[System]
You are a **scientific‐rigor auditor**. You will receive the parsed contents of a research paper. Your job is to identify **only** those errors or flaws that directly undermine the **scientific validity** of the paper’s methods, analyses, or conclusions.

Your sole focus is to identify flaws—such as errors in experimental design, data integrity, calculations, statistical inference, or reproducibility—that directly call into question the validity of a specific claim, paragraph, or the paper as a whole.

**Do not** report issues that are purely presentational, rhetorical, stylistic, or related to citation practices.

---

After you’ve done a **detailed walkthrough** of the paper, output exactly in this format—no extra keys or commentary:

```
<analysis>
{detailed walk‑through of how you checked each section/figure and why you flagged (or did not flag) any flaw}
</analysis>

<response>
{
  "has_error": <true|false>,
  "errors": [
    {
      "location": "Section 2.1",
      "description": "Claim that ‘all X are Y’ is contradicted by citation [5], which shows examples of non‑Y X."
    },
    {
      "location": "Figure 3",
      "description": "XAxis labeled ‘Time (s)’ but units appear to be milliseconds; scale bar mismatches caption."
    }
    // …more entries…
  ]
}
</response>
```

- **Do not** include any other keys or prose outside these two tagged blocks.
- **Do not** report stylistic or citation issues.
- Be as precise as possible about **where** (section ID or figure/table) and **what** the scientific flaw is.
- Each description for the errors must be rooted in a scientific rationale explaining why it is 'wrong' (not how it could be improved).

Begin your analysis now.

[User]
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Xue Li, Bo Zhu, Yue Lu, Feng Zhao, Qian Liu, Junhao Wang, Miaomiao Ye, Siyuan Chen, Junwei Nie, Lizhong Xiong, et al.

Xue Li, Bo Zhu, Yue Lu, Feng Zhao, Qian Liu, et al.. DNA methylation remodeling and the functional implication during male gametogenesis in rice. Genome Biology, 2024, 25 (1), pp.84. 10.1186/s13059-024-03222-w. hal-04557022

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Submitted on 23 May 2024

**Background:** Epigenetic marks are reprogrammed during sexual reproduction. In flowering plants, DNA methylation is only partially remodeled in the gametes and the zygote. However, the timing and functional significance of the remodeling during plant gametogenesis remain obscure.

**Results:** Here we show that DNA methylation remodeling starts after male meiosis in rice, with non-CG methylation, particularly at CHG sites, being first enhanced in the microspore and subsequently decreased in sperm. Functional analysis of rice CHG methyltransferase genes CMT3a and CMT3b indicates that CMT3a functions as the major CHG methyltransferase in rice meiocyte, while CMT3b is responsible for the increase of CHG methylation in microspore. The function of the two histone demethylases JMJ706 and JMJ707 that remove H3K9me2 may contribute to the decreased CHG methylation in sperm. During male gametogenesis CMT3a mainly silences TE and TE-related genes while CMT3b is required for repression of genes encoding factors involved in transcriptional and translational activities. In addition, CMT3b functions to repress zygotic gene expression in egg and participates in establishing the zygotic epigenome upon fertilization.

**Conclusion:** Collectively, the results indicate that DNA methylation is dynamically remodeled during male gametogenesis, distinguish the function of CMT3a and CMT3b in sex cells, and underpin the functional significance of DNA methylation remodeling during rice reproduction.

**Keywords:** Rice male gametogenesis, Egg, Zygote, DNA methylation, CMT3, JMJ706, JMJ707

Xue Li¹†, Bo Zhu¹†, Yue Lu², Feng Zhao¹, Qian Liu¹, Jiahao Wang¹, Miaomiao Ye¹, Siyuan Chen¹, Junwei Nie³, Lizhong Xiong¹, Yu Zhao¹, Changyin Wu¹ and Dao-Xiu Zhou¹,⁴*

†Xue Li and Bo Zhu contributed equally to this work.

*Correspondence: dao-xiu.zhou@universite-paris-saclay.fr

¹ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
² Key Laboratory of Plant Functional Genomics of the Ministry of Education/ Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou 225009, China
³ Vazyme Biotech Co., Ltd, Nanjing 210000, China
⁴ Institute of Plant Science Paris-Saclay (IPS2), CNRS, INRAE, Université Paris-Saclay, 91405 Orsay, France

DNA cytosine methylation is a hallmark for repression of transposable elements (TE) and related sequences in complex genomes such as flowering plants and vertebrates. In flowering plants, DNA cytosine methylation occurs in the context of CG, CHG and...

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

CHH (where H is A, C, or T) sequences. CG methylation is maintained during cell division by DNA methyltransferase1 (MET1), which recognizes and methylates hemi-methylated CG sites in the newly replicated DNA. Non-CG (i.e., CHG and CHH) methylation in heterochromatin is maintained by plant-specific Chromomethylases3 (CMT3, at CHG sites) and CMT2 (at CHH and CHG sites), which bind to the histone methylation mark H3K9me2, while CHH methylation in euchromatin regions is maintained by Domains Rearranged Methyltransferase2 (DRM2) guided by related siRNA [1]. DRM2 also mediates de novo DNA methylation regardless of sequence contexts [1]. All three methylation sites are found in TE and TE-like sequences and in about 10–20% of genes depending on plant species. DNA methylation in positive regulatory sequences impairs gene transcription and causes gene silencing, but enhances gene activity when occurring in repressive DNA elements [2]. CG methylation is also common within the transcribed regions of genes, where it is associated with gene activity [3]. In rice, DNA methylation displays some differences in methylation levels and genomic distribution compared with Arabidopsis. For instance, rice mCHH is enriched at euchromatin regions and many genes are found to be methylated at CHH sites, especially around TSS [4, 5]. There are also quite a number of genes methylated at CHG sites which are related to allelic-specific expression in rice hybrids [6].

Epigenetic marks are reprogrammed in the gametes and after fertilization. In mammals, there are two distinct phases of epigenetic reprogramming to prevent inheritance of ancestral epigenetic signatures. The first phase consists of a genome-wide erasure of DNA methylation in the primordial germ cells (PGCs), the gamete precursors, followed by the reestablishment of epigenetic signatures to enable gamete maturation and function [7, 8]. Global DNA methylation is once again erased post-fertilization in early embryos, followed by another round of global de novo methylation during embryo development [9–11]. In contrast with mammals, DNA methylation is not globally erased in gametes in flowering plants [12–17]. Unlike mammalian germ lines that are defined already at an early stage of embryogenesis, thus before meiosis, the plant male and female sexual lineages initiate as diploid meiocytes from somatic cells, which give rise to haploid microspores after meiosis. The male microspores subsequently undergo mitosis, producing the vegetative and generative cells. The generative cell is further divided to produce two sperm cells in the mature pollen grain, the male gametophyte. Previous studies showed that plant sperm DNA methylation is remodeled and shows variation relative to somatic tissues [12, 14, 16–19]. However, it remains unclear whether the remodeling process is initiated in male meiocytes before meiosis or at the subsequent steps after meiosis and whether DNA methylation remodeling is required for male gametogenesis.

In this work, we analyzed DNA methylation in rice male meiocyte, microspore and sperm and studied the function of a set of chromatin regulators during the process. The results indicate that DNA methylation remodeling starts after meiosis of the male meiocytes and that non-CG methylation, particularly at CHG sequences, is dynamically remodeled during rice male gametogenesis. The work reveals distinct function of DNA methyltransferases and histone demethylase in the remodeling of CHG methylation and suggests that the CHG methylation remodeling has functional significance for male gametogenesis and fertilization.

(2024) 25:84

Li et al. Genome Biology

From cluster A of the microspore hyper DMRs, 315 genes were identified. These genes are enriched for translation, ribonucleoprotein complex, and cellular protein metabolic functions, implying that mCHG in protein translation and RNA-binding pathway genes was particularly dynamic during meiocyte to sperm development (Additional file 1: Fig. S3e, Additional file 4: Table S3). Several genes showed lower expression in microspore

than meiocyte and/or sperm (Additional file 1: Fig. S4a), suggesting that hypermethylation might play a role in their repression in microspore.

CMT3a and CMT3b function during male gametogenesis

CMT3a is a major CHG methyltransferase gene expressed at high levels throughout the sporophytic development in rice [25, 26]. However, its expression became low or undetectable in sperm in several rice varieties (Fig. 2a). By contrast, CMT3b expression was low or undetectable in vegetative tissues/organs but showed expression in reproductive cells including meiocyte, microspore, and sperm (Fig. 2a). To study the function of

CMT3 genes during male gametogenesis, we produced cmt3a and cmt3b knockout (KO) plants in the ZH11 background using the CRISPR technique and two independent lines for each gene were obtained (Additional file 1: Fig. S5a). The cmt3a mutants produced mainly defective pollens and were infertile (Fig. 2b). Cytology sections revealed that the cmt3a pollen development was arrested likely at the bicellular microspore stage (Additional file 1: Fig. S5b).

By contrast, the cmt3b mutants showed no clear plant morphological phenotype. To check the effects of cmt3 mutations on DNA methylation during male gametogenesis, we performed BS-seq of meiocyte, unicellular microspore, and sperm isolated from two independent CRISPR/Cas9-free lines of cmt3a and/or cmt3b at T3-4 generation (as well as a tissue culture-regenerated wild type line) (Additional file 2: Table S1). Violin plots of the data revealed that mCHG was almost absent from cmt3a meiocyte (Fig. 2c, Additional file 1: Fig. S6a), consistent with the CMT3a loss-of-function effects in somatic tissues [25]. To a lesser extent, mCG was also reduced in cmt3a meiocyte. However, cmt3a sperm mCHG (as well as mCHH) levels became higher than the mutant meiocyte (Fig. 2c), suggesting that additional activities partially restored mCHG and/or ectopically mediated mCHH in the mutant sperm. DRM2 and CMT2 being highly expressed in rice sperm (Additional file 1: Fig. S7a), the residual mCHG level in cmt3a sperm could be maintained by RdDM or CMT2. This hypothesis is supported by the observation that the drm2/cmt2 mutations also reduced the mCHG levels of those loci in leaves (Additional file 1: Fig. S7b).

