More mitochondria, less efficient respiration

COX3, ND1, reactive oxygen species, and the path from colitis to colorectal cancer

How inflammation-driven mitochondrial turnover can amplify mtDNA damage, sustain colitis, and reshape cancer risk.

An inflamed epithelial cell can make more mitochondria while extracting less useful energy from each one. That apparent paradox matters because respiratory-chain stress is not only a consequence of inflammation: it can become an amplifier. Higher mitochondrial turnover and electron flow create more opportunities for reactive oxygen species (ROS) to damage mitochondrial DNA (mtDNA), weaken oxidative phosphorylation (OXPHOS), and release signals that keep mucosal immunity active. Over years of repeated injury and repair, that loop can help create a tissue environment permissive to colitis-associated colorectal cancer.

Mitochondrial biogenesis in inflammation

Mitochondrial biogenesis is coordinated rather than spontaneous. PGC-1α coactivates nuclear respiratory factors including NRF1; these induce TFAM and other proteins needed to replicate, package, and transcribe mtDNA. Inflammatory and redox signals can engage this repair-and-replacement program, although cytokines such as TNF-α and IL-6 can also suppress or distort it depending on dose, timing, cell type, and disease stage. Thus, biogenesis is better understood as a stress response than as proof of healthy respiration (Cherry and Piantadosi, 2015; Cunningham, Vincent, Sodhi, Novak, Ranganathan, Egan, Stolz, Rogers, Firek, Morowitz, Gittes, Zuckerbraun, Hackam, and Mollen, 2016). Figure 1 shows the compensatory logic.

from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.patches import Circle, Ellipse, FancyBboxPatch, FancyArrowPatch, Rectangle
from scipy import stats

OUT = Path("images")
OUT.mkdir(exist_ok=True)
sns.set_theme(style="whitegrid", context="notebook")
navy, teal, coral, gold, pale = "#17324D", "#17807E", "#D95D4F", "#E5A93D", "#EAF3F2"

fig, ax = plt.subplots(figsize=(11, 5.3))
ax.set(xlim=(0, 12), ylim=(0, 6)); ax.axis("off")
steps = [(0.3, 4.65, 2.2, 0.75, "TNF-α · IL-6\nredox stress", coral),
         (3.15, 4.65, 2.2, 0.75, "PGC-1α", teal),
         (6.0, 4.65, 2.2, 0.75, "NRF1", teal),
         (8.85, 4.65, 2.2, 0.75, "TFAM + mtDNA\nreplication", teal)]
for x, y, w, h, txt, col in steps:
    ax.add_patch(FancyBboxPatch((x,y),w,h,boxstyle="round,pad=.08",fc=col,ec="none"))
    ax.text(x+w/2,y+h/2,txt,ha="center",va="center",color="white",weight="bold")
for x in [2.55,5.4,8.25]:
    ax.add_patch(FancyArrowPatch((x,5.03),(x+.55,5.03),arrowstyle="-|>",mutation_scale=16,color=navy,lw=2))
for cx, label, count in [(2.7,"Before: fewer",3),(9.0,"After: more",8)]:
    ax.add_patch(Circle((cx,2.15),1.65,fc="#F7FAFC",ec=navy,lw=2))
    coords = [(cx-0.75+(i%4)*0.5,1.65+(i//4)*0.85) for i in range(count)]
    for mx,my in coords:
        ax.add_patch(Ellipse((mx,my),.7,.34,angle=15,fc=gold,ec=coral,lw=1.4))
        ax.plot([mx-.2,mx+.2],[my,my],color="white",lw=1)
    ax.text(cx,.2,label+" mitochondria",ha="center",weight="bold",color=navy)
ax.add_patch(FancyArrowPatch((4.55,2.15),(7.15,2.15),arrowstyle="-|>",mutation_scale=22,color=teal,lw=3))
ax.text(5.85,2.5,"compensatory\nbiogenesis",ha="center",color=teal,weight="bold")
ax.text(5.85,.65,"More organelles ≠ more efficient OXPHOS",ha="center",color=coral,weight="bold")
fig.tight_layout(); fig.savefig(OUT/"figure-1-biogenesis.png",dpi=220,bbox_inches="tight")
plt.show()

Figure 1: Inflammatory signaling can engage the PGC-1α–NRF1–TFAM biogenesis program, increasing mitochondrial number without guaranteeing respiratory quality.

