# Whole Blood RNA Profiles Associated with Pulmonary Arterial Hypertension and Clinical Outcome

C. J. Rhodes, P. Otero-Nunez, J. Wharton, E. M. Swietlik, S. Kariotis, L. Harbaum, M. J. Dunning, J. M. Elinoff, N. Errington, A. A. R. Thomson, et al.

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## To cite this version:

C. J. Rhodes, P. Otero-Nunez, J. Wharton, E. M. Swietlik, S. Kariotis, et al. Whole Blood RNA Profiles Associated with Pulmonary Arterial Hypertension and Clinical Outcome. American Journal of Respiratory and Critical Care Medicine, 2020, 202 (4), pp.586-594. 10.1164/rccm.202003-0510OC. hal-03152298

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# ORIGINAL ARTICLE

## Whole-Blood RNA Profiles Associated with Pulmonary Arterial Hypertension and Clinical Outcome

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Christopher J. Rhodes¹*, Pablo Otero-Núñez¹*, John Wharton¹, Emilia M. Swietlik², Sokratis Kariotis³,⁴, Lars Harbaum¹, Mark J. Dunning⁵, Jason M. Elinoff⁶, Niamh Errington³,⁴, A. A. Roger Thompson⁴, James Iremonger⁴, J. Gerry Coghlan⁷, Paul A. Corris⁸, Luke S. Howard¹, David G. Kiely⁴, Colin Church⁹, Joanna Pepke-Zaba¹⁰, Mark Toshner²,¹⁰, Stephen J. Wort¹, Ankit A. Desai¹¹, Marc Humbert¹²,¹³,¹⁴, William C. Nichols¹⁵, Laura Southgate¹⁶, David-Alexandre Trégouët¹⁷, Richard C. Trembath¹⁸, Inga Prokopenko¹⁹, Stefan Gräf²,²⁰, Nicholas W. Morrell², Dennis Wang³,⁵, Allan Lawrie⁴, and Martin R. Wilkins¹; on behalf of the NIHR BioResource–Rare Diseases PAH Consortium and the UK National PAH Cohort Study Consortium

¹National Heart and Lung Institute, Imperial College London, London, United Kingdom; ²Department of Medicine, University of Cambridge, Cambridge, United Kingdom; ³Sheffield Institute for Translational Neuroscience, ⁴Department of Infection, Immunity & Cardiovascular Disease, and ⁵Sheffield Bioinformatics Core, The University of Sheffield, Sheffield, United Kingdom; ⁶Critical Care Medicine Department, NIH Clinical Center, Bethesda, Maryland; ⁷University College London, London, United Kingdom; ⁸Newcastle University, Newcastle upon Tyne, United Kingdom; ⁹University of Glasgow, Glasgow, United Kingdom; ¹⁰Royal Papworth Hospital, Cambridge, United Kingdom; ¹¹Indiana University, Indianapolis, Indiana; ¹²Université Paris-Sud, Faculté de Médecine, Université Paris-Saclay, Le Kremlin-Bicêtre, France; ¹³Service de Pneumologie, Hôpital Bicêtre, Assistance Publique – Hôpitaux de Paris, Le Kremlin-Bicêtre, France; ¹⁴INSERM UMR_S 999, Hôpital Marie Lannelongue, Le Plessis Robinson, France; ¹⁵Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio; ¹⁶Molecular and Clinical Sciences Research Institute, St. George's University of London, London, United Kingdom; ¹⁷INSERM UMR_S 1219, Bordeaux Population Health Research Center, University of Bordeaux, Bordeaux, France; ¹⁸Division of Genetics and Molecular Medicine, King's College London, London, United Kingdom; ¹⁹Department of Clinical and Experimental Medicine, University of Surrey, Guildford, United Kingdom; and ²⁰NIHR BioResource for Translational Research, Cambridge, United Kingdom

ORCID IDs: 0000-0001-8110-2575 (J.W.); 0000-0002-2090-1450 (L.S.); 0000-0002-1315-8873 (S.G.); 0000-0003-3926-1171 (M.R.W.).

## Abstract

**Rationale:** Idiopathic and heritable pulmonary arterial hypertension (PAH) are rare but comprise a genetically heterogeneous patient group. RNA sequencing linked to the underlying genetic architecture can be used to better understand the underlying pathology by identifying key signaling pathways and stratify patients more robustly according to clinical risk.

**Objectives:** To use a three-stage design of RNA discovery, RNA validation and model construction, and model validation to define a set of PAH-associated RNAs and a single summarizing RNA model score. To define genes most likely to be involved in disease development, we performed Mendelian randomization (MR) analysis.

**Methods:** RNA sequencing was performed on whole-blood samples from 359 patients with idiopathic, heritable, and drug-induced PAH and 72 age- and sex-matched healthy volunteers. The score was evaluated against disease severity markers including survival analysis using all-cause mortality from diagnosis. MR used

known expression quantitative trait loci and summary statistics from a PAH genome-wide association study.

**Measurements and Main Results:** We identified 507 genes with differential RNA expression in patients with PAH compared with control subjects. A model of 25 RNAs distinguished PAH with 87% accuracy (area under the curve 95% confidence interval: 0.791–0.945) in model validation. The RNA model score was associated with disease severity and long-term survival (P = 4.66 × 10⁻⁶) in PAH. MR detected an association between SMAD5 levels and PAH disease susceptibility (odds ratio, 0.317; 95% confidence interval, 0.129–0.776; P = 0.012).

