Graphical abstract

   [47]graphic file with name 00486-2024.GA01.jpg
   [48]Open in a new tab

   PAH: pulmonary arterial hypertension; PDE5i: phosphodiesterase-5
   inhibitor; EDDY: Evaluation of Differential DependencY; NK: natural
   killer.

Abstract

Background

   Pulmonary arterial hypertension (PAH) is a deadly disease without
   effective non-invasive diagnostic and prognostic testing. It remains
   unclear whether vasodilators reverse inflammatory activation, a part of
   PAH pathogenesis. Single-cell profiling of inflammatory cells in blood
   could clarify these PAH mechanisms.

Methods

   We evaluated a University of Pittsburgh Medical Center cohort
   consisting of idiopathic PAH (iPAH) and systemic sclerosis-associated
   PAH (sscPAH) patients and non-PAH controls. We performed single-cell
   RNA sequencing of peripheral blood mononuclear cells (PBMCs) from
   controls (n=3) and from PAH patients (iPAH and sscPAH) naïve to
   treatment (n=4), PAH patients 3 months after phosphodiesterase-5
   inhibitor (PDE5i) treatment (n=7) and PAH patients 3 months after
   PDE5i+macitentan treatment (n=6). We compared the transcriptomes of
   five PBMC subtypes from iPAH and sscPAH to observe their serial
   responses to treatments. Furthermore, we utilised network analysis to
   illuminate the altered connectivity of biological networks in this
   complex disease.

Results

   We defined differential gene expression and perturbed network
   connectivity in PBMCs of PAH patients following treatment with PDE5i or
   PDE5i+macitentan. Importantly, we identified significant reversal of
   inflammatory transcripts and pathways in the combined PAH patient
   cohort after vasodilator therapy in every PBMC type assessed. The
   “glucagon signalling in metabolic regulation” pathway in monocytes was
   reversed after vasodilator therapy via two independent analysis
   modalities.

Conclusion

   Via a systems-biology approach, we define inflammatory reprogramming in
   the blood of PAH patients and the anti-inflammatory activity of
   vasodilators. Such findings establish diagnostic and prognostic
   blood-based tools for tracking inflammatory progression of PAH and
   response to therapy.

Shareable abstract

   In pulmonary hypertension, immune cell transcriptional reprogramming
   was reversed by vasodilators. This work establishes blood-based tools
   for tracking pulmonary arterial hypertension (PAH) and response to
   therapy. [49]https://bit.ly/46g1DQQ

Introduction

   Pulmonary arterial hypertension (PAH) is a progressive disease of the
   pulmonary vasculature without cure. The diagnosis is established after
   right heart catheterisation [[50]1], an invasive haemodynamic
   procedure. There is a need for early and accurate non-invasive
   diagnostic and prognostic tools.

   Systemic inflammation is increasingly associated with PAH pathogenesis
   [[51]2]. Perivascular inflammation in the lungs of PAH patients
   contributes to vascular remodelling [[52]3–[53]5]. In particular,
   myeloid cells are recruited from the bone marrow contributing to the
   engraftment of immune cells from blood to lung [[54]5–[55]7]. Thus,
   inflammatory cells in the blood are likely to reflect initiation and
   progression of PAH in the lung and are translationally relevant to PAH
   pathogenesis.

   Blood-based measures of immune activation in PAH could address a
   controversy in the field, namely whether current vasodilator drugs
   reverse immune reprogramming. It has been assumed that these drugs act
   through vasomotor tone pathways and do not prevent or regress the
   anti-apoptotic and pro-proliferative phenotypes of vascular cells in
   PAH. Phosphodiesterase-5 inhibitors (PDE5i) primarily control nitric
   oxide, a vasodilator, while endothelin-receptor antagonists, such as
   macitentan, inhibit endothelin vasoconstriction signalling [[56]8].
   PDE5i [[57]9, [58]10] and endothelin-receptor antagonists [[59]11,
   [60]12] may harbour anti-inflammatory properties. But this mechanism
   has not been well-defined in PAH patients, and whether such putative
   actions are due to direct drug effects on inflammatory cells or to
   indirect actions on systemic immunoactivation has not been fully
   explored. Furthermore, while transcriptomes in circulating immune cells
   have been reported [[61]13–[62]19], serial stages of PAH with or
   without treatment have not been evaluated. Such longitudinal profiling
   across untreated, single and combination drug treatments is especially
   challenging to study in the current era where immediate dual and
   sometimes triple combination vasodilator therapy is recommended for
   symptomatic PAH patients, based on the Ambrisentan and Tadalafil
   Combination Therapy in Subjects with Pulmonary Arterial Hypertension
   (AMBITION) trial [[63]20]. Single-cell analyses could address these
   gaps and pave the way for clinical platforms. To do so, we leveraged
   orthogonal analyses beyond differential gene expression (DGE), which
   often underestimate intergenic relationships [[64]21–[65]23]. We
   further defined intergenic relationships by analysing single-cell RNA
   sequencing (scRNA-seq) data via the Evaluation of Differential
   DependencY (EDDY) [[66]24, [67]25], which defines differential
   dependency networks (DDNs) based on the differential interconnectivity
   of genes in a pathway. Such connectivity can be perturbed in untreated
   versus treated disease. We sought to identify the effect of PDE5i and
   PDE5i+macitentan therapy on disease-based networks. A combination of
   DGE and EDDY analyses of scRNA-seq data could identify the
   comprehensive landscape of transcriptional inflammatory cell
   reprogramming in PAH.

   Therefore, this systems-biology study in PAH incorporated both DGE and
   differential network connectivity analyses of longitudinal patient
   samples collected from untreated patients, from patients undergoing
   single and combination vasodilator therapy and from patients recruited
   prior to the initiation of combination therapy in PAH. In doing so, we
   sought to define at single-cell resolution: 1) any differences between
   idiopathic PAH (iPAH) and systemic sclerosis-associated PAH (sscPAH)
   patients in such responses, and 2) any evidence of immune cell
   reprogramming after either single or combination vasodilator therapy,
   as reflected by reversal of immune cell transcriptomic changes in PAH.

