Integrative "omic" analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes

Abstract

Rationale: Sepsis is a leading cause of morbidity and mortality. Currently, early diagnosis and the progression of the disease are difficult to make. The integration of metabolomic and transcriptomic data in a primate model of sepsis may provide a novel molecular signature of clinical sepsis.

Objectives: To develop a biomarker panel to characterize sepsis in primates and ascertain its relevance to early diagnosis and progression of human sepsis.

Methods: Intravenous inoculation of Macaca fascicularis with Escherichia coli produced mild to severe sepsis, lung injury, and death. Plasma samples were obtained before and after 1, 3, and 5 days of E. coli challenge and at the time of killing. At necropsy, blood, lung, kidney, and spleen samples were collected. An integrative analysis of the metabolomic and transcriptomic datasets was performed to identify a panel of sepsis biomarkers.

Measurements and main results: The extent of E. coli invasion, respiratory distress, lethargy, and mortality was dependent on the bacterial dose. Metabolomic and transcriptomic changes characterized severe infections and death, and indicated impaired mitochondrial, peroxisomal, and liver functions. Analysis of the pulmonary transcriptome and plasma metabolome suggested impaired fatty acid catabolism regulated by peroxisome-proliferator activated receptor signaling. A representative four-metabolite model effectively diagnosed sepsis in primates (area under the curve, 0.966) and in two human sepsis cohorts (area under the curve, 0.78 and 0.82).

Conclusions: A model of sepsis based on reciprocal metabolomic and transcriptomic data was developed in primates and validated in two human patient cohorts. It is anticipated that the identified parameters will facilitate early diagnosis and management of sepsis.

Keywords: bacteremia; metabolomics; mitochondrial dysfunction; nonhuman primates; transcriptomics.

Figure 1.

Escherichia coli challenge of cynomolgus macaques leads to increased mortality, tissue colonization, and inflammation in a dose-dependent manner. (A) Kaplan-Meier curve for live E. coli challenge. Low-dose challenge (prime, 1 × 10⁵ to 1 × 10⁸; live, 1 × 10⁴ to 5 × 10⁹); high-dose challenge (prime, 1 × 10⁹; live, 1 × 10¹⁰ to 5 × 10¹²). Four animals in the high-dose range died shortly after live infusion (1–2 h) and were not included in subsequent analyses other than for baseline metabolomics. (B) E. coli tissue colony-forming units from animals after killing in low-dose challenge (low), high-dose challenge (high) survivors, and death. *P < 0.05. Necropsy of all animals was performed at 5 days post-challenge, except for the animals that were killed because of moribund state at 6–60 hours post-challenge. (C) Relative tissue mass after killing. *P < 0.05; **P < 0.001. (D) Lung sections (×200) were examined blindly for histologic evidence of lung injury. Control animals demonstrated normal lung architecture, animals in the low-dose challenge (CFU 10⁸, CFU 10⁹) groups exhibited mild inflammation and interstitial edema, and animals in the high-dose challenge (CFU 10¹⁰, CFU 10¹¹) groups exhibited pronounced tissue injury with lung inflammation and edema.

Figure 2.

Metabolomic changes in nonhuman primates (NHP) two-hit sepsis model. (A) Experimental design. Arrows indicate the time blood was collected. Scaled metabolomic intensities were log2+1 transformed, and analysis of variance with 5% false discovery rate was performed against time course of low-dose challenge (baseline, n = 20; 1 d post-challenge, n = 7; 3 d post-challenge, n = 7; 5 d post-challenge, n = 12) or high-dose sepsis challenge (baseline, n = 20; 3 d post-challenge, n = 4; 5 d post-challenge, n = 3; and death, n = 4). Unsupervised principal components analysis using Pearson moment correlation for (B) infection low-dose comparison (baseline, red; 1 d post-challenge, green; 3 d post-challenge, blue; 5 d post-challenge, gold) and (C) infection high-dose comparison (baseline, red; 3 d post-challenge, green; 5 d post-challenge, blue; death, gold). (D) Cell plot of significant metabolomic changes affected by sepsis grouped by metabolic pathway. (E–J) Bar charts of representative metabolic pathways affected by sepsis challenge (E, lactate; F, 1-stearoyl-glycerophosphocholine; G, taurolithocholate-3-sulfate; H, isovalerylcarnitine; I, succinate; J, pregnen-diol disulfate). *Significantly different from control. ^#Significantly different comparison 1–5 d low-dose, or 3–5 d high-dose. Low-dose comparison (−log10 P value ≥ 2.14) or high-dose comparison (−log10 P value ≥ 2.00). TCA = tricarboxylic acid.

