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Progressive hypothalamic neuroinflammation in ovariectomized mice parallels aging-related transcriptomic changes in the female human hypothalamus.

Code ↔ Paper

6 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 6 matches
  1. [1] § Methods › Human studies › Human data analysis. ↔ Cell type deconvolution/Human/celltype_deconvolutionHUMAN.Rmd, lines 69–81 · score 0.70 · 61–65, 46–50, 56–60, 51–55, status, menopausal
  2. [2] § Methods › Human studies › Human data analysis. ↔ Differential Gene Expression Analysis/Human/DEanalysis_human.Rmd, lines 171–229 · score 0.66 · 61–65, 46–50, 56–60, 51–55, Human
  3. [3] § Results › Age-related transcriptomic changes in the human female hypothalamus. ↔ Cell type deconvolution/Human/celltype_deconvolutionHUMAN.Rmd, lines 69–81 · score 0.64 · 61–65, 46–50, 56–60, 51–55, menopausal, human
  4. [4] § Results › Age-related transcriptomic changes in the human female hypothalamus. ↔ Differential Gene Expression Analysis/Human/DEanalysis_human.Rmd, lines 171–229 · score 0.63 · 61–65, 46–50, 56–60, 51–55, human, genes
  5. [5] § Results › Age-related transcriptomic changes in the human female hypothalamus. ↔ LOESS Analysis/LOESS_analysis.Rmd, lines 8–122 · score 0.52 · KISS1R, TAC1, TAC3, PDYN, scores, clustering
  6. [6] § Methods › RNA-seq data processing and analysis ↔ Differential Gene Expression Analysis/Mouse/tximport_DEanalysis_mouse.Rmd, lines 8–46 · score 0.50 · tximport, GENCODE, M30, kallisto, imported, gene expression

Paper

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The authors' code

R Markdown · 81 lines · 2.3 KB · no license · 2 matches

  1. ---
  2. title: "celltype_deconvolutionHUMAN"
  3. author: "Jordana Bloom"
  4. date: "2024-12-01"
  5. output: html_document
  6. ---
  9. ```{r}
  10. #devtools::install_github("cozygene/bisque")
  11. library(BisqueRNA)
  12. library(Biobase)
  13. library(dplyr)
  14. #library(tibble)
  15. library(matrixStats)
  16. library(stringr)
  17. library(ggplot2)
  18. library(ggpubr)
  19. library(reshape2)
  20. library(readr)
  21. library(RColorBrewer)
  22. library(viridis)
  24. sessionInfo()
  25. ```
  27. ## Input Format
  29. Bisque requires expression data in the ExpressionSet format from the Biobase package.
  31. Bulk RNA-seq data can be converted from a matrix (columns are samples, rows are genes) to an ExpressionSet as follows:
  33. ```{r}
  34. #use supplemental data table S9
  35. bulk.matrix <- read.delim("path_to_supplementalTableS9.txt", header=TRUE, check.names = FALSE)
  37. colnames(bulk.matrix)[colnames(bulk.matrix) == "gene_id"] <- "gene_col"
  39. #remove column with gene.type information
  40. bulk.matrix <- bulk.matrix[, -ncol(bulk.matrix)]
  42. #remove gene.names column
  43. bulk.matrix <- bulk.matrix[, -ncol(bulk.matrix)]
  45. # Set the row names to the first column
  46. rownames(bulk.matrix) <- make.unique(bulk.matrix$gene_col)
  47. # Remove the first column
  48. bulk.matrix <- bulk.matrix[, -1]
  49. bulk.matrix <- as.matrix(bulk.matrix)
  51. bulk.eset <- ExpressionSet(assayData = bulk.matrix)
  52. #sampleNames(bulk.eset)
  53. ```
  55. ```{r}
  56. #use supplemental data table S5 (marker genes obtained from Hajdarovic et al. 2022)
  57. marker.data.frame <- read.delim("path_to_supplementalTableS5.txt", header=TRUE, check.names = FALSE)
  59. #Call the marker-based decomposition method:
  60. res <- MarkerBasedDecomposition(bulk.eset, markers = marker.data.frame, gene_col = "gene_col", min_gene = 1, weighted=FALSE)
  62. marker.based.estimates <- as.data.frame(res$bulk.props)
  64. write.table(marker.based.estimates,"path_to_markerestimate_output.txt", sep="\t",quote=FALSE, col.names=NA)
  66. ```
  69. ```{r}
  70. #marker estimate output file with menopause status group information included
  71. data <- read.delim("markerestimates_withgroupinfo.txt", header=TRUE, check.names = FALSE)
  73. data$Group <- factor(data$Group, levels = c("< 45", "46-50", "51-55", "56-60", "61-65"))
  75. pdf("Celltypedecon_stackedBar_humandata.pdf")
  76. # Stacked
  77. ggplot(data, aes(fill=Cell_type, y=Value, x=Group)) +
  78. geom_bar(position="fill", stat="identity") + theme_bw() + scale_fill_viridis(discrete = T) +
  79. theme(axis.text.x = element_text(angle = 45, hjust = 1))
  80. dev.off()
  81. ```