Why COX3 and ND1 are vulnerable

MT-ND1 encodes a core membrane subunit of Complex I, where electrons enter from NADH; MT-CO3 encodes cytochrome-c-oxidase subunit III in Complex IV, where oxygen is reduced. Both genes reside on the compact mitochondrial genome. Compared with nuclear DNA, mtDNA is not organized in canonical histone chromatin, has a narrower repair repertoire, and lies close to respiratory ROS production. Those features make oxidative lesions and replication errors consequential, but not uniquely confined to these two genes: COX3 and ND1 are mechanistically useful sentinels within a genome-wide vulnerability (Nishikawa, Oshitani, Matsumoto, Nishigami, Arakawa, and Inoue, 2005; Turrens, 2003). Their ETC positions are highlighted in Figure 2.

fig, ax = plt.subplots(figsize=(11,5.2)); ax.set(xlim=(0,12),ylim=(0,6)); ax.axis("off")
ax.add_patch(Rectangle((.2,2.25),11.6,1.35,fc="#DCECF2",ec="#8BAAB5",lw=1.5))
ax.text(.35,3.75,"Intermembrane space",color=navy); ax.text(.35,1.85,"Matrix",color=navy)
xs=[1.3,4.2,6.8,9.6]; labels=["Complex I\nND1","Complex II","Complex III","Complex IV\nCOX3"]
for x,lbl in zip(xs,labels):
    hi = x in (1.3,9.6)
    ax.add_patch(FancyBboxPatch((x,2.0),1.45,2.1,boxstyle="round,pad=.08",fc=coral if hi else navy,ec="white",lw=2))
    ax.text(x+.725,3.05,lbl,ha="center",va="center",color="white",weight="bold")
for a,b in [(2.78,4.05),(5.68,6.65),(8.28,9.45)]:
    ax.add_patch(FancyArrowPatch((a,3.05),(b,3.05),arrowstyle="-|>",mutation_scale=18,color=gold,lw=2.5))
ax.text(5.9,4.55,"electron flow  →  O₂ → H₂O",ha="center",color=navy,weight="bold")
for x in [2.0,7.5]:
    ax.add_patch(FancyArrowPatch((x,2.05),(x,1.2),arrowstyle="-|>",mutation_scale=15,color=coral,lw=2))
    ax.text(x,0.95,"ROS leak",ha="center",color=coral,weight="bold")
ax.text(5.9,5.55,"MT-ND1 and MT-CO3: mtDNA-encoded",ha="center",fontsize=13,color=coral,weight="bold")
ax.text(5.9,5.05,"no canonical histone protection · limited repair repertoire · near ROS source",ha="center",color=navy)
fig.tight_layout(); fig.savefig(OUT/"figure-2-etc.png",dpi=220,bbox_inches="tight"); plt.show()

Figure 2: Electron flow through the inner mitochondrial membrane. MT-ND1 in Complex I and MT-CO3 in Complex IV are mtDNA-encoded; ROS leakage is shown at the principal Complex I and III sites.

The ROS feedback loop

Greater ETC throughput can raise electron leakage, especially when respiratory complexes are damaged or substrate supply exceeds downstream capacity. ROS then damage mtDNA, including MT-CO3 and MT-ND1; dysfunctional complexes lower coupling efficiency and can generate still more ROS. Permeabilized or dying cells release mtDNA and mitochondrial formyl peptides, adding immune stimulation to metabolic failure (Turrens, 2003; Boyapati, Dorward, Tamborska, Kalla, Ventham, Doherty, Whitfield, Gray, Loane, Rossi, Satsangi, and Ho, 2018). This is the cycle summarized in Figure 3. It is a plausible integrated mechanism, not proof that every arrow occurs in every cell at every stage.

