Email Alert | RSS

Journal of Peking University (Health Sciences) ›› 2022, Vol. 54 ›› Issue (3): 532-540. doi: 10.19723/j.issn.1671-167X.2022.03.020

Previous Articles     Next Articles

Characteristics of amino acid metabolism in myeloid-derived suppressor cells in septic mice

Yuan MA1,Yue ZHANG2,3,Rui LI2,3,Shu-wei DENG2,3,Qiu-shi QIN1,Liu-luan ZHU1,2,3,*()   

  1. 1. Institute of Infectious Diseases, Peking University Ditan Teaching Hospital, Beijing 100015, China
    2. Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
    3. Beijing Key Laboratory of Emerging Infectious Diseases, Beijing 100015, China
  • Received:2022-01-18 Online:2022-06-18 Published:2022-06-14
  • Contact: Liu-luan ZHU E-mail:zhuliuluan@aliyun.com
  • Supported by:
    the National Natural Science Foundation of China(81871586);the National Natural Science Foundation of China(82172128)

RICH HTML

 11   

PDF (PC)

113

Abstract:

Objective: To explore the amino acid metabolomics characteristics of myeloid-derived suppressor cells (MDSCs) in mice with sepsis induced by the cecal ligation and puncture (CLP). Methods: The sepsis mouse model was prepared by CLP, and the mice were randomly divided into a sham operation group (sham group, n = 10) and a CLP model group (n = 10). On the 7th day after the operation, 5 mice were randomly selected from the surviving mice in each group, and the bone marrow MDSCs of the mice were isolated. Bone marrow MDSCs were separated to measure the oxygen consumption rate (OCR) by using Agilent Seahorse XF technology and to detect the contents of intracellular amino acids and oligopeptides through ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) technology. Different metabolites and potential biomarkers were analyzed by univariate statistical analysis and multivariate statistical analysis. The major metabolic pathways were enriched using the small molecular pathway database (SMPDB). Results: The proportion of MDSCs in the bone marrow of CLP group mice (75.53% ± 6.02%) was significantly greater than that of the sham group (43.15%± 7.42%, t = 7.582, P < 0.001), and the basal respiratory rate [(50.03±1.20) pmol/min], maximum respiration rate [(78.07±2.57) pmol/min] and adenosine triphosphate (ATP) production [(25.30±1.21) pmol/min] of MDSCs in the bone marrow of CLP group mice were significantly greater than the basal respiration rate [(34.53±0.96) pmol/min, (t = 17.41, P < 0.001)], maximum respiration rate [(42.57±1.87) pmol/min, (t = 19.33, P < 0.001)], and ATP production [(12.63±0.96) pmol/min, (t = 14.18, P < 0.001)] of sham group. Leucine, threonine, glycine, etc. were potential biomarkers of septic MDSCs (all P < 0.05). The increased amino acids were mainly enriched in metabolic pathways, such as malate-aspartate shuttle, ammonia recovery, alanine metabolism, glutathione metabolism, phenylalanine and tyrosine metabolism, urea cycle, glycine and serine metabolism, β-alanine metabolism, glutamate metabolism, arginine and proline metabolism. Conclusion: The enhanced mitochondrial oxidative phosphorylation, malate-aspartate shuttle and alanine metabolism in MDSCs of CLP mice may provide raw materials for mitochondrial aerobic respiration, thereby promoting the immunosuppressive function of MDSCs. Blocking the above metabolic pathways may reduce the risk of secondary infection in sepsis and improve the prognosis.

Key words: Sepsis, Myeloid-derived suppressor cell, Aerobic respiration, Amino acid metabolomics

CLC Number: 

  • R363.1

Figure 1

Proportion and oxidative respiration of bone marrow MDSCs in CLP and sham mice A, survival curve of CLP group mice; B, representative plot of flow cytometry of mouse bone marrow MDSCs; C, comparison of MDSCs content between sham group and CLP group; D, OCR curves of MDSCs in sham and CLP groups; E, F, G, basal, ATP production and maximum respiration OCR of MDSCs in sham and CLP groups. CLP, cecal ligation and puncture; MDSCs, myeloid-derived suppressor cells; OCR, oxygen consumption rate; FCCP, car-bonyl cyanide-p-trifluoromethoxyphenolhydrazone; ATP, adenosine triphosphate. *P < 0.001."

