A pairwise cytokine code explains the organism-wide response to sepsis [Spatial transcriptomics by Array-seq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE248904 | GEO | 2024/01/08
REPOSITORIES: GEO
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Project description:Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the molecular and cellular impact of sepsis across organs remains rudimentary. Here, we characterize the pathogenesis of sepsis by measuring dynamic changes in gene expression across organs. To pinpoint molecules controlling organ states in sepsis, we compare the effects of sepsis on organ gene expression to those of 6 singles and 15 pairs of recombinant cytokines. Strikingly, we find that the pairwise effects of tumor necrosis factor plus interleukin (IL)-18, interferon-gamma or IL-1 suffice to mirror the impact of sepsis across tissues. Mechanistically, we map the cellular effects of sepsis and cytokines by computing changes in the abundance of 195 cell types across 9 organs, which we validate by whole-mouse spatial profiling. Our work decodes the cytokine cacophony in sepsis into a pairwise cytokine message capturing the gene, cell and tissue responses of the host to the disease.
2024-01-08 | GSE224145 | GEO
Project description:Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the molecular and cellular impact of sepsis across organs remains rudimentary. Here, we characterize the pathogenesis of sepsis by measuring dynamic changes in gene expression across organs. To pinpoint molecules controlling organ states in sepsis, we compare the effects of sepsis on organ gene expression to those of 6 singles and 15 pairs of recombinant cytokines. Strikingly, we find that the pairwise effects of tumor necrosis factor plus interleukin (IL)-18, interferon-gamma or IL-1 suffice to mirror the impact of sepsis across tissues. Mechanistically, we map the cellular effects of sepsis and cytokines by computing changes in the abundance of 195 cell types across 9 organs, which we validate by whole-mouse spatial profiling. Our work decodes the cytokine cacophony in sepsis into a pairwise cytokine message capturing the gene, cell and tissue responses of the host to the disease.
2024-01-08 | GSE224127 | GEO
Project description:Sepsis is an uncontrolled, systemic response to infection with life-threatening consequences. Our understanding of the pathogenesis of sepsis across organs of the body is rudimentary. Here, using mouse models of sepsis, we generate a dynamic, organism-wide map of the pathogenesis of the disease, revealing the spatiotemporal patterns of well-known and previously unrecognized effects of sepsis on the body. By combining functional perturbations with organism-wide profiling, we discover two interorgan mechanisms that are key to the pathophysiology of sepsis. First, we find that a hierarchical cytokine circuit arising from the pairwise effects of TNF plus IL-18, IFN-γ, or IL-1β suffices to explain a large fraction of the molecular effects of sepsis on the body. Moreover, the effects of these three cytokine pairs on the abundance of nearly two hundred cell types across nine organ types recapitulate half of all the cellular effects of sepsis. Second, we uncover an interorgan pathway whereby a gut-derived, secreted phospholipase, Pla2g5, mediates hemolysis in the blood circulation and contributes to multi-organ failure during sepsis. Thus, a simplifying principle in the systemic behavior of the cytokine network and a lipase misdirected from gut to blood provide fundamental insights to help build a unifying mechanistic framework for the pathophysiological effects of sepsis on the organ systems of the body.
2024-01-11 | GSE253061 | GEO
Project description:Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events are unclear. This has resulted in ineffective development of sepsis-specific biomarkers and therapies, and suboptimal treatment regimens in preventing and reversing organ damage. Here, we used pharmacoproteomics to score treatment effects in a preclinical Escherichia coli sepsis model based on changes in the organ, cell, and plasma proteome landscapes. A combination of pathophysiological read-outs and time-resolved proteome maps of organs and blood enabled the definition of time-dependent and organotypic proteotypes of dysfunction and damage that was used to guide the administration of several combinations of beta-lactam antibiotic meropenem and immunomodulatory glucocorticoid methylprednisolone. The proteome-based scoring strategy revealed three distinct response patterns defined as intervention-specific reversions, non-reversions, and specific intervention-induced effects, revealing that the intervention effects depended on the underlying proteotype and varied significantly between organs. In the later stages of the disease, administration of glucocorticoids accentuated some of the positive effects exerted by Mem leading to superior reduction of the inflammatory response in the kidneys and partial restoration of the metabolic dysfunction instigated by sepsis. Unexpectedly, antibiotics introduced sepsis-independent perturbations in the mitochondrial proteome that was to some degree counteracted by glucocorticoids. In summary, this study provides a pharmacoproteomic resource describing the time-resolved septic organ failure landscape across organs and the blood compartment, and a novel scoring strategy that captures unintended secondary drug effects as an important criterion to consider while assessing therapeutic efficacy. Such information is critically important to enable a quantitative, objective and organotypic assessment of treatment benefits and unintended effects, drug synergies of candidate treatments as well as the effect of dose and time in murine sepsis models.
