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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: J Invest Dermatol. 2021 Mar 22;141(9):2197–2207. doi: 10.1016/j.jid.2021.02.742

In-Depth Analysis of the Hidradenitis Suppurativa Serum Proteome Identifies Distinct Inflammatory Subtypes

Kristina Navrazhina 1,2, Sandra Garcet 1, Juana Gonzalez 1, David Grand 1, John W Frew 1, James G Krueger 1,*
PMCID: PMC8384651  NIHMSID: NIHMS1685829  PMID: 33766512

Abstract

Hidradenitis Suppurativa (HS) is a chronic inflammatory dermatosis with presentations ranging from painful nodules and abscesses to draining tunnels. Using an unbiased proteomics approach, we assessed 368 cardiovascular, cardiometabolic and inflammation-related biomarkers in serum of moderate to severe HS patients. HS serum clustered separately from healthy controls and had an up-regulation of neutrophil-related markers (Cathepsin D, IL-17A, CXCL1). Patients with histologically diagnosed dermal tunnels had higher serum Lipocalin-2 (LCN2) levels compared to those without tunnels. Consistent with this, patients with tunnels had a more neutrophilic-rich serum signature, marked by Cathepsin D, IL-17A, IL-17D, and MMP-2 alterations. There was a significant serum-skin correlation between proteins in the serum and the corresponding mRNA levels in skin biopsies, with healthy-appearing perilesional skin demonstrating a significant correlation with neutrophil-related proteins in the serum. Granulocyte-colony stimulating factor (G-CSF, CFS3) mRNA levels in lesional skin significantly correlated with neutrophil-related proteins in the serum, suggesting that CFS3 in the skin may be a driver of neutrophilic inflammation. Clinical parameters significantly correlated with levels of LCN2 and IL-17A in the serum. Using an unbiased, large-scale proteomic approach, we demonstrate that HS is a systemic neutrophilic dermatosis, with a specific molecular signature associated with the presence of dermal tunnels.

Graphical Abstract

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INTRODUCTION:

Hidradenitis Suppurativa (HS) is a chronic inflammatory disease with a prevalence of 1% (Jemec et al., 1996, Jemec and Kimball, 2015, Sabat et al., 2020). HS has a wide spectrum of clinical presentations, ranging from inflamed nodules and abscesses, to interconnecting draining tunnels and late-stage fibrotic disease. Patients with HS face multiple co-morbidities, including inflammatory bowel disease, depression, sexual dysfunction and an increased risk of cardiovascular disease and metabolic syndrome (Kurek et al., 2013, Matusiak et al., 2010, Miller et al., 2014, Reddy et al., 2020, Sabat et al., 2012, van der Zee et al., 2014). Current treatment options for HS have limited efficacy (Frew et al., 2020a), and are hindered by a lack of blood and serum biomarkers for assessment of inflammatory activity and therapeutic response.

While the exact pathogenesis of HS remains unclear, recent studies have led to a paradigm shift from the traditional model of follicular occlusion as a driver of disease to appreciating HS as a systemic inflammatory disorder with alterations involving plasma cells and B-cells (Gudjonsson et al., 2020, Lowe et al., 2020), neutrophils (Byrd et al., 2019), dendritic cells (Lowe et al., 2020), macrophages (Byrd et al., 2018, Thomi et al., 2018) and multiple other pro-inflammatory axis, including the Th17 pathway (Navrazhina et al., 2020, Wolk et al., 2011). However, most of this work has been based on histological and transcriptomic profiling of skin biopsies. Several studies have examined the serum proteome to identify potential disease biomarkers, with varying results regarding the abundance of these cytokines and proteins compared to healthy controls (Blok et al., 2016, Jimenez-Gallo et al., 2017, Vossen et al., 2019, Wolk et al., 2017). A study assessing the in-depth proteomic profile of HS serum in order to identify biomarkers of disease is lacking.

Multiple studies have utilized the Olink broad proteomic panels to gain molecular insight into disease activity in the serum of inflammatory dermatoses including atopic dermatitis (AD) (Brunner et al., 2019, Brunner et al., 2017), alopecia areata (Glickman et al., 2020) and psoriasis vulgaris, as well as for biomarkers of therapeutic response in psoriasis (Kim et al., 2018). In addition to an increase in inflammatory proteins, these studies have identified alterations in cardiovascular biomarkers, consistent with systemic inflammation associated with these disorders. In this study, we aimed to evaluate protein expression in HS serum.

