Literature DB >> 25920543

Oxidative phosphorylation gene expression falls at onset and throughout the development of meningococcal sepsis-induced multi-organ failure in children.

Sainath Raman1, Nigel Klein, Antonia Kwan, Mike Hubank, Shamima Rahman, Asrar Rashid, Mark J Peters.   

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Year:  2015        PMID: 25920543      PMCID: PMC4502289          DOI: 10.1007/s00134-015-3817-y

Source DB:  PubMed          Journal:  Intensive Care Med        ISSN: 0342-4642            Impact factor:   17.440


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Dear Editor, Sepsis-induced critical illness differs between adults and children. Children deteriorate more quickly and exhibit a ‘cold shock’ haemodynamic pattern more often. Organ failure scores characteristically peak earlier in septic children (<2 ICU days) than adults (days 3–4). A high proportion of deaths in children occur very early [1]. Yet, ICU stays are shorter and survival better in children [2]. Might these differences in clinical phenotype—more rapid onset and recovery—be due to differences in the underlying mechanisms of multi-organ failure (MOF)? Acute mitochondrial dysfunction may contribute to MOF. Reduced mitochondrial oxygen utilisation and gene expression has been observed in established sepsis in adults and children [3]. There is an association between recovery of mitochondrial function and survival but the contribution to the onset of organ failure is less clear. We investigated whether mitochondrial oxidative phosphorylation gene expression (Oxphos) alters early enough in the clinical course of sepsis to remain a candidate element of MOF pathophysiology. To do this, we selected a population with the most rapid onset of profound sepsis-MOF: previously healthy children with acute meningococcal septicaemia. We investigated the time-course of gene expression in peripheral blood with gene set enrichment analysis (GSEA), at 0, 4, 8, 12, 24, and 48 h from time of admission to the emergency room. Extracted RNA was hybridised in Affymetrix microarrays. Methodological details are published elsewhere [4]. The dataset is available to download from the European Bioinformatics Institute database (ArrayExpress id: E-MEXP-3850). Emergency room venesection was designated time 0 as a pragmatic reference time point while acknowledging that children may be at different stages of illness at presentation. The GSEA ranks changes in single gene expression. It notes the distribution of elements of a predefined gene set in this overall ranked list. We focused on the Reactome TCA cycle and respiratory electron transport chain (RETC) genes within the 3655 included gene sets. GSEA describes the probability of a non-random distribution of RTEC elements within the ranked list [5]. On the group level analysis, with a false discovery rate (FDR) at <25 % and ranked according to their normalised enrichment score (NES), 1039 out of 3655 gene sets showed a decreasing profile with time. The RETC set was ranked 75th of the 1039 gene sets. On the individual-level analysis and FDR <25 %, all five patients had a highly ranked fall in RETC set expression. Patient 1 showed a low correlation of RETC gene expression to a decreasing profile. This might be relevant as, unfortunately, this child died. Figure 1 shows the NES, FDR and nominal p values for the RETC set for the individual patients and the overall group.
Fig. 1

Enrichment plot of the Respiratory Electron Transport Chain (RETC) gene set showing the normalised enrichment score (NES), the false discovery rate (FDR) and nominal p values for the gene set. Gene set enrichment analysis compares the variation in the gene expression in our overall dataset compared to a preselected gene set (RETC gene set) over the time-course. We have observed that RETC gene set expression is down-regulated more than would be expected by background variation in expression. The top portion of the plot shows the running enrichment score (ES) for the RETC gene set. The middle portion of the plot shows where the members of the gene set appear in the ranked list of genes. The lower portion of the plot shows the value of the ranking metric as you move down the list of ranked genes. The ranking metric measures a gene’s correlation with a phenotype. For our continuous phenotype (time series), a positive value indicates correlation with the phenotype profile (decreasing profile)