fig, ax = plt.subplots(figsize=(10,8)); ax.set(xlim=(-5,5),ylim=(-4.5,4.8)); ax.axis("off")
nodes=["Inflammation","Biogenesis","ETC activity","ROS","mtDNA damage\nCOX3 / ND1","OXPHOS decline","DAMP release"]
angles=np.linspace(np.pi/2,np.pi/2-2*np.pi,len(nodes),endpoint=False); pts=[]
for i,(lab,a) in enumerate(zip(nodes,angles)):
    x,y=3.25*np.cos(a),3.25*np.sin(a); pts.append((x,y))
    col=coral if lab in ("ROS","mtDNA damage\nCOX3 / ND1") else teal
    ax.add_patch(FancyBboxPatch((x-1.0,y-.38),2,.76,boxstyle="round,pad=.08",fc=col,ec="white",lw=1.5))
    ax.text(x,y,lab,ha="center",va="center",color="white",weight="bold",fontsize=10)
for (x1,y1),(x2,y2) in zip(pts,pts[1:]+pts[:1]):
    ax.add_patch(FancyArrowPatch((x1,y1),(x2,y2),connectionstyle="arc3,rad=-.13",arrowstyle="-|>",mutation_scale=15,color=navy,lw=2,shrinkA=45,shrinkB=45))
ax.text(0,0,"self-reinforcing\nmitochondrial stress",ha="center",va="center",color=navy,weight="bold",fontsize=14)
ax.add_patch(FancyArrowPatch((3.0,-2.1),(4.25,-3.35),arrowstyle="-|>",mutation_scale=18,color=coral,lw=2))
ax.text(4.05,-3.8,"chronic colitis",ha="center",color=coral,weight="bold")
ax.add_patch(FancyArrowPatch((-2.7,-2.45),(-4.1,-3.45),arrowstyle="-|>",mutation_scale=18,color=coral,lw=2))
ax.text(-3.55,-4.05,"genomic instability\n→ colon cancer risk",ha="center",color=coral,weight="bold")
fig.tight_layout(); fig.savefig(OUT/"figure-3-feedback-loop.png",dpi=220,bbox_inches="tight"); plt.show()

Figure 3: A proposed feed-forward loop linking inflammation, mitochondrial turnover, respiratory stress, DAMP release, chronic colitis, and cancer risk.

Link to colitis

The intestinal epithelium depends heavily on mitochondrial metabolism to maintain ion transport, mucus production, tight-junction renewal, and rapid repair. Respiratory dysfunction therefore weakens the barrier while inflammatory infiltrates increase oxidant exposure. Released mtDNA, with bacterial-like CpG motifs, can signal through TLR9; cytosolic oxidized mtDNA can also promote NLRP3 inflammasome activation. Mitochondrial formyl peptides recruit and activate innate immune cells. In active IBD, plasma and fecal mtDNA, circulating formyl peptides, damaged mucosal mitochondria, and TLR9-positive lamina propria cells have all been observed together, supporting—without by itself proving—a self-sustaining DAMP circuit (Boyapati, Dorward, Tamborska, Kalla, Ventham, Doherty, Whitfield, Gray, Loane, Rossi, Satsangi, and Ho, 2018).

Direct evidence from OXPHOS protein studies

Schneider et al. examined 286 colonic biopsies from 124 pediatric and adult participants with Crohn’s disease, ulcerative colitis, or no IBD. Immunohistochemistry measured representative subunits from all five OXPHOS complexes plus VDAC1 as a marker of mitochondrial mass. Inflamed IBD mucosa showed disease-, age-, and compartment-dependent changes, including reduced epithelial respiratory-complex staining and altered VDAC1. This is unusually direct tissue-level evidence that respiratory machinery and mitochondrial mass change in inflamed colon (Schneider, Özsoy, Zimmermann, Brunner, Feichtinger, Mayr, Kofler, Neureiter, Klieser, Aigner, Schütz, Stummer, Sperl, and Weghuber, 2022). It complements sequencing studies, which identify mtDNA alterations but generally infer rather than directly measure respiratory protein function. Earlier biochemical work likewise reported ETC-complex dysfunction in UC mucosa (Santhanam, Rajamanickam, Motamarry, Ramakrishna, Amirtharaj, Ramachandran, Pulimood, and Venkatraman, 2012).

Link to colon cancer

Repeated oxidative injury can damage nuclear as well as mitochondrial DNA while proliferative repair expands altered epithelial clones. Nishikawa et al. found higher mucosal 8-OHdG and mtDNA mutation frequencies in UC and colitic cancer (Nishikawa, Oshitani, Matsumoto, Nishigami, Arakawa, and Inoue, 2005); MitoChip analysis also detected somatic mtDNA changes in gastrointestinal preneoplasia (Sui, Zhou, Wang, Canto, Lee, Eshleman, Montgomery, Sidransky, Califano, and Maitra, 2006). Circulating MT-ND1 copy number and variants have consequently been explored as CRC biomarkers (Xu, Zhou, Yuan, Su, Li, Lu, Zhang, Cai, and Han, 2021).

