OSCR

Gut microbiome alterations are sex-dependently associated with brain abnormalities in a mouse model of Neurofibromatosis type I.

Code ↔ Paper

4 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 4 matches
  1. [1] § Results › Microbiota composition changes in +/- mice suggest limited functional impacts, while supervised integration of gut function, brain anatomy and 16S data reveals host-microbe signatures of neu ↔ microbiome_diversity.R, lines 222–310 · score 0.66 · Clostridium sp, stool score, gut permeability, SY8519, Colon, microbial
  2. [2] § Materials and methods › Full-length 16S rRNA amplicon analysis › Full-length 16S rRNA amplicon sequencing and bioinformatic analysis ↔ microbiome_diversity.R, lines 88–132 · score 0.64 · mixOmics, stool score, gut permeability, Variable, sequencing, DIABLO
  3. [3] § Materials and methods › Statistics and figures ↔ microbiome_diversity.R, lines 222–310 · score 0.56 · stool score, gut permeability, Circos, boxplots, Omics, microbial
  4. [4] § Materials and methods › Full-length 16S rRNA amplicon analysis › Full-length 16S rRNA amplicon sequencing and bioinformatic analysis ↔ microbiome_diversity.R, lines 1–42 · score 0.53 · beta diversity, Emu, library, transformed, abundance, sequencing