**Conclusions:** A whole-blood RNA signature of PAH, which includes RNAs relevant to disease pathogenesis, associates with disease severity and identifies patients with poor clinical outcomes. Genetic variants associated with lower SMAD5 expression may increase susceptibility to PAH.

**Keywords:** pulmonary arterial hypertension; RNAseq; whole-blood RNA

(Received in original form March 4, 2020; accepted in final form April 29, 2020)

©This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Correspondence and requests for reprints should be addressed to Martin R. Wilkins, M.D., F.Med.Sci., Imperial College London, 2S4 Commonwealth Building, Hammersmith Campus, Du Cane Road, London W12 0NN, UK. E-mail: m.wilkins@imperial.ac.uk.
**At a Glance Commentary**

**Scientific Knowledge on the**
**Subject:** Blood transcriptomic
profiles in pulmonary arterial
hypertension have been examined in
small limited studies indicating their
potential utility in differentiating
patients from control subjects and by
clinical phenotypes.

**What This Study Adds to the Field:**
This study represents the most
comprehensive analysis of whole-blood
RNA profiles in pulmonary arterial
hypertension and identifies and
validates a signature that both
differentiates patients from control
subjects and stratifies patients by
clinical risk and severity. Genetic
analyses (Mendelian randomization)
indicate potential causal roles for some
genes including the TGF-β signaling
molecule SMAD5.

Pulmonary arterial hypertension (PAH) is associated with vasoconstriction and occlusion of distal pulmonary arteries, characterized by endothelial damage, smooth muscle and fibroblast proliferation, and inflammation. Increased pulmonary vascular resistance leads to right heart failure, with survival rates estimated at 52-75% at 5 years, even with modern-day therapy (1). The rate of deterioration and response to therapy varies between patients, driving a search for better predictors of clinical outcome and tools to inform drug

selection. Molecular profiling using
multiple omics technologies offers greater
granularity than standard clinical
phenotypes for characterizing patients with
PAH and could improve initial risk
stratification, treatment selection, and
monitoring as well as provide insights into
biological pathways not yet targeted by
current therapies (2-4).

Transcriptome profiling through RNA
sequencing permits a comprehensive
analysis of gene expression in tissue samples.
Whole-blood RNA analysis offers an
alternative “liquid biopsy” to lung biopsy,
which carries a high risk in PAH, and can
be performed sequentially. This approach
can also investigate immune mechanisms in
PAH that have recently been highlighted
(5). Previous studies of blood RNA in PAH
have been limited by patient numbers and
the use of microarrays, which are less
sensitive than high-quality RNA
sequencing (RNAseq) and limited by the
probe set of each specific array. A recent
meta-analysis of these microarray studies
identified some consistent differentially
expressed genes not appreciated in
individual studies (6). Explanted lung
tissues from patients with late-stage PAH
have also shown differences in RNA
profiles (7). The results from both studies
remain to be validated in independent
cohorts.

The aim of this study was to
characterize gene pathways associated with
PAH and to assess their association with
disease heterogeneity, specifically in terms of
disease severity and outcomes including
response to vasodilators and mortality, and
genetic background. We compare gene

expression in whole-blood samples from
359 patients with idiopathic, heritable, or
drug-induced PAH from the UK PAH
Cohort study with 72 age- and sex-matched
healthy volunteers without any cardiac or
respiratory disease as control subjects. Using
equal distribution of samples into a three-
stage design, we identified reproducible
RNA expression differences by RNAseq.
Two distinct computational approaches
were used to estimate white blood cell
(WBC) fractions and thus account for the
potential effect of different cell numbers on
gene transcript levels across samples. A
predictive statistical model that combined
gene expression differences performed well
at identifying patients with PAH in a
separate case-control analysis. The RNA-
based score was also associated with disease
severity and clinical outcomes (all-cause
mortality). Enrichment of genetic variants
that determine the levels of PAH RNAs was
detected, implicating these RNAs in the
pathogenesis of the disease.

**Methods**

Comprehensive methods are included in the
online supplement.

**Study Participants and Sample
Analysis**

Patients with idiopathic, heritable, or drug-
induced pulmonary arterial hypertension
(referred to throughout as PAH) were
recruited from expert centers across the
UK as part of the PAH Cohort study
(www.ipahcohort.com). In each case,
diagnosis was confirmed by right heart

Supported in part by the Assistance Publique-Hopitaux de Paris, INSERM, University Paris-Sud, and Agence Nationale de la Recherche (Departement Hospitalo-Universitaire Thorax Innovation; LabEx LERMIT, ANR-10-LABX-0033; and RHU BIO-ART LUNG 2020, ANR-15-RHUS-0002) and British Heart Foundation Centre for Research Excellence award RE/18/4/34215. C.J.R. is supported by a BHF Intermediate Basic Science Research fellowship (FS/15/59/31839) and Academy of Medical Sciences Springboard fellowship (SBF004\1095). L.H. is a recipient of ERS Fellowship (LTRF 2016-6884). A.A.R.T. is supported by a BHF Intermediate Clinical Fellowship (FS/18/13/3328). L.S. is supported by the Wellcome Trust Institutional Strategic Support Fund (204809/Z/16/Z) awarded to St. George's, University of London. D.W. is supported by an Academy of Medical Sciences Springboard fellowship (SBF004\1052). N.W.M. is a British Heart Foundation Professor and NIHR Senior Investigator. A.L. is supported by a BHF Senior Basic Science Research fellowship (FS/13/48/30453 and FS/18/52/33808). The authors also acknowledge funding from NHLBI, R01HL136603 (A.A.D.). The UK National Cohort of Idiopathic and Heritable PAH is supported by the National Institute of Health Research BioResource, the BHF (SP/12/12/29836), and the UK Medical Research Council (MR/K020919/1). The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the Department of Health and Social Care.