By contrast, the cmt3b mutation led to a clear loss of overall mCHG in microspore and sperm but the mutation effect was less clear in meiocyte (Fig. 2c, Additional file 1: Fig. S6a). The cmt3b mutation also resulted in some increases of mCHH in sperm. Density plots confirmed the observations (Additional file 1: Fig. S6b). The increases of mCHH in cmt3a/b sperm might be of indirect effects to compensate mCHG loss in the mutants. Nearly all of the cmt3b hypo-CHG DMRs in meiocyte and sperm overlapped with those of cmt3a (Fig. 2d, e), indicating that CMT3b functioned to maintain mCHG on a fraction of the CMT3a targets. The cmt3b mutation resulted in a large number (33,412) of hypo-CHG DMRs in microspore (Fig. 2d), and diminished the mCHG difference between

(See figure on next page.)
Fig. 2 Effects of cmt3a and cmt3b mutations on DNA methylation in meiocyte, microspore and sperm. a Transcript levels in FPKM of rice CMT3a and CMT3b in seedling (Se), roots (Ro), meiocyte (Me), unicellular microspore (UM), sperm (S), egg (E), zygote (Z), endosperm nuclei (En, 1.5 days after fertilization) and globular embryo (GE, 3 days after fertilization) from RNA-seq data. The sperm (Kit-S) in Kitaake background was reported by Anderson et al., (2013). b The pollen grains of wild type and cmt3a and cmt3b mutants were I2-KI stained. Bars = 50 μm. c Violin plots comparing overall cytosine methylation levels of wild type and cmt3a and cmt3b mutant meiocyte (Me), unicellular microspore (UM) and sperm (S). The average methylation levels (white dots) and median values (black bars) in transposable elements (TE) are shown. Values of the methylomes are averages from the two replicates. d Number of differential methylated regions (DMR) in cmt3a and cmt3b relative to wild type. Relative portions in TE (> 500 bp), TEG, gene, and Intergenic regions are indicated by different colors. e Venn diagrams showing overlapping of hypo-CHG DMRs in cmt3a and cmt3b meiocyte (left) and sperm (right) relative to wild type cells. f Box plots of DNA methylation levels of hypo-CHG DMRs in meiocyte (Me) versus microspore (UM) (upper) and sperm (S) relative to microspore (UM) (lower) in wild type, cmt3a (3a) and cmt3b (3b) cells. The significance was calculated with multiple comparison tests. Different letters on top of the bars indicate a significant difference (p < 0.05). g Genome Browser screen captures showing high CHG methylation sites in microspore relative to meiocyte and sperm decreased in cmt3b mutants (highlighted by grey)

microspore and sperm observed in wild type (Additional file 1: Fig. S6c). In fact, the methylation levels of hyper-CHG DMRs in wild type microspore versus meiocyte were decreased to the meiocyte levels in cmt3b microspore, and the methylation levels of the hypo-CHG DMRs in wild type sperm versus microspore were decreased to the sperm levels in cmt3b microspore (Fig. 2f, g). For the three clusters of DMRs shown in Additional file 1: Fig. S3a, the cmt3b mutation largely reduced the mCHG levels in microspore (Additional file 1: Fig. S3c). In addition, the 315 genes (from cluster A) showed higher mCHG in exons than introns in microspore (Additional file 1: Fig. S4b), while

the cmt3b mutation reduced the mCHG levels from both exons and introns, suggesting that CMT3b may preferentially target gene exons in microspore (Additional file 1: Fig. S4a, b). The analysis indicates that CMT3b is required for the increase of mCHG in microspore.

As CMT3a is not or lowly expressed in sperm, mCHG diluted by mitosis may not be maintained in sperm. Alternatively, active DNA demethylation may be involved, as mutations of DNA demethylases locally remodeled DNA methylation in sperm [17]. Since mCHG is linked to H3K9me2 through a positive feedback loop [27, 28], we investigated whether H3K9me2 demethylases were also involved in the decrease of mCHG in sperm. JMJ706 was shown to function as a H3K9 demethylase in rice [29]. JMJ707 is closely related to JMJ706 [29], of which JMJ707 showed high expression in sperm (Additional file 1: Fig. S8a). To test whether the genes were involved in mCHG during male gametogenesis, we produced jmj706 and jmj707 double knockout (KO) plants and obtained two independent lines (Additional file 1: Fig. S8b). The KO lines (j67) showed a reduced pollen viability and seed setting rate (Additional file 1: Fig. S8c, d). We analyzed the DNA methylome of male meiocyte and sperm of the mutant lines (Cas9-free at T3-4 generation) and found that the mutations had no drastic effect on the overall methylation in the cells (Fig. 3a, Additional file 1: Fig. S9a). However, the methylation levels of the hypo-DMRs in wild type meiocyte versus microspore were increased in j67 meiocyte (Fig. 3b, c, d). Similarly, the methylations levels of the hypo-DMRs in wild type sperm versus microspore were augmented in j67 sperm (Fig. 3b, c, d). The jmj706/7 mutations clearly elevated mCHG levels of cluster B DMRs in meiocyte and cluster C in sperm (Additional file 1: Fig. S3c). There was no overlap between cmt3b and jmj706/7-affected DMRs (Additional file 1: Fig. S9b). The analysis indicated that JMJ706/707 play a role to reduce mCHG at a set of genomic loci (mainly TE or TEG) in male meiocyte and sperm.

It is shown that Arabidopsis male sex cells show sexual-lineage-specific methylation (SLM) or sexual-lineage-hypermethylation (SLH) [18]. Using the published methods [18], we identified 555 SLH loci in rice meiocyte, microspore and sperm relative to somatic cells (seedling) (Additional file 1: Fig. S10a). The cmt3b mutations reduced the SLH levels in all three male sex cell types, especially in microspore (Additional file 1: Fig. S10b, c). By contrast, the jmj706/7 had no clear effect on SLH in the sex cells (Additional file 1: Fig. S10b). Further analysis could divide the 555 SLH into 340 canonical SLH and 215 SLM loci (Additional file 1: Fig. S11a) [18]. The canonical SLH loci corresponded mainly to TEs, while the SLM loci were located mainly in genes (body and promoter regions) (Additional file 1: Fig. S11b), suggesting that SLM mainly targets genic regions during male gametophyte and sperm development. In total, 132 genes were targeted by SLM, which are enriched for translational function (Additional file 1: Fig. S11c, Additional file 5: Table S4). The 132 genes appeared to be repressed in sperm compared to meiocyte or microspore (Additional file 1: Fig. S11d, e). The cmt3b mutation reduced SLM and increased expression of some of the genes in sperm (Additional file 1: Fig.

Li et al. Genome Biology

(2024) 25:84

Function of CMT3a and CMT3b in egg and zygote DNA methylation

Unlike in sperm, CMT3a is highly expressed in egg and zygote. CMT3b transcripts are detected in egg and zygote (Fig. 2a). To study CMT3 function in egg and zygote, we compared wild type, cmt3a and/or cmt3b egg and zygote methylomes by BS-seq analysis (Additional file 2: Table S1). In wild type, egg and zygote mCHG levels were higher than sperm (Fig. 4a, Additional file 1: Fig. S12a). Because cmt3a was infertile, we only analyzed cmt3a egg methylome. As in male meiocyte and somatic tissues [25], the cmt3a mutation eliminated almost all mCHG in egg (Fig. 2c and Fig. 4a, Additional file 1: Fig. S12a). The cmt3b mutation had a limited effect on overall mCHG in egg, but caused a clear decrease of mCHG in zygote (Fig. 4a, Additional file 1: Fig. S12a). The cmt3b mutation caused a clear decrease of mCHG in zygote (Fig. 4a, Additional file 1: Fig. S12a), suggesting that CMT3b may be involved in SLM and repression of some of the genes in sperm.

| a    |    |        |   | b    |    |        |   | c    |    |        |   |
| ---- | -- | ------ | - | ---- | -- | ------ | - | ---- | -- | ------ | - |
| mCG  |    |        |   | mCHG |    |        |   | mCHH |    |        |   |
| 1.0  |    |        |   | 1.0  |    |        |   | 0.4  |    |        |   |
| 0.75 |    |        |   | 0.75 |    |        |   | 0.3  |    |        |   |
| 0.5  |    |        |   | 0.5  |    |        |   | 0.2  |    |        |   |
| 0.25 |    |        |   | 0.25 |    |        |   | 0.1  |    |        |   |
| 0    |    |        |   | 0    |    |        |   | 0    |    |        |   |
|      | Me | j67-Me | S |      | Me | j67-Me | S |      | Me | j67-Me | S |

| d           |   |   |   | e           |   |   |   |
| ----------- | - | - | - | ----------- | - | - | - |
| Chr03g10180 |   |   |   | Chr03g32320 |   |   |   |
| Me          |   |   |   | UM          |   |   |   |
| j67-Me      |   |   |   | S           |   |   |   |
| UM          |   |   |   | j67-S       |   |   |   |
| S           |   |   |   | Geno        |   |   |   |

Fig. 3 Effects of jmj706/707 mutations on DNA methylation in male sex cells. a Comparison of overall TE methylation levels in wild type and jmj706/707 mutant meiocyte (Me) and sperm (S). Values of the methylomes are averages from the two replicates. The average methylation levels (white dots) and median values (black bars) are shown. b Density plots of CHG methylation differences between jmj706/707 mutant and wild-type meiocyte (upper) and sperm (lower) (black lines). The red traces are density plots confined to the hypo DMRs between meiocyte and microspore (Me-UM) (upper) or between sperm and microspore (S-UM) (lower). c Box plots showing DNA methylation levels of 50-bp hypo-CHG methylation regions in wild type meiocyte (Me) (upper) and sperm (S) (lower) relative to microspore (UM) and in jmj706/707 meiocyte (j67-Me) and sperm (j67-S). The significance was calculated with multiple comparison tests. Different letters on top of the bars indicate a significant difference (p < 0.05). d Genome browser screen shots showing low CHG methylation sites in meiocyte (upper) or sperm (lower) relative to microspore but elevated in j67 mutants. Grey illustrates differentially methylated regions.

Fig. 4 Effect of cmt3a and cmt3b mutations on DNA methylation in zygote and/or egg cells.
a Comparison of overall TE methylation levels in sperm (S), egg (E), and zygote (Z) of wild type and cmt3a, cmt3b mutants. Values of the methylomes are averages from the two replicates. The average methylation levels (white dots) and median values (black bars) are shown.
b Number of differentially methylated regions (DMR) in the indicated mutant cells relative to wild type. Different colors indicate the distribution of DMRs in TE (red), gene (light green), intergenic region (pink), and TEG (yellow).
c Venn diagrams showing overlapping of hyper-CHG DMRs in zygote relative to sperm (Z-S) (upper) or egg (Z-E) (lower) and hypo-CHG DMRs in cmt3b zygote relative to wild type zygote (3bZ-Z).
d Box plots showing DNA methylation levels of Z-S (upper) or Z-E (lower) hyper-CHG DMRs of the indicated cell type. The significance was calculated with multiple comparison tests. Different letters on top of the bars indicate a significant difference (p < 0.05).
e Screenshots of CHG methylation levels of 5 representative genes in the indicated cell.

| TF   | CHG hypo DMR | C | TE |
| ---- | ------------ | - | -- |
| 1.0  | 90,680       |   |    |
| 0.75 |              |   |    |
| 0.5  |              |   |    |
| 0.25 |              |   |    |
| 0    | 0            |   |    |

|   |                        |
| - | ---------------------- |
| 1 | 1. cmt3a S–WT S        |
| 2 | 2. cmt3b S–WT S        |
| 3 | 3. cmt3a E–WT E        |
| 4 | 4. cmt3b E–WT E        |
| 5 | 5. cmt3a Z–WT Z        |
| 6 | 6. cmt3b Z–WT Z        |

| mCHG | S | E | Z |
| ---- | - | - | - |
| 1.0  |   |   |   |
| 0.75 |   |   |   |
| 0.5  |   |   |   |
| 0.25 |   |   |   |
| 0    |   |   |   |

|       |   |   |   |
| ----- | - | - | - |
| WT    |   |   |   |
| cmt3a |   |   |   |
| cmt3b |   |   |   |

```mermaid
graph TD
A[17,870] --> B((5,623))
C[23,460] --> B
```

```mermaid
graph TD
A[12,101] --> B((1,234))
C[23,460] --> B
```

| mCHG | S | E | Z | 3bZ |
| ---- | - | - | - | --- |
| 1.0  |   |   |   |     |
| 0.75 |   |   |   |     |
| 0.5  |   |   |   |     |
| 0.25 |   |   |   |     |
| 0    |   |   |   |     |

mutation produced more hypo-CHG DMRs (23,460) in zygote than egg (13,249) or sperm (17,576) (Fig. 4b). About 24% (5,623/23,460) of the hypo-DMRs in cmt3b zygote overlapped with the hyper-DMRs in wild type zygote versus sperm (Fig. 4c). In fact, the cmt3b mutation reduced the methylation differences of the DMRs between wild type zygote and sperm (Z–S) or egg (Z–E) (Fig. 4d, e). Together, the data indicate that CMT3b

