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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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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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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

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.

[1] Palomba H, Correa TD, Silva E, Pardini A, Assuncao MS (2015). Comparative analysis of survival between elderly and non-elderly severe sepsis and septic shock resuscitated patients. Einstein (Sao Paulo), 13: 357-363.
[2] Radé F, Bretagnol F, Auguste M, Di Guisto C, Huten N, Calan L (2014). Determinants of outcome following laparoscopic peritoneal lavage for perforated diverticulitis. Br J Surg, 101:1602-1606; discussion 1606.
[3] Greenwood H, Patel J, Mahida R, Wang Q, Parekh D and Dancer RC (2014). Simvastatin to modify neutrophil function in older patients with septic pneumonia (SNOOPI): study protocol for a randomised placebo-controlled trial. Trials, 15: 332.
[4] Sfera A, Price AI, Gradini R, Cummings M, Osorio C (2015). Proteomic and epigenomic markers of sepsis-induced delirium (SID). Front Mol Biosci, 2: 59.
[5] Bagshaw SM, Webb SA, Delaney A, George C, Pilcher D, Hart GK, et al. (2009). Very old patients admitted to intensive care in Australia and New Zealand: a multi-centre cohort analysis. Crit care, 13: R45.
[6] Pugh RJ, Thorpe CM, Subbe CP (2017). A critical age: can we reliably measure frailty in critical care? Crit care, 21: 121.
[7] Luis A Destarac EWE (2002). Sepsis in older patients: an emerging concern in critical care. Advances in sepsis, 2: 15-22.
[8] Jiang L, Huang J, Wang Y, Tang H (2012). Metabonomic analysis reveals the CCl4-induced systems alterations for multiple rat organs. J Proteome Res, 11: 3848-3859.
[9] Patin F, Baranek T, Vourc'h P, Nadal-Desbarats L, Goossens JF, Marouillat S, et al. (2016). Combined metabolomics and transcriptomics approaches to assess the IL-6 blockade as a therapeutic of ALS: deleterious alteration of lipid metabolism. Neurotherapeutics, 13: 905-917.
[10] Rittirsch D, Schoenborn V, Lindig S, Wanner E, Sprengel K, Gunkel S, et al. (2016). An integrated clinico-transcriptomic approach identifies a central role of the heme degradation pathway for septic complications after trauma. Ann Surg, 264: 1125-1134.
[11] HR W (2012). Clinical review: sepsis and septic shock--the potential of gene arrays. Crit Care, 16: 204-212.
[12] Jian B (2011). Aging influences cardiac mitochondrial gene expression and cardiovascular function following hemorrhage injury. Mol Med, 17: 542-549.
[13] Rittirsch D, Huber-Lang MS, Flierl MA, Ward PA (2009). Immunodesign of experimental sepsis by cecal ligation and puncture. Nature protocols, 4: 31-36.
[14] Tang BM, McLean AS, Dawes IW, Huang SJ, Cowley MJ, Lin RC (2008). Gene-expression profiling of gram-positive and gram-negative sepsis in critically ill patients. Crit Care Med, 36: 1125-1128.
[15] Inoue S, Suzuki-Utsunomiya K, Okada Y, Taira T, Iida Y and Miura N (2013). Reduction of immunocompetent T cells followed by prolonged lymphopenia in severe sepsis in the elderly. Crit Care Med, 41: 810-819.
[16] Javier Corral JYl, David Herna´ndez-Espinosa, Yolanda Monreal, Ruben Mota, Isabel Arcas, Antonia Min˜an, et al. (2005). Role of lipopolysaccharide and cecal ligation and puncture on blood coagulation and inflammation in sensitive and resistant mice models. Am J Pathol, 166: 1089-1098.
[17] Bjerrum JT, Rantalainen M, Wang Y, Olsen J, Nielsen OH (2014). Integration of transcriptomics and metabonomics: improving diagnostics, biomarker identification and phenotyping in ulcerative colitis. Metabolomics, 10: 280-290.
[18] Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, et al. (2004). Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. P Natl Acad Sci USA, 101: 10205-10210.
[19] Wiklund S, Johansson E, Sjostrom L, Mellerowicz EJ, Edlund U, Shockcor JP, et al. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal Chem, 80: 115-122.
[20] Fink MP (2001). Cytopathic hypoxia: mitochondrial dysfunction as mechanism contributing to organ dysfunction in sepsis. Crit Care Clin, 17: 219-237.
[21] Pechlivanis A, Papaioannou KG, Tsalis G, Saraslanidis P, Mougios V, Theodoridis GA (2015). Monitoring the response of the human urinary metabolome to brief maximal exercise by a combination of RP-UPLC-MS and 1H NMR spectroscopy. J Proteome Res, 14: 4610-4622.
[22] Casserly B, Phillips GS, Schorr C, Dellinger RP, Townsend SR, Osborn TM, et al. (2015). Lactate measurements in sepsis-induced tissue hypoperfusion: results from the Surviving Sepsis Campaign database. Crit Care Med, 43: 567-573.
[23] Karbowskia M, Kurono C, Wozniak M, Ostrowski M, Teranishi M, Nishizawa Y, et al. (1999). Free radical-induced megamitochondria formation and apoptosis. Free Radical Bio Med, 26: 396-409.
[24] Van Cromphaut SJIV, G Van den Berghe (2008). Glucose metabolism and insulin resistance in sepsis. Curr Pharm Design, 14: 1887-1899.
[25] Hu W-C (2013). Sepsis is a syndrome with hyperactivity of TH17-like innate immunity and hypoactivity of adaptive immunity. Quantitative Biology.