RESULTS:

The proteomic profile of HS serum is molecularly distinct from healthy controls and other systemic inflammatory dermatoses

Using the Olink proteomic platform, we assessed the serum proteome of patients with untreated Hurley II and III HS (n=22) and BMI-matched healthy control individuals (n=9) using the inflammation (92 biomarkers), cardiometabolic (92 biomarkers), cardiovascular II (92 biomarkers) and cardiovascular III (92 biomarkers) panels. Patient demographics can be found in Supplementary Table 1). Principal component analysis (PCA) demonstrated that HS samples clustered separately from healthy volunteers (Figure 1a). Unsupervised two-dimensional hierarchical clustering algorithm was used to group the samples based on differentially expressed proteins (DEPs, defined as abs(FCH) ≥ 1.2 and p ≤ 0.05) (Figure 1b). HS serum had a significant increase in neutrophil-related proteins (IL-17A, CXCL1, Cathepsin D), mediators of atherosclerosis (HGF), chemotactic cytokines and receptors (CCL5, IL-4RA) and growth factors (TGF-α, HGF, HB-EGF) (Figure 1b) (Bell et al., 2018). HS exhibited elevations of ST2 protein, a biomarker for cardiovascular stress and fibrosis that is a potential predictor for outcomes in patients with heart failure (Villacorta and Maisel, 2016). This finding is consistent with increased risk of cardiovascular complications in patients with HS (Miller et al., 2014, Reddy et al., 2020).

Figure 1: HS serum is molecularly distinct from healthy controls and other systemic skin diseases.

Figure 1:

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a) Principal Component Analysis and b) unsupervised hierarchical clustering of all differentially expressed proteins (abs(FCH) ≥1.2, and p≤0.05) between HS and healthy volunteer (HV) controls. Fold changes relative to healthy controls are shown; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. c-Kit-ligand is found in both inflammation and cardiovascular panel. c) Gene Ontology (GO) biological process pathway enrichment analysis of differentially expressed proteins in HS relative to HV using the exploring Genomic Relations (XGR) tool. Vertical line is FDR=0.05. d) PANTHER Statistical overrepresentation test for GO cellular component for differentially expressed proteins in HS relative to HV e) Venn diagram of differentially expressed proteins between HS, psoriasis and atopic dermatitis (AD).

We then conducted an enrichment analysis of DEPs for Gene Ontology (GO) biological processes terms. Pathways that were significantly enriched in the serum of HS patients relative to healthy volunteers are shown in Figure 1c, with the vertical line demonstrating a false discovery rate (FDR) of 0.05. The most significantly enriched pathways were related to general immune response (positive chemotaxis, chemokine-mediated signaling pathway, lymphocyte chemotaxis, immune response, regulation of signaling receptor activity, inflammatory response, signal transduction) and neutrophil-mediated inflammation (neutrophil chemotaxis, neutrophil degranulation). Since the serum contains secreted proteins, we assessed the cellular structures in which the DEPs are localized to function. PANTHER statistical overrepresentation test for GO cellular component terms identified the tertiary granule lumen (p=3.95E-05), specific granule lumen (neutrophil-associated) (p=5.57E-05), and extracellular space (p=9.42E-13) as the cellular locations in which HS-specific proteins functioned (Mi et al., 2019) (Figure 1d). Given that smoking may be associated with HS, we performed a sensitivity analysis to account for smoking status. There were 7 proteins differentially expressed between HS smokers and nonsmokers (SLAMF7, QPCT, CHIL1, SELE, NCAM1, MB, and SERPINA7). None of these proteins were differentially expressed between HS and healthy control patients regardless of the smoking status.

