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Abstract

Mitochondrial sites of contact with the nucleus, hereafter referred to as Nucleus-Associated Mitochondria (NAM), are specialised domains that enable communication, influencing cellular function. Previous studies have shown that these contacts can be stabilised by protein scaffolds acting as tethers to promote retrograde signalling, particularly during apoptotic stress. This is facilitated via the mitochondrial protein TSPO. In this study, we have investigated a mitochondrial DNA (mtDNA)-depleted (ρ⁰) 4T1 cell model to further inform the role of NAM in retrograde communication between corrupted mitochondria and the nucleus. Our data report an increase in NAM frequency in mtDNA-depleted cells compared to the mtDNA-retaining parental 4T1 line. Using a combination of cellular assays, transmission electron microscopy, and epigenetic profiling, we have found that under conditions of mtDNA loss, mitochondria become enriched in TSPO, evading mitophagic clearance and are prone to forming stable contacts with the nucleus. This coincides with an extreme reduction in DNA methylation, as well as histone modifications associated with chromatin decondensation.

Keywords

mitochondria contact sites NAM TSPO VDAC

Introduction

Intracellular organelles are often connected through regions of close apposition known as membrane contact sites (MCS) (Jing et al., 2020; Prinz et al., 2020). As the understanding of MCS biology and function has grown, it has dramatically changed the traditional view of the interplay between biochemically compartmentalised entities. A growing body of research is at the forefront of uncovering a novel and emerging form of mitochondrial communication with the nucleus, which occurs via direct physical contacts between the two organelles (Desai et al., 2020; Strobbe et al., 2021; Campanella and Kannan, 2024). Although the existence of interactions between mitochondria and the nucleus dates back to the 1950s (Walker and Moraes, 2022), the molecular mechanisms underlying these interactions and their pathophysiological relevance have only begun to be elucidated. It is now understood that during mitochondrial stress, mitochondria trigger intense communication with the nucleus (Kotiadis et al., 2014; Kim and Lee, 2024). We have uncovered that this process is facilitated by the redistribution of mitochondria within the cell towards the nucleus. This proximity allows mitochondria to form physical tethers with the nuclear membrane in response to chemical stress. This process requires stabilisation of the outer mitochondrial membrane by the protein known as Translocator Protein (TSPO) (Desai et al., 2020).

These structures, referred to as nuclear-associated mitochondria (NAM), play a crucial role in mitochondrial retrograde signalling, a communication pathway where mitochondria influence nuclear gene expression (Strobbe et al., 2021; Campanella and Kannan, 2024). Among their functions, NAM mediate the transport of cholesterol to the nucleus, supporting transcriptional regulation and priming cellular pro-survival responses (Desai et al., 2020). Beyond its structural role in NAM formation, TSPO is essential for the redistribution of mitochondria toward the nucleus and helps prevent their removal via mitophagy, preserving mitochondrial integrity during stress (Gatliff et al., 2014; Gatliff and Campanella, 2016; Desai et al., 2020).

While the full spectrum of NAM functions and mechanisms remains to be clarified, they are present across a wide range of organisms, including mammals (Desai et al., 2020), yeast (Eisenberg-Bord et al., 2021), and protozoa (Ovciarikova et al., 2024). This suggests that these contacts represent an evolutionarily conserved core element in cellular homeostasis (Walker and Moraes, 2022; Campanella and Kannan, 2024). Mitochondrial DNA (mtDNA) encodes essential subunits of the respiratory chain complexes and therefore plays a critical role in maintaining mitochondrial function. Damage to mtDNA, whether by means of oxidative stress, mutations, or depletion, leads to impaired mitochondrial bioenergetics and altered production of reactive oxygen species (ROS), which can modulate cellular stress and dysfunction (Shokolenko et al., 2009; El-Hattab and Scaglia, 2013; Ryzhkova et al., 2018). mtDNA damage is a hallmark of various pathologies and is intimately linked with mitochondrial quality control mechanisms, including mitophagy, biogenesis and mtDNA repair (Lee and Wei, 2005; Srivastava, 2017; Rong et al., 2021; Vodicka et al., 2025). Mounting evidence suggests a hierarchical relationship between mtDNA depletion and nuclear epigenome remodelling (Delsite et al., 2002; Castegna et al., 2015; Santos, 2021) whereby damage or depletion of mtDNA triggers mitochondrial dysfunction. In turn, this initiates retrograde signalling pathways that influence nuclear gene expression and chromatin architecture (Butow and Avadhani, 2004; Jazwinski, 2013; Stoccoro and Coppedè, 2021). This signalling cascade could result in widespread epigenetic changes, including DNA methylation and histone modification, ultimately reprogramming cellular identity and defining stress responses (Smiraglia et al., 2008; Vivian et al., 2017; Lozoya et al., 2019). Understanding this interplay could be essential for deciphering how mitochondrial perturbations contribute to cellular adaptation, disease progression, and ageing. This study aims to further elucidate the mechanisms and functional implications of mito-nuclear contacts by examining an mtDNA-depleted ρ⁰ (Rho 0) model derived from 4T1 cells. The prevalence of mito-nuclear contact sites in this model is assessed alongside mitochondrial proteins and epigenetic markers, providing insight into how mitochondrial dysfunction influences nuclear communication and cellular adaptation.