Figure 2

Composition of various metabolites of MDSCs in CLP and sham groups CLP, cecal ligation and puncture; MDSCs, myeloid-derived suppressor cells. *P < 0.001."

Table 1

One-dimensional analysis of amino acid and oligopeptide contents in MDSCs of mice in sham-operated group and CLP group  /(μmol/L)"

Amino acids Sham group (n=5) CLP group (n=5) t/U value P log2FC
Glycine 386.660±77.645 995.180±65.567 13.390 < 0.001 1.364
Leucine 219.580±34.095 587.560±79.165 9.546 < 0.001 1.420
Aspartic acid 122.680±44.956 555.440±118.618 7.628 0.001 2.179
Lysine 224.020±47.647 666.820±134.086 6.958 0.001 1.574
Histidine 98.200±16.996 202.380±36.155 5.831 0.001 1.043
Asparagine 64.500±28.406 140.580±23.324 4.629 0.002 1.124
Valine 195.400±45.947 548.020±136.323 5.481 0.003 1.488
Threonine 112.160±5.280 269.220±40.306 0.000 0.008 1.322
Serine 444.380±60.045 1 251.500±187.629 0.000 0.008 1.676
Phenylalanine 116.560±11.250 301.400±51.594 0.000 0.008 1.563
Tyrosine 89.080±14.074 243.940±47.185 0.000 0.008 1.393
Glutamate 522.220±186.936 2 490.800 (1 678.900, 2 631.900) 0.000 0.008 2.475
4-hydroxyproline 217.480±54.856 729.100±189.424 0.000 0.008 1.995
Tryptophan 24.700±9.100 103.200±30.850 0.000 0.008 2.405
Alanine 921.900 (870.000, 948.200) 3 029.320±780.214 0.000 0.008 1.801
Proline 96.800 (95.400, 104.800) 375.700±121.282 0.000 0.008 2.085
Taurine 4 646.620±2 425.105 8 336.720±739.823 0.000 0.008 0.472
Citrulline 49.140±18.249 124.040±39.186 3.874 0.009 1.336
Methionine 62.860±20.195 127.240±6.354 0.000 0.012 1.122
5-hydroxylysine 2.516±2.567 15.620±8.233 0.000 0.012 2.204
Aminoadipic acid 14.500 (13.800, 14.900) 42.380±13.225 1.000 0.016 1.814
Glutamine 297.100 (277.400, 300.600) 1 452.940±559.859 1.000 0.016 2.448
α-aminobutyric acid 27.740±14.200 83.200±34.351 3.336 0.019 1.585
Arginine 137.380±68.239 305.500±105.690 2.988 0.021 1.153
Cystine 0.180±0.000 12.376±12.628 2.500 0.025 6.207
Ornithine 91.960±58.453 312.800±160.012 2.899 0.033 1.766
Kynurenine 8.520±1.359 10.340±1.383 2.099 0.069 0.279
Carnosine 20.138±15.920 40.700±24.508 1.573 0.160 1.015
Glutathione 273.980±94.760 189.800 (187.300, 268.800) 7.000 0.310 -0.591

Figure 3

OPLS-DA multidimensional analysis of metabolites in MDSCs of mice in CLP and sham groups CLP, cecal ligation and puncture; MDSCs, myeloid-derived suppressor cells; OPLS-DA, orthogonal partial least square discriminant analysis."

