The Impact of Histone Post-translational Modifications H3K9me on Same Gene Mutation Rate in Fission Yeast

doi:10.21203/rs.3.rs-4342402/v1

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The Impact of Histone Post-translational Modifications H3K9me on Same Gene Mutation Rate in Fission Yeast

https://doi.org/10.21203/rs.3.rs-4342402/v1

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Mutations are the driving force behind genetic variation, fueling both the oncogenesis and evolution of species. The mutation rate varies across the genome, potentially in response to chromatin organization by histone modifications and other factors. However, the exact relationship between the two is yet to be fully understood and requires further investigation. One modification involves the methylation of histone H3 at lysine 9, which creates heterochromatin and represses transcription in euchromatin to maintain genome stability for organism survival. This study aimed to determine the effect of H3K9 methylation alone, without other histone markers, on the mutation rate in fission yeast using fluctuation assays and statistical analysis. Our groundbreaking method has been proven to accurately estimate mutation rates of a single gene under two different conditions in a single experiment using one isogenic clone. Our research results demonstrate that the H3K9me markers increase the phenotypic mutation rate of the same gene. For prospective researchers, this study presents an innovative experimental approach that ensures unparalleled accuracy in gene analysis for genetics applications and epigenetic therapy.

Molecular Genetics

Bioinformatics

histone modification

H3K9me3

mutation

chromatin

fluctuation assay

yeast

1.1 The Significance of Genetic Material

Genetic information is crucial for the growth and survival of human beings. Every one of us is made up of trillions of cells containing genetic material that plays a significant role in shaping our identity as human beings and all other cellular organisms. This genetic material, such as deoxyribonucleic acid (DNA) [1], is packaged and condensed by nuclear proteins to form chromatin and categorized into heterochromatin and euchromatin based on the condensation level[2] [3]. In essence, genetic material like DNA is responsible for three fundamental functions [4]: the genotypic function, which involves replication; the phenotypic function, which affects gene expression; and the evolutionary function, which involves mutations to control the growth and development of the organism, enabling it to adapt to environmental changes

One of the biggest challenges affecting human development in daily life is changes in genetic information, also known as mutation. In other words, DNA is constantly at risk of being damaged due to metabolic by-products, DNA processing enzymes, errors during DNA synthesis, and other environmental or external mutagens. This damage causes spontaneous mutations, which can provide new genetic variability that helps organisms adapt to environmental changes or threaten the epigenome by altering how genetic information is accessed and expressed. To solve these issues, DNA damage response (DDR) pathways play a crucial role in dealing with these mutations and repairing them precisely through DNA repair pathways, which ensure the maintenance of genome integrity[5] and prevent cancer[6] [7]

1.2 H3K9- Methylated heterochromatin's Impact on Mutation Rate and Disease.

The connection between chromatin and these DNA repair pathways is intricate in eukaryotes, with many factors influencing chromatin dynamics [8]. These factors encompass histone post-translational modifications (PTMs), histone variants [9], chaperones [10], chromatin remodeling complexes [11], and more. Recent studies have identified various types of modifications (PTMs), including methylation, acetylation, phosphorylation, ADP ribosylation, ubiquitylation, and SUMOylation, and their relationship with diseases has been explored [12], [13]. For example, the process of H3K9me, or methylation of histone H3 at lysine 9, is responsible for creating heterochromatin and transcriptional repression within the euchromatin. In eukaryotic cells, this methylation process can happen in three ways - mono-methylation (me1), di-methylation (me2), or tri-methylation (me3). Methyltransferases like SUV39H and G9a are responsible for this process. On the other hand, lysine demethylases (KDMs) remove H3K9me, and H3K9 demethylases can reverse the function of H3K9 methylation [14]. These two processes regulate gene transcription, chromatin structure, and epigenetic state [15]. These epigenetic regulators, including H3K9 demethylases, have been found to positively impact repairing the mutation called DNA double-strand breaks (DSBs), making them crucial for maintaining genomic integrity [16]. Any aberrations [17] in these methylation and demethylation processes are linked to several diseases, including diabetes [12] and cancer [16] [18]. Thus, post-translational modifications are an essential and critical area of scientific investigation due to their significance in normal development [19] [20]and their correlation with various therapies [21]and translational oncology [22].

1.3 The Limitations of the Previous Studies:

In this regard, understanding how H3K9 methylation affects the spontaneous mutation rates of a gene is a crucial area of research. Despite this, it has remained incomplete for many years. As shown in Fig. 1, several scientists have studied the effects of H3K9me modification levels and heterochromatin on mutation rates in eukaryotes[23][24][25][26] [27], etc. However, their findings have needed to be clarified due to questionable experimental approaches and contradictory results. Most studies failed to consider the significant influence of the genomic landscape on mutation rates[28] [29]. In other words, most studies compared mutation rates between euchromatin and heterochromatin domains in the presence of various variables such as different domains, distinct genes, chromosomes, and strain, or during two different experiments. Thus, the findings of the previous study are still incomplete. It is essential to conduct further research to gain a more comprehensive understanding of this genetic dilemma by controlling these differences in one experiment.

