Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation

Li,Zhenliang; Zhang,Ying; Liu,Yaling; Liu,Yanchun; Li,Youyi

doi:10.3892/mmr.2017.7013

Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation

Authors:
- Zhenliang Li
- Ying Zhang
- Yaling Liu
- Yanchun Liu
- Youyi Li
View Affiliations

Affiliations: Intensive Care Unit, Pinggu Hospital Affiliated to Capital Medical University, Beijing 101200, P.R. China, Department of Infection Diseases, Beijing Pinggu Hospital of Traditional Chinese Medicine, Beijing 101200, P.R. China
Published online on: July 15, 2017 https://doi.org/10.3892/mmr.2017.7013
Pages: 3133-3146
Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Sepsis is an inflammatory response to pathogens (such as Gram‑positive and Gram‑negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram‑positive and Gram‑negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram‑positive samples and 25 Gram‑negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram‑negative and Gram‑negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram‑positive and Gram‑negative samples. A total of 340 and 485 DEGs were obtained in Gram‑positive and Gram‑negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram‑positive and Gram‑negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol‑cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram‑negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram‑positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram‑negative sepsis, whereas LATS2 may be a biomarker for Gram‑positive sepsis. These findings may promote the therapies of sepsis caused by Gram‑positive and Gram‑negative bacteria.