Table 2

Multivariate statistical analysis of amino acid and oligopeptide contents in MDSCs of mice in sham and CLP groups  /(μmol/L)"

Amino acids Sham group (n=5) CLP group (n=5) VIP Corr.Coeffs.
Leucine 219.580±34.095 587.560±79.165 1.144 0.977
Threonine 112.160±5.280 269.220±40.306 1.142 0.976
Glycine 386.660±77.645 995.180±65.567 1.135 0.970
Serine 444.380±60.045 1251.500±187.629 1.129 0.964
Phenylalanine 116.560±11.250 301.400±51.594 1.124 0.960
Tyrosine 89.080±14.074 243.940±47.185 1.121 0.957
Aspartic acid 122.680±44.956 555.440±118.618 1.117 0.954
Glutamate 522.220±186.936 2 490.800 (1 678.900, 2 631.900) 1.107 0.945
Methionine 62.860±20.195 127.240±6.354 1.107 0.946
Lysine 224.020±47.647 666.820±134.086 1.107 0.946
4-hydroxyproline 217.480±54.856 729.100±189.424 1.087 0.928
Histidine 98.200±16.996 202.380±36.155 1.076 0.919
Tryptophan 24.700±9.100 103.200±30.850 1.075 0.918
Valine 195.400±45.947 548.020±136.323 1.074 0.918
Alanine 921.900 (870.000, 948.200) 3 029.320±780.214 1.024 0.874
Proline 96.800 (95.400, 104.800) 375.700±121.282 1.021 0.872
Asparagine 64.500±28.406 140.580±23.324 1.009 0.862
Aminoadipic acid 14.500 (13.800, 14.900) 42.380±13.225 0.997 0.852
Citrulline 49.140±18.249 124.040±39.186 0.982 0.838
Glutamine 297.100 (277.400, 300.600) 1 452.940±559.859 0.967 0.826
Taurine 4 646.620±2 425.105 8 336.720±739.823 0.961 0.821
α-aminobutyric acid 27.740±14.200 83.200±34.351 0.945 0.807
5-hydroxylysine 2.516±2.567 15.620±8.233 0.925 0.790
Ornithine 91.960±58.453 312.800±160.012 0.888 0.758
Arginine 137.380±68.239 305.500±105.690 0.863 0.737
Cystine 0.180±0.000 12.376±12.628 0.775 0.662
Kynurenine 8.520±1.359 10.340±1.383 0.704 0.601
Carnosine 20.138±15.920 40.700±24.508 0.540 0.461
Glutathione 273.980±94.760 189.800 (187.300, 268.800) 0.362 -0.310

Figure 4

Pathway enrichment analysis of marker metabolites in MDSCs of CLP group mice A, heat map of changes in marker metabolite content in MDSCs in the sham group and CLP group; B, bar graphs of enrichment analysis of metabolite pathways for MDSCs markers in sepsis, all P < 0.001. CLP, cecal ligation and puncture; MDSCs, myeloid-derived suppressor cells."