Paper

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The authors' code

R · 311 lines · 13 KB · no license · 4 matches

  1. # Import libraries
  2. library(tidyverse)
  4. # Import metadata
  5. meta <- read.csv("sample_metadata/NF1_MB1_all_data_forGK_updated.csv")#, row.names = 1)
  7. # Format metadata and remove sample with poor sequencing data
  8. meta <- meta %>%
  9. filter(!is.na(X16Ssampleid), X16Ssampleid != 'DMG2400369', X16Ssampleid != "") %>%
  10. arrange(X16Ssampleid) %>%
  11. mutate(Genotype = factor(Genotype, levels = c("WT","HET")),
  12. Sex = factor(Sex, levels = c("m","f")),
  13. sex_geno = paste0(Sex, "_", Genotype)) %>%
  14. tibble::column_to_rownames(var = 'X16Ssampleid')
  15. # meta <- meta[order(rownames(meta)),]
  16. # meta <- meta[which(rownames(meta) != "DMG2400369"),]
  18. #########
  19. # Beta diversity
  20. #########
  21. ## Import data
  22. data <- read.delim("data/emu-combined-abundance-species.tsv", row.names = 1)
  24. ## Replace NA if any
  25. data[is.na(data)] <- 0
  27. ## Subset and order
  28. common <- intersect(colnames(data), rownames(meta))
  29. data <- data[, sort(common), drop = FALSE]
  31. colSums(data)
  33. ## Filter out taxa < 0.1% (data currently in proportions 0-1)
  34. data.filt <- t(data[rowMeans(data) >= 0.001, ])
  35. dim(data.filt)
  37. ## Robust CLR-transformation (for samples as rows x taxa as columns)
  38. data.clr = vegan::decostand(data.filt, MARGIN = 1, method = 'rclr')
  40. ### Attach column and rownames
  41. rownames(data.clr) <- rownames(data.filt)
  42. colnames(data.clr) <- colnames(data.filt)
  44. ## PCA
  45. pca.res <- prcomp(data.clr, scale = T, center = T)
  46. plot(pca.res)
  47. summ <- summary(pca.res)
  48. comp_var <- round(summ$importance["Proportion of Variance",]*100, digits = 1)
  50. pca.df <- pca.res$x |> as.data.frame()
  51. pca.df <- pca.df[order(rownames(pca.df)),]
  52. table(rownames(pca.df) == rownames(meta))
  54. ## Merge with metadata
  55. pca.df <- cbind(pca.df, meta)|>
  56. as.data.frame() %>%
  57. mutate(sex_geno = factor(sex_geno, levels = c("male_WT","male_HET","female_WT","female_HET")))
  59. ## Save data
  60. pca.data <- pca.df %>%
  61. select(PC1, PC2, mouse, reads, genotype, sex, label, analysis, sex_geno)
  62. pca_data <- list(pca_data = pca.data,
  63. var_exp = comp_var)
  64. saveRDS(pca_data, file = "analysis/beta_div/pca_data.RDS")
  67. ## Plot
  68. p_aitch_facet <- pca.df %>%
  69. ggplot(aes(x = PC1, y = PC2)) +
  70. geom_point(size = 2, aes(color = sex_geno, shape = sex), alpha = 0.85) + # med_hist
  71. scale_shape_manual(values = c(16,15,17,8)) +
  72. xlab(paste0("Component1: ", comp_var[1],"%")) +
  73. ylab(paste0("Component2: ", comp_var[2],"%")) +
  74. ggtitle("Beta diversity: Aitchison") +
  75. facet_wrap(~sex) +
  76. theme_bw(base_size = 8.5) +
  77. theme(plot.title = element_text(hjust = 0.5, size = 9),
  78. legend.position = "right",
  79. #legend.title = element_blank()
  80. )
  82. ## Save plot
  83. jpeg("analysis/beta_div/aitchison_faceted_by_sex.jpg", res= 300, units = 'cm',
  84. width = 10, height = 7)
  85. p_aitch_facet
  86. dev.off()
  88. ## PERMANOVA
  89. dist_aitch <- vegan::vegdist(data.clr, method = 'euclidean')
  90. table(rownames(data.clr) == rownames(meta))
  91. perm_res <- vegan::adonis2(dist_aitch ~ genotype * sex, data = meta, permutations = 999, by = 'terms')
  93. vegan::adonis2(dist_aitch ~ genotype * sex, data = meta, permutations = 999, by = 'margin')
  95. vegan::adonis2(dist_aitch ~ genotype, data = meta, permutations = 999, by = 'margin')
  96. vegan::adonis2(dist_aitch ~ sex, data = meta, permutations = 999, by = 'margin')
  98. perm_res <- as.data.frame(perm_res) %>%
  99. tibble::rownames_to_column(var = "variables") %>%
  100. filter(!variables %in% c("Residual", "Total")) %>%
  101. mutate(sig = case_when(`Pr(>F)` < 0.01 ~ "**",
  102. `Pr(>F)` < 0.05 ~ "*",
  103. .default = ""))
  104. write.table(perm_res, file = "analysis/beta_div/aitch_permanova_res_filt_0.1.txt", sep = "\t",quote = F, row.names = F)
  106. ## Pairwise PERMANOVA
  107. pairwise_perm <- RVAideMemoire::pairwise.perm.manova(dist_aitch, fact = meta$sex_geno)
  108. pairwise_perm$p.value %>%
  109. as.data.frame()
  111. write.table(pairwise_perm$p.value, file = "analysis/beta_div/aitch_pairwise_permanova_res_filt_0.1.txt", sep = "\t", row.names = T, quote = F)
  113. ############
  114. # mixOmics DIABLO integration of microbiome with behavioural data
  115. ############
  116. ## Import behavioural data
  117. meta_behav <- read.csv("sample_metadata/NF1_MB1_all_data_forGK_updated.csv")#, row.names = 1)
  119. ## Samples wtih missing values in gut params: f HET: 5/8, f WT:1/8, m WT:2/9, m HET: 0/7
