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. 2016 Sep;186(3):219-34.
doi: 10.1667/RR14306.1. Epub 2016 Aug 11.

An Integrated Multi-Omic Approach to Assess Radiation Injury on the Host-Microbiome Axis (VSports app下载)

Maryam Goudarzi  1 , Tytus D Mak  2 , Jonathan P Jacobs  3 , Bo-Hyun Moon  1 , Steven J Strawn  1 , Jonathan Braun  3 , David J Brenner  4 , Albert J Fornace Jr  1   5 , Heng-Hong Li  1
Affiliations

An Integrated Multi-Omic Approach to Assess Radiation Injury on the Host-Microbiome Axis

V体育官网入口 - Maryam Goudarzi et al. Radiat Res. 2016 Sep.

Abstract

Medical responders to radiological and nuclear disasters currently lack sufficient high-throughput and minimally invasive biodosimetry tools to assess exposure and injury in the affected populations. For this reason, we have focused on developing robust radiation exposure biomarkers in easily accessible biofluids such as urine, serum and feces. While we have previously reported on urine and serum biomarkers, here we assessed perturbations in the fecal metabolome resulting from exposure to external X radiation in vivo. The gastrointestinal (GI) system is of particular importance in radiation biodosimetry due to its constant cell renewal and sensitivity to radiation-induced injury. While the clinical GI symptoms such as pain, bloating, nausea, vomiting and diarrhea are manifested after radiation exposure, no reliable bioindicator has been identified for radiation-induced gastrointestinal injuries. To this end, we focused on determining a fecal metabolomic signature in X-ray irradiated mice. There is overwhelming evidence that the gut microbiota play an essential role in gut homeostasis and overall health VSports手机版. Because the fecal metabolome is tightly correlated with the composition and diversity of the microorganism in the gut, we also performed fecal 16S rRNA sequencing analysis to determine the changes in the microbial composition postirradiation. We used in-house bioinformatics tools to integrate the 16S rRNA sequencing and metabolomic data, and to elucidate the gut integrated ecosystem and its deviations from a stable host-microbiome state that result from irradiation. The 16S rRNA sequencing results indicated that radiation caused remarkable alterations of the microbiome in feces at the family level. Increased abundance of common members of Lactobacillaceae and Staphylococcaceae families, and decreased abundances of Lachnospiraceae, Ruminococcaceae and Clostridiaceae families were found after 5 and 12 Gy irradiation. The metabolomic data revealed statistically significant changes in the microbial-derived products such as pipecolic acid, glutaconic acid, urobilinogen and homogentisic acid. In addition, significant changes were detected in bile acids such as taurocholic acid and 12-ketodeoxycholic acid. These changes may be associated with the observed shifts in the abundance of intestinal microbes, such as R. gnavus , which can transform bile acids. .

