Report on DNA Methylation and Cancer Cell Senescence: Implications for Sustainable Development Goals

Abstract

Inhibitors of DNA methylation, such as 5-aza-deoxycytidine, are commonly used in experimental and clinical settings but cause DNA damage alongside loss of DNA methylation, complicating the understanding of their effects. This study disentangles the effects of decreased DNA methylation from DNA damage in cancer cells using degron alleles targeting key DNA methylation regulators. Findings reveal that cancer cells with reduced DNA methylation, absent DNA damage, enter cellular senescence characterized by G1 arrest, senescence-associated secretory phenotype (SASP), and SA-β-gal positivity. This senescence is independent of p53 and Rb pathways but involves cytoplasmic p21 and nuclear cGAS functioning independently of STING. Xenograft experiments confirm that tumor cells can be induced to senesce in vivo by decreasing DNA methylation. These insights have significant implications for therapeutic strategies aligned with Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure).

Introduction

DNA methylation is a prevalent epigenetic modification in mammals, influencing genome activity without altering DNA sequence. Approximately 80% of cytosines in CpG contexts are methylated in differentiated cells. DNA methylation patterns vary dynamically across cell types, maintained by enzymes DNMT1 and UHRF1. In cancer cells, aberrant DNA methylation patterns include global hypomethylation and focal hypermethylation, affecting tumor suppressor gene expression. Epigenetic therapies aim to modulate these patterns to inhibit cancer progression.

Results

1. Induction of Senescence by Prolonged Depletion of DNMT1 and/or UHRF1

1. Using colorectal cancer cell lines with auxin-inducible degron systems, prolonged depletion of DNMT1 and/or UHRF1 resulted in complete protein degradation and progressive loss of DNA methylation.
2. Cell proliferation decreased significantly, with accumulation of cells in G1 phase and reduced S phase entry, indicating cell cycle arrest.
3. Apoptosis was not significantly increased, suggesting senescence as the primary cause of growth arrest.
4. Senescence markers, including enlarged nuclei, increased SA-β-gal activity, and irreversible growth arrest, were observed, correlating with the extent of DNA methylation loss.

2. Senescence is Linked to DNA Methylation Loss Across Multiple Cancer Types

• Rescue experiments with mutant UHRF1 and DNMT1 variants demonstrated that maintenance of DNA methylation prevents senescence, confirming the causal role of methylation loss.
• Senescence induction was consistent across various cancer cell lines from colon, lung, breast, cervix, and osteosarcoma origins.
• Treatment with a DNMT1-selective inhibitor (GSK3685032) that does not cause DNA damage also induced senescence, validating the findings.

3. Molecular Characterization of Senescence

1. Bulk RNA sequencing revealed an interferon response and SASP gene expression accompanying senescence.
2. Gene set enrichment analysis showed downregulation of cell cycle genes and upregulation of interferon-related pathways.
3. Joint analysis of DNA methylation and gene expression identified genes activated by promoter demethylation, including EMT and p53 pathway genes, but interferon and SASP responses were not directly linked to promoter demethylation.
4. Expression of transposable elements increased moderately, with changes in chromatin marks such as increased H3K9 acetylation.
5. Single-cell RNA sequencing revealed heterogeneous cell populations with distinct proliferation, interferon, p53, and SASP signatures.

4. Senescence is Independent of DNA Damage, p53, and p16/Rb Pathways

• Immunofluorescence and western blot analyses showed no increase in DNA damage markers (γ-H2AX, Chk1/2 phosphorylation, p53) upon depletion of DNMT1 and/or UHRF1.
• Knockdown of p16 or inactivation of p53 and Rb via HPV16 E6/E7 proteins did not prevent senescence, indicating non-canonical pathways are involved.
• Senescence was observed in multiple cell lines lacking functional p53 and/or Rb, supporting the independence from these pathways.

5. Role of p21 and cGAS in Senescence

1. p21 expression increased in senescent cells, predominantly localized in the cytoplasm, where it inhibits apoptosis and promotes cell survival.
2. Knockdown of p21 increased apoptosis and reduced cell viability in senescent cells but did not affect SASP expression.
3. cGAS expression was induced upon DNA methylation loss, correlating with promoter demethylation, and localized mainly in the nucleus.
4. Knockdown of cGAS reduced SA-β-gal positivity and SASP gene expression, indicating its role in SASP activation.
5. STING knockout did not affect senescence or SASP, suggesting cGAS acts independently of STING in this context.