Enrichment plot of the Respiratory Electron Transport Chain (RETC) gene set showing the normalised enrichment score (NES), the false discovery rate (FDR) and nominal p values for the gene set. Gene set enrichment analysis compares the variation in the gene expression in our overall dataset compared to a preselected gene set (RETC gene set) over the time-course. We have observed that RETC gene set expression is down-regulated more than would be expected by background variation in expression. The top portion of the plot shows the running enrichment score (ES) for the RETC gene set. The middle portion of the plot shows where the members of the gene set appear in the ranked list of genes. The lower portion of the plot shows the value of the ranking metric as you move down the list of ranked genes. The ranking metric measures a gene’s correlation with a phenotype. For our continuous phenotype (time series), a positive value indicates correlation with the phenotype profile (decreasing profile) Oxidative phosphorylation gene expression reduced early and continued to decrease for at least 48 h in septic critically ill children. These findings are consistent with mitochondrial dysfunction contributing to the development of organ failure in both adults and children despite the differences in sepsis phenotype in these groups.
  5 in total

1.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

2.  Timing of death in children referred for intensive care with severe sepsis: implications for interventional studies.

Authors:  Mirjana Cvetkovic; Daniel Lutman; Padmanabhan Ramnarayan; Nazima Pathan; David P Inwald; Mark J Peters
Journal:  Pediatr Crit Care Med       Date:  2015-06       Impact factor: 3.624

3.  Mortality related to invasive infections, sepsis, and septic shock in critically ill children in Australia and New Zealand, 2002-13: a multicentre retrospective cohort study.

Authors:  Luregn J Schlapbach; Lahn Straney; Janet Alexander; Graeme MacLaren; Marino Festa; Andreas Schibler; Anthony Slater
Journal:  Lancet Infect Dis       Date:  2014-12-01       Impact factor: 25.071

4.  Transcriptional instability during evolving sepsis may limit biomarker based risk stratification.

Authors:  Antonia Kwan; Mike Hubank; Asrar Rashid; Nigel Klein; Mark J Peters
Journal:  PLoS One       Date:  2013-03-27       Impact factor: 3.240

5.  Differential expression of the nuclear-encoded mitochondrial transcriptome in pediatric septic shock.

Authors:  Scott L Weiss; Natalie Z Cvijanovich; Geoffrey L Allen; Neal J Thomas; Robert J Freishtat; Nick Anas; Keith Meyer; Paul A Checchia; Thomas P Shanley; Michael T Bigham; Julie Fitzgerald; Sharon Banschbach; Eileen Beckman; Kelli Howard; Erin Frank; Kelli Harmon; Hector R Wong
Journal:  Crit Care       Date:  2014-11-19       Impact factor: 9.097

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1.  Transcriptomic data from two primary cell models stimulating human monocytes suggest inhibition of oxidative phosphorylation and mitochondrial function by N. meningitidis which is partially up-regulated by IL-10.

Authors:  Unni Gopinathan; Reidun Øvstebø; Berit Sletbakk Brusletto; Ole Kristoffer Olstad; Peter Kierulf; Petter Brandtzaeg; Jens Petter Berg
Journal:  BMC Immunol       Date:  2017-10-27       Impact factor: 3.615

2.  A community approach to mortality prediction in sepsis via gene expression analysis.

Authors:  Timothy E Sweeney; Thanneer M Perumal; Ricardo Henao; Marshall Nichols; Judith A Howrylak; Augustine M Choi; Jesús F Bermejo-Martin; Raquel Almansa; Eduardo Tamayo; Emma E Davenport; Katie L Burnham; Charles J Hinds; Julian C Knight; Christopher W Woods; Stephen F Kingsmore; Geoffrey S Ginsburg; Hector R Wong; Grant P Parnell; Benjamin Tang; Lyle L Moldawer; Frederick E Moore; Larsson Omberg; Purvesh Khatri; Ephraim L Tsalik; Lara M Mangravite; Raymond J Langley
Journal:  Nat Commun       Date:  2018-02-15       Impact factor: 14.919

  2 in total

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