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Gli3R-mediated inhibition of hedgehog signaling alters the embryonic transcriptome in zebrafish.

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The authors' code

R · 248 lines · 9.1 KB · no license

  1. library("DESeq2")
  2. library("RColorBrewer")
  3. library("gplots")
  4. library("ggplot2")
  5. library("dplyr")
  6. library("regionReport")
  7. library("pathview")
  8. library("gage")
  9. library("GenomicAlignments")
  10. library("biomaRt")
  11. library("pheatmap")
  12. library("dplyr")
  13. library("EnhancedVolcano")
  14. library("biomaRt")
  15. library("clusterProfiler")
  16. library("tidyverse")
  17. library("data.table")
  18. library("ReactomePA")
  19. library("org.Dr.eg.db")
  20. library("formattable")
  21. library("stringr")
  22. library("recount")
  23. library("gageData")
  24. library("tidyr")
  25. library(optparse)
  27. option_list = list(
  28. make_option(c("-d", "--directory"), type="character", default=NULL,
  29. help="working directory", metavar="character"),
  30. # make_option(c("-o", "--out"), type="character", default="out.txt",
  31. # help="output file name [default= %default]", metavar="character"),
  32. make_option(c("-p", "--prefix"), type="character", default="defaultname",
  33. help="prefix name for all graphs [default= %default]", metavar="character"),
  34. make_option(c("-s", "--sampletable"), type="character", default="defaultname",
  35. help="sample table csv file [default= %default]", metavar="character")
  36. # make_option(c("-c", "--mincell"), type="integer", default=3,
  37. # help="minimum number of cells [default= %default]", metavar="integer"),
  38. # make_option(c("-d", "--minfeat"), type="integer", default=200,
  39. # help="minimum number of molecules [default= %default]", metavar="integer")
  40. );
  42. opt_parser = OptionParser(option_list=option_list);
  43. opt = parse_args(opt_parser);
  45. #if (is.null(opt$file)){
  46. # print_help(opt_parser)
  47. # stop("At least one argument must be supplied (input file)", call.=FALSE)
  48. #}
  50. outputPrefix <- opt$prefix
  51. directory <- opt$directory
  52. sampleTablefile <- opt$sampletable
  54. #sampleTable <- read.csv("input.csv")
  55. sampleTable <- read.csv(sampleTablefile)
  56. # CHANGE BELOW
  57. treatments = c("wt","hom") ##genotype again
  59. ddsHTSeq <-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable,directory=directory,
  60. design=~condition)
  61. colData(ddsHTSeq)$condition <- factor(colData(ddsHTSeq)$condition,levels=treatments)
  62. dds <-DESeq(ddsHTSeq)
  64. # filtering might need to change depending on sample number
  65. keep <- rowSums(counts(dds) == 0) < 4
  66. dds <- dds[keep,]
  68. # CHANGE BELOW
  69. res <-results(dds,contrast=c("condition","hom","wt")) ##contrast= sets the order of analysis to hom vs wt. If obmitted, the order will be alphabetical
  70. res<-res[order(res$pvalue),]
  71. rlog<-rlog(dds)
  72. vst <-vst(dds)
  75. gene_name <-read.csv("llgeneid_genename.csv") ##this file has LLgeneID as column 1 and LLgeneAbbrev/gene name as column 2
  76. dataframe_res <-as.data.frame(res)
  77. dataframe_res$LLgeneID<-row.names(dataframe_res)
  78. dataframe_res<-dataframe_res[c(7,1:6)]
  79. res_gene <-inner_join(dataframe_res,gene_name,by="LLgeneID")
  80. res_gene<-res_gene[!is.na(res_gene$padj),]
  81. res_gene_p<-subset(res_gene,padj<0.05)
  83. res_gene_p_up<-subset(res_gene_p,log2FoldChange>0)
