Aug 05, 2023
multi
Volume de Biologia da Comunicação
Communications Biology volume 6, Número do artigo: 525 (2023) Citar este artigo
689 Acessos
2 Altmétrica
Detalhes das métricas
As células endoteliais vasculares (ECs) formam uma interface dinâmica entre o sangue e o tecido e desempenham um papel crucial na progressão da inflamação vascular. Aqui, pretendemos dissecar os mecanismos moleculares de todo o sistema de respostas inflamatórias endoteliais-citocinas. Aplicando uma biblioteca de citocinas imparcial, determinamos que TNFα e IFNγ induzem a maior resposta de EC, resultando em assinaturas inflamatórias proteômicas distintas. Notavelmente, a estimulação combinada de TNFα + IFNγ induziu uma assinatura inflamatória sinérgica adicional. Empregamos uma abordagem multi-ômica para dissecar esses estados inflamatórios, combinando (fosfo-) proteoma, transcriptoma e secretoma e encontramos, dependendo do estímulo, uma ampla gama de processos alterados de imunomodulação, incluindo proteínas do complemento, complexos MHC e distintos citocinas secretoras. A sinergia resultou na ativação cooperativa da indução da transcrição. Este recurso descreve os intrincados mecanismos moleculares que estão na base da inflamação endotelial e apóia o papel imunomodulador adaptativo do endotélio na defesa do hospedeiro e na inflamação vascular.
As células endoteliais (ECs) revestem o interior de nossos vasos sanguíneos e formam uma interface dinâmica entre o sangue e os tecidos circundantes. Além de facilitar a troca de oxigênio, nutrientes e resíduos, os CEs controlam a hemostasia atraindo plaquetas para selar brechas nas paredes vasculares durante a hemostasia primária1. Além disso, os CEs são porteiros cruciais que controlam o tráfego de células imunes para dentro e para fora dos tecidos durante a inflamação. Por seu papel nesta sinapse adaptativa, os CEs estão bem equipados para detectar sinais ambientais, como estresse mecânico, hormônios (por exemplo, vasopressina, histamina), células (por exemplo, neutrófilos, monócitos, plaquetas) e outros estímulos externos (por exemplo, trombina , citocinas)2,3,4,5. Além da transmigração de células imunes, as CEs possuem diversas capacidades imunomoduladoras, como apresentação de antígenos e secreção de citocinas5,6. No entanto, embora as ECs carreguem essas propriedades imunomoduladoras e estejam entre as primeiras células a entrar em contato com patógenos, elas raramente são mencionadas nas redes de células imunes7,8,9.
A desregulação da homeostase da CE pode resultar em estados superinflamatórios ou hipercoagulação do endotélio. Esta disfunção endotelial está implicada em várias doenças inflamatórias multifacetadas, incluindo lesão pulmonar aguda relacionada à transfusão, sepse, artrite reumatoide, síndrome do desconforto respiratório agudo (SDRA), vasculopatias oculares, doença renal crônica e COVID-1910,11,12,13, 14,15,16,17,18,19.
Embora tanto a homeostase endotelial quanto as citocinas estejam desreguladas nessas doenças, a base molecular que orquestra as interações endoteliais-citocinas adaptativas é principalmente confinada à pesquisa do fator de necrose tumoral-alfa (TNFα). Além disso, o sinergismo entre citocinas como TNFα e interferon-gama (IFNy) foi observado em ECs e relacionado a efeitos deletérios em distúrbios inflamatórios20,21,22,23. Embora mecanismos subjacentes tenham sido propostos, uma resposta de CE em todo o sistema não foi caracterizada.
Portanto, neste estudo, nos propusemos a dissecar as assinaturas moleculares das respostas de citocinas endoteliais, empregando células endoteliais de crescimento sanguíneo (BOECs), também conhecidas como células formadoras de colônias endoteliais, como nossa fonte de ECs por causa de sua expansão extensa e robusta, expressão de marcadores EC vasculares maduros e capacidade de isolamento de doadores adultos24,25.
