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Targeting m<sup>6</sup>A writer METTL3 with engineered nanovesicles reduces neuroinflammation in vitro and in vivo.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › METTL3 knockdown inhibits the JAK2/STAT3 pathway through the m6A-SOCS3 axis in neuroinflammation ↔ R/01_merip_exomePeak2_from_bam.R, lines 21–108 · score 0.70 · METTL3 KO, m6A peaks, MeRIP, WT, hypomethylated, GSE264486
  2. [2] § Methods › Bioinformatics analysis ↔ R/01_merip_exomePeak2_from_bam.R, lines 21–108 · score 0.65 · Peak calling, exomePeak2, mm10, GSE264486, BAM, seq
  3. [3] § Methods › RNA-seq assay and data analysis ↔ R/10_bulk_rnaseq_edgeR_limma.R, lines 65–100 · score 0.60 · RNA seq, fold change, KEGG, log2, GSEA, enrichment
  4. [4] § Methods › Statistics and reproducibility ↔ R/10_bulk_rnaseq_edgeR_limma.R, lines 1–63 · score 0.55 · limma voom, Bayes
  5. [5] § Results › METTL3 knockdown inhibits the JAK2/STAT3 pathway through the m6A-SOCS3 axis in neuroinflammation ↔ R/02_merip_precomputed_peak_downstream.R, the whole file · a weak match · score 0.52 · METTL3 KO, peaks, WT, overlap, hypomethylated, GSE264486

