Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics.
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ABSTRACT:
SUBMITTER: Malmstrom E
PROVIDER: S-EPMC4729823 | biostudies-literature | 2016 Jan
REPOSITORIES: biostudies-literature
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Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics.
Malmström Erik E Kilsgård Ola O Hauri Simon S Smeds Emanuel E Herwald Heiko H Malmström Lars L Malmström Johan J
Nature communications 20160106
The plasma proteome is highly dynamic and variable, composed of proteins derived from surrounding tissues and cells. To investigate the complex processes that control the composition of the plasma proteome, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in murine plasma. The strategy relies on the construction of a comprehensive protein tissue atlas from cells and highly vascularized organs using shotgun mass spectrometry. The protein tissue a ...[more]
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Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.
2010-05-25 | E-GEOD-6535 | biostudies-arrayexpress
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. Keywords: disease state analysis, gram-positive sepsis, gram-negative sepsis
2007-12-01 | GSE6535 | GEO
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Project description:Sepsis is a principal cause of death in critical care units worldwide and consumes considerable healthcare resources. The aim of our study was to determine whether the early cytokine profile can discriminate between Gram-positive and Gram-negative bacteraemia (GPB and GNB, respectively) and to assess the prognostic value regarding outcome in critically ill patients with severe abdominal sepsis. The outcome measure was hospital mortality. Blood samples were obtained from 165 adult patients with confirmed severe abdominal sepsis. Levels of the proinflammatory mediators TNF-α, IL-8, IL-12 and IFN-γ and the anti-inflammatory mediators IL-1ra, IL-4, IL-10 and TGF-β1 were determined and correlated with the nature of the bacteria isolated from the blood culture and outcome. The cytokine profile in our study indicated that the TNF-α levels were 2-fold, IL-8 were 3.3-fold, IFN-γ were 13-fold, IL-1ra were 1.05-fold, IL-4 were 1.4-fold and IL-10 were 1.83-fold higher in the GNB group compared with the GPB group. The TNF-α levels were 4.7-fold, IL-8 were 4.6-fold, IL-1ra were 1.5-fold and IL-10 were 3.3-fold higher in the non-survivors compared with the survivors.
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Project description:BackgroundThe host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique.MethodsThe training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA.ResultsTwo immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA.ConclusionsThe identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.
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Project description:BackgroundThe wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics.MethodsInvestigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments.ResultsWe show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts.ConclusionsThe automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.
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Project description:ObjectivesTo identify important pathogen recognition receptor (PRR) pathways regulating innate immune responses and outcome in Staphylococcus aureus sepsis.MethodsWe analysed whether candidate PRR pathway genetic variants were associated with killed S. aureus-induced cytokine responses ex vivo and performed follow-up in vitro studies. We tested the association of our top-ranked variant with cytokine responses and clinical outcomes in a prospective multicentre cohort of patients with staphylococcal sepsis.ResultsAn intronic TLR4 polymorphism and expression quantitative trait locus, rs1927907, was highly associated with cytokine release induced by stimulation of blood from healthy Thai subjects with S. aureus ex vivo. S. aureus did not induce TLR4-dependent NF-κB activation in transfected HEK293 cells. In monocytes, tumor necrosis factor (TNF)-α release induced by S. aureus was not blunted by a TLR4/MD-2 neutralizing antibody, but in a monocyte cell line, TNF-α was reduced by knockdown of TLR4. In Thai patients with staphylococcal sepsis, rs1927907 was associated with higher interleukin (IL)-6 and IL-8 levels as well as with respiratory failure. S. aureus-induced responses in blood were most highly correlated with responses to Gram-negative stimulants whole blood.ConclusionsA genetic variant in TLR4 is associated with cytokine responses to S. aureus ex vivo and plasma cytokine levels and respiratory failure in staphylococcal sepsis. While S. aureus does not express lipopolysaccharide or activate TLR4 directly, the innate immune response to S. aureus does appear to be modulated by TLR4 and shares significant commonality with that induced by Gram-negative pathogens and lipopolysaccharide.
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Project description:BackgroundMost plasmids replicate only within a particular genus or family.Methodology/principal findingsHere we describe an engineered high copy number expression vector, pBAV1K-T5, that produces varying quantities of active reporter proteins in Escherichia coli, Acinetobacter baylyi ADP1, Agrobacterium tumefaciens, (all gram-negative), Streptococcus pneumoniae, Leifsonia shinshuensis, Peanibacillus sp. S18-36 and Bacillus subtilis (gram-positive).Conclusions/significanceOur results demonstrate the efficiency of pBAV1K-T5 replication in different bacterial species, thereby facilitating the study of proteins that don't fold well in E. coli and pathogens not amenable to existing genetic tools.
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