Methods

Patient data

   We isolated peripheral blood mononuclear cells (PBMCs) from patients
   seen at the University of Pittsburgh Medical Center via peripheral
   venous blood draw. sscPAH patients (n=4) and iPAH patients (n=5) were
   recruited to the study. Serial samples were obtained at the untreated
   PAH stage, after receiving stable PDE5i for 3 months and 3 months after
   the addition of macitentan treatment. A similar population of cells was
   isolated from non-diseased matched control persons (non-PAH control
   patients (n=3): a 66-year-old female, a 33-year-old male and a
   28-year-old female). Blood was analysed from untreated PAH patients
   (sscPAH, n=2; iPAH, n=2; total untreated PAH, n=4), PAH patients
   receiving PDE5i treatment (sscPAH, n=3; iPAH, n=4; total PAH+PDE5i,
   n=7) and PAH patients receiving PDE5i and macitentan treatment (sscPAH,
   n=3; iPAH, n=3; total PAH PDE5i+macitentan, n=6). sscPAH and iPAH
   patient samples were combined to increase the statistical power of the
   analyses for the PAH group.

   The inclusion criteria for sscPAH and iPAH patients were as follows:
   patients aged 18–75 years; a confirmed diagnosis of sscPAH or iPAH by
   right heart catheterisation; mean pulmonary arterial pressure >25 mmHg;
   pulmonary capillary wedge pressure <15 mmHg; pulmonary vascular
   resistance >3 Wood Units; New York Heart Association Class I–III;
   current pulmonary vasodilator therapy with PDE5i; and ability to take
   10 mg of macitentan p.o. daily, for at least 3 months, as per standard
   of care. The exclusion criteria included group II–V pulmonary
   hypertension, treatment-naïve pulmonary hypertension patients, patients
   on combination pulmonary vasodilator therapy, patients on prostacyclin,
   riociguat or endothelin-receptor antagonist therapy, inability to
   maintain dosing of macitentan (10 mg p.o. daily) for the 3-month trial
   period. [68]Supplementary table S1 lists the demographics of PAH
   patients and control individuals recruited for this study. The number
   of recruited patients was determined primarily by the availability of
   clinical samples. iPAH and sscPAH patient PBMCs were utilised for the
   single-cell analysis of monocytes, B cells, CD8+ T cells, CD4+ T cells
   and natural killer (NK) cells. Experimental procedures involving human
   tissue were approved by institutional review boards at the University
   of Pittsburgh (IRB STUDY19070123). Ethics approval for this study and
   informed consent conformed to the standards of the Declaration of
   Helsinki.

Single-cell RNA sequencing

   Blood samples collected in EDTA purple-top Vacutainers were centrifuged
   at 2000 rpm for 15 min and plasma was removed for banking. Buffy coats
   were collected and used for Ficoll gradient to isolate PBMCs, which
   were used for the scRNA-seq analysis. RNA libraries were prepared using
   Chromium Single Cell 3' Reagent Kits v.2 per manufacturer's guidelines
   (10x Genomics manual CG00052, catalogue no. PN-12237). scRNA-seq was
   performed using the GemCode Single Cell Instrument (Chromium, 10x
   Genomics), which included at least 800 cells (∼50,000 reads per cell)
   per each patient sample. Lung samples were processed and analysed as
   described previously [[69]26].

Bioinformatics analysis

   Raw data from scRNA-seq were processed using 10x Genomics’ Cell Ranger
   v.8 to generate raw counts data for individual cells. Raw counts data
   were first analysed for normalised counts and potential batch
   corrections among different patient samples using SCTransform in Seurat
   v.3 [[70]24, [71]27, [72]28]. Cell types were determined with SingleR
   [[73]25] using the Blueprint ENCODE reference [[74]29, [75]30].
   Normalised counts were then analysed for differential gene dependency
   networks and for DGE analysis, followed by pathway enrichment analysis.
   For DGE analysis of each cell type between different conditions, the
   normalised expression matrices were analysed using the Wilcoxon
   rank-sum test implemented in the FindMarker function in Seurat [[76]24,
   [77]27, [78]28] with 10 minimum percent expression threshold and 0.1
   logfc threshold. The Benjamini–Hochberg-adjusted p-value cut-off was
   set to 0.05. Gene set enrichment analysis (GSEA) was conducted on
   differentially expressed genes (DEGs) with the Enrichr R package using
   the “Reactome_2022” databases [[79]27]. The false discovery rate
   (FDR)-adjusted p-value cut-off was set to 0.05. Statistical testing of
   cell proportions was conducted using propeller [[80]28].

EDDY analysis to identify pathways with differential dependency

   Traditional pathway enrichment analysis following differential
   expression analysis often underestimates the systems-level interactions
   underlying complex biological processes present in drug response. To
   complement DGE-based pathway analysis, higher-level network analyses
   were undertaken. That is, we engineered a computational strategy that
   identifies DDNs, which are crucial to defining the landscape of
   transcriptional network reprogramming expected in cells’ response to
   therapies across the sampled cell types. We built upon the capabilities
   of a previously developed computational method, EDDY [[81]24, [82]25],
   a highly specific and sensitive computational pipeline that defines
   DDNs based on the rewiring of interactions among genes in a network
   when comparing two conditions. Previously, we applied EDDY to identify
   rewired DDNs in PAH lung tissue versus control lung tissue [[83]31]. We
   leveraged the principles of EDDY for use in single-cell transcriptomic
   data, defining the comprehensive gene expression rewiring across the
   sampled blood-borne cell types, in the context of PAH disease states
   and vasodilator treatment.