This does not mean “cancer equals failed OXPHOS.” In a staged CRC cell-line model, advanced cells retained substantial oxidative metabolism and acquired greater metabolic flexibility, alongside altered mitochondrial networks (Legge, Collard, Stanko, Hoskin, Holt, Bull, Kollareddy, Bellamy, Groves, Ma, Hazelwood, Qualtrough, Amulic, Malik, Williams, Jones, and Vincent, 2024). Separately, 5-FU-resistant colon-cancer cells shifted toward OXPHOS dependence, connecting mitochondrial plasticity to chemoresistance (Denise, Paoli, Calvani, Taddei, Giannoni, Kopetz, Kazmi, Morelli, Pettazzoni, Sacco, Caselli, Vanoni, Landriscina, Cirri, and Chiarugi, 2015). The apparent contrast is temporal and selective: inflamed mucosa may lose respiratory capacity, whereas surviving neoplastic clones can rewire or restore mitochondrial metabolism. Baker et al. add a crucial qualification: mtDNA mutations rose in UC preneoplasia but tended to be negatively selected in established cancers (Baker, Nachmanson, Kumar, Emond, Ussakli, Brentnall, Kennedy, and Risques, 2019). The trajectory is therefore non-monotonic.

Toy data analysis

WarningSimulation disclaimer

Every number below is simulated for pedagogical illustration. None comes from a patient, clinical cohort, GEO record, or the cited studies; the simulations reproduce only broad qualitative patterns and cannot estimate biological effect sizes.

Relative mtDNA copy number

This two-group simulation follows the requested means and variances and is loosely motivated by the heterogeneous mtDNA-copy-number/IBD literature, including causal-inference evidence in Crohn’s disease (Cai, Li, Liang, Zhang, Xu, Dong, Weng, and Yu, 2023). Tissue injury can deplete mtDNA even when surviving cells activate compensatory biogenesis, so direction depends on specimen, cell mixture, timing, and normalization.

rng = np.random.default_rng(20260825)
copy = pd.DataFrame({
    "group": np.repeat(["Healthy", "Inflamed / IBD"], 30),
    "mtDNA_copy_number": np.r_[rng.normal(205,70,30), rng.normal(95,30,30)]
})
t_copy, p_copy = stats.ttest_ind(copy.loc[copy.group=="Healthy","mtDNA_copy_number"], copy.loc[copy.group=="Inflamed / IBD","mtDNA_copy_number"], equal_var=False)
print(f"Welch t = {t_copy:.2f}; p = {p_copy:.3g}")
fig, ax = plt.subplots(figsize=(7,5))
sns.violinplot(data=copy,x="group",y="mtDNA_copy_number",inner=None,color="#B9DCD8",ax=ax)
sns.boxplot(data=copy,x="group",y="mtDNA_copy_number",width=.25,showfliers=False,color="white",ax=ax)
sns.stripplot(data=copy,x="group",y="mtDNA_copy_number",color=navy,alpha=.7,jitter=.16,size=5,ax=ax)
ax.set(xlabel="",ylabel="Relative mtDNA copy number (a.u.)",title=f"Welch p = {p_copy:.2g}")
sns.despine(); plt.show()

Welch t = 6.50; p = 8.17e-08

Figure 4: Simulated relative mtDNA copy number in healthy and inflamed colon (n = 30 per group). Points are synthetic.

In this synthetic draw, inflamed tissue has lower relative mtDNA copy number (Welch p reported above), illustrating depletion despite a possible compensatory biogenesis signal.

ROS and COX3/ND1 mutation frequency

The next simulation encodes a positive association inspired by oxidative-adduct and mtDNA-mutation findings in UC mucosa (Nishikawa, Oshitani, Matsumoto, Nishigami, Arakawa, and Inoue, 2005).

ros = rng.uniform(.15,1.25,50)
mut = np.clip(.02 + .15*ros + rng.normal(0,.025,50),0,None)
fit = stats.linregress(ros,mut)
ros_df = pd.DataFrame({"ROS":ros,"Mutation frequency":mut})
print(f"R² = {fit.rvalue**2:.3f}; slope p = {fit.pvalue:.3g}")
fig, ax = plt.subplots(figsize=(7,5))
sns.regplot(data=ros_df,x="ROS",y="Mutation frequency",color=coral,scatter_kws={"s":42,"alpha":.75},ax=ax)
ax.set(xlabel="Relative ROS level (a.u.)",ylabel="COX3 + ND1 mutation frequency",title=f"R² = {fit.rvalue**2:.2f}; p = {fit.pvalue:.2g}")
sns.despine(); plt.show()

R² = 0.738; slope p = 1.4e-15

Figure 5: Simulated ROS level versus combined COX3/ND1 mutation frequency (n = 50), with ordinary least-squares fit and 95% confidence band.