Since we observed systemic inflammation in HS serum, we compared the HS serum proteome to previously published Olink cardiovascular and inflammation panels in atopic dermatitis (AD) and psoriasis vulgaris (Brunner et al., 2017) using abs(FCH) ≥ 1.2, and P-value of ≤ 0.05 (Figure 1E). All three dermatoses had an up regulation of mediators involved in atherosclerosis (HGF) (Bell et al., 2018). Compared to AD and psoriasis, HS only had 11 unique DEPs. HS was characterized by a significant upregulation of proteins related to neutrophil chemotaxis (CXCL1) and biomarkers of cardiovascular disease (ST2), with downregulation of IL-17D. HS was more akin to psoriasis, with an up-regulation of Th17 pathway (IL-17A), neutrophil-related proteins (IL-17A, Cathepsin D, CCL24). Both HS and AD had an upregulation of growth factors (TGF;α) and IL-4 immune signaling (IL4-RA, SLAMF1).

Lipocalin 2 differentiates HS subtypes in serum

Lipocalin-2 (LCN2), also known as neutrophil gelatinase-associated lipocalin, has been suggested as a potential biomarker in HS, with reports demonstrating elevated levels of LCN2 in serum of patients with HS and palmoplantar pustular psoriasis (PPP) (Wolk et al., 2018, Wolk et al., 2017). LCN2 is a potent chemoattractant for neutrophils, promoting adhesion and extravasation of granulocytes (Schroll et al., 2012). LCN2 can also be used to measure of inflammation in the context of inflammatory bowel disease (Chassaing et al., 2012, Thorsvik et al., 2017). Given the neutrophilic signature of HS serum and the applicability of LCN2 as a biomarker of inflammatory disease, we asked whether LCN2 is elevated in our HS cohort. Unsupervised two-dimensional hierarchical clustering of all samples arranged by LCN2 levels identified two HS subgroups: subset of HS patients with high LCN2 levels and a subset of HS patients with low LCN2 levels which clustered more closely to the controls (Figure 2a). We identified a node of the dendrogram which was associated with increasing levels of LCN2 (black box, Figure 2a). This cluster identifies proteins directly proportional to LCN2 levels in serum, including neutrophil-related proteins (AZU1, MPO, EN-RAGE, DEFA1, CEACAM8, MMP-9, CXCL8) (Figure 2b–c). Phylogenetic tree clustering of all the samples demonstrated two distinct subtypes of HS based on high or low LCN2 levels in the serum (Figure 2d). We then evaluated the clinical and histological parameters associated with the patients in each subgroup. The majority of the patients in the LCN2-high subgroup had histologically diagnosed dermal HS tunnels (based on ultrasound examination of HS skin as well as the presence of a visible tunnel on the histological assessment of the biopsy), compared to the LCN2-low subgroup, in which patients did not have histologically diagnosed tunnels. Since the criteria of histologically-confirmed dermal tunnels was used, it is plausible that a patient may have had a tunnel that was missed by the punch biopsy, thus explaining the two outliers in the cohort (Figure 2d).

Figure 2: Serum LCN2 levels differentiate HS into two subgroups.

Figure 2:

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a) Unsupervised hierarchical clustering of all proteins in HS and healthy volunteers (HV) control serum arranged by increasing levels of LCN2 in the serum. Red indicates upregulated and blue indicates downregulated protein expression levels. Blue circle demonstrates node of interest on the dendrogram with black box identifying a cluster of proteins that are directly proportional with LCN2 levels b) Magnification of the LCN2-related cluster of proteins identifies a clear demarcation of two HS sub-groups based on protein level of LCN2 in serum c) Pearson’s correlation between LCN2 protein levels and levels of other neutrophil-related proteins in serum is shown. r is Pearson correlation. d) High LCN2 sub-group consists of patients with histologically diagnosed tunnels, which cluster separately and away from patients without tunnels and low LCN2 levels.

HS patients with tunnels have a different serum proteomic profile compared to patients without HS tunnels

We then compared DEPs in sera of HS patients with and without histologically confirmed tunnels, with FCH relative to healthy control shown (Figure 3a) and conducted an enrichment analysis of DEPs unique to HS samples with tunnels (relative to healthy controls) using the canonical/KEGG/REACTOME/biocarta pathways. Serum of HS patients with tunnels had an enrichment of pathways involved in proliferation and signal transduction, inflammation, extracellular matrix remodeling and tissue-development (development biology, axon guidance, pathways in cancer) (Figure 3b). There were 41 unique DEPs in tunnel samples compared to 23 proteins unique to non-tunnel samples, both relative to healthy controls (Figure 3c). There was minimal overlap between tunnel and non-tunnel samples (5 proteins). Smoking status did not influence the serum proteome between patients with and without tunnels; of the 7 differentially expressed proteins between HS smokers and HS nonsmokers, only 1 (SERPINA7) was differentially expressed between tunnel and non-tunnel HS samples.

