Table 1.
Clinical characteristics of liver transplant recipients.
Fig 1.
Experimental design for stratification of tacrolimus-treated liver transplant patients and multi-omics sample analyses.
Fig 1 illustrates the experimental design where liver transplant patients taking tacrolimus were divided into ST and LT groups based on medication duration, and into LC, MC, and HC groups (N = 3) based on the drug concentration of tacrolimus in whole blood, as well as into LCD and HCD groups based on the drug concentration-to-dose ratio. Fecal samples were collected for metagenomic testing, and whole blood samples were analyzed for metabolomics.
Fig 2.
Relative abundances of dominant bacteria and viruses in tacrolimus-treated LT patient subgroups.
Panel A, Panel D, Panel G show the bar graphs of the relative abundance of dominant bacteria at the genus and species levels and dominant viruses at the genus level for the ST and LT groups. Panel B, Panel E, Panel H show the bar graphs of the relative abundance of dominant bacteria at the genus and species levels and dominant viruses at the genus level for the LC, MC, and HC (N = 3) groups. Panel C, Panel F, Panel I show the bar graphs of the relative abundance of dominant bacteria at the genus and species levels and dominant viruses at the genus level for the LCD and HCD groups.
Fig 3.
Gut bacterial and viral α-diversity comparisons across tacrolimus-treated liver transplant patient subgroups.
Panel A presents a comparison of the gut bacterial α-diversity indices between the ST and LT groups across four different indices: the Chao1 index, ACE index, Shannon diversity index, and Simpson index. Each subplot features box plots that show the distribution range, median, and quartiles of the data for each group, with p-values less than 0.05 on each box indicating significant differences between groups. Panel B shows the comparison of gut viral α-diversity indices between the ST and LT groups across the same four indices. Panel C-F respectively display the Chao1 index and Shannon diversity index for bacteria and viruses in the LC, MC, and HC (N = 3) groups, as well as the LCD and HCD groups. Note: The “a” labels in the figure are formatting artifacts and can be ignored.
Fig 4.
PCoA Plots and PERMANOVA (Adonis) analysis of gut microbiota dissimilarities in stratified patient cohorts.
Panel A-C present the PCoA analysis for the different groups, reflecting the differences in the data on a two-dimensional coordinate plot, with the axes representing the two principal components that maximize the variance. Samples with more similar compositions are closer together on the PCoA plot. The results of the PERMANOVA (Adonis) analysis (R² and p-values) are shown in the figure.
Fig 5.
Identification and analysis of key biomarkers, functional traits, and their abundance changes across patient subgroups.
Panel A, C are combination plots of biomarker abundance changes between groups as determined by the wilcox rank-sum test. The left bar graphs show the average abundance of identified key species in the study groups, with the X-axis representing the average proportion and the Y-axis listing different bacterial genera. Red squares represent Group ST, and blue squares represent Group LT. The right scatter plots show the abundance changes of key species between groups, with the X-axis displaying the difference in average proportions. The numeric value on the far right is the P-value obtained from the Wilcox rank-sum test between species groups. Panel B shows the key species in different blood drug concentration groups, with the left dot plot displaying the identified Biomarkers and their importance to the grouping, retaining results with importance higher than 1.5. The right heatmap uses a color gradient to show the abundance of Biomarkers in the study samples, with redder colors indicating higher abundance, and gray representing the absence of the Biomarker in the sample. Panel D, F list multiple features on the left, related to the duration of medication. Each feature has different red shaded areas in each group, indicating the relative importance of the feature in these two classifiers. The darker the color, the higher the importance of the feature in the classifier. Panel E is a combination plot of key functional abundance changes between groups as determined by the Wilcox rank-sum test. The left chart shows the average proportion for each process, with red representing Group ST and blue representing Group LT. The right chart shows the average difference and 95% confidence intervals for these processes between the two groups.
Fig 6.
Profiling of metabolite categories and differential metabolite analysis (PCA, Volcano Plots, Radar Plots) in tacrolimus-treated cohorts.
Panel A shows each color representing a metabolite classification, with unknown classifications represented as Other. The percentage indicates the proportion of metabolites belonging to this type out of all identified metabolites. Panel B-D are PCA score plots. After dimensionality reduction analysis, samples have relative coordinate points on the principal components PC1 and PC2. The distance between the coordinate points represents the degree of clustering and dispersion among samples; a closer distance indicates higher similarity between samples, while a greater distance indicates greater differences. PCA analysis can be used to observe the trend of separation between groups in the experimental model, as well as the presence of any outliers, and reflects the variability between and within groups from the original data. The confidence ellipses represent the “true” samples of the group, which are distributed within this area at a 95% confidence level; samples outside this area can be considered as potential outliers. Panel E, Panel F are volcano plots of differential metabolites for different medication duration and different drug concentration-to-dose ratio groups, respectively. These volcano plots use consistent screening thresholds: fold-change cutoff of |log₂(FC)| ≥ 1 (actual fold change ≥ 2 or ≤ 0.5) to distinguish biologically significant differences from experimental fluctuations, and independent samples t-test p < 0.05 to control the false positive rate. OPLS-DA model VIP ≥ 1 is additionally used as an auxiliary criterion to enhance screening accuracy. The color of the points in the graph represents the category of the metabolites, with red indicating significantly up-regulated metabolites that meet the threshold, blue indicating significantly down-regulated metabolites that meet the threshold, gray indicating metabolites that do not meet the fold change screening threshold, and tan indicating metabolites that meet the fold change threshold but do not meet the P-value threshold. Panel F, Panel G are radar plots of differential metabolites for different medication duration and different drug concentration-to-dose ratio groups, respectively. For each group comparison, we calculate the corresponding ratio for the quantitative values of differential metabolites and apply a logarithmic transformation with base 2, selecting the top 10 differential metabolites with significant changes (up and down regulation combined) for display. The grid lines correspond to the log2FC. The green colored area is formed by the lines connecting the dots (metabolites).
Fig 7.
Spearman correlation analysis of clinical parameters, top 30 bacterial genera, and key differential metabolites.
Panel A and Panel B show heatmaps of clinical indicators and the top 30 abundant genera, alongside the Spearman correlation coefficient for top 30 differentially accumulated metabolites (ST vs LT). The color scale shows the correlation coefficients, with red representing positive correlations and blue representing negative ones. Asterisks indicate statistical significance (* P < 0.05, ** P < 0.01).
Fig 8.
Heatmaps showing Spearman correlations between top 30 abundant genera and top 30 differentially accumulated metabolites.
Heatmaps illustrate Spearman correlations between the top 30 abundant genera and the top 30 differentially accumulated metabolites (ST vs. LT groups). Red and blue colors on the scale represent positive and negative correlations, respectively, and asterisks indicate statistical significance (* P < 0.05, ** P < 0.01, *** P < 0.001).