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Aging and disease    2019, Vol. 10 Issue (4) : 854-870     DOI: 10.14336/AD.2018.1027
Orginal Article |
Metabolomics Coupled with Transcriptomics Approach Deciphering Age Relevance in Sepsis
Dingqiao Xu1, Shanting Liao1, Pei Li1, Qian Zhang1, Yan Lv1, Xiaowei Fu1, Minghua Yang1, Junsong Wang2,*, Lingyi Kong1,*
1Jiangsu Key Laboratory of Bioactive Natural Product Research and State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
2Center for Molecular Metabolism, Nanjing University of Science and Technology, Nanjing, China.
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Abstract  

Sepsis is a severe disease frequently occurred in the Intenisive Care Unit (ICU), which has a very high morbidity and mortality, especially in patients aged over 65 years. Owing to the aging effect and the ensuing deterioration of body function, the elder patients may have atypical responses to sepsis. Diagnosis and pathogenesis of sepsis in this population are thus difficult, which hindered effective treatment and management in clinic. To investigated age effects on sepsis, 158 elderly septic patients and 71 non-septic elderly participants were enrolled, and their plasma samples were collected for transcriptomics (RNA-seq) and metabolomics (NMR and GC-MS) analyses, which are both increasingly being utilized to discover key molecular changes and potential biomarkers for various diseases. Protein-protein interaction (PPI) analysis was subsequently performed to assist cross-platform integration. Real time polymerase chain reaction (RT-PCR) was used for validation of RNA-seq results. For further understanding of the mechanisms, cecal ligation and puncture (CLP) experiment was performed both in young and middle-aged rats, which were subjected to NMR-based metabolomics study and validated for several key inflammation pathways by western blot. Comprehensive analysis of data from the two omics approaches provides a systematic perspective on dysregulated pathways that could facilitate the development of therapy and biomarkers for elderly sepsis. Additionally, the metabolites of lactate, arginine, histamine, tyrosine, glutamate and glucose were shown to be highly specific and sensitive in distinguishing septic patients from healthy controls. Significant increases of arginine, trimethylamine N-oxide and allantoin characterized elderly patient incurred sepsis. Further analytical and biological validations in different subpopulations of septic patients should be carried out, allowing accurate diagnostics and precise treatment of sepsis in clinic.

Keywords sepsis      biomarker      aging      transcriptomics      metabolomics     
Corresponding Authors: Wang Junsong,Kong Lingyi   
About author:

These authors contributed equally.

Just Accepted Date: 26 November 2018   Issue Date: 01 August 2019
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Dingqiao Xu
Shanting Liao
Pei Li
Qian Zhang
Yan Lv
Xiaowei Fu
Minghua Yang
Junsong Wang
Lingyi Kong
Cite this article:   
Dingqiao Xu,Shanting Liao,Pei Li, et al. Metabolomics Coupled with Transcriptomics Approach Deciphering Age Relevance in Sepsis[J]. Aging and disease, 2019, 10(4): 854-870.
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http://www.aginganddisease.org/EN/10.14336/AD.2018.1027     OR     http://www.aginganddisease.org/EN/Y2019/V10/I4/854
Figure 1.  An overview workflow of the comprehensive analysis of metabolomics and transcriptomics in sepsis.
Figure 2.  OPLS-DA analysis of metabolic profiles between ESEP and EVOL groups for plasma

Score plots (A) and color-coded coefficient loadings plots (B, C) for the plasma of septic patients based on 1H NMR analysis; Score plots (D) and color-coded coefficient loadings plots (E, F) for the plasma of septic patients based on GC-MS analysis. Significantly changed metabolites were assigned in the loading’s plots. Downward and upward peaks represent increased and decreased concentrations in pathogenic group. Symbols of ? (black filled circles), | (red filled squares) represent the control and pathogenic groups, respectively.

Figure 3.  Plasma transcriptomics response caused by sepsis in elderly patients

(A) Heat map of differentially expressed genes (DEGs) identified by RNA-seq between groups (P <0.05). Hierarchical clustering of DEGs in EVOL samples (N-1, N-2, and N-3) compared with the ESEP samples (S-1, S-2, and S-3). (B) Volcano plots of DEGs. A total of 1636 DEGs, including 1088 up-regulated and 548 down-regulated genes, threshold of significance as fold change was >2, FDR <0.05. (C) Histogram diagram of Gene Ontology (GO) classification. The results of DEGs are summarized in three major categories: biological process (red), molecular function (green), and cellular component (blue). The y-axis on the left indicates the enriched GO terms; the x-axis indicates the number of DEGs.