Materials and Methods

Cell Culture

ρ⁰ cells were generated by long-term maintenance in the presence of sub-lethal doses of ethidium bromide (Tan et al., 2015). Cells were maintained under standard culture conditions (37 °C, 5% CO₂). Medium: RPMI 1640 ATCC, 10% FBS, Penicillin/Streptomycin, and 50 μg/mL uridine. Drugs were pre-diluted in DMSO.

MEF WT, TSPO-/-, and TSPO overexpressing cell lines were all maintained at 37°C under humidified conditions and 5% CO2 and grown in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, penicillin (100 U/ml), and streptomycin (100 mg/ml).

TSPO KO MEF cells were generated by transient transfection with GeneArt™ CRISPR Nuclease Vector with OFP Reporter Kit, using a gRNA (5′-AGAGCTGGGAGGTTTCACAGAGG −3′) targeting exon 2. TSPO overexpressing MEF were generated by generating a lentivirus expressing full length ms TSPO(atgCCTGAATCCTGGGTGCCTGCCGTGGGCCTCACTCTGGTGCCCAGCCTGGGGGGCTTCATGGGAGCCTACTTTGTACGTGGCGAGGGCCTCCGGTGGTATGCTAGCTTGCAGAAACCCTCTTGGCATCCGCCTCGCTGGACACTGGCTCCCATCTGGGGCACACTGTATTCAGCCATGGGGTATGGCTCCTACATAGTCTGGAAAGAGCTGGGAGGTTTCACAGAGGACGCTATGGTTCCCTTGGGTCTCTACACTGGTCAGCTGGCTCTGAACTGGGCGTGGCCCCCCATCTTCTTTGGTGCCCGGCAGATGGGCTGGGCCTTGGCCGATCTTCTGCTTGTCAGTGGGGTGGCGACTGCCACAACCCTGGCTTGGCACCGAGTGAGCCCGCCGGCTGCCCGCTTGCTGTACCCTTACCTGGCCTGGCTGGCTTTTGCCACCGTGCTCAACTACTATGTATGGCGTGATAACTCTGGCCGGCGAGGGGGCTCCCGGCTCCCAGAGTGA). TSPO sequence was purchased from twistbioscience in pTwist CMV Hygro plasmid. Lentivirus was produced using 3rd generation lentiviral protocol previously published (Dull et al., 1998). Lentivirus was produced using : pRSV-Rev (Plasmid #12253, https://www.addgene.org/12253/), PMDLg/pRRE (Addgene#12251 https://www.addgene.org/12251/), pMD2.G (Plasmid #12259) https://www.addgene.org/12259/ and pTwist CMV Hygro (Gene of Interest)

MEF WT cells were transduced with lentivirus twice, 48 h and 72 h after transfection. 48 h Transduction was performed by collecting media from HEK293 T cells and replacing it with fresh 3 ml of media. Collected supernatant was filtered with a 0.22uM filter and added to MEF cells together with 3 ml fresh media. At 72h, supernatant was collected from the same HEK293 T cells and MEF cells were transduced again. 48 h after transduction 7-day Blasticidine S hydrochloride (Sigma, 15205-25MG) selection(8 µg/mL) was started. Once selection was completed, single-cell cloning was performed.

