https://orcid.org/0000-0003-4536-4492
Figures
Abstract
Cholangiocarcinoma (CCA) is a diverse collection of malignant tumors that originate in the bile ducts. Mitochondria, the energy converters in eukaryotic cells, contain circular mitochondrial DNA (mtDNA) which has a greater mutation rate than nuclear DNA. Heteroplasmic variations in mtDNA may suggest an increased risk of cancer-related mortality, serving as a potential prognostic marker. In this study, we investigated the mtDNA variations of five CCA cell lines, including KKU-023, KKU-055, KKU-100, KKU213A, and KKU-452 and compared them to the non-tumor cholangiocyte MMNK-1 cell line. We used Oxford Nanopore Technologies (ONT), a long-read sequencing technology capable of synthesizing the whole mitochondrial genome, which facilitates enhanced identification of complicated rearrangements in mitogenomics. The analysis revealed a high frequency of SNVs and INDELs, particularly in the D-loop, MT-RNR2, MT-CO1, MT-ND4, and MT-ND5 genes. Significant mutations were detected in all CCA cell lines, with particularly notable non-synonymous SNVs such as m.8462T > C in KKU-023, m.9493G > A in KKU-055, m.9172C > A in KKU-100, m.15024G > C in KKU-213A, m.12994G > A in KKU-452, and m.13406G > A in MMNK-1, which demonstrated high pathogenicity scores. The presence of these mutations suggests the potential for mitochondrial dysfunction and CCA progression. Analysis of mtDNA structural variants (SV) revealed significant variability among the cell lines. We identified 208 SVs in KKU-023, 185 SVs in KKU-055, 231 SVs in KKU-100, 69 SVs in KKU-213A, 172 SVs in KKU-452, and 217 SVs in MMNK-1. These SVs included deletions, duplications, and inversions, with the highest variability observed in KKU-100 and the lowest in KKU-213A. Our results underscore the diverse mtDNA mutation landscape in CCA cell lines, highlighting the potential impact of these mutations on mitochondrial function and CCA cell line progression. Future research is required to investigate the functional impacts of these variants, their interactions with nuclear DNA in CCA, and their potential as targets for therapeutic intervention.
Citation: Faipan A, Sitthirak S, Wangwiwatsin A, Namwat N, Klanrit P, Titapun A, et al. (2025) Mitochondrial genomic alterations in cholangiocarcinoma cell lines. PLoS One 20(6): e0323844. https://doi.org/10.1371/journal.pone.0323844
Editor: Matias A. Avila, University of Navarra School of Medicine and Center for Applied Medical Research (CIMA), SPAIN
Received: September 23, 2024; Accepted: April 16, 2025; Published: June 9, 2025
Copyright: © 2025 Faipan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The sequencing data can be accessed at NCBI under the project identifier PRJEB78381.
Funding: This work was supported by the NSRF under the Basic Research Fund of Khon Kaen University and Srinagarind Diamond Research Fund (DR63201) to WL. The financial assistance from the Invitation Research Grant (IN67051) and a Postgraduate Study Support Grant of Faculty of Medicine, Khon Kaen University to AF.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cholangiocarcinoma (CCA) is a malignant tumor originating in the biliary tree and can be categorized into three sub-types based on anatomical location: intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA). Perihilar CCA is the most prevalent form of this cancer, comprising 50–60% of CCA cases, followed by distal CCA (20–30%) and intrahepatic CCA (10–20%) [1]. These tumors are the second most common primary liver malignancy, accounting for roughly 15% of all primary liver tumors and 3% of gastrointestinal malignancies worldwide [2]. Notably, northeastern Thailand has the highest global incidence rate of CCA, with 85 cases per 100,000 persons annually [3].
CCA is a complicated and highly heterogeneous cancer distinguished by tumor site, etiology, genetic characteristics, and varied prognostic outcomes. These variables confound much of the analysis of CCA genetics. Cancer cell lines are critical for studying many aspects of tumor biology and therapeutic strategies [4]. Therefore, we leveraged a bioresource of CCA cell lines, including KKU-213A, KKU-100, KKU-055, KKU-452, and KKU-023, which demonstrate differential differentiation abilities to investigate the mtDNA profile of in CCA and provide insight for further prognosis biomarker development in CCA patients.
Mitochondria serve as the energy converters in eukaryotic cells, synthesizing adenosine triphosphate (ATP) through the mitochondrial electron transport chain (ETC) within the oxidative phosphorylation (OXPHOS) system. Mitochondria possess circular double-stranded DNA genomes, termed mitochondrial DNA (mtDNA), comprising 16,569 base pairs. The mtDNA includes a G-enriched inner light strand (L-strand) and a C-enriched heavy strand (H-strand). MtDNA encodes 2 rRNAs and 22 tRNAs for protein synthesis, as well as 13 peptides for ETC and OXPHOS [5,6]. MtDNA has a greater mutation rate than nuclear DNA, estimated to be 10–17 times higher than the nuclear genome due to an absence of complex DNA repair mechanisms [6]. Mitochondria also demonstrate high levels of heteroplasmy, defined as the coexistence of several mtDNA variants within a single cell or organism and which has significant implications for mitochondrial function and disease etiology [7]. A recent study revealed that heteroplasmic variations in mtDNA, particularly single nucleotide variants (SNVs), are associated with an elevated risk of cancer-related mortality in leukemia, highlighting heteroplasmic variation as a potential prognostic indicator for cancer [8].