Mostramos que os ECs expressam o repertório de receptores para facilitar vários sinais de citocinas. No entanto, após estimulação com uma biblioteca de citocinas imparcial, observamos estados inflamatórios predominantemente únicos para TNFα e IFNγ. Além disso, a estimulação combinada de TNFα e IFNγ resultou em uma resposta EC sinérgica. Combinando vários níveis ômicos, dissecamos a base molecular desses estados inflamatórios desde a sinalização (fosfoproteoma) até a transcrição do mRNA (transcriptoma), regulação de proteínas (proteoma) e secreção de proteínas (secretoma). Este estudo revela estados inflamatórios EC adaptativos em todo o sistema, enfatizando o papel das interações EC-citocina na patogênese inflamatória e reiterando ECs como um jogador adaptativo na inflamação.
5). b Cytoscape interaction network of receptors and potential ligands (red dots: receptors, yellow dots: ligands, purple dots: proteins fulfilling both receptor and ligand criteria), edges represent STRING-DB scores. Inserts show zooms of example cytokine-receptor interactions; for network with labels, see Supplementary Fig. 1b./p> 1). b Summarizing network of differentially abundant proteins between stimuli. Node labels show cytokine stimuli. Node size represents amount of statistically significant proteins. Edges show overlap between proteomes, color intensities (white to black) of edges indicate amount of overlapping proteins as a ratio of the smaller node. See Supplementary Fig. 3 for non-summarized network. c Profile plots of modules describing cytokine proteomic responses with cytokine annotation. Gradient scale indicated z-scores of median LFQ-score of genes in a module per stimulus, Yellow: cytokines related to an increased abundance response profile; purple: cytokine(s) related to a decreased protein abundance response, cytokines which contribute to the module regulation are highlighted. Replicates have been summarized to medians for visualization, modules are indicated by color, M1 (pink), M2 (blue), M3 (green), M4 (red) and M5 (yellow). d Proteins with high modules membership scores plotted as median label-free intensities (LFQ). e Enriched GO terms and Wikipathways per module. MF molecular function, CC cellular component, BP biological process./p> 1). d LFQ intensities of hallmark proteins per stimulation./p> 1). c Area plots of cumulative temporal dynamics of changes in the phosphoproteome (teal areas and solid lines), transcriptome (brown area and dotted lines) and proteome (purple areas and dashed lines). d Tile plot of the top enriched GO terms per stimulation: IFNγ (pink), TNFα (orange), TNFα + IFNγ (green) and omics level (as indicated). Color gradient indicated –log10 BH-adjusted p values. MF molecular function, CC cellular component, BP biological process. e Line plots of phosphorylation events, transcript levels and relative protein abundances of members of highly enriched GO:terms. Circles indicate medians; error bars show standard deviations (n = 3 biological replicates)./p>0.95) interactions. This resulted in a network containing 2306 interactions, revealing 9 high-density hubs summarized in biological processes: "Viral sensors", "Cytokines", "Complement factors", "JAK/STAT signaling", "Cell cycle", "NFKB signaling", "Proteasome", "NFKB complex" and "Antigen presentation" (Fig. 5d and Supplementary Fig. 6). Plotting the ratio of response classifications per hub, only two were majorly TNFα induced: NF-κB complex proteins (47% TNFα) and cytokines (44% TNFα), while all others were primarily IFNγ-induced. Especially the hubs, "Complement factors", "Viral sensors", "Proteasome" and "Antigen presentation" were predominantly IFNγ-induced (>75%). None of the hubs were majorly synergistically induced, suggesting synergy is confined to specific proteins and not entire biological processes./p>5-fold) (Fig. 6b). C3, crucial in the activation of the alternative pathway, is the only uniquely TNFα-induced transcript in this hub. However, whether transcript expression translated to protein increases is unclear as corresponding proteins were not detected. IFNγ also induced a strong antigen-presenting hub (Fig. 6c). We previously reported