Figure 3: HS patients with tunnels have a different serum proteome profile compared to patients without tunnels.

Figure 3:

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a) Heatmap of all differentially expressed proteins (abs(FCH) ≥1.2, and p≤0.05) between healthy control volunteers (HV) and HS patients without tunnels, HV and HS patients with tunnels, or HS patients with and without tunnels. Fold changes relative to HV are shown; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001. b) Enriched biological processes in serum of HS patients with tunnels by canonical/KEGG/REACTOME/biocarta pathways using the exploring Genomic Relations (XGR) tool. Vertical line is FDR=0.05 c) Venn Diagram of differentially expressed proteins in HS patients with and without tunnels relative to HV d) Olink expression of serum protein levels, shown in Log2(Expression) for neutrophil and cardiovascular-risk related proteins in serum of HS and HV, HS patients with and without tunnels, and HS patients with draining and non-draining tunnels. Each dot represents an individual sample.

HS samples with tunnels had a neutrophilic signature (CTSD, IL-17A, IL-17D, LCN2) compared to HS without tunnels. Serum of patients with tunnels had an upregulation of known cardiovascular biomarkers (HGF, ST2, PGLYRP1). Given the neutrophilic signature associated with tunnels, and that pus draining from tunnels is neutrophil-mediated, we asked whether patients with draining or non-draining tunnels had a different proteome profile (Figure 3d). There was a significant difference in levels of neutrophilic-related proteins (IL-17A, LCN2, CXCL8, EN-RAGE, DEFA-1, MMP-9) between HS samples with draining versus nondraining tunnels. In cases where there was no significant difference in protein levels between healthy volunteers and HS, or tunnel versus non-tunnel samples, there was a significant difference in protein levels between draining and non-draining tunnel (EN-RAGE, DEFA1, MMP-9). This suggests that patients with actively draining tunnels have a different serum proteome profile. Furthermore, cardiovascular biomarker ST2 was significantly elevated in patients with draining tunnels, further linking the role of tunnels in HS and the increased risk for cardiovascular co-morbidities (Figure 3d).

Correlation of biomarkers suggests a skin-blood interaction in HS

We examined lesional (LS), and healthy-appearing perilesional (PL) and nonlesional (NL) skin biopsies as previously described (Frew et al., 2019b) (Figure 4a). We first asked if there was any correlation between HS skin and serum by studying IL-6, which has been previously suggested as a biomarker in HS serum (Jimenez-Gallo et al., 2017). IL-6 protein level in serum was significantly correlated with IL-6 mRNA in LS (r=0.62, p=0.0096), PL (r=0.56, p=0.0138) and NL (r=0.5, p=0.0261) skin (Figure 4b). We focused this analysis on PL skin biopsies since the overall RNA-quality was better in the PL compared to LS samples, consistent with the increased presence of neutrophils in lesions of LS skin. There was a significant correlation between proteins involved in the interferon axis (CXCL9, CXCL11), known psoriasis-related proteins (PI3/elastin, SELP), B-cell related protein (IGCL2), neutrophil related markers (LCN2, SERPINA5, SLAMF1) and biomarkers of general inflammation (HGF, HO-1) (Figure 4c)

Figure 4: There is a significant serum-skin correlation in HS.

Figure 4:

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a) Lesional (LS) skin was biopsied at an edge of an active inflammatory lesion. Perilesional (PL) and nonlesional (NL) skin biopsies were taken from healthy-appearing skin 2cm and 10cm from the edge of the active inflammatory lesion, respectively, and biopsied on the same anatomical area as the LS biopsy. b) Correlation plots of IL-6 protein serum levels and the IL-6 mRNA levels in LS, PL and NL skin; scatterplots are shown with estimated linear regression and 95% confidence interval; r Pearson Correlation. c) Serum-skin correlation of serum protein levels (Log2 Olink expression) with their corresponding mRNA levels in PL skin (Log2 mRNA expression) d) Serum-skin correlation between mRNA levels of CSF3 in LS skin and levels of neutrophil-related proteins in the serum.

