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Coexpression among eastern oyster host and microbiome genes suggests coordinated regulation of calcifying fluid chemistry.

Code ↔ Paper

9 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 9 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Oyster and Microbiome Coexpression Analysis. ↔ 2_downstream_analyses/2.4_Host_Microbiome_WGCNA/WGCNA_markdown.Rmd, lines 68–81 · score 0.93 · soft thresholding power, TOMType, blockwiseModules, mergeCutHeight, minModuleSize, pamRespectsDendro
  2. [2] § Methods › Oyster Transcriptome Statistical Analyses. ↔ 2_downstream_analyses/2.2_Host_Analyses/biomineralization_toolkit_DESeq2_results.R, lines 1–54 · score 0.77 · molluscan biomineralization toolkit, GO terms, DESeq2, curated, filtering, transcripts
  3. [3] § Results › Coexpression Among Host and Microbiome Genes Suggests Coordinated Regulation of Calcifying Fluid Chemistry. ↔ 2_downstream_analyses/2.3_Microbiome_analyses/pH_relevant_DESeq2_results_plot.R, lines 1–41 · score 0.71 · assimilatory sulfate reduction, carbonic anhydrase, carbonate chemistry, nitrate, oxidation, urease
  4. [4] § Methods › Oyster and Microbiome Coexpression Analysis. ↔ 2_downstream_analyses/2.3_Microbiome_analyses/pH_relevant_DESeq2_results_plot.R, lines 62–145 · score 0.71 · biochemical reactions, carbonic anhydrase, KEGG Modules, urease, pathway, nitrogen
  5. [5] § Results › Coexpression Among Host and Microbiome Genes Suggests Coordinated Regulation of Calcifying Fluid Chemistry. ↔ 2_downstream_analyses/2.3_Microbiome_analyses/pH_relevant_DESeq2_results_plot.R, lines 62–145 · score 0.66 · biochemical reactions, KEGG orthologs, carbonic anhydrase, subunits, urease, KOs
  6. [6] § Methods › Oyster Transcriptome Analyses. ↔ 2_downstream_analyses/2.2_Host_Analyses/load_salmon_host_counts_and_annotations.R, the whole file · a weak match · score 0.59 · Oyster transcriptome annotations, Salmon, NCBI, downloaded, quantify, frames
  7. [7] § Results › Decoupling of pH and DIC in Oyster Calcifying Fluid Over the Tidal Cycle. ↔ 2_downstream_analyses/2.3_Microbiome_analyses/pH_relevant_DESeq2_results_plot.R, lines 43–60 · score 0.58 · log2 fold change, log2FC, DESeq2
  8. [8] § Results › Microbiome Transcriptional Changes in Response to Calcifying Fluid Chemistry. ↔ 2_downstream_analyses/2.3_Microbiome_analyses/pH_relevant_DESeq2_results_plot.R, lines 1–41 · score 0.54 · carbonic anhydrase, nitrate reduction, oxidation, denitrification, KEGG, carbonate
  9. [9] § Methods › Microbiome Metatranscriptome Statistical Analyses. ↔ 2_downstream_analyses/2.3_Microbiome_analyses/Microbiome_DESeq2.R, lines 88–155 · score 0.51 · low abundance, DESeq2, KOs, KEGG, Treatment, downstream

