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Comparative analysis of milk and brain fatty acids reveals human-specific signatures in brain development.

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  1. [1] § Results › Species-specific FA signatures in milk ↔ milk_FA.LS22_40.lmAdjustedNoPopulation.R, lines 1–31 · score 0.52 · lactation stage, milk FA, Shanghai, Moscow, parity, population

Paper

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The authors' code

R · 97 lines · 3.5 KB · no license · 1 match

  1. rm(list=ls())
  3. library(ggplot2)
  4. library(reshape2)
  5. library(car)
  6. library(gridExtra)
  8. setwd("D://work//skoltech//lipid//writing//GitHub//data")
  10. data <- read.csv("milk_FA.normalized.csv", row.names = 1)
  12. info.shg <- read.csv("milk_FA.info.humanShangHai.csv", row.names = 1)
  13. info.msk <- read.csv("milk_FA.info.humanMoscow.csv", row.names = 1)
  15. info.shg.filt <- info.shg[info.shg$LactationStage >=22 & info.shg$LactationStage<=40, ]
  16. info.msk.filt <- info.msk[info.msk$LactationStage <=40, ]
  17. info.msk.filt <- info.msk.filt[info.msk.filt$Parity %in% c("1", "2"), ]
  19. info.hm <- rbind(info.shg.filt[, c("LactationStage", "Parity", "N_C", "Sex")], info.msk.filt[, c("LactationStage", "Parity", "N_C", "Sex")])
  21. info.hm$Population <- factor(c(rep("Shanghai", nrow(info.shg.filt)), rep("Moscow", nrow(info.msk.filt))))
  22. info.hm$Parity <- factor(info.hm$Parity)
  23. info.hm$N_C <- factor(info.hm$N_C)
  24. info.hm$Sex <- factor(info.hm$Sex)
  26. info.hm$species <- c(rep("HSs", nrow(info.shg.filt)), rep("HSm", nrow(info.msk.filt)))
  27. info.hm$species <- factor(info.hm$species, levels = c("HSm", "HSs"), order = TRUE)
  29. all.species <- unique(as.character(info.hm$species))
  31. logdata.hm <- log2(data[, rownames(info.hm)])
  34. milkFactorAttribution <- function(dat, inf)
  35. {
  36. cf <- dat
  37. cf[] <- NA
  38. result <- c()
  39. for(i in 1:nrow(dat)) {
  40. ints <- as.numeric(as.character(dat[i,]))
  41. allDat <- cbind(ints, inf)
  43. lmmod1 <- lm(ints ~ log2(LactationStage) + Parity + N_C + Sex + Population, data = allDat)
  44. aov <- Anova(lmmod1, type = 2)
  45. coef <- coef(lmmod1)
  46. names(coef) <- c("Intercept", "LS", "Parity", "N_C", "Sex", "Population")
  47. cf[i, ] <- ints - coef["LS"]*log2(inf$LactationStage) - coef["Parity"]*as.numeric(inf$Parity) - coef["N_C"]*as.numeric(inf$N_C) - coef["Sex"]*as.numeric(inf$Sex)
  49. sq <- aov[1:5, "Sum Sq"]/sum(aov[, "Sum Sq"])
  50. pr <- aov[1:5, "Pr(>F)"]
  51. fdr <- rep("NA", 5)
  52. sub <- c(as.character(sq), as.character(pr), fdr)
  53. result <- rbind(result, sub)
  54. }
  56. colnames(result) <- c("LactationStage.R2", "Parity.R2", "N_C.R2", "Sex.R2", "Population.R2", "LactationStage.Pvalue", "Parity.Pvalue", "N_C.Pvalue", "Sex.Pvalue", "Population.Pvalue", "LactationStage.FDR", "Parity.FDR", "N_C.FDR", "Sex.FDR", "Population.FDR")
  57. result[, 11:15] <- apply(result[, 6:10], 2, function(x) {x[is.na(x)] <- 1; p.adjust(x, "fdr")})
  58. result <- data.frame(apply(result, 2, function(x) as.numeric(as.character(x))))
  59. rownames(result) <- rownames(dat)
  61. result$Population.sig <- ifelse(result$Population.FDR <0.05, "yes", "no")
  63. return(list(result, cf))
  64. }
  67. attrib <- milkFactorAttribution(logdata.hm, info.hm)
  69. write.csv(data.frame(attrib[2]), "milk_FA.LS22_40.lmAdjustedNoPopulation.csv", row.names = TRUE, quote = FALSE)
  72. specificity <- data.frame(attrib[1])
  74. proportionPercentage <- function(dat)
  75. {
  76. pro.sp <- c()
  77. for(i in 1:length(all.species)) {
  78. sampn.sp <- rownames(info.hm[info.hm$species==all.species[i], ])
  79. dt.sp <- dat[, sampn.sp]
  80. pro.tmp <- rowMeans(dt.sp, na.rm = TRUE)
  81. pro.sp <- cbind(pro.sp, pro.tmp)
  82. }
  84. colnames(pro.sp) <- all.species
  85. pro.sp[is.nan(pro.sp)] <- 0
  86. mean.sp <- pro.sp
  88. return(pro.sp)
  89. }
  92. spMe.data <- data.frame(proportionPercentage(data))
  93. specificity$HSm <- spMe.data[rownames(specificity), "HSm"]
  94. specificity$HSs <- spMe.data[rownames(specificity), "HSs"]
  95. specificity$pop.sig <- apply(specificity, 1, function(x) if(x["Population.sig"]=="yes") {ifelse(x["HSm"] >x["HSs"], "HSm", "HSs")} else {NA})
  97. write.csv(specificity, "milk_FA.specific.humans.csv", row.names = TRUE, quote = FALSE)

milk_FA.LS22_40.lmAdjustedNoPopulation.R at commit 02f910e, no license · at the source