1 Singer M , Deutschman CS , Seymour CW , et al. The third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315 (8): 801- 810.
doi: 10.1001/jama.2016.0287
2 徐静媛, 邱海波. 脓毒症治疗的现状与展望[J]. 国际流行病学传染病学杂志, 2021, 48 (4): 259- 262.
doi: 10.3760/cma.j.cn331340-20210618-00126
3 Rudd KE , Johnson SC , Agesa KM , et al. Global, regional, and national sepsis incidence and mortality, 1990—2017: analysis for the Global Burden of Disease Study[J]. Lancet, 2020, 395 (10219): 200- 211.
doi: 10.1016/S0140-6736(19)32989-7
4 Schrijver IT , Théroude C , Roger T . Myeloid-derived suppressor cells in sepsis[J]. Front Immunol, 2019, 10, 327.
doi: 10.3389/fimmu.2019.00327
5 Mira JC , Gentile LF , Mathias BJ , et al. Sepsis pathophysiology, chronic critical illness, and persistent inflammation-immuno-suppression and catabolism syndrome[J]. Crit Care Med, 2017, 45 (2): 253- 262.
doi: 10.1097/CCM.0000000000002074
6 Veglia F , Perego M , Gabrilovich D . Myeloid-derived suppressor cells coming of age[J]. Nat Immunol, 2018, 19 (2): 108- 119.
doi: 10.1038/s41590-017-0022-x
7 Consonni FM , Porta C , Marino A , et al. Myeloid-Derived suppressor cells: ductile targets in disease[J]. Front Immunol, 2019, 10, 949.
doi: 10.3389/fimmu.2019.00949
8 Karin N . The Development and homing of myeloid-derived suppressor cells: from a two-stage model to a multistep narrative[J]. Front Immunol, 2020, 11, 557- 586.
doi: 10.3389/fimmu.2020.00557
9 Venet F , Monneret G . Advances in the understanding and treatment of sepsis-induced immunosuppression[J]. Nat Rev Nephrol, 2018, 14 (2): 121- 137.
doi: 10.1038/nrneph.2017.165
10 Mathias B , Delmas AL , Ozrazgat-Baslanti T , et al. Human myeloid-derived suppressor cells are associated with chronic immune suppression after severe sepsis/septic shock[J]. Ann Surg, 2017, 265 (4): 827- 834.
doi: 10.1097/SLA.0000000000001783
11 Darden DB , Bacher R , Brusko MA , et al. Single-cell RNA-seq of human myeloid-derived suppressor cells in late sepsis reveals multiple subsets with unique transcriptional responses: a pilot study[J]. Shock, 2021, 55 (5): 587- 595.
doi: 10.1097/SHK.0000000000001671
12 Wegiel B , Vuerich M , Daneshmandi S , et al. Metabolic switch in the tumor microenvironment determines immune responses to anti-cancer therapy[J]. Front Oncol, 2018, 8, 284.
doi: 10.3389/fonc.2018.00284
13 Won WJ , Deshane JS , Leavenworth JW , et al. Metabolic and functional reprogramming of myeloid-derived suppressor cells and their therapeutic control in glioblastoma[J]. Cell Stress, 2019, 3 (2): 47- 65.
doi: 10.15698/cst2019.02.176
14 Leinwand J , Miller G . Regulation and modulation of antitumor immunity in pancreatic cancer[J]. Nat Immunol, 2020, 21 (10): 1152- 1159.
doi: 10.1038/s41590-020-0761-y
15 Grzywa TM , Sosnowska A , Matryba P , et al. Myeloid cell-derived arginase in cancer immune response[J]. Front Immunol, 2020, 11, 938.
doi: 10.3389/fimmu.2020.00938
16 Rittirsch D , Huber-Lang MS , Flierl MA , et al. Immunodesign of experimental sepsis by cecal ligation and puncture[J]. Nat Protoc, 2009, 4 (1): 31- 36.
doi: 10.1038/nprot.2008.214
17 Su L , Li H , Xie A , et al. Dynamic changes in amino acid concentration profiles in patients with sepsis[J]. PLoS One, 2015, 10 (4): e0121933.
doi: 10.1371/journal.pone.0121933
18 Gunst J , Vanhorebeek I , Thiessen SE , et al. Amino acid supplements in critically ill patients[J]. Pharmacol Res, 2018, 130, 127- 131.
doi: 10.1016/j.phrs.2017.12.007
19 Al-Khami AA , Rodriguez PC , Ochoa AC . Metabolic reprogramming of myeloid-derived suppressor cells (MDSCs) in cancer[J]. Oncoimmunology, 2016, 5 (8): e1200771.
doi: 10.1080/2162402X.2016.1200771
20 Mohammadpour H , MacDonald CR , McCarthy PL , et al. β2-adrenergic receptor signaling regulates metabolic pathways critical to myeloid-derived suppressor cell function within the TME[J]. Cell Rep, 2021, 37 (4): 109883.
doi: 10.1016/j.celrep.2021.109883
21 Altinok O , Poggio JL , Stein DE , et al. Malate-aspartate shuttle promotes l-lactate oxidation in mitochondria[J]. J Cell Physiol, 2020, 235 (3): 2569- 2581.
doi: 10.1002/jcp.29160