Paper

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The authors' code

R · 145 lines · 6.2 KB · no license · 5 matches

  1. library(dplyr)
  2. library(stringr)
  3. library(tidyr)
  4. library(tibble)
  5. library(ggplot2)
  7. # -----------------------------
  8. # 1) Define KO set of interest
  9. # -----------------------------
  10. # Curated KOs relevant to carbonate chemistry / pH regulation / redox cycling
  11. kegg_kos_ph <- c(
  12. # Carbonic anhydrase
  13. "ko:K01672","ko:K01673","ko:K01674","ko:K01743","ko:K18245","ko:K18246",
  14. # Urease
  15. "ko:K01427","ko:K01428","ko:K01429","ko:K01430","ko:K14048",
  16. # Denitrification (subset you listed)
  17. "ko:K00370","ko:K00371","ko:K00374","ko:K02567","ko:K02568","ko:K00368","ko:K15864","ko:K04561","ko:K02305","ko:K00376",
  18. # Assimilatory sulfate reduction
  19. "ko:K13811","ko:K00958","ko:K00860","ko:K00955","ko:K00957","ko:K00956","ko:K00390","ko:K05907","ko:K00380","ko:K00381","ko:K00392",
  20. # Dissimilatory sulfate reduction
  21. "ko:K00394","ko:K00395","ko:K11180","ko:K11181","ko:K27196","ko:K27187","ko:K27188","ko:K27189","ko:K27190","ko:K27191",
  22. # Assimilatory nitrate reduction
  23. "ko:K00367","ko:K10534","ko:K00372","ko:K00360","ko:K00366","ko:K17877","ko:K26139","ko:K26138","ko:K00361",
  24. # Dissimilatory nitrate reduction
  25. "ko:K00362","ko:K00363","ko:K03385","ko:K15876",
  26. # Methanogenesis-related (combined list from your code)
  27. "ko:K00925","ko:K00625","ko:K01895","ko:K00193","ko:K00197","ko:K00194",
  28. "ko:K00577","ko:K00578","ko:K00579","ko:K00580","ko:K00581","ko:K00582","ko:K00583","ko:K00584",
  29. "ko:K00399","ko:K00401","ko:K00402","ko:K22480","ko:K22481","ko:K22482",
  30. "ko:K03388","ko:K03389","ko:K03390","ko:K08264","ko:K08265",
  31. "ko:K14127","ko:K14126","ko:K14128","ko:K22516","ko:K00125",
  32. "ko:K00200","ko:K00201","ko:K00202","ko:K00203","ko:K11261","ko:K00205","ko:K11260","ko:K00204","ko:K00672","ko:K01499",
  33. "ko:K00319","ko:K13942","ko:K00320",
  34. # Sulfide oxidation (SOX)
  35. "ko:K17222","ko:K17223","ko:K17224","ko:K17225","ko:K22622","ko:K17226","ko:K17227",
  36. # Ammonium oxidation
  37. "ko:K20932","ko:K20933","ko:K20934","ko:K20935",
  38. "ko:K10944","ko:K10945","ko:K10946","ko:K10535",
  39. # Extra (in your methylotrophic block)
  40. "ko:K14080","ko:K04480","ko:K14081"
  41. ) %>% unique()
  43. # -------------------------------------------------------
  44. # 2) Helper: subset DESeq2 results to significant KOs only
  45. # -------------------------------------------------------
  46. # - Adds KEGG_KO from rownames
  47. # - Computes diffexpressed label from padj + direction of log2FC
  48. # - Filters to your KO list of interest
  49. make_tornado_df <- function(res_obj, time_label, ko_keep, padj_cutoff = 0.05) {
  50. as.data.frame(res_obj) %>%
  51. rownames_to_column("KEGG_KO") %>%
  52. mutate(
  53. Timepoint = time_label,
  54. diffexpressed = if_else(!is.na(padj) & padj < padj_cutoff,
  55. if_else(log2FoldChange > 0, "exposure", "control"),
  56. "NO")
  57. ) %>%
  58. filter(diffexpressed != "NO") %>%
  59. filter(KEGG_KO %in% ko_keep)
  60. }
  62. # ------------------------------------------
  63. # 3) Build one table across all timepoints
  64. # ------------------------------------------
  65. tornado_dat <- bind_rows(
  66. make_tornado_df(control_6hour_vs_exposure_6hour, "12:50", kegg_kos_ph),
  67. make_tornado_df(control_9hour_vs_exposure_9hour, "16:02", kegg_kos_ph),
  68. make_tornado_df(control_12hour_vs_exposure_12hour, "19:14", kegg_kos_ph)
  69. ) %>%
  70. mutate(Timepoint = factor(Timepoint, levels = c("12:50","16:02","19:14")))
  72. # ------------------------------------------
  73. # 4) Join KO definitions + module metadata
  74. # ------------------------------------------
  75. # Expectation:
  76. # KO_definitions: KEGG_KO, KO_definition
  77. # KO_to_Module_key: KEGG_KO, KEGG_Pathway, KEGG_Module
  78. # Module_definitions: KEGG_Module, <module definition columns>
  80. colnames(KO_definitions) <- c("KEGG_KO", "KO_definition")
  81. colnames(KO_to_Module_key) <- c("KEGG_KO", "KEGG_Pathway", "KEGG_Module")
  83. tornado_dat_annot <- tornado_dat %>%
  84. left_join(KO_definitions, by = "KEGG_KO") %>%
  85. mutate(
  86. # KO symbol = first chunk before ";" in the KO definition field
  87. KO_Symbol = str_split(KO_definition, ";", simplify = TRUE)[, 1] %>% str_trim()
  88. ) %>%
  89. left_join(KO_to_Module_key, by = "KEGG_KO") %>%
  90. left_join(Module_definitions, by = "KEGG_Module") %>%
  91. # Keep one row per KO x timepoint (prevents duplicate joins exploding rows)
  92. distinct(KEGG_KO, Timepoint, .keep_all = TRUE)
  94. # ----------------------------------------------------
  95. # 5) Clean labels for module coloring (stable mapping)
  96. # ----------------------------------------------------
  97. # Map KEGG module IDs to the exact “clean” labels you want to show in the plot.
  98. module_clean_map <- c(
  99. "M00529" = "M00529 Denitrification, nitrate => nitrogen",
  100. "M00530" = "M00530 Dissimilatory nitrate reduction, nitrate => ammonia",
  101. "M00531" = "M00531 Assimilatory nitrate reduction, nitrate => ammonia",
  102. "M00176" = "M00176 Assimilatory sulfate reduction, sulfate => H2S",
  103. "M00595" = "M00595 Thiosulfate oxidation by SOX complex, thiosulfate => sulfate"
  104. )
  106. tornado_dat_annot <- tornado_dat_annot %>%
  107. mutate(
  108. # Override to keep CA + urease colored as their own categories
  109. KEGG_Module_definitions_clean = case_when(
  110. str_detect(KO_Symbol, regex("^ure", ignore_case = TRUE)) ~ "urease subunit alpha",
  111. str_detect(KO_Symbol, regex("cah|cynT|can", ignore_case = TRUE)) ~ "carbonic anhydrase",
  112. TRUE ~ unname(module_clean_map[KEGG_Module])
  113. ),
  114. KEGG_Module_definitions_clean = factor(
  115. KEGG_Module_definitions_clean,
  116. levels = c(
  117. "M00529 Denitrification, nitrate => nitrogen",
  118. "M00530 Dissimilatory nitrate reduction, nitrate => ammonia",
  119. "M00531 Assimilatory nitrate reduction, nitrate => ammonia",
  120. "M00176 Assimilatory sulfate reduction, sulfate => H2S",
  121. "M00595 Thiosulfate oxidation by SOX complex, thiosulfate => sulfate",
  122. "urease subunit alpha",
  123. "carbonic anhydrase"
  124. )
  125. )
  126. )
  128. # ------------------------------------------
  129. # 6) Tornado plot (faceted by timepoint)
  130. # ------------------------------------------
  131. important_genes <- ggplot(
  132. tornado_dat_annot,
  133. aes(x = log2FoldChange, y = KO_Symbol, fill = KEGG_Module_definitions_clean)
  134. ) +
  135. geom_col() +
  136. facet_grid(. ~ Timepoint, scales = "free_y") +
  137. scale_y_discrete(limits = rev) +
  138. labs(
  139. x = "log2 fold change (exposure vs control)",
  140. y = "KEGG Orthologue",
  141. fill = "Biochemical reaction (KEGG module)"
  142. ) +
  143. theme_classic()
  145. important_genes