TNFα induces MHCI proteins, including HLA-A, HLA-B and HLA-C, which we observed here too36. However, these MHCI complex proteins as well as immunoproteasome (PSMB8, PSMB9 and PSMB10) and immunoproteasome regulator subunits (PSME1 and PSME2), peptide loading proteins (TAP1, TAP2, ERAP1 and ERAP2)37,38 and immune checkpoint protein Programmed death- ligand 1 (CD274) were higher induced by IFNγ compared to TNFα. Moreover, IFNγ also induced MHCII complexes required for exogenous antigen presentation. HLA-DR, HLA-DQ and HLA-DP transcripts were upregulated 4–7-fold at 12–24 h of IFNγ stimulation. Interestingly, in contrast to MHCI proteins, which were detected abundantly on the protein level, we were only able to detect HLA-DRA and HLA-DRB in separate LFQ workflow experiments (Supplementary Fig. 7a). To visualize the discrepancy between MHCI and MHCII protein expression, we stained BOECs for HLA-A/B/C or HLA-DR after stimulation of TNFα, IFNγ or combined stimulation. MHCI showed a clear distribution over the cell membrane, also in steady-state condition (Supplementary Fig. 7b) and in line with both transcriptome and protein data, HLA-DR was only observed in IFNγ stimulated conditions. However, in contrast to the membrane distribution of HLA-A/B/C, HLA-DR was mostly localized to compartments inside the cell (Fig. 6d)./p> 1). Colors indicate stimulus: TNFα (green), IFNγ (blue), TNFα + IFNγ (red). c Interaction network of differentially regulated proteins after 24 h TNFα + IFNγ stimulation showing protein type per hub indicated in gray. d Heatmap of enriched proteins in the cytokine registry per omics level showing correlating transcripts and proteins in lysate after TNFα, IFNγ and TNFα + IFNγ stimulation. Color gradient indicates z-scores. Several proteins are highlighted in line plots showing VST and LFQ values, error bars show standard deviation (n = 3 biological replicates). e Number of papers enriching for cell type interactions by cytokines induced per stimulation. Node size represents number of papers, per stimulus largest node is set to most cited cell type (TNFα: n citations = 566, IFNγ: n = 140, TNFα + IFNγ: n = 153)./p>95% in the total proteome./p>1./p> 1 was considered significant and relevant. For label-free secretomics data, a BH-adjusted p < 0.01 and log2 fold change > 1 was used as the significance threshold./p>0.4) were selected and annotated for Uniprot "secreted" and "signal" keywords or GO:CC "extracellular space" and "extracellular region" terms to define receptor ligands. Connections between receptors and ligands were visualized in Cytoscape 3.8.0./p>0.7 with TNFα and <0.7 with IFNγ stimulation); S2-IFNγ shape (correlation coefficient <0.7 with TNFα and >0.7 with IFNγ stimulation); S3-common shape (correlation coefficient >0.7 with TNFα and >0.7 with IFNγ stimulation) or S4-TNFα + IFNγ shape (correlation coefficient <0.3 with TNFα and <0.3 with IFNγ stimulation). Effect sizes were categorized as E1-TNFα effect (AUC ratios TNFα + IFNγ stimulation/TNFα stimulation <2 and TNFα + IFNγ stimulation/IFNγ stimulation >2); E2-IFNγ effect (AUC ratios TNFα + IFNγ stimulation/TNFα stimulation >2 and TNFα + IFNγ stimulation/IFNγ stimulation <2), and E3-TNFα + IFNγ effect (AUC ratios TNFα + IFNγ stimulation/TNFα stimulation >2 and TNFα + IFNγ stimulation/IFNγ stimulation >2). Classifications were set to common if S3 but not E3 criteria were fulfilled; TNFα classification: S1 or S3 + E1; IFNγ classification: S2 or S3 + E2; TNFα + IFNγ classification: S3 or E3 classifications were fulfilled. If not fulfilling any shape or effect size cutoffs, classification was set to "not classified"./p>0.9 gene names were connected with edges. Edges between phosphosites, corresponding proteins and transcript were manually appended to the network. This network was visualized in Cytoscape 3.8.0. We first obtained the "EdgeBetweenness" using the "Analyse Network" function, after which we used "Edge-weighted Spring Embedded Layout" to visualize the network. We highlighted interaction hubs based on closeness of nodes, overall regulation levels and biological overlap./p>
Português
English
Français
Deutsch
Español
Italiano
日本語
한국어
Русский
Lar