View Figures

View References

1	Taylor FB Jr, Kinasewitz GT and Lupu F: Pathophysiology, staging and therapy of severe sepsis in baboon models. J Cell Mol Med. 16:672–682. 2012. View Article : Google Scholar : PubMed/NCBI
2	Brodská H, Malíčková K, Adámková V, Benáková H, Šťastná MM and Zima T: Significantly higher procalcitonin levels could differentiate Gram-negative sepsis from Gram-positive and fungal sepsis. Clin Exp Med. 13:165–170. 2013. View Article : Google Scholar : PubMed/NCBI
3	Deutschman C and Tracey K: Sepsis: Current dogma and new perspectives. Immunity. 40:463–475. 2014. View Article : Google Scholar : PubMed/NCBI
4	Lyle NH, Pena OM, Boyd JH and Hancock REW: Barriers to the effective treatment of sepsis: Antimicrobial agents, sepsis definitions, and host-directed therapies. Ann N Y Acad Sci. 1323:101–114. 2014. View Article : Google Scholar : PubMed/NCBI
5	Hotchkiss RS and Karl IE: The pathophysiology and treatment of sepsis. N Engl J Med. 348:138–150. 2003. View Article : Google Scholar : PubMed/NCBI
6	Koh EM, Lee SG, Kim CK, Kim M, Yong D, Lee K, Kim JM, Kim DS and Chong Y: Microorganisms isolated from blood cultures and their antimicrobial susceptibility patterns at a university hospital during 1994–2003. Korean J Lab Med. 27:265–275. 2007.(In Korean). View Article : Google Scholar : PubMed/NCBI
7	Glik J, Kawecki M, Gaździk T and Nowak M: The impact of the types of microorganisms isolated from blood and wounds on the results of treatment in burn patients with sepsis. Pol Przegl Chir. 84:6–16. 2012.PubMed/NCBI
8	Tang BM, McLean AS, Dawes IW, Huang SJ, Cowley MJ and Lin RC: Gene-expression profiling of gram-positive and gram-negative sepsis in critically ill patients. Crit Care Med. 36:1125–1128. 2008. View Article : Google Scholar : PubMed/NCBI
9	Mahabeleshwar GH, Qureshi MA, Takami Y, Sharma N, Lingrel JB and Jain MK: A myeloid hypoxia-inducible factor 1α-Krüppel-like factor 2 pathway regulates gram-positive endotoxin-mediated sepsis. J Biol Chem. 287:1448–1457. 2012. View Article : Google Scholar : PubMed/NCBI
10	Giamarellos-Bourboulis EJ, van de Veerdonk FL, Mouktaroudi M, Raftogiannis M, Antonopoulou A, Joosten LA, Pickkers P, Savva A, Georgitsi M, van der Meer JW and Netea MG: Inhibition of caspase-1 activation in Gram-negative sepsis and experimental endotoxemia. Crit Care. 15:R272011. View Article : Google Scholar : PubMed/NCBI
11	Kager LM, Weehuizen TA, Wiersinga WJ, Roelofs JJ, Meijers JC, Dondorp AM, van't Veer C and van der Poll T: Endogenous α2-antiplasmin is protective during severe gram-negative sepsis (melioidosis). Am J Respir Crit Care Med. 188:967–975. 2013. View Article : Google Scholar : PubMed/NCBI
12	Kager LM, Wiersinga WJ, Roelofs JJ, de Boer OJ, Weiler H, van't Veer C and van der Poll T: A thrombomodulin mutation that impairs active protein C generation is detrimental in severe pneumonia-derived gram-negative sepsis (melioidosis). PLoS Negl Trop Dis. 8:e28192014. View Article : Google Scholar : PubMed/NCBI
13	Afsari B, Geman D and Fertig EJ: Learning dysregulated pathways in cancers from differential variability analysis. Cancer Inform. 13:(Suppl 5). S61–S67. 2014.
14	Calandra T, Cohen J, et al: International Sepsis Forum Definition of Infection in the ICU Consensus Conference: The international sepsis forum consensus conference on definitions of infection in the intensive care unit. Crit Care Med. 33:1538–1548. 2005. View Article : Google Scholar : PubMed/NCBI
15	Murie C, Barette C, Lafanechère L and Nadon R: Control-plate regression (CPR) normalization for high-throughput screens with many active features. J Biomol Screen. 19:661–671. 2014. View Article : Google Scholar : PubMed/NCBI
16	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
17	Chen H and Boutros PC: VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 12:352011. View Article : Google Scholar : PubMed/NCBI
18	Gene Ontology Consortium, . Blake JA, Dolan M, Drabkin H, Hill DP, Li N, Sitnikov D, Bridges S, Burgess S, Buza T, et al: Gene ontology annotations and resources. Nucleic Acids Res. 41:(Database issue). D530–D535. 2013. View Article : Google Scholar : PubMed/NCBI
19	da Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2008. View Article : Google Scholar
20	Zhou J, Dong X, Zhou Q, Wang H, Qian Y, Tian W, Ma D and Li X: microRNA expression profiling of heart tissue during fetal development. Int J Mol Med. 33:1250–1260. 2014.PubMed/NCBI