Oxford Nanopore Technology (ONT) provides long-read sequencing capable of generating sequences thousands of base pairs long. This enables the sequencing of the entire mitochondrial genome (~16.6kb) in a single read. Furthermore, ONT allows for a more detailed investigation of heteroplasmic deletions inside a single read as well as the identification of complicated, large rearrangements, e.g., duplications, which are difficult to identify using short-read sequencing [9]. Lastly, the study demonstrated the ability of long-read ONT sequencing to detect large-scale deletions and rearrangements in mtDNA, which is important in understanding and diagnosing primary mitochondrial disorders. Hence, nanopore sequencing represents a promising approach for sequencing the whole mitochondrial genome [10] and determining the presence of tumour-associated SNVs.
In this investigation, we conducted comprehensive sequencing of the entire mtDNA using ONT across five distinct CCA cell lines, alongside one non-tumor cholangiocyte cell line, MMNK-1. We then investigated the mtDNA profiles of each cell line to define whether mtDNA contained candidate pathogenic variants within CCA.
Materials and methods
Cell lines and cell cultivation
Five Opisthorchis viverrini (Ov)-associated CCA cell lines including KKU-213A, KKU-100, KKU-055, KKU-452, and KKU-023, as well as non-tumor cholangiocyte MMNK-1, as listed in the Table 8, were acquired from the Cholangiocarcinoma Research Institute in Khon Kaen, previously documented in the literature [4,11,12]. The cell line collection and study were approved by the Ethic Committee for Human Research, Khon Kaen University (HE671263). Among these, KKU-213A, KKU-100, KKU-055, and KKU-023 were derived from patients with intrahepatic CCA, whereas KKU-452 originated from patients with perihilar CCA. These cell lines were cultivated as a monolayer culture in Dulbecco’s Modified Eagle Medium (DMEM) (Life Technologies, Inc.), supplemented with 10% heat-inactivated fetal bovine serum (FBS), 100 U/ml penicillin and 100 mg/ml streptomycin (Life Technologies, Inc.), maintained at 37°C with 5% CO2. In this study, each of the six cell lines had three replicates, a total of 18 samples in each following experiment.
DNA extraction and mtDNA enrichment
For each of the six cell lines, at least 106 cells were collected for DNA extraction using the QIAamp® DNA Mini Kit (QIAGEN, Germany) according to the manufacturer’s instructions. The DNA samples were pre-treated with Exonuclease V (New England BioLabs, Inc.) to digest any linear nDNA, enrich circular mtDNA, and reduce the risk of nuclear mitochondrial segments (NUMTs) contamination [9]. Each 3 µg of each gDNA sample was treated with 30 units of Exonuclease V at 37°C for 3 h. The reaction was halted by adding 11 mM EDTA and heat-inactivation at 70°C for 30 min. We used 10 ng of circular mtDNA templates to amplify the entire mitochondrial genome using Multiple Displacement Amplification (MDA) and isothermal amplification at 33°C with REPLI-g Midi DNA Polymerase for 8 h., following the protocol outlined by the manufacturer of the REPLI-g® Mitochondrial DNA Kit (QIAGEN, Germany). The amplified DNA was then quantified using a Qubit Fluorometer (ThermoFisher Scientific Inc.). To debranch the amplified mtDNA, 500 ng was treated with 2 µL of T7 endonuclease I, 4 µL of 10X Buffer, and adjusted to a final volume of 40 µL with nuclease-free water. The reaction mixture was then incubated at 37°C for 2 h (New England BioLabs Inc.). The debranched mtDNA was quality-checked using the Genomic DNA ScreenTape assay with the TapeStation system (Agilent Technologies). Before proceeding with any experiments, the DNA was purified using QIAseq Beads (QIAGEN, Germany), following the manufacturer’s instructions precisely.
ONT library preparation and sequencing
During the library preparation process, the Native Barcoding Kit 24 V14 (SQK-NB114) protocol, endorsed by the Nanopore Community (https://nanoporetech.com), was employed to optimize the capability of multiplexing long reads. The sequencing technique using ONT was executed using a Flongle flow cell (R10.4.1). The library preparation protocol included repairing and end-prepping/dA-tailing samples with the NEBNext End Repair/dA-tailing module. A separate dT-tailed barcode adapter was then added to the dA-tailed templates. Subsequently, the barcoded samples were combined into a single pool. Each barcode adapter had a cohesive end that was connected to the provided sequencing adapter. Finally, the sequencing mixture was loaded into the flow cell. This sequencing run was set up on the MinION instrument for an average of 20 h.
Pre-processing of mitochondrial genome variation analysis
The raw data was generated by the MinKNOW software (Oxford Nanopore Technologies). During real-time acquisition, the Dorado base-calling method was used in super-accuracy mode to call this data. All FASTQ files associated with each barcode were combined into a single FASTQ file for each sample_code_replicate. The FASTQ files received a first quality check with NanoPlot [13]. Low-quality reads (those with a quality score of less than 9) were eliminated using Nanofilt [14].
We performed the Minimap2 [15] alignment by aligning them to the mtDNA reference genome, revised the Cambridge Reference Sequence (rCRS), and generated aligned reads in SAM format. All SAM files were converted to BAM files and sorted with tools from the Samtools repository [16]. The alignment statistics were derived by estimating the reads that mapped entirely to the mitochondrial sequence using Qualimap [17].