Immunocytochemistry

Post treatment, cells were fixed (methanol), permeabilised (0.5% Triton-X), then blocked (1 h, 10% GS, 3% BSA). Primary antibody incubation (16 h, 4°C). Secondary antibody incubation (1 h, RT). Cells were mounted with DAPI mounting medium. Antibodies are displayed in Table 1. Fluorescent confocal imaging was carried out using a Leica SP8 Confocal Microscope or Zeiss LSM 800. Acquisition was carried out with an oil immersion lens (x63). A minimum of 20 images was acquired per well. Analysis via ImageJ. Colocalization analysis was carried out using Fiji (ImageJ). The total colocalization area value (represented by the yellow channel) was divided by the total area of red. Value of yellow/red indicates the portion of mitochondria that are colocalized with autophagic machinery. Data were presented as mean colocalization for each treatment type, normalised to control values.

Table 1.

Antibodies Used in Immunocytochemistry Assays.

Primary Antibody	Concentration	Supplier
SSBP1 (Rb)	1:400	proteintech, 12212-1-AP
Anti-DNA AC-30-10 (Ms)	1:400	PROGEN, 61014
MTCO1 (Ms)	1:400	Abcam, 14705
ATPB (Ms)	1:400	Abcam, 14730
SQSTM1/P62 (Rb)	1:400	Abcam, 109012
Secondary Antibody
Goat anti Mouse Alexa Flour 568	1:1000	Invitrogen
Goat anti Rabbit Alexa Flour 488	1:1000	Invitrogen

Immunoblotting

Electrophoresis and Protein Transfer

Whole cell lysates were boiled (95°C) and run on a 12% polyacrylamide gel (1.5 h, 110 V) against a protein ladder. Transfer to PVDF membrane was via transblot cell (Bio-Rad) (110 V, 70 min).

Immunoprobing

Total protein was recorded with No-Stain™ Protein Labelling Reagent. Membranes were blocked in 5% milk (1 h, RT), then incubated with the primary antibody overnight (4°C). Then incubated (1 h, RT) with secondary antibody. Antibodies are displayed in Table 2. Band visualisation was carried out via the Amersham ECL detection kit. Density was analysed using ImageJ to generate a lane profile of the ROI. Curve area was calculated to measure density. Values normalised to total protein.

Table 2.

Antibodies Used in Western Blot Assays.

Primary Antibody	Concentration	Supplier
Anti-TSPO/PBR (Rb)	1:1000	Abcam, 109497
Anti-VDAC1/Porin (Rb)	1:1000	Abcam, 154856
Secondary Antibody
Goat Anti-Rabbit Immunoglobin/HRP	1:5000	Dako, p0448

Transmission Electron Microscopy

Electron micrographs were acquired at the QMUL TEM facility. Cells were fixed in 2–4% glutaraldehyde (Sorensen). Images were captured of the entire cell. Higher magnification images were taken around the nucleus. Pixel measurements were converted to µm against the scale bar via ImageJ. Contacts were measured using guidelines for TEM analysis and for mitochondrial contacts (Lam et al., 2021).

Proximity Ligation Assay (PLA)

To visualise and quantify NAM events in intact cells, WT, TSPO KO, and TSPO overexpressing MEF cells were seeded in 8-well chamber slides and fixed in 4% PFA after 24 h. PLA was performed by using the Duolink^® PLA Red reagents (DUO92008, Sigma-Aldrich). Cells were permeabilised with 0.1% Triton X-100 in PBS. Blocking was performed with the Duolink^® Blocking Solution for 1 h at 37 °C. Cells were incubated with the primary antibody solution (Duolink Antibody Diluent) overnight at 4 °C and then washed twice in 1× Wash Buffer A for 5 min. The following primary antibodies were used: anti anti-Lamin B1 (1:500; Proteintech, 80906-1-RR) and anti-TOMM20 (1:400, Abcam, ab56783).