  120. ### Pick samples with 16S sequences + selected behav columns
  121. params <- c("BW_final", "brain", "GTT", "stool_score", "gut_permeability")
  122. meta_behav_filt <- meta_behav_filt %>%
  123. filter(!is.na(X16Ssampleid), X16Ssampleid != 'DMG2400369', X16Ssampleid != "") %>%
  124. arrange(X16Ssampleid) %>%
  125. dplyr::select(Sex, Genotype, ID, cage, sex_geno, X16Ssampleid, all_of(params)) %>%
  126. dplyr::select(-calprotectin,-cecum_g, -colon_cm, ) %>% #
  127. filter(!is.na(gut_permeability), !is.na(brain)) %>%
  128. mutate(sex_geno = paste0(Sex, "_", Genotype)) %>%
  129. mutate(sex_geno = factor(sex_geno, levels = c('m_WT',"m_HET","f_WT","f_HET"))) %>%
  130. tibble::column_to_rownames(var = 'X16Ssampleid')
  132. meta_behav_filt %>% count(sex_geno)
  134. ## Standardize behavioural data
  135. meta_behav_filt_std <- vegan::decostand(meta_behav_filt[,-which(colnames(meta_behav_filt) %in% c('Sex', 'Genotype', 'ID', 'cage','sex_geno'))], MARGIN = 2, method = 'standardize')
  137. ## Subset species data to those with metadata
  138. data.clr.meta <- data.clr[which(rownames(data.clr) %in% rownames(meta_behav_filt_std)),]
  139. data.clr.meta <- data.clr.meta[order(rownames(data.clr.meta)),]
  140. dim(data.clr.meta)
  142. meta_behav_filt_std <- meta_behav_filt_std[which(rownames(meta_behav_filt_std) %in% rownames(data.clr.meta)),]
  143. meta_behav_filt_std <- meta_behav_filt_std[order(rownames(meta_behav_filt_std)),]
  144. dim(meta_behav_filt_std)
  146. ## Subset to male samples
  147. meta_behav_filt.male <- meta_behav_filt %>%
  148. filter(Sex == 'm')
  149. table(meta_behav_filt.male$Genotype)
  151. meta_behav_filt_std.male <- meta_behav_filt_std[which(rownames(meta_behav_filt_std) %in% rownames(meta_behav_filt.male)),]
  152. meta_behav_filt_std.male <- meta_behav_filt_std.male[order(rownames(meta_behav_filt_std.male)),]
  154. length(which(rownames(data.clr.meta) %in% rownames(meta_behav_filt.male))) # missing 1
  156. rownames(meta_behav_filt.male)[which(!rownames(meta_behav_filt.male ) %in% rownames(data.clr.meta))]
  158. data.clr.meta.male <- data.clr.meta[which(rownames(data.clr.meta) %in% rownames(meta_behav_filt.male)),]
  159. data.clr.meta.male <- data.clr.meta.male[order(rownames(data.clr.meta.male)),]
  160. table(rownames(meta_behav_filt_std.male) == rownames(data.clr.meta.male))
  162. ## Remove features with close to no variance
  163. feat_var <- apply(data.clr.meta.male, MARGIN = 2, var)
  164. feat_var <- feat_var[order(feat_var, decreasing = T)]
  165. summary(feat_var)
  166. feat_var <-feat_var[feat_var > 0.1]
  167. data.clr.meta.male <- data.clr.meta.male[,which(colnames(data.clr.meta.male) %in% names(feat_var))]
  169. ## Set DIABLO data
  170. data.diablo.male = list(microb = data.clr.meta.male,
  171. meta = meta_behav_filt_std.male)
  173. ## Check that samples for all datasets match
  174. lapply(data.diablo.male, dim)
  176. ## Set up grid for tuning
  177. test.keepX = list (microb = c(5:9, seq(10, 18, 2), seq(20,30,5)),
  178. meta = c(2:5))
  180. library(mixOmics)
  181. tune.diablo = tune.block.splsda(X = data.diablo.male,
  182. Y = meta_behav_filt.male$Genotype, ncomp = 2,
  183. test.keepX = test.keepX, design = design,
  184. validation = 'loo', #folds = 10, nrepeat = 1,
  185. dist = "centroids.dist")
  186. list.keepX = tune.diablo$choice.keepX # set the optimal values of features to retain
  187. list.keepX
  189. ## Final number of features to keep
  190. list.keepX$microb <- c(7,5)
  191. list.keepX$meta <- c(2,1)
  193. ## Final DIABLO model
  194. final.diablo.male = block.splsda(X = data.diablo.male,
  195. Y = meta_behav_filt.male$Genotype,
  196. ncomp = 2,
  197. keepX = list.keepX, design = design)
  199. ### Sample plot
  200. jpeg('analysis/diablo_omics_int/DIABLO_sample_plot_male.jpg', res = 300, units = 'cm', width = 19, height = 11)
  201. plotIndiv(final.diablo.male, ind.names = FALSE, legend = TRUE,
  202. group = meta_behav_filt.male$Genotype, abline = TRUE,
  203. title = 'DIABLO Sample Plot: Male')
  204. dev.off()
  206. diablo_sample_plot <- plotIndiv(final.diablo.male, ind.names = FALSE, legend = TRUE,
  207. group = meta_behav_filt.male$Genotype, abline = TRUE,
  208. title = 'DIABLO Sample Plot: Male')
  209. diablo_sample_plot_microb_coords <- diablo_sample_plot[['df']] |> as.data.frame() %>%
  210. tibble::rownames_to_column(var = 'sample')
  211. write.table(diablo_sample_plot_microb_coords, file = 'analysis/diablo_omics_int/DIABLO_sample_plot_male_coords.tsv', sep = '\t', quote = F, row.names = F)
  213. ### Variable plot
  214. jpeg('analysis/diablo_omics_int/DIABLO_var_plot_male.jpg', res = 300, units = 'cm', width = 14, height = 14)