Mitochondrial genome variations analysis
We utilized mtDNA-Server 2 to detect mitochondrial genome variations with the default parameters. This enhanced platform is accessible through the mitoverse cloud at https://mitoverse.i-med.ac.at [18]. To detect SVs, we utilized Sniffles2 to calls SVs in long-read sequencing data [19]. We investigated depth coverage cutoffs 50x. We used the Ensembl browser to annotate each VCF file to determine the start and end points of each SV [20]. Subsequently, circos plots were generated using ShinyCircos-V2.0, a graphic user interface visualization tool accessible online at https://venyao.xyz/shinyCircos/ [21]. Finally, we reviewed previous studies on the MITOMAP database [22].
Results
Defining mitochondrial genome diversity: INDELs, SNVs and SV analysis
The mitoverse results encompass a QC report, haplogroup analysis, and mean depth coverage (S14 Table in S1 File) along with the annotated variants. The number of mtDNA SNVs, insertions, and deletions identified in each cell line showed no significant differences among the cell lines (Fig 1A). However, the number of INDELs within each gene varied, with the D-loop, MT-RNR2, MT-CO1, MT-ND4, and MT-ND5 genes exhibiting a high frequency of INDELs across all cell lines (Fig 1C and S6–S11 Tables in S1 File). The number of SNVs per gene revealed that the D-loop, MT-CO1, MT-ND4, MT-ND5, and MT-CYB genes had a higher number of SNVs in CCA cell lines compared to MMNK-1, particularly in KKU-213A and KKU-452. Interestingly, five CCA cell lines had SNVs in the MT-ND6 gene, while the MMNK-1 cell line had none (Fig 1D).
Number of mtDNA SNVs and INDELs (A) mtDNA structural variant types (B) INDELs in each mitochondrial gene (C) SNVs in each mitochondrial gene and (D) SNVs in each OXPHOS Complex.
To further understand the landscape of mtDNA SVs in CCA cell lines, we performed a comprehensive analysis of deletions, duplications, and inversions using Sniffles2, applying a 50x depth coverage cutoff. The analysis revealed significant variation in the number of SVs among the different cell lines, identifying 208 SVs in KKU-023, 185 SVs in KKU-055, 231 SVs in KKU-100, 69 SVs in KKU-213A, 172 SVs in KKU-452, and 217 SVs in the non-tumorigenic cholangiocyte cell line MMNK-1 (Fig 1B). This variation underscores the genetic diversity within the mtDNA of these cell lines.
KKU-023 cell line
In the KKU-023 cell line, 48 SNVs were identified, including 14 non-synonymous mutations. Notable mutations such as m.3571C > T, m.3916G > A, m.8462T > C, m.9712T > C, m.14249G > A, m.14772C > T, m.15506G > A, and m.7090G > C exhibited high pathogenicity based on MutPred scores (>0.5), Selection scores (>0.5), and Conservation Index (CI) values (>0.8). Five non-synonymous SNVs—m.8462T > C, m.8860A > G, m.14766C > T, m.15326A > G, and m.13928G > C—displayed high heteroplasmy levels of 98.3%, 99.1%, 99.9%, 99.9%, and 97.7%, respectively (Table 1). Most SNVs occurred in the MT-CYB and MT-ND1 genes, located in Complex III and I, respectively. SV analysis revealed 66 deletions, 69 duplications, and 73 inversions (Fig 2A).
The circos plots depict the positions of SNVs and INDELs, marked by red dots along the circumference. Structural deletions are indicated by violet lines, structural duplications by blue-green lines, and structural inversions by red-wine lines.
KKU-055 cell line
The KKU-055 cell line presented 59 SNVs, including 16 non-synonymous mutations. Significant mutations such as m.6667C > T, m.9493G > A, m.9712T > C, m.13406G > A, and m.14249G > A were observed, all demonstrating high scores in MutPred, Selection, and CI. Mutations like m.8860A > G, m.9053G > A, m.10609T > C, m.12406G > A, m.13759G > A, m.15326A > G, and m.13928G > C had heteroplasmy levels exceeding 96% (Table 2). Most SNVs occurred in the MT-ND5 and MT-ATP6 genes, located in Complex I and V, respectively, indicating these may be specific pathogenic genes of this poorly differentiated cell line. The SVs in KKU-055 included 59 deletions, 55 duplications, and 71 inversions (Fig 2B).
KKU-100 cell line
In the KKU-100 cell line, 61 SNVs were detected, with 16 non-synonymous mutations. Noteworthy mutations such as m.3890G > A, m.4818G > A, m.6762G > A, m.10797C > T, m.11234C > T, m.13633G > A, m.13804G > A, m.15128T > C, m.9172C > A, and m.10095C > A were found, each having high MutPred, Selection, and CI scores. Additionally, mutations m.7775G > A, m.8701A > G, m.8860A > G, m.10398A > G, m.14766C > T, and m.15326A > G showed heteroplasmy levels above 98% (Table 3). Most SNVs occurred in the MT-CYB and MT-ATP6 genes, located in Complex III and V, respectively. This cell line had 72 deletions, 74 duplications, and 85 inversions (Fig 2C).
KKU-213A cell line
The KKU-213A cell line exhibited the highest number of SNVs, with 91 identified, including 21 non-synonymous mutations. Key mutations such as m.3571C > T, m.5464T > C, m.8584G > A, m.9529C > T, m.9712T > C, m.14291T > C, m.14904T > C, m.15005G > A, m.15056T > C, m.9537C > A, and m.15024G > C were notable for their high scores in MutPred, Selection, and CI. Mutations m.7853G > A, m.8584G > A, m.8701A > G, m.8860A > G, m.10398A > G, m.12541G > A, m.14291T > C, m.14318T > C, m.14766C > T, m.14904T > C, m.15326A > G, and m.15024G > C exhibited heteroplasmy levels above 92% (Table 4). Most SNVs occurred in the MT-CYB, MT-ATP6, and MT-CO3 genes, located in Complex III, V, and IV, respectively. SV analysis revealed 25 deletions, 25 duplications, and 19 inversions (Fig 2D).
