Monocyte transcriptomes from patients with axial spondyloarthritis reveal dysregulated monocytopoiesis and a distinct inflammatory imprint

Monocyte transcriptomes were generated from two independent cohorts by either GeneChip microarrays (cohort 1) or RNA-sequencing (RNA-seq — cohort 2). Cohort 1 included 25 axSpA patients and 10 healthy controls (HC). Cohort 2 included 32 axSpA patients and 22 HC. All axSpA patients were HLA-B27+ and fulfilled the ASAS criteria of axSpA [16]. AS was defined according to the modified New York criteria [17]. Disease activity was determined by BASDAI [18] and ASDAS [19]. Patients in cohort 1 were treated with non-steroidal anti-inflammatory drugs (NSAIDs) on demand or continuously but not by biological DMARDs; in cohort 2, 34% (11/32 patients) were treated by biological DMARDs. Patient characteristics are given in Table 1.

Analysis of serological translocation markers was performed in cohort 1 and in 59 axSpA patients from a third cohort, the Gent Inflammatory Arthritis and spoNdylitis cohort (GIANT), in which microscopic evaluation of gut biopsies taken upon colonoscopy was performed [20]. None of GIANT patients were treated with biological DMARDs. Gut biopsies were classified according to microscopic evaluation as “no colonic inflammation”, “acute” (neutrophilic) and “chronic” (lymphocytic) inflammation.

All participants gave written consent to the study, which was approved by the local ethical committees of the university hospitals Charité (Berlin, Germany), Ile-de-France XI (Saint-Germaine-en-Laye, France), and Ghent University Hospital (Ghent, Belgium).

Serum was collected and stored until measurement at least at − 20 to – 80 °C. Commercial ELISA or limulus based detection kits were used for measurement of I-FABP (Hycult Biotech), (1→3)-β-D-Glucan (Fujifilm Wako), and LBP (USCN).

Extended methodological details and details on statistical analysis are given in a supplementary file (Supplementary data 1). In brief, CD14⁺ monocytes were isolated from heparinized whole blood by positive magnetic selection using CD14 microbeads. In cohort 1, RNA isolation and gene chip hybridization were performed as previously described [21]. In cohort 2, monocyte transcriptomes were generated by RNA-sequencing using the Illumina HiSeq4000 sequencer. For GeneChip microarray analysis, significant differences in expression were identified using high performance chip data analysis (HPCDA) (for details see Supplementary data 1). A HPCDA score of > 100 was considered significant. For RNA-sequencing, analysis of differential gene expression was carried out with edgeR using a linear model including the disease status, the gender and the sequencing experiment. Scores reaching a p-value < 0.05 were considered significant.

Hierarchical clustering of transcriptomes was performed with Genes@Work software [22]. Principal component analysis (PCA) was performed using Qlucore (Lund, Sweden). For functional allocation, gene set enrichment analysis (GSEA) was performed using the Molecular Signature Database (MSigDB) v7.2 [23, 24]. Differentially expressed probe-sets and genes were analyzed for co-expression in 70 reference transcriptomes (all Affymetrix HG-U133 Plus 2.0 transcriptomes) generated by our own or retrieved from Gene expression omnibus (GEO) data repository as previously described [25]. In brief, data sets were harmonized by quantile normalization before application in co-expression analysis. Pearson correlation coefficients were calculated between the differentially expressed probe-sets of individual comparisons on the basis of the 70 reference transcriptomes. Euclidian distance and average linkage as an agglomeration rule were applied for hierarchical clustering of this gene-to-gene correlation. For analysis of cohort 2, differentially expressed genes were matched to GeneChip probe-sets before analysis.