Paper

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The authors' code

R · 108 lines · 5.2 KB · no license · 2 matches

  1. #!/usr/bin/env Rscript
  2. suppressPackageStartupMessages({
  3. library(data.table)
  4. library(dplyr)
  5. library(exomePeak2)
  6. library(TxDb.Mmusculus.UCSC.mm10.knownGene)
  7. library(BSgenome.Mmusculus.UCSC.mm10)
  8. })
  10. source("R/functions_enrichment_plot.R")
  11. make_dir("output/exomePeak2")
  12. make_dir("output/tables")
  13. make_dir("output/plots")
  15. meta <- fread("config/sample_info.csv")
  16. meta[, bam := file.path("bam", paste0(srr, "_sorted.bam"))]
  17. missing_bam <- meta[!file.exists(bam), bam]
  18. if (length(missing_bam) > 0) stop("Missing BAM files:\n", paste(missing_bam, collapse = "\n"))
  20. get_bams <- function(condition, assay) meta[condition == !!condition & assay == !!assay][order(replicate), bam]
  21. # data.table-safe selection
  22. wt_ip <- meta[condition == "WT" & assay == "IP"][order(replicate), bam]
  23. wt_input <- meta[condition == "WT" & assay == "Input"][order(replicate), bam]
  24. ko_ip <- meta[condition == "KO" & assay == "IP"][order(replicate), bam]
  25. ko_input <- meta[condition == "KO" & assay == "Input"][order(replicate), bam]
  27. cat("WT IP BAMs:\n", paste(wt_ip, collapse = "\n"), "\n")
  28. cat("WT Input BAMs:\n", paste(wt_input, collapse = "\n"), "\n")
  29. cat("KO IP BAMs:\n", paste(ko_ip, collapse = "\n"), "\n")
  30. cat("KO Input BAMs:\n", paste(ko_input, collapse = "\n"), "\n")
  32. # exomePeak2 differential mode: WT is control; KO is treated.
  33. # If library strandness is known, change library_type to "1st_strand" or "2nd_strand".
  34. sep <- exomePeak2(
  35. bam_ip = wt_ip,
  36. bam_input = wt_input,
  37. bam_treated_ip = ko_ip,
  38. bam_treated_input = ko_input,
  39. txdb = TxDb.Mmusculus.UCSC.mm10.knownGene,
  40. bsgenome = BSgenome.Mmusculus.UCSC.mm10,
  41. paired_end = FALSE,
  42. library_type = "unstranded",
  43. fragment_length = 100,
  44. glm_type = "DESeq2",
  45. LFC_shrinkage = "none",
  46. parallel = FALSE,
  47. export_results = TRUE,
  48. export_format = c("CSV", "BED", "RDS"),
  49. table_style = "bed",
  50. save_plot_GC = TRUE,
  51. save_plot_analysis = TRUE,
  52. save_plot_name = "GSE264486_Mettl3KO_vs_WT_LPS",
  53. save_dir = "output/exomePeak2",
  54. peak_calling_mode = "exon"
  55. )
  57. saveRDS(sep, "output/exomePeak2/exomePeak2_object.rds")
  59. # Locate exported exomePeak2 CSV. Different versions may name it slightly differently.
  60. csvs <- list.files("output/exomePeak2", pattern = "\\.csv$", recursive = TRUE, full.names = TRUE, ignore.case = TRUE)
  61. if (length(csvs) == 0) {
  62. stop("exomePeak2 finished but no CSV was found in output/exomePeak2. Check exported BED/RDS manually.")
  63. }
  64. peak_file <- csvs[which.max(file.info(csvs)$mtime)]
  65. cat("Using exported peak table: ", peak_file, "\n")
  66. peak_df <- fread(peak_file)
  67. fwrite(peak_df, "output/tables/merip_all_peaks_exomePeak2.csv")
  69. # Standard differential peak filter. Column names differ by exomePeak2 versions, so use flexible matching.
  70. cn <- colnames(peak_df)
  71. lfc_col <- cn[grepl("log2.*FC|log2.*Fold|log2FoldChange", cn, ignore.case = TRUE)][1]
  72. fdr_col <- cn[grepl("padj|fdr|adj", cn, ignore.case = TRUE)][1]
  73. gene_col <- cn[grepl("geneName|symbol|gene_name|gene", cn, ignore.case = TRUE)][1]
  74. if (is.na(lfc_col) || is.na(fdr_col)) stop("Cannot identify log2FC/FDR columns in exomePeak2 result. Please inspect output/tables/merip_all_peaks_exomePeak2.csv")
  76. peak_df[, log2FC_for_filter := as.numeric(get(lfc_col))]
  77. peak_df[, FDR_for_filter := as.numeric(get(fdr_col))]
  78. if (!is.na(gene_col)) peak_df[, geneName := as.character(get(gene_col))]
  80. hypo <- peak_df[FDR_for_filter < 0.05 & log2FC_for_filter < 0]
  81. hyper <- peak_df[FDR_for_filter < 0.05 & log2FC_for_filter > 0]
  82. fwrite(hypo, "output/tables/merip_hypomethylated_peaks.csv")
  83. fwrite(hyper, "output/tables/merip_hypermethylated_peaks.csv")
  84. cat("Hypo peaks: ", nrow(hypo), "\n")
  85. cat("Hyper peaks:", nrow(hyper), "\n")
  87. if ("Annotation" %in% colnames(peak_df)) {
  88. peak_df[, Region := standardize_region(Annotation)]
  89. hypo[, Region := standardize_region(Annotation)]
  90. hyper[, Region := standardize_region(Annotation)]
  91. fwrite(peak_df[, .N, by = Region][order(Region)], "output/tables/merip_all_region_summary.csv")
  92. fwrite(hypo[, .N, by = Region][order(Region)], "output/tables/merip_hypo_region_summary.csv")
  93. fwrite(hyper[, .N, by = Region][order(Region)], "output/tables/merip_hyper_region_summary.csv")
  94. save_region_pie(hypo, "Hypomethylated m6A peak regions", "output/plots/merip_hypo_region_pie.pdf")
  95. save_region_pie(hyper, "Hypermethylated m6A peak regions", "output/plots/merip_hyper_region_pie.pdf")
  96. reg <- rbind(data.frame(hypo[, .N, by = Region], Type = "Hypo"), data.frame(hyper[, .N, by = Region], Type = "Hyper"))
  97. if (nrow(reg) > 0) {
  98. reg <- reg %>% group_by(Type) %>% mutate(percent = N / sum(N) * 100)
  99. p <- ggplot(reg, aes(x = Region, y = percent, fill = Type)) + geom_col(position = "dodge") + theme_bw() + coord_flip() + labs(x = NULL, y = "Percentage (%)", title = "Hypo vs Hyper peak region distribution")
  100. ggsave("output/plots/merip_hypo_hyper_region_barplot.pdf", p, width = 8, height = 5)
  101. }
  102. }
  104. if ("geneName" %in% colnames(hypo)) run_enrichment(hypo$geneName, "merip_hypo", "output/tables", "output/plots")
  105. if ("geneName" %in% colnames(hyper)) run_enrichment(hyper$geneName, "merip_hyper", "output/tables", "output/plots")
  106. if ("geneName" %in% colnames(peak_df)) run_gsea(data.frame(geneName = peak_df$geneName, log2FoldChange = peak_df$log2FC_for_filter), "merip_all_peaks", "output/tables", "output/plots")
  108. cat("MeRIP-seq exomePeak2 analysis finished.\n")