pH_relevant_DESeq2_results_plot.R at commit bfeef13, no license · at the source

Overview

Authors: Andrea Unzueta-Martínez1, Jennifer A. Delaney1, Kate Morkeski2, Abby Ross3, Zhaohui Aleck Wang2, Peter R. Girguis1
ORCID iDs: Andrea Unzueta-Martínez, Kate Morkeski, Abby Ross, Peter R. Girguis
  1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138
  2. Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA 02543
  3. Department of Ecology and Evolutionary Biology and Institute of Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO 80303
Institutions: Harvard University (United States); Woods Hole Oceanographic Institution (United States); Institute of Arctic and Alpine Research (United States); University of Colorado Boulder (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 11, article e2521539123
Dates: received 4 September 2025; accepted 23 January 2026; published online 10 March 2026; in print 17 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2521539123 · PMID 41805583 · PMCID PMC12994172 · OpenAlex W7134897737
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Spectral & time-frequency
Keywords: shell, microbiome, oyster, host-microbiome
MeSH: Calcification, Physiologic*, Crassostrea*, Microbiota*, Ostreidae*, Animals, Gene Expression Regulation, Hydrogen-Ion Concentration, Seawater, Transcriptome (* major topic)
Journal subjects: Biological Sciences, Applied Biological Sciences
Topic: Ocean Acidification Effects and Responses (Oceanography, Earth and Planetary Sciences), according to OpenAlex
Funding: NSF (2109473); Gordon and Betty Moore Foundation (GBMF) (9208)
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Marine animals that build shells, such as oysters, carefully regulate the chemistry of their internal calcifying fluids, but the molecular mechanisms behind this control, as well as whether microbes play a role in calcification, are poorly understood. To better understand oysters’ molecular mechanisms and the role of their calcifying-fluid microbes, we conducted experiments that simulated a tidal cycle, measured calcifying fluid pH and total dissolved inorganic carbon, and characterized host and microbial gene expression via transcriptomics. These experiments showed that calcifying fluid pH remained relatively stable throughout tidal pH fluctuations, with corresponding increases in oyster transcripts for ion transport and acid–base regulation. These data provide direct evidence that tidal fluctuations drive rapid changes in oyster calcifying fluid chemistry. Most surprisingly, increases in microbial transcripts related to nitrogen and sulfur cycling correlated to higher calcifying fluid DIC, and coexpression network analysis revealed patterns of gene expression that linked oyster immune and neural pathways to microbial redox processes, providing molecular evidence of potential host modulation of microbial metabolism. Together, these results reveal that oysters actively regulate their calcifying fluid pH over short timescales, and the endemic microbiome metabolic responses can yield metabolites that influence calcifying fluid pH, alkalinity, and ultimately calcification. These data offer a perspective on oyster physiological capacity and, most importantly, the potential role of microbes in oyster calcification. In light of ongoing changes in ocean pH and temperature, oysters provide a model for studying animal–microbial responses to environmental acidification and how their interactions may shape biomineralization.