Author Contributions: All authors made substantial contributions to the conception or design and data acquisition of the work. C.J.R., P.O.-N., J.W., E.M.S., S.K., L.H., M.J.D., J.M.E., N.E., A.A.R.T., J.I., D.W., A.L., S.G., and M.R.W. performed the analysis and/or interpretation of data. C.J.R., P.O.-N., J.W., and M.R.W. drafted the work, and all authors revised it critically for important intellectual content, give final approval of the version submitted for publication, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

This article has a related editorial.

This article has an online supplement, which is accessible from this issue's table of contents at www.atsjournals.org.

<footnote>*These authors contributed equally to this work.</footnote>
catheterization following established international guidelines (1), which remained unchanged for the duration of this study. Healthy volunteers were recruited at the same centers and samples processed using the same standard operating procedure at all sites. All individuals gave written, informed consent with local ethical committee approval. Whole blood (3 ml) was collected in Tempus Blood RNA Tubes, and RNAseq was performed using established Illumina methodologies (see online supplement for further details). Genomics data were obtained from a published PAH genome-wide association study (8).

Three hundred fifty-nine patients with PAH were randomized into three data analysis groups for RNA discovery (n = 120), RNA validation (n = 120), and model validation (n = 119). Each of these three groups was then compared with an independent set of age- and sex-matched healthy volunteers as control subjects (n = 24 in each set; Table 1 and Figure E1 in the online supplement).

RNAseq Data Analysis

Fastq files (raw reads from RNAseq) were analyzed using Salmon v0.9.1 (9) and

GENCODE release 28 to produce transcript abundance estimates that were converted to gene expression data using tximport in R with Rstudio (10). Quality control is detailed in the online supplement.

RNAseq analysis of tissues with mixed cell types such as blood can be affected significantly by the cell composition of each sample. Two different computational approaches, CIBERsort (11) and quanTISeq (12), were used to predict WBC profiles associated with PAH, which were included as covariates in secondary differential gene expression analyses (see online supplement). Differential expression analysis was performed using edgeR v3.22.5 (13) correcting for the three principal components, which each explained more than 1% of the variance in the data set. Differentially expressed genes were defined in analyses both with and without WBC fractions as covariates in distinct discovery and validation sample sets. These sets were then combined, and only significant genes (P < 0.05) directionally consistent in the initial analyses and meeting false discovery rate (FDR) multiple test corrections (based on all detected genes, α = 0.1) in the combined analysis were taken forward. Five hundred seven genes meeting these criteria

were considered to generate a model to distinguish PAH from control subjects. Subset selection of RNAs that best distinguish PAH in combination was performed by least absolute shrinkage and selection operator (LASSO) regression analysis, using the glmnet v2.0-18 package from CRAN, with k-fold cross-validation (k = 10) selecting the largest value of lambda such that error is within 1 SE of the minimum. This produces an RNA score from a linear weighted combination of the mRNAs identified by the LASSO analysis. Receiver operating characteristic analysis was performed using the pROC v1.14.0 package from Bioconductor (14).

Survival curves from date of diagnostic right heart catheterization were constructed using Kaplan-Meier estimates with left truncation for date of sampling for this study to correct for survival bias. Differences in survival estimates were assessed by log-rank test. RNA scores were also compared across disease severity markers 6-minute-walk test (Spearman's rank) and World Health Organization (WHO) functional class (Kruskal-Wallis ANOVA) and by cardiac biomarkers (circulating BNP [brain natriuretic peptide] or NT-proBNP [N-terminal pro-brain natriuretic peptide] as

<table><caption>**Table 1.** Basic Demographics of Control Subjects and Patients with PAH in Three Analysis Groups and More Detailed Clinical Characteristics, Including Disease Severity of Patients with PAH as a Cohort</caption><thead><tr><th rowspan="2"></th><th colspan="2">A: RNA Discovery</th><th colspan="2">B: RNA Validation</th><th colspan="2">C: Model Validation</th></tr><tr><th>Control Subjects</th><th>PAH</th><th>Control Subjects</th><th>PAH</th><th>Control Subjects</th><th>PAH</th></tr></thead><tbody><tr><td>Female</td><td>17</td><td>89</td><td>16</td><td>80</td><td>17</td><td>86</td></tr><tr><td>Male</td><td>7</td><td>31</td><td>8</td><td>40</td><td>7</td><td>33</td></tr><tr><td>Age, yr</td><td>43.9 (30.2-53.4)</td><td>45.2 (35.7-55)</td><td>45.1 (31-53.5)</td><td>43.2 (33.3-54.8)</td><td>44.4 (31.7-51.6)</td><td>47.1 (36.7-61.2)</td></tr><tr><th colspan="7">PAH Patient Characteristics in A, B, and C</th></tr><tr><th colspan="7">Median (25th-75th Percentile) or Counts</th></tr><tr><td>Age at diagnosis</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">44.8 (34.3-58.1)</td></tr><tr><td>Female/male</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">255/104</td></tr><tr><td>Ethnicity, white/other</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">314/45</td></tr><tr><td>WHO functional class, I/II/III/IV</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">36/138/155/18</td></tr><tr><td>Six-minute-walk distance, m</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">330 (224.5-411)</td></tr><tr><td>Mean pulmonary artery pressure, mm Hg</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">53 (46-61)</td></tr><tr><td>Mean right atrial pressure, mm Hg</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">8 (6-12)</td></tr><tr><td>Pulmonary capillary wedge pressure, mm Hg</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">10 (7-12)</td></tr><tr><td>Q, L/min</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">3.8 (3.05-4.9)</td></tr><tr><td>Cardiac index, L/min/m<sup>2</sup></td><td colspan="2"></td><td colspan="2"></td><td colspan="2">2.07 (1.68-2.57)</td></tr><tr><td>Pulmonary vascular resistance, Wood units</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">11.55 (7.93-15.78)</td></tr><tr><td>Years since diagnosis sampled</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">3.95 (1.38-7.7)</td></tr><tr><td>Years survived since sampling</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">3.14 (2.3-3.67)</td></tr><tr><td>Years survived since diagnosis</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">6.99 (4.19-10.88)</td></tr><tr><td>Vasoresponders</td><td colspan="2"></td><td colspan="2"></td><td colspan="2">21</td></tr></tbody></table>