KKU-452 cell line
KKU-452 showed 69 SNVs, with 17 being non-synonymous mutations. Significant mutations included m.5464T > C, m.8725A > G, m.9026G > A, m.9712T > C, m.12994G > A, m.14178T > C, m.15005G > A, m.15557G > A, and m.11873A > T, all with high MutPred, Selection, and CI scores. Moreover, mutations m.4491G > A, m.8701A > G, m.8725A > G, m.8860A > G, m.10398A > G, m.12994G > A, m.13204G > A, m.14178T > C, m.14766C > T, m.15317G > A, and m.15326A > G had heteroplasmy levels over 95% (Table 5). Most SNVs occurred in the MT-CYB and MT-ATP6 genes, located in Complex III and V, respectively. The SV analysis revealed 57 deletions, 46 duplications, and 69 inversions (Fig 2E). The identified mtDNA genes and SVs suggest that KKU-452 might have a similar mutation pattern to KKU-100.
MMNK-1 cell line
The MMNK-1 cell line had 46 SNVs, with 9 non-synonymous mutations. Among these, mutations m.8160A > G, m.13406G > A, and m.13970G > A showed high MutPred, Selection, and CI scores. Additionally, mutations m.5442T > C, m.8701A > G, m.8860A > G, m.10398A > G, m.14766C > T, and m.15326A > G had heteroplasmy levels exceeding 95% (Table 6). There was no high mutation concentration in any specific gene. For SVs, there were 74 deletions, 58 duplications, and 85 inversions (Fig 2F). These mutation patterns highlight the distinct mtDNA mutational landscape observed in the CCA cell lines.
In all cell lines, we identified one shared SNV and seven INDELs across five CCA cell lines (Table 7). Most mutations were concentrated in Complex I genes and the D-loop region. Non-synonymous SNVs ranged from 9 in MMNK-1–21 in KKU-213A, with common SNVs such asm.1787G > T and m.4334A > C found in all six cell lines, noted for their potential pathogenicity and conservation across species. High heteroplasmy levels in mutations like m.8860A > G, m.8701A > G, and m.15326A > G suggest these mutations play a key role in mitochondrial dysfunction in CCA. Additionally, the SNV m.750A > G and seven common INDELs were present in CCA cell lines while high heteroplasmy was also observed in specific SNVs (e.g., m.8462T > C in KKU-023, m.9493G > A in KKU-055, m.9172C > A in KKU-100, m.15024G > C in KKU-213A, m.12994G > A in KKU-452, and m.13406G > A in MMNK-1), indicating their potential involvement in carcinogenesis. Table 8 provides clinical characteristics of patient donors and highlights the high pathogenic mitochondrial genes for each cell line. Specifically, KKU-023 has MT-CYB and MT-ND1, KKU-055 has MT-ND5 and MT-ATP6, KKU-100 and KKU-452 have MT-CYB and MT-ATP6, while KKU-213A contains MT-CYB, MT-ATP6, and MT-CO3 genes. The substantial variation in SVs among the cell lines, ranging from 69 in KKU-213A to 231 in KKU-100, indicates differences in genetic alterations that may reflect distinct mechanisms driving disease progression. This diversity suggests possible therapeutic targets, warranting further research into the functional impact of these SVs and their role in CCA development.
Discussion
This study explored the entire mitochondrial genome in CCA lines using ONT. We compared the sequence data to the Revised Cambridge Reference Sequence (rCRS) to identify nucleotide changes. Due to the lack of a corresponding non-cancerous cholangiocyte cell line, we utilized the rCRS and the MMNK-1 cell line, which is an immortalized cholangiocyte created via SV40T and hTERT transduction [23], and origin from OUMS-21 which derived from the human embryo liver tissue [24].
Our analysis revealed that most SNVs and INDELs occurred in the D-loop region of mtDNA (Figs 1C–1E), which is known for its hypervariable nature and role in controlling replication. The hypervariable (HV) segments of D-loop, particularly nucleotides 16519 (HV-I) and 73 (HV-II), are commonly associated with cancer risk, as seen in salivary gland tumors and other cancers [25]. The m.73A > G variant has been reported in various conditions, including aging brain, polymerase gamma-progressive external ophthalmoplegia (POLG/PEO), thyroid, and prostate tumors [26–31] and was detected in CCA and hepatocellular carcinoma (HCC) cell lines [32]. Although the m.73A > G mutation has been reported to have no significant impact the stability of mtDNA secondary structure, it has been linked to certain cancers. Interestingly, its role in myocardial infarction seems to be beneficial, potentially by affecting mitochondrial function and gene expression [33]. Similarly, the m.16519T > C mutation has been found in glioblastoma, gastric, lung, ovarian, and prostate tumors [31,34–37] and CCA and HCC cell [32], there was a report indicated that the presence of this mitochondrial variant might predispose individuals with knee osteoarthritis to a heightened baseline inflammatory state involving IL6 [38]. The m.301T > TC insertion and m.16223C > T have been reported in multiple tumor types [39], while m.16304T > C has been linked to esophageal and breast tumors [40,41]. These findings are consistent with the notion that alterations in the D-loop region may be involved in the regulation of the mitochondrial genome and could be associated with carcinogenesis and the progression of CCA cell lines.