[26] Stechmiller Joyce KBCaTP (2004). Arginine immunonutrition in critically ill patients: a clinical dilemma. Am J Crit Care, 13: 17-23.
[27] Luiking YC, Poeze M, Ramsay G, Deutz NE (2005). The role of arginine in infection and sepsis. JPEN, 29: S70-74.
[28] Badurdeen S, Mulongo M, Berkley JA (2015). Arginine depletion increases susceptibility to serious infections in preterm newborns. Pediatr Res, 77: 290-297.
[29] Chiarla C, Giovannini I, Siegel JH (2006). Plasma arginine correlations in trauma and sepsis. Amino acids, 30: 81-86.
[30] Jia YX, Pan CS, Yang JH, Liu XH, Yuan WJ, Zhao J, et al. (2006). Altered L-arginine/nitric oxide synthase/nitric oxide pathway in the vascular adventitia of rats with sepsis. Clin Exp Pharmacol P, 33: 1202-1208.
[31] Kale S S, Yende S (2011). Effects of aging on inflammation and hemostasis through the continuum of critical illness. Aging Dis, 2: 501-511.
[32] Marti L, Cervera C, Filella X, Marin JL, Almela M, Moreno A (2007). Cytokine-release patterns in elderly patients with systemic inflammatory response syndrome. Gerontology, 53: 239-244.
[33] Kim SJ, Baek KS, Park HJ, Jung YH, Lee SM (2016). Compound 9a, a novel synthetic histone deacetylase inhibitor, protects against septic injury in mice by suppressing MAPK signalling. Brit J Clin Pharmaco, 173: 1045-1057.
[34] Ci X, Zhou J, Lv H, Yu Q, Peng L, Hua S (2017). Betulin exhibits anti-inflammatory activity in LPS-stimulated macrophages and endotoxin-shocked mice through an AMPK/AKT/Nrf2-dependent mechanism. Cell Death Dis, 8: e2798.
[35] Atsumi T, Cho Y-R, Leng L, McDonald C, Yu T, Danton C, et al. (2007). The proinflammatory cytokine macrophage migration inhibitory factor regulates glucose metabolism during systemic inflammation. J Immunol, 179: 5399-5406.
[36] Reisberg R (1957). Biological significance of choline acetylase. Yale J Biol Med, 29: 404-435.
[37] Godshall CJ, Scott MJ, Peyton JC, Gardner SA, Cheadle WG (2002). Genetic background determines susceptibility during murine septic peritonitis. J Surg Res, 102: 45-49.
[38] Hotchkiss RS, Nicholson DW (2006). Apoptosis and caspases regulate death and inflammation in sepsis. Nat Rev Immunol, 6: 813-822.
[39] Tang BM, McLean AS, Dawes IW, Huang SJ, Lin RC (2007). The use of gene-expression profiling to identify candidate genes in human sepsis. Am J Resp Crit Care, 176: 676-684.
[40] Hao E, Lang F, Chen Y, Zhang H, Cong X, Shen X, et al. (2013). Resveratrol alleviates endotoxin-induced myocardial toxicity via the Nrf2 transcription factor. PloS one, 8: e69452.
[41] Klaassen CD, Reisman SA (2010). Nrf2 the rescue: effects of the antioxidative/electrophilic response on the liver. Toxicol Appl Pharm, 244: 57-65.
[42] Hyung JH, Ahn CB, Il Kim B, Kim K, Je JY (2016). Involvement of Nrf2-mediated heme oxygenase-1 expression in anti-inflammatory action of chitosan oligosaccharides through MAPK activation in murine macrophages. Eur J Pharmacol, 793: 43-48.
[43] AP Klippel HM, TH Covey (1977). The ues of silver-zinc-allantoin powder for the prehospital treatment of burns. JACEP, 6: 184-186.
[44] Dong OuYang JX, Heguang Huang, Zhong Chen (2011). Metabolomic profiling of serum from human pancreatic cancer patients using 1H NMR spectroscopy and principal component analysis. Appl Biochem Biotech, 165: 148-154.
[45] Seymour CW, Yende S, Scott MJ, Pribis J, Mohney RP, Bell LN, et al. (2013). Metabolomics in pneumonia and sepsis: an analysis of the GenIMS cohort study. Intensive Care Med, 39: 1423-1434.
[46] Schiattarella GG, Sannino A, Toscano E, Giugliano G, Gargiulo G, Franzone A, et al. (2017). Gut microbe-generated metabolite trimethylamine-N-oxide as cardiovascular risk biomarker: a systematic review and dose-response meta-analysis. Eur Heart J, 38: 2948-2956.
[47] Baron P TL, Traber DL, Nguyen T, Hollyoak M, Heggers JP, Herndon DN (1994). Gut failure and translocation following burn and sepsis. J Surg Res, 57: 197-204.
[48] Moraes C, Fouque D, Amaral AC, Mafra D (2015). Trimethylamine N-Oxide from gut microbiota in chronic kidney disease patients: focus on diet. J Renal Nutr, 25: 459-465.
[49] Haak BW, Wiersinga WJ (2017). The role of the gut microbiota in sepsis. Lancet Gastroenterol Hepatol, 2: 135-143.
[50] Seldin MM, Meng Y, Qi H, Zhu W, Wang Z, SL Hazen, AJ Lusis, DM Shih (2016). Trimethylamine N-oxide promotes vascular inflammation through signaling of mitogen-activated protein kinase and nuclear factor -kappaB. J Am Heart Assoc, 5.
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