Definition of abbreviations: PAH = pulmonary arterial hypertension; WHO = World Health Organization. Control subjects are healthy volunteers without any cardiac or respiratory disease.
available, using cutoffs from European
guidelines for risk assessment (1)).

Functional annotation and enrichment
of the genes associated with PAH was
performed using DAVID (david.ncifcrf.gov)
and Ingenuity Pathway Analysis (IPA) using
in-built FDR corrections for multiple tests.

Mendelian randomization (MR)
analysis using all independent genome-wide
significant whole-blood expression
quantitative trait loci (eQTL) from two
published studies (15, 16) and PAH
association from our published genome-
wide association study (8) was performed
using the TwoSampleMR package (17).

Results

Identification of RNAs Differentially
Expressed in Patients with PAH
Compared with Control Subjects

The three-stage study design is depicted in
Figure 1. Five hundred seven RNAs were
significantly different (P < 0.05) between
PAH and control subjects with directional
concordance in both discovery and
validation analyses (Figure 2 and Table E3)
before and after accounting for WBC
fractions (see *METHODS* and Tables E1–E3
and Figure E2). All 507 genes were included
after accounting for multiple testing (FDR
α < 0.1, Table E3). None were associated
with exposure to the main PAH therapies
(see online supplement for further details).
These included RNAs in pathways relevant
to PAH; for example, *SMAD5* (mothers
against decapentaplegic homolog-5),
encoding a downstream mediator of
signaling of *BMPR2* (3), was reduced in
patients with PAH (Figure 2C), consistent
with documented reduced BMPR2
signaling in this condition, and the
transient receptor potential cation channel,
*TRPC1* (Figure 2D), also associated with
the development of PH (18), was also
reduced in patients.

A set of differentially expressed RNAs
relevant to PAH pathobiology were selected
for validation of RNAseq quantification by
real-time quantitative PCR, namely, *NRG1*,
*TRPC1*, *FBLN2*, *SESN1*, *SMAD5*, and
*CCND3*. *SPAST* was also selected for its
stability across samples as a potential
control gene (see *METHODS* for details). All
real-time quantitative PCR measurements
correlated significantly with RNAseq
quantification (*P* < 0.05, Spearman's
rho = 0.3–0.89, Table E4).

Patients and control subjects from the
RNA discovery and validation analyses were
then combined and LASSO analysis was
used to determine the combination of RNAs
that performed best in discriminating
patients with PAH from control subjects in a
single model; this analysis yielded 25 RNAs
(Table E5). The model was then tested in an
independent validation set (n = 119 patients
with PAH and n = 24 control subjects) and
demonstrated an area under the curve
of 0.868 (95% confidence interval,
0.791–0.945) (Figure 3). The optimum
cutoff for identifying PAH with the LASSO
model of 1.768 recognized 88.9% of
patients with 72.2% specificity.

Survival Association of RNA Model in
Clinically Diagnosed Idiopathic and
Heritable PAH

We next examined whether the RNA model
was also associated with patients with the
poorest outcomes, using all 359 patients.
The optimum cutoff (1.910) for identifying
PAH nonsurvivors with the LASSO model
separated patients with PAH into low- and
high-risk groups in survival analysis
(Figures 4A and 4B and E3).

To determine which of the 25
transcripts in the model were responsible
for the association with survival, we
tested each transcript for associations
with all-cause mortality during follow-up.

After FDR correction for multiple testing,
three intronic long noncoding RNAs
(RP4–751H13.6/ATP6V0E2-AS1,
RP4–534N18.4/AL136115.3/Lnc-PTP4A2–13,
RP11–701H24.5/Lnc-SNRPN-1:6)
were associated with survival in
all the patients with PAH analyzed
and cutoffs distinguished high- and
low-risk patient subgroups (Table E6
and Figure E4).

To further characterize the RNA
signature, we analyzed its association
with three clinical measures of disease
severity—WHO functional class, exercise
capacity (6-minute-walk), and cardiac
biomarkers (BNP or NT-proBNP). We
found a significant difference in RNA
scores between patients in different WHO
functional classes (P = 0.008; Figure 4C)
and BNP and NT-proBNP levels
(P = 5.10 × 10⁻⁴; Figure 4D) and a
significant negative correlation with
exercise capacity (Spearman's
rho = −0.256; P = 8.7 × 10⁻⁵).