   Gene–gene interactions are highly context-dependent; hence,
   identification of the rewiring of such interactions may reveal complex
   biological processes involved in the therapeutic responses of cells, as
   well as the development of disease. EDDY4 was developed to capture such
   context-dependent gene interactions by combining robust statistical
   analysis of gene expression data and pathway databases, such as
   Reactome.

   Given a gene set, EDDY constructs graphs for gene dependencies in each
   condition, for example, B cells of untreated PAH, where edges between
   nodes were defined by a pairwise independence test (chi-square test) of
   gene expression, with known interactions (edges) were given priority.
   By repeated resampling of each group (B cells of untreated PAH versus
   non-PAH control), multiple unique networks were constructed for each
   group; upon scoring, each group was characterised with a network
   likelihood distribution. Divergence between two network likelihood
   distributions was computed via Jensen–Shannon entropy and the
   statistical significance (p-value) of the divergence between the two
   distributions was assessed via permutation test. Thus, each gene set in
   a pathway database, for example Reactome, was assessed for
   Jensen–Shannon entropy and corresponding p-value, and gene sets with
   statistically significant differential dependency among gene networks
   between two conditions were catalogued. A pathway with differential
   dependency can be visualised as a DDN. For visualisation, each
   characteristic line in DDNs indicates the identified relationship
   between nodes (genes). A line indicates a gene-to-gene connection.

Cell culture

   THP-1 (human leukaemia monocytic cell line) monocytes (ATCC, TIB-202)
   were grown to confluency and serum-starved in 0.1% FBS+RPMI-1640
   (Gibco, 11875093) either with or without exposure to 10 ng⋅mL^−1 of
   lipopolysaccharide (LPS) (Sigma, L4391-1MG) for 24 h. After 24 h, cells
   were re-plated at 5×10^5 cells per well in a six-well plate in 10%
   FBS+RPMI-1640. Cells with exposure to LPS were treated with 25 μM of
   sildenafil+25 μM of macitentan (Sigma, S-010-1ML; Sigma, SML3386-10MG);
   cells without exposure to LPS were treated with drug vehicles. Methanol
   or methanol+dimethyl sulfoxide (DMSO) were used as vehicle controls.

RNA isolation, reverse transcription and quantitative real-time PCR

   Total RNA from cells was isolated using QIAzol lysis reagent (QIAGEN)
   according to the manufacturer's protocol. 500 ng of total RNA was
   reverse transcribed into cDNA using a high-capacity cDNA reverse
   transcription kit (Thermo Fisher Scientific). Quantitative real-time
   PCRs were performed on a QuantStudio 6 Flex Real-time PCR System
   (Applied Biosystems). TaqMan Fast Advanced Master Mix (Thermo Fisher
   Scientific, 4444965) and TaqMan primers were used for GNG5
   (Hs00893832_g1), GNG11 (Hs00914578_m1) and GNB2 (Hs00929275_g1). PGK1
   (Hs00943178_g1) was used as a housekeeping gene control to normalise
   the data.

Results

scRNA-seq identified transcriptomic and connectome changes in PBMCs in PAH
(naïve to PAH treatment) versus non-PAH individuals

   To define the transcriptional landscape across blood inflammatory
   cells, we compared scRNA-seq datasets from PBMCs of PAH patients
   (combined iPAH and sscPAH) naïve to vasodilator treatment (untreated
   PAH, n=4) with those of non-PAH control individuals (n=3) ([84]figure
   1a and [85]supplementary tables S1 and [86]S2). Alterations of single
   gene expression were assessed through DGE analysis, and alterations of
   network connectivity were defined via EDDY.

FIGURE 1.

   [87]FIGURE 1
   [88]Open in a new tab

   The single-cell RNA sequencing (scRNA-seq) analysis modalities
   differential gene expression (DGE)/gene set enrichment analysis (GSEA)
   and Evaluation of Differential DependencY (EDDY) reveal pulmonary
   arterial hypertension (PAH)-specific signatures in patient peripheral
   blood mononuclear cells (PBMCs). a) scRNA-seq data were obtained from
   PBMC samples harvested from the following conditions: non-PAH control,
   untreated PAH (PAH patients naïve to vasodilator treatment), PAH
   patients treated with phosphodiesterase-5 inhibitor (PDE5i) and PAH
   patients treated with PDE5i+macitentan. Pre-processing and DGE analyses
   were conducted for each group. Reactome pathways were identified using
   GSEA to reveal changes in immune gene expression under disease and
   treatment conditions. As an orthogonal method for validating the
   enrichment of significant pathways, biological networks were analysed
   via EDDY. EDDY connects differentially expressed genes (DEGs) via
   characteristic lines forming networks based on disease or treatment
   state. Thus, each condition (health versus disease) can change how
   different genes are connected. b,c) Representative dot plots showing
   the top 20 b) upregulated genes and c) downregulated genes in untreated
   PAH, compared with non-PAH controls, in each major PBMC type (adjusted
   p-value <0.05). NK: natural killer. *: HLA or S100 superfamily
   transcripts.

   We utilised post hoc identification of cell type-specific transcripts
   to generate uniform manifold approximation and projection (UMAP) plots
   displaying clustering of major cell types ([89]supplementary figure
   S1a–d). Upon cell type identification, modest differences were noted in
   untreated PAH versus non-PAH controls in terms of cell proportions
   across the major cell type populations ([90]supplementary figure S1e).
   Based on consistent identification of predominant cell types across
   samples, further analysis focused on B cells, CD4+ T cells, CD8+ T
   cells, monocytes and NK cells.