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Figures

Fig. 1
Fig. 1
Perturbations in the fecal metabolomic signature in mice 3 days after irradiation was assessed via MetaboLyzer. Here we show these perturbations after 5 Gy irradiation with 12 Gy irradiation showing similar changes (Supplementary Fig. S2; http://dx.doi.org/10.1667/RR14306.1.S3). Panel A shows a volcano plot, where the statistically significant (Kolmogorov-Smirnov test, FDR < 0.10) spectral features are shown in red. The x-axis of the volcano plot, log2 fold change, highlights the direction of the shift in the abundance of the spectral features. On this axis the positive scale represents an increase and the negative scale represents a decrease in the abundance of spectral features at day 3 after 5 Gy irradiation. The y-axis of the volcano plot, −log P value, specifies the significance of the change, decrease or increase, in the abundance of the spectral features. Panel B shows a principle component analysis (PCA) scores plot and highlights the distinct separation between the overall fecal metabolomic profile of the control mice vs. those irradiated at 5 Gy at day 3. The statistically significant ions in panel A contribute to the separation seen in panel B. Panel C is a heatmap of individual ions with most significant contribution to the separation of the fecal metabolomic signature of mice exposed to 5 Gy external beam irradiation compared to the control mice. The top four-fifth of the heatmap shows spectral features with increasing abundances after 5 Gy irradiation, while those at the bottom one-fifth of the heatmap show a decreasing pattern in their abundances after irradiation. To evaluate the predictive ability of the statistically significant spectral features, an ROC curve (panel D) was constructed utilizing an SVM based classification model coupled with Monte Carlo cross-validation.
Fig. 2
Fig. 2
Differential correlation analysis showed shifts in the coregulation of several key pathways. Panel A: Dissimilarity heatmap generated from the Pearson's correlation coefficients calculated for the control group exhibiting a high degree of coregulation and coordination, as represented by the abundance of dark red hues. Panel B: Dissimilarity heatmap generated from the Pearson's correlation coefficients calculated for mice exposed to 5 Gy (30-day time point) showing a clear loss of correlation compared to panel A, as exhibited by the lighter red hues in the periphery of the top right corner where darker hues existed in the previous heatmap. There is also evidence of increased coregulation as a result of radiation exposure, as exhibited by the newly darkened areas in the bottom left corner when compared to panel A. These differences are emphasized in the differential correlation heatmap in panel C, where orange hues represent a gain of correlation (and thus increased coregulation), while blue hues represent a loss of correlation (i.e. dysregulation). Panel D: The results of “double hit” pathway enrichment analysis on this subset of correlation shifts. The y-axis is the −1*log10 (P value) for each KEGG pathway, as calculated by the uncorrected (blue bar) and FDR corrected (red bar) hypergeometric test. Several pathways are highlighted based on their statistical and biological significance including tyrosine metabolism, tryptophan metabolism, cyanoamino acid metabolism, bile acid secretion and phenylalanine metabolism all undergoing increased coregulatory activity.
Fig. 3
Fig. 3
Panel A: Seven fecal metabolite markers known to be of bacterial origin show statistically significant changes in their abundances after irradiation. Glyceric acid, homogentisic acid, glutaconic acid and pipecolic acid show decreasing abundances after 5 and 12 Gy at day 3. This decrease is dose specific as the mice exposed to 12 Gy show greater decrease in the fecal abundance of these metabolites than those exposed to 5 Gy. Hippuric acid, taurin and urobilinogen show increase in their fecal abundances postirradiation in a dose specific pattern. Because these metabolites are products of the gut microbiota, significant changes in their abundances imply changes in the microbial metabolism and a shift toward gut dysbiosis. Panel B: Multidimensional scaling plot showing the separation of fecal metabolomic profile after 5 Gy irradiation throughout the 30 days. The fecal metabolomic signature of the irradiated mice are well separated from the preirradiation mice (control group). In addition, the metabolomic signatures of day 30 and 14 are closer while that of day 3 is further separated and closer to that of the control group. Panel C: The heatmap of top 50 important variables highlights the time dependence of the metabolomic response to 5 Gy irradiation. The two yellow boxes show individual spectral features with gradual decreasing abundances throughout the 30-day study while the features at the bottom of the heatmap show a rapid drop. Several ions from panel C are shown in panel D. Panel D: these individual microbial markers show time-specific responses to 5 Gy irradiation throughout the 30-day study. Sebacic acid and serotonin showed decreasing levels while 2-ketobutyric acid and hypoxanthine show increasing levels throughout the 30-day study at 5 Gy irradiation.
Fig. 4
Fig. 4
Three bile acids were detected to have statistically significant changing levels postirradiation. Secondary bile acid ketodeoxycholic acid shows decreasing levels postirradiation while the conjugated bile acids, taurocholic acid and sulfocholic acid, show increasing levels at day 3 postirradiation.
Fig. 5
Fig. 5
Microbiome diversity analysis of fecal samples of pre- and postirradiation. (panel A) Alpha diversity measures of fecal microbiome, observed richness, Chao1 measure and Shannon index are shown. In this plot each closed circle represents an individual mouse. The “0 day” denotes preirradiation (panel B). Proportion of OTUs present in mouse feces classified at the phylum level.
Fig. 6
Fig. 6
Panel A: The abundance of Lachnospiraceae (Ruminococcus) detected in the experimental groups. Panel B: The box and whiskers plot of the abundance of the species R. gnavus observed at day 3 post irrradiation (5 and 12 Gy). The data for individual samples are shown in Supplementary Fig. S1 (http://dx.doi.org/10.1667/RR14306.1.S3).
Fig. 7
Fig. 7
Inter-omic network between metabolites and microbes. The network was created from correlations between 20 validated metabolites and OTUs with differential abundance after irradiation that were identified at the family to species level. These correlations were based on the residuals of fitted models to account for radiation status. The metabolites are in yellow and the taxa in blue, with red lines showing negative correlation and gray showing positive correlation. Each line represents a correlation between a single OTU within the taxa and a validated metabolite.
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