Here, our study seeks to solve these challenges of previous research in the genetic field. Here are the aims and significant outcomes of this study.

Firstly, our study aimed to introduce a novel and precise experimental approach for estimating mutation rates throughout history for the first time. Figure 1 clearly illustrates the novelty of this present study. We employed a comprehensive research approach on the same gene, switching between euchromatin and heterochromatin states using one isogenic clone in the same experiment. This breakthrough is of immense significance as it corrects the current understanding of the link between chromatin organization and mutation rates[28]. For future scientists, this method can be reused to enhance epigenetic therapy in general [30], cancer treatment [31] [32], understand the use of epigenetic biomarkers in the diagnosis and treatment of liver diseases [25] and positively contribute to evolutionary biology [33].
Secondly, this study attempted to present a good opportunity to investigate H3K9 methylated heterochromatin in isolation on the same loci to produce more accurate, precise, and conclusive results. This is because other methylated histone modifications related to heterochromatin formation, such as H3K27me or H3K20me, can coexist with H3K9me in the same domain, as illustrated in Fig. 1 of this review [34]. Any investigation of H3k9me with other histone methylations like H3K27me, H3K20me, or different protein markers could potentially dilute or obscure the findings. Therefore, the results of our study can help researchers gain a deeper understanding of the role of H3K9me on the mutation, which could pave the way for other drug development for liver regeneration [35], leukemia patients [36], and beyond.

Finally, the utilization of yeast as a model organism maximizes the significance of our study for some reasons. Generally, yeast has played a vital role in facilitating our understanding of the molecular processes that cause cancer through evolutionary preservation [37]. It has many essential genes that regulate cellular functions, just like in higher organisms. These genes are similar in yeast and humans, making yeast a valuable tool for studying gene function. Additionally, yeast is easy to manipulate and grow compared to mammalian cells. Decades of research on yeast have accumulated a wealth of information and various tools for manipulating genes, including knockout and ectopic expression of both endogenous and exogenous genes. Moreover, yeast's genetic and metabolic similarity to tumor cells, along with its capacity for precise engineering, makes it a highly suitable model organism for cancer research [38] [39] [40] [40]. Yeast research often provides insights that can be generalized to mammalian counterparts. Therefore, we believe that our study's results will benefit not only future scientists in evolutionary biology and oncology but also those who can use this experimental approach, as outlined in the methods section, to further their research.

To achieve the research goals mentioned above with remarkable significance, we conducted experiments to evaluate the impact of H3K9-methylated heterochromatin on the spontaneous mutation rate, using the fission yeast Schizosaccharomyces pombe as a model system [41]. Also, the ura4⁺ gene was used as a reporter gene to evaluate gene silencing using a (5-FOA) drug [42]. In addition, we utilized gene regulation by the tetracycline-activatable system or the inducible heterochromatin system at the ura4⁺ gene, which was developed by[43] as mentioned in the method section (Figure. 2A). This innovative system enabled us to switch between activating and silencing the ura4⁺ gene by adding (tet) into the cell culture to remove H3K9 me during the same experiment for a single clone using fluctuation assay, as shown in Figure. 3. This approach allowed us to investigate the impact of H3K9-methylated heterochromatin on mutation rates of a single gene with identical DNA sequences while accurately determining the influence of H3K9me alone.

Thus, our study has been designed with precision, and it holds the potential to make significant contributions to evolutionary biology and epigenetic fields.

2.1. Strain and Media:

The fission yeast or Schizosaccharomyces pombe strain used in this study was obtained from Moazed Danesh's group and named SPY5101, as described in Ragunathan's study [43]. These researchers inserted 10XtetO sites upstream of a 114bp fragment of the ura4⁺ promoter at the endogenous locus of the ura4⁺ gene. Moreover, the GFP fusion protein was inserted downstream of the ura4⁺gene. Under such conditions, TetR-Clr4-I can bind with 10XtetO, leading to the establishment of ectopic heterochromatin and loss of ura4⁺ gene function after silencing, as shown in (Figure. 2A).

However, adding (tet) is crucial in regulating ura4⁺ gene expression, leading to the removal of ectopic heterochromatin and subsequent activation of the ura4 + gene, as shown in Fig. 4B and Figure S3's red bar in Ragunathan's study [43]. Consequently, the (10XtetO- ura4 -GFP) marker shows sensitivity to (5-FOA) drug in the presence of (tet).