RNA Profiles in Responders to
Vasodilator Therapy

Several RNAs have been proposed as
biomarkers for identifying vasoresponders
to calcium antagonists using cultured
lymphocytes (19). We compared the
transcriptome of the 17 vasoresponders and
223 nonvasoresponders in the RNA

<figure><img src="image_5.png" /></figure>

**Figure 1.** Study design. Three hundred fifty-nine consecutively recruited patients with pulmonary arterial hypertension (PAH) from the UK National Cohort study of PAH were randomized into three groups. Age- and sex-matched control subjects for each group were then identified from the same study. The first two groups were analyzed separately for RNA discovery and RNA validation and then combined for modeling of the best combination of RNAs to distinguish between control subjects and PAH. This model was then tested in the final group of individuals. All patients were combined for subsequent clinical analyses. LASSO = least absolute shrinkage and selection operator.
A

<figure><img src="image_6.png" /></figure>

B

<figure><img src="image_7.png" /></figure>

C

<figure><img src="image_8.png" /></figure>

D

<figure><img src="image_9.png" /></figure>

**Figure 2.** Identification of RNAs with different levels in patients with pulmonary arterial hypertension (PAH) and control subjects. (A and B) Volcano plot of RNA log fold differences between PAH and control subjects in discovery (A) and validation (B) analyses. Selected genes were identified in further analyses presented throughout the manuscript including modeling, external validation, and Mendelian randomization. (C and D) Box plots of SMAD5 (C) and TRPC1 (D) levels (log₁₀ reads) in control subjects and patients with PAH in the RNA discovery, validation, and model validation (validation 2) analysis groups. Horizontal dashed lines in C and D indicate the median level in healthy control subjects.

discovery and first validation groups but found that no transcripts consistently distinguished responders and nonresponders and that none of the previously implicated RNAs were even nominally different (Table E7).

## Comparison with Differentially Expressed Genes Identified in Previous Studies

A meta-analysis of previous transcriptomic studies in blood samples from patients with PAH (6) and a study of PAH lung tissue samples (7) were compared with results from the current RNAseq analysis.

Four hundred sixteen out of the 507 top dysregulated genes from the current RNAseq study were present in the PAH blood transcriptomic meta-analysis (6). Out of those, 300/416 (72%) were directionally consistent with results from the current RNAseq study. One hundred twenty-six out of 300 (42%) of these were nominally significant and 70 met FDR corrected significance. Thirty-seven out of 70 genes also met FDR corrected significance in the idiopathic PAH subgroup analysis from that same study (Table E8).

Three hundred seventy-two out of the 507 top dysregulated genes from the current RNAseq study were present in the lung

tissue microarray study and 161/372 (43%) were also directionally consistent. Forty-one out of 161 (25%) genes were nominally significant and 26 met FDR corrected significance (Table E9). Only one gene was found to be dysregulated in patients with PAH across all three studies: *AMD1* (encoding a polyamine biosynthesis intermediate enzyme, adenosylmethionine decarboxylase 1) was consistently lower in PAH.

## Functional Characterization of RNAs Related to PAH

Of the 507 RNAs found to be differentially expressed in PAH, 435 were present in
<figure><img src="image_10.png" /><figcaption>**Figure 3.** Least absolute shrinkage and selection operator (LASSO) model performance in an independent validation group of age- and sex-matched subjects. (A) Box plot showing LASSO model scores for control subjects (n = 24) and patients with pulmonary arterial hypertension (PAH) (n = 119). (B) Receiver operating curve showing the performance of LASSO model scores for determining PAH status in the model validation group.</figcaption></figure>

<figure><img src="image_11.png" /></figure>

the functional annotations in DAVID. Enrichment of DNA-binding TFs (transcription factors), such as HIF1α (hypoxia-inducible factor 1α) and KLF10 (Krüppel-like factor 10), and many zinc finger-containing TFs was observed compared with a background of the genes detected (Table E10). The Ingenuity Knowledge Base mapped 505/507 transcripts. Double-stranded DNA repair, T-cell receptor, PI3K signaling in B lymphocytes, the role of JAK family kinases in IL6-type cytokine signaling and hypoxia signaling were among the top canonical pathways identified by IPA (Figure E5 and Table E11). *AMD1*, the only gene in common between the current RNAseq study, the recent lung tissue microarray study and the gene expression meta-analysis, was part of the top IPA gene network (Figure E6).

## MR Analysis for Association of RNAs with PAH Development

To determine which of the 507 RNAs associated with PAH are most likely to be causal in disease pathogenesis, we performed a two-sample MR analysis using whole-blood eQTL from two population-based studies (15, 16) and summary statistics from a published PAH genome-wide association study of 2,085 patients with PAH and 9,659 control subjects (8).

MR analysis determines whether genetic variation associated with a trait (e.g., high or low RNA expression) is itself associated with a phenotype, in this case the development of PAH. In this exploratory analysis in which eQTL were available for 293/507 RNAs, two genes, *SESN1* (Sestrin-1) and *SMAD5*, reached nominal significance using eQTL in both data sets (15, 16); eleven more reached nominal significance using eQTL from one or the other of the two studies (Tables E12 and E13).