  215. plotVar(final.diablo.male, var.names = c(FALSE, TRUE),
  216. style = 'graphics', legend = TRUE,
  217. title = 'Correlation circle: Male',
  218. pch = c(16, 17), cex = c(1,0.8),
  219. col = c('darkorchid', 'lightgreen'))
  220. dev.off()
  222. ### Loadings
  223. loadings_comp1_male <- rbind(selectVar(final.diablo.male)$microb$value, selectVar(final.diablo.male)$meta$value) %>%
  224. as.data.frame() %>%
  225. tibble::rownames_to_column(var = 'features') %>%
  226. mutate(sex = 'm',
  227. block = case_when(features %in% colnames(meta_behav) ~ 'Metadata',
  228. .default = 'Microbiome'))
  230. loadings_plot_male <- loadings_comp1_male %>% # loadings_comp1_male
  231. # filter(features != 'Clostridium sp. SY8519') %>%
  232. mutate(Outcome = case_when(value.var > 0 ~ "WT",
  233. .default = 'HET'),
  234. block = factor(block, levels = c('Microbiome','Metadata'))) %>%
  235. ggplot(aes(x = value.var, y = reorder(features, value.var, decreasing = F))) +
  236. geom_col(aes(fill = Outcome)) +
  237. labs(title = 'Comp 1 Loadings: Male', x = 'Loadings', y = 'Features') +
  238. facet_wrap(~block, scales = 'free_y') +
  239. scale_fill_manual(values = c('#388ECC', '#F68B33')) +
  240. theme_bw() +
  241. theme(plot.title = element_text(hjust = 0.5),
  242. )
  245. jpeg('analysis/diablo_omics_int/Loadings_comp1_male.jpg', res = 300, units = 'cm', width = 18, height = 8)
  246. loadings_plot_male
  247. dev.off()
  249. #### Export as table
  250. write.table(loadings_comp1_male, file = 'analysis/diablo_omics_int/Loadings_comp1_Male.tsv', sep = '\t', row.names = F, quote = F)
  252. ### Circos plot
  253. jpeg('analysis/diablo_omics_int/DIABLO_circos_comp1_male_blank.jpg', res = 300, units = 'cm', width = 17, height = 16)
  254. circosPlot(final.diablo.male, cutoff = 0.7, line = TRUE, comp = 1, color.Y = c("#00cc99","#0000ff"),legend.title = "", var.names = var_names_hide,
  255. color.blocks= c('darkorchid', 'lightgreen'),
  256. color.cor = c("chocolate3","grey20"),
  257. size.labels = 0,
  258. size.variables = 0)
  259. dev.off()
  261. ############
  262. # Check if the subset of samples are representative of the whole cohort
  263. ############
  264. meta_boxplot <- meta_behav %>%
  265. rename('sample' = 'X16Ssampleid') %>%
  266. pivot_longer(cols = c('BW_final', 'brain', 'GTT', 'stool_score', 'gut_permeability','colon_cm'), values_to = 'measure', names_to = 'readout') %>% # params
  267. mutate(sex_geno = paste0(Sex, "_", Genotype)) %>%
  268. mutate(sex_geno = factor(sex_geno, levels = c('m_WT',"m_HET","f_WT","f_HET"))) %>%
  269. ggplot(aes(x = sex_geno, y = measure)) +
  270. geom_boxplot(outlier.shape = NA, aes(fill = sex_geno)) +
  271. geom_jitter(width = 0.25, alpha = 0.9) +
  272. facet_wrap(~ readout, scales = 'free_y') +
  273. labs(title = 'All samples') +
  274. theme_bw() +
  275. theme(axis.title.x = element_blank(),
  276. legend.position = 'none',
  277. plot.title = element_text(hjust = 0.5))
  279. meta_boxplot_sub <- meta_behav %>%
  280. filter(!is.na(X16Ssampleid), X16Ssampleid != 'DMG2400369', X16Ssampleid != "") %>%
  281. dplyr::select(Sex, Genotype, ID, cage, X16Ssampleid, all_of(params)) %>%
  282. dplyr::select(-calprotectin, -cecum_g) %>%
  283. filter(!is.na(gut_permeability), !is.na(brain)) %>%
  284. mutate(sex_geno = paste0(Sex, "_", Genotype)) %>%
  285. mutate(sex_geno = factor(sex_geno, levels = c('m_WT',"m_HET","f_WT","f_HET"))) %>%
  286. pivot_longer(cols = any_of(params), values_to = 'measure', names_to = 'readout') %>%
  287. ggplot(aes(x = sex_geno, y = measure)) +
  288. geom_boxplot(outlier.shape = NA, aes(fill = sex_geno)) +
  289. geom_jitter(width = 0.25, alpha = 0.9) +
  290. facet_wrap(~ readout, scales = 'free_y') +
  291. labs(title = 'Subset of samples for DIABLO') +
  292. theme_bw() +
  293. theme(axis.title.x = element_blank(),
  294. legend.position = 'none',
  295. plot.title = element_text(hjust = 0.5))
  297. jpeg('analysis/diablo_omics_int/meta_boxplot.jpg', res = 300, width = 18, height = 20, units = 'cm')
  298. cowplot::plot_grid(meta_boxplot, meta_boxplot_sub, nrow = 2)
  299. dev.off()
  301. meta_boxplot_sub_data <- meta_behav %>%
  302. filter(!is.na(X16Ssampleid), X16Ssampleid != 'DMG2400369', X16Ssampleid != "") %>%
  303. dplyr::select(Sex, Genotype, ID, cage, X16Ssampleid, all_of(params)) %>%
  304. dplyr::select(-calprotectin, -cecum_g) %>%
  305. filter(!is.na(gut_permeability), !is.na(brain)) %>%
  306. mutate(sex_geno = paste0(Sex, "_", Genotype)) %>%
  307. mutate(sex_geno = factor(sex_geno, levels = c('m_WT',"m_HET","f_WT","f_HET")))
  309. ### Export as table
  310. write.table(meta_boxplot_sub_data, file = 'analysis/diablo_omics_int/meta_boxplot_sub_data.tsv', sep = "\t", row.names = F,quote = F)