For transcriptome analysis, blood monocytes were isolated by positive magnetic selection using CD14 microbeads. This selection method resulted in ≥ 95% purity and induces minor transcriptional changes [26]. First, transcriptomes from monocytes of 25 HLA-B27+ axSpA patients and 10 HC from cohort 1 were generated by microarray. Using pair-wise comparisons and a HPCDA score > 100 as cut-off, 957 probe-sets referring to 805 genes were found differentially expressed between axSpA patients and HC (Fig. 1A, Supplementary Table 1). Of these 957 probe-sets, 661 probe-sets were up- and 296 probe-sets were downregulated. Although differences in expression of individual probe-sets were low (mean FC 1.20), both PCA and hierarchical clustering demonstrated clear separation of monocyte transcriptomes of axSpA patients from HC (Fig. 1B, C). For functional classification of differentially expressed genes, the MSigDB as integral part of GSEA was applied. This analysis mapped axSpA upregulated probe-sets with functional groups such as “protein secretion”, “TNFα signaling via NFkB”, “inflammatory responses”, “apoptosis”, and “interferon gamma responses” suggesting a proinflammatory activation of the axSpA monocytes. For axSpA-downregulated genes GSEA showed overlap with pathways such as “oxidative phosphorylation”, “hypoxia”, and “G2M checkpoint” mostly indicative of metabolic changes (Fig. 1D). A few of the downregulated genes also mapped to the “TNF signaling via NFkB” functional group. Most of these genes including IFNGR2, ETS2, and FOS rather downregulate proinflammatory responses in monocytes/ macrophages [27, 28] suggesting profound alteration of TNF signaling pathway in monocytes in axSpA patients.

In cohort 2, monocyte transcriptomes were generated by RNA-seq. Group-wise comparison of transcriptomes of HLA-B27+ axSpA patients and HLA-B27- HC identified 48 genes as up- and 47 as downregulated (corresponding to 109 and 129 GeneChip probe-sets, respectively,) in axSpA patients compared to HC (p-value < 0.05; Supplementary Table 1). The lower number of differentially expressed genes is most likely related to more stringent analysis (HPCDA scoring compared to edgeR computed p-values). Furthermore, the overall overlap in significant genes between cohort 1 and 2 was distressingly small (only one gene of the up- and the downregulated gene sets was shared). This is partly explained by limited coverage of RNA-seq genes by the microarrays, which was about 60%. Apart from different methodology, low similarities in individual genes in independent studies analyzing primary tissue are a known problem [23]. However, GSEA can reveal shared pathways between independent studies. Indeed, GSEA of the upregulated genes in cohort 2 overlapped with functional pathways “TNF signaling via NFkB”, “inflammatory responses”, and “IL-2-STAT5 signaling”, which showed strong similarity with cohort 1 functional gene sets. Downregulated genes were enriched in functional groups such as “notch signaling”, “epithelial-mesenchymal transition”, “G2M checkpoint”, and “hypoxia function” according to GSEA which overlapped with cohort 1 in “G2M checkpoint” and “hypoxia” functional groups (Fig. 1D).

Thus, GSEA of both cohorts suggests an imprint of inflammation in axSpA monocytes.

To decipher transcriptional alterations of axSpA monocytes in the context of BM adaptation and LPS/cytokine activation, we applied mapping of monocyte profiles with previously published profiles from GEO data repositories. Up- and downregulated probe-sets are analyzed separately; for analysis of cohort 2 the matched GeneChip probe-sets were used.

The reference profiles comprised (1) 34 transcriptomes of 11 different cell types of early and late myelopoiesis from bone marrow (BM; GSE42519) [29]; (2) 15 transcriptomes from blood cells comprising BDCA1⁺ (n = 3) and BDCA3⁺ DCs (n = 3), CD15⁺ PMN (n = 3) and blood monocyte subsets including classical CD14⁺CD16⁻ monocytes (Mo-CD16⁻; n = 3), and non-classical monocytes CD14⁻CD16⁺ (Mo-CD16⁺; n = 3) [8] (GSE18565); (3) six transcriptomes of blood leukocytes from healthy donors before and after treatment with G-CSF (GSE7400) [30]; and (4) 12 transcriptomes of blood purified monocytes incubated without stimulus or stimulated with TNFα, LPS, IFNγ, or IFNα (GSE38351) [21, 31].

By applying the differentially expressed probe-sets to these transcriptomes, their regulation during myelopoiesis, in subsets of blood cells and upon in vitro activation of monocytes can be studied. The list of differentially expressed probe-sets is retrieved from the reference transcriptomes; quantile normalized and co-regulated probe-sets are identified by calculating Pearson correlation coefficients. Hierarchical clustering of this gene-to-gene correlation (correlation matrix) provides the order for display of the reference transcriptomes showing relative expression values. This allows visual identification of co-expression clusters among the differentially expressed probe-sets in the reference transcriptomes.