Overview

Authors: Aleksandra Mitina1, Yunmei Wang2, Waltraud Mair2, Anna Vanyushkina3, Nickolay Anikanov4, Olga Efimova2, Song Guo2, Pavel Mazin5, Philipp Khaitovich6,7
ORCID iDs: Aleksandra Mitina, Olga Efimova
  1. Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON Canada
  2. Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Moscow, Russia
  3. Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
  4. Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
  5. Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
  6. NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
  7. Center for Bio- and Medical Technologies, Moscow, Russia
Institutions: Hospital for Sick Children (Canada); Weizmann Institute of Science (Israel); Wellcome Sanger Institute (United Kingdom); Skolkovo Institute of Science and Technology (Russia); The Affiliated Yongchuan Hospital of Chongqing Medical University (China)
Journal: Communications biology, volume 9, issue 1, article 631
Dates: received 20 June 2025; accepted 10 December 2025; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-025-09401-0 · PMID 42020529 · PMCID PMC13161393 · OpenAlex W7155205856
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), developmental (subfield)
Methods: Statistics, Preprocessing, Connectivity
Keywords: Evolutionary developmental biology, Lipidomics
MeSH: Brain*, Fatty Acids*, Milk*, Milk, Human*, Animals, Female, Humans, Infant, Infant Formula, Neurodevelopment, Species Specificity (* major topic)
Topic: Fatty Acid Research and Health (Nutrition and Dietetics, Nursing), according to OpenAlex
Funding: Russian Science Foundation (22-15-00474)
Citations: cited by 1 paper (Europe PMC); 63 references in the paper

Abstract

Lipids constitute the majority of brain dry weight and play essential structural and signaling roles. During early life, their supply depends largely on breast milk, yet how milk composition aligns with brain fatty acids (FA) across species has not been systematically explored. We analyzed 837 milk samples from seven mammalian species and 194 brain samples from five species using LC-MS. We identified 81 FA in milk and 33 in brain, with 31 shared across both tissues. FA composition in milk and brain was strongly correlated, particularly in humans and macaques, with the strongest associations observed in the prefrontal cortex and during the first four weeks postpartum. Humans were uniquely enriched in very- and ultra-long-chain unsaturated FAs (≥24 carbons) in both milk and brain, suggesting a role in species-specific neurodevelopment. Infant formula clustered closer to bovids than to human milk, underscoring compositional differences of potential nutritional relevance. These findings reveal conserved and human-specific features of milk and brain FAs, highlight the importance of early milk supply for neurodevelopment, and provide evolutionary and translational insights into infant nutrition.

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 1 match between paragraphs and lines of code.

Zenodo 10799382

License: CC-BY-4.0
State: the link answers, verified on 29 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: ggplot2 (10 files), tidyverse (9 files), reshape2 (7 files), car (3 files), cowplot (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
14 files
At the source: zenodo.org/doi/10.5281/zenodo.10799382

wanymen/mammal-lipids

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 02f910e58f0dba6244c8d63c05aafd1c5bd7da51, 8 March 2024
Languages: R (13)
Size: 19 files, 13 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (10 files), tidyverse (9 files), reshape2 (7 files), car (3 files), cowplot (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
14 files
At the source: github.com/WanYMEN/mammal-lipids/tree/v1.0.0-alpha

Code availability

The GitHub library containing the code for the lipidomics data analysis is available on Zenodo under “Coevolution between mammalian brain and milk” (https://zenodo.org/doi/10.5281/zenodo.10799382). The deposited version corresponds to that used in this study and can be freely accessed without restriction. Analyses were performed in R (v3.4.0) using XCMS (v3.4.4), CAMERA (v1.33.3), and IPO (v3.5), with parameters described in the Methods section.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 1 match 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

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

Data availability

The metabolomics data generated in this study have been deposited in the MetaboLights repository under the study identifier MTBLS12481 (https://www.ebi.ac.uk/metabolights/MTBLS12481). All numerical source data underlying the main and Supplementary Figs. are provided in Supplementary Material.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 11 MeSH terms, 1 funder, 58 references.

Cite

This paper

Mitina, A., Wang, Y., Mair, W., Vanyushkina, A., Anikanov, N., Efimova, O., Guo, S., Mazin, P., & Khaitovich, P. (2026). Comparative analysis of milk and brain fatty acids reveals human-specific signatures in brain development. Communications biology, 9(1), 631. https://doi.org/10.1038/s42003-025-09401-0

BibTeX

@article{mitina2026comparative,
author = {Mitina, Aleksandra and Wang, Yunmei and Mair, Waltraud and Vanyushkina, Anna and Anikanov, Nickolay and Efimova, Olga and Guo, Song and Mazin, Pavel and Khaitovich, Philipp},
title = {{Comparative analysis of milk and brain fatty acids reveals human-specific signatures in brain development}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {631},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-025-09401-0},
url = {https://doi.org/10.1038/s42003-025-09401-0},
pmid = {42020529},
pmcid = {PMC13161393}
}

RIS

TY - JOUR
AU - Mitina, Aleksandra
AU - Wang, Yunmei
AU - Mair, Waltraud
AU - Vanyushkina, Anna
AU - Anikanov, Nickolay
AU - Efimova, Olga
AU - Guo, Song
AU - Mazin, Pavel
AU - Khaitovich, Philipp
TI - Comparative analysis of milk and brain fatty acids reveals human-specific signatures in brain development
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/22
VL - 9
IS - 1
SP - 631
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-025-09401-0
UR - https://doi.org/10.1038/s42003-025-09401-0
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Mitina",
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"PMCID": "PMC13161393",
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