microbiome_diversity.R at commit b378435, no license · at the source

Overview

Authors: Sonali N. Reisinger1, Geraldine Kong2,3, Nicholas van de Garde1,4, Asim Muhammad1,4, Pranav Adithya1,4, Da Lu1,4, Pamudika Kiridena1,4, Carolina Gubert1, Gabriel Dabscheck5,6, Jonathan M. Payne5,6,7, Anthony J. Hannan1,4,8
ORCID iDs: Sonali N. Reisinger, Geraldine Kong, Nicholas van de Garde, Da Lu, Pamudika Kiridena, Carolina Gubert, Jonathan M. Payne, Anthony J. Hannan
  1. Florey Institute of Neuroscience and Mental Health,Parkville, VIC Australia
  2. Peter Doherty Institute of Infection and Immunity, Department of Microbiology and Immunology, University of Melbourne,Parkville, VIC Australia
  3. Centre for Pathogen Genomics Innovation Hub, Department of Microbiology and Immunology, University of Melbourne,Parkville, VIC Australia
  4. Florey Department of Neuroscience and Mental Health, University of Melbourne,Parkville, VIC Australia
  5. Royal Children’s Hospital Melbourne,Parkville, VIC Australia
  6. Murdoch Children’s Research Institute,Parkville, VIC Australia
  7. Department of Paediatrics, University of Melbourne,Parkville, VIC Australia
  8. Department of Anatomy and Physiology, University of Melbourne,Parkville, VIC Australia
Institutions: Florey Institute of Neuroscience and Mental Health (Australia); The University of Melbourne (Australia); Royal Children's Hospital (Australia); Murdoch Children's Research Institute (Australia)
Journal: Molecular psychiatry, volume 31, issue 9, pages 5184-5203
Dates: received 22 February 2025; accepted 9 April 2026; published online 27 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41380-026-03609-0 · PMID 42045433 · PMCID PMC13441884 · OpenAlex W7156240034
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), autism (population), ADHD (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: Neuroscience, Autism spectrum disorders, Diagnostic markers, ADHD, Physiology
MeSH: Gastrointestinal Microbiome*, Neurofibromatosis 1*, Animals, Autistic Disorder, Behavior, Animal, Brain, Disease Models, Animal, Female, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Neurofibromin 1, RNA, Ribosomal, 16S (* major topic)
Topic: Neurofibromatosis and Schwannoma Cases (Neurology, Medicine), according to OpenAlex
Funding: National Health and Medical Research Council; Hereditary Disease Foundation (HDF)
Citations: not cited yet (Europe PMC); 141 references in the paper