Among the probe-sets upregulated in axSpA patients of cohort 1, we found five clusters with distinct co-expression in BM and blood cells (C1-C5, Fig. 2A). Most strikingly, genes of cluster 1–3 and cluster 5 were overrepresented in late stage granulocytopoiesis and PMNs but weakly expressed in DCs. Moreover, cluster 1 and 5 probe-sets were also enriched after G-CSF treatment. These co-expression cluster included genes such as TLR6, CCR1, NFkB, STAT3, and CSF3R which are related to inflammatory and PMN effector functions such as bacterial recognition, ROS production and phagocytosis [32].

In addition, a co-expression cluster comprising fewer genes was found in in vitro activated monocytes (cluster 4). GSEA of genes in this cluster indicated overlap with TNF signaling via NFkB, IL-6-JAK-STAT3-signaling and inflammatory responses among others.

Co-expression analysis of the 296 downregulated probe-sets of the axSpA signature of cohort 1 revealed three clusters (Fig. 2B). Most strikingly, genes of cluster 1 and 3, comprising > 50% of the downregulated probe-sets, were highly expressed in DCs but strongly downregulated in PMNs and late granulocytopoiesis. GSEA of these cluster genes revealed overlap with metabolic pathways such as oxidative phosphorylation, cholesterol homeostasis, and adipogenesis, cell cycle control, and mTORC1 signaling. These metabolic pathways are constitutively active in resting DCs while PMNs relay on glycolytic pathways. Genes of cluster 2, comprising less than 10% of the downregulated genes in axSpA, were upregulated in late granulopoiesis and related to TNFα signaling and hypoxia.

Co-expression profiling of significant genes of cohort 2 revealed similar changes as in cohort 1. Thus, co-expression of the upregulated genes was found in late granulocytopoiesis and blood PMNs as well as in LPS/cytokine stimulated monocyte signatures. Among the downregulated genes a co-expression cluster was found in DCs (Fig. 2C, D). Genes of this cluster were in opposite downregulated in PMNs and late granulocytopoiesis.

Altogether, upregulated genes of the axSpA signature are found in late PMNs of the BM, in blood PMNs, and in response to G-CSF while downregulated genes are DC associated. Thus, axSpA monocytes exhibit a neutrophil-like phenotype but deprivation of DC features which is compatible with a shift towards GMP-dependent generation of monocytes. Together with the LPS/TNF imprint, this may reflect response and adaptation to continuous microbial stimulation possibly originating from the gut.

To assess putative microbial translocation we measured serum concentration of I-FABP, βDG and LBP in patients of cohort 1, in which none of the patient suffered from clinical IBD. Pathological elevations of these parameters were reported before in clinical conditions of intestinal inflammation such as coeliac disease, HIV enteropathy, and Crohns disease [33‐35].

As a result, we found no difference in I-FABP serum levels between axSpA and HC (Fig. 3A) and βDG levels were undetectable in both axSpA and HC samples (while pathological elevation was found in patients with metabolic syndrome in our hands; unpublished observations B.S.). However, LBP serum levels were significantly higher in axSpA patients compared to HC (p < 0.001; Fig. 3B). LBP levels tended to be higher in patients with active disease (BASDAI > 4) although this did not reach statistical difference (Fig. 3C) and did not differ between nr-axSpA and AS patients (Fig. 3D).

To determine whether LBP levels are related to intestinal inflammation in axSpA patients, we analyzed LBP levels in axSpA patients of the GIANT cohort in whom presence of intestinal inflammation was assessed by colonoscopy and microscopic evaluation. Interestingly, LBP levels were higher in patients with microscopic gut inflammation than in patients without gut inflammation. The highest LBP levels were found in patients with chronic gut inflammation suggesting a relation between LBP levels and gut inflammation in axSpA patients (Fig. 3E).

To determine, if disease activity is associated with distinct transcriptional changes in monocytes, we compared transcriptomes of axSpA patients from cohort 1 separated into active patients (BASDAI ≥ 4 = BASDAI^high, n = 12) and inactive patients (BASDAI < 4 = BASDAI^low, n = 13). The group-wise comparison of these two groups of patients identified 1367 probe-sets differentially expressed (HPCDA score > 100). Hierarchical clustering showed the heterogeneity between patients but very distinctive separation of nine BASDAI^high patients from eight BASDAI^low patients, while three BASDAI^high patients clustered outside these groups and five BASDAI^low patients clustered in-between these groups (Fig. 4A).