6. In Vivo Validation of Senescence Induction

• Xenograft models using HCT116 cells with degron alleles for UHRF1 and/or DNMT1 showed efficient protein degradation upon treatment with auxin analog 5-Ph-IAA.
• Treated tumors exhibited reduced growth, decreased proliferation (Ki67 staining), increased senescence (SA-β-gal staining), and macrophage infiltration (F4/80 marker), indicating an inflammatory response.
• These results demonstrate that loss of DNA methylation induces cancer cell senescence in vivo, with potential implications for tumor suppression and immune system engagement.

Discussion

Implications for Sustainable Development Goals (SDGs)

1. SDG 3: Good Health and Well-being
   • Understanding the mechanisms by which DNA methylation loss induces senescence in cancer cells opens avenues for novel cancer therapies that minimize DNA damage, reducing side effects and improving patient outcomes.
   • Therapeutic strategies targeting DNA methylation maintenance proteins like UHRF1 and DNMT1 can induce senescence even in tumors lacking p53 or Rb, addressing treatment resistance.
2. SDG 9: Industry, Innovation, and Infrastructure
   • Development of specific inhibitors or degraders for UHRF1 and DNMT1 represents innovative approaches in cancer treatment, fostering pharmaceutical advancements.
   • Use of degron technology exemplifies cutting-edge research tools enabling precise modulation of protein function, accelerating drug discovery and development.
3. SDG 10: Reduced Inequalities
   • Advancing therapies effective across diverse cancer types and genetic backgrounds may contribute to equitable healthcare by providing treatment options for patients with resistant tumors.

Conclusions and Future Directions

• Loss of DNA methylation induces a distinct form of cellular senescence in cancer cells, characterized by non-canonical pathways involving cytoplasmic p21 and nuclear cGAS.
• Senescence induction is independent of DNA damage, p53, and p16/Rb pathways, broadening the scope of potential therapeutic targets.
• In vivo evidence supports the translational potential of targeting DNA methylation maintenance for cancer therapy.
• Future research should explore the molecular mechanisms linking DNA methylation loss to senescence, the role of chromatin reorganization, and the development of specific UHRF1 inhibitors.
• Integrating these findings with SDG frameworks can guide sustainable and equitable innovations in cancer treatment.

1. Sustainable Development Goals (SDGs) Addressed or Connected to the Issues Highlighted in the Article

1. SDG 3: Good Health and Well-being
   • The article focuses on cancer biology, specifically mechanisms of DNA methylation and senescence in cancer cells, which directly relates to improving health outcomes and combating diseases such as cancer.
   • Therapeutic implications discussed include novel approaches to cancer treatment by inducing senescence in tumor cells without causing DNA damage.
2. SDG 9: Industry, Innovation and Infrastructure
   • The study employs advanced molecular biology techniques such as degron alleles, RNA sequencing, and genome editing, reflecting innovation in biomedical research infrastructure.
   • Development of novel inhibitors and potential drug targets like UHRF1 suggests contributions to innovative cancer therapies.
3. SDG 17: Partnerships for the Goals
   • The research involves collaboration among multiple institutions and international teams, exemplifying partnerships to advance scientific knowledge and health solutions.

2. Specific Targets Under Those SDGs Identified Based on the Article’s Content

1. SDG 3: Good Health and Well-being
   • Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
   • Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including encouraging innovation and substantially increasing the number of research and development workers.
3. SDG 17: Partnerships for the Goals
   • Target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation.

3. Indicators Mentioned or Implied in the Article to Measure Progress Towards the Identified Targets

1. For SDG 3 (Good Health and Well-being)
   • Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease.
   • Indicator 3.b.1: Proportion of the population with access to affordable essential medicines and vaccines on a sustainable basis.
   • Implied indicators from the article:
     • Effectiveness of cancer therapies measured by tumor growth inhibition and induction of senescence markers (e.g., SA-β-gal positivity, SASP expression).
     • Measurement of proliferation markers such as Ki67 to assess tumor cell growth.
     • Levels of DNA methylation as a biomarker for therapeutic efficacy.
2. For SDG 9 (Industry, Innovation and Infrastructure)
   • Indicator 9.5.1: Research and development expenditure as a proportion of GDP.
   • Indicator 9.5.2: Researchers (in full-time equivalent) per million inhabitants.
   • Implied indicators from the article:
     • Number and quality of innovative molecular tools and inhibitors developed (e.g., degron alleles, DNMT1 inhibitors).
     • Publication and dissemination of scientific findings.
3. For SDG 17 (Partnerships for the Goals)
   • Indicator 17.6.2: Fixed Internet broadband subscriptions per 100 inhabitants, by speed.
   • Implied indicators from the article:
     • Number of collaborative research projects and publications involving international and interdisciplinary teams.
     • Data sharing and open access to research data (as indicated by data availability statements).

4. Table of SDGs, Targets, and Indicators Relevant to the Article

Source: nature.com