2023-05-10 | PXD036847 | Pride
Project description:Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events are unclear. This has resulted in ineffective development of sepsis-specific biomarkers and therapies, and suboptimal treatment regimens in preventing and reversing organ damage. Here, we used pharmacoproteomics to score treatment effects in a preclinical Escherichia coli sepsis model based on changes in the organ, cell, and plasma proteome landscapes. A combination of pathophysiological read-outs and time-resolved proteome maps of organs and blood enabled the definition of time-dependent and organotypic proteotypes of dysfunction and damage that was used to guide the administration of several combinations of beta-lactam antibiotic meropenem and immunomodulatory glucocorticoid methylprednisolone. The proteome-based scoring strategy revealed three distinct response patterns defined as intervention-specific reversions, non-reversions, and specific intervention-induced effects, revealing that the intervention effects depended on the underlying proteotype and varied significantly between organs. In the later stages of the disease, administration of glucocorticoids accentuated some of the positive effects exerted by Mem leading to superior reduction of the inflammatory response in the kidneys and partial restoration of the metabolic dysfunction instigated by sepsis. Unexpectedly, antibiotics introduced sepsis-independent perturbations in the mitochondrial proteome that was to some degree counteracted by glucocorticoids. In summary, this study provides a pharmacoproteomic resource describing the time-resolved septic organ failure landscape across organs and the blood compartment, and a novel scoring strategy that captures unintended secondary drug effects as an important criterion to consider while assessing therapeutic efficacy. Such information is critically important to enable a quantitative, objective and organotypic assessment of treatment benefits and unintended effects, drug synergies of candidate treatments as well as the effect of dose and time in murine sepsis models.
2023-05-10 | PXD036832 | Pride
Project description:An early aspect of sepsis is dysregulated activation of endothelial cells (EC), initiating a cascade of inflammatory signaling leading to leukocyte adhesion/migration and organ damage. Therapeutic targeting of ECs has been hampered by concerns regarding ECs heterogeneity from different organs. Using a combination of in vitro and in silico analysis, we present a comprehensive analysis of proteomic changes in mouse lung, kidney and liver ECs following exposure to a clinically relevant cocktail of proinflammatory cytokines. Mouse lung, liver and kidney ECs were incubated with TNFα/IL-1β/IFNγ for 4 or 24 hrs to model inflammatory responses during sepsis. Quantitative label-free global proteomics was performed on the ECs to identify differentially expressed proteins (DEP). Proteins with an abundance ratio >2.0 or <0.5 and an adjusted p<0.05 were characterized as upregulated or downregulated respectively. Gene Ontology (GO) classification was used to determine biological processes that regulate EC function during sepsis and PANTHER was used to classify the molecular function of the identified proteins. Finally, an interactive pathway was developed to investigate signaling within ECs and across organs. Proteomic analysis identified both unique and shared DEPs between the ECs specific to lung, liver and kidney. Using GO, 5 Biological Processes (BP) for each of the organ specific ECs were identified. Interestingly, 4 of the top 5 GO BPs were observed in all 3 ECs at 4 and 24 hrs: defense response to other organism, innate immune response, response to bacterium and response to cytokine. However, these BPs were found to be differentially regulated. For example, at 4 and 24 hrs, lung ECs which have the highest number and highest fold expression of upregulated proteins (189 and 316 respectively), also have higher numbers of unique upregulated proteins (124 vs 194) compared to liver (98 vs 121) and kidney ECs (109 vs 136). The liver showed the greatest number of conserved proteins between 4 and 24 hrs (56) compared to lung (50) and kidney (51). The number of common proteins between all three ECs increases from 38 at 4 hrs to 79 at 24 hrs, indicating more uniformity in ECs proteomic expression during the progression of sepsis. The PANTHER database was used to classify proteins in different functional groups (e.g. defense/immunity) at 4 and 24 hrs. Thus, BPs and PANTHER hits can provide insight into why some organs are more susceptible to sepsis early on and show that as sepsis progresses, some protein expression patterns become more uniform while additional organ specific proteins are expressed. Proteomic analysis of organ-specific ECs provides further understanding of how sepsis affects multiple organs across time and supports future proteomic, temporal studies of EC dysfunction.