PLA probes were diluted 1:5 in Duolink^® Antibody Diluent. Cells were incubated with the PLA probes for 1 h at 37 °C and then washed twice in 1× Wash Buffer A for 5 min. Ligation was performed for 30 min at 37 °C with 1 µl Ligase in 40 µl Duolink^® Ligation Solution per reaction, followed by two washes in 1× Wash Buffer A for 5 min. 0.5 µl Polymerase was added to 40 µl Duolink^® Amplification Buffer per reaction for probe amplification. Coverslips were incubated in a pre-heated humidity chamber for 100 min at 37 °C. Amplification was followed by two washes in 1× Wash Buffer B for 10 min, followed by one wash with 0.01× Wash Buffer B. A cover glass pre-heated at 37 °C was then mounted using a mounting medium with DAPI (Abcam, ab104139). For analysis of PLA, images were acquired using a Nikon Ti2-E inverted microscope platform with Yokogawa CSU-W1 SoRa spinning disk confocal unit (Nikon; Tokyo, Japan) and a 60× oil Plan apochromat λD objective (Nikon). 3D deconvolution of images acquired with SDCM was conducted with the built-in 3D deconvolution module of the NIS-Elements Advanced Research software, using the Lucy-Richardson deconvolution algorithm with the following parameters: numerical aperture: 1.42; refractive index: 1.515; calibration: 0.282 μm; z-step: 0.1 μm; noise level: medium; iterations: 11.Image analysis was conducted using ImageJ (CLSM images) by measuring the area of puncta dots over the area of the nuclei.

5mC Methylation

5mC methylation quantification was achieved via MethylFlash™ Methylated 5mC DNA Quantification Kit, as per the manufacturer's instructions.

Bead-Chip Profiling

Procedure carried out by the Blizard Institute genomics department (QMUL) via Infinum Mouse Methylation BeadChip. Data analysis was via GenomeStudio Methylation Module v1.8. Methylation status was ascertained via assessment of β-value (Chowdhury et al., 2011). For each locus, a mean β-value (Avgβ) with a range of 0–1 (0 = unmethylated, 1 = completely methylated) was generated.

Differential Methylation Analysis

Differential methylation was determined via an Illumina custom model. Software categorises differentially methylated CpGs by assigned difference score (DiffScore) (Chudin et al., 2006), a transformed P-value providing directionality of difference between average signals of each group. Calculated as follows:

\begin{aligned} DiffScore = & 10 s g n (β_{condition} - β_{reference}) l o g_{10} P, \\ where β_{condition} = β_{TL} / β_{PTL}, β_{reference} \\ = β_{TNL} or β_{condition} = β_{TNL} / β_{PTL}, \\ β_{reference} = β_{TL}) . \end{aligned}

DiffScores were converted to Absolute DiffScore for each detected site as per the following formula:

Absolute DiffScore = ∣ DiffScore ∣ .

Absolute DiffScores were used to determine significance based on equivalent P value:

P value = 10^{(- | D I F F S C O R E | / 10)} .

Statistical Analysis

Data presented as mean ± SD. Normally distributed data were subject to unpaired t-tests or ANOVA (multiple-group). Mann–Whitney U test was used for non-normally distributed variables. Significance was considered P < 0.05. Pearson's correlation test was used to determine linear correlation (Parab and Bhalerao, 2010). Analyses were performed in Microsoft Excel, GraphPad, or GenomeStudio.

Results

mtDNA-Depleted Cells Display Increased Mitochondria–Nucleus Contacts

To confirm mtDNA depletion in ρ⁰ cells, fluorescent immunolabelling of the single-stranded DNA-binding protein SSBP1 was performed. SSBP1 selectively binds single-stranded mtDNA during replication (Chase and Williams, 1986; Gustafson et al., 2019). Mean SSBP1 fluorescence intensity was compared between ρ⁰ and wild-type 4T1 cells, together with double-stranded DNA staining and DAPI to distinguish mtDNA from nDNA. As expected, wild-type 4T1 cells displayed a clear mitochondrial SSBP1 pattern, whereas ρ⁰ cells showed no detectable signal (Figure 1A). Quantitative analysis confirmed a significant reduction in mean SSBP1 fluorescence intensity in ρ⁰ cells (Figure 1B).

Figure 1.

