (DKK1),
sex-determining region Y-box 2 (SOX2), and octamer-binding
transcription factor 4 (OCT4), were normalised to those of two
reference genes, namely glyceraldehyde 3-phosphate dehydrogenase
(GAPDH) and β-actin (ACTB). No-template (NTC) and no-reverse
transcriptase (NRT) controls were included in every qPCR run. The
sequences of the primers (Integrated DNA Technologies Inc., USA) are
provided in Supplementary Table [192]S6.
MtDNA gene-specific qPCR
qPCR was performed to determine intersample differences in mtDNA
content according to a published protocol^[193]126. Briefly, total DNA
from confluent cultured cells was extracted using DNeasy Blood & Tissue
kit (QIAGEN, Germany) according to the manufacturer’s instructions. The
DNA concentrations and their purity based on the ratio of absorbance at
260 nm and 280 nm were determined via the OPTIZEN NanoQ Microvolume
UV/Visible Spectrophotometer (Mecasys Co. Ltd, Korea). The
A[260]/A[280]ratios varied from 1.8 to 2.1, indicating that the samples
were pure. qPCR was performed to amplify two mitochondrial genes,
tRNA^Leu(UUR) and 16 S rRNA, and a nuclear gene, β2-microglobulin
(β2M). The cycling protocol consisted of an initial denaturation step
of 95 °C for 3 min, followed by 35 cycles of 95 °C for 5 sec, 62 °C for
10 sec, and 72 °C for 20 sec and concluded with melting curve analysis.
The qPCR was performed on the CFX Connect Real-Time PCR Detection
System (Bio-rad Laboratories Inc., USA) using SensiFAST SYBR No-ROX Kit
(Bioline, Australia). The mtDNA content was calculated using the Eq.
([194]1), where ∆Cq is the difference in Cq values between mtDNA
(tRNA^Leu(UUR) or 16 S rRNA) and β2M genes. The sequences of the
primers (Integrated DNA Technologies Inc., USA) are provided in
Supplementary Table [195]S7.
[MATH: MtDNAcontent=2×2−ΔCq :MATH]
1
MtDNA sequencing
MtDNA was extracted from 10–15 millions of cells using QIAprep Miniprep
Kit (QIAGEN, Germany) according to a published protocol^[196]80.
Further DNA purification was performed using a solid-phase reversible
immobilization paramagnetic bead technique with Agencourt AMPure XP
(Beckman Coulter Inc., USA). The DNA concentrations and their purity
based on the ratio of absorbance at 260 nm and 280 nm were determined
via OPTIZEN NanoQ Microvolume UV/Visible Spectrophotometer (Mecasys Co.
Ltd, Korea). The A[260]/A[280] ratios varied from 1.8 to 2.1,
indicating that the samples were pure.
Six MinION sequencing runs were performed using two MinION SpotOn Flow
Cells version R9.5 (FLO-MIN107; Oxford Nanopore Technologies, UK). Both
PCR amplicons and native DNA of each sample were used as the sequencing
input. The details of the sequencing runs are described in
Table [197]2. Two ~8-kb products, which span the entire mitochondrial
genome (~16 kb), were amplified with the following cycling protocol: an
initial denaturation step of 94 °C for 2 min, 35 cycles of 94 °C for
12 sec, 62 °C for 30 sec, and 68 °C for 9 min, and final extension for
7 min at 68 °C. The long PCR was performed using an AtMax Taq DNA
Polymerase (Vivantis Technologies Sdn. Bhd., Malaysia) on an Arktik
Thermal Cycler (Thermo Scientific Inc., USA). For barcoded PCR samples,
the DNA samples were amplified using a previously described protocol
using the same two sets of primers with universal sequences. All PCR
primers were synthesised by Integrated DNA Technologies Inc., USA and
are listed in Supplementary Table [198]S8. The native DNA samples were
digested by BamHI (Vivantis Technologies Sdn. Bhd., Malaysia) to
linearise the circular mitochondrial genome.