Figure 3.

Kynurenine pathway modification. (A) Kynurenine metabolic pathway. (B–E) Bar charts of raw scaled metabolic data for tryptophan (B), kynurenine (C), kynurenate (D), and quinolinate (E). Analysis of variance (5% false discovery rate). *Significantly different from control, ^#Significantly different comparison 1–5 d low-dose or 3–5 d high-dose. Low-dose comparison (−log10 P value ≥ 2.14) or high-dose comparison (−log10 P value ≥ 2.00). (F) Bar chart for transcriptomic counts data for kynureninase (KYNU) in control (CON), low-dose challenge, high-dose challenge, and death. *−Log10 P value ≥ 3.00.

Figure 4.

Transcriptomic analysis of whole-lung RNA. (A) Experimental design: total RNA was isolated from whole lungs, Truseq multiplex sequence libraries were built using rRNA depletion rather than poly-A selection. Samples were sequenced and aligned with Tophat and read counts were determined using BedTools. Analysis of variance (ANOVA) (false discovery rate = 0.03) was performed and significant gene lists were uploaded into the Database for Annotation, Visualization and Integrated Discovery (DAVID) with results suggesting mitochondrial dysfunction. (B) Venn diagram of significant differences in high-dose challenge versus low-dose challenge, low-dose challenge versus nonsurvivor, and high-dose challenge versus nonsurvivor; 82 genes were significantly different in control versus the other groups (high dose, low dose, or nonsurvivor) that were not significantly different in the other comparisons. (C) Heatmap of significant gene transcript abundance differences for control (CON; n = 2), sepsis low-dose challenge (n = 8), sepsis high-dose challenge (n = 4), and sepsis death (n = 4). (D) DAVID analysis. Official gene symbols for significant genes, minus duplicates and unknown Ensembl-reported genes, were uploaded into DAVID v6.7. The Homo sapiens gene list was used for functional annotation analysis. The analysis found 109 gene pathways significantly enriched (P ≤ 0.006; false discovery rate ≤ 10) (see Table E4). Fifteen metabolomic, inflammatory, and transcription factor enriched pathways are presented.

Figure 5.

Hypothetical pathway analysis depicts mitochondrial dysfunction. Gene-by-metabolite associations highlight problems with tricarboxylic acid (TCA) cycle regulation, β-oxidation, branched-chain amino acid (BCAA) degradation, and peroxisomal activation likely leading to increased production of various acyl-carnitines, a strong predictive signature of poor sepsis outcomes. This mitochondrial dysregulation is likely mediated by peroxisomal proliferator-activated receptor-γ family transcription factors and/or novel zinc finger proteins. Problems with lipid metabolism are manifest in the strong association of kynurenine family metabolites and peroxisomal deactivation. Perhaps most interesting is the association of succinate with inflammatory gene IL-1β, suggesting a potential link to mitochondrial dysfunction and initiation of the inflammatory response. Nicotinamide adenine dinucleotide (NAD+); key TCA cycle rate-limiting enzymes pyruvate dehydrogenase phosphatase 2 (PDP2) and pyruvate dehydrogenase kinase 2 (PDK2) were down-regulated in high-dose challenges and nonsurvivors.