The *SMAD5* eQTL SNP rs4146187 was clearly associated with *SMAD5* RNA levels in patients with PAH in this study ($P = 3.56 \times 10^{-6}$, Figure 5; gnomAD database allele frequency in non-Finnish European population = 0.275). Patients with the A/A genotype had comparable *SMAD5* RNA levels to control subjects, whereas patients with the C/C genotype had a median 18% lower *SMAD5* RNA level. The C/C genotype was present in 49.4% of patients with PAH, and in the PAH genome-wide association study (8), each copy of the A allele (associated with higher *SMAD5* levels) was associated with an 8.5% reduction in the risk of developing PAH (odds ratio, 0.915; 95% confidence interval, 0.846–0.990; $P = 0.0266$). *SMAD5* levels were similarly reduced in patients with PAH with and

without pathogenic *BMPR2* variants (Figure E7A), supporting the observation that impaired signaling in the BMPR2 pathway is more common in PAH than rare mutations in *BMPR2* suggest. The PAH RNA model score was similarly elevated in patients with PAH with and without pathogenic *BMPR2* variants (Figure E7B).

## Discussion

Here we report an RNA signature that separates idiopathic and heritable PAH from healthy individuals. The signature also stratifies patients according to disease severity and risk of early death, adding plausibility to its association with PAH. Several of the discriminating mRNAs encode TFs, including SMAD5, HIF-1α and KLF10. MR analysis to integrate genomic data and identify underlying pathogenic signaling pathways revealed that genetic variants associated with lower expression levels of *SMAD5* are more common in patients with PAH.

MR is a powerful tool for separating cause from consequence, as an individual's genetic status predates the development of PAH. We harnessed genetic data from a published international genome-wide association study (8) and information on previously identified eQTL, common genetic variants that alter gene expression levels, from large published studies in whole-blood RNA samples (15, 16). The identification of SMAD5 by this unbiased strategy has biological plausibility as *SMAD5* encodes an intracellular transcriptional modulator that is activated by ligand binding of BMPR2, the most common genetic risk factor in heritable PAH (3). SMAD5 also has BMP-independent roles, including regulation of cellular metabolism, directly interacting with hexokinase to increase glycolysis, and responding to changes in intracellular pH (20). SMAD5 also controls levels of the master iron regulator hepcidin (21), which may be elevated and drive iron deficiency associated with poor outcomes in PAH (22). Novel therapeutics under development target restoration of BMPR2 signaling in a variety of ways, including ligand or coactivator replacement and allowing read-through of premature stop codons (23–25).
A

<figure><img src="image_12.png" /></figure>

B

<figure><img src="image_13.png" /></figure>

C

<figure><img src="image_14.png" /></figure>

D

<figure><img src="image_15.png" /></figure>

**Figure 4.** Diagnostic RNA model and survival in pulmonary arterial hypertension (PAH). (A) Box plot of LASSO model score in control subjects and patients with PAH separated by survival status during follow-up. Dashed line shows the cutoff that identified 88.9% of nonsurviving patients with PAH. (B) Kaplan-Meier survival plot separating patients on the basis of the 88.9% sensitive cutoff. (C) Box plot of RNA model score in healthy volunteers and patients with PAH divided by WHO functional class. (D) Box plot of RNA model score in patients with PAH divided by presence of low, intermediate, or high levels of cardiac biomarkers BNP (brain natriuretic peptide) (<50, 50-300, or >300 pg/ml) or NT-proBNP (N-terminal pro-brain natriuretic peptide) (<300, 300-1,400, or >1,400 pg/ml), as per European guidelines for PAH assessment. LASSO = least absolute shrinkage and selection operator; WHO = World Health Organization.

Whole-blood SMAD5 profiling may represent an accessible measurement of BMPR2 pathway dysfunction in patients with PAH.

We were able to externally validate our findings with a meta-analysis of published PAH blood transcriptome studies (6) and a PAH lung RNA profiling study (7). The concordance of our whole-blood RNAseq findings is better with

blood transcriptomics than with lung tissue data, as might be expected.

AMD1 was the only gene consistently dysregulated in both these studies and our data set, with lower levels in PAH samples in all three studies. AMD1 encodes a key enzyme controlling the supply of decarboxylated S-adenosylmethionine for polyamine biosynthesis and is regulated by several mechanisms, including

increased protein degradation in the presence of elevated polyamines and the inhibition of mRNA translation by spermidine and spermine (26).

We have previously demonstrated that elevated circulating levels of polyamine metabolites, such as acisoga, 4-acetamidobutanoate, and N-acetylputrescine, are associated with poor outcomes in PAH (4). Reduced AMD1
<figure><img src="image_16.png" /><figcaption>**Figure 5.** Whole-blood RNA levels of SMAD5 in control subjects and patients with pulmonary arterial hypertension stratified by genotype at the SMAD5 expression quantitative trait locus rs4146187.</figcaption></figure>

expression in patients with PAH may be
in part due to negative feedback from
elevated polyamines. These data contrast
with observations in hypoxic rodents.
*Amd1* expression is increased in hypoxic
animals, and both AMD1<sup>+/-</sup> mice and
mice treated with the AMD1 inhibitor,
SAM486a, were partially protected from
the development of hypoxic PH (27).
Interestingly, TRPC1 transcript levels
were also reduced in PAH, in contrast
to findings in hypoxic mice (18). This
might reflect the limitations of the
hypoxic rodent as a model of human
PAH. Support for further investigation
of polyamine metabolism in PAH comes
from the recent association of rare
deleterious mutations in *ATP13A3*
with PAH (3). *ATP13A3* is linked
with polyamine biosynthesis and is a
potential target for drug development
(28).