In parallel, we analyzed the egg and zygote transcriptomes of wild type and cmt3a/b plants. Because cmt3a was infertile, we only analyzed cmt3a egg transcriptome. PCA analysis indicated high levels of reproducibility of the replicates. The cmt3b egg and

zygote transcriptomes were close to, but distinct from, the wild type (Additional file 1: Fig. S13b). By contrast, the cmt3a egg transcriptome was largely distal from the wild type (Additional file 1: Fig. S13b), consistent with the drastic effect of cmt3a mutation on mCHG in egg. There were in total 4,868 upregulated genes (> two-fold, Q < 0.01), of which 1,648 were hypomethylated at CHG sites in cmt3a egg (Additional file 1: Fig. S12b, c). Interestingly, among the up-regulated genes, 1,461 were TEGs, of which 982 (67.2%) were hypomethylated at CHG sites in cmt3a egg (Additional file 1: Fig. S12b, c). The analysis indicates that CMT3a-mediated mCHG is required mainly for TEGs repression in egg, consistent with previous results showing that cmt3a mutation resulted in burst of TE expression [26]. The cmt3b mutation resulted in totally 3,022 upregulated genes (> two-fold, Q < 0.01) in egg, of which few were TEGs and only 120 (including 26 TEG) were identified as hypo-CHG methylated genes in the mutant egg (Additional file 1: Fig. S12b, c), indicating that unlike cmt3a, the cmt3b mutation de-repressed mainly non-TE-related genes, which was likely independent of a clear loss of mCHG. However, about 40% of the upregulated genes in cmt3b overlapped with those detected in cmt3a eggs (Additional file 1: Fig. S12d), suggesting that both CMT3 genes are required for gene (mainly non-TEGs) repression in egg. The cmt3b mutation resulted in upregulation of 2,179 and downregulation of 1,870 genes in zygote (Additional file 1: Fig. S12b). Similar to that observed in cmt3b egg, relatively few upregulated genes were TEGs or showed hypo mCHG in cmt3b zygote (Additional file 1: Fig. S12c).

## CMT3b represses zygotic genes in egg cells

In wild type zygote we identified 1804 down- and 2628 upregulated (> two-fold, Q < 0.01) genes relative to egg (Fig. 6a). Among the upregulated genes, 416 overlapped with previously identified genes expressed in rice zygotes (Fig. 6b) [31]. The 416 genes were enriched for chromatin replication and cell division functions (Fig. 6c), consistent with zygotic genome activation to promote zygote cell division in plants [32]. Within the 416 zygotic genes, 83 were upregulated in cmt3b egg. By contrast, although the cmt3a mutation caused a larger number of upregulated genes in egg, only 39 were among the 416 zygotic genes (Fig. 6d). Among the 83 zygotic genes upregulated in cmt3b egg were those encode histones, chromatin proteins (HMGs, SMC2, TOP2), DNA methyltransferases (MET1 and CMT3a), transcription factors (E2F, HAP3, NAC), and cell division-related proteins (Fig. 6e, f, g, Additional file 7: Table S6). The analysis suggested that CMT3b has a function to repress zygote gene expression program in egg.

## Discussion

### Non-CG methylation dynamics during rice male gametogenesis

Previous results showed that DNA methylation in plants is partially remodeled or reconfigured in male and female gametes [12–17, 19]. In this work, we provided evidence that non-CG (mainly CHG) methylations are dynamically remodeled during rice male gametogenesis with the highest levels observed in microspore and the lowest levels in sperm. It is known that during male germline development there is a cell cycle arrest [33], which could account for the reduced mCHG and mCHH in sperm. Unlike in Arabidopsis meiocyte that has higher mCG but lower mCHH than in somatic tissues [18], DNA methylation levels in rice male meiocyte were similar to somatic tissues (Additional

(2024) 25.84

The increase of mCHG observed in microspore was unexpected, as the methylation levels would be reduced after the meiotic cell divisions. The enhanced mCHG may have functional significance in microspore development. It remains to be studied whether the increased mCHG is related to chromatin reorganization of the haploid genome that was gametogenesis.

shown to be activated before the bicellular microspore stage in cereals [34]. As hyper-methylated genes are enriched for ribonucleoprotein complex (such as mRNA-binding) and translation proteins in microspore, the mCHG increase may be also related to repression of genes involved in transcriptional and translational activities that are shut-down during meiosis or male gametogenesis [26].

The decrease of mCHG in sperm may be required for sperm development and/or reshaping the sperm chromatin that lacks the compact silent center (CSC) found in egg and zygote chromatin 3D structures [21], and contains specific histone variants and modification patterns [35–39]. Alternatively, the decreased mCHG might facilitate the decondensation of the sperm chromatin upon fusion with the egg cell nucleus after fertilization, allowing initiation of transcription from the paternal genome [40].

The remodeling of non-CG methylation in rice sperm seemed to differ from Arabidopsis sperm where mCHH is lost or largely reduced [12]. This may be due to a different mCHH landscape in the rice genome, which is mainly scattered in genic regions [4, 5]. In fact, in rice sperm mCHH was found to be reduced in genic regions, but appeared to be enhanced in TE-rich pericentromeric regions (Additional file 1: Fig. S16a, b). The increased mCHH at pericentromeric region in sperm may be indirectly promoted by DNA demethylation at TE and repetitive sequence in the pollen vegetative cell in rice [16]. Alternatively, siRNAs produced by microspore may be inherited into sperm, in which they target heterochromatin LTR retrotransposon silencing through the RdDM pathway. In addition, the reduced genic mCHG and mCHH may also involve DNA demethylases that were shown to function in rice sperm [17].

The present work indicated CMT3a/b also have distinct functions in egg. CMT3a plays the major role in silencing TEGs, whereas CMT3b is required for repression of zygote gene expression program in egg. The repression of cell vision genes by CMT3b in egg is consistent with previous results showing that rice and maize egg cells are almost devoid of transcripts encoding histone proteins [31, 41]. The cmt3b effects on zygote mCHG and gene expression indicate that CMT3b participates in zygotic genome reprogramming by reestablishing mCHG methylation and by regulating gene expression. In conclusion, the work reveals non-CG DNA methylation dynamics during male gametogenesis and distinguishes CMT3a/b functions in mCHG and gene expression in reproductive cells in rice.

CMT3a is the major CHG methyltransferase in rice. CMT3a expression was detected in meiocyte, but barely in sperm (Fig. 2a), which may contribute to the low mCHG levels in sperm cells. However, CMT3a-mediated mCHG appeared essential for post-meiotic development, as the cmt3a mutation that eliminated mCHG in meiocyte stopped the pollen development at bicellular microspore stage. The expression pattern and mutation effects indicate that CMT3b plays an important role in increasing mCHG in the microspore in addition to complementing CMT3a for mCHG maintenance in meiocyte and sperm. However, the present data suggest that CMT3b preferentially targets protein-coding genes for methylation and is required for repression of transcriptional and/or translational genes during male gametogenesis.

Li et al. Genome Biology (2024) 25:84

The work shows that during male gametogenesis mCHG level is first enhanced in microspores but subsequently reduced to the lowest level in sperm. CMT3a is the main CHG methyltransferase in somatic cells and meiocyte to silence TE and TE-related genes, while CMT3b is required for the surge of mCHG in microspore to repress transcription and translation-related genes, and is involved in the establishment of the zygotic epigenome after fertilization. The histone H3K9me2 demethylases JMJ706 and JMJ707 contribute to the reduction of mCHG in sperm. This study reveals a dynamic remodeling of mCHG during male gametogenesis, which has functional significance for pollen development and fertilization.

Rice variety Zhonghua11 (ZH11) (Oryza sativa spp. japonica) was used for transformation of CMT3a, CMT3b, and JMJ706/707 CRISPR/Cas9 vectors in this study. Single-guide RNAs (sgRNA) of CRISPR/Cas9 system were designed as previously reported [42]. The sgRNA target sequences of CMT3a, CMT3b, and JMJ706/707 as well as primers for genotyping are listed in Additional file 8: Table S7. Mutations in CMT3a, CMT3b and JMJ706/707 were decoded by DSDecodeM (http://skl.scau.edu.cn/dsdecode/) [43, 44]. Cas9-free transgenic plants from T3-T4 segregating populations were utilized for phenotypic analysis and isolation of reproductive cells. For field growth, germinated rice seedlings were planted in Wuhan from May to October. For greenhouse growth, germinated rice seedlings were planted in soil-filled boxes under a 14-h light/10-h dark cycle at temperatures of 32 °C (in light) and 26 °C (in dark).

Rice sperm, meiocyte and unicellular microspore were isolated from anthers of Cas9-free transgenic plants and tissue culture-regenerated wild type plants. For sperm isolation, mature anthers were soaked in 45% (w/v) sucrose and then transferred into 15% (w/v) sucrose to release sperm pairs. For isolation of meiocytes, panicles of about 5 cm length were chosen and middle parts of the panicles were collected. Intact anthers were carefully selected using dissecting needles, placed onto an RNAase-free slide, and suspended in a PBS solution containing 1% proteinase inhibitors. The outer wall of anthers was then removed by capillary to release meiocytes. Isolated meiocytes were checked under a light microscope (Additional file 1: Fig. S1a). For isolation of unicellular microspores, panicles of about 8 cm length were harvested and the middle parts of the panicles were collected. Anthers were dissected out and made a hole with a capillary tube in the same solution as used for meiocyte isolation. Squeeze gently from the opposite side of the hole to release unicellular microspore. Isolated microspores were checked under a light unicellular microscope (Additional file 1: Fig. S1b). All isolated cells were collected by a micromanipulator system (Eppendorf, TransferMan® 4r).

(2024) 25:84

Li et al. Genome Biology

Rice eggs and unicellular zygotes were isolated from ovules before and after fertilization of Cas9-free transgenic plants and tissue culture-regenerated wild type [17, 21]. Briefly, ovaries of non-pollinated and pollinated florets (about 6.5 h after pollination) were manually collected in RNAase-free water under a dissection microscope. The collected ovules were transferred to a solution containing 0.53 M mannitol and 1% proteinase inhibitors to release eggs or zygotes. All isolated cells were stained by FDA (Fluorescein diacetate, Sangon, 596–09-8) and collected by a micromanipulator system (Eppendorf, TransferMan® 4r).

