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Integrative host-microbiome modeling uncovers the implication of oral-gut translocation in advanced cirrhosis.

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  1. [1] § REACTOTYPING REVEALS SEVERITY‐ASSOCIATED METABOLIC ORGANIZATION AND ORAL–GUT FUNCTIONAL CONVERGENCE ↔ Figure_1/Figure_1.Rmd, lines 240–270 · score 0.58 · gut reactobiome, cirrhosis severity, Oral Gut, Distance, MELD, OGMD
  2. [2] § COMMUNITY METABOLIC MODELING REVEALS ECOSYSTEM‐LEVEL AMPLIFICATION OF AMMONIA PRODUCTION ↔ Figure_2/Figure_2.Rmd, lines 176–207 · score 0.54 · muscle GSMMs, log2FC, Brain, metabolic, Figure 2
  3. [3] § TRANSLOCATING ORAL‐ASSOCIATED SPECIES ENHANCED AMMONIA‐PRODUCING METABOLIC POTENTIAL ↔ Figure_2/GSMM/Bacterial_community_generate.m, lines 17–62 · score 0.51 · gut microbial, composition, zero, profiles, metagenomic, reactobiome

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The authors' code

R Markdown · 663 lines · 21 KB · no license · 1 match

  1. ---
  2. title: "Figure 1.Reactotype-associated metabolic convergence between oral and gut microbiomes in liver cirrhosis."
  3. author: "Yi Jin"
  4. date: "2026-04-09"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(
  10. echo = TRUE,
  11. warning = FALSE,
  12. message = FALSE,
  13. fig.width = 5,
  14. fig.height = 5
  15. )
  16. ```
  18. ## Load packages
  20. ```{r}
  21. library(microbiome)
  22. library(DirichletMultinomial)
  23. library(reshape2)
  24. library(magrittr)
  25. library(dplyr)
  26. library(parallel)
  27. library(tidyverse)
  28. library(palmerpenguins)
  29. library(ggdist)
  30. library(ggrepel)
  31. library(ggfun)
  32. library(ggtext)
  33. library(readxl)
  34. library(ggpubr)
  35. library(ggpmisc)
  36. library(knitr)
  37. ```
  39. ## Data preparation
  41. ```{r}
  42. load("dmngut.RData")
  43. load("dmnoral.RData")
  45. gut.rxty.pcoa.tab <- read.csv("Tables/gut_rxty_pcoa_tab.csv", check.names = FALSE)
  46. oral.rxty.pcoa.tab <- read.csv("Tables/oral_rxty_pcoa_tab.csv", check.names = FALSE)
  47. paired_distances_df.m <- read.csv("Tables/paired_distances_df.csv", check.names = FALSE)
  49. reactotype_colors <- c("#B0E0E6", "#5F9EA0", "#1F78B4")
  50. severity_color <- c(
  51. "Healthy" = "gray",
  52. "Mild Severity" = "cyan",
  53. "Low Severity" = "skyblue",
  54. "Moderate Severity" = "blue",
  55. "High Severity" = "purple"
  56. )
  58. oral.rxty.pcoa.tab$reactotype <- factor(oral.rxty.pcoa.tab$reactotype)
  59. gut.rxty.pcoa.tab$reactotype <- factor(gut.rxty.pcoa.tab$reactotype)
  61. paired_distances_df.m$Severity_group <- factor(
  62. paired_distances_df.m$Severity_group,
  63. levels = c("Healthy", "Mild Severity", "Low Severity", "Moderate Severity", "High Severity")
  64. )
  66. H <- read_excel("Tables/Commensal_MSP_FBA_results.xlsx", sheet = "cleaned_Ex")
  67. low <- read_excel("Tables/tMSP_FBA_results.xlsx", sheet = "cleaned_Ex")
  69. colnames(H)[1] <- "Flux"
  70. colnames(low)[1] <- "Flux"
  72. low[] <- lapply(low, function(x) if (is.factor(x)) as.numeric(as.character(x)) else x)
  73. H[] <- lapply(H, function(x) if (is.factor(x)) as.numeric(as.character(x)) else x)
  74. ```
  76. ------------------------------------------------------------------------
  78. ## Fig S1. Reactotype model fitting results.
  80. ```{r }
  81. plot(lplc_gutall, type="b", xlab="Number of Dirichlet Components",ylab="Model Fit")
  82. plot(lplc_oralall, type="b", xlab="Number of Dirichlet Components",ylab="Model Fit")
  83. ```
  85. ## Figure 1A. Relative proportions of gut reactotypes.
  87. ```{r }
  88. ggplot(gut.rxty.pcoa.tab, aes(x = reactotype, fill = as.factor(Severity_group))) +
  89. geom_bar(position = "fill") +
  90. labs(x = "Severity Group", y = "Count", fill = "Reactotype") +
  91. ggtitle("Relative Proportions Gut Reactotype") +
  92. scale_fill_manual(values = c("gray", "cyan", "skyblue", "blue", "purple")) +
  93. theme_minimal()
  94. ```
  96. ## Figure 1B. Relative proportions of oral reactotypes.
  98. ```{r }