By utilizing this system during our experiments, the lack of tetracycline or (- tet condition) implies silencing the ura4⁺ gene due to the presence of H3K9-methylated heterochromatin. On the other hand, the presence of (tet) in the cell culture or (+ tet condition) means activation of the ura4⁺ gene in the euchromatin region, which leads to cell death because the active ura4⁺gene is sensitive to (5-FOA) drug. Under these 2 conditions during one experiment, mutant colonies might appear on solid media with (5-FOA drug + tet) only if there is a mutation within the ura4⁺ gene. This procedure allowed us to estimate the phenotypic mutation rate in the absence and presence of (tet) at the same gene locus (ura4⁺ gene). Thus, we could investigate whether H3K9-methylated heterochromatin raises or reduces mutation rates of one specific gene and thoroughly analyze its effect alone to ensure accurate results.

Regarding the media for cell growth, we cultivated this strain using a fully synthetic medium EMMG[44] during fluctuation assays. The drug concentrations were adjusted. (5-FOA) was set to (1 g/L), while (tet) was set to (10 µM/L).

2.2. Silencing Assay:

Before estimating the mutation rate, it is crucial to ensure the strain's sensitivity to media containing both (5-FOA + tet) drugs. Thus, the strain with the modified reporter (10XtetO- ura4⁺-GFP) was spotted on plates containing yeast extract (YE), (5-FOA) (1 g/L), and (tet) (10 µM/L) after a four- or five-fold serial dilution.

2.3. Developing Fluctuation Assay for Increasing Precision:

Accurately estimating the rate of mutations is critical to understanding the mechanisms of evolution and disease. Choosing the correct method is essential to accurately estimate the parameter (m), which represents the number of mutation events per culture. While several methods are available to estimate mutation rates, the fluctuation assays are the most accurate and straightforward way to detect parameters (m). We used this method to ensure the accuracy of our findings and avoid the pitfalls of other methods, such as mutation accumulations and mutant accumulation assays.

To achieve precise results, it is essential to adopt a rigorous approach to experimental design and data analysis. Our proposed improvements, grounded in the latest [45] and original fluctuation assay protocols [46], enabled us to obtain reliable and compelling data.

Fluctuation Assays:

These assays were done for 2 isogenic clones of the previously mentioned strain after doing single-clone isolation, as mentioned in Fig. 3. A single clone was grown in liquid EMMG media at 32°C for each assay until saturation. The preculture was then diluted into fresh media to represent an initial inoculum (No) of approximately 200 cells and split into two different media types. The first media condition contains only liquid EMMG media, representing the absence of tetracycline (-tet condition). The second media condition has EMMG with (tet) (10 µM/L), representing the presence of tetracycline (+ tet condition). By utilizing 96-well plates, we could dispense 10 µl of diluted cultures for each experimental condition, thus enhancing the overall precision of the essay. This 96-well format allowed us to adjust the volume of all parallel cultures to 10 microliters, use foil covers to prevent evaporation of cultures and keep the samples in the dark. The cultures in 96-well plates were allowed to grow until saturation. Then, 24 parallel cultures were taken for each experimental condition to determine the average number of cells per culture after saturation. The remaining 72 parallel cultures were plated separately for each condition with and without (tet) on a 9 cm petri dish containing EMMG, (5-FOA) (1 g/L), and (tet) (10 µM/L). After incubation at 32°C for 3–4 days in the dark, the number of mutants (r) for each parallel culture was recorded. In the meantime, some of these colonies were picked up and replated on a 9 cm petri dish containing EMMG, (5-FOA) (1 g/L), and (tet) (10 µM/L) to double-check their phenotypic resistance on these drugs. Finally, these colonies were taken for DNA sequencing.

2.4. Statistical Analysis for the Results of Fluctuation Assays:

To calculate the parameter (m) representing the expected number of mutation events per culture in R-software, we individually calculated the number of mutants or colonies on the agar plates for each parallel culture. According to Eq. 18 from [47], the phenotypic mutation rates (µ) were determined using (µ = parameter m/Nt), where (Nt) represents the average number of cells per culture before plating on the agar plates. This was followed by calculating the 95% confidence intervals for the parameter (m) using the MSS Maximum Likelihood or Ma–Sandri–Sarkar maximum likelihood [48]. This method has been the most reliable over the years, making it the optimal choice for this calculation. Therefore, we used the MSS Maximum Likelihood[47] approach in R with the recursive likelihood function [49].

Our research led us to valuable data beyond the conventional values of (r), which posed a significant challenge when using the recursive version of the likelihood in R - software. Despite this, we developed a non-recursive version of the likelihood function, along with non-parametric bootstrapping [50], to implement the MSS Maximum Likelihood approach. Our innovative approach produced significant results and filled a gap in the literature, as no other non-recursive version of the likelihood appears to be available. For more information on how we calculated the 95% confidence intervals for the parameter (m), refer to [50]. By using this technique for each of the 4 experimental setups, we could estimate the confidence intervals for our findings with unprecedented accuracy.

