May 08, 2023
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Communications Biology volume
Biologia delle comunicazioni volume 6, numero articolo: 525 (2023) Citare questo articolo
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Le cellule endoteliali vascolari (EC) formano un'interfaccia dinamica tra sangue e tessuto e svolgono un ruolo cruciale nella progressione dell'infiammazione vascolare. Qui, miriamo ad analizzare i meccanismi molecolari a livello di sistema delle risposte infiammatorie endoteliali-citochine. Applicando una libreria di citochine imparziale, abbiamo determinato che TNFα e IFNγ inducevano la più ampia risposta EC con conseguente distinte firme infiammatorie proteomiche. In particolare, la stimolazione combinata di TNFα + IFNγ ha indotto un’ulteriore firma infiammatoria sinergica. Abbiamo utilizzato un approccio multi-omico per analizzare questi stati infiammatori, combinando (fosfo-)proteoma, trascrittoma e secretoma e abbiamo trovato, a seconda dello stimolo, un'ampia gamma di processi immunomodulanti alterati, comprese proteine del complemento, complessi MHC e distinti citochine secretorie. La sinergia ha portato all'attivazione cooperativa dell'induzione della trascrizione. Questa risorsa descrive gli intricati meccanismi molecolari che sono alla base dell'infiammazione endoteliale e supporta il ruolo immunomodulatore adattivo dell'endotelio nella difesa dell'ospite e nell'infiammazione vascolare.
Le cellule endoteliali (EC) rivestono l'interno dei nostri vasi sanguigni e formano un'interfaccia dinamica tra il sangue e i tessuti circostanti. Oltre a facilitare lo scambio di ossigeno, sostanze nutritive e prodotti di scarto, le EC controllano l'emostasi attirando le piastrine per sigillare le brecce nelle pareti vascolari durante l'emostasi primaria1. Inoltre, le EC sono guardiani cruciali che controllano il traffico di cellule immunitarie dentro e fuori i tessuti durante l’infiammazione. Per il loro ruolo in questa sinapsi adattativa, le EC sono ben attrezzate per percepire segnali ambientali, come stress meccanico, ormoni (ad esempio, vasopressina, istamina), cellule (ad esempio, neutrofili, monociti, piastrine) e altri stimoli esterni (ad esempio, trombina). , citochine)2,3,4,5. Oltre alla trasmigrazione delle cellule immunitarie, le EC hanno diverse capacità immunomodulatorie come la presentazione dell'antigene e la secrezione di citochine5,6. Tuttavia, sebbene le EC portino queste proprietà immunomodulanti e siano tra le prime cellule a entrare in contatto con gli agenti patogeni, sono raramente menzionate nelle reti di cellule immunitarie7,8,9.
La deregolamentazione dell’omeostasi della CE può provocare stati di infiammazione eccessiva o di ipercoagulazione dell’endotelio. Questa disfunzione endoteliale è implicata in diverse malattie infiammatorie multi-sfaccettate, tra cui danno polmonare acuto correlato alla trasfusione, sepsi, artrite reumatoide, sindrome da distress respiratorio acuto (ARDS), vasculopatie oculari, malattia renale cronica e COVID-1910,11,12,13, 14,15,16,17,18,19.
Sebbene sia l'omeostasi endoteliale che le citochine siano deregolamentate in queste malattie, la base molecolare che orchestra le interazioni adattative endotelio-citochine è per lo più limitata alla ricerca sul fattore di necrosi tumorale alfa (TNFα). Inoltre, il sinergismo tra citochine come TNFα e interferone gamma (IFNy) è stato osservato negli EC e collegato a effetti dannosi nei disturbi infiammatori20,21,22,23. Sebbene siano stati proposti i meccanismi sottostanti, non è stata caratterizzata una risposta a livello di sistema da parte della CE.
Pertanto, in questo studio, abbiamo deciso di analizzare le firme molecolari delle risposte endoteliali-citochine, impiegando cellule endoteliali ematiche (BOEC), note anche come cellule formanti colonie endoteliali, come fonte di EC a causa della loro estesa e robusta espansione, espressione di marcatori EC vascolari maturi e capacità di essere isolati da donatori adulti24,25.
Mostriamo che le EC esprimono il repertorio di recettori per facilitare vari segnali di citochine. Tuttavia, dopo stimolazione con una libreria di citochine imparziale, abbiamo osservato stati infiammatori prevalentemente unici per TNFα e IFNγ. Inoltre, la stimolazione combinata di TNFα e IFNγ ha prodotto una risposta EC sinergica. Combinando più livelli omici, abbiamo analizzato le basi molecolari di questi stati infiammatori dalla segnalazione (fosfoproteoma) alla trascrizione dell'mRNA (trascrittoma), alla regolazione delle proteine (proteoma) e alla secrezione proteica (secretoma). Questo studio rivela stati infiammatori EC adattativi a livello di sistema, sottolineando il ruolo delle interazioni EC-citochine nella patogenesi infiammatoria e ribadendo le EC come un attore adattivo nell'infiammazione.
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>