The number of mitochondrial sites of contact with the nucleus is greater in cells devoid of mtDNA. (A) Representative images obtained in 4T1 Wildtype (left) and ρ⁰ (right) cells, immunolabelled against SSBP1 (green) and Anti-DNA (red). Nuclei were stained with DAPI (blue); Scale bar = 10 µM. (B) Quantification of SSBP1 mean intensity normalised to background fluorescence (4T1: 42.28 ± 7.016; ρ⁰: 0.5512 ± 2.188) (C) Representative images obtained in Wildtype (left) and ρ⁰ (right) cells, immunolabelled against MT-CO1 (green). Nuclei were stained with DAPI (blue); Scale bar = 10 µM. (D) Quantification of MT-CO1 mean intensity normalised to background fluorescence (4T1:47.7 ± 6.4; ρ⁰: 0.99 ± 0.94); n = 10. (E) Visual representation of normal mitochondrial distribution (i) and mitochondrial redistribution leading to contact formation (ii). (F) Representative TEM images of mitochondria (red), which are in relative proximity of the nucleus (cyan), circles indicate sites of contact between a mitochondrion and the nucleus, scale bar = 500 nm (G) Quantification of mean number of contact sites per cell (4T1:0.12 ± 0.08; ρ⁰: 0.79 ± 0.21). (H) Quantification of mean cell area (4T1:30776 ± 10,357; ρ⁰: 22,874 ± 10,173). (I) Quantification of mean nucleus area (4T1: 11,270 ± 5185; ρ⁰: 7538 ± 5407). * = P ≤ 0.05, ** = P ≤ 0.01, n = 20.

This finding was further validated by assessing the expression of MT-CO1, a mitochondrial protein encoded by mtDNA (Taanman, 1999). Consistent with mtDNA depletion, ρ⁰ cells lacked detectable MT-CO1, with a markedly reduced mean fluorescence intensity compared with 4T1 cells (Figure 1C and D).

Transmission electron microscopy (TEM) was used to examine ultrastructural differences between 4T1 and ρ⁰ cells. Mitochondria located in proximity to the nucleus were identified, and their distance from the nuclear envelope was measured (Figure 1F). Mitochondria–nucleus contact sites were defined as either direct contact or extreme apposition, with a maximum separation of <30 nm, in accordance with established criteria (Scorrano et al., 2019). Quantification revealed a striking increase in contact site frequency in ρ⁰ cells, corresponding to an approximately 6.7-fold elevation compared with 4T1 cells (Figure 1G).

In parallel, morphometric analysis showed that ρ⁰ cells were significantly smaller, exhibiting a 26% reduction in mean cell area (Figure 1H) and a 33% reduction in nuclear area (Figure 1I).

Assessment and Modulation of the NAM-Forming Protein TSPO

Formation of nucleus-associated mitochondria (NAM) is mediated by multiprotein complexes that stabilise and bridge the two organelles. One key mitochondrial component is the translocator protein TSPO, whose overexpression has been shown to increase mitochondria–nucleus contacts (Figure 2A). We therefore investigated whether the increased prevalence of NAM in ρ⁰ cells was associated with altered TSPO expression.

Figure 2.

mt-DNA depleted cells present increased expression of the NAM tethering protein TSPO.

Immunoblot analysis revealed that the mitochondrial outer membrane protein VDAC, which functionally interacts with TSPO (Gatliff et al., 2014), was downregulated by more than 50% in ρ⁰ cells (Figure 2B and C). In contrast, TSPO expression was nearly doubled (Figure 2D). Consequently, the TSPO:VDAC ratio was markedly elevated in ρ⁰ cells (Figure 2E).

To determine whether increased TSPO expression contributed directly to enhanced NAM formation, we attempted TSPO downregulation. However, genetic suppression of TSPO was incompatible with ρ⁰ cell viability (data not shown), prompting a pharmacological approach. Cells were treated with the TSPO-specific ligand PK11195, which impairs TSPO-mediated cholesterol handling and reduces its functional activity.

Following PK11195 treatment, mito-nuclear contacts were reassessed by TEM (Figure 2F). As expected, untreated ρ⁰ cells exhibited a significantly higher mean number of contact sites (1.2-fold increase). PK11195 had no significant effect on contact frequency in 4T1 cells, where TSPO levels were unchanged. In contrast, PK11195 treatment of ρ⁰ cells resulted in a significant reduction (0.81-fold) in the mean number of contact sites (Figure 2G).

To further examine the relationship between TSPO and mito-nuclear proximity, a proximity ligation assay targeting Lamin B1 (nucleus) and TOMM20 (mitochondria) was performed in mouse embryonic fibroblasts (MEFs) following TSPO knockout or overexpression (Figure 2H). TSPO knockout produced a modest but significant decrease in mito-nuclear proximity, whereas TSPO overexpression led to a greater than threefold increase in contact frequency (Figure 2I).

mtDNA Depletion Drives Redistribution of Epigenetic Markers

To assess whether mtDNA depletion affects the nuclear epigenome, global DNA methylation was measured by ELISA-like quantification of 5-methylcytosine (5mC) in nDNA isolated from 4T1 and ρ⁰ cells (Figure 3A). ρ⁰ cells exhibited an approximately sevenfold reduction in mean 5mC levels compared with 4T1 cells (Figure 3B).