2.5. Computational Analysis:

To verify the accuracy of our fluctuation data, we compared the predicted cumulative frequency distribution of mutants to the experimental data and created a plot using MATLAB. By utilizing a MATLAB file from a comparable study [51], we could execute accurate computational simulations in this article. According to Lang's study, the one-parameter model accounts for the Luria-Delbrück distribution [46]. Also, the probability distribution for the number of mutants per culture in the two-parameter model is the joint distribution of the Luria-Delbrück (parameter m) and the Poisson (parameter n = md) distributions. As a result, we could accurately assess mutation following the plating process (d) by utilizing a two-parameter model. This Div. post-plating or value (d) represents the total number of cell divisions capable of producing mutants after plating the cells on the selective media with both (5-FOA drug and tet).

By fitting our study's fluctuation assays into these one-parameter and two-parameter distributions, we could accurately simulate the results of a fluctuation assay.

3.1 Eliminating Ectopic H3K9-methylated Heterochromatin and Cell Sensitivity to the (5-FOA) drug.

Before beginning the fluctuation assays, it is crucial to perform a silencing assay to confirm the removal of H3K9me from the (10XtetO-ura4+-GFP) gene by the tetracycline and assess cell sensitivity to (5-FOA) drug.

In Fig. 2B of our study, the results show that (tet) effectively eradicated ectopic H3K9-methylated heterochromatin, consistent with the results of Ragunathan's study shown in Fig. 4B of his article [43].

As a result of this removal, the (10XtetO-ura4+-GFP) marker simultaneously showed sensitivity to the (5-FOA) drug in the presence of (tet), like wild-type cells that contain the ura4 + gene, as vividly depicted in (Fig. 2B).

In this case, we could accurately calculate the mutation rate by simply counting the colonies or mutants (r) produced at the end of each fluctuation assay after plating the cells on the selective media with both (5-FOA drug and tet).

3.2 Ectopic H3K9-methylated Heterochromatin Increases the Phenotypic Mutation Rate.

After conducting a fluctuation assay for each clone, it is essential to accurately count the number of mutants or colonies on agar plates and estimate the parameter (m), representing the number of mutation events per culture.

To ensure the most reliable data, we calculated this number individually for every parallel culture (-tet condition) and (+ tet condition). Next, we separately calculated the phenotypic mutation rates for each clone, following the specific steps outlined in the Materials and Methods section.

According to the statistical data presented in Table 1, it can be concluded that establishing H3k9me on the ura4 + gene locus increases the number of mutation events per culture (parameter m). For clones 1 and 2, the values of the parameter (m) under (-tet) condition are approximately 1.4773 and 9.3216, respectively. Similarly, the mutation rates of clones 1 and 2 under the (-tet) condition are about 4.6310344 × 10^− 6 and 3.7436144 × 10^− 5, subsequently. Based on all the previous values, the results suggest that the number of mutation events per culture (parameter m) and mutation rates are higher than what is observed in clone 1 and clone 2 cell cultures when there are no H3K9me markers and tetracycline is present, as in the (+ tet condition). Furthermore, we could calculate 95% confidence intervals of all parameters (m) separately, as detailed in Table 1.

By statistically combining the phenotypic mutation rates of Clone 1 (4.6310344 × 10^− 6) and Clone 2 (3.7436144 × 10^− 5) under (-tet conditions), we were able to determine a phenotypic mutation rate of (1.907426 × 10^− 5) with a 95% confidence interval via bootstrap [50] [1.653× 10^− 5, 2.211× 10^− 5]. Similarly, when we statistically combined the phenotypic mutation rates of Clone 1 (2.3932659 × 10^− 6) and Clone 2 (6.9356807 × 10^− 6) under (+ tet conditions), we obtained a phenotypic mutation rate of (4.755245 × 10^− 6) with a 95% confidence interval via bootstrap [50] [3.794 × 10^− 6, 5.960 × 10^− 6].

Thus, our results showed that the presence of H3k9me on the ura4⁺ gene locus under (-tet conditions) increased the phenotypic mutation rate (µ) in clones 1 and 2.

3.3 Fluctuation Assay Results Align Perfectly With Luria-Delbrück's one-parameter model distributions.

After this statistical analysis, we evaluated the precision of our fluctuation results and parameter (m) in Table 1 by comparing mutants' predicted cumulative frequency distribution to the experimental data. To present our findings, we created a plot using MATLAB, allowing us to demonstrate our results' accuracy and reliability.