The simulated regression is positive, as constructed; it demonstrates how correlation and uncertainty would be displayed, not a measured dose-response.

mtDNA DAMP marker across disease activity

This simulation mirrors the direction of the active-IBD plasma/fecal mtDNA finding, with deliberately greater heterogeneity in active disease (Boyapati, Dorward, Tamborska, Kalla, Ventham, Doherty, Whitfield, Gray, Loane, Rossi, Satsangi, and Ho, 2018).

groups = ["Non-IBD", "Mild / inactive IBD", "Active IBD"]
damp = pd.DataFrame({"group":np.repeat(groups,24), "mtDNA_DAMP":np.r_[rng.normal(18,4,24),rng.normal(31,8,24),rng.normal(62,20,24)]})
samples=[damp.loc[damp.group==g,"mtDNA_DAMP"] for g in groups]
f_damp,p_damp=stats.f_oneway(*samples)
print(f"One-way ANOVA F = {f_damp:.2f}; p = {p_damp:.3g}")
fig, ax = plt.subplots(figsize=(8,5))
sns.boxplot(data=damp,x="group",y="mtDNA_DAMP",hue="group",palette=["#AFC5D5",gold,coral],legend=False,showfliers=False,ax=ax)
sns.stripplot(data=damp,x="group",y="mtDNA_DAMP",color=navy,alpha=.6,jitter=.18,size=4,ax=ax)
ax.set(xlabel="",ylabel="mtDNA DAMP marker (a.u.)",title=f"One-way ANOVA p = {p_damp:.2g}")
sns.despine(); plt.show()

One-way ANOVA F = 92.22; p = 3.21e-20

Figure 6: Simulated mtDNA DAMP marker across non-IBD, mild/inactive IBD, and active IBD groups (n = 24 each).

The synthetic group difference increases with activity and variance, matching the intended illustration rather than estimating a clinical threshold.

Emerging methods: EM-seq as a future tool

Enzymatic methyl-sequencing (EM-seq) measures cytosine methylation; it is not a mutation assay and therefore is not a direct extension of COX3/ND1 mutation-biomarker studies. Traditional bisulfite conversion is harsh, fragments DNA, and can bias recovery and mapping—problems that are particularly relevant to low-input, low-methylation mtDNA. EM-seq uses milder enzymatic conversion and preserves more usable molecules (Vaisvila, Ponnaluri, Sun, and others, 2021). Targeted plasma cfDNA EM-seq has already supported an HCC classifier in 596 samples, based on nuclear differentially methylated CpGs (Guo, Zheng, Li, Li, Xiao, Zheng, Zhu, Xu, He, Zhang, Chen, Qiu, Jiang, Liu, and Chen, 2023). Applying it specifically to mitochondrial methylation in plasma from colitis or CRC is promising but forward-looking: to our knowledge, no published study has yet established that application.