Figure 3.

Epigenetic analysis of mt-DNA depleted cells. (A) Representative standard curve of optical density (OD) plotted against a diluted range of 5mC positive control, used as a reference to calculate 5mC % of DNA samples. (B) Comparison of global 5mC % between 4T1 and ρ⁰ cells Represented as 5mC % of sample DNA normalized against total DNA (4T1:0.47 ± 0.2151; ρ⁰: 0.07 ± 0.02) N = 3. (C) Representative images of histone H3K27Me3 (red) obtained in wild-type 4T1 and ρ⁰ cells. Nuclei were stained with DAPI (blue); Scale bar = 10 µM. Cells were treated for 48 h with PK11195 (0.2 µM) or vehicle control (DMSO). (D) Quantification of H3K27Me3 mean intensity normalized to DAPI (4T1: 0.94 ± 0.43; 4T1 PK11195: 0.95 ± 0.35; ρ⁰: 0.73 ± 0.24; ρ⁰ PK11195: 0.75 ± 0.27). (E) Representative images of histone H3K27Ac (green) obtained in wild-type 4T1 and ρ⁰ cells. Nuclei were stained with DAPI (blue); Scale bar = 10 µM. Cells were treated for 48 h with PK11195 (0.2 µM) or vehicle control (DMSO). (F) Quantification of H3K27Ac mean intensity normalized to DAPI (4T1: 1.02 ± 0.33; 4T1 PK11195: 1.04 ± 0.33; ρ⁰: 1.13 ± 0.33; ρ⁰ PK11195: 1.18 ± 0.41). * = P ≤ 0.05, **** = P ≤ 0.0001.

We next investigated histone modifications by fluorescent immunolabelling of H3K27 trimethylation (H3K27me3) and acetylation (H3K27ac) in both cell types (Figure 3C and E). Analyses were also performed following PK11195 treatment, which reduced NAM formation (Figure 2F and G). Quantification revealed a significant 22% decrease in H3K27me3 levels in ρ⁰ cells (Figure 3D), accompanied by an 11% increase in H3K27ac (Figure 3F). PK11195 treatment did not significantly affect either histone mark. These findings led to a wider investigation of epigenomic modifications using genome-wide DNA methylation profiling.

Methylation Microarray Profiling Reveals Patterns Associated with NAM Formation

Genome-wide DNA methylation profiling of 4T1 and ρ⁰ nDNA was performed using the Infinium Mouse Methylation BeadChip. Probe detection rates exceeded 98% (P ≥ 0.05), indicating high data quality (Figure 4A). Comparison of median average β values (Avgβ) revealed a reduction of 0.13 in ρ⁰cells relative to 4T1 cells (Figure 4B).

Figure 4.

Methylation profile associates with NAM level. (A) Number of detected Illumina probes (detection p < 0.05). Mean values: 4T1 = 282418.67 (98.40%); ρ⁰ = 283119 (98.30%). Total probes = 285000. n = 3. (B) Boxplot comparison of relative methylation levels, represented by average beta values (Avgβ) across all jointly detected CpG sites. Median values: 4T1 = 0.6955; ρ⁰= 0.5648. P = <0.0001. (C) Scatterplot displaying the relationship between Avgβ of 4T1 and ρ⁰ cells. R² = 0.6640 (D) Volcano plot showing possibly differentially methylated CpG sites. X-axis represents difference in magnitude of signal intensity between probes for each group, expressed as δβ = β (ρ⁰) - β(4T1). Y-axis represents the absolute DiffScore with a score > ± 13 being deemed significant (equivalent to P-value of 0.05) (red) and < ± 13 as non-significant (black). (E) Pie charts representing total significantly differentiated (red) and non-differentiated (black) sites detected / the percentage of which are hypomethylated (purple) and hypermethylated (yellow). (F) Distribution of absolute DiffScores across chromosomes (20 = X, 21 = Y). Significant (red) and non-significant (black). (G) Heatmap of significantly differentiated methylated sites denoted by hypermethylated (yellow) and hypomethylated (purple) loci. Ordered by descending DiffScore value from positive to negative.