2022-10-14 | PXD031804 | Pride
Project description:The immune system makes decisions in response to combinations of multiple microbial inputs. We do not understand the combinatorial logic governing how higher-order combinations of microbial signals shape immune responses. Here, using coculture experiments and statistical analyses, we discover a general property for the combinatorial sensing of microbial signals, whereby the effects of triplet combinations of microbial signals on immune responses can be predicted by combining the effects of single and pairs. Mechanistically, we find that singles and pairs dictate the information signaled by triplets in mouse and human DCs at the levels of transcription, chromatin and protein secretion. We exploit this simplifying property to develop cell-based immunotherapies prepared with adjuvant combinations that trigger protective responses in mouse models of cancer. We conclude that the processing of multiple input signals by innate immune cells is governed by pairwise effects, which will inform the rationale combination of adjuvants to manipulate immunity.
2020-07-01 | GSE134867 | GEO
Project description:The immune system makes decisions in response to combinations of multiple microbial inputs. We do not understand the combinatorial logic governing how higher-order combinations of microbial signals shape immune responses. Here, using coculture experiments and statistical analyses, we discover a general property for the combinatorial sensing of microbial signals, whereby the effects of triplet combinations of microbial signals on immune responses can be predicted by combining the effects of single and pairs. Mechanistically, we find that singles and pairs dictate the information signaled by triplets in mouse and human DCs at the levels of transcription, chromatin and protein secretion. We exploit this simplifying property to develop cell-based immunotherapies prepared with adjuvant combinations that trigger protective responses in mouse models of cancer. We conclude that the processing of multiple input signals by innate immune cells is governed by pairwise effects, which will inform the rationale combination of adjuvants to manipulate immunity.
2020-07-01 | GSE134869 | GEO
Project description:The immune system makes decisions in response to combinations of multiple microbial inputs. We do not understand the combinatorial logic governing how higher-order combinations of microbial signals shape immune responses. Here, using coculture experiments and statistical analyses, we discover a general property for the combinatorial sensing of microbial signals, whereby the effects of triplet combinations of microbial signals on immune responses can be predicted by combining the effects of single and pairs. Mechanistically, we find that singles and pairs dictate the information signaled by triplets in mouse and human DCs at the levels of transcription, chromatin and protein secretion. We exploit this simplifying property to develop cell-based immunotherapies prepared with adjuvant combinations that trigger protective responses in mouse models of cancer. We conclude that the processing of multiple input signals by innate immune cells is governed by pairwise effects, which will inform the rationale combination of adjuvants to manipulate immunity.
2020-07-01 | GSE134874 | GEO
Project description:<p>The mechanisms by which macrophage metabolism is regulated and the effects of metabolism on diseases remain largely unknown. We show here that TGF-β regulates the glycolysis of macrophages independently of inflammatory cytokine production, and thus affects the survival in experimental sepsis. Specifically, TGF-β increased expression and activity of phosphofructokinase-1 liver type (PFKL) in macrophages and thus promoted their glycolysis during cell activation, yet paradoxically suppressed the production of proinflammatory cytokines in the same macrophages. The upregulation of glycolysis was mediated by a mTOR-c-MYC dependent pathway, whereas the inhibition of cytokines was ascribed to the activation of SMAD3 and a downregulated activation of the pro-inflammatory transcription factors AP-1, NFkB and STAT1. Importantly, in an LPS-induced endotoxemia and CLP-sepsis models, TGF-β enhancement of macrophage glycolysis led to a decreased survival in mice, which was associated with increased blood coagulation. Analysis of cohorts of patients with sepsis and covid-19 revealed that the expression of PFKL, TGF-β receptor TGFBRI and coagulation factor F13A1 in myeloid cells positively correlated with the progression of the disease. Thus, TGF-β is emerging as a critical cytokine regulating macrophage metabolism and could serve as a therapeutic target in patients with sepsis.</p>
2023-02-17 | MTBLS6886 | MetaboLights