  99. ggplot(oral.rxty.pcoa.tab, aes(x = reactotype, fill = as.factor(Severity_group))) +
  100. geom_bar(position = "fill") +
  101. labs(x = "Severity Group", y = "Count", fill = "Reactotype") +
  102. ggtitle("Relative Proportions Oral Reactotype") +
  103. scale_fill_manual(values = c("gray", "cyan", "skyblue", "blue", "purple")) +
  104. theme_minimal()
  105. ```
  107. ## Figure 1C. MELD between gut reactotypes.
  109. ```{r }
  110. ggplot(gut.rxty.pcoa.tab[-which(gut.rxty.pcoa.tab$Severity_group=="Healthy"),], aes(x = reactotype, y = MELD, fill = reactotype)) +
  111. stat_slab(aes(thickness = stat(pdf*n)),
  112. scale = 0.7) +
  113. stat_dotsinterval(side = "bottom",
  114. scale = 0.7,
  115. slab_size = NA)+
  116. stat_compare_means(comparisons = list(c("1", "2"), c("1", "3"), c("2", "3")), label = "p.format") +
  117. labs(x = "Gut Reactotype", y = "MELD") +
  118. scale_fill_manual(values = reactotype_colors) +
  119. ggtitle("MELD by Reactotype of LC") +
  120. theme_minimal()+
  121. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  122. stat_compare_means(aes(group = reactotype), label.y = 53)
  123. ```
  125. ## Figure 1D. MELD between oral reactotypes.
  127. ```{r }
  128. ggplot(oral.rxty.pcoa.tab[-which(oral.rxty.pcoa.tab$Severity_group=="Healthy"),], aes(x = factor(reactotype), y = MELD, fill = factor(reactotype))) +
  129. stat_slab(aes(thickness = stat(pdf*n)),
  130. scale = 0.7) +
  131. stat_dotsinterval(side = "bottom",
  132. scale = 0.7,
  133. slab_size = NA)+
  134. stat_compare_means(comparisons = list(c("1", "")), label = "p.format") +
  135. labs(x = "Oral Reactotype", y = "MELD") +
  136. scale_fill_manual(values = reactotype_colors[1:2]) +
  137. ggtitle("MELD by Reactotype of LC") +
  138. theme_minimal()+
  139. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  140. stat_compare_means(aes(group = reactotype), label.y = 34)
  141. ```
  143. ## Figure S2C. BMI between gut reactotypes.
  145. ```{r }
  146. ggplot(gut.rxty.pcoa.tab[,], aes(x = reactotype, y = BMI, fill = reactotype)) +
  147. stat_slab(aes(thickness = stat(pdf*n)),
  148. scale = 0.7) +
  149. stat_dotsinterval(side = "bottom",
  150. scale = 0.7,
  151. slab_size = NA)+
  152. labs(x = "Gut Reactotype", y = "BMI") +
  153. scale_fill_manual(values = reactotype_colors) +
  154. ggtitle("BMI by Reactotype of LC") +
  155. theme_minimal()+
  156. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  157. stat_compare_means(aes(group = reactotype))
  158. ```
  160. ## Figure S2D. Age between gut reactotypes.
  162. ```{r }
  163. ggplot(gut.rxty.pcoa.tab[,], aes(x = reactotype, y = Age, fill = reactotype)) +
  164. stat_slab(aes(thickness = stat(pdf*n)),
  165. scale = 0.7) +
  166. stat_compare_means(comparisons = list( c("1", "3")), label = "p.format", hide.ns = TRUE) + #p.signif
  167. stat_dotsinterval(side = "bottom",
  168. scale = 0.7,
  169. slab_size = NA)+
  170. labs(x = "Gut Reactotype", y = "Age") +
  171. scale_fill_manual(values = reactotype_colors) +
  172. ggtitle("Age by Reactotype of LC") +
  173. theme_minimal()+
  174. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  175. stat_compare_means(aes(group = reactotype), label.y = 90)
  177. ```
  179. ## Figure S2E. BMI between oral reactotypes.
  181. ```{r }
  182. ggplot(oral.rxty.pcoa.tab[-which(oral.rxty.pcoa.tab$Severity_group=="Healthy"),], aes(x = factor(reactotype), y = BMI, fill = factor(reactotype))) +
  183. stat_slab(aes(thickness = stat(pdf*n)),
  184. scale = 0.7) +
  185. stat_dotsinterval(side = "bottom",
  186. scale = 0.7,
  187. slab_size = NA)+
  188. # stat_compare_means(comparisons = list(c("1", "2")), label = "p.signif") +
  189. labs(x = "Oral Reactotype", y = "BMI") +
  190. scale_fill_manual(values = reactotype_colors[1:2]) +
  191. ggtitle("BMI by Reactotype of LC") +
  192. theme_minimal()+
  193. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  194. stat_compare_means(aes(group = reactotype), label.y = 55)
  195. ```
  197. ## Figure S2F. Age between oral reactotypes.
  199. ```{r }