22 Mansouri S , Shahriari A , Kalantar H , et al. Role of malate dehydrogenase in facilitating lactate dehydrogenase to support the glycolysis pathway in tumors[J]. Biomed Rep, 2017, 6 (4): 463- 467.
doi: 10.3892/br.2017.873
23 Li X , Li Y , Yu Q , et al. Metabolic reprogramming of myeloid-derived suppressor cells: an innovative approach confronting challenges[J]. J Leukoc Biol, 2021, 110 (2): 257- 270.
doi: 10.1002/JLB.1MR0421-597RR
24 Bozkus CC , Elzey BD , Crist SA , et al. Expression of cationic amino acid transporter 2 is required for myeloid-derived suppressor cell-mediated control of t cell immunity[J]. J Immunol, 2015, 195 (11): 5237- 5250.
doi: 10.4049/jimmunol.1500959
25 Srivastava MK , Sinha P , Clements VK , et al. Myeloid-derived suppressor cells inhibit T-cell activation by depleting cystine and cysteine[J]. Cancer Res, 2010, 70 (1): 68- 77.
doi: 10.1158/0008-5472.CAN-09-2587
26 Zhao H , Raines LN , Huang SC . Carbohydrate and amino acid metabolism as hallmarks for innate immune cell activation and function[J]. Cells, 2020, 9 (3): 562.
doi: 10.3390/cells9030562
[1] LIU Yu-qing, LU Jian, HAO Yi-chang, XIAO Chun-lei, MA Lu-lin. Predicting model based on risk factors for urosepsis after percutaneous nephrolithotomy [J]. Journal of Peking University(Health Sciences), 2018, 50(3): 507-513.
[2] CHEN Wei, HU Fan-lei, LIU Hong-jiang, XU Li-ling, LI Ying-ni, LI Zhan-guo. Myeloid-derived suppressor cells promoted autologous B cell proliferation in rheumatoid arthritis [J]. Journal of Peking University(Health Sciences), 2017, 49(5): 819-823.
[3] ZHANG Xin, RU Xi-fang, WANG Ying, LI Xing, SANG Tian, FENG Qi. Clinical characteristics of neonatal fungal sepsis in neonatal intensive care unit [J]. Journal of Peking University(Health Sciences), 2017, 49(5): 789-793.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Yun-fei LIU,Jia-jia DANG,Pan-liang ZHONG,Ning MA,Di SHI,Yi SONG. Injury mortality among Chinese aged 5 to 24 years from 1990 to 2019[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 498 -504 .
[2] Wu-ping ZHOU,Shu-han YANG,Nan MU,Wei-yan JIAN. Analysis of variation trend in health workforce equity allocation in China[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 477 -482 .
[3] Guang-qi LIU,Yuan-jie PANG,Jiang WU,Min LV,Meng-ke YU,Yu-tong LI,Yang-mu HUANG. Trend analysis of influenza vaccination among hospitalized elderly people in Beijing, 2013-2019[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 505 -510 .
[4] . [J]. Journal of Peking University(Health Sciences), 2009, 41(2): 226 -229 .
[5] Li-ye LAI,Chang-song DOU,Cui-na ZHI,Jie CHEN,Xue MA,Peng ZHAO,Bi-yun YAO. Curcumin alleviates the manganese-induced neurotoxicity by promoting autophagy in rat models of manganism[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 400 -411 .
[6] TIAN Jing,QIN Man,CHEN Jie,XIA Bin. Early loss of primary molar and permanent tooth germ caused by the use of devitalizer during primary molar root canal therapy: Two cases report[J]. Journal of Peking University (Health Sciences), 2022, 54(2): 381 -385 .
[7] He-wei MIN,Yi-bo WU,Xin-ying SUN. Relation of smoking status to family health and personality traits in residents aged over 18 years in China[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 483 -489 .
[8] Yu-han DENG,Yong JIANG,Zi-yao WANG,Shuang LIU,Yu-xin WANG,Bao-hua LIU. Long short-term memory and Logistic regression for mortality risk prediction of intensive care unit patients with stroke[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 458 -467 .
[9] Ming-long CHEN,Xiao-han LIU,jing GUO. Relationship between social support and parental burnout in COVID-19 among Chinese young parents[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 520 -525 .
[10] Jia-min WANG,Qiu-ping LIU,Ming-lu ZHANG,Chao GONG,Shu-dan LIU,Wei-ye CHEN,Peng SHEN,Hong-bo LIN,Pei GAO,Xun TANG. Effectiveness of different screening strategies for type 2 diabete on preventing cardiovascular diseases in a community-based Chinese population using a decision-analytic Markov model[J]. Journal of Peking University (Health Sciences), 2022, 54(3): 450 -457 .
Copyright © Journal of Peking University (Health Sciences), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd