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Gut <i>Proteobacteria</i> glycine metabolism regulates neuroplasticity, motivation, and reinstatement of cocaine self-administration in mice.

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  1. [1] § Materials and methods › Liquid chromatography–tandem mass spectrometry (LC–MSMS) ↔ R/quasiSpectral.r, the whole file · a weak match · score 0.50 · quasiSpectral, Benjamini, Hochberg, shrink

Paper

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The authors' code

R · 74 lines · 3 KB · no license · 1 match

  1. #' Compare mutant to wild type spectral count data, using "QuasiSeq" methodology
  2. #'
  3. #' @param spectralDataAll data frame of gene/protein identifiers (geneName) and their spectral counts in mutant and wildtype groups.
  4. #' @param n.mut number of mutant animals
  5. #' @param n.wt number of wild type animals
  6. #' @return A dataframe with the log2 ratios, p-values, and q-values. Also eta0, the estimated proportion of null samples
  8. quasiSpectral <- function(spectralDataAll, n.mut, n.wt) {
  9. # the file "spectralDataAll" must be formatted as follows:
  10. #have gene names in the first column, named "geneName"
  11. # next n.mut columns must contain spectral counts for mutant (or comparative) group
  12. # next n.wt columns must contain specctral counts for wild type (or control) group
  14. spectralDataUse <- spectralDataAll[,1 + 1:(n.mut + n.wt)] # just use the data
  15. spectralDataUse[is.na(spectralDataUse)] <- 0
  16. trt <- c(rep(0, n.mut), rep(1, n.wt)) # assume mutants are first, followed by wild type
  18. # set up design matrix for QL.fit
  19. design.list<-vector("list",2)
  20. design.list[[1]]<-model.matrix(~as.factor(trt))
  21. design.list[[2]]<- matrix(1,length(trt))
  23. # define offset, which allows one to compare columns adjusted for the total counts
  24. log.offset <- log(apply(spectralDataUse,2,sum)) # offset is log of column totals
  26. #log.offset<-log(apply(spectralDataUse,2,quantile,.75))
  28. ### Analyze using QL, QLShrink and QLSpline methods applied to quasi-Poisson model
  30. fit <- QL.fit(spectralDataUse, design.list,log.offset=log.offset, Model="Poisson", print.progress=F)
  31. results <- QL.results(fit)
  34. #######
  35. # recommend using Model="Poisson" instead of the following:
  36. #fit.nb <- QL.fit(spectralDataUse, design.list,log.offset=log.offset, Model="NegBin")
  37. #results.nb <- QL.results(fit.nb)
  39. #results <- results.nb
  40. #fit <- fit.nb
  43. ### How many significant genes at FDR=.05 from QLSpline method?
  44. #apply(results$Q.values[[3]]<.05,2,sum)
  46. ### Indexes for Top 10 most significant genes from QLSpline method
  47. #head(order(results$P.values[[3]]), 10)
  49. # convert coefficients to log base 2
  50. coef.main <- fit$coefficients[,2] / log(2)
  51. #set maximum and minimum
  52. max.value <- 16 # 2^4
  53. coef.main[coef.main > 16] <- 16
  54. coef.main[coef.main < -16] <- -16
  56. p.values <- as.numeric(results$P.values[[3]]) # QLSpline method is third component
  57. q.values <- results$Q.values[[3]]
  59. p.values.bonf <- p.adjust(p=p.values, method="bonferroni")
  60. p.values.holm <- p.adjust(p=p.values, method="holm")
  61. q.values.BH <- p.adjust(p=p.values, method="BH") # Benjamini and Hochberg
  63. library(fdrtool) # must be downloaded and installed
  64. fdrout <- fdrtool(p.values, statistic="pvalue")
  65. q.values.strimmer <- fdrout$qval
  66. eta0 <- fdrout$param[3] # proportion of null samples in population
  67. eta0
  69. geneName <- as.character(spectralDataAll$geneName)
  70. QLfit <- data.frame(geneName, spectralDataUse, coef.main, p.values, p.values.bonf, p.values.holm, q.values.BH, q.values.strimmer)
  72. results <- list(QLfit=QLfit, eta0=eta0)
  73. results
  74. }

quasiSpectral.r at commit a1c4c3d, no license · at the source

Overview

Authors: Susana Delgado Ocaña1, Guadalupe Herrera1, Daniel Guzmán2, David W Self2, Santiago Cuesta1,3
ORCID iDs: Susana Delgado Ocaña, Guadalupe Herrera, David W Self, Santiago Cuesta
  1. Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
  2. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
  3. Rutgers Addiction Research Center, Brain Health Institute, Rutgers Health, Piscataway, NJ, USA
Institutions: Rutgers, The State University of New Jersey (United States); The University of Texas Southwestern Medical Center (United States); Rutgers Health (United States)
Journal: Gut microbes, volume 18, issue 1, article 2693397
Dates: received 5 March 2026; accepted 17 June 2026; published online 26 June 2026; in print December 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1080/19490976.2026.2693397 · PMID 42359815 · PMCID PMC13313183 · OpenAlex W7166014926
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, Connectivity
Keywords: Cocaine, Proteobacteria, addiction, glycine, gut–brain axis, proteomics
MeSH: Cocaine*, Cocaine-Related Disorders*, Gastrointestinal Microbiome*, Glycine*, Motivation*, Neuronal Plasticity*, Proteobacteria*, Animals, Escherichia coli, Humans, Male, Mice, Mice, Inbred C57BL, Nucleus Accumbens, Self Administration (* major topic)
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Research Corporation for Science Advancement and the Frederick Gardner Cottrell Foundation (#SA-MND-2023-038b); Duncan and Nancy MacMillan Faculty Development Chair Endowment Fund; National Institute on Drug Abuse (5T32DA055569-04); NIH Office of the Director (1DP2AT013279-01); NCCIH NIH HHS (DP2 AT013279)
Citations: not cited yet (Europe PMC); 103 references in the paper

Abstract

Addiction is a chronic and relapsing disorder that affects millions of people worldwide; nonetheless, currently available FDA-approved treatments are limited in number and effectiveness. In past years, the gut–brain axis has emerged as a key modulatory factor associated with different psychiatric disorders, including addiction. Working in mice, we have shown that cocaine exposure alters the composition of the gut microbiome, increasing the abundance of Proteobacteria. This microbial shift, in turn, leads to a depletion in host glycine levels, altering cocaine-induced transcriptional changes in the Nucleus Accumbens (NAc) and facilitating the development of behavioral sensitization and conditioned place preference. Among the behavioral models to study psychostimulant use disorders, cocaine self-administration (SA) remains the most translational. Therefore, here we investigated whether Proteobacteria-induced glycine depletion can affect cocaine SA in mice. Using the human Escherichia coli HS and the glycine-uptake-deficient mutant E. coli HS ΔCycA, we build upon our previous findings and demonstrate that the ability of gut Proteobacteria to use glycine during cocaine SA shapes the trajectory and long-term neurobehavioral plasticity induced by the drug. Furthermore, we show that this bacterial-induced glycine depletion impacts the NAc proteome, altering its vulnerability to undergo molecular adaptations across different stages of the SA paradigm. Altogether, our findings show that the gut microbiome, and particularly the Proteobacteria phylum, is a crucial factor influencing short and long-term adaptation underlying motivation and cocaine-seeking behaviors.

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 1 match between paragraphs and lines of code.

mooredf22/quasispectral

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a1c4c3d2cfba40d5d8c8e201948c38be212bab41, 12 May 2021
Languages: R (2)
Size: 11 files, 2 scripts
Software Heritage: archived
Found in: the text, “Liquid chromatography–tandem mass spectrometry (”
Holds: environment (DESCRIPTION), documentation, 1 notebook
Not found: README, license file, CITATION.cff, tests, continuous integration
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/mooredf22/quasispectral

Tracing map

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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;
  • 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

Datasets cited

Data availability statement

Proteomic and 16S rRNA sequencing data that support the findings of this study are available at https://doi.org/10.5281/zenodo.17362536 and from the corresponding author, S.C., upon reasonable request.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 15 MeSH terms, 5 funders, 95 references.

Cite

This paper

Delgado Ocaña, S., Herrera, G., Guzmán, D., Self, D. W., & Cuesta, S. (2026). Gut &lt;i&gt;Proteobacteria&lt;/i&gt; glycine metabolism regulates neuroplasticity, motivation, and reinstatement of cocaine self-administration in mice. Gut microbes, 18(1), 2693397. https://doi.org/10.1080/19490976.2026.2693397

BibTeX

@article{delgadoocana2026gut,
author = {Delgado Ocaña, Susana and Herrera, Guadalupe and Guzmán, Daniel and Self, David W and Cuesta, Santiago},
title = {{Gut \&lt;i\&gt;Proteobacteria\&lt;/i\&gt; glycine metabolism regulates neuroplasticity, motivation, and reinstatement of cocaine self-administration in mice}},
journal = {Gut microbes},
year = {2026},
month = jun,
volume = {18},
number = {1},
pages = {2693397},
publisher = {Taylor \& Francis},
issn = {1949-0976},
doi = {10.1080/19490976.2026.2693397},
url = {https://doi.org/10.1080/19490976.2026.2693397},
pmid = {42359815},
pmcid = {PMC13313183}
}

RIS

TY - JOUR
AU - Delgado Ocaña, Susana
AU - Herrera, Guadalupe
AU - Guzmán, Daniel
AU - Self, David W
AU - Cuesta, Santiago
TI - Gut &lt;i&gt;Proteobacteria&lt;/i&gt; glycine metabolism regulates neuroplasticity, motivation, and reinstatement of cocaine self-administration in mice
T2 - Gut microbes
J2 - Gut Microbes
PY - 2026
DA - 2026/06/26
VL - 18
IS - 1
SP - 2693397
SN - 1949-0976
PB - Taylor & Francis
DO - 10.1080/19490976.2026.2693397
UR - https://doi.org/10.1080/19490976.2026.2693397
LA - en
ER -

CSL-JSON

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"title": "Gut &lt;i&gt;Proteobacteria&lt;/i&gt; glycine metabolism regulates neuroplasticity, motivation, and reinstatement of cocaine self-administration in mice",
"container-title": "Gut microbes",
"author": [
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"given": "Susana"
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"PMCID": "PMC13313183",
"ISSN": "1949-0976",
"publisher": "Taylor & Francis",
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"language": "en",
"issued": {
"date-parts": [
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2026,
6,
26
]
]
}
}

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