Many of the transcripts that passed
robust statistical evaluation in this study are
novel. We identified three specific long
noncoding RNAs that are reduced in PAH
and associated with poor outcomes. These
transcripts are not well studied, and their
role in regulating lysosomal proton pump
protein ATP6V0E2, protein tyrosine
phosphatase PTP4A2, or small nuclear
ribonucleoprotein-associated protein N
remain to be established. Their association
with survival selects these out as worthy
of further investigation. In addition to

suggesting biological relevance, the
association of the RNA model, developed to
distinguish PAH from control subjects, with
survival and disease severity in patients with
PAH suggests that the differences observed
could be useful to identify patients who
may require a more aggressive treatment
strategy, such as upfront triple therapy (29).
This strategy could also be used to prioritize
patients more likely to have events to power
clinical trials. RNA profiles and, in
particular, RNAs with known eQTLs,
could be of further use in clinical trial
design to identify patients more likely to
respond to specific therapeutics. It would
be of great interest, for example, to
observe whether patients with lower
SMAD5 levels, or simply with the variant
associated with lower SMAD5 levels,
showed a differential response to novel
therapeutics targeting relevant signaling,
such as the TGF-β ligand trap,
Sotatercept.

There is considerable interest in
developing a biochemical test to identify
patients with PAH who respond well to
calcium antagonists that would supersede
the current test, namely, an acute
vasodilator challenge while undergoing
cardiac catheterization. In contrast to
previous reports, we were unable to
demonstrate the association of a
peripheral transcript signature to
vasoresponder status, including
previously studied RNAs (19). A strength

of our study is the number of patients
studied. Previous reports have relied on
much smaller patient numbers. That we
were able to demonstrate consistency
between our main analysis results and
those of a recently published meta-
analysis of PAH blood transcriptome
studies (6) suggests our methodological
approaches are robust.

One of the limitations of sampling
whole blood to derive transcripts is that
samples comprise a mixed population of
cells. We used established deconvolution
methods to correct for potential
confounding in RNA expression analysis. In
support of the validity of this approach, we
noted altered numbers of regulatory T cells
and CD8+ T cells in patients with PAH,
consistent with previous reports (30, 31),
although there is debate on precise
changes in cell subpopulations and
this may be due in part to differing
methodologies and definitions of cell
types (32).

This study does not assess the role of
post-translational modifications in the
pathogenesis of PAH, which could add
further information to the circulating
transcriptome. It is important that further
mechanistic studies consider the role of
the genes highlighted in this study in the
tissues of primary interest, namely, the
lung and heart. The study design included
more patient samples than control
subjects to account for the higher
heterogeneity typically observed in
patient populations. This heterogeneity is
observed in the overlap in box plots of
individual RNA levels or scores between
subsets of patients with PAH and control
subjects and emphasizes the importance
of using any molecular markers in
combination with best practice clinical
assessments. We chose to use LASSO
regression modeling not only as it is
known to perform well in these kinds of
data sets but also because it is widely
applied and often easier to interpret
than other methodologies. Another
consideration is that the patients
studied here were prevalent cases.
Moreover, all the patients recruited had a
clinical diagnosis of idiopathic, heritable,
or drug-induced PAH. It would be of
interest to sample patients with other
presentations of PAH and other
cardiopulmonary diseases to better
understand the clinical utility of our RNA
signature.
In summary, we report a whole-blood RNA profile that distinguishes patients with PAH from healthy control subjects and reflects disease severity. Integration with genomic data suggests that SMAD5 is important in the development of PAH, prioritizing restoration of normal SMAD5 function as a target for therapeutic intervention. ■

**Author disclosures** are available with the text of this article at www.atsjournals.org.

**Acknowledgment:** The authors thank Dr. Robert L. Danner and Dr. Junfeng Sun at the NIH Clinical Center for their discussions and for sharing results from the blood transcriptome meta-analysis and Prof. Winston Hide at Beth Israel Deaconess Medical Center for his insights into analysis of RNAseq data sets. The authors thank National Institute for Health Research

(NIHR) BioResource volunteers for their participation and thank NIHR Biomedical Research centers and National Health Service (NHS) Trusts and staff for their contribution. The authors thank the NIHR Imperial Clinical Research Facility, NIHR Sheffield Biomedical Research Centre, and NHS Blood and Transplant. The authors also gratefully acknowledge the participation of patients recruited to the U.S. NIH/NHLBI-sponsored National Biological Sample and Data Repository for PAH (also known as PAH Biobank).

## References

1. Galiè N, Humbert M, Vachieri JL, Gibbs S, Lang I, Torbicki A, et al. 2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS). Endorsed by: association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT). *Eur Respir J* 2015;46:903–975.

2. Rhodes CJ, Wharton J, Ghataorhe P, Watson G, Girerd B, Howard LS, et al. Plasma proteome analysis in patients with pulmonary arterial hypertension: an observational cohort study. *Lancet Respir Med* 2017;5:717–726.

3. Gráf S, Haimel M, Bleda M, Hadinnapola C, Southgate L, Li W, et al. Identification of rare sequence variation underlying heritable pulmonary arterial hypertension. *Nat Commun* 2018;9:1416.

4. Rhodes CJ, Ghataorhe P, Wharton J, Rue-Albrecht KC, Hadinnapola C, Watson G, et al. Plasma metabolomics implicates modified transfer RNAs and altered bioenergetics in the outcomes of pulmonary arterial hypertension. *Circulation* 2017;135:460–475.

5. Rabinovitch M, Guignabert C, Humbert M, Nicolls MR. Inflammation and immunity in the pathogenesis of pulmonary arterial hypertension. *Circ Res* 2014;115:165–175.