The DNA libraries were prepared using the Ligation Sequencing Kit 1D
(SQK-LSK108; Oxford Nanopore Technologies, UK) according to the
manufacturer’s instructions. For barcoded PCR samples, the sequencing
libraries were prepared using the Ligation Sequencing Kit 1D and the
PCR Barcoding Kit (EXP-PBC001; Oxford Nanopore Technologies, UK).
Briefly, the end-repair, dA-tailing, sequencing adapter ligation, and
final purification of DNA libraries were performed according to the
manufacturer’s instructions. The MinION SpotON Flow Cell was primed and
the DNA library was loaded according to the manufacturer’s
instructions. The duration of all sequencing runs was set to 48 h.
MinION sequencing output processing
HDF5 raw data were acquired by MinKNOW version 1.7 (Oxford Nanopore
Technologies, UK), and local base-calling with demultiplexing setting
was performed using Albacore version 2.3.4 (Oxford Nanopore
Technologies, UK). The quality of the sequencing raw data was assessed
using NanoStat^[199]127. The data was indexed using Nanopolish^[200]128
to link each sequencing read with its signal-level data in the HDF5
files. The base-called and indexed sequencing reads were aligned to the
human reference genome assembly GRCh38 using BWA-MEM^[201]129 with the
ont2d mode. SAMtools^[202]130 was used to sort and index the aligned
sequencing reads prior to variant- and methylation-calling via
Nanopolish. The pipelines for the variant- and methylation-calling are
described in more detail in the Supplementary Notes. The functional
effect of the variants were predicted using open-source algorithms
including PolyPhen-2^[203]131, PANTHER^[204]132, Envision^[205]133,
MutationAssessor^[206]134, MutPred2^[207]135 and SNPs&GO^[208]136. The
mtDNA deletion was also determined via MitoDel^[209]137 and
eKLIPse^[210]138. MitoDel is a tool for detecting and quantifying mtDNA
deletions even at low heteroplasmy levels via the BLAT split read
mapping method. The eKLIPse pipeline uses soft clipping alignment
analysis of sequencing reads to predict mtDNA deletions.
Sanger sequencing
The mutations identified by MinION were cross-checked with Sanger
sequencing. 24 pairs of primers (Supplementary Table [211]S9) were used
to amplify overlapping PCR products that spanned the whole
mitochondrial genome, using MyTaq Mix (Bioline, Australia). The
products varied in size from 767 to 1079 bases. Touchdown PCR was
performed on the CFX Connect Real-Time PCR Detection System (Bio-rad
Laboratories Inc., USA) to obtain PCR products with high specificity. A
two-phase cycling protocol was used. The first PCR phase consisted of
an initial denaturation step of 95 °C for 3 min, followed by 10 cycles
of gradually decreasing the annealing temperature from 68 °C to a
target temperature of 58 °C; the second phase continued with 25 cycles
of 95 °C for 5 sec, 58 °C for 10 sec, and 72 °C for 1 min. The PCR
products were then Sanger-sequenced using the BigDye Terminator v3.1
Cycle Sequencing Kit chemistry (Thermo Scientific Inc., USA) on an
Applied Biosystems Genetic Analyzer (Thermo Scientific Inc., USA;
outsourced to First BASE Laboratories Sdn. Bhd. Malaysia). The
resultant mtDNA sequences were visualized and analysed using Geneious
version 10.2.3 (Biomatters Ltd., Auckland, New Zealand).