Probe-wise comparison demonstrated low overall correlation between cell types (R² = 0.66), consistent with extensive differential methylation (Figure 4C). Differential methylation analysis identified significant changes (DiffScore ≥ ± 13) at 143,589 sites, representing over 50% of interrogated loci (Figure 4E). Of these, 103,138 sites were hypomethylated (negative δβ), whereas 40,451 were hypermethylated (positive δβ), yielding a negatively skewed volcano plot (Figure 4D).

Chromosomal distribution analysis revealed significant differential methylation across all mouse chromosomes (Figure 4F), with chromosome 2 harbouring the highest number of affected loci (10,599) and chromosome Y the fewest (1,638). Heat map visualisation of Avgβ values across significantly altered sites highlighted a global shift from hypermethylation towards hypomethylation following mtDNA depletion (Figure 4G).

Discussion

Mitochondria engage in dynamic crosstalk with other organelles by means of MCS, specialised structures that bring membranes of distinct organelles into proximity without fusion. These contacts orchestrate critical cellular processes, including calcium signalling, lipid trafficking, and metabolic coordination. Notably, the discovery of nuclear-associated mitochondria (NAM) highlights a novel type of MCS that directly links mitochondria to the nucleus, offering an architectural basis for mitochondrial retrograde signalling, a process by which mitochondria influence nuclear gene expression and chromatin organisation.

A key molecular player in this context is the Translocator Protein (TSPO), which is significantly accumulated in mtDNA-depleted ρ⁰ cells (Figure 2D, E). Located in the outer mitochondrial membrane (OMM), TSPO prevents ubiquitination of mitochondrial proteins downstream of the PINK1/Parkin pathway, thereby curtailing P62 recruitment and repressing mitophagy (Gatliff et al., 2014; Gatliff and Campanella, 2016). Conversely, VDAC proteins, which function as docking sites for Parkin and serve as ubiquitination substrates (Geisler et al., 2010; Sun et al., 2012), are reduced in ρ⁰ cells (Figure 2C, E). An elevated TSPO:VDAC ratio (Figure 2E) therefore, reflects an environment biased against mitochondrial clearance, potentially stabilising mitochondria that would otherwise be degraded. This is further supported due to ρ⁰ cells displaying a resistance to mitophagy induction (Supplementary Figure G/H). These changes raise two complementary possibilities: mitochondrial stress from mtDNA depletion could directly modulate TSPO and VDAC expression, or mitochondria inherently enriched in TSPO and depleted in VDAC may be selectively spared from mitophagy, progressively enriching this subpopulation. Either scenario suggests a mitochondrial network resistant to quality control, which could favour the persistence of mitochondria positioned closer to the nucleus (Figure 5). Importantly, TSPO is not only protective against mitophagic degradation but also instrumental in NAM formation, acting as a tether that anchors mitochondria to the nuclear envelope (Desai et al., 2020). The elevated TSPO:VDAC ratio observed aligns with earlier findings that NAM assembly requires a critical threshold of TSPO relative to VDAC, as these proteins often display coordinated regulation (Arif et al., 2016; Shoshan-Barmatz et al., 2019). The observation that PK11195, a TSPO ligand, reduces NAM frequency (Figure 2F/G) further supports TSPO's functional role in sustaining mito–nuclear contacts. PK11195 has a high affinity for TSPO, selectively binding to its active site and acting as an antagonist to receptor activity. Inhibition of TSPO activity by PK11195 may act to restore mitophagic clearance of TSPO-enriched mitochondria by reducing the associated overproduction of reactive oxygen species (ROS), thereby lowering the number of functional contact sites. This would be consistent with previous findings regarding the impact of TSPO ROS production and the capacity for PK11195 to reduce ROS levels (Gatliff et al., 2014; Sun et al., 2018; Feng et al., 2020; Seidlmayer et al., 2021). PK11195 may also diminish the protein's ability to form and maintain its tethering activity. Notably, PK11195 also modulates VDAC activity (Seidlmayer et al., 2021), suggesting that TSPO:VDAC dynamics are central to regulating NAM stability and abundance. To verify this relationship and refine the model suggested in Figure 5, future studies should implement additional modulators of TSPO/VDAC activity (e.g Etifoxine). Figure 2H/I provides further support for the pivotal influence of TSPO on contact frequency, with its knockout and overexpression having an opposing impact on the frequency of extreme spatial proximity between mitochondria and the nucleus.