6. Elinoff JM, Mazer AJ, Cai R, Lu M, Graninger G, Harper B, et al. Meta-analysis of blood genome-wide expression profiling studies in pulmonary arterial hypertension. *Am J Physiol Lung Cell Mol Physiol* 2020;318:L98–L111.

7. Stearman RS, Bui QM, Speyer G, Handen A, Cornelius AR, Graham BB, et al. Systems analysis of the human pulmonary arterial hypertension lung transcriptome. *Am J Respir Cell Mol Biol* 2019;60:637–649.

8. Rhodes CJ, Batai K, Bleda M, Haimel M, Southgate L, Germain M, et al.; UK NIHR BioResource Rare Diseases Consortium; UK PAH Cohort Study Consortium; US PAH Biobank Consortium. Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis. *Lancet Respir Med* 2019;7:227–238.

9. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. *Nat Methods* 2017;14:417–419.

10. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016 [accessed 2020 Feb 29]. Available from: https://www.R-project.org/.

11. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. *Nat Methods* 2015;12:453–457.

12. Finotello F, Mayer C, Plattner C, Laschober G, Rieder D, Hackl H, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. *Genome Med* 2019;11:34.

13. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. *Bioinformatics* 2010;26:139–140.

14. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. *BMC Bioinformatics* 2011;12:77.

15. Joehanes R, Zhang X, Huan T, Yao C, Ying SX, Nguyen QT, et al. Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. *Genome Biol* 2017;18:16.

16. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. *Nat Genet* 2013;45:1238–1243.

17. Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. *Stat Med* 2016;35:1880–1906.

18. Malczyk M, Veith C, Fuchs B, Hofmann K, Storch U, Schermuly RT, et al. Classical transient receptor potential channel 1 in hypoxia-induced pulmonary hypertension. *Am J Respir Crit Care Med* 2013; 188:1451–1459.

19. Hemnes AR, Trammell AW, Archer SL, Rich S, Yu C, Nian H, et al. Peripheral blood signature of vasodilator-responsive pulmonary arterial hypertension. *Circulation* 2015;131:401–409.

20. Fang Y, Liu Z, Chen Z, Xu X, Xiao M, Yu Y, et al. Smad5 acts as an intracellular pH messenger and maintains bioenergetic homeostasis. *Cell Res* 2017;27:1083–1099.

21. Wang CY, Core AB, Canali S, Zumbrennen-Bullough KB, Ozer S, Umans L, et al. Smad1/5 is required for erythropoietin-mediated suppression of hepcidin in mice. *Blood* 2017;130:73–83.

22. Rhodes CJ, Howard LS, Busbridge M, Ashby D, Kondili E, Gibbs JS, et al. Iron deficiency and raised hepcidin in idiopathic pulmonary arterial hypertension: clinical prevalence, outcomes, and mechanistic insights. *J Am Coll Cardiol* 2011;58:300–309.

23. Drake KM, Dunmore BJ, McNelly LN, Morrell NW, Aldred MA. Correction of nonsense BMPR2 and SMAD9 mutations by ataluren in pulmonary arterial hypertension. *Am J Respir Cell Mol Biol* 2013;49:403–409.

24. Long L, Ormiston ML, Yang X, Southwood M, Gráf S, Machado RD, et al. Selective enhancement of endothelial BMPR-II with BMP9 reverses pulmonary arterial hypertension. *Nat Med* 2015;21:777–785.

25. Spiekerkoetter E, Sung YK, Sudheendra D, Scott V, Del Rosario P, Bill M, et al. Randomised placebo-controlled safety and tolerability trial of FK506 (tacrolimus) for pulmonary arterial hypertension. *Eur Respir J* 2017;50:1602449.

26. Casero RA Jr, Murray Stewart T, Pegg AE. Polyamine metabolism and cancer: treatments, challenges and opportunities. *Nat Rev Cancer* 2018;18:681–695.

27. Weisel FC, Kloepping C, Pichl A, Sydykov A, Kojonazarov B, Wilhelm J, et al. Impact of S-adenosylmethionine decarboxylase 1 on pulmonary vascular remodeling. *Circulation* 2014;129: 1510–1523.

28. Madan M, Patel A, Skruber K, Geerts D, Altomare DA, Iv OP. ATP13A3 and caveolin-1 as potential biomarkers for difluoromethylomithine-based therapies in pancreatic cancers. *Am J Cancer Res* 2016;6:1231–1252.

29. Sitbon O, Jaïs X, Savale L, Cottin V, Bergot E, Macari EA, et al. Upfront triple combination therapy in pulmonary arterial hypertension: a pilot study. *Eur Respir J* 2014;43:1691–1697.

30. Ulrich S, Nicolls MR, Taraseviciene L, Speich R, Voelkel N. Increased regulatory and decreased CD8+ cytotoxic T cells in the blood of patients with idiopathic pulmonary arterial hypertension. *Respiration* 2008;75:272–280.

31. Huertas A, Phan C, Bordenave J, Tu L, Thuillet R, Le Hiress M, et al. Regulatory T cell dysfunction in idiopathic, heritable and connective tissue-associated pulmonary arterial hypertension. *Chest* 2016;149: 1482–1493.

32. Qiu H, He Y, Ouyang F, Jiang P, Guo S, Guo Y. The role of regulatory T cells in pulmonary arterial hypertension. *J Am Heart Assoc* 2019;8:e014201.