In the coding regions, the highest number of SNVs were found in the genes encoding Complexes I, IV, III, and V, with counts of 122, 55, 43, and 20, respectively (Fig 1E). Previous studies have noted that Complex I mutations are particularly prevalent in CCA tumors [42]. Complex I, also known as NADH-ubiquinone oxidoreductase, is the largest component of the mitochondrial electron transport chain and provides about 40% of the proton motive force necessary for ATP synthesis. It also plays a crucial role in biosynthesis, redox regulation, cell proliferation, resistance to apoptosis, and metastasis. Mutations in Complex I genes are associated with the progression of various cancers, including prostate, thyroid, breast, lung, renal, colorectal, and head and neck tumors [43]. Maintaining the NAD+/NADH ratio via Complex I is critical for adaptive responses to hypoxia, including stabilization of hypoxia-inducible factor 1-alpha (HIF1α) [44], and promoting a metabolic shift towards aerobic glycolysis, known as the Warburg effect [45,46]. Complex IV, or cytochrome c oxidase (COX), is a vital enzyme in the mitochondrial ETC responsible for the final electron transfer to oxygen, producing water [47]. Mutations in Complex IV genes can influence breast cancer progression and may serve as potential genetic markers [48]. Complex III, also known as cytochrome c reductase, transfers electrons from coenzyme Q (CoQ) to cytochrome c and moves four protons into the intermembrane space (IMS) [49]. It plays a key role in cellular signaling, and its modulation can significantly impact gene expression, potentially linking its activity to advanced tumor stages and drug resistance, particularly in highly oxidative tumors [50]. Complex V, known as ATP synthase, is crucial for ATP production. In cancer, Complex V is vital due to the high energy demands of rapidly growing cancer cells [49]. High expression levels of ATP synthase-related proteins have been observed in several cancers, including glioma, ovarian, prostate, breast, and clear cell renal cell carcinoma, correlating with poor prognosis [51]. These underscore the significant presence of SNVs in the mitochondrial genes encoding Complexes I, IV, III, and V in CCA cell lines.
The study also identified varying numbers of non-synonymous SNVs across the CCA cell lines: 14 in KKU-023, 16 in KKU-055, 16 in KKU-100, 21 in KKU-213A, 17 in KKU-452, and 9 in MMNK-1. Each cell line exhibited unique INDELs (S6–S11 Tables in S1 File) and SNV profiles (Tables 2–7), but some SNVs were shared. For instance, the m.8701A > G mutation, present in four CCA cell lines (except KKU-023) and MMNK-1, has been reported in thyroid tumors [28,39,49]. The m.10398A > G mutation was found in all six cell lines and has been reported in various cancers [31,49,52,53]. Both SNVs have been observed in CCA and HCC cell lines [32]. Additionally, the m.10609T > C mutation was found in CCA tumors and is located in the MT-ND4L gene, resulting in an amino acid change from isoleucine (I) to threonine (T) [42]. Additionally, in the KKU-213A CCA cell line, which harbors numerous mtDNA SNVs, prior studies have shown that ALDH1A3 expression significantly rises under lactic acidosis (LA) conditions and correlates with LDHA expression. Higher ALDH1A3 levels have been linked to poorer patient survival, indicating its potential as a prognostic marker. The EGFR pathway has been identified as a main regulator of ALDH1A3 in LA, promoting tumor aggressiveness and resistance to gemcitabine. Therefore, targeting the EGFR-ALDH1A3 axis could be a promising therapeutic approach for metastatic CCA in this cell line [54]. The doubling time and the total number of mtDNA SNVs and INDELs for each cell line (S15 Table and S6 Fig in S1 File) illustrating the negative relationship, which suggested a potential link to proliferative capacity. However, this correlation was not statistically significant. Additionally, the expression level of IL-6 and CK19 has been investigated using western blot (S7–S8 Fig in S1 File) and droplet digital PCR (S11 Fig in S1 File) analyses. The correlation of their expression with the total number of mtDNA SNVs and INDELs for each cell line was evaluated, however, the analyses did not yield statistically significant results (S9–S10, S12–S13 Fig in S1 File). These findings suggest that further molecular characterization with the larger number of samples is necessary. These findings highlight the presence of potentially pathogenic mutations within mitochondrial complexes and emphasize the substantial accumulation of SNVs in genes encoding key mitochondrial complexes (I, IV, III, and V) of the electron transport chain in CCA cell lines. The high occurrence of SNVs in the D-loop and coding regions of mtDNA. The unique mutation profiles and shared SNVs across cell lines underscore the relevance of mitochondrial genome alterations as both potential biomarkers and therapeutic targets in CCA.
For the mtDNA SVs in this study can be detected by ONT. The distribution helps highlight the specific types of genetic alterations predominant in each cell line, providing a baseline to assess the impact of these variants in CCA cell lines. The variability observed could be due to differences in the cell lines’ origins, genetic backgrounds, or levels of mitochondrial dysfunction. MtDNA deletions and duplications can disrupt mitochondrial gene function, often caused by spontaneous changes or mutations in nuclear-encoded proteins like DNA polymerase γ (POLγ) and Twinkle helicase. These structural alterations are commonly linked to mitochondrial disorders and conditions such as cancer, diabetes, neurodegenerative diseases, and aging. The accumulation of these mtDNA changes in cancer can lead to increased oxidative stress and altered apoptotic pathways, which may promote tumorigenesis [55]. While mtDNA inversions can disrupt mitochondrial genes and may result in the formation of hybrid gene products that impair mitochondrial function and disrupt proteostasis, crucial for cellular health. Inversions caused by inverted repeats (IRs) are suggested to be more mutagenic than deletions caused by direct repeats (DRs). This heightened mutagenicity can lead to greater instability in the mitochondrial genome, increasing the risk of diseases [56]. The incorporation of mtDNA segments into nuclear DNA is often associated with specific processes that lead to structural variations in the nuclear genome.