  200. ggplot(oral.rxty.pcoa.tab[-which(oral.rxty.pcoa.tab$Severity_group=="Healthy"),], aes(x = factor(reactotype), y = Age, fill = factor(reactotype))) +
  201. stat_slab(aes(thickness = stat(pdf*n)),
  202. scale = 0.7) +
  203. stat_dotsinterval(side = "bottom",
  204. scale = 0.7,
  205. slab_size = NA)+
  206. # stat_compare_means(comparisons = list(c("1", "2")), label = "p.signif") +
  207. labs(x = "Oral Reactotype", y = "Age") +
  208. scale_fill_manual(values = reactotype_colors[1:2]) +
  209. ggtitle("Age by Reactotype of LC") +
  210. theme_minimal()+
  211. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  212. stat_compare_means(aes(group = reactotype), label.y = 76)
  213. ```
  215. ## Figure 1E. Oral-gut metabolic distance (OGMD) by severity groups.
  217. ```{r }
  218. ggplot(paired_distances_df.m, aes(x = Severity_group, y = Paired_Distance, fill = Severity_group)) +
  219. stat_slab(aes(thickness = stat(pdf*n)),
  220. scale = 0.7) +
  221. stat_dotsinterval(side = "bottom",
  222. scale = 0.7,
  223. slab_size = NA)+
  224. labs(x = "Severity", y = "Oral-gut Rxbiome Bray distance", fill = "Severity group")+
  225. ggtitle("Boxplot of oral-gut distance by severity")+
  226. scale_fill_manual(values = severity_color, labels = c("Healthy","Mild Severity (3-8)", "Low Severity (9-14)", "Moderate Severity (15-24)", "High Severity (≥25)")) +
  227. theme_minimal()+
  228. stat_summary(fun.y = mean, geom = "point", shape = 23, size=4)+
  229. stat_compare_means(aes(group = Severity_group), label.y = 0.95)+
  230. theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  231. stat_compare_means(comparisons = list(
  232. # c("Healthy","Mild Severity"),
  233. # c("Healthy", "Low Severity"),
  234. # c("Healthy", "Moderate Severity"),
  235. c("Moderate Severity", "High Severity"),
  236. c("Healthy", "High Severity")
  237. ), label = "p.format")
  238. ```
  240. ## Figure 1F. OGMD and MELD (cirrhosis severity) associations.
  242. ```{r }
  243. ggplot(paired_distances_df.m[-which(is.na(paired_distances_df.m$MELD)),],
  244. aes(x = MELD, y = Paired_Distance)) +
  245. geom_point(aes(color = distance_group), size = 4) +
  246. geom_smooth(method = "lm", se = TRUE, color = "black") +
  247. labs(x = "MELD", y = "Oral-gut Reactobiome Distance") +
  248. # ggtitle("Linear Regression between MELD and OGD") + # Add title
  249. scale_color_manual(values = c("high_distance" = "#809FFF", "low_distance" = "#00008B")) + # Specify colors
  250. stat_poly_eq(aes(label = paste(..rr.label.., ..p.value.label.., sep = "~~~")),
  251. label.x = 0.1, label.y = 0.1, # Adjust these values for the p-value label position based on data coordinates
  252. formula = y ~ x, parse = TRUE, size = 6) + # Increase font size for the equation label
  253. theme(
  254. plot.title = element_text(size = 15), # Increase title font size
  255. axis.title.x = element_text(size = 15), # Increase x-axis label font size
  256. axis.title.y = element_text(size = 15), # Increase y-axis label font size
  257. axis.text = element_text(size = 15), # Increase axis text size
  258. legend.text = element_text(size = 13), # Increase legend text size
  259. # legend.title = element_text(size = 13), # Increase legend title font size
  260. legend.title = element_blank(), # Remove legend title
  261. legend.position = "top", # Move legend to the top
  262. # legend.position = c(0.4, 0.2), # Set legend position inside the plot (x = 0.8, y = 0.8)
  263. legend.direction = "horizontal", # Arrange legend horizontally
  264. panel.background = element_rect(fill = "white", color = NA), # Set panel background to white
  265. plot.background = element_rect(fill = "white", color = NA), # Set overall plot background to white
  266. panel.grid.major = element_blank(), # Remove major grid lines
  267. panel.grid.minor = element_blank() # Remove minor grid lines
  268. )
  270. ```
  272. ## Figure 1I. Wilcoxon test for predicted metabolic producion.
  274. ## Statistical results & Volcano plot
  276. ```{r }
  277. merged_data <- merge(low, H, by = "Flux", all = TRUE)