For RNA-seq library construction, mRNA isolated from different types of cells were reverse transcribed and amplified by using a Single Cell Full Length mRNA Amplification Kit (Vazyme, Cat.# N712) according to manufacturer's instruction. cDNAs were sheared into 200–400 bp DNA fragments followed by purification using VAHTS DNA Clean Beads (Vazyme, Cat.# N411). The rest steps were performed using the TruePrep DNA Library Prep Kit V2 for Illumina (Vazyme, Cat.# TD502). About 3000 sperm cells and 400 meiocytes were used to construct the transcriptome libraries. Fifty cells of egg or unicellular zygote were used for each replicate of the transcriptome library construction. BS-seq libraries were constructed using reported protocol [23]. About 300–400 cells were pooled for each replicate for sperm, meiocyte and unicellular microspore bisulfite seq library construction and fifty cells of egg or unicellular zygote were used to construct bisulfite seq library.

Proper florets were gathered and 50% FAA (50 ml absolute ethanol, 10 ml 37% formaldehyde solution, 5 ml glacial acetic acid, add double distilled water to 100 ml) was used to fix samples. Semi-thin embedding were performed using Herau Kulzer Technovit 7100 resin. Briefly, the materials were first transferred to a gradient alcohol solution (70% for 4 h at 4°C, 85% for 1 h at room temperature, 95% for 1 h at room temperature, 100% for 2 h at room temperature), then immersed separately in a pre-infiltration solution (equal parts of 96% or absolute ethanol and base liquid Technovit 7100) and an infiltration solution (1 g hardener I dissolved in 100 ml base liquid) for two hours. The treated materials were embedded in a 65°C film stand with polymerisation solution (1 ml hardener is added with the help of a pipette and stirred into 15 ml of preparation solution) and then sliced.

First, raw RNA-seq data were cleaned using fastp software to remove connectors and filter low-quality reads [45]. Clean read was then matched to the MSU7.0 rice reference genome using hisat2 software [46]. FeatureCounts were used for quantitative

analysis and the differentially expressed genes (Fold change > 2, q < 0.01) were calculated by DESeq2 [47].

## BS-seq data analysis

Fastp software was used to remove connectors and filter low-quality reads from the acquired raw BS-seq data. Clean reads were mapped to the MSU7.0 rice genome. Bismark software was used to match, deduplicate and extract methylation sites [48]. To obtain more loci for analysis, we combined the two biological replicates. Duplicates were removed and uniquely mapped reads were retained and each cytosine covered by at least five reads for further analysis. To avoid methylation being strongly influenced by single cytosine sites, the methylation level of each cytosine is calculated separately and then averaged for all cytosines to represent the methylation level for each bin (bin methylation level = (sum of individual cytosine methylations) / (number of cytosines within 100 bp).

To identify differential methylated regions (DMRs), the whole genome was divided into 100-bp bins. Bins that contained at least five cytosines each and every cytosine with at least a five-fold coverage were retained, absolute methylation difference of 0.5, 0.3, and 0.1 for CG, CHG, and CHH, respectively, and P values < 0.01 (Fisher’s exact test) were considered as DMRs. Neighboring DMRs within 200 bp were merged.

TE gene and gene Annotation information was downloaded from the Rice Genome Annotation project (http://rice.uga.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_7.0/all.dir/all.locus_brief_info.7.0). Differentially methylated genes (DMG) were identified using bedtools software [49], filtrated by > 80% of overlap between DMRs and genes.

Density plots show the frequency distribution of DNA methylation differences between 50-bp window of two samples with at least 20 informative sequenced cytosines in both samples and 70% CG, 30% CHG, or 10% CHH methylation in either of the samples as previously described [16].

## Identification of sexual lineage-specific methylation loci

The previously reported method [18] was used to identify SLH and SLM in rice male sex cells. Briefly, Average sex cell mCG and mCHH levels within 100 bp bins were calculated from meiocytes, microspores and sperm, and average sex cell mCHG levels were calculated from meiocytes and microspores. Fractional methylation in 100 bp windows across the genome was compared between an average of selected sex cells (SexAV) and somatic tissues (Seedling) (Diff = SexAV—Seedling). The total methylation level was significantly different (Fisher’s exact test p-value < 0.01), and the methylation level of all sex cell replicates was higher than that of all somatic tissues and selected windows meeting the following criterion: Diff_CG > 0 & Diff_CHG > 0.05 & Diff_CHH > 0.1 & (Diff_CG + Diff_CHG + Diff_CHH) > 0.4.

The refined list of SLHs (555 loci) was then separated into two groups based on the level of CHH/G methylation in somatic tissues: 1) SLMs with CHH and CHG methylation lower than 0.05 and 0.1, respectively, in somatic tissues (215 loci). 2) canonical SLHs with CHH methylation higher than 0.05 or CHG methylation higher than 0.1, in somatic tissues (340 loci) [18].

The online version contains supplementary material available at https://doi.org/10.1186/s13059-024-03222-w.

Additional file 1: Figure S1. Isolation of rice meiocytes and unicellular microspores and quality control of rice male germ cells BS-seq. Figure S2. DNA methylation levels in reproduction cells compared with somatic tissues. Figure S3. Analysis of microspore hyper DMRs relative to meiocyte and sperm. Figure S4. Analysis of mCHG genes in unicellular microspore. Figure S5. Effects of cmt3a and cmt3b in pollen development. Figure S6. CMT3b maintains high mCHG level in microspore. Figure S7. Analysis had no effect on mCHG in cmt3a mutant sperm cells. Figure S8. Production and phenotypic analysis of jmj706/707 double mutants. Figure S9. Average methylation level of TEG and genes in jmj706/707 meiocyte and sperm cells compared with wild type. Figure S10. Effect of the cmt3a/b and jmj706/7 mutations on sexual lineage-specific methylation. Figure S11. Analyze the Canonical SLH and lineage-specific methylation (SLM). Figure S12. Effects of cmt3a and cmt3b mutations in zygote and/or egg DNA methylation. Figure S13. Transcriptome data analysis. Figure S14. Enrichment of upregulated genes in cmt3b meiocyte and sperm. Figure S15. DNA methylation levels in rice reproductive cells and seedlings. Figure S16. DNA CHH methylation landscape in rice reproductive cells and seedlings.

Additional file 2: Table S1. Summary of BS-seq data.

Additional file 3: Table S2. Summary of RNA-seq data.

Additional file 4: Table S3. 315 genes annotated by Cluster A.

Additional file 5: Table S4. 132 genes annotated by SLM.

Additional file 6: Table S5. CMT3b regulates gene in meiocyte and sperm.

Additional file 7: Table S6. 83 zygotic genes upregulated in cmt3b egg.

Additional file 8: Table S7. Primers used in this study.

Additional file 9. Review history.

Acknowledgements
We thank Mr. Hao Liu from the National Key Laboratory of Crop Genetic Improvement for essential help in managing the high-throughput computing clusters.

Peer review information
Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Review history
The review history is available as Additional file 9.

Authors’ contributions
XL produced the mutants, isolated the cells and participated in data production, BZ produced and analyzed the data; YL, FZ and QL participated in data analysis; JW, MY and SC participated in material production; LX, CW participated in the project design and YZ participated in supervision and management of the project; DXZ coordinated the project, analyzed the data and wrote the paper.

Funding
The work was supported by National Natural Science Foundation of China [32070563, 31730049], the Fundamental Research Funds for the Central Universities [2662023SKPY002], and the Agence Nationale de la Recherche (LANDSREC, ANR-21-CE20-0012–01).

Availability of data and materials
Genes sequence data from this article can be found in the Rice Genome Annotation Project website under the following accession numbers: CMT3a, LOC_Os10g01570; CMT3b, LOC_Os03g12570; JMJ706, LOC_Os10g42690; JMJ707, LOC_Os02g46930. All high throughput data in support of the finding of this study deposited to the Gene Expression Omnibus (GEO) under the accession number GSE235680 [50]. The transcription profiles of reported rice sperm were downloaded from Gene Expression Omnibus (accession no. GSE50777) [51]. The rice central cells and vegetative cells BS-seq data was under the accession number GSE89789 [52] and GSE126791 [53]. The rice cmt2 and drm2 mutant BS-seq data was under the accession number GSE138705 [54]. No other scripts and software were used other than those mentioned in the Methods section.

Declarations

Ethics approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Competing interests
Junwei Nie claims competing interests, the remaining authors declare that they have no competing interests.

Published online

02 April 2024

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


[System]
You are a **scientific‐rigor auditor**. You will receive the parsed contents of a research paper. Your job is to identify **only** those errors or flaws that directly undermine the **scientific validity** of the paper’s methods, analyses, or conclusions.

Your sole focus is to identify flaws—such as errors in experimental design, data integrity, calculations, statistical inference, or reproducibility—that directly call into question the validity of a specific claim, paragraph, or the paper as a whole.

**Do not** report issues that are purely presentational, rhetorical, stylistic, or related to citation practices.

---

After you’ve done a **detailed walkthrough** of the paper, output exactly in this format—no extra keys or commentary:

```
<analysis>
{detailed walk‑through of how you checked each section/figure and why you flagged (or did not flag) any flaw}
</analysis>

<response>
{
  "has_error": <true|false>,
  "errors": [
    {
      "location": "Section 2.1",
      "description": "Claim that ‘all X are Y’ is contradicted by citation [5], which shows examples of non‑Y X."
    },
    {
      "location": "Figure 3",
      "description": "XAxis labeled ‘Time (s)’ but units appear to be milliseconds; scale bar mismatches caption."
    }
    // …more entries…
  ]
}
</response>
```

- **Do not** include any other keys or prose outside these two tagged blocks.
- **Do not** report stylistic or citation issues.
- Be as precise as possible about **where** (section ID or figure/table) and **what** the scientific flaw is.
- Each description for the errors must be rooted in a scientific rationale explaining why it is 'wrong' (not how it could be improved).

Begin your analysis now.

[User]
Background After myocardial infarction, the lost myocardium is replaced by fibrotic tissue, eventually progressively leading to myocardial dysfunction. Direct reprogramming of fibroblasts into cardiomyocytes via the forced overexpression of cardiac transcription factors Gata4, Mef2c, and Tbx5 (GMT) offers a promising strategy for cardiac repair. The limited reprogramming efficiency of this approach, however, remains a significant challenge.

Methods We screened seven factors capable of improving direct cardiac reprogramming of both mice and human fibroblasts by evaluating small molecules known to be involved in cardiomyocyte differentiation or promoting human-induced pluripotent stem cell reprogramming.

Results We found that vitamin C (VitC) significantly increased cardiac reprogramming efficiency when added to GMT-overexpressing fibroblasts from human and mice in 2D and 3D model. We observed a significant increase in reactive oxygen species (ROS) generation in human and mice fibroblasts upon Doxy induction, and ROS generation was subsequently reduced upon VitC treatment, associated with increased reprogramming efficiency. However, upon treatment with dehydroascorbic acid, a structural analog of VitC but lacking antioxidant properties, no difference in reprogramming efficiency was observed, suggesting that the effect of VitC in enhancing cardiac reprogramming is partly dependent of its antioxidant properties.