Biomarkers and methods at a glance

Study	Focus	Methods & data	Key biomarker
Nishikawa et al. (Nishikawa, Oshitani, Matsumoto, Nishigami, Arakawa, and Inoue, 2005)	mtDNA mutation accumulation in UC-associated cancer	Mucosal biopsy sequencing + 8-OHdG oxidative stress assay	8-OHdG, mtDNA mutation frequency
Sui et al. (MitoChip study) (Sui, Zhou, Wang, Canto, Lee, Eshleman, Montgomery, Sidransky, Califano, and Maitra, 2006)	mtDNA mutations in GI preneoplasia incl. colitis-dysplasia	Whole mitochondrial genome microarray sequencing (MitoChip) on preneoplastic tissue vs. matched normal	Somatic mtDNA sequence alterations
Baker et al., Mol Cancer Res 2019 (Baker, Nachmanson, Kumar, Emond, Ussakli, Brentnall, Kennedy, and Risques, 2019)	mtDNA mutation dynamics from UC to cancer	Duplex Sequencing (ultra-accurate NGS) on 46 colon biopsies across dysplastic stages	Clonal/subclonal mtDNA mutation burden
Boyapati et al., Inflamm Bowel Dis 2018 (Boyapati, Dorward, Tamborska, Kalla, Ventham, Doherty, Whitfield, Gray, Loane, Rossi, Satsangi, and Ho, 2018)	mtDNA as a released inflammatory DAMP in active IBD	Fecal/plasma mtDNA quantification, formylpeptide detection, TLR9+ cell counts	Circulating/fecal mtDNA + TLR9 expression
Xu et al., Bosn J Basic Med Sci 2021 (Xu, Zhou, Yuan, Su, Li, Lu, Zhang, Cai, and Han, 2021)	Circulating MT-ND1 as a CRC biomarker	Droplet digital PCR on plasma cfDNA, variant + copy number analysis	Plasma cfDNA MT-ND1 copy number & variants
Cai et al., Sci Rep 2023 (Cai, Li, Liang, Zhang, Xu, Dong, Weng, and Yu, 2023)	mtDNA copy number and IBD risk	Mendelian randomization (MR-Egger, IVW, weighted median) meta-analysis	Genetically predicted mtDNA copy number
Schneider et al., Mediators Inflamm 2022 (Schneider, Özsoy, Zimmermann, Brunner, Feichtinger, Mayr, Kofler, Neureiter, Klieser, Aigner, Schütz, Stummer, Sperl, and Weghuber, 2022)	Direct OXPHOS protein and mitochondrial mass changes in IBD tissue	Immunohistochemistry for all 5 OXPHOS complexes + VDAC1 on 286 biopsies from 124 subjects (CD, UC, controls)	OXPHOS complex expression + VDAC1 (mitochondrial mass)
Targeted EM-seq/HCC study, 2023 (Guo, Zheng, Li, Li, Xiao, Zheng, Zhu, Xu, He, Zhang, Chen, Qiu, Jiang, Liu, and Chen, 2023)	Plasma cfDNA methylation detection via enzymatic conversion (methodological reference, not colitis/CRC-specific)	Targeted EM-seq + capture on 596 plasma samples, methylation-based classifier	Panel of differentially methylated CpGs (nuclear, not mtDNA)

References

Anne D. Cherry and Claude A. Piantadosi. (2015). Regulation of mitochondrial biogenesis and its intersection with inflammatory responses. Antioxidants & Redox Signaling. https://doi.org/10.1089/ars.2014.6200.

Katie E. Cunningham, Gabriel Vincent, Chhinder P. Sodhi, Elizabeth A. Novak, S. Ranganathan, Christine E. Egan, Donna B. Stolz, Martha B. Rogers, Brian Firek, Michael J. Morowitz, George K. Gittes, Brian S. Zuckerbraun, David J. Hackam, and Kevin P. Mollen. (2016). Peroxisome proliferator-activated receptor-\(\gamma\) coactivator 1-\(\alpha\) (PGC1\(\alpha\)) protects against experimental murine colitis. Journal of Biological Chemistry. https://doi.org/10.1074/jbc.M115.688812.

Masanori Nishikawa, Nobuhide Oshitani, Tetsuya Matsumoto, Takahiro Nishigami, Tetsuo Arakawa, and Masayasu Inoue. (2005). Accumulation of mitochondrial DNA mutation with colorectal carcinogenesis in ulcerative colitis. British Journal of Cancer. https://doi.org/10.1038/sj.bjc.6602664.

Julio F. Turrens. (2003). Mitochondrial formation of reactive oxygen species. The Journal of Physiology. https://doi.org/10.1113/jphysiol.2003.049478.

Ray K. Boyapati, David A. Dorward, Arina Tamborska, Rahul Kalla, Nicholas T. Ventham, Michael K. Doherty, Phil D. Whitfield, Mohini Gray, Jason Loane, Adriano G. Rossi, Jack Satsangi, and Gwo-Tzer Ho. (2018). Mitochondrial DNA is a pro-inflammatory damage-associated molecular pattern released during active IBD. Inflammatory Bowel Diseases. https://doi.org/10.1093/ibd/izy095.