Abstract

Neurofibromatosis type 1 (NF1) is a genetic condition presenting with variable symptomatology, however most individuals will demonstrate cognitive and behavioural difficulties, including autism. Using a heterozygous germline knockout mouse model of NF1 (Nf1 +/-), we performed in-depth behavioural evaluations encompassing learning and memory, stereotypy, social interaction, anxiety- and depression-like behaviour. Anatomical and functional studies of the brain and gastrointestinal tract were followed by the first investigation of gut microbiota composition (via full-length 16S rRNA sequencing) in a Nf1 +/- mouse model. The cognitive and autism-like behavioural phenotype seen in Nf1 +/- mice was accompanied by a striking increase in relative brain size which is highly relevant to clinical NF1. Furthermore, brain size was correlated with behaviour, supporting a potential mechanistic link. Nf1 +/- mice showed significant alterations in gut microbiota composition vs. Nf1 +/+ wild-type controls, with males additionally showing significant changes to species abundance of the Clostridium and Blautia genera, and the Lachnospiraceae family, findings which partially overlap with those in preclinical and clinical autism. Composition of associated functional pathways was not globally altered, however +/- mice showed significant changes in a pyrimidine deoxynucleotide biosynthesis pathway. In male Nf1 +/- mice, we also identified a genotype-specific host-microbial signature, pointing towards a mechanistic link between gut microbiome composition and brain size. These findings significantly expand our understanding of brain and behavioural abnormalities in this preclinical model of NF1 and, importantly, have uncovered the gut microbiome as a highly promising new area of research and a potential therapeutic target for these symptom clusters.

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

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gkong1/Reisinger_et_al_2026_Mol_Psych

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Evidence: files inventoried
Commit: b378435540fa8179b801876a320ec4fe08e51b36, 27 April 2026
Languages: R (1)
Size: 5 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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2 files
At the source: github.com/gkong1/Reisinger_et_al_2026_Mol_Psych

Code availability

Code used for 16S and integrative statistical analyses is available on github: https://github.com/gkong1/Reisinger_et_al_2026_Mol_Psych.

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

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Data

Datasets cited

Data availability

Datasets generated by this study will be made publicly available in online data repositories upon publication (16S sequencing data: NCBI Sequence Read Archives (SRA) under BioProject PRJNA1441776; other datasets – Figshare: 10.6084/m9.figshare.31839700).

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

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Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 14 MeSH terms, 2 funders, 128 references.

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Reisinger, S. N., Kong, G., van de Garde, N., Muhammad, A., Adithya, P., Lu, D., Kiridena, P., Gubert, C., Dabscheck, G., Payne, J. M., & Hannan, A. J. (2026). Gut microbiome alterations are sex-dependently associated with brain abnormalities in a mouse model of Neurofibromatosis type I. Molecular psychiatry, 31(9), 5184-5203. https://doi.org/10.1038/s41380-026-03609-0

BibTeX

@article{reisinger2026gut,
author = {Reisinger, Sonali N. and Kong, Geraldine and van de Garde, Nicholas and Muhammad, Asim and Adithya, Pranav and Lu, Da and Kiridena, Pamudika and Gubert, Carolina and Dabscheck, Gabriel and Payne, Jonathan M. and Hannan, Anthony J.},
title = {{Gut microbiome alterations are sex-dependently associated with brain abnormalities in a mouse model of Neurofibromatosis type I}},
journal = {Molecular psychiatry},
year = {2026},
month = apr,
volume = {31},
number = {9},
pages = {5184--5203},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03609-0},
url = {https://doi.org/10.1038/s41380-026-03609-0},
pmid = {42045433},
pmcid = {PMC13441884}
}

RIS

TY - JOUR
AU - Reisinger, Sonali N.
AU - Kong, Geraldine
AU - van de Garde, Nicholas
AU - Muhammad, Asim
AU - Adithya, Pranav
AU - Lu, Da
AU - Kiridena, Pamudika
AU - Gubert, Carolina
AU - Dabscheck, Gabriel
AU - Payne, Jonathan M.
AU - Hannan, Anthony J.
TI - Gut microbiome alterations are sex-dependently associated with brain abnormalities in a mouse model of Neurofibromatosis type I
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/04/27
VL - 31
IS - 9
SP - 5184
EP - 5203
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03609-0
UR - https://doi.org/10.1038/s41380-026-03609-0
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41380-026-03609-0",
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"container-title-short": "Mol Psychiatry",
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"page": "5184-5203",
"DOI": "10.1038/s41380-026-03609-0",
"PMID": "42045433",
"PMCID": "PMC13441884",
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"language": "en",
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