01_merip_exomePeak2_from_bam.R at commit bdefa34, no license · at the source

Overview

Authors: Liangfu Xu1,2,3, Yuanwei Pan3, Guanjun Li1,2, Peng She4, Qian-Fang Meng3, Zhigang Liu1,2, Lang Rao3
ORCID iDs: Zhigang Liu, Lang Rao
  1. Cancer Center, Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, The Tenth Affiliated Hospital, Southern Medical University (Dongguan People’s Hospital),Dongguan, China
  2. Shenzhen School of Clinical Medicine, Southern Medical University,Shenzhen, China
  3. Institute of Chemical Biology, Shenzhen Bay Laboratory,Shenzhen, China
  4. Department of Orthopedics, The Seventh Affiliated Hospital of Sun Yat-Sen University,Shenzhen, China
Institutions: Dongguan People’s Hospital (China); Southern Medical University (China); Shenzhen Bay Laboratory (China); The Seventh Affiliated Hospital of Sun Yat-sen University (China)
Journal: Nature communications, volume 17, issue 1, article 9534
Dates: received 24 December 2025; accepted 8 July 2026; published online 8 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-75862-4 · PMID 42702629 · PMCID PMC13547405 · OpenAlex W7201982615
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: Drug delivery, Tumour immunology
MeSH: Adenosine*, Methyltransferases*, Neuroinflammatory Diseases*, Animals, Blood-Brain Barrier, Cytokines, Disease Models, Animal, Epigenome Editing, Epitranscriptome, Female, Humans, Liposomes, Mice, Mice, Inbred C57BL, Microglia, RNA Methylation, RNA, Small Interfering (* major topic)
Topic: RNA modifications and cancer (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82372106 and 82222035, 82502547, 82402456)
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Epigenetic editing, particularly N6-methyladenosine (m6A) modification, represents a promising therapeutic strategy by silencing genes without altering DNA sequence. However, in vivo epigenetic intervention of neuroinflammation remains challenging and has rarely been explored. Here we developed a hybrid epigenetic nanomodulator, siMETTL3-hNVs, by integrating natural microglia-derived nanovesicles (NVs) with synthetic liposomes pre-loading small interfering RNA targeting the m6A writer methyltransferase-like 3 (METTL3). Natural NVs enabled siMETTL3-hNVs to achieve inflamed-brain delivery through CCR2-CCL2 chemotaxis and caveolae-mediated transcytosis across the blood-brain barrier. More importantly, relying on abundant cytokine receptors on the NVs, siMETTL3-hNVs served as decoys to neutralize pro-inflammatory cytokines, synergizing with the intracellular silencing of METTL3 to drive microglial M2 repolarization. In female mouse models of acute neuroinflammation and radiation-induced brain injury, siMETTL3-hNVs treatment significantly reduced cytokine levels, attenuated hippocampal damage, and ameliorated cognitive deficits. This work overcomes critical delivery bottlenecks in m6A-based therapeutics and establishes a robust strategy for epigenetic reprogramming of neuroinflammation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

Zenodo 20627531

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (4 files), tidyverse (4 files), clusterProfiler (2 files), ggplot2 (2 files), edgeR (1 file), limma (1 file), pheatmap (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
12 files
At the source:

xulf1996-arch/mettl3_m6a_bulk_pipeline

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bdefa34adfeae024d569248ca75c8f0ce8c6907d, 10 June 2026
Languages: Shell (6), R (5)
Size: 17 files, 11 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (environment.yml), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: data.table (4 files), tidyverse (4 files), clusterProfiler (2 files), ggplot2 (2 files), edgeR (1 file), limma (1 file), pheatmap (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/xulf1996-arch/METTL3_m6A_bulk_pipeline/tree/v1.0.0

Code availability

The R and shell scripts used for the sequencing data analysis have been deposited in Zenodo68 and are available at https://doi.org/10.5281/zenodo.20627531.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

All data generated or analyzed during this study are available within the article, Supplementary Information, or Source Data file. The RNA-seq data generated in this study have been deposited in the Sequence Read Archive under accession number PRJNA1400104 (https://www.ncbi.nlm.nih.gov/sra?term=PRJNA1400104). The public m6A-seq dataset analyzed in this study is available from the Gene Expression Omnibus under accession number GSE264486 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE264486). Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 17 MeSH terms, 1 funder, 68 references.

Cite

This paper

Xu, L., Pan, Y., Li, G., She, P., Meng, Q.-F., Liu, Z., & Rao, L. (2026). Targeting m&lt;sup&gt;6&lt;/sup&gt;A writer METTL3 with engineered nanovesicles reduces neuroinflammation in vitro and in vivo. Nature communications, 17(1), 9534. https://doi.org/10.1038/s41467-026-75862-4

BibTeX

@article{xu2026targeting,
author = {Xu, Liangfu and Pan, Yuanwei and Li, Guanjun and She, Peng and Meng, Qian-Fang and Liu, Zhigang and Rao, Lang},
title = {{Targeting m\&lt;sup\&gt;6\&lt;/sup\&gt;A writer METTL3 with engineered nanovesicles reduces neuroinflammation in vitro and in vivo}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9534},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75862-4},
url = {https://doi.org/10.1038/s41467-026-75862-4},
pmid = {42702629},
pmcid = {PMC13547405}
}

RIS

TY - JOUR
AU - Xu, Liangfu
AU - Pan, Yuanwei
AU - Li, Guanjun
AU - She, Peng
AU - Meng, Qian-Fang
AU - Liu, Zhigang
AU - Rao, Lang
TI - Targeting m&lt;sup&gt;6&lt;/sup&gt;A writer METTL3 with engineered nanovesicles reduces neuroinflammation in vitro and in vivo
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/08
VL - 17
IS - 1
SP - 9534
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75862-4
UR - https://doi.org/10.1038/s41467-026-75862-4
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Targeting m&lt;sup&gt;6&lt;/sup&gt;A writer METTL3 with engineered nanovesicles reduces neuroinflammation in vitro and in vivo",
"container-title": "Nature communications",
"author": [
{
"family": "Xu",
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
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"PMID": "42702629",
"PMCID": "PMC13547405",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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"issued": {
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8
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]
}
}

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