Out of 1367 probe-sets of this “active axSpA signature”, 535 were up- and 832 were downregulated in monocytes of BASDAI^high patients. GSEA of upregulated genes identified associations with inflammatory pathways such as “IL-2-STAT5”, “TNFα”, “TGFβ”, and “Notch signaling”, as well as with “IFNγ response” among other pathways. The downregulated genes showed an overlap with functional groups such as “myc target”, “unfolded protein response”, and “adipogenesis” (Fig. 4B).

Up- and downregulated genes of the “active axSpA signature” were analyzed for co-expression in 70 reference transcriptomes as described above. For the upregulated genes, in total four gene clusters were identified, where cluster 1 and 2 genes showed co-expression in early and late granulocytopoiesis, cluster 3 in LPS/cytokine-activated monocytes and cluster 4 showed distinct co-expression in the non-classical CD16⁺ monocyte subset in the blood.

GSEA of cluster 3 und 4 genes indicated overlap with functional pathways “interferon gamma response”, “IL2-STAT5 signaling”, “inflammation response”, “TNFα signaling via NFkB”, “TGFβ signaling”, “mTORC1”, and “Notch signaling”.

In contrast, among the 832 probe-sets downregulated in BASDAI^high patients (and hence upregulated in BASDAI^low patients), high expression was found in DCs (cluster 1a–d; Fig. 4D). Genes of this cluster were underrepresented in PMNs and progressively downregulated in late granulocytopoieses. GSEA revealed overlap with metabolic pathways such as “mTORC1” (cluster 1a), “Myc targets”, “G2M checkpoint”, “E2F targets”, “DNA repair”, and “adipogenesis” (cluster 1b). A small co-expression cluster was found in PMNs (cluster 2).

Thus, the “active SpA signature” points towards LPS/cytokine activation, changes in monocyte subset mobilization (CD16⁺-associated gene enhancement of non-classical monocytes) and suppression of DC-associated genes in monocytes of active axSpA patients.

Nr-axSpA and AS are considered consecutive stages of axSpA differing in presence of structural damage. However, progression to the radiographic stage is slow and limited to a small group of patients [36] suggesting that genetic heterogeneity among patients may determine progression to AS rather than time.

Since monocytes give rise to osteoclast which promote bone destruction in AS [37], we performed pair-wise comparisons of transcriptomes of nr-axSpA and AS patients of cohort 1 to search for an AS-specific monocyte transcriptomic profile. In total, 562 probe-sets were identified to be differentially expressed, again with low magnitudes of changes in their expression. Hierarchical clustering demonstrated that all 10 nr-axSpA clustered separately from the AS group, but 3 AS clustered with the nr-axSpA patients (Fig. 5A). PCA with these 562 significant probe-sets confirmed distinctive differences in monocyte transcriptomes of 10 nr-axSpA and 15 AS patients (Fig. 5B).

GSEA of probe-sets upregulated in AS showed overlap with the functional group “apoptosis”, while probe-sets downregulated in AS patients mapped to metabolic pathways such as oxidative phosphorylation, myc and mTORC1 (Fig. 5C).

Co-expression analysis of the 145 upregulated AS signature probe-sets revealed co-expression of cluster 1 and 2 in early and late myelopoiesis (Fig. 5D). In contrast, probe-sets downregulated in AS (Fig. 5E) where clearly overrepresented in DCs (cluster 1, 2, and 4) and repressed in PMNs and late granulocytopoiesis. Only genes of cluster 3, comprising less than 25% of the downregulated probe-sets, were higher expressed in late granulocytopoiesis and downregulated in DCs (cluster 3). Cluster 1, 2, and 4 genes overlapped with metabolic pathways such as “myc pathway”, “unfolded protein response”, and “oxidative phosphorylation”.

Thus, monocytes of AS patients show a stronger shift towards a neutrophil-like phenotype as compared to nr-axSpA patients which express more DC-like features suggesting a stronger adaptation of monocytopoiesis in the BM of AS patients.