Figure 5.

Proposed model for TSPO:VDAC regulation of contact sites. Working model for the regulation of mito-nuclear contacts by an altered TSPO:VDAC mitochondrial population via avoidance of the PINK1/Parkin mitophagy pathway.

Through NAM structures, mitochondria can directly influence nuclear architecture and gene expression. This may include ATP surge-driven alterations to chromatin, cell cycle, and DNA repair (Ghose et al., 2025). Our results show that ρ⁰ cells exhibit persistent mito–nuclear contacts and display extensive epigenetic reprogramming. Global methylation analysis reveals widespread loss of 5mC and predominantly negative differential methylation (Figure 4), shifting chromatin towards a more open and transcriptionally accessible state (Barlow and Bartolomei, 2014; Bommarito and Fry, 2019). This is complemented by histone modification patterns: reduced H3K27me3, associated with facultative heterochromatin and transcriptional repression (Kuzmichev et al., 2002; Saksouk et al., 2015), alongside increased H3K27ac, which promotes chromatin decondensation and active transcription (Wu et al., 2010; Dasgupta et al., 2022). Together, these changes suggest that mitochondrial dysfunction via mtDNA depletion triggers a retrograde signalling cascade culminating in epigenetic remodelling.

Although NAM appear to play a role in maintaining mito–nuclear communication under stress, short-term TSPO inhibition by PK11195 did not significantly affect the levels of H3K27me3 or H3K27ac (Figures 3C-F). This would indicate that the epigenetic modifications reported here may be independent of TSPO activity and contact site prevalence. Alternatively, it is also possible that histone modifications may be more resilient to short-term modulation of NAM activity or may require longer interventions to induce a meaningful impact. Future experiments should test chronic TSPO blockade or genetic approaches to clarify the direct contribution of NAM to chromatin remodelling.

Altogether, these findings reinforce the concept that mito–nuclear contacts represent a specialised subset of MCS critical for mitochondrial retrograde signalling. By integrating mitochondrial metabolic state and positioning, these contacts coordinate programs which enable cells to adapt to stress and maintain homeostasis. The modulation of TSPO emerges as a key regulatory axis governing both mitochondrial persistence and the physical architecture of mito–nuclear crosstalk. Understanding this axis offers new insight into how cellular adaptation and disease processes may hinge on the dynamic behaviour of membrane contact sites.

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Footnotes

Author's Note

Jiri Neuzil, Faculty of Science and First Faculty of Medicine, Charles University, Prague, Czech Republic.

Acknowledgments

I would like to extend my greatest appreciation towards my colleagues of the Mitochondrial Cell Biology and Pharmacology Research Group and the staff of QMUL for their continuous support. In particular, I would like to thank Prof. Michelangelo Campanella for supervising this project and for continuous guidance. Additional thanks to Dr. Giulia Mastroianni for assisting with electron microscope aquisition.

Author Contributions

Conceptualisation: M.C. and R.B.M.; Supervision: M.C.; Methodology: R.B.M and M.C.; Material preparation and knowledge transfer: J.N. ; Investigation & Data Collection: R.B.M., E.S and M.C.; Writing - Original Draft: R.B.M., E.S and M.C.; Writing – Review & Editing: R.B.M. J.N. and M.C.;

Consent to Participate

Not applicable

Consent for Publication

Not applicable

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Considerations

This article does not contain any studies with human or animal participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research activities led by M.C. are supported by the following funders, who are gratefully acknowledged: Biotechnology and Biological Sciences Research Council [grant numbers BB/M010384/1 and BB/N007042/1; The European Research Council COG 2018 - 819600_FIRM; Fondation ARC pour la Recherche sur le Cancer ARCLEADER2022020004901; AIRC-MFAG 21903, PRIN Project (MUR code 202252ZLSX) and Barts Charity Project (G-002762). An iCase Studentship award to M.C. by the BBSRC LiDO DTP, in partnership with Shift Bioscience, has supported R.B.M in his endeavours.

Patent

No patents or patent applications have been derived from this work.

ORCID iDs

Richard Boulton-McDonald

Eva Sidlauskaite

Michelangelo Campanella

Supplemental Material

Supplemental material for this article is available online.

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