As shown in Fig. 4, our findings align perfectly with one-parameter models. This data is best described by the Luria-Delbrück distribution or the one-parameter model, suggesting that post-plating growth and mutation do not occur. This is supported by the (d = 0) for the two-parameter model. Therefore, these results provide strong evidence supporting the conclusion that H3K9me has raised the phenotypic mutation rate of the same gene.

Having a clear understanding of how H3K9 methylation affects gene mutation rates is crucial. However, some researchers have used incorrect research designs while studying this phenomenon. Specifically, they have relied on data from two genes located in different chromatin regions across the chromosome [26] [27], as shown in Fig. 1, or other methods. It is, therefore, necessary to correct this approach and use a more precise experimental plan to drive our research forward. In addition, it is vital to focus our attention on investigating H3K9me solely and its impact on the mutation rate of the same gene, as it significantly affects tumorigenesis.

Here, we present our innovative and cutting-edge approach, combining multiple successful factors and critical achievements. We introduced the tetracycline-activatable system, allowing researchers to alternate a single gene's activation and silencing in one experiment, as illustrated in Fig. 2A. This experimental design enables the investigation of the effects of H3K9-methylated heterochromatin on the mutation rates of a gene with identical DNA sequences. Furthermore, we have provided a clear and concise step-by-step process for performing the fluctuation assay, resulting in increased accuracy and improved results, as depicted in Fig. 3. This comprehensive approach enables evolutionary biologists to advance their research with greater accuracy. Furthermore, we calculated the parameter (m), indicating the number of mutational events per culture.

Thirdly, we successfully overcame a difficult challenge: calculating the 95% confidence intervals for the parameter (m) based on MSS- Maximum Likelihood using R. By doing so, we have made it effortless for future researchers to maximize the accuracy of their research results [50].

Our results demonstrated that clones 1 and 2 exhibit a significant difference in parameter (m) irrespective of the presence or absence of H3k9me.

Based on the values of (µ), we can infer that the presence of H3k9me on the ura4⁺ gene increased the phenotypic mutation rates.

No study is perfect. Although qualitative research agreement exists between the calculated and observed trend of increasing the phenotypic mutation rates (µ) due to the existence of H3K9me in clones 1 and 2, semi-quantitative research has yet to be achieved. In other words, our study, though meticulous, revealed a lack of the DNA sequences of the mutant (r). So, we could not determine the mutational spectra from the phenotypic mutation rate yet. We still working on this step.

Nevertheless, our developed experimental design presents a fresh perspective and concepts to investigate multiple epigenetic modifications on the same gene mutation rate. Our findings are more likely to profoundly impact the future of drug development for H3K9me-related diseases and evolutionary biology [33]. This highlights the significance of pushing the boundaries of scientific research and the necessity for continued exploration of H3K9-methylated heterochromatin to enhance epigenetic therapy for H3K9me inhibitors [52] and life science research.

In conclusion, this study has successfully tackled two significant scientific challenges through an innovative experimental approach by introducing a developed research perspective to estimate the impact of H3K9-methylated heterochromatin on gene mutation rates using the exact isogenic clone under different experimental conditions in one experiment. This experimental approach allows scientists to accurately examine the relationship between H3K9me and mutation rates. Moreover, our study suggests that these H3K9m3 histone markers alone significantly increase the phenotypic mutation rate. By unlocking H3k9me-associated mutation secrets, this breakthrough empowers scientists to improve tumorigenesis research [38] and develop more effective tumor treatments [37] [39], which could ultimately save the lives of millions of people worldwide.

Acknowledgments:

We are grateful to Hokkaido University for providing us with excellent lab facilities and resources that were instrumental in the success of our research work. The project is funded by a grant (JPMJSP2119) from Hokkaido University, Japan. The first author thanks the Japanese Ministry of Education, Culture, Sports, Science, and Technology (Monbukagakusho) for supporting her through MEXT scholarships. We also collaborated with Auckland University in New Zealand on this study and appreciate their contribution and resources. Next, We thank Prof. Murakami for sharing his thoughts and engaging in the discussions. In addition, we would like to thank the Institute for Advancement of Graduate Education staff at Hokkaido University for their administrative support. Finally, we thank Dr. Kaushik Ragunathan for granting permission to modify and reuse a part of Fig. 1-A from his study [43].

Travers A, Muskhelishvili G (2015) DNA structure and function. FEBS J 282:2279–2295. https://doi.org/10.1111/febs.13307
Morrison O, Thakur J (2021) Molecular Complexes at Euchromatin, Heterochromatin and Centromeric Chromatin. Int J Mol Sci 22:6922. https://doi.org/10.3390/ijms22136922
Maeshima K, Iida S, Tamura S (2021) Physical Nature of Chromatin in the Nucleus. Cold Spring Harb Perspect Biol 13:a040675. https://doi.org/10.1101/cshperspect.a040675
Minchin S, Lodge J (2019) Understanding biochemistry: structure and function of nucleic acids. Essays Biochem 63:433–456. https://doi.org/10.1042/ebc20180038
Huang R, Zhou P-K (2021) DNA damage repair: historical perspectives, mechanistic pathways and clinical translation for targeted cancer therapy. Signal Transduct Target Ther 6:254. https://doi.org/10.1038/s41392-021-00648-7
Ui A, Chiba N, Yasui A (2020) Relationship among DNA double-strand break (DSB), DSB repair, and transcription prevents genome instability and cancer. Cancer Sci 111:1443–1451. https://doi.org/10.1111/cas.14404
Stead ER, Bjedov I (2021) Balancing DNA repair to prevent ageing and cancer, Exp. Cell Res 405:112679. https://doi.org/10.1016/j.yexcr.2021.112679
Nair N, Shoaib M, Sørensen CS (2017) Chromatin Dynamics in Genome Stability: Roles in Suppressing Endogenous DNA Damage and Facilitating DNA Repair. Int J Mol Sci 18:1486. https://doi.org/10.3390/ijms18071486
Ferrand J, Plessier A, Polo SE (2021) Control of the chromatin response to DNA damage: Histone proteins pull the strings, Semin. Cell Dev Biol 113:75–87. https://doi.org/10.1016/j.semcdb.2020.07.002
Chakraborty U, Shen Z-J, Tyler J (2021) Chaperoning histones at the DNA repair dance. DNA Repair 108:103240. https://doi.org/10.1016/j.dnarep.2021.103240
Stadler J, Richly H (2017) Regulation of DNA Repair Mechanisms: How the Chromatin Environment Regulates the DNA Damage Response. Int J Mol Sci 18:1715. https://doi.org/10.3390/ijms18081715
Wu X, Xu M, Geng M, Chen S, Little PJ, Xu S, Weng J (2023) Targeting protein modifications in metabolic diseases: molecular mechanisms and targeted therapies, Signal Transduct. Target Ther 8:220. https://doi.org/10.1038/s41392-023-01439-y
Xu H, Wang Y, Lin S, Deng W, Peng D, Cui Q, Xue Y (2018) Genom Proteom Bioinform 16:244–251. https://doi.org/10.1016/j.gpb.2018.06.004. A Database of Human Disease-associated Post-translational Modifications
Hyun K, Jeon J, Park K, Kim J (2017) Writing, erasing and reading histone lysine methylations. Exp Mol Med 49:e324–e324. https://doi.org/10.1038/emm.2017.11
Nicetto D, Zaret KS (2019) Role of H3K9me3 heterochromatin in cell identity establishment and maintenance. Curr Opin Genet Dev 55:1–10. https://doi.org/10.1016/j.gde.2019.04.013
Jeon H-Y, Hussain A, Qi J (2020) Role of H3K9 demethylases in DNA doublestrand break repair. J Cancer Biol 1:10–15. https://doi.org/10.46439/cancerbiology.1.003
Gong F, Miller KM (2019) Histone methylation and the DNA damage response, Mutat. Res Rev Mutat Res 780:37–47. https://doi.org/10.1016/j.mrrev.2017.09.003
Zhou M, Yan J, Chen Q, Yang Y, Li Y, Ren Y, Weng Z, Zhang X, Guan J, Tang L, Ren Z (2022) Association of H3K9me3 with breast cancer prognosis by estrogen receptor status. Clin Epigenetics 14:135. https://doi.org/10.1186/s13148-022-01363-y
Wang Y-C, Peterson SE, Loring JF (2014) Protein post-translational modifications and regulation of pluripotency in human stem cells. Cell Res 24:143–160. https://doi.org/10.1038/cr.2013.151
Moutin M, Bosc C, Peris L, Andrieux A (2021) Tubulin post-translational modifications control neuronal development and functions. Dev Neurobiol 81:253–272. https://doi.org/10.1002/dneu.22774
Chen L, Liu S, Tao Y (2020) Regulating tumor suppressor genes: post-translational modifications, Signal Transduct. Target Ther 5:90. https://doi.org/10.1038/s41392-020-0196-9
Yang Y, Zhang M, Wang Y (2022) The roles of histone modifications in tumorigenesis and associated inhibitors in cancer therapy. J Natl Cancer Cent 2:277–290. https://doi.org/10.1016/j.jncc.2022.09.002
de la Peña MV, Summanen PAM, Liukkonen M, Kronholm I (2023) Chromatin structure influences rate and spectrum of spontaneous mutations in Neurospora crassa. Genome Res 33:599–611. https://doi.org/10.1101/gr.276992.122
Habig M, Lorrain C, Feurtey A, Komluski J, Stukenbrock EH (2021) Epigenetic modifications affect the rate of spontaneous mutations in a pathogenic fungus. Nat Commun 12:5869. https://doi.org/10.1038/s41467-021-26108-y
Polak P, Karlić R, Koren A, Thurman R, Sandstrom R, Lawrence MS, Reynolds A, Rynes E, Vlahoviček K, Stamatoyannopoulos JA, Sunyaev SR (2015) Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518:360–364. https://doi.org/10.1038/nature14221
Prendergast JG, Campbell H, Gilbert N, Dunlop MG, Bickmore WA, Semple CA (2007) Chromatin structure and evolution in the human genome. BMC Evol Biol 7:72. https://doi.org/10.1186/1471-2148-7-72
Xia J, Han L, Zhao Z (2012) Investigating the relationship of DNA methylation with mutation rate and allele frequency in the human genome. BMC Genom 13:S7. https://doi.org/10.1186/1471-2164-13-s8-s7
Makova KD, Hardison RC (2015) The effects of chromatin organization on variation in mutation rates in the genome. Nat Rev Genet 16:213–223. https://doi.org/10.1038/nrg3890
Gonzalez-Perez A, Sabarinathan R, Lopez-Bigas N (2019) Local Determinants of the Mutational Landscape of the Human Genome. Cell 177:101–114. https://doi.org/10.1016/j.cell.2019.02.051
Heerboth S, Lapinska K, Snyder N, Leary M, Rollinson S, Sarkar S (2014) Use of Epigenetic Drugs in Disease: An Overview. Genet Epigenetics 6. https://doi.org/10.4137/geg.s12270. GEG.S12270
Liu Z, Ren Y, Weng S, Xu H, Li L, Han X (2022) A New Trend in Cancer Treatment: The Combination of Epigenetics and Immunotherapy. Front Immunol 13:809761. https://doi.org/10.3389/fimmu.2022.809761
Ahuja N, Sharma AR, Baylin SB (2016) Epigenetic Therapeutics: A New Weapon in the War Against Cancer. Annu Rev Med 67:73–89. https://doi.org/10.1146/annurev-med-111314-035900
Lind MI, Spagopoulou F (2018) Evolutionary consequences of epigenetic inheritance. Heredity 121:205–209. https://doi.org/10.1038/s41437-018-0113-y
Fortuny A, Polo SE (2018) The response to DNA damage in heterochromatin domains. Chromosoma 127:291–300. https://doi.org/10.1007/s00412-018-0669-6
Du J, Liao W, Wang H, Hou G, Liao M, Xu L, Huang J, Yuan K, Chen X, Zeng Y (2023) MDIG-mediated H3K9me3 demethylation upregulates Myc by activating OTX2 and facilitates liver regeneration, Signal Transduct. Target Ther 8:351. https://doi.org/10.1038/s41392-023-01575-5
Monaghan L, Massett ME, Bunschoten RP, Hoose A, Pirvan P-A, Liskamp RMJ, Jørgensen HG, Huang X (2019) The Emerging Role of H3K9me3 as a Potential Therapeutic Target in Acute Myeloid Leukemia. Front Oncol 9:705. https://doi.org/10.3389/fonc.2019.00705
Ferreira R, Limeta A, Nielsen J (2019) Tackling Cancer with Yeast-Based Technologies. Trends Biotechnol 37:592–603. https://doi.org/10.1016/j.tibtech.2018.11.013
Guaragnella N, Palermo V, Galli A, Moro L, Mazzoni C, Giannattasio S (2014) The expanding role of yeast in cancer research and diagnosis: insights into the function of the oncosuppressors p53 and BRCA1/2. FEMS Yeast Res 14:2–16. https://doi.org/10.1111/1567-1364.12094
Bjornsti M-A (2002) Cancer therapeutics in yeast. Cancer Cell 2:267–273. https://doi.org/10.1016/s1535-6108(02)00160-5
Cazzanelli G, Pereira F, Alves S, Francisco R, Azevedo L, Carvalho PD, Almeida A, Côrte-Real M, Oliveira M, Lucas C, Sousa M, Preto A (2018) The Yeast Saccharomyces cerevisiae as a Model for Understanding RAS Proteins and their Role in Human Tumorigenesis. Cells 7:14. https://doi.org/10.3390/cells7020014
Hoffman CS, Wood V, Fantes PA (2015) An Ancient Yeast for Young Geneticists: A Primer on the Schizosaccharomyces pombe Model System. Genetics 201:403–423. https://doi.org/10.1534/genetics.115.181503
Cam HP, Whitehall S (2016) Reporter Gene Silencing Assays in Fission Yeast, Cold Spring Harb. Protoc. (2016) pdb.prot091512. https://doi.org/10.1101/pdb.prot091512
Ragunathan K, Jih G, Moazed D (2015) Epigenetic inheritance uncoupled from sequence-specific recruitment. Science 348:1258699. https://doi.org/10.1126/science.1258699
Petersen J (2016) P. Russell, Growth and the Environment of Schizosaccharomyces pombe, Cold Spring Harb. Protoc. (2016) pdb.top079764. https://doi.org/10.1101/pdb.top079764
Lang GI, Instability G, Methods and, Protocols (2017) Methods Mol Biol 1672:21–31. https://doi.org/10.1007/978-1-4939-7306-4_3
Luria SE, Delbrück M, MUTATIONS OF BACTERIA FROM VIRUS SENSITIVITY TO VIRUS RESISTANCE, Genetics (1943) 28 491–511. https://doi.org/10.1093/genetics/28.6.491
Foster PL, Methods for Determining Spontaneous Mutation Rates (2006) Methods Enzym 409:195–213. https://doi.org/10.1016/s0076-6879(05)09012-9
Rosche WA, Foster PL (2000) Determining Mutation Rates in Bacterial Populations. Methods 20:4–17. https://doi.org/10.1006/meth.1999.0901
R Core Team (2021) R A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. - References - Scientific Research Publishing, (n.d.). https://www.scirp.org/(S(czeh2tfqw2orz553k1w0r45))/reference/referencespapers.aspx?referenceid=3131254 (accessed October 14, 2023)
Abdalla O, Walker C (2023) R-codes for Calculating Fluctuation Assay Results and 95% Confidence Intervals Based on Ma-Sandri-Sarkar Maximum Likelihood. https://doi.org/10.21203/rs.3.rs-3646152/v1
Lang GI, Murray AW (2008) Estimating the Per-Base-Pair Mutation Rate in the Yeast Saccharomyces cerevisiae. Genetics 178:67–82. https://doi.org/10.1534/genetics.107.071506
Rowbotham SP, Li F, Dost AFM, Louie SM, Marsh BP, Pessina P, Anbarasu CR, Brainson CF, Tuminello SJ, Lieberman A, Ryeom S, Schlaeger TM, Aronow BJ, Watanabe H, Wong KK, Kim CF (2018) H3K9 methyltransferases and demethylases control lung tumor-propagating cells and lung cancer progression. Nat Commun 9:4559. https://doi.org/10.1038/s41467-018-07077-1