  278. merged_data[is.na(merged_data)] <- 0
  279. wilcoxon_test_results <- apply(merged_data[, -1], 1, function(row) {
  280. low_values <- as.numeric(row[1:(ncol(low) - 1)])
  281. H_values <- as.numeric(row[(ncol(low)):(ncol(merged_data) - 1)])
  283. wilcoxon_test <- wilcox.test(low_values, H_values)
  285. low_mean <- mean(low_values)
  286. H_mean <- mean(H_values)
  288. log2fc_low_H <- log2((low_mean + 0.000001) / (H_mean + 0.000001))
  290. return(c(low_mean, H_mean, log2fc_low_H, wilcoxon_test$p.value))
  291. })
  293. # data frame
  294. wilcoxon_test_results_df <- data.frame(
  295. Metabolite = merged_data$Flux,
  296. Low_Mean = wilcoxon_test_results[1, ],
  297. H_Mean = wilcoxon_test_results[2, ],
  298. log2FC_Low_H = wilcoxon_test_results[3, ],
  299. p_value = wilcoxon_test_results[4, ]
  300. )
  302. # show stats results
  303. print(wilcoxon_test_results_df)
  306. library(readxl)
  307. FBA_16MSP_stat_for_volcano <- read_excel("Tables/FBA_16MSP_stat_for_volcano.xlsx")
  309. data = FBA_16MSP_stat_for_volcano
  310. data$log10_p = -log(data$p_value,10)
  312. # volcano
  313. ggplot(data = data) +
  314. geom_point(aes(x = log2FoldChange, y = -log10(p_value),
  315. color = log2FoldChange,
  316. size = -log10(p_value))) +
  317. geom_point(data = data %>%
  318. tidyr::drop_na() %>%
  319. dplyr::filter(change != "Normal") %>%
  320. dplyr::arrange(desc(-log10(p_value))) %>%
  321. dplyr::slice(1:20),
  322. aes(x = log2FoldChange, y = -log10(p_value),
  323. size = -log10(p_value)),
  324. shape = 21, show.legend = FALSE, color = "#000000") +
  325. geom_text_repel(data = data %>%
  326. tidyr::drop_na() %>%
  327. dplyr::filter(change != "Normal") %>%
  328. dplyr::arrange(desc(-log10(p_value))) %>%
  329. dplyr::slice(1:15) %>%
  330. dplyr::filter(change == "Up"),
  331. aes(x = log2FoldChange, y = -log10(p_value), label = SYMBOL),
  332. box.padding = 0.5,
  333. nudge_x = 0.5,
  334. nudge_y = 0.2,
  335. segment.curvature = -0.1,
  336. segment.ncp = 3,
  337. direction = "y",
  338. hjust = "left") +
  339. scale_color_gradientn(
  340. colours = c("#3288bd", "#66c2a5", "#ffffbf", "#f46d43", "#9e0142"),
  341. values = scales::rescale(c(-20, -10, 0, 10, 20), to = c(0, 1))
  342. ) +
  343. geom_vline(xintercept = c(-log2(1.5), log2(1.5)), linetype = 2) +
  344. geom_hline(yintercept = -log10(0.05), linetype = 4) +
  345. xlim(c(-20,20)) +
  346. ylim(c(-1, 10)) +
  347. theme_bw() +
  348. theme(panel.grid = element_blank(),
  349. legend.background = element_roundrect(color = "#808080", linetype = 1),
  350. axis.text = element_text(size = 13, color = "#000000"),
  351. axis.title = element_text(size = 15),
  352. plot.title = element_text(hjust = 0.5),
  353. plot.subtitle = element_text(hjust = 0.5)) +
  354. annotate(geom = "text", x = 15, y = 2, label = "p = 0.05", size = 5) +
  355. coord_cartesian(clip = "off") +
  356. annotation_custom(
  357. grob = grid::segmentsGrob(
  358. y0 = unit(-10, "pt"),
  359. y1 = unit(-10, "pt"),
  360. arrow = arrow(angle = 45, length = unit(.2, "cm"), ends = "first"),
  361. gp = grid::gpar(lwd = 3, col = "#74add1")
  362. ),
  363. xmin = -20,
  364. xmax = -1,
  365. ymin = 10,
  366. ymax = 10
  367. ) +
  368. annotation_custom(
  369. grob = grid::textGrob(
  370. label = "Down",
  371. gp = grid::gpar(col = "#74add1")
  372. ),
  373. xmin = -20,
  374. xmax = -1,
  375. ymin = 10,
  376. ymax = 10
  377. ) +
  378. annotation_custom(
  379. grob = grid::segmentsGrob(
  380. y0 = unit(-10, "pt"),
  381. y1 = unit(-10, "pt"),
  382. arrow = arrow(angle = 45, length = unit(.2, "cm"), ends = "last"),
  383. gp = grid::gpar(lwd = 3, col = "#d73027")
  384. ),
  385. xmin = 20,
  386. xmax = 1,
  387. ymin = 10,
  388. ymax = 10
  389. ) +
  390. annotation_custom(
  391. grob = grid::textGrob(
  392. label = "Up",
  393. gp = grid::gpar(col = "#d73027")
  394. ),
  395. xmin = 20,
  396. xmax = 1,
  397. ymin = 10,
  398. ymax = 10
  399. )
  400. ```
  402. ## Figure 1J. FBA & FVA predicted NH3 flux.
  404. ```{r }