21	Zhai Y, Tchieu J and Saier MH Jr: A web-based tree view (TV) program for the visualization of phylogenetic trees. J Mol Microbiol Biotechnol. 4:69–70. 2002.PubMed/NCBI
22	Weaver B and Wuensch KL: SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients. Behav Res Methods. 45:880–895. 2013. View Article : Google Scholar : PubMed/NCBI
23	Demchak B, Hull T, Reich M, Liefeld T, Smoot M, Ideker T and Mesirov JP: Cytoscape: The network visualization tool for GenomeSpace workflows. F1000Res. 3:1512014.PubMed/NCBI
24	Kotera M, Moriya Y, Tokimatsu T, Kanehisa M and Goto S: KEGG and GenomeNet, New Developments. Metagenomic Analysis. 329–339. 2015.
25	Liberti L, Lavor C, Maculan N and Mucherino A: Euclidean distance geometry and applications. Quantitative Biol. 56:3–69. 2012.
26	Bódai T, Altmann EG and Endler A: Stochastic perturbations in open chaotic systems: Random versus noisy maps. Phys Rev E Stat Nonlin Soft Matter Phys. 87:0429022013. View Article : Google Scholar : PubMed/NCBI
27	Schmidt MV, Paulus P, Kuhn AM, Weigert A, Morbitzer V, Zacharowski K, Kempf VA, Brüne B and von Knethen A: Peroxisome proliferator-activated receptor γ-induced T cell apoptosis reduces survival during polymicrobial sepsis. Am J Respir Crit Care Med. 184:64–74. 2011. View Article : Google Scholar : PubMed/NCBI
28	Chang K, Svabek C, Vazquez-Guillamet C, Sato B, Rasche D, Wilson S, Robbins P, Ulbrandt N, Suzich J, Green J, et al: Targeting the programmed cell death 1: Programmed cell death ligand 1 pathway reverses T cell exhaustion in patients with sepsis. Crit Care. 18:R32014. View Article : Google Scholar : PubMed/NCBI
29	Härter L, Mica L, Stocker R, Trentz O and Keel M: Mcl-1 correlates with reduced apoptosis in neutrophils from patients with sepsis. J Am Coll Surg. 197:964–973. 2003. View Article : Google Scholar : PubMed/NCBI
30	Galley H: Oxidative stress and mitochondrial dysfunction in sepsis. Br J Anaesth. 107:57–64. 2011. View Article : Google Scholar : PubMed/NCBI
31	Lee I and Hüttemann M: Energy crisis: The role of oxidative phosphorylation in acute inflammation and sepsis. Biochim Biophys Acta. 1842:1579–1586. 2014. View Article : Google Scholar : PubMed/NCBI
32	Petruzzella V, Vergari R, Puzziferri I, Boffoli D, Lamantea E, Zeviani M and Papa S: A nonsense mutation in the NDUFS4 gene encoding the 18 kDa (AQDQ) subunit of complex I abolishes assembly and activity of the complex in a patient with Leigh-like syndrome. Hum Mol Genet. 10:529–535. 2001. View Article : Google Scholar : PubMed/NCBI
33	Ugalde C, Hinttala R, Timal S, Smeets R, Rodenburg RJ, Uusimaa J, van Heuvel LP, Nijtmans LG, Majamaa K and Smeitink JA: Mutated ND2 impairs mitochondrial complex I assembly and leads to Leigh syndrome. Mol Genet Metab. 90:10–14. 2007. View Article : Google Scholar : PubMed/NCBI
34	Bottley A, Phillips NM, Webb TE, Willis AE and Spriggs KA: eIF4A inhibition allows translational regulation of mRNAs encoding proteins involved in Alzheimer's disease. PLoS One. 5:pii: e13030. 2010. View Article : Google Scholar : PubMed/NCBI
35	Dröse S and Brandt U: Molecular mechanisms of superoxide production by the mitochondrial respiratory chainMitochondrial Oxidative Phosphorylation. Springer; pp. 145–169. 2012, View Article : Google Scholar
36	Van Raamsdonk JM and Hekimi S: Superoxide dismutase is dispensable for normal animal lifespan. Proc Natl Acad Sci USA. 109:5785–5790. 2012. View Article : Google Scholar : PubMed/NCBI
37	Schulte J, Struck J, Köhrle J and Müller B: Circulating levels of peroxiredoxin 4 as a novel biomarker of oxidative stress in patients with sepsis. Shock. 35:460–465. 2011. View Article : Google Scholar : PubMed/NCBI
38	Owens KM, Kulawiec M, Desouki MM, Vanniarajan A and Singh KK: Impaired OXPHOS complex III in breast cancer. PLoS One. 6:e238462011. View Article : Google Scholar : PubMed/NCBI
39	Modena P, Testi MA, Facchinetti F, Mezzanzanica D, Radice MT, Pilotti S and Sozzi G: UQCRH gene encoding mitochondrial Hinge protein is interrupted by a translocation in a soft-tissue sarcoma and epigenetically inactivated in some cancer cell lines. Oncogene. 22:4586–4593. 2003. View Article : Google Scholar : PubMed/NCBI
40	Li Y, Pei J, Xia H, Ke H, Wang H and Tao W: Lats2, a putative tumor suppressor, inhibits G1/S transition. Oncogene. 22:4398–4405. 2003. View Article : Google Scholar : PubMed/NCBI
41	Yang QH, Liu DW, Long Y, Liu HZ, Chai WZ and Wang XT: Acute renal failure during sepsis: Potential role of cell cycle regulation. J Infect. 58:459–464. 2009. View Article : Google Scholar : PubMed/NCBI