	Clone 1		Clone 2
Experimental conditions	Presence of H3K9me	Absence of H3K9me	Presence of H3K9me	Absence of H3K9me
Experimental conditions	(- tet condition)	(+ tet condition)	(- tet condition)	(+ tet condition)
Parameter (m)	1.4773 (Figure 4 A)	0.7108 ( Figure 4 B)	9.3216 ( Figure 4 C)	1.4773 ( Figure 4 D)
Division post-plating (d)	0	0	0	0
95% confidence intervals (CI) for parameter (m) via bootstrap [1]	[0.995, 2.018]	[0.469, 1.028]	[7.942, 11.015]	[1.146, 1.884]
Phenotypic mutation rate (μ)	4.6310344 × 10^-6	2.3932659 × 10^-6	3.7436144 × 10^-5	6.9356807 × 10^-6

References:

[1] O. Abdalla, C. Walker, R-codes for Calculating Fluctuation Assay Results and 95% Confidence Intervals Based on Ma-Sandri-Sarkar Maximum Likelihood, (2023). https://doi.org/10.21203/rs.3.rs-3646152/v1.

^{Abbreviations: (5-FOA) is a 5-fluoroorotic acid drug, and (tet) is tetracycline.}^{(-tet condition) refers to the absence of tetracycline in cell culture and presence of H3k9me on ura4+ gene or a silenced ura4+ gene}^.^{(+tet condition) means the presence of tetracycline in the cell cultures and absence of H3k9me on ura4+ gene or an active ura4+ gene.}

The authors declare no competing interests.

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The Impact of Histone Post-translational Modifications H3K9me on Same Gene Mutation Rate in Fission Yeast

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Abstract

Figures

1. Introduction

1.1 The Significance of Genetic Material

1.2 H3K9- Methylated heterochromatin's Impact on Mutation Rate and Disease.

1.3 The Limitations of the Previous Studies:

2. Materials and Methods

2.1. Strain and Media:

2.2. Silencing Assay:

2.3. Developing Fluctuation Assay for Increasing Precision:

2.4. Statistical Analysis for the Results of Fluctuation Assays:

2.5. Computational Analysis:

3. Results

3.1 Eliminating Ectopic H3K9-methylated Heterochromatin and Cell Sensitivity to the (5-FOA) drug.

3.2 Ectopic H3K9-methylated Heterochromatin Increases the Phenotypic Mutation Rate.

3.3 Fluctuation Assay Results Align Perfectly With Luria-Delbrück's one-parameter model distributions.

4. Discussion

Declarations

Acknowledgments:

References

Table 1

Additional Declarations

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