Anna M. Schneider, Mihriban Özsoy, Franz A. Zimmermann, Susanne M. Brunner, René G. Feichtinger, Johannes A. Mayr, Barbara Kofler, Daniel Neureiter, Eckhard Klieser, Elmar Aigner, Sebastian Schütz, Nathalie Stummer, Wolfgang Sperl, and Daniel Weghuber. (2022). Expression of oxidative phosphorylation complexes and mitochondrial mass in pediatric and adult inflammatory bowel disease. Mediators of Inflammation. https://doi.org/10.1155/2022/9151169.

Srikanth Santhanam, Sankar Rajamanickam, Aditya Motamarry, B. S. Ramakrishna, J. G. Amirtharaj, A. Ramachandran, Anna Pulimood, and Anuradha Venkatraman. (2012). Mitochondrial electron transport chain complex dysfunction in the colonic mucosa in ulcerative colitis. Inflammatory Bowel Diseases. https://doi.org/10.1002/ibd.22926.

Guoping Sui, Shaoyu Zhou, Jean Wang, Marcia Canto, Edward E. Lee, James R. Eshleman, Elizabeth A. Montgomery, David Sidransky, Joseph A. Califano, and Anirban Maitra. (2006). Mitochondrial DNA mutations in preneoplastic lesions of the gastrointestinal tract: A biomarker for the early detection of cancer. Molecular Cancer. https://doi.org/10.1186/1476-4598-5-73.

Yong Xu, Jing Zhou, Qing Yuan, Jin Su, Qiang Li, Xin Lu, Lin Zhang, Zhe Cai, and Jing Han. (2021). Quantitative detection of circulating MT-ND1 as a potential biomarker for colorectal cancer. Bosnian Journal of Basic Medical Sciences. https://doi.org/10.17305/bjbms.2021.5576.

Danny N. Legge, Tracey J. Collard, Ewelina Stanko, Ashley J. Hoskin, Amy K. Holt, Caroline J. Bull, Madhu Kollareddy, Jake Bellamy, Sarah Groves, Eric H. Ma, Emma Hazelwood, David Qualtrough, Borko Amulic, Karim Malik, Ann C. Williams, Nicholas Jones, and Emma E. Vincent. (2024). Identifying targetable metabolic dependencies across colorectal cancer progression. Molecular Metabolism. https://doi.org/10.1016/j.molmet.2024.102037.

Corti Denise, Paolo Paoli, Maura Calvani, Maria Letizia Taddei, Elisa Giannoni, Scott Kopetz, Syed Mohammad Ali Kazmi, Maria Pia Morelli, Piergiorgio Pettazzoni, Elena Sacco, Anna Caselli, Marco Vanoni, Matteo Landriscina, Paolo Cirri, and Paola Chiarugi. (2015). 5-fluorouracil resistant colon cancer cells are addicted to OXPHOS to survive and enhance stem-like traits. Oncotarget. https://doi.org/10.18632/oncotarget.5991.

Kelsey T. Baker, Deborah Nachmanson, Sumeet Kumar, Mary J. Emond, Cemal Ussakli, Teresa A. Brentnall, Scott R. Kennedy, and Rosa A. Risques. (2019). Mitochondrial DNA mutations are associated with ulcerative colitis preneoplasia but tend to be negatively selected in cancer. Molecular Cancer Research. https://doi.org/10.1158/1541-7786.MCR-18-0520.

Xianlei Cai, Xueying Li, Chao Liang, Miaozun Zhang, Yuan Xu, Zhebin Dong, Yihui Weng, and Weiming Yu. (2023). Mitochondrial DNA copy number is associated with crohn’s disease: A comprehensive mendelian randomization analysis. Scientific Reports. https://doi.org/10.1038/s41598-023-48175-5.

Romualdas Vaisvila, Vamsi K. C. Ponnaluri, Zhe Sun, and others. (2021). Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA. Genome Research. https://doi.org/10.1101/gr.266551.120.

Ping Guo, Hailing Zheng, Yihan Li, Yuntong Li, Yue Xiao, Jin Zheng, Xingqiang Zhu, Huan Xu, Zhi He, Qian Zhang, Jinchun Chen, Mingshan Qiu, Min Jiang, Pingguo Liu, and Hongliang Chen. (2023). Hepatocellular carcinoma detection via targeted enzymatic methyl sequencing of plasma cell-free DNA. Clinical Epigenetics. https://doi.org/10.1186/s13148-022-01420-6.