  84. res_gene_p_down<-subset(res_gene_p,log2FoldChange<0)
  87. write.csv(res_gene,file=paste0(outputPrefix,"_allresults_wt_hom-with-normalized.csv")) ##this output file is normalized change in expression of all genes
  88. write.csv(res_gene_p,file=paste0(outputPrefix,"_topp_wt_hom-with-normalized.csv")) ##this output file is normalized changed in expression of genes with an adjusted p values of <0.05
  89. write.csv(res_gene_p_up,file=paste0(outputPrefix,"_upreg_topp_wt_hom-with-normalized.csv")) ##this output file is normalized changed in expression of genes with an adjusted p values of <0.05
  90. write.csv(res_gene_p_down,file=paste0(outputPrefix,"_downreg_topp_wt_hom-with-normalized.csv")) ##this output file is normalized changed in expression of genes with an adjusted p values of <0.05
  94. {
  95. listMarts()
  96. ensembl=useMart("ensembl")
  97. listDatasets(ensembl)
  98. ensembl=useDataset("drerio_gene_ensembl", mart=ensembl)
  99. listAttributes(mart=ensembl)
  101. annoDRerio <- getBM(attributes = c("ensembl_gene_id", "ensembl_gene_id_version","entrezgene_id",
  102. "zfin_id_symbol", "description", "external_gene_name"), mart=ensembl)
  103. }
  105. anno_gene_list_up <- inner_join(res_gene_p_up,annoDRerio,by=c("LLgeneAbbrev" ="external_gene_name"))
  107. keggPA_up = as.data.frame(enrichKEGG(gene=anno_gene_list_up$entrezgene_id, organism="dre",pvalueCutoff=0.05))
  108. write.csv(keggPA_up,file=paste0(outputPrefix,"_up_pathways.csv")) ##this output file give kegg pathways IDs, names, and statistical values
  113. {
  114. listMarts()
  115. ensembl=useMart("ensembl")
  116. listDatasets(ensembl)
  117. ensembl=useDataset("drerio_gene_ensembl", mart=ensembl)
  118. listAttributes(mart=ensembl)
  120. annoDRerio <- getBM(attributes = c("ensembl_gene_id", "ensembl_gene_id_version","entrezgene_id",
  121. "zfin_id_symbol", "description", "external_gene_name"), mart=ensembl)
  122. }
  124. anno_gene_list_down <- inner_join(res_gene_p_down,annoDRerio,by=c("LLgeneAbbrev" ="external_gene_name"))
  127. keggPA_down = as.data.frame(enrichKEGG(gene=anno_gene_list_down$entrezgene_id, organism="dre",pvalueCutoff=0.05))
  128. write.csv(keggPA_down,file=paste0(outputPrefix,"_down_pathways.csv")) ##this output file give kegg pathways IDs, names, and statistical values
  131. # pathview <-pathview(gene.data=res_gene_p[,3],pathway.id="dre04110",species="dre")
  135. ## the next few plots will be saved in your working directory
  137. ##MA plot
  138. jpeg(file=paste0(outputPrefix,"_MA_basemeans.jpeg"))
  139. plotMA(dds) ##MA plot of base means
  140. dev.off()
  142. jpeg(file=paste0(outputPrefix,"_MA_logfoldchange.jpeg"))
  143. plotMA(res) ##MA plot of logfold change
  144. dev.off()
  147. ##PCA
  148. jpeg(file=paste0(outputPrefix,"_PCA.jpeg"))
  149. plotPCA(rlog,"condition")
  150. dev.off()
  152. #print(res_gene)
  153. #print(res_gene$LLgeneAbbrev)
  154. #head(res)
  155. #head(res_gene)
  156. #rownames(res_gene) <- res_gene$LLgeneAbbrev
  157. # there are non-unique values here, need to solve this
  158. #head(res_gene)
  159. ##volcano
  160. pdf(file=paste0(outputPrefix,"_volcano.pdf"))
  161. EnhancedVolcano(res, x = 'log2FoldChange', lab = rownames(res), labSize = 6.0,
  162. y = 'padj',ylab=bquote(~-Lot[10] ~ italic(Padj)),col=c("grey","grey","magenta","magenta"),pCutoff=0.05,
  163. legendPosition = 'none',cutoffLineType="blank",xlim=c(-3,3),ylim=0,5)
  164. dev.off()