No.MetabolitesECLP vs EShamYCLP vs YShamECLP vs YShamECLP vs YCLP

FCPFCPFCPFCP
1Isoleucine1.211.070.921.11
2Leucine1.221.041.071.31
3Valine1.291.260.841.16
4Hydroxybutyrate3.6**2.76*0.32*1.89*
5Lactate1.371.290.72*1.14
6Alanine1.65*0.861.391.2
7Allantoin1.55**0.68**1.9***1.29*
8Acetate1.161.240.931.76**
9NAA1.051.051.081.13
10Methionine1.021.28*0.91.15
11Glutamate2.14*1.181.111.31
12Glutathione0.961.061.211.28
13Succinate1.77*1.57*0.59*1.12
14Glutamine1.071.39*1.031.43*
15Arigine1.79*0.91.281.33
16Creatine.Pcr1.53*1.380.851.78**
17Ornithine1.58*1.290.841.08
18Ethanolamine1.270.921.111.01
19Choline2.27*1.140.761.86**
20Choline.
/O.Phosphocholine
2.53*1.370.6*1.82*
21Glucose0.61*1.211.210.38**
22Taurine0.720.960.990.95
23Betaine0.93111
24Glycine1.69*0.940.910.85
25Maltose1.231.250.720.9
26Ascorbate0.941.010.950.96
27Inosine1.311.040.950.99
28Uridine1.141.130.840.95
29TMAO1.25*1.111.121.65***
30Fumarate1.30.880.990.87
31Tyrosine1.050.961.58*1.51*
32Histamine2.06*0.821.62*1.32
33Tryptophan0.910.991.231.22
34Phenylalanine1.361.62*0.81.3
35Cytidine1.091.170.961.12
36Methylxanthine1.111.45*0.921.33
37Formate0.710.941.381.3
Table 1  Identified metabolites with fold changes between groups and p-values in rat plasma.
Figure 4.  A network of protein-protein interaction (PPI)

The PPI analysis was based on fold change of gene/protein, protein-protein interaction, KEGG pathway enrichment and biological process enrichment. Circle nodes refer to genes/proteins. Rectangle refers to KEGG pathway or biological process, which was filled with color gradient from yellow (low p-value) to blue (high p-value).

Figure 5.  Scores on the first and second principal components and average scores for the factor Age (A) and CLP (B)

Interaction ‘Age × CLP’ model (C) scores on the first principal component of the corresponding submodels. The loadings (D, E, and F) belonging to the first component for the factor Age, CLP and the interaction ‘Age × CLP’.

Figure 6.  Biochemical parameters including inflammation markers and immune cytokines were determined in rats

Histograms for clinical chemistry results of MCP-1, MIP-1α, RAG-1, LDH, IL-6, CX3CR1, ALT, AST, SOD, GSSG, GSH, NO and for creatine of serum in septic middle-aged (ECLP) and young rats (YCLP) (n=6). Data in serum are expressed as mean ± S.D. #p < 0.05, ##p < 0.01 and ###p < 0.005 for YCLP vs. ECLP group.

Figure 7.  Aging effects on TLR4/NF-κB and MAPK signal pathway in rat livers

(A) Aging effects on TLR4/ NF-κB signal pathway. The expression of proteins in TLR4/NF-κB were up-regulated in ECLP group (B, C, D). (E) Shown are western blots for MAPK signal pathway. The expression of proteins in MAPK were up-regulated in ECLP group (F, G). (H) Histone, c-Jun, c-Fos, Cleaved PARP and Nrf2 expression level were analyzed by western blotting. The accompanying bars represent intensity ratio of protein relative to β-actin. Results are expressed as mean ± SD. *p < 0.05, **p < 0.01 and ***p < 0.005 for YCLP (vehicle-treated young CLP) group vs. ECLP group. #p < 0.05, ##p < 0.01 and ###p < 0.005 for ESham (vehicle-treated sham) group vs. ECLP group. (n=6).

Figure 8.  Hypothetical pathway constructed based on integration of gene-by-metabolite interactions

Gene-by-metabolite interactions determined by average absolute value correlations for metabolomics families (e.g., TCA - succinate, fumarate, malate, citrate) to individual genes.

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