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 9 matches between paragraphs and lines of code.

Andrea-Unzueta-Martinez/oyster-host-microbiome-transcriptomics

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: bfeef1304653fd8b065b894716eb81a921b937b4, 24 December 2025
Languages: R (7), Python (2)
Size: 18 files, 9 scripts
Software Heritage: not archived
Found in: “Data, Materials, and Software Availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), DESeq2 (2 files), pandas (2 files), data.table (1 file), ggplot2 (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/Andrea-Unzueta-Martinez/oyster-host-microbiome-transcriptomics

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;
  • 9 scripts, each with its path and the digest of its content;
  • 9 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, Materials, and Software Availability

Raw RNA-seq reads generated for this study are publicly available under NCBI BioProject accession PRJNA1313129 (https://www.ncbi.nlm.nih.gov/bioproject?term=PRJNA1313129) (79). Processed count matrices for both the oyster (C. virginica) host and its associated microbiome, along with all scripts used to generate and format these data, are available in the GitHub repository [https://github.com/Andrea-Unzueta-Martinez/oyster-host-microbiome-transcriptomics; (80)]. The repository contains the raw (unnormalized) host and microbiome count matrices, annotated Python scripts used for generating count tables and reformatting functional annotation files, and a detailed step-by-step pipeline describing the quality control, mapping, and quantification procedures.

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, 6 authors, 4 keywords, 9 MeSH terms, 2 funders, 47 references.

Cite

This paper

Unzueta-Martínez, A., Delaney, J. A., Morkeski, K., Ross, A., Wang, Z. A., & Girguis, P. R. (2026). Coexpression among eastern oyster host and microbiome genes suggests coordinated regulation of calcifying fluid chemistry. Proceedings of the National Academy of Sciences of the United States of America, 123(11), e2521539123. https://doi.org/10.1073/pnas.2521539123

BibTeX

@article{unzuetamartinez2026coexpression,
author = {Unzueta-Martínez, Andrea and Delaney, Jennifer A. and Morkeski, Kate and Ross, Abby and Wang, Zhaohui Aleck and Girguis, Peter R.},
title = {{Coexpression among eastern oyster host and microbiome genes suggests coordinated regulation of calcifying fluid chemistry}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = mar,
volume = {123},
number = {11},
pages = {e2521539123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2521539123},
url = {https://doi.org/10.1073/pnas.2521539123},
pmid = {41805583},
pmcid = {PMC12994172}
}

RIS

TY - JOUR
AU - Unzueta-Martínez, Andrea
AU - Delaney, Jennifer A.
AU - Morkeski, Kate
AU - Ross, Abby
AU - Wang, Zhaohui Aleck
AU - Girguis, Peter R.
TI - Coexpression among eastern oyster host and microbiome genes suggests coordinated regulation of calcifying fluid chemistry
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/03/10
VL - 123
IS - 11
SP - e2521539123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2521539123
UR - https://doi.org/10.1073/pnas.2521539123
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Coexpression among eastern oyster host and microbiome genes suggests coordinated regulation of calcifying fluid chemistry",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
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"family": "Unzueta-Martínez",
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"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "123",
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"PMID": "41805583",
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