  405. FBA_FVA_long <- read.csv("Tables/FBA_FVA_long.csv", check.names = FALSE)
  406. FBA_FVA_long_H <- read.csv("Tables/FBA_FVA_long_H.csv", check.names = FALSE)
  408. ggplot(FBA_FVA_long, aes(x = species, y = variable, fill = value)) +
  409. geom_tile(width = 0.9, height = 0.9) +
  410. labs(x = element_blank(), y = element_blank(), fill = "Flux Value") +
  411. theme_classic() +
  412. theme(legend.position = "bottom",
  413. axis.text.x = element_text(angle = 0, hjust = 1)) +
  414. geom_text(data = subset(FBA_FVA_long, value == 0), aes(label = "X")) +
  415. scale_fill_gradient(low = "white", high = "blue", na.value = "white", limits = c(0, 5))+
  416. coord_flip()
  419. ggplot(FBA_FVA_long_H, aes(x = species, y = variable, fill = value)) +
  420. geom_tile(width = 0.9, height = 0.9) +
  421. labs(x = element_blank(), y = element_blank(), fill = "Flux Value") +
  422. theme_classic() +
  423. theme(legend.position = "bottom",
  424. axis.text.x = element_text(angle = 90, hjust = 1)) +
  425. geom_text(data = subset(FBA_FVA_long_H, value == 0), aes(label = "X")) +
  426. scale_fill_gradient(low = "white", high = "blue", na.value = "white")
  427. ```
  429. ## Figure 1K. Predicted NH3 production accross different diets.
  431. ```{r }
  432. Predicted_NH3_tMSPs <- read.csv("Tables/Predicted_NH3_tMSPs.csv", check.names = FALSE)
  434. set2_colors <- RColorBrewer::brewer.pal(6, "Set2")
  436. custom_colors <- c(
  437. UK_avg_FBA = set2_colors[1],
  438. UK_avg_FVA = set2_colors[6],
  439. HFD_P = set2_colors[2],
  440. HFD_O = set2_colors[3],
  441. HPD_P = set2_colors[4],
  442. HPD_O = set2_colors[5]
  443. )
  445. ggplot(Predicted_NH3_tMSPs, aes(x = reorder(species, Flux), y = Flux)) +
  446. geom_boxplot(
  447. fill = "grey",
  448. color = "black",
  449. width = 0.5,
  450. outlier.shape = 21,
  451. outlier.alpha = 0.5,
  452. linewidth = 0.4
  453. ) +
  454. geom_jitter(
  455. aes(color = Condition),
  456. size = 2,
  457. width = 0.15,
  458. alpha = 0.8
  459. ) +
  460. coord_flip() +
  461. theme_bw(base_size = 12) +
  462. labs(
  463. x = "Species",
  464. y = "Estimated ammonia production"
  465. ) +
  466. scale_color_manual(
  467. values = custom_colors,
  468. name = "Diet"
  469. ) +
  470. theme(
  471. panel.grid.major.x = element_blank(),
  472. panel.grid.minor = element_blank(),
  473. axis.text.y = element_markdown(face = "italic"),
  474. axis.text = element_text(color = "black"),
  475. legend.title = element_text(size = 11, face = "bold"),
  476. legend.text = element_text(size = 10),
  477. panel.border = element_rect(color = "black", size = 0.4)
  478. )
  479. ```
  481. ## Figure 1L. Predicted acetate production across different diets.
  483. ```{r }
  484. Predicted_acetate_tMSPs <- read.csv("Tables/Predicted_acetate_tMSPs.csv", check.names = FALSE)
  486. ggplot(Predicted_acetate_tMSPs, aes(x = reorder(species, Flux), y = Flux)) +
  487. geom_boxplot(
  488. fill = "grey",
  489. color = "black",
  490. width = 0.5,
  491. outlier.shape = 21,
  492. outlier.alpha = 0.5,
  493. linewidth = 0.4
  494. ) +
  495. geom_jitter(
  496. aes(color = Condition),
  497. size = 2,
  498. width = 0.15,
  499. alpha = 0.8
  500. ) +
  501. coord_flip() +
  502. theme_bw(base_size = 12) +
  503. labs(
  504. x = "Species",
  505. y = "Estimated acetate production"
  506. ) +
  507. scale_color_manual(
  508. values = custom_colors,
  509. name = "Diet"
  510. ) +
  511. theme(
  512. panel.grid.major.x = element_blank(),
  513. panel.grid.minor = element_blank(),
  514. axis.text.y = element_markdown(face = "italic"),
  515. axis.text = element_text(color = "black"),
  516. legend.title = element_text(size = 11, face = "bold"),
  517. legend.text = element_text(size = 10),
  518. panel.border = element_rect(color = "black", size = 0.4)
  519. )
  521. ```
  523. ## Figure 1M. Correlation analysis of relative abundance with disease severity.
  525. ```{r }
  526. # stat
  527. gut_16MSP_MELD <- read.csv("Tables/gut_16MSP_MELD.csv", check.names = FALSE)
  529. MSP_16 = c("msp_0005", "msp_0166", "msp_0313", "msp_0380", "msp_0570","msp_0573",