   To define cell type-specific transcriptome changes, we compared
   untreated PAH with non-PAH conditions in these cell types and
   investigated the top 20 up- or downregulated DEGs ([91]figure 1b and c,
   and [92]supplementary table S3). Notably, genes related to the human
   leukocyte antigen (HLA) clusters, such as HLA-A, HLA-DPA1 and HLA-B,
   were upregulated ([93]figure 1b). These findings are consistent with
   those of genome-wide association studies whereby a gene locus HLA-DPA1
   is associated with risk and severity of PAH [[94]32–[95]34]. The S100
   superfamily was upregulated in multiple cell types, consistent with its
   known role in immune and vascular regulation in PAH [[96]35]. Several
   cell types had prominent downregulation of ribosomal protein (RP)
   superfamily genes, which are associated with fundamental processes such
   as protein translation ([97]figure 1c and [98]supplementary table S3).
   Furthermore, monocytes displayed prominent downregulation of HLA-DQB1,
   -DRA and -DQA1, while HLA-C was downregulated in B cells, CD4+ T cells,
   CD8+ T cells and NK cells, highlighting the dynamic role of immune
   processes and antigen presentation to the vascular endothelium in PAH.

   To further define regulatory pathways represented by the gene
   expression alterations in untreated PAH versus non-PAH control in each
   major cell type, we performed GSEA [[99]27]. Overall, GSEA identified
   enrichment of 44 pathways in B cells, 45 in CD4+ T cells, 70 in CD8+ T
   cells, 177 in monocytes and 54 in NK cells ([100]supplementary table
   S4). Top 20 pathways as defined by the Reactome pathway database
   [[101]36] were overrepresented in all five immune cell types and were
   associated with transcription and translation, cell cycle and infection
   ([102]figure 2a and [103]supplementary table S7).

FIGURE 2.

   [104]FIGURE 2
   [105]Open in a new tab

   Enrichment of cell-specific pathways in pulmonary arterial hypertension
   (PAH) peripheral blood mononuclear cells (PBMCs) via gene set
   enrichment analysis (GSEA) and Evaluation of Differential DependencY
   (EDDY). a) Heat map showing the top 20 Reactome pathways in untreated
   PAH patients versus non-PAH controls in each cell type (adjusted
   p-value <0.05). Red indicates pathway enrichment. Blue indicates no
   enrichment. Significant pathway overlap was noted in all five major
   cell types. b) Dot plot showing differentially connected pathways
   identified by EDDY (p-value <0.05). The rewiring score indicates the
   degree of differential connectivity in each pathway in untreated PAH
   patients versus non-PAH controls. Highlighted pathways indicate overlap
   with GSEA analysis. NK: natural killer; SARS-CoV-2: severe acute
   respiratory syndrome coronavirus 2; eIF: eukaryotic initiation factor;
   ROBO: Roundabout; SRP: signal recognition particle; NMD:
   nonsense-mediated decay; EJC: exon junction complex; GTP: guanosine
   triphosphate; FGFR: fibroblast growth factor receptor; Nef: negative
   regulatory factor; VEGF: vascular endothelial growth factor; CDK:
   cyclin-dependent kinase; MHC: major histocompatibility complex; TCR: T
   cell receptor; TCA: tricarboxylic acid; FGF: fibroblast growth factor;
   CCT/TRiC: chaperonin containing TCP-1/T-complex protein ring complex.

   Beyond the DGE and GSEA analytics, EDDY offered orthogonal evidence
   into connectivity within intergenic networks across immune cells in
   each condition, annotated by the Reactome database. Disease states or
   response to treatment can alter intergenic connectivity, resulting in
   loss or gain of connections ([106]figure 1a). EDDY revealed 57
   differentially connected networks (p<0.05, as annotated by Reactome) in
   the untreated PAH condition (compared with non-PAH individuals)
   ([107]figure 2b and [108]supplementary table S5). To define
   particularly significant immune reprogramming in PAH, we focused on
   pathways that were identified by both GSEA and EDDY analyses.
   Comparisons between these modalities revealed no overlapping pathways
   in B cells, CD4+ T cells, NK cells or CD8+ T cells, but four unique
   overlapping pathways (GSEA: adjusted p<0.05 and EDDY: p<0.05) were
   observed in monocytes, suggesting a particularly active reprogramming
   of this pro-inflammatory cell type in PAH ([109]figure 2b and
   [110]supplementary tables S3 and [111]S4). Specifically, the pathways
   entitled “antigen presentation: folding assembly and peptide loading of
   class I MHC” (GSEA: adjusted p=4.7589×10^−5; EDDY: p=0.003147),
   “complement cascade” (GSEA: adjusted p=0.03495598; EDDY: p=0.004407),
   “downstream TCR signalling” (GSEA: adjusted p=0.00468134; EDDY:
   p=0.006211) and “SPRY regulation of FGF signalling” (GSEA: adjusted
   p=0.00982496; EDDY: p=0.03102) were identified. Thus, overall,
   combining a GSEA and EDDY analytic approach offered a more
   comprehensive landscape of transcriptional reprogramming across the
   PBMC cell types involved, revealing a shared signature of enriched
   pathways, particularly in monocytes. The full analysis generated via
   EDDY is accessible at
   [112]https://chan.vmi.pitt.edu/PBMC_scRNAseq_EDDY/.

iPAH and sscPAH patients display both similarities and key differences in
immune response to vasodilator therapy