Conclusions Our findings demonstrate that VitC supplementation significantly enhances the efficiency of cardiac reprogramming, partially by suppressing ROS production in the presence of GMT.

Keywords Vitamin C, Cardiac reprogramming, ROS, Cardiac regeneration

Juntao Fang1,3, Qiangbing Yang1,2†, Renée G. C. Maas1,3†, Michele Buono1, Bram Meijlink1, Dyonne Lotgerink Bruinenberg1, Ernest Diez Benavente1, Michal Mokry1,2, Alain van Mil1,3, Li Qian4, Marie-José Goumans5, Raymond Schiffelers2, Zhiyong Lei1,2,3* and Joost P. G. Sluijter1,3*

†Qiangbing Yang and Renée G. C. Maas have contributed equally and share the second authorship.

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Various approaches have been explored to identify gene combinations or conditions that enhance cardiac reprogramming, such as altering GMT stoichiometry, adding supplementary growth factors like fibroblast growth factor (FGF) and vascular endothelial growth factor (VEGF), modulating different signaling pathways such as the Hippo pathway, or manipulating gene expression including Beclin1 and Bmi1 [8, 12–18]. However, most of these approaches and improvements have been studied and observed primarily in mouse embryonic fibroblasts (MEFs). In the current study, we investigated the impact of several factors on enhancing GMT-reprogramming efficiency in both MEFs and human fetal cardiac fibroblasts. We selected a panel of small molecules previously reported to facilitate cardiac differentiation or hiPSC reprogramming [19–25]. The ability of these molecules alone to sufficiently enhance cardiac reprogramming efficiency, however, remains uncertain.

Here, we present the first evidence that Vitamin C (VitC) significantly promotes the direct reprogramming of fibroblasts by GMT into cardiomyocytes, partially mediated by reactive oxygen species (ROS) reduction. Our findings offer a cost-effective, safe, and efficient strategy for generating iCMs.

Cardiovascular disease (CVD) remains a leading cause of death worldwide, with the associated costs expected to rise further [1]. Myocardial infarction (MI) is a primary contributor to CVD, in which lost myocardium is replaced with fibrotic tissue due to the limited regenerative capacity of cardiomyocytes. An increasing number of patients post-MI develop heart failure [2], a state in which the heart provides too little oxygenated blood into the circulation [3]. Currently, there are only a few curative treatments available for end-stage heart failure patients post-MI, limited to left ventricle assistant devices (LVADs) and heart transplantation [4]. However, LVADs can lead to complications including bleeding, infection, thrombosis, and dysrhythmia and typically only provide support for up to 5 years [5]. Heart transplantation serves as the ultimate solution for end-stage heart failure patients, but the application is limited due to donor scarcity and immune rejection issues [6]. Consequently, numerous studies have focused on repopulating the lost myocardial tissue post-MI in an attempt to regenerate the infarcted region and restore heart function at the basis of the problem and repopulate lost tissues. One such approach involves cell-based strategies that generate functional cardiomyocytes through cellular reprogramming, in which non-cardiomyocytes can be converted into cardiomyocytes via the overexpression of cardiac-specific transcription factors such as Gata4, Mef2c, and Tbx5 (GMT) [7]. Several studies have demonstrated that cardiac reprogramming can reduce scar size and improve cardiac function in mice post-MI [7–10]. This strategy offers several advantages, including the prevention of cell wash-out upon injection due to the presence of fibroblasts in the infarcted myocardial area, a reduction in fibrosis when fibroblasts can be converted into induced cardiomyocytes (iCMs), and the cell-free approach, thereby avoiding wash-out upon injection, a decreased likelihood of immune rejection and occurrence of arrhythmias. Unfortunately despite previous attempts, the direct cardiac reprogramming efficiency remains very low, particularly in human cells [11]. Therefore, further investigation of the underlying molecular mechanisms is required.

Mouse embryonic fibroblasts (MEF) were obtained from the αMHC/GFP transgenic mouse on embryonic day

Generation of inducible cardiac reprogramming reporter in mouse embryonic fibroblasts

13.5, as previously described [7, 9, 26]. In short, hearts were digested with 0.1% trypsin and plated on plastic dishes. Attached fibroblasts were cultured for 7 days, and α-MHC-GFP⁺ cells were sorted and cultured in DMEM/M199 medium containing 10% FBS at a density of 10⁴/cm².

To generate a MEF GMT cell line, MEFs were transfected with a PiggyBac vector [27] carrying an inducible TetOn3G promotor, the mouse GMT reprogramming factors, and mCherry. After co-transfection by using Lipofectamine 3000 (Invitrogen, Catalog number: L3000001), the cells were selected with puromycin at a final concentration of 2 μg/ml for 3 days (Gibco, A11138-03) to obtain a stable cell line.

Human fetal heart tissue was obtained by individual permission using standard written informed consent procedures and prior approval of the ethics committee of the Leiden University Medical Center, the Netherlands (No. METC P08-087). This is in accordance with the principles outlined in the Declaration of Helsinki for the use of human tissue or subjects. Human fetal cardiac fibroblasts (hFCFs) cell line carrying an inducible cardiac reprogramming cassette was created with the PiggyBac (PB) transposon system as described above. hFCFs were transfected with either a GMT vector or an empty vector (Neg) together with a transposase plasmid. As a control, the Neg hFCFs cell line was created containing the same TetOn 3G promotor and mCherry tag, but not containing the reprogramming cassette. After co-transfection by using Lipofectamine 3000 (Invitrogen, Catalog number: L3000001), the cells were selected with puromycin at a final concentration of 2 μg/ml (Gibco, A11138-03) to obtain a stable cell line. Due to the tetOn3G promotor it is possible to activate and inactivate the reprogramming cassette at every preferred time point by adding or removing doxycycline (Doxy) [26].

Cells were seeded at a density of 5 × 10⁴ cells per well in a 6-well plate coated with 0.1% gelatin and cultured in the reprogramming medium (DMEM with 10% FBS and 1% P/S). Reprogramming medium was changed every other day. Doxy (1 μg/ml, Clontech, 631,311) was added one day after the cells were plated to start reprogramming and maintained during cardiac reprogramming. VitC (Sigma, A4544) was given with a final concentration of 20 μg/ml or at indicated concentrations. The day of Doxy administration was considered as day 0.

ROS was determined using a 2′,7′–dichlorofluorescin diacetate (DCFDA, ab113851) assay following manufacturer's instruction. In brief, cells were washed and trypsinized into single cells, and then cells were stained with DCFDA for 30 min (without washing) at 37°C and analyzed by Cytoflex (Beckman Coulter, #A00-1-1102). Data were analyzed using FlowJo 10.4.

Total RNA was isolated at the indicated time points using the Nucleospin RNA isolation kit from Macherey–Nagel (740.955.250). The qScript cDNA synthesis kit from Quanta BioSciences (95,047–100) was used to synthesize cDNA from the isolated RNA samples (300 ng per sample). The quantitative real-time polymerase chain reaction (qRT-PCR) was performed with iQ SYBR Green Supermix (Bio-Rad). β-actin was selected as the housekeeping gene and was used for the calculation of normalized gene expression levels (ΔCt). The primers required in this study are listed in Table 1 and purchased from Integrated DNA Technologies.

RNA concentration and integrity were analyzed with the Agilent 2100 Bio analyzer prior to proceeding with sequencing (Agilent RNA 6000 Nano Kit). RNA concentration for each single sample must be at least more than 15 ng/μl, and the total RNA amount is no less than 0.3 μg. The library construction kit is MGIEasy RNA Directional Library Prep Set (1,000,006,385), made by MGI. Sequencing was performed on the DNBSEQ-G400 platform with PE150. The sequencing data were filtered with SOAPnuke by (1) removing reads containing sequencing adapter; (2) removing reads whose low-quality base ratio (base quality less than or equal to 15) is more than 20%; (3) removing reads whose unknown base (‘N’ base) ratio is more than 5%, afterwards clean reads were obtained and stored in FASTQ format. The clean reads were mapped to the reference genome using HISAT2. After that, Ericscript (v0.5.5) and rMATS (V3.2.5) were used to detect fusion genes and differential splicing genes (DSGs), respectively. Bowtie2 was applied to align the clean reads to the gene set, a database built by BGI (Beijing Genomic Institute in ShenZhen), in which known and novel, coding and noncoding transcripts were included. Expression level of each gene was calculated by RSEM (v1.3.1). Read counts obtained from RNA sequencing data were normalized, and differential gene expression between groups was performed using DESeq2 (v1.4.5) in R. The screening criteria for DEGs were P-value < 10⁻⁶ and Log2 fold change ≥ 0.5 and ≤ (−0.5). Pathway enrichment analysis was performed using differentially upregulated genes, and the package “EnrichR” enriched for “GO_Biological_Process_2015” was used. Gene expression profiles were also analyzed using Omics Explorer 3.2 (Qlucore),

and principal component analysis (PCA) was used to visualize the data. Gene set enrichment analysis (GSEA) was also performed with Qlucore to determine whether a gene set of interest was significantly enriched in one condition compared to another. Gene sets with (false discovery rate [FDR] < 0.1) were considered significantly enriched in the comparison made. RNA sequencing and data mapping are conducted in BGI Hongkong Tech Solution NGS Lab.

For MEFs, cells from different groups were first washed with PBS, detached by trypsin and collected in 15-ml Eppendorf tube, filtered with Flowmi™ Cell Strainers, 40 um (southern labware, BAH136800040) and were analyzed by Cytoflex flow cytometer (Beckman Coulter, #A00-1-1102). For hFCFs, cells were washed and fixated in 70% cold ethanol for 30 min. Later, cells were washed twice with PBS and blocked with 5% goat serum for 30 min, washed twice with PBS and stained with primary antibody α-Actinin (Sigma, A7811, 22ug/ml) overnight at 4 ℃, washed with PBS twice and stained with goat anti-mouse IgG Alexa Fluor 488 s antibody (Invitrogen, A11001, in 1:400 dilution) for one hour at room temperature, and then washed with PBS twice, filtered with Flowmi™ Cell Strainers, 40 um and analyzed by Cytoflex flow cytometer in the similar setting.

For MEFs spheroid formation, 0.5 million cells were added to one well of a 6-well ultra-low attachment well plate (Corning) in DMEM + 10% FBS + 1% PS with 10% KOSR and 10 μM Y-27632. Spheroid plates were kept on a rotation plate, 70 rpm at 37 °C and 5% CO2. Medium change was performed after 24 h with the basal medium without KOSR and Y-27632. After 3 days, the spheroids were treated with Doxy at a final concentration of 1 µg/ml with/without vitamin C at a final concentration of 20 µg/ml. After 7 days, the spheroids were collected and used for flow cytometry and immunofluorescent analysis.