  530. "msp_0627", "msp_0881", "msp_0884","msp_1219", "msp_1782", "msp_1786",
  531. "msp_1787","msp_1788", "msp_1793", "msp_1799")
  532. cor_gut16_MELD_results <- data.frame(msp = MSP_16, correlation = NA, p_value = NA)
  534. for (i in seq_along(MSP_16)) {
  535. msp_col <- MSP_16[i]
  537. test_result <- cor.test(gut_16MSP_MELD[[msp_col]], gut_16MSP_MELD$MELD, method = "spearman")
  539. cor_gut16_MELD_results$correlation[i] <- test_result$estimate
  540. cor_gut16_MELD_results$p_value[i] <- test_result$p.value
  541. }
  543. cor_gut16_MELD_results$FDR <- p.adjust(
  544. cor_gut16_MELD_results$p_value,
  545. method = "BH"
  546. )
  548. # View stats results
  549. print(cor_gut16_MELD_results)
  551. # Corr plot
  552. cor_gut16_MELD_results$significance <- ifelse(cor_gut16_MELD_results$FDR <= 0.001, "***",
  553. ifelse(cor_gut16_MELD_results$FDR <= 0.01, "**",
  554. ifelse(cor_gut16_MELD_results$FDR <= 0.05, "*",
  555. ifelse(cor_gut16_MELD_results$FDR <= 0.1, "·", ""))))
  556. cor_gut16_MELD_results$msp <- factor(
  557. cor_gut16_MELD_results$msp,
  558. levels = cor_gut16_MELD_results$msp[order(cor_gut16_MELD_results$correlation)]
  559. )
  561. ggplot(cor_gut16_MELD_results, aes(x = "MELD", y = msp)) +
  562. geom_tile(aes(fill = correlation), color = "white") +
  563. scale_fill_gradient2(
  564. low = "dodgerblue",
  565. mid = "white",
  566. high = "firebrick",
  567. midpoint = 0,
  568. limits = c(-0.4, 0.4),
  569. name = "Spearman\nrho"
  570. ) +
  571. geom_text(aes(label = significance), color = "black", size = 5) +
  572. labs(
  573. x = "",
  574. y = "Gut MSPs"
  575. ) +
  576. theme_minimal() +
  577. theme(
  578. axis.text.x = element_text(angle = 0, hjust = 0.5),
  579. axis.text.y = element_text(size = 8),
  580. panel.grid = element_blank()
  581. )
  583. ```
  585. ## Figure 1N. Co-abundance analysis of 16 tMSPs.
  587. Code for making the table suitable for Cytoscape visualization.
  589. ```{r }
  590. abundance_data_t <- read.csv("Tables/abundance_data_t.csv", row.names = 1)
  591. abundance_data_t <- as.data.frame(abundance_data_t)
  592. abundance_data_t[] <- lapply(abundance_data_t, function(x) as.numeric(as.character(x)))
  593. feature_names <- colnames(abundance_data_t)
  595. ## Spearman stat
  597. edge_list <- data.frame(
  598. source = character(),
  599. target = character(),
  600. correlation = numeric(),
  601. p_value = numeric(),
  602. stringsAsFactors = FALSE
  603. )
  605. for (i in 1:(ncol(abundance_data_t) - 1)) {
  606. for (j in (i + 1):ncol(abundance_data_t)) {
  608. feature1 <- colnames(abundance_data_t)[i]
  609. feature2 <- colnames(abundance_data_t)[j]
  611. x <- abundance_data_t[[i]]
  612. y <- abundance_data_t[[j]]
  614. complete_idx <- complete.cases(x, y)
  615. x_use <- x[complete_idx]
  616. y_use <- y[complete_idx]
  618. if (length(x_use) < 3) next
  620. if (sd(x_use) == 0 || sd(y_use) == 0) next
  622. cor_test_spearman <- suppressWarnings(
  623. cor.test(x_use, y_use, method = "spearman", exact = FALSE)
  624. )
  626. edge_list <- rbind(
  627. edge_list,
  628. data.frame(
  629. source = feature1,
  630. target = feature2,
  631. correlation = unname(cor_test_spearman$estimate),
  632. p_value = cor_test_spearman$p.value,
  633. stringsAsFactors = FALSE
  634. )
  635. )
  636. }
  637. }
  639. # FDR
  640. edge_list$FDR <- p.adjust(edge_list$p_value, method = "BH")
  642. # Table for Cytoscape network visualization
  643. edge_list <- edge_list %>%
  644. mutate(
  645. abs_correlation = abs(correlation),
  646. correlation_sign = ifelse(correlation > 0, "positive", "negative"),
  647. significance = case_when(
  648. FDR <= 0.001 ~ "***",
  649. FDR <= 0.01 ~ "**",
  650. FDR <= 0.05 ~ "*",
  651. FDR <= 0.1 ~ "·",
  652. TRUE ~ ""
  653. )
  654. )
  656. cytoscape_edges_all <- edge_list %>%
  657. arrange(FDR, desc(abs_correlation))
  659. cytoscape_edges_filtered <- cytoscape_edges_all %>%
  660. filter(FDR <= 0.05)
  662. head(cytoscape_edges_filtered)
  663. ```