  166. ##each gene (with llgene ID, gene name, and entrez ID) and then normalized counts for each sample
  167. countsdata <-counts(dds,normalized=TRUE)
  168. namelist <-read.csv("LLgeneID_entrezID.csv")
  169. ##want to merge countsdata with namelist and gene_name
  170. rownames(namelist) <- namelist[,1]
  171. rownames(gene_name) <- gene_name[,1]
  172. genecounts<-merge(gene_name,namelist,by=0)
  173. rownames(genecounts)<-genecounts[,1]
  174. genecounts<-merge(genecounts,countsdata,by=0)
  175. genecounts<-genecounts[,-(1:3)]
  177. write.csv(genecounts,file=paste0(outputPrefix,"_normalized_reads_gene_list.csv")) ##this output file is normalized change in expression of all genes
  180. ##dot plot of only significant genes (in res_gene_p)
  182. #make counts data with 1st column is LLgeneID, then merge with res_gene_p
  183. countsdata<-as.data.frame(countsdata)
  184. countsdata1 <-setDT(countsdata, keep.rownames = "LLgeneID")
  185. countsdata1<-countsdata1[match(res_gene_p$LLgeneID,countsdata1$LLgeneID,)]
  186. countsdata1_good<-as.data.frame(t(countsdata1))
  187. colnames(countsdata1_good) <-countsdata1_good[1,]
  188. countsdata1_good<-countsdata1_good[-c(1),]
  191. #for(i in 1:ncol(countsdata1_good)) {
  192. # jpeg(file=paste(outputPrefix,"_expression_",i,".jpeg",sep=""))
  193. # print(ggplot(countsdata1_good,aes(x=rownames(countsdata1_good),y=countsdata1_good[,i]))+
  194. # geom_point()+labs(x="sample",y="normalized counts",title=colnames(countsdata1_good[i])))
  195. # dev.off()
  196. #}
  200. ##dot plot of every gene---this is really a lot so I would recommend only using it if you really want it or altering this code to get a specific gene graph
  201. #genecountst<-as.data.frame(t(genecounts))
  202. #genecounts_good <-genecountst[-c(1,3),]
  203. #colnames(genecounts_good) <-genecounts_good[1,]
  204. #genecounts_good<-genecounts_good[-c(1),]
  206. #for(i in 1:ncol(genecounts_good)) {
  207. # jpeg(file=paste0(outputPrefix,"_expression_[i].jpeg"))
  208. #print(ggplot(genecounts_good,aes(x=rownames(genecounts_good),y=genecounts_good[,i]))+geom_point())
  209. # dev.off()
  210. #}
  213. ##pathway graph
  215. keggPA_down$NegLogPAdj <-log10(keggPA_down$p.adjust)
  217. keggPA_up$NegLogPAdj <--log10(keggPA_up$p.adjust)
  219. keggPA_forplot <-rbind(keggPA_down,keggPA_up) ##only use this for making a plot because I flipped the log adjusted p values in order to force the down regulated pathways to be on the left....
  223. color<-ifelse(keggPA_forplot$NegLogPAdj<0,"blue","yellow")
  224. jpeg(file=paste0(outputPrefix,"_pathways.jpeg"))
  225. ggplot(keggPA_forplot, aes(y=reorder(Description,NegLogPAdj), x=NegLogPAdj))+geom_bar(stat="identity",fill=color)+
  226. labs(x="log 10 padj",y="pathway")
  227. dev.off()
  231. jpeg(file=paste0(outputPrefix,"_pathways_up.jpeg"))
  232. ggplot(keggPA_up, aes(y=Description, x=NegLogPAdj))+geom_bar(stat="identity")
  233. dev.off()
  235. jpeg(file=paste0(outputPrefix,"_pathways_down.jpeg"))
  236. ggplot(keggPA_down, aes(y=Description, x=-NegLogPAdj))+geom_bar(stat="identity")
  237. dev.off()
  241. ###grouping of samples in tree and in heatmap
  242. sampleDists <-dist(t(assay(vst)))
  243. plot(hclust(sampleDists))
  245. sampleDistMatrix <- as.matrix(sampleDists)
  246. rownames(sampleDistMatrix) <- paste(vst$condition, vst$type, sep="-")
  247. colnames(sampleDistMatrix) <- NULL
  248. pheatmap(sampleDistMatrix, clustering_distance_rows=sampleDists, clustering_distance_cols=sampleDists)