For immunofluorescence staining, an optimized protocol was used that allows imaging with single-cell resolution of the whole 3D microtissues [28]. In short, spheroids were collected by carefully aspirating media and adding 1 mL cold phosphate-buffered saline (PBS) with bovine serum albumin (BSA) (Roche)-coated tips into BSA-coated tubes. The spheroids were washed with 10 mL ice-cold PBS and spun down for 3 min at 70 × g and 4 °C. Next, the spheroids were resuspended in 1 mL 4% paraformaldehyde (PFA, Santa Cruz) solution and fixed for 45 min at 4 °C. After the fixation, the spheroids were spun down and washed with 10 mL ice-cold PBS for 3 times. Spheroids were then blocked with the spheroid washing buffer (SWB) consisting of 0.1% Triton X-100 (Sigma), 0.2% of 10% (w/v) SDS (Sigma) and 0.2% bovine serum albumin (BSA, Sigma) in PBS (store at 4 °C, up to 2 weeks) for 15 min at 4 °C. After fixation and blocking, the spheroids were transferred to a 24-well suspension plate where immunolabeling was performed. Spheroids were incubated with antibodies anti-GFP (goat, R1091P, Origene) to stain newly developed cardiomyocytes. DNA Hoechst stain for nuclei labeling (Thermo Fisher Scientific) was added together with the secondary antibodies (Donkey anti goat-488, Thermo Fisher Scientific, 0.5ug/ml). Specimens were mounted using FUnGI consisting of 50% (v/v) glycerol (Sigma), 9.4% (v/v) dH2O, 10.6 mM

To facilitate our screening, we established a stable Doxy-inducible cardiac reprogramming cell model in which the Gata4, Mef2c and Tbx5 polycistron are under the control of a Doxy-inducible tetOn3G promoter (Figs. 1A and 2A). To validate the inducible cardiac reprogramming system, MEFs were treated with Doxy. Following Doxy induction, GMT gene expression inductions were confirmed in MEFs (Fig. 1B). After a successful cardiac reprogramming, GFP signal will become detectable in α-MHC-GFP MEFs by

tris base (Roche), 1.1 mM EDTA (Sigma), 2.5 M fructose (Sigma) and 2.5 M urea (Sigma). Fluorescent imaging was performed on a SP8 Confocal Microscope (Leica). Optical sectioning along the Z axis was performed, and the images were projected into a single focal plane using the manufacturer’s software.

Data were analyzed with GraphPad Prism 9, and comparisons were performed with a t-test (non-parametric tests) and a two-way ANOVA. Data were presented as mean ± SEM. A value of p < 0.05 is considered as significantly different.

| Fang et al. Stem Cell Research & Therapy (2024) 15:19 |    |   | Page 6 of 16 |
| ----------------------------------------------------- | -: | - | ------------ |

A.
```mermaid
graph TD
A[GFP+ Relative ratio] --> B[2.5]
B --> C[2.0]
C --> D[1.5]
D --> E[1.0]
E --> F[0.5]
F --> G[0.0]
```

B. Representative fluorescence microscopy images showing GFP expression under various conditions: Control, VitC 10 μg/ml+Doxy, VitC 20 μg/ml+Doxy, VitC 40 μg/ml+Doxy, VitC 100 μg/ml+Doxy, and Doxy alone.

C. Flow cytometry analysis plots for: Doxy, VitC 10 μg/ml+Doxy, VitC 20 μg/ml+Doxy, VitC 40 μg/ml+Doxy, VitC 100 μg/ml+Doxy.

D.
```mermaid
graph TD
A[GFP+%] --> B[80]
B --> C[70]
C --> D[60]
D --> E[50]
E --> F[40]
F --> G[30]
G --> H[20]
H --> I[10]
I --> J[0]
```

E. VitC treatment schematic
```mermaid
graph LR
A[Mouse fibroblast] --> B[D1-D7]
A --> C[D5-D7]
A --> D[D3-D5]
A --> E[D1-D3]
A --> F[GMT]
F --> G[Day 1]
G --> H[Day 2]
H --> I[Day 3]
I --> J[Day 4]
J --> K[Day 5]
K --> L[Day 6]
L --> M[Day 7]
A --> N[D3]
A --> O[D5]
A --> P[D7]
M --> Q[Induced cardiomyocyte]
```

F.
```mermaid
graph TD
A[GFP+ Relative ratio] --> B[2.5]
B --> C[2.0]
C --> D[1.5]
D --> E[1.0]
E --> F[0.5]
F --> G[0.0]
```

G.
```mermaid
graph TD
A[GFP+ Relative ratio] --> B[2.0]
B --> C[1.5]
C --> D[1.0]
D --> E[0.5]
E --> F[0.0]
```

Fig. 2 (See legend on next page.)

microscopic or flow cytometric analyses (Fig. 1C, D). Similarly, hFCFs were also treated with Doxy. After Doxy induction, mCherry expression was observed in hFCFs (Fig. 2B), and GMT gene expression inductions were confirmed in hFCFs as well (Fig. 2F). After a successful cardiac reprogramming, α-actinin-positive population will become detectable in hFCFs upon α-actinin staining with microscopic or flow cytometric analyses (Fig. 2C). Five days after Doxy addition, hFCFs cells with empty vectors (Neg) remained to display a cardiac fibroblast morphology with an elongated and stretched state (Fig. 2D). The morphology of hFCFs GMT cells, however, changed significantly and appeared like the previously reported cardiomyocyte progenitor cells (CMPCs), isolated from the human heart [30]. We measured cardiac-specific MYH6 and ACTN2 gene expression by qPCR and observed that the expression of these early cardiac-specific markers was significantly increased after Doxy induction, as compared to control groups (Fig. 2G). These results showed that the Doxy-inducible direct cardiac reprogramming system was functional, enabling both mice and human fibroblasts to be converted into induced cardiomyocytes (iCM), while the efficiency is still not very high.

Many factors have been shown to potentially modulate cardiac reprogramming (Table 2), including histone deacetylase (HDAC) inhibitors Suberoylanilide hydroxamic acid (SAHA), and valproic acid (VPA), demethylating agent 5-azacytidine (5-AZA), Wnt agonists CHIR 99021, and ascorbic acid (VitC), mTOR and p53 inhibition [19, 22–25, 29]. We hypothesized that these molecules may enhance direct cardiac reprogramming of GMT. Therefore, we screened various concentrations of these molecules in the α-MHC-GFP/GMT MEF cell line (Fig. 3A). VPA (500 μM and 1 mM) and SAHA (500 nM and 1 μM), however, concentration dependently decreased the cardiac reprogramming efficiency. We assumed that this might be due to the toxicity of VPA and SAHA at high concentrations, whereas iCMs are more susceptible to this toxicity. Interestingly, we observed that VitC showed a consistent and robust cardiac reprogramming enhancing effect and achieved the highest proportion of GFP⁺ cells (Fig. 3A). In the absence of Doxy, no GFP signal was observed upon VitC exposure which indicates the robustness of our system (Fig. 3B top row). The GFP signal was clearly visible when treated for 7 days with Doxy and Doxy+VitC (Fig. 3B bottom row). These initial results were further validated by FACS analysis, demonstrating that approximately 25% GFP⁺ cells were observed 7 days after Doxy addition, whereas VitC (with a concentration of 10 μg/mL, 20 μg/mL and 40 μg/mL) can significantly increase the proportion of GFP⁺ cells to 32%, 46.4% and 36.4%, respectively, as compared to Doxy alone. The proportion of GFP⁺ cells decreased to 22.6% when treated with VitC at a concentration of 100 μg/mL (Fig. 3C, D), and this probably linked to increased cell death during

Fig. 2 VitC promotes the direct reprogramming of MEFs into induced cardiomyocytes. A Summary of reprogramming efficiency with indicated small molecules to explore their cardiomyocyte forming-enhancing effect. Reprogramming efficiency of indicated conditions was measured by flow cytometric analyses. The GFP⁺ expression for each condition was normalized to the Doxy treated condition. B Representative fluorescence images of MEFs with or without Doxy exposure, together with exposure to different concentrations of VitC. Upon exposure to Doxy, cells started to express GFP while not being present in non-exposed MEFs, reflecting the success of cardiac reprogramming. Scale bar 200 μm. C, D Representative FACS images and statistical analysis of GFP⁺ MEFs with different concentrations of VitC treatment. E Experimental layout for exploring the duration effect of VitC on cardiac reprogramming. F Quantification of GFP⁺ MEFs of different duration with VitC administration. G, Quantification of GFP⁺ MEFs of different time slots with VitC administration. Mean values ± SEM of three independent experiments is shown (n = 3). Data were analyzed with two-way ANOVA. *p < 0.05 vs Doxy, **p < 0.01 vs Doxy, ***p < 0.001 vs Doxy. (Doxy: Doxycycline)

VPA Valproic acid, mTORi mammalian target of rapamycin inhibitor, 5-AZA 5-azacytidine, P53i P53 inhibitor, SAHA suberoylanilide hydroxamic acid, CHIR-99021

(See figure on previous page.)

reprogramming and exposure to VitC, as the iCMs are more sensitive to this response.

To determine the exact phase of cardiac reprogramming in which VitC participates, we added VitC (20 μg/ml) in the early-phase (day 1–3), mid-phase (day 3–5), and late-phase (day 5–7) during reprogramming in the MEF cell line (Fig. 3E). Here, the addition of VitC increased the ratio of GFP+ cells again and upon exposure during all the phases (Fig. 3F). We treated MEFs with VitC (20 μg/ml) for the different durations (3 days,

Fang et al. Stem Cell Research & Therapy           (2024) 15:19                                                                     Page 9 of 16

A
Control | Doxy | Doxy+VitC
--- | --- | ---
Control 1 | Doxy 1 | Doxy+VitC 1
Control 2 | Doxy 2 | Doxy+VitC 2
Control 3 | Doxy 3 | Doxy+VitC 3

B
Control | Doxy | Doxy+VitC | C
--- | --- | --- | ---
Control FACS | Doxy FACS | Doxy+VitC FACS | Bar Graph
03-MEF-control-F4 P1 | 03-MEF-dox-F5 P1 | 03-MEF-vitC-F6 P1 | 20
P2(0.27%) | P2(6.43%) | P2(18.76%) | 15
| | | 10
| | | 5
| | | 0
FITC-A | FITC-A | FITC-A | Control Doxy Doxy+VitC

Fig. 4 VitC promotes direct cardiac reprogramming efficiency of 3D spheroid derived from MEFs. A Immunofluorescence images of spheroid derived from MEFs, stained with GFP antibody. The blue color indicates DAPI staining and the green color indicates GFP staining that represented α-MHC signal. Upon exposure to Doxy, cells started to express GFP while not being present in non-exposed MEFs, reflecting the success of cardiac reprogramming. Scale bar 40 μm. B, C, Representative FACS image and quantification of GFP+ MEFs dissociated from MEFs spheroid. Mean values + SEM of three independent experiments is shown (n = 3). Data were analyzed with two-way ANOVA. *p ≤ 0.05 vs Doxy. (Doxy: Doxycycline)

5 days and 7 days) and observed that VitC promotes cardiac reprogramming at all the different time durations (Fig. 3F). Interestingly, we found that administration in none of these phases achieved the maximal effect observed upon exposure to VitC for the entire follow-up period of the cardiac reprogramming (Fig. 3G). These results suggested that the treatment of VitC is essential throughout the entire reprogramming process.