celltype_deconvolutionHUMAN.Rmd at commit c220d35, no license · at the source

Overview

Authors: Jordana CB Bloom1,2, Encarnación Torres2,3, Sidney A Pereira2,3, Liliana Arvizu-Sanchez2, Audrey N Fontes2, Hadine Joffe3,4, David C Page1,5,6, Victor M Navarro2,3,7
ORCID iDs: Sidney A Pereira, Victor M Navarro
  1. Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA
  2. Division of Endocrinology, Mass General Brigham, Boston, Massachusetts, USA
  3. Harvard Medical School, Boston, Massachusetts, USA
  4. Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
  5. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  6. Howard Hughes Medical Institute, Cambridge, Massachusetts, USA
  7. Harvard Graduate Program in Neuroscience, Boston, Massachusetts, USA
Institutions: Whitehead Institute for Biomedical Research (United States); Mass General Brigham (United States); Harvard University (United States); Beth Israel Deaconess Medical Center (United States); Howard Hughes Medical Institute (United States); Massachusetts Institute of Technology (United States)
Journal: JCI insight, volume 11, issue 17, article e207270
Dates: received 24 March 2026; accepted 20 July 2026; published online 23 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1172/jci.insight.207270 · PMID 42490144 · PMCID PMC13564084 · OpenAlex W7170133919
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: Endocrinology, Neuroscience, Reproductive biology, Neuroendocrine regulation
MeSH: Aging*, Hypothalamus*, Neuroinflammatory Diseases*, Transcriptome*, Animals, Estrogens, Female, Gene Expression Profiling, Humans, Hypothalamic-Pituitary-Gonadal Axis, Luteinizing Hormone, Mice, Mice, Inbred C57BL, Neurons, Ovariectomy (* major topic)
Topic: Menopause: Health Impacts and Treatments (Endocrinology, Diabetes and Metabolism, Medicine), according to OpenAlex
Funding: National Institutes of Health (HD090151,DK133760,HD099084,U54 AG062322); NIDDK NIH HHS (R01 DK133760); The Brit Jepson d’Arbeloff Center on Women’s Health (N/A); NICHD NIH HHS (R01 HD090151, R01 HD099084); NIA NIH HHS (U54 AG062322)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