Nested-PCR
A common 4977-bp mtDNA deletion was detected using a previously
reported nested PCR assay^[212]91,[213]92. We chose nested PCR because
it can detect low-abundance mtDNA deletion by two successive rounds of
amplification. The reaction mix for the first round of PCR consisted of
5 ng/µl of total DNA, 0.2 µM each of the forward and reverse PCR
primers, and 1× MyTaq Mix (Bioline, Australia). The cycling protocol
consisted of initial denaturation at 95 °C for 1 min, followed by 15
cycles of 95 °C for 15 sec, 62 °C for 15 sec, and 72 °C for 10 sec. A
second round of PCR was performed using the first PCR product (diluted
1:100) and a different set of primers, amplifying a smaller fragment in
25 cycles of PCR. The PCR protocol was otherwise identical to what we
used for the first round of PCR. A synthetic 595-bp DNA fragment was
used as a positive control. The DNA fragment comprised the sequences
that flank the 4977-bp deletion, which is known to occur between two
13-bp repeats in the mitochondrial genome, namely 8470–8482 bp and
13447–13459 bp. The sequences of the primers and the DNA fragment
(Integrated DNA Technologies Inc., USA) are provided in Supplementary
Table [214]S10. The presence of the mtDNA deletion would be indicated
by the amplification of a 358-bp product. When the mtDNA deletion was
absent, no products would be obtained, as the short extension time
would not allow the PCR to amplify the large interposing region between
the 13-bp repeats (>5 kb).
Intracellular reactive oxygen species assay
The generation of intracellular reactive oxygen species (ROS) in cells
treated with cisplatin was measured via fluorescence microplate-based
analysis following staining with 2′, 7′-dichlorodihydrofluorescein
diacetate (H[2]DCFDA; Sigma Aldrich Inc., USA). Briefly, cells were
seeded in 12-well plates, at a density of 5 × 10^3 cells/well, and
incubated overnight at 37 °C in 5% CO[2] humidified air. Cells were
then treated with their respective IC[50] doses of cisplatin for 72 h
and 100 µM of H[2]O[2] for 1 h. The cells were harvested by
trypsinization and washed using serum-free media. In parallel, the
cells were subjected to incubation with 10 µM H[2]DCFDA for 30 min at
37 °C for ROS staining and cell viability determination. After
H[2]DCFDA staining, the excess dye was removed via centrifugation. The
cells were then transferred into a black 96-well plate, and the
fluorescence intensity (excitation: 504 nm; emission: 529 nm) was
measured using Varioskan Flash (Thermo Scientific Inc., USA). Changes
in intracellular ROS production relative to a control were obtained
after the fluorescence intensity (F) was normalized to the absorbance
(Abs) values from the cell viability assay using the following Eq.
([215]2);
[MATH:
Relativ
eFlu
orescenc
eInt
ensity=(FTreatment−<
mrow>FBla
nk)/(AbsTreatment−AbsBlank)(FCon<
mi>trol−<
msub>FBlank)/(Ab<
mrow>sCon
trol−
AbsBlank) :MATH]
2
where treatment, control, and blank represent cisplatin- or
H[2]O[2]-treated cells, untreated cells, and solvent alone,
respectively.
Statistical analysis
All data are presented as means and standard deviations of three
independent experiments and were statistically analysed using the
Student’s t-test (Western blotting, flow cytometry, IC[50] value, and
qPCR), Fisher’s exact test (variant allele fraction), and one-way ANOVA
(mtDNA gene-specific qPCR and intracellular ROS assay). All the
statistical analyses were performed using GraphPad Prism version 7
(GraphPad Software, Inc., USA). Differences between groups were
considered statistically significant when P < 0.05.
Supplementary information
[216]Supplementary Information.^ (430.6KB, pdf)
[217]Dataset S1.^ (12.6KB, xlsx)
[218]Dataset S2.^ (11KB, xlsx)
[219]Dataset S3.^ (15KB, xlsx)
[220]Dataset S4.^ (18.7KB, xlsx)
[221]Dataset S5.^ (12.6KB, xlsx)
[222]Dataset S6.^ (13.6KB, xlsx)
[223]Dataset S7.^ (18.7KB, xlsx)
[224]Dataset S8.^ (707.9KB, zip)
Acknowledgements