Figure_1.Rmd at commit 0749f7e, no license · at the source

Overview

Authors: Yi Jin1, Frederick Clasen1, Fernando Garcia‐Guevara1, Sania Arif2, Robert Schierwagen2, Gholamreza Bidkhori1, Michael Praktiknjo2, Maximilian J Brol2, Frank E Uschner2, Florence A Castelli3, Nicolas Pons4, Benoit Quinquis4, Nathalie Galleron4, Kevin Da Silva4, Christophe Junot3, Debbie L Shawcross5, David L Moyes1, Rajiv Jalan6,7, S Dusko Ehrlich8, Vishal C Patel5, Jonel Trebicka2,7,9, Saeed Shoaie1,10
ORCID iDs: Yi Jin, Debbie L Shawcross, David L Moyes, Saeed Shoaie
  1. Centre for Host‐Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
  2. Department of Internal Medicine B, University of Münster, Münster, Germany
  3. CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), MetaboHUB‐IDF, Université Paris‐Saclay, Gif‐sur‐Yvette, France
  4. Université Paris‐Saclay, INRAE, MGP, Jouy‐en‐Josas, France
  5. Roger Williams Institute of Liver Studies, School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
  6. Liver Failure Group, UCL Institute for Liver and Digestive Health, London, UK
  7. European Foundation for the Study of Chronic Liver Failure, EF CLIF, Barcelona, Spain
  8. Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
  9. Department of Medical Gastroenterology and Hepatology, University of Southern Denmark, Odense, Denmark
  10. Quantitative Systems Biology, Faculty of Medicine, Biruni University, Istanbul, Turkey
Institutions: King's College London (United Kingdom); University of Münster (Germany); University Hospital Münster (Germany); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Université Paris-Saclay (France); Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (France); MetaboHUB (France); Médicaments et Technologies pour la Santé (France); European Foundation for the Study of Chronic Liver Failure (Spain); University College London (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); University of Southern Denmark (Denmark); Biruni University (Türkiye)
Journal: iMeta, volume 5, issue 3, article e70131
Dates: received 31 March 2026; accepted 24 April 2026; published online 7 May 2026
Type: Letter · Language: English
License: CC BY
Identifiers: DOI 10.1002/imt2.70131 · PMID 42491347 · PMCID PMC13377410 · OpenAlex W7160500719
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Statistics, Connectivity
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Foundation for Liver Research (268211/1134579); China Scholarship Council; Engineering and Physical Sciences Research Council (EP/S001301/1)
Citations: not cited yet (Europe PMC); 20 references in the paper

Abstract

Liver cirrhosis is associated with profound disruption of host-microbiome metabolic interactions. Using paired oral and fecal metagenomics combined with genome-scale metabolic modeling, we investigated how microbial translocation along the oral-gut axis influences microbial metabolism at different cirrhosis severities. Reactobiome-based functional profiling revealed progressive metabolic convergence between oral and gut microbiomes, quantified by a decrease in oral-gut metabolic distance. Translocation-associated microbial species enriched in patients with cirrhosis were predicted to have elevated capacities for ammonia and acetate production. Microbial-community and host metabolic modeling further suggested that these microbial metabolic shifts may influence host energy metabolism and redox balance across the liver, brain, and skeletal muscle. Together, these findings suggest a potential acetate-ammonia metabolic axis linking oral-gut microbial translocation with systemic metabolic stress in advanced cirrhosis.