deseq2gene.R at commit 78a5c66, no license · at the source

Overview

Authors: Anna J Moyer1, Summer B Thyme1
ORCID iDs: Summer B Thyme
  1. Department of Biochemistry and Molecular Biotechnology, The University of Massachusetts Chan Medical School, Worcester, MA 01605, United States
Institutions: University of Massachusetts Chan Medical School (United States)
Journal: G3 (Bethesda, Md.), volume 16, issue 3, article jkaf306
Dates: received 17 September 2025; accepted 11 December 2025; published online 16 January 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/g3journal/jkaf306 · PMID 41546624 · PMCID PMC12958812 · OpenAlex W7124540269
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), zebrafish (organism), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials
Keywords: zebrafish, embryonic development, RNA-seq, hedgehog, Gli3R, Gli, ionocytes, ion homeostasis, Foxi3, H+-ATPase-rich
MeSH: Hedgehog Proteins*, Signal Transduction*, Transcriptome*, Zebrafish*, Zebrafish Proteins*, Animals, Embryo, Nonmammalian, Gene Expression Profiling, Gene Expression Regulation, Developmental, Zinc Finger Protein Gli3 (* major topic)
Journal subjects: Omics Report
Topic: Hedgehog Signaling Pathway Studies (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH (R01 HD115159); Jérôme Lejeune Foundation postdoctoral fellowship
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Hedgehog signaling is a conserved developmental pathway that patterns diverse tissues during vertebrate embryogenesis. In zebrafish, disruptions to the hedgehog pathway cause well-characterized defects in specific cell types including neurons and glia derived from the ventral neural tube. We inhibited hedgehog signaling by overexpressing the Gli3 repressor ubiquitously and performed bulk RNA sequencing of 30 h postfertilization zebrafish embryos. Consistent with known roles of hedgehog signaling, we observed reduced expression of genes marking lateral floor plate, motor neurons, Kolmer–Agduhr cells, dopaminergic neurons, slow muscle cells, and anterior pituitary. Gene set enrichment analysis using marker genes derived from the Daniocell atlas also revealed downregulation of genes marking H+-ATPase-rich ionocytes, which are located in the embryonic skin and are responsible for osmotic homeostasis. Reduced expression of ionocyte-specific transporter genes and the transcription factor foxi3a suggests that Gli activity may play a previously unrecognized role in the specification of this cell type.

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

Repository

Its files are read in the Code ↔ Paper reader above.

thymelab/BulkRNASeq

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 78a5c66c052e34741360ac8057e77038f570d772, 6 January 2024
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not archived
Found in: the text, “RNA-seq analysis”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: clusterProfiler (2 files), data.table (2 files), DESeq2 (2 files), ggplot2 (2 files), pheatmap (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/thymelab/BulkRNASeq

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

No dataset and no data link were found in the paper.

Data Availability

Plasmids are available upon request. Raw RNA-seq data and gene counts have been deposited in GEO (GSE307979; Thyme 2025). Processed data are available in Supplementary Table 1.

Supplemental material available at G3 online.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 10 keywords, 10 MeSH terms, 2 funders, 52 references.

Cite

This paper

Moyer, A. J., & Thyme, S. B. (2026). Gli3R-mediated inhibition of hedgehog signaling alters the embryonic transcriptome in zebrafish. G3 (Bethesda, Md.), 16(3), jkaf306. https://doi.org/10.1093/g3journal/jkaf306

BibTeX

@article{moyer2026gli3r,
author = {Moyer, Anna J and Thyme, Summer B},
title = {{Gli3R-mediated inhibition of hedgehog signaling alters the embryonic transcriptome in zebrafish}},
journal = {G3 (Bethesda, Md.)},
year = {2026},
month = mar,
volume = {16},
number = {3},
pages = {jkaf306},
publisher = {Oxford University Press},
issn = {2160-1836},
doi = {10.1093/g3journal/jkaf306},
url = {https://doi.org/10.1093/g3journal/jkaf306},
pmid = {41546624},
pmcid = {PMC12958812}
}

RIS

TY - JOUR
AU - Moyer, Anna J
AU - Thyme, Summer B
TI - Gli3R-mediated inhibition of hedgehog signaling alters the embryonic transcriptome in zebrafish
T2 - G3 (Bethesda, Md.)
J2 - G3 (Bethesda)
PY - 2026
DA - 2026/03/01
VL - 16
IS - 3
SP - jkaf306
SN - 2160-1836
PB - Oxford University Press
DO - 10.1093/g3journal/jkaf306
UR - https://doi.org/10.1093/g3journal/jkaf306
LA - en
ER -

CSL-JSON

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"container-title": "G3 (Bethesda, Md.)",
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"family": "Moyer",
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"container-title-short": "G3 (Bethesda)",
"volume": "16",
"issue": "3",
"page": "jkaf306",
"DOI": "10.1093/g3journal/jkaf306",
"PMID": "41546624",
"PMCID": "PMC12958812",
"ISSN": "2160-1836",
"publisher": "Oxford University Press",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

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