This highlights the importance of the mitochondrial genome in understanding the complex molecular patterns observed in cancer genomes and in identifying potential cancer-driving events. The positive correlation between the mutation burdens of mitochondrial and nuclear genomes in various cancer types suggests that SVs in mtDNA may influence or be indicative of changes in the nuclear genome [57]. Nevertheless, this is the initial study examining SVs in mtDNA within CCA cell lines. Future research should focus on elucidating the mechanisms by which these mtDNA mutations contribute to mitochondrial dysfunction and CCA progression. Exploring the interactions between mitochondrial and nuclear gene variants could provide deeper insights into the molecular underpinnings of CCA and identify potential therapeutic targets. Speculatively, targeting specific mtDNA mutations or their resulting dysfunctions may offer new avenues for CCA treatment.
This study explored the mtDNA profiles in five CCA cell lines (KKU-023, KKU-055, KKU-100, KKU-213A, KKU-452) and a non-tumorous cholangiocyte cell line (MMNK-1), with a focus on identifying INDELs, SNVs, and SVs. Our key findings reveal a significant presence of SNVs and INDELs, particularly in the D-loop region and genes encoding mitochondrial respiratory complexes, with Complex I mutations being notably prevalent. Additionally, we observed a considerable number of SVs, including deletions, duplications, and inversions, across the different cell lines. Our study contributes to the existing knowledge by highlighting the variability in mtDNA alterations among CCA cell lines and suggesting potential roles these mutations may play in mitochondrial dysfunction and cancer progression. Notably, this is the first study to comprehensively investigate mtDNA SVs in CCA cell lines, providing a baseline for future studies.
References
- 1. Loilome W, Namwat N, Jusakul A, Techasen A, Klanrit P, Phetcharaburanin J, et al. The Hallmarks of Liver Fluke Related Cholangiocarcinoma: Insight into Drug Target Possibility. Recent Results Cancer Res. 2023;219:53–90. pmid:37660331
- 2. Banales JM, Marin JJG, Lamarca A, Rodrigues PM, Khan SA, Roberts LR, et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management. Nat Rev Gastroenterol Hepatol. 2020;17(9):557–88. pmid:32606456
- 3. Strijker M, Belkouz A, van der Geest LG, van Gulik TM, van Hooft JE, de Meijer VE, et al. Treatment and survival of resected and unresected distal cholangiocarcinoma: a nationwide study. Acta Oncol. 2019;58(7):1048–55. pmid:30907207
- 4. Saensa-Ard S, Leuangwattanawanit S, Senggunprai L, Namwat N, Kongpetch S, Chamgramol Y, et al. Establishment of cholangiocarcinoma cell lines from patients in the endemic area of liver fluke infection in Thailand. Tumour Biol. 2017;39(11):1010428317725925. pmid:29110582
- 5. Kozakiewicz P, Grzybowska-Szatkowska L, Ciesielka M, Rzymowska J. The Role of Mitochondria in Carcinogenesis. Int J Mol Sci. 2021;22(10):5100. pmid:34065857
- 6. Wu Q, Tsai H-I, Zhu H, Wang D. The Entanglement between Mitochondrial DNA and Tumor Metastasis. Cancers (Basel). 2022;14(8):1862. pmid:35454769
- 7. Stefano GB, Bjenning C, Wang F, Wang N, Kream RM. Mitochondrial Heteroplasmy. Adv Exp Med Biol. 2017;982:577–94. pmid:28551808
- 8. Hong YS, Battle SL, Shi W, Puiu D, Pillalamarri V, Xie J, et al. Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality. Nat Commun. 2023;14(1):6113. pmid:37777527
- 9. Macken WL, Falabella M, Pizzamiglio C, Woodward CE, Scotchman E, Chitty LS, et al. Enhanced mitochondrial genome analysis: bioinformatic and long-read sequencing advances and their diagnostic implications. Expert Rev Mol Diagn. 2023;23(9):797–814. pmid:37642407
- 10. Frascarelli C, Zanetti N, Nasca A, Izzo R, Lamperti C, Lamantea E, et al. Nanopore long-read next-generation sequencing for detection of mitochondrial DNA large-scale deletions. Front Genet. 2023;14:1089956. pmid:37456669
- 11. Sripa B, Seubwai W, Vaeteewoottacharn K, Sawanyawisuth K, Silsirivanit A, Kaewkong W, et al. Functional and genetic characterization of three cell lines derived from a single tumor of an Opisthorchis viverrini-associated cholangiocarcinoma patient. Hum Cell. 2020;33(3):695–708. pmid:32207095
- 12. Sripa B, Leungwattanawanit S, Nitta T, Wongkham C, Bhudhisawasdi V, Puapairoj A, et al. Establishment and characterization of an opisthorchiasis-associated cholangiocarcinoma cell line (KKU-100). World J Gastroenterol. 2005;11(22):3392–7. pmid:15948244
- 13. De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics. 2023;39(5):btad311. pmid:37171891
- 14. De Coster W, D’Hert S, Schultz DT, Cruts M, Van Broeckhoven C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics. 2018;34(15):2666–9. pmid:29547981