   To offer increased granularity of the analysis, we investigated the
   comparisons between the iPAH and sscPAH patient subgroups. sscPAH
   patients have a worse prognosis and response to treatment, partially
   due to the systemic involvement and the inflammatory nature of the
   disease [[113]37]. Therefore, we hypothesised that iPAH and sscPAH
   patients may display different immune responses in the setting of PAH.
   To track the responses of iPAH and sscPAH patients to therapy, we
   utilised scRNA-seq data from iPAH or sscPAH patients prior to treatment
   (iPAH: n=2, sscPAH: n=2), 3 months post PDE5i (iPAH: n=4, sscPAH: n=3)
   and 3 months post PDE5i+macitentan (iPAH: n=3, sscPAH: n=3)
   ([114]supplementary tables S1 and [115]S2). After performing DGE
   analysis comparing untreated iPAH and sscPAH patients, we identified
   differentially up- and downregulated genes, emphasising the differences
   in immune and vascular disease processes ([116]supplementary figure S2a
   and b). Furthermore, we performed GSEA comparing iPAH with sscPAH. The
   top 50 pathways were related to immune system regulation, response to
   stress, metabolism, amino acid, transcription and translation
   mechanisms ([117]figure 3a). In addition, we compared iPAH with sscPAH
   in the setting of PDE5i treatment alone ([118]supplementary figure S3a)
   and in the setting of PDE5i+macitentan treatment ([119]supplementary
   figure S3b). After the addition of treatments, the pathways became more
   uniformly enriched in all immune cell types.

FIGURE 3.

   [120]FIGURE 3
   [121]Open in a new tab

   Comparison between idiopathic pulmonary arterial hypertension (iPAH)
   and systemic sclerosis-associated pulmonary arterial hypertension
   (sscPAH) at baseline and post treatment. a) Dot plot showing pathways
   enriched in iPAH patients versus those enriched in sscPAH patients
   prior to treatment in each immune cell type (adjusted p<0.05). b)
   Proportions (%) of each cell type (B cells, CD4+ T cells, CD8+ T cells,
   monocytes, natural killer (NK) cells) in iPAH or sscPAH patients prior
   to treatment, post phosphodiesterase-5 inhibitor (PDE5i) or post
   PDE5i+macitentan. Significance of changes between proportions in iPAH
   versus sscPAH patients were assessed based on a t-test (false discovery
   rate (FDR) <0.01) [[122]28]. c) Number of differentially expressed
   genes (DEGs) assessed in each cell type in respect of sscPAH (compared
   with iPAH) in each condition, untreated PAH (no tx), after PDE5i and
   after PDE5i+macitentan. DEGs going upwards (+) indicate upregulated
   genes; DEGs in the downwards (−) direction indicated downregulated
   genes. ROBO: Roundabout; SRP: signal recognition particle; GTP:
   guanosine triphosphate; TCA: tricarboxylic acid; NMD: nonsense-mediated
   decay; EJC: exon junction complex; ER: endoplasmic reticulum; TCR: T
   cell receptor; eIF: eukaryotic initiation factor; SARS-CoV-2: severe
   acute respiratory syndrome coronavirus 2; CFTR: cystic fibrosis
   transmembrane conductance regulator; ERAD: endoplasmic
   reticulum-associated protein degradation; PCP/CE: planar cell
   polarity/convergent extension; GAP: GTPase activating protein.

   When comparing the proportions of each cell type in iPAH and sscPAH,
   monocytes were significantly enriched prior to treatment (FDR<0.01);
   however, no differences were observed following PDE5i and
   PDE5i+macitentan treatments [[123]28] ([124]figure 3b). Consistent with
   the more pro-inflammatory sscPAH programme, we observed significantly
   more upregulated DEGs in sscPAH immune cell types than in iPAH immune
   cell types ([125]figure 3c). After the addition of PDE5i, the number of
   DEGs was substantially attenuated in sscPAH, followed by an increase
   after the addition of macitentan. These findings underscore the
   differences in therapy responses in these two PAH subtypes and provide
   evidence for the complex processes occurring at the level of immune
   cells.

   Finally, we investigated individual longitudinal iPAH and sscPAH
   patient samples to track their response to each treatment. We
   established the following longitudinal comparisons: 1) iPAH or sscPAH
   prior to treatment versus non-PAH control, 2) iPAH or sscPAH after
   PDE5i versus iPAH or sscPAH prior to treatment and 3) iPAH or sscPAH
   after PDE5i+macitentan versus iPAH or sscPAH after PDE5i alone. The top
   50 pathways were represented in each comparison, and enrichment or
   depletion of pathways was analysed. Compared with iPAH
   ([126]supplementary figure S4a), sscPAH ([127]supplementary figure S4b)
   displayed more pathway enrichment following PDE5i+macitentan therapy,
   consistent with the increase in DEGs. Overall, these findings highlight
   the differences in transcriptional inflammatory responses between iPAH
   and sscPAH, as well as in responses to vasodilator therapy.

Pathway and network analyses revealed potentially therapeutic reversal of
inflammatory cell reprogramming in PAH patients by vasodilators

   We sought to determine whether vasodilator therapy controls and/or
   reverses inflammatory reprogramming in the blood of PAH patients.
   Specifically, we searched for pathways across inflammatory cell types
   that displayed DGE in untreated PAH versus non-PAH control groups but
   reversed directionality with single (PDE5i) or combinatorial
   (PDE5i+macitentan) treatment, indicating a therapeutic reprogramming of
   these immune cells with vasodilator treatment. Thus, we compared
   scRNA-seq of PBMCs in the combined iPAH and sscPAH patient cohort
   across therapeutic time points: 1) untreated PAH patients (prior to
   therapy, n=4); 2) 3 months after PDE5i therapy (n=7); and 3) 3 months
   after PDE5i+macitentan therapy (n=6) ([128]supplementary tables S1 and
   [129]S2). Across these datasets, we applied the systems-biology
   analytic pipeline, including GSEA and EDDY analyses ([130]figure 1a).