To further confirm the robust cardiac-inducing effect of VitC on direct cardiac reprogramming efficiency and explore the underlying mechanisms, we performed RNA sequencing to identify the differentially expressed genes between Doxy alone and Doxy plus VitC group. In total, 622 genes were found to be differentially expressed between these two groups. Transcriptional differences within these two groups were visualized in a PCA plot (Fig. 4A). To investigate the transcriptional differences between these two groups further, differential gene expression analysis was performed. Heatmap shows that gene sets related to cardiac muscle cell development were significantly upregulated in Doxy plus VitC group in the comparison of the control group and certain cardiac genes such as TNNT2, ACTC1 and ACTN2 were demonstrated to be significantly increased in VitC treated group, as compared to Doxy alone (Fig. 4E). We performed GSEA to identify statistically enriched gene sets in transcriptomic data between these two groups and found that VitC treatment displayed a significant enrichment of genes associated with muscle cell development during cardiac reprogramming (Fig. 4D). Furthermore, we also observed that epigenetic modification changes were occurring upon VitC exposure, including histone methylation and histone acetylation (Fig. 4B, F). Heatmap analyses show that gene sets related to histone methylation and histone acetylation were significantly downregulated in Doxy plus VitC group (Fig. 4C, G). Enrichment analysis of differentially expressed genes revealed the presence of ongoing heart development processes (Fig. 4H).

To further explore if this VitC effect could be validated in a 3D environment, representing a more translational in vitro model [31, 32], MEF-containing spheroids were generated and treated with VitC for 7 consecutive days. Hereafter, the spheroids were either fixed by 4% PFA for GFP staining or dissociated into single cells for flow cytometry measurements. We observed that the expression of GFP was indeed increased in the VitC group compared to Doxy alone (Fig. 5A), which is consistent with the Flow cytometry results, where VitC addition significantly increases the GFP+ population from 5.5 to 11.2% (Fig. 5B, C). These results indicate that VitC is also sufficient to enhance the direct cardiac reprogramming efficiency in a 3D in vitro cell model.

To determine if VitC exposure enhanced cardiac reprogramming efficiency in the human setting, we treated hFCFs with VitC with a concentration of 20 µg/ml for one week. hFCFs were collected either for α-actinin staining evaluated by FACS or for RNA isolation to determine mRNA expression of cardiac genes. We observed that VitC administration significantly increased the α-actinin positive cells, enhancing the percentage from 13.5 to 25.5%, as detected by FACS (Fig. 6B, C). Furthermore, mRNA expression level of cardiac genes ACTN2 and MYH6 (Fig. 6D) was increased 3.6- and 5.3-fold, respectively. Finally, in line with the 3D MEF spheroid results, we also observed that VitC significantly enhanced direct cardiac reprogramming efficiency of hFCFs spheroids (Fig. 6F, G), increasing the proportion of α-actinin+ positive cells from 13.9 to 26.7%.

ROS is an important player in hiPSCs reprogramming, having an inhibitory role, whereas VitC is known for its antioxidant properties [33]. To investigate if VitC

Fig. 5 Forced expression of GATA4, MEF2C and TBX5 converts human fetal cardiac fibroblast (hFCFs) into iCMs. A Strategy for creating a direct cardiac reprogramming system with hFCFs: hFCFs carrying a stable Doxy-inducible TetO promotor, mouse GMT reprogramming factors and a mCherry tag, can be converted into iCM upon exposure to Doxy. B Representative fluorescent images of mCherry expression in hFCFs after Doxy exposure. Upon exposure to Doxy, cells started to express mCherry while not being present in control cells, reflecting the presence of GMT factors. Scale bar 200 μm. C Representative FACS images of α-actinin staining positive hFCFs upon Doxy exposure. Upon exposure to Doxy, cardiac marker α-actinin expression is induced in hFCFs while not being present in non-exposed hFCFs. D Light microscopy images of GMT or Neg-transduced hFCFs on day 5, compared to cultured cardiomyocyte progenitor cells (CMPcs). Scale bar 500 μm. E, Magnified images from (D), Scale bar 250 μm. F, G Relative mRNA expression of GMT and cardiac genes MYH6 and ACTN2 during reprogramming in GMT or Neg-transduced hFCFs at different time points. RNA samples were collected from three independent experiments. Mean values±SEM of three independent experiments is shown (n=3). Data were analyzed with two-way ANOVA. *p≤0.05 vs GMT, **p≤0.01 vs GMT, ***p≤0.001 vs GMT and ****p≤0.0001 vs GMT. (Doxy: Doxycycline; Neg: Empty vector)

Fang et al. Stem Cell Research & Therapy      (2024) 15:19                                                          Page 11 of 16

A

```mermaid
graph LR
A[Human fibroblasts] -->|Doxy| B[Induced cardiomyocyte]
A -->|Promoter GMT mCherry| A
```

B
Control                       Doxy

C
Control                       Doxy
```
10^6                          10^6
10^5                          10^5
10^4    a-actinin+            10^4    a-actinin+
10^3      0.23%               10^3      12.4%
10^2                          10^2
10^1                          10^1
10^1 10^2 10^3 10^4 10^5 10^6 10^1 10^2 10^3 10^4 10^5 10^6
FL1-A : FITC-A                FL1-A : FITC-A
```

D
Neg Doxy(-)             Neg Doxy(+)              GMT Doxy(-)         GMT Doxy(+)                   CMPC

[Microscopy images showing different cell conditions with zoomed-in sections]

F
```
GATA4                                         MEF2C                                       TBX5

0.6                           Neg         1.5×10^-2 _                        Neg         0.03                            Neg
Neg+Doxy   1.0×10^-2 _                        Neg+Doxy                                  Neg-Doxy
GMT                                         GMT         0.02-                          GMT
0.2                           GMT+Doxy  5.0×10^-3 _                        GMT+Doxy    0.01                            GMT+Doxy

0.0                                     0                                             0.00
Day 3   Day 7   Day 14                  Day 3   Day 7   Day 14                        Day 3   Day 7   Day 14

MYH6                                        ACTN2
5×10^-5 _                      Neg         8×10^-3 _                        Neg
4×10^-5 _                      Neg+Doxy    6×10^-3 _                        Neg+Doxy
3×10^-5 _                      GMT         4×10^-3 _                        GMT
2×10^-5 _                      GMT+Doxy    2×10^-3 _                        GMT+Doxy
1×10^-5 _

Day 3   Day 7   Day 14                  Day 3   Day 7   Day 14
```

Fig. 5 (See legend on previous page.)

enhances cardiac reprogramming via a ROS mediated mechanism, we determined ROS production by measuring DHA level upon Doxy-induced direct cardiac reprogramming. We observed a significant increase in ROS generation in MEFs and hFCFs upon Doxy induction, as compared to the control cell lines (Fig. 6I, K). ROS generation was subsequently reduced upon VitC treatment, associated with increased reprogramming efficiency (Figs. 4D, 6C, I, K). Interestingly, upon treatment with dehydroascorbic acid, a structural analog of VitC but lacking antioxidant properties (chemical structure shown in Fig. 6H), no difference in reprogramming efficiency was observed (Fig. 6I, L), suggesting that the effect of VitC in enhancing cardiac reprogramming is partly dependent on its antioxidant properties. We also tested the effect of α-lipoic acid (LA), an antioxidant, in MEFs with different concentrations to see if it can boost cardiac reprogramming efficiency, and we observed LA with a concentration of 0.5 mM can significantly increase reprogramming efficiency (shown in Additional file 2; Figure S1), this further indicated that the reduction of ROS facilitates cardiac reprogramming efficiency. In addition, we found that direct cardiac reprogramming induces P53 expression while this was reduced with VitC treatment (shown in Additional file 2; Figure S2), indicating that VitC promotes cardiac reprogramming by reducing ROS-induced cell death.

## Discussion

Direct reprogramming of fibroblasts into induced cardiomyocytes (iCMs) offers a promising therapeutic approach for myocardial infarction (MI) patients by simultaneously eliminating fibroblasts and generating new cardiomyocytes in the infarcted region. However, the efficiency of direct cardiac reprogramming remains suboptimal. In this study, we investigated the effects of seven small molecules, on the efficiency of direct cardiac reprogramming in mouse and human fibroblasts. These molecules were selected based on their previously reported roles in cardiac differentiation and human-induced pluripotent stem cell (hiPSC) reprogramming.

For instance, suberoylanilide hydroxamic acid (SAHA), also known as vorinostat and valproic acid (VPA) are well-known histone deacetylase (HDAC) inhibitor that have been reported to significantly enhance hiPSC reprogramming [26]. In particular, VPA emerged as a more potent modulator in cellular reprogramming through relaxation of chromatin structure, making it more accessible to transcription factors and other regulatory proteins [26, 34]; 5-azacytidine (5-AZA), a demethylating agent, was shown to enhance the differentiation of human embryonic stem cells into cardiomyocytes by DNA methylation regulation [35]. CHIR 99021, a Wnt agonist, can also robustly enhance cardiomyocyte differentiation from hiPSCs [29]. VitC, known as ascorbic acid, has emerged as a promising small molecule that can remarkably promote cardiac differentiation of embryonic stem cells (ESC) and hiPSCs, and enhances the generation of mouse and human-induced pluripotent stem cells [24, 25, 36]. The cell-cycle regulator P53 functions as an important safeguard and was also shown as a critical barrier to reprogramming process, P53 deletion significantly promotes hiPSC reprogramming [28]. In addition, mTOR inhibitor was also reported to enhance the generation of hiPSC [30]. Eventually, we identified vitamin C (VitC) as a potent enhancer of direct cardiac reprogramming efficiency. VitC, an essential nutrient for human health, has been widely utilized as a supportive compound in various cellular differentiation and reprogramming processes due to its beneficial properties, such as alleviating cell senescence and enhancing cellular proliferation [19, 21]. Our RNA sequencing data corroborate the transition of fibroblasts into induced cardiomyocytes, as evidenced by the upregulation of cardiac development genes such as TNNT2, ACTC1 and ACTN2 in the Doxy + VitC group. Interestingly, VitC-induced enhancement of cardiac reprogramming was also observed in a 3D spheroid model derived from both mouse embryonic fibroblasts (MEFs) and human fetal cardiac fibroblasts (hFCFs), indicating that VitC effectively promotes fibroblast-to-cardiomyocyte conversion in both the 2D and 3D environment. Intriguingly, other small molecules, such as

(See figure on next page.)
Fig. 6  VitC enhances direct conversion of cardiomyocytes from hFCFs via ROS suppression. **A** Schematic overview of direct cardiac reprograming in hFCFs. **B** Representative FACS images of α-actinin+ hFCFs upon Doxy exposure. **C** Statistical analysis of α-actinin+ hFCFs upon Doxy exposure. **D** Relative mRNA expression of cardiac genes ACTN2 and MYH6 seven days after reprogramming. **E** Schematic overview of direct cardiac reprograming in spheroid derived from hFCFs. **F, G** Representative FACS images and statistical analysis of α-actinin+ spheroid derived from hFCFs. **H** Chemical structure of ascorbic acid and dehydroascorbic acid. **I** Flow cytometry analysis of DCFDA detection to measure MEFs ROS production upon Doxy exposure, treated with ascorbic acid or dehydroascorbic acid. **J** Quantification of GFP+ MEFs treated with ascorbic acid or dehydroascorbic acid. **K** Flow cytometry analysis of DCFDA detection to measure hFCFs ROS production upon Doxy exposure, treated with ascorbic acid or dehydroascorbic acid. **L** Quantification of α-actinin+ hFCFs upon Doxy exposure, treated with dehydroascorbic acid. Mean values + SEM of three independent experiments is shown (n = 3). Data were analyzed with two-way ANOVA. *p ≤ 0.05 vs Doxy, ****p ≤ 0.0001 vs Doxy, ##p ≤ 0.01 vs control. (Doxy: Doxycycline)

Figure 6

A. Diagram outlining the process: Human fibroblasts → Induced cardiomyocytes (by Doxy treatment) → FACS analysis (a-actinin staining).