- 15. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34(18):3094–100. pmid:29750242
- 16. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. pmid:19505943
- 17. Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics. 2016;32(2):292–4. pmid:26428292
- 18. Weissensteiner H, Forer L, Kronenberg F, Schönherr S. mtDNA-Server 2: advancing mitochondrial DNA analysis through highly parallelized data processing and interactive analytics. Nucleic Acids Res. 2024;52(W1):W102–7. pmid:38709886
- 19. Smolka M, Paulin LF, Grochowski CM, Horner DW, Mahmoud M, Behera S, et al. Detection of mosaic and population-level structural variants with Sniffles2. Nat Biotechnol. 2024;42(10):1571–80. pmid:38168980
- 20. Harrison PW, Amode MR, Austine-Orimoloye O, Azov AG, Barba M, Barnes I, et al. Ensembl 2024. Nucleic Acids Res. 2024;52(D1):D891–9. pmid:37953337
- 21. Wang Y, Jia L, Tian G, Dong Y, Zhang X, Zhou Z, et al. shinyCircos-V2.0: Leveraging the creation of Circos plot with enhanced usability and advanced features. Imeta. 2023;2(2):e109. pmid:38868422
- 22. Brandon MC, Lott MT, Nguyen KC, Spolim S, Navathe SB, Baldi P, et al. MITOMAP: a human mitochondrial genome database--2004 update. Nucleic Acids Res. 2004;33(Database issue):D611-3. pmid:15608272
- 23. Maruyama M, Kobayashi N, Westerman KA, Sakaguchi M, Allain JE, Totsugawa T, et al. Establishment of a highly differentiated immortalized human cholangiocyte cell line with SV40T and hTERT. Transplantation. 2004;77(3):446–51. pmid:14966424
- 24. Miyazaki M, Mihara K, Bai L, Kano Y, Tsuboi S, Endo A, et al. Immortalization of Epithelial-like Cells from Human Liver Tissue with SV40 T-Antigen Gene. Experimental Cell Research. 1993;206(1):27–35.
- 25. Singh H, Kumar S, Urs AB, Kapoor S. Identification of sequence polymorphisms in the D-loop region of mitochondrial DNA as valuable biomarkers for salivary gland tumors: an observational study. Egypt J Otolaryngol. 2022;38(1).
- 26. Calloway CD, Reynolds RL, Herrin GL Jr, Anderson WW. The frequency of heteroplasmy in the HVII region of mtDNA differs across tissue types and increases with age. Am J Hum Genet. 2000;66(4):1384–97. pmid:10739761
- 27. Nekhaeva E, Bodyak ND, Kraytsberg Y, McGrath SB, Van Orsouw NJ, Pluzhnikov A, et al. Clonally expanded mtDNA point mutations are abundant in individual cells of human tissues. Proc Natl Acad Sci U S A. 2002;99(8):5521–6. pmid:11943860
- 28. Máximo V, Soares P, Lima J, Cameselle-Teijeiro J, Sobrinho-Simões M. Mitochondrial DNA somatic mutations (point mutations and large deletions) and mitochondrial DNA variants in human thyroid pathology: a study with emphasis on Hürthle cell tumors. Am J Pathol. 2002;160(5):1857–65. pmid:12000737
- 29. Del Bo R, Bordoni A, Sciacco M, Di Fonzo A, Galbiati S, Crimi M, et al. Remarkable infidelity of polymerase γA associated with mutations in POLG1 exonuclease domain. Neurology. 2003;61(7):903–8.
- 30. Coskun PE, Beal MF, Wallace DC. Alzheimer’s brains harbor somatic mtDNA control-region mutations that suppress mitochondrial transcription and replication. Proc Natl Acad Sci U S A. 2004;101(29):10726–31. pmid:15247418
- 31. Brandon M, Baldi P, Wallace DC. Mitochondrial mutations in cancer. Oncogene. 2006;25(34):4647–62.
- 32. Bahitham W, Liao X, Peng F, Bamforth F, Chan A, Mason A, et al. Mitochondriome and cholangiocellular carcinoma. PLoS One. 2014;9(8):e104694. pmid:25137133
- 33. Umbria M, Ramos A, Aluja MP, Santos C. The role of control region mitochondrial DNA mutations in cardiovascular disease: stroke and myocardial infarction. Sci Rep. 2020;10(1).
- 34. Bragoszewski P, Kupryjanczyk J, Bartnik E, Rachinger A, Ostrowski J. Limited clinical relevance of mitochondrial DNA mutation and gene expression analyses in ovarian cancer. BMC Cancer. 2008;8(1).
- 35. Wu C-W, Yin P-H, Hung W-Y, Li AF-Y, Li S-H, Chi C-W, et al. Mitochondrial DNA mutations and mitochondrial DNA depletion in gastric cancer. Genes Chromosomes Cancer. 2005;44(1):19–28. pmid:15892105
- 36. Kirches E, Krause G, Warich-Kirches M, Weis S, Schneider T, Meyer-Puttlitz B, et al. High frequency of mitochondrial DNA mutations in glioblastoma multiforme identified by direct sequence comparison to blood samples. Int J Cancer. 2001;93(4):534–8. pmid:11477557
- 37. Fliss MS, Usadel H, Caballero OL, Wu L, Buta MR, Eleff SM, et al. Facile detection of mitochondrial DNA mutations in tumors and bodily fluids. Science. 2000;287(5460):2017–9. pmid:10720328
- 38. Durán-Sotuela A, Fernández-Moreno M, Hermida-Gómez T, Lourido-Salas L, Fernández-Tajes J, Relaño S, et al. Mtdna D-Loop Variant M.16519c Modifies The Expression Pattern Of Transmitochondrial Cybrids Conditioning The Risk Of Developing The Rapidly Progressive Knee Osteoarthritis. Potential Role Of Inflammation. Osteoarthritis and Cartilage. 2023;31:S102–3.