The hypothalamic changes that occur after the loss of ovarian estrogen remain poorly characterized. Here, we performed a comprehensive temporal characterization of the mouse hypothalamus after ovariectomy (OVX), combining physiological measurements with bulk RNA-seq of the posterior hypothalamus (PH) and preoptic area at 14 days and 4 months after OVX. Serum luteinizing hormone levels rose progressively and then declined, and core temperature peaked early and subsequently normalized, recapitulating the endocrine and thermoregulatory dynamics of reproductive aging in humans. Transcriptomic analysis revealed time-dependent activation of inflammatory pathways, glial markers, and KNDy neuron-related gene networks, with the most pronounced changes emerging at 4 months after OVX, particularly in the PH. Immunofluorescence confirmed increased neurokinin B release, declining KNDy neuronal activity, and heightened astrocytic reactivity in the arcuate nucleus after prolonged estrogen withdrawal. To contextualize these findings, we analyzed publicly available human hypothalamic RNA-seq data across chronological age. Age-related transcriptomic patterns, including progressive inflammatory signaling, glial activation, and altered KNDy gene expression, showed significant correlation with the OVX mouse model, particularly at the pathway level. These findings establish a temporal framework for hypothalamic molecular changes after estrogen withdrawal, identify conserved neuroinflammatory signatures across species, and provide a preclinical platform for testing interventions targeting menopause-associated hypothalamic dysfunction.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

jcbloom/hypo_menopauseNavarro

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c220d3566603e50cc9fb91304766115874bd16e7, 22 June 2026
Languages: R (8)
Size: 22 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggplot2 (6 files), ggpubr (5 files), reshape2 (5 files), DESeq2 (2 files), limma (2 files), pheatmap (2 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), data.table (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/jcbloom/hypo_menopauseNavarro

The paper's code and data availability statement is in the Data section.

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;
  • 8 scripts, each with its path and the digest of its content;
  • 6 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

Raw RNA-seq data from mouse PH and POA regions have been deposited in NCBI’s Gene Expression Omnibus (GSE288245 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE288245)). This paper analyzed publicly available GTEx consortium data available on the GTEx Portal (https://www.gtexportal.org/home/). Original code to perform analyses has been deposited at GitHub (https://github.com/jcbloom/hypo_menopauseNavarro). In addition to the supplemental materials, the Supporting Data Values file accompanying the figures has also been provided. Any additional information required to reanalyze the data reported in this study is available from the corresponding author upon request.

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, 8 authors, 4 keywords, 15 MeSH terms, 5 funders, 69 references.

Cite

This paper

Bloom, J. C., Torres, E., Pereira, S. A., Arvizu-Sanchez, L., Fontes, A. N., Joffe, H., Page, D. C., & Navarro, V. M. (2026). Progressive hypothalamic neuroinflammation in ovariectomized mice parallels aging-related transcriptomic changes in the female human hypothalamus. JCI insight, 11(17), e207270. https://doi.org/10.1172/jci.insight.207270

BibTeX

@article{bloom2026progressive,
author = {Bloom, Jordana CB and Torres, Encarnación and Pereira, Sidney A and Arvizu-Sanchez, Liliana and Fontes, Audrey N and Joffe, Hadine and Page, David C and Navarro, Victor M},
title = {{Progressive hypothalamic neuroinflammation in ovariectomized mice parallels aging-related transcriptomic changes in the female human hypothalamus}},
journal = {JCI insight},
year = {2026},
month = jul,
volume = {11},
number = {17},
pages = {e207270},
publisher = {American Society for Clinical Investigation},
issn = {2379-3708},
doi = {10.1172/jci.insight.207270},
url = {https://doi.org/10.1172/jci.insight.207270},
pmid = {42490144},
pmcid = {PMC13564084}
}

RIS

TY - JOUR
AU - Bloom, Jordana CB
AU - Torres, Encarnación
AU - Pereira, Sidney A
AU - Arvizu-Sanchez, Liliana
AU - Fontes, Audrey N
AU - Joffe, Hadine
AU - Page, David C
AU - Navarro, Victor M
TI - Progressive hypothalamic neuroinflammation in ovariectomized mice parallels aging-related transcriptomic changes in the female human hypothalamus
T2 - JCI insight
J2 - JCI Insight
PY - 2026
DA - 2026/07/23
VL - 11
IS - 17
SP - e207270
SN - 2379-3708
PB - American Society for Clinical Investigation
DO - 10.1172/jci.insight.207270
UR - https://doi.org/10.1172/jci.insight.207270
LA - en
ER -

CSL-JSON

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"author": [
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"publisher": "American Society for Clinical Investigation",
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