   Among those who received treatment, significant transcript changes were
   identified in nearly every inflammatory and immune cell type screened,
   with a substantial proportion displaying reversal of gene expression
   directionality, compared with untreated PAH versus non-PAH controls
   (individual DEG reversal adjusted p-value <0.05). Reversal of gene
   expression was observed after PDE5i alone, while other DEGs displayed
   reversal with PDE5i combined with macitentan ([131]figure 4a and
   [132]supplementary table S6). The reversal DEGs obtained from the
   dataset for each cell type were highly significant, based on empirical
   null distribution (B cells: n=24, p-value <2.2×10^−16; CD4+ T cells:
   n=67, p-value <2.2×10^−16; CD8+ T cells: n=108, p-value <2.2×10^−16;
   monocytes: n=183, p-value <2.2×10^−16; NK cells: n=66, p-value
   <2.2×10^−16) ([133]supplementary figure S5a–e). Of relevance for
   macitentan, a specific cohort of genes displayed reversal of disease
   expression only after addition of macitentan, but not with PDE5i alone.
   Similarly, enhanced fold change was noted in some gene subsets,
   particularly after macitentan therapy, compared with treatment with
   PDE5i only. These findings define extensive transcriptional alterations
   in inflammatory cells triggered by PAH, but reversed with PDE5i or
   combinatorial drug treatment. To identify the regulatory pathways
   reversed by PAH treatment, we performed GSEA of reversed DEGs and
   constructed a representative heat map of the top 20 Reactome terms for
   each immune cell type ([134]figure 4b and [135]supplementary table S7).
   Most of the pathways significantly overlapped in each cell type.
   Fundamental cellular processes were represented consistently across all
   cell types, including DNA damage, RNA processing, metabolism and
   adaptive and innate immune functions.

FIGURE 4.

   [136]FIGURE 4
   [137]Open in a new tab

   Single-cell RNA sequencing (scRNA-seq) analysis showed reversal of
   differential gene expression after treatment with phosphodiesterase-5
   inhibitor (PDE5i) and with PDE5i+macitentan in each cell type. a) Heat
   map diagrams showing differentially expressed genes (DEGs) (adjusted
   p-value <0.05) reversed in each condition: untreated PAH versus non-PAH
   control (PAH no treatment (tx) versus non-PAH control); PAH patients
   treated with PDE5i versus untreated PAH (PAH+PDE5i versus PAH no tx);
   PAH patients treated with PDE5i+macitentan versus untreated PAH
   (PDE5i+M versus PAH no tx). b) The top 20 Reactome pathways constructed
   using genes reversed following vasodilator therapy (false discovery
   rate (FDR) <0.05) in each cell type. NK: natural killer; SRP: signal
   recognition particle; ROBO: Roundabout; NMD: nonsense-mediated decay;
   EJC: exon junction complex; GTP: guanosine triphosphate; eIF:
   eukaryotic initiation factor. *: representative DEGs that underwent
   reversal only after the addition of macitentan. ^#: representative DEGs
   that showed stronger log fold change only after the addition of
   macitentan. Statistical analysis for DEG reversal is shown in
   [138]supplementary figure S5.

   In parallel, by application of EDDY, we identified a complementary set
   of networks that were differentially connected in any of the following
   comparisons: 1) untreated PAH versus non-PAH control, 2) PDE5i versus
   untreated PAH and 3) PDE5i+macitentan versus untreated PAH
   ([139]supplementary figure S6a–e). Differential connectivity implicated
   the activity of these gene networks in response to disease or to
   vasodilator treatment. As with the DGE analysis, we were specifically
   interested in identifying pathways that were reprogrammed with
   treatment – that is, pathways that showed altered connectivity in PAH
   and displayed reversal to baseline non-diseased connectivity after
   PDE5i or PDE5i+macitentan treatment. In untreated PAH, we identified
   reprogramming in four pathways in B cells, seven in CD4+ T cells, seven
   in CD8+ T cells and seven in NK cells. As with the DGE analysis,
   monocytes carried the most pathways (24) with altered connectivity in
   PAH ([140]supplementary figure S6a–e). We then sought to further define
   reversal of individual connections (presence or absence) between genes
   in serial stages of treatment: untreated PAH, after PDE5i and after
   PDE5i+macitentan. Reversal of gene connectivity was defined as “ON”
   when a connection was present in non-PAH individuals, absent in
   untreated PAH and present again after the addition of either PDE5i or
   PDE5i+macitentan. Conversely, the “OFF” reversal state was computed,
   defined by a connection absent in non-PAH, present in untreated PAH and
   absent again with either PDE5i or PDE5i+macitentan use. After
   quantification of the number of “ON” and “OFF” states
   ([141]supplementary figure S7), such patterns were identified in every
   cell type. However, they were most prominently observed in monocytes,
   for which more significant “ON”/“OFF” reversals were acquired after the
   addition of macitentan, highlighting its potential additive effect to
   PDE5i ([142]figure 5a).

FIGURE 5.

   [143]FIGURE 5
   [144]Open in a new tab

   Gene set enrichment analysis (GSEA) and Evaluation of Differential
   DependencY (EDDY) revealed an overlapping reversed pathway “glucagon
   signalling in metabolic regulation”. a) Reversal of pathway
   connectivity in monocytes identified by EDDY. Reversal was defined as
   “ON” when connections were present in non-pulmonary arterial
   hypertension (PAH) control, absent in untreated PAH and present again
   after the phosphodiesterase-5 inhibitor (PDE5i) or PDE5i+macitentan
   therapy. Alternatively, reversal was defined as “OFF” when connections
   were absent in the non-PAH control, present in untreated PAH and absent
   again after PDE5i or PDE5i+macitentan therapy. The highlighted
   “glucagon signalling in metabolic regulation” pathway identified by
   EDDY was also identified via GSEA analysis. “ON” reversal is shown with
   blue bars; “OFF” reversal is shown with red bars. b) Heat map for the
   “glucagon signalling in metabolic regulation” pathway showing reversed
   differentially expressed genes (DEGs) in untreated PAH followed by
   PDE5i+macitentan. Conditions shown were matched to those of [145]figure
   4a. Reversal was represented by log fold change (0.1 to −0.1; red to
   blue), showing the effects of each treatment. c) “ON” and “OFF”
   networks were generated via EDDY for the “glucagon signalling in
   metabolic regulation” pathway, and showed reversal of intergenic
   connections with PDE5i+macitentan use. Each panel demonstrates a
   dependency network (DN) for the non-PAH control, PAH untreated and
   PDE5i+macitentan groups. Lines represent connections that have
   previously been reported in literature and predicted computationally.
   Only bold lines indicate “ON” and “OFF” states. Orange connections show
   interactions unique to the non-PAH condition; blue connections show
   PDE5i+macitentan-specific interactions; green connections show
   untreated PAH. Grey lines show connections that are shared in all
   networks. TCR: T cell receptor; TCA: tricarboxylic acid; CDK:
   cyclin-dependent kinase; tx: treatment; M: macitentan.