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 3 matches between paragraphs and lines of code.

sysbiomelab/Oral-gut-liver

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0749f7e0b4d0e8bba5b71e74d0b16eeb8733a8bf, 12 May 2026
Languages: MATLAB (3), R (2)
Size: 39 files, 5 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), circlize (1 file), ComplexHeatmap (1 file), ggplot2 (1 file), ggpubr (1 file), Statistics and Machine Learning Toolbox (1 file), pheatmap (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files
At the source: github.com/sysbiomelab/Oral-gut-liver

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

Tracing map

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  • 5 scripts, each with its path and the digest of its content;
  • 3 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

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

Data availability statement

The shotgun metagenomic raw data used in this study are publicly available from the European Nucleotide Archive (ENA) under the project accessions PRJEB52891 (GLA cohort, https://www.ebi.ac.uk/ena/browser/view/PRJEB52891) and PRJEB38481 (RIFSYS cohort, https://www.ebi.ac.uk/ena/browser/view/PRJEB38481), and from https://www.microbiomeatlas.org under the project accession PRJEB38483 (healthy cohort, https://www.ebi.ac.uk/ena/browser/view/PRJEB38483). Metagenomic and metabolomic data for the TIPS cohort from the NEPTUN study (NCT03628807 (https://clinicaltrials.gov/ct2/show/NCT03628807), https://clinicaltrials.gov/study/NCT03628807) can be available upon request via the European Association for the Study of the Liver (EASL). Genome‐scale metabolic models corresponding to the MSPs can be obtained from the Microbiome Atlas website (https://www.microbiomeatlas.org). The full summary statistics to support the findings of this study are included within the supplementary information files. The data used for the figures and scripts used in this study can be found on GitHub: https://github.com/sysbiomelab/Oral-gut-liver. Supplementary materials (methods, figures, tables, graphical abstract, slides, videos, Chinese translated version, and updated materials) may be found in the online DOI or iMeta Science http://www.imeta.science/. The data that support the findings of this study are available in the supplementary material of this article.

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 2, 28 September 2026

  • Funding: added Foundation for Liver Research: 268211/1134579; China Scholarship Council; Engineering and Physical Sciences Research Council: EP/S001301/1

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 20 references.

Cite

This paper

Jin, Y., Clasen, F., Garcia‐Guevara, F., Arif, S., Schierwagen, R., Bidkhori, G., Praktiknjo, M., Brol, M. J., Uschner, F. E., Castelli, F. A., Pons, N., Quinquis, B., Galleron, N., Da Silva, K., Junot, C., Shawcross, D. L., Moyes, D. L., Jalan, R., Ehrlich, S. D., . . . Shoaie, S. (2026). Integrative host-microbiome modeling uncovers the implication of oral-gut translocation in advanced cirrhosis. iMeta, 5(3), e70131. https://doi.org/10.1002/imt2.70131

BibTeX

@article{jin2026integrative,
author = {Jin, Yi and Clasen, Frederick and Garcia‐Guevara, Fernando and Arif, Sania and Schierwagen, Robert and Bidkhori, Gholamreza and Praktiknjo, Michael and Brol, Maximilian J and Uschner, Frank E and Castelli, Florence A and Pons, Nicolas and Quinquis, Benoit and Galleron, Nathalie and Da Silva, Kevin and Junot, Christophe and Shawcross, Debbie L and Moyes, David L and Jalan, Rajiv and Ehrlich, S Dusko and Patel, Vishal C and Trebicka, Jonel and Shoaie, Saeed},
title = {{Integrative host-microbiome modeling uncovers the implication of oral-gut translocation in advanced cirrhosis}},
journal = {iMeta},
year = {2026},
month = may,
volume = {5},
number = {3},
pages = {e70131},
publisher = {Wiley},
issn = {2770-5986},
doi = {10.1002/imt2.70131},
url = {https://doi.org/10.1002/imt2.70131},
pmid = {42491347},
pmcid = {PMC13377410}
}

RIS

TY - JOUR
AU - Jin, Yi
AU - Clasen, Frederick
AU - Garcia‐Guevara, Fernando
AU - Arif, Sania
AU - Schierwagen, Robert
AU - Bidkhori, Gholamreza
AU - Praktiknjo, Michael
AU - Brol, Maximilian J
AU - Uschner, Frank E
AU - Castelli, Florence A
AU - Pons, Nicolas
AU - Quinquis, Benoit
AU - Galleron, Nathalie
AU - Da Silva, Kevin
AU - Junot, Christophe
AU - Shawcross, Debbie L
AU - Moyes, David L
AU - Jalan, Rajiv
AU - Ehrlich, S Dusko
AU - Patel, Vishal C
AU - Trebicka, Jonel
AU - Shoaie, Saeed
TI - Integrative host-microbiome modeling uncovers the implication of oral-gut translocation in advanced cirrhosis
T2 - iMeta
J2 - Imeta
PY - 2026
DA - 2026/05/07
VL - 5
IS - 3
SP - e70131
SN - 2770-5986
PB - Wiley
DO - 10.1002/imt2.70131
UR - https://doi.org/10.1002/imt2.70131
LA - en
ER -

CSL-JSON

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"id": "10.1002/imt2.70131",
"type": "article-journal",
"title": "Integrative host-microbiome modeling uncovers the implication of oral-gut translocation in advanced cirrhosis",
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"author": [
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{
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"family": "Schierwagen",
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{
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{
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"given": "Christophe"
},
{
"family": "Shawcross",
"given": "Debbie L"
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{
"family": "Moyes",
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{
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"given": "Rajiv"
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