B. FACS analysis plots for Control, Doxy, and Doxy+VitC treated samples.

C-D. Bar graphs showing the following:

| Treatment   | a-actinin (%) | ACTN2 (Relative expression) | MYH6 (Relative expression) |
|-------------|--------------|----------------------------|---------------------------|
| Control     | ~12          | ~1                         | ~1                        |
| Doxy        | ~18          | ~3                         | ~2                        |
| Doxy+VitC   | ~28          | ~6                         | ~5                        |

E. Diagram outlining process: hFCFs → Spheroids → Dissociation → Single cells → FACS analysis (a-actinin staining).

F. FACS analysis plots for Control, Doxy, and Doxy+VitC in hFCFs-derived samples.

G. Bar graph of a-actinin (%) in hFCFs:

| Treatment   | a-actinin (%) |
|-------------|--------------|
| Control     | ~10          |
| Doxy        | ~15          |
| Doxy+VitC   | ~22          |

H. Chemical structures of Ascorbic acid (VitC) and Dehydroascorbic acid (DHA).

I-L. Bar graphs for various measurements:

I. DCF/Hoechst ratio:

| Treatment   | DCF/Hoechst (ratio) |
|-------------|---------------------|
| Control     | ~0.4                |
| Doxy        | ~1.1                |
| Doxy+VitC   | ~0.6                |
| Doxy+DHA    | ~0.9                |

J. GFP/Hoechst ratio:

| Treatment      | GFP/Hoechst (ratio) |
|----------------|---------------------|
| Control        | ~1.0                |
| DHA 10 μg/ml   | ~1.0                |
| DHA 20 μg/ml   | ~1.0                |
| DHA 40 μg/ml   | ~1.0                |
| DHA 100 μg/ml  | ~1.0                |

K. DCF/Hoechst ratio:

| Treatment   | DCF/Hoechst (ratio) |
|-------------|---------------------|
| Control     | ~0.3                |
| Doxy        | ~1.1                |
| Doxy+VitC   | ~0.7                |
| Doxy+DHA    | ~1.0                |

L. a-actinin (relative ratio):

| Treatment          | a-actinin (relative ratio) |
|--------------------|---------------------------|
| Control            | ~1.0                      |
| DHA                | ~1.0                      |
| Doxy               | ~0.8                      |
| Doxy+DHA 10 μg/ml  | ~0.8                      |
| Doxy+DHA 20 μg/ml  | ~0.8                      |
| Doxy+DHA 40 μg/ml  | ~0.8                      |

Fig. 6 (See legend on previous page.)

5-AZA, VPA, and SAHA, known to facilitate cardiac differentiation or hiPSC reprogramming, did not enhance direct cardiac reprogramming. Notably, higher concentrations of VPA and SAHA impeded cardiac reprogramming, while lower dosages exhibited no effect. This observation may be attributed to the toxicity of these compounds at higher concentrations.

ROS generation is elevated in the early stage of hiPSC reprogramming, and excessive ROS accumulation impairs hiPSC generation [37]. Molecules with antioxidant properties have been shown to boost the reprogramming of fibroblasts into hiPSCs such as resveratrol [35], N-acetylcysteine and VitC [38]. Nonetheless, the involvement of ROS in the process of direct cardiac reprogramming remains elusive. In our study, we discovered that VitC enhanced direct cardiac reprogramming efficacy and reduced ROS generation during the reprogramming process. In contrast, dehydroascorbic acid, which lacks potent antioxidant properties, did not affect cardiac reprogramming efficiency or ROS production. In this scenario, the function of VitC appears to be associated with its antioxidant activity. Further investigation into the underlying mechanisms is warranted.

The involvement of ROS in the process of direct cardiac reprogramming was unknown for a long time. Although vitamin E nicotinate, an antioxidant, has been shown to facilitate the application of direct cardiac reprogramming approach for cardiac repair [39], a direct link to the observed effects by the elimination of ROS was lacking. In our study, we demonstrated that the reduction of ROS by VitC facilitated cardiac reprogramming, this may bring new insights in direct cardiac conversion. Although this study suggests that enhanced direct cardiac reprogramming efficiency by VitC might be modulated by decreasing ROS production, it is possible that VitC effects are also mediated via e.g. enhanced epigenetic modifications. For instance, histone demethylases are important in regulating hiPSC reprogramming [34] and direct cardiac reprogramming [40], and VitC can affect the activity of these enzymes [41]; therefore, VitC might allow the direct reprogramming to proceed more smoothly by promoting histone demethylation. In our study, we observed that epigenetic modification changes were present in these processes, including histone methylation and histone acetylation, and thereby providing new insights into the mechanism of direct cardiac reprogramming. Despite our encouraging observation regarding VitC-mediated enhanced cardiac reprogramming, we did not see spontaneous beating of the cells in the end, even not with our iPS-derived cardiomyocyte culture media. We speculate that due to the reprogramming-induced cell death, cells lose essential interconnections and signal cascades. Besides, we merely have focused on in vitro models in our study. Further studies are required to investigate the ROS-mediated effect in vivo and explore new delivery systems to achieve higher efficiency for cardiac repair. In addition, exogenous ROS incubation, treatment of antioxidants in different induction timing, and loss-of-function studies of ROS-associated genes would be helpful to uncover the role of ROS in both in vitro and in vivo direct cardiac reprogramming in future studies [39].

Although VitC is known to boost induced pluripotent stem cells reprogramming and enhance cardiac differentiation of induced pluripotent stem cells [19, 21], its effect in facilitating somatic cell reprogramming into cardiomyocytes remained unclear. Our study provides the first evidence that VitC enables the enhancement of direct cardiac reprogramming efficiency of key cardiac transcriptional factors (GMT), either in human or mouse cells, as well as on both 2D and 3D level. Finally, we used RNAseq to get mechanistic insights and could demonstrate that ROS plays an important role during this process. The other identified mechanisms included epigenetic modifications, histone methylation, and histone acetylation, which could be additional potential mechanisms that affect direct cardiac reprogramming efficiency, but will need more future investigations.

In conclusion, direct cardiac reprogramming represents a promising strategy for cardiac regeneration with the potential to become a novel therapeutic approach for heart failure. Our study, for the first time, demonstrates that VitC enhances the direct reprogramming of cardiomyocytes from fibroblasts using GMT transfection in vitro. This enhancement is partly attributed to a decrease in ROS production. Our findings pave the way for a combined gene and small molecule therapy for cardiac regeneration.

Our findings demonstrate that VitC significantly enhances the efficiency of cardiac reprogramming, partially by suppressing ROS production in the presence of GMT, which provides novel insights into the regulation of direct cardiac reprogramming and its potential for therapeutic applications in cardiac repair.

Vascular endothelial growth factor

Mouse embryonic fibroblasts

Left ventricle assistant devices

Human-induced pluripotent stem cell

hFCFs

Human fetal cardiac fibroblasts

Embryonic stem cell

Fibroblast growth factor

Cardiovascular disease

Induced cardiomyocytes

Gata4, Mef2c, Tbx5

CMPCs: Cardiomyocyte progenitor cells
VitC: Vitamin C
HDAC: Histone deacetylase
SAHA: Suberoylanilide hydroxamic acid
VPA: Valproic acid
5-AZA: 5-Azacytidine
mTORi: Mammalian target of rapamycin inhibitor
P53i: P53 inhibitor
DHA: Dehydroascorbic acid
ROS: Reactive oxygen species
PB: PiggyBac
Neg: Empty vector
Doxy: Doxycycline
DCFDA: 2',7'-Dichlorofluorescin diacetate
RT-PCR: Real-time polymerase chain reaction
BSA: Bovine serum albumin
SWB: Spheroid washing buffer
PFA: Paraformaldehyde
ACTN2: Actinin alpha 2
MYH6: Myosin heavy chain 6
TNNT2: Troponin T2
ACTC1: Actin alpha cardiac muscle 1

of Helsinki for the use of human tissue or subjects. All experiments were conducted according to the criteria of the code of proper use of human tissue used in the Netherlands.

Consent for publication
Not applicable.

Competing interests
The authors declare that they have no competing interests.

Author details
¹Experimental Cardiology laboratory, Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands. ²CDL Research, University Medical Center Utrecht, Utrecht, The Netherlands. ³Circulatory Health Laboratory, UMC Utrecht, Regenerative Medicine Center Utrecht, University Utrecht, 3508 GA Utrecht, The Netherlands. ⁴McAllister Heart Institute, University of North Carolina, Chapel Hill, NC, USA. ⁵Department of Cell and Chemical Biology, Leiden University Medical Centre, Leiden, The Netherlands.

Received: 8 June 2023   Accepted: 14 December 2023
Published online: 17 January 2024

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13287-023-03615-x.

Additional file 1. Supplementary figure S1 and S2.

Additional file 2. Processed NGS data with and without VitC treatment.

## Acknowledgements

We would like to thank Inge Dokter, and Tess Beekink for their excellent technical support.

## Author contributions

ZL, JPG designed and directed the project. ZL and JF planned experiments. JF, QY, RM, BM and DLB carried out the experiments. JF, MB, EDB and MM performed the RNA-seq data analysis. LQ provides the inducible cardiac reprogramming cassette and aMHC/GFP transgenic mouse cell lines. GM contributes to the ethical issue application. ZL, JPG, AM and RS offer funding and resources. JF drafted the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript. All authors read and approved the final manuscript.

## Funding

This work is supported by the Project EVICARE (No. 725229) of the European Research Council (ERC) to J.P.G.S., the ZonMw-TAS program (No. 116002016) to J.P.G.S./Z.L., the Dutch Ministry of Economic Affairs, Agriculture and Innovation and the Netherlands CardioVascular Research Initiative (CVON): the Dutch Heart Foundation to J.P.G.S. Juntao Fang is supported by a Chinese Scholarship Council (CSC) fellowship program (No.201906210082). Renee G.C Maas is supported by the PLN Foundation and by the HARVEY project (18747 NWO OTP). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

## Availability of data and material

Our data have been deposited and can be fully accessed via: https://dataverse.nl/dataset.xhtml?persistentId=doi:https://doi.org/10.34894/EZRRDE.

## Declarations

### Ethics approval and consent to participate

Human fetal heart tissue was obtained by individual permission using standard written informed consent procedures and has been approved by De medisch-ethische toetsingscommissie Leiden Den Haag Delft (METC LDD) with the number B21.053 entitled "Cardiopulmonary circulation" on December 16, 2021. This is in accordance with the principles outlined in the Declaration

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