   Notably, “glucagon signalling in metabolic regulation” in monocytes was
   the lone network found to be reversed by vasodilator therapy via both
   GSEA and EDDY pipelines ([146]figure 5a and b). Specifically, DEGs such
   as GNG5, GNB2 and GNG11 within this pathway were reversed by
   vasodilators ([147]figure 5b). Via EDDY analytics, this network
   contained 14 connections in the “ON” state – present in the non-PAH
   state, absent in the PAH state and regained with PDE5i+macitentan use;
   15 connections were present in the “OFF” state – absent in the non-PAH
   state, gained in untreated PAH and absent again with PDE5i+macitentan
   use ([148]figure 5c). No reversal of intergenic connections was
   detected with PDE5i treatment alone.

   To determine if such vasodilator therapy carries direct versus indirect
   effects on “glucagon signalling in metabolic regulation” in monocytes,
   we performed additional validation of the DEGs identified in the
   “glucagon signalling in metabolic regulation” pathway in THP-1
   monocytes ([149]supplementary figure S10). We stimulated THP-1
   monocytes with LPS to induce inflammation and subsequently treated them
   with PDE5i+macitentan. Pro-inflammatory LPS upregulated a known
   inflammation-responsive IRF7 transcript, which was also present in our
   DEG dataset. Notably, sildenafil and macitentan did not alter
   transcript levels of GNG5, GNB2 or GNG11, suggesting that there is not
   a direct vasodilator effect on “glucagon signalling in metabolic
   regulation” in monocytes.

   Taken together, these findings converge on a notion that these
   molecules, and their modulation by PDE5i and macitentan, carry roles in
   regulating the dynamics of monocytes and their role in mediating
   metabolism in PAH – events that may be the drivers of insulin and
   glucagon signalling and could go far beyond control of simply vasomotor
   tone in PAH.

Classical and non-classical monocytes may have distinct roles in PAH

   Since monocytes were strongly activated in the setting of PAH, we
   further investigated the differences between classical (CD14^high
   CD16^low) and non-classical monocytes (CD14^low CD16^high) subtypes.
   Classical monocytes are known to differentiate into tissue resident
   macrophages in the lung, while non-classical have a pro-inflammatory
   role; both play roles in PAH pathogenesis [[150]7, [151]38]. In the
   setting of PAH, classical and non-classical monocytes displayed similar
   top 20 pathway activations ([152]supplementary figure S8a). Treatment
   with PDE5i+macitentan activated different classical and non-classical
   monocyte pathways, suggesting that combinatorial treatment has more
   distinct effects on both cell types than PDE5i alone
   ([153]supplementary figure S8b and c). Furthermore, we investigated the
   activation of the key “glucagon signalling in metabolic regulation”
   pathway in classical and non-classical monocytes ([154]supplementary
   figure S9a and b). Classical monocytes most comprehensively
   recapitulated the regulatory pattern observed in the combined monocyte
   group ([155]figure 5b). Overall, these findings suggest that the
   addition of macitentan has a unique role in activating classical and
   non-classical monocyte cell populations.

Discussion

   In this study, we performed DGE/GSEA and EDDY analytics on serial
   scRNA-seq PAH patient samples to investigate inflammatory reprogramming
   and its relationship to vasodilatory agents. We observed 1) baseline
   and post-treatment differences in iPAH and sscPAH; 2) reversed DEGs and
   networks after either single or combination vasodilator use in the
   combined PAH group (iPAH+sscPAH); and 3) a key reversed pathway,
   indicating a role for glucagon signalling in PAH. These results are
   especially unique since contemporary recruitment of patients undergoing
   both single and dual therapy longitudinally is limited by current PAH
   treatment recommendations for upfront combination therapy [[156]20].
   Ultimately, these findings address a long-standing question regarding
   the anti-inflammatory activity of vasodilator therapy in PAH. In doing
   so, our findings set the stage for the possibility that some of the
   observed efficacy of vasodilators may extend beyond vasodilation alone
   and depend upon their anti-inflammatory properties. Future studies
   should be geared towards determining the contribution of the
   anti-inflammatory nature of these drugs on overall disease progression.
   In doing so, this work also establishes the foundation for single-cell
   “liquid biopsy” tools that could track inflammatory reprogramming in
   PAH.

   Single-cell technology is accelerating rapidly, necessitating advances
   in large-scale analytics of complex data [[157]39]. This study offers
   evidence of significant expanded transcriptional reprogramming,
   otherwise missed from traditional approaches, by applying EDDY to
   scRNA-seq datasets from individual PAH patients alongside DGE and GSEA.
   At a translational level, the DGE/GSEA and EDDY analytics pipelines
   pave the way to monitor real-time evolution of disease, as well as
   identification of new druggable pathways and reversal after delivery of
   therapies, thus informing clinical decision-making.