- 39. Stafford P, Chen-Quin EB. The pattern of natural selection in somatic cancer mutations of human mtDNA. J Hum Genet. 2010;55(9):605–12. pmid:20613764
- 40. Kumimoto H, Yamane Y, Nishimoto Y, Fukami H, Shinoda M, Hatooka S, et al. Frequent somatic mutations of mitochondrial DNA in esophageal squamous cell carcinoma. Int J Cancer. 2004;108(2):228–31. pmid:14639607
- 41. Tseng L-M, Yin P-H, Chi C-W, Hsu C-Y, Wu C-W, Lee L-M, et al. Mitochondrial DNA mutations and mitochondrial DNA depletion in breast cancer. Genes Chromosomes Cancer. 2006;45(7):629–38. pmid:16568452
- 42. Muisuk K, Silsirivanit A, Imtawil K, Bunthot S, Pukhem A, Pairojkul C, et al. Novel mutations in cholangiocarcinoma with low frequencies revealed by whole mitochondrial genome sequencing. Asian Pac J Cancer Prev. 2015;16(5):1737–42. pmid:25773818
- 43. Urra FA, Muñoz F, Lovy A, Cárdenas C. The Mitochondrial Complex(I)ty of Cancer. Front Oncol. 2017;7:118. pmid:28642839
- 44. Calabrese C, Iommarini L, Kurelac I, Calvaruso MA, Capristo M, Lollini P-L, et al. Respiratory complex I is essential to induce a Warburg profile in mitochondria-defective tumor cells. Cancer Metab. 2013;1(1):11. pmid:24280190
- 45. Mullen AR, Wheaton WW, Jin ES, Chen P-H, Sullivan LB, Cheng T, et al. Reductive carboxylation supports growth in tumour cells with defective mitochondria. Nature. 2012;481(7381):385–8. pmid:22101431
- 46. Wu M, Neilson A, Swift AL, Moran R, Tamagnine J, Parslow D, et al. Multiparameter metabolic analysis reveals a close link between attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells. Am J Physiol Cell Physiol. 2007;292(1):C125-36. pmid:16971499
- 47. Uchenunu O, Zhdanov AV, Hutton P, Jovanovic P, Wang Y, Andreev DE, et al. Mitochondrial complex IV defects induce metabolic and signaling perturbations that expose potential vulnerabilities in HCT116 cells. FEBS Open Bio. 2022;12(5):959–82. pmid:35302710
- 48. de Oliveira RC, Dos Reis SP, Cavalcante GC. Mutations in Structural Genes of the Mitochondrial Complex IV May Influence Breast Cancer. Genes (Basel). 2023;14(7):1465. pmid:37510369
- 49. Grasso D, Zampieri LX, Capelôa T, Van de Velde JA, Sonveaux P. Mitochondria in cancer. CST. 2020;4(6):114–46.
- 50. Matassa DS, Criscuolo D, Avolio R, Agliarulo I, Sarnataro D, Pacelli C, et al. Regulation of mitochondrial complex III activity and assembly by TRAP1 in cancer cells. Cancer Cell Int. 2022;22(1).
- 51. Wang T, Ma F, Qian H. Defueling the cancer: ATP synthase as an emerging target in cancer therapy. Molecular Therapy - Oncolytics. 2021;23:82–95.
- 52. Yeh JJ, Lunetta KL, van Orsouw NJ, Moore FD Jr, Mutter GL, Vijg J, et al. Somatic mitochondrial DNA (mtDNA) mutations in papillary thyroid carcinomas and differential mtDNA sequence variants in cases with thyroid tumours. Oncogene. 2000;19(16):2060–6. pmid:10803467
- 53. Koshikawa N, Akimoto M, Hayashi J-I, Nagase H, Takenaga K. Association of predicted pathogenic mutations in mitochondrial ND genes with distant metastasis in NSCLC and colon cancer. Sci Rep. 2017;7(1):15535. pmid:29138417
- 54. Thamrongwaranggoon U, Detarya M, Seubwai W, Saengboonmee C, Hino S, Koga T, et al. Lactic acidosis promotes aggressive features of cholangiocarcinoma cells via upregulating ALDH1A3 expression through EGFR axis. Life Sci. 2022;302:120648. pmid:35598658
- 55. Basu S, Xie X, Uhler JP, Hedberg-Oldfors C, Milenkovic D, Baris OR, et al. Accurate mapping of mitochondrial DNA deletions and duplications using deep sequencing. PLoS Genet. 2020;16(12):e1009242.
- 56. Yang J-N, Seluanov A, Gorbunova V. Mitochondrial Inverted Repeats Strongly Correlate with Lifespan: mtDNA Inversions and Aging. PLoS ONE. 2013;8(9):e73318.
- 57. Yuan Y, Ju YS, Kim Y, Li J, Wang Y, Yoon CJ, et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nat Genet. 2020;52(3):342–52. pmid:32024997