   Through this process, firstly, we found fundamental differences between
   iPAH and sscPAH and their response to treatments, thus expanding our
   understanding of the immune response in PAH. Our results are consistent
   with the notion that sscPAH represents a pro-inflammatory subtype of
   PAH [[158]40], potentially mediated by expansion of monocytes. Compared
   with iPAH, sscPAH patients also displayed more robust reprogramming
   after treatment with PDE5i+macitentan, which could shed light on the
   proposed unique benefits of PDE5i and/or macitentan for each PAH
   subtype [[159]41].

   Secondly, our findings also support the paradigm whereby vasodilator
   treatment can reprogramme the inflammatory landscape of PAH. At a more
   granular level, several pathways were activated across all major immune
   cell types with disease and treatment, suggesting the potential
   existence of multiple master regulators that orchestrate an
   inflammatory state. Our in vitro data ([160]supplementary figure S10)
   suggest that the anti-inflammatory effect of vasodilators in PAH is
   likely to be due to an indirect mechanism resulting from vasodilation,
   with potential consequent effects on myeloid stimulation at the level
   of the bone marrow [[161]5–[162]7]. It is possible that improved
   haemodynamics via vasodilator treatment attenuate the stress responses
   from the pulmonary vasculature, dampening the pro-inflammatory
   stimulation on the bone marrow. Therefore, in response to less
   inflammatory stimuli, the bone marrow could suppress the activation of
   CD34+/CD133+ pro-angiogenic myeloid progenitor cells, thus leading to
   less pro-inflammatory circulating myeloid lineage cells in the blood
   [[163]42, [164]43].

   Furthermore, the reversal of immune pathways only by the addition of
   macitentan, but not PDE5i alone, indicates a unique immunomodulatory
   activity of each drug. The response to PDE5i and macitentan in
   combination corresponds with recent findings presented in the clinical
   study to compare the efficacy and safety of macitentan and tadalafil
   monotherapies with the corresponding fixed-dose combination therapy in
   subjects with PAH (A DUE) [[165]44], which showed significant
   improvements in outcomes in patients with combination PDE5i and
   macitentan therapy, compared with monotherapy. However, given that
   macitentan was administered in combination with PDE5i here, future work
   will be necessary to decipher the precise immunomodulatory role of
   macitentan alone.

   At a molecular level, via GSEA and EDDY, we identified monocyte
   alterations in PAH [[166]45]. Perhaps most intriguingly, we identified
   reversal of the pathway “glucagon signalling in metabolic regulation”
   in monocytes after therapy. This pathway carries numerous G-protein
   subunit genes, such as GNG5, GNG11 and GNB2, that are linked to
   glucagon and glucagon-like peptide (GLP)-1 signalling, but have never
   previously been linked to PAH, to our knowledge; however, increasingly,
   they are being targeted by GLP-1 agonists to improve cardiovascular
   mortality [[167]46]. Pharmacologic GLP-1 activation in preclinical
   models of PAH have been explored [[168]47–[169]49], and a recent
   clinical trial has indicated the haemodynamic benefits of such therapy
   for patients with PAH and those with chronic thromboembolic pulmonary
   hypertension [[170]50]. Furthermore, GLP-1 agonists harbour
   anti-inflammatory properties [[171]51], which may disrupt monocyte
   recruitment and activation to the perivascular space in the setting of
   pulmonary hypertension. Our findings now set the stage for future
   studies to define whether glucagon signalling alterations in immune
   cells in PAH may be a key cellular target for GLP-1 agonism.

   The limitations of this study should be acknowledged. First, our study
   included a modest sample size, which could limit the full
   generalisability of our findings across all PAH patients and across all
   therapies of the same vasodilator classes. While we would have
   preferred to recruit more participants, our ability to offer sequential
   single and dual combination vasodilator therapy in a single PAH patient
   became ethically challenging as the study progressed, given the
   advancing standard of care to initiate immediate dual combination
   therapy, based on the results of the AMBITION trial [[172]20].
   Nonetheless, given the longitudinal nature of the study, whereby each
   patient could be used as their own control, and the multimodal analytic
   pipeline available for each individual subject, the results still hold
   substantial merit, especially considering the granularity of the
   findings and the now unique nature of molecular profiling across
   longitudinal untreated and single and dual vasodilator therapy
   contexts. Second, although we identified major inflammatory gene
   networks that were reversed with vasodilator use, future work will be
   needed to determine if these immune changes constitute causative
   pathogenic mechanisms in PAH [[173]52].

   In sum, we leveraged a systems-biology approach by combining DGE/GSEA
   and EDDY analyses of scRNA-seq data to define immune pathways
   dysregulated in the PBMCs of PAH patients and to identify pathways that
   were reversed by vasodilator use. These results offer new insights into
   the inflammatory mechanisms involved in PAH and emphasise the utility
   of PBMCs in predicting the pro-inflammatory processes in the pulmonary
   vasculature. Our findings now serve as a foundation for clinical tools
   of scRNA-seq for the diagnosis and monitoring of disease progression in
   real time.

Supplementary material

   Please note: supplementary material is not edited by the Editorial
   Office, and is uploaded as it has been supplied by the author.

   Supplementary material [174]00486-2024.SUPPLEMENT^ (20.4MB, pdf)

   Table S1 [175]00486-2024.TABLES1^ (16KB, xlsx)

   Table S2 [176]00486-2024.TABLES2^ (14.5KB, xlsx)

   Table S3 [177]00486-2024.TABLES3^ (83.5KB, xlsx)

   Table S4 [178]00486-2024.TABLES4^ (17.8KB, xlsx)

   Table S5 [179]00486-2024.TABLES5^ (49.4KB, xlsx)

   Table S6 [180]00486-2024.TABLES6^ (58.7KB, xlsx)

   Table S7 [181]00486-2024.TABLES7^ (58.9KB, xlsx)

   Table S8 [182]00486-2024.TABLES8^ (51.3KB, xlsx)

Acknowledgements