Granulocyte colony-stimulating factor (G-CSF/CSF3) is a major hematopoietic cytokine regulating granulopoiesis, and is involved in both inducing granulocyte production and release from the bone marrow (Furze and Rankin, 2008, Semerad et al., 2002). Given the strong correlation between neutrophil-related biomarkers in the serum and skin, and the neutrophilic signature associated with HS overall, we asked if CSF3 is a possible driver of increased neutrophil activity is. The mRNA levels of CSF3 were elevated in LS and PL skin of HS patients compared to skin from healthy volunteers. Therefore, we asked if there was a correlation between CFS3 levels in LS skin and neutrophil-related biomarkers in serum (Figure 4d). Indeed, many of the neutrophil-related markers correlated with increased expression of CSF3 mRNA in the skin, suggesting that the active inflammatory lesion may be driving the recruitment of neutrophils and thus increasing the expression of neutrophil-related protein in the serum (Figure 4d).

Levels of neutrophil-related proteins in the serum correlate with HS clinical activity

We then asked whether clinical characteristics correlated with LCN2 and IL-17A protein levels in the serum (Figure 5). Given that only advanced HS patients (Hurley Stage II and III) were included in this study, we could not correlate the serum levels of LCN2 and IL-17A with Hurley Staging. However, patients with Hurley Stage III were more likely to present with draining tunnels (p=0.00905) and thus are more likely to have neutrophilic inflammation in the serum, consistent with analysis in Figure 3d. Unlike the IHS4 criteria, Hurley staging does not take into account the presence of nodules, abscesses and number of draining tunnels in a weighted approach. IHS4 scoring, which assigns points to the number of nodules, abscesses and draining tunnels/fistulae, correlated the most with the serum protein levels of neutrophilic LCN2 and IL-17A, suggesting its utility as a tool to quantify disease activity and clinical response in HS. Therefore, our data suggests that the IHS4 score is more representative of disease activity than Hurley staging. Furthermore, IHS4 levels also correlated with other markers of general inflammation (TNF-α, IL-6) and biomarkers of cardiovascular risk (ST2, HGF, TIE2). This may suggest that patients with more severe HS are at an increased risk of cardiovascular disease.

Figure 5: Serum protein correlations with clinical markers and skin disease severity.

Figure 5:

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Correlation of White Blood Cell Count (WBC), Absolute Neutrophil Count (Abs. Neutrophil), Absolute Nodule Count (defined as the sum of nodules and abscesses), Draining Tunnel Count, Pain Score, and IHS4 scores with A) LCN2 and B) IL-17A protein levels in the serum. Scatterplots are shown with estimated linear regression and 95% confidence interval; r Pearson Correlation. Blue dots represent healthy control samples and red dots are HS samples.

DISCUSSION:

This study is a large-scale proteomic analysis of serum from moderate-to-severe HS patients. Consistent with previous reports in skin, we identified elevation of IL-17A in HS serum (Kelly et al., 2015, Lima et al., 2016, Navrazhina et al., 2020). We demonstrate systemic neutrophilic inflammation in HS, consistent with elevated absolute neutrophil counts in the blood (Supplementary Table 1). Unbiased analysis of samples demonstrated clustering of HS based on high and low LCN2 levels in the serum, which corresponded with histologically-confirmed presence or absence of epithelialized dermal tunnels, respectively. This is consistent with neutrophils and keratinocytes (likely from the epithelized tunnels) being the source of LCN2 elevation (Wolk et al., 2017). There was a significant serum-skin correlation of neutrophilic markers present in perilesional skin, suggesting that there is an ongoing systemic inflammation even in healthy-appearing skin. Consistent with this, smaller-scale studies have shown that there is an upregulation of pro-inflammatory pathways even in healthy-appearing unaffected skin, further giving credence to the concept of HS as a systemic dermatosis (Navrazhina et al., 2020, Sanchez et al., 2019, van der Zee et al., 2011). ELISA-based analysis of HS serum has demonstrated dysregulation in pathways involving general inflammation (Blok et al., 2016), neutrophil activation (Wolk et al., 2017), complement pathway (Hoffman et al., 2018) and antibody formation (Assan et al., 2020). We identified CSF3 in the skin as a potential regulator of neutrophilic inflammation in the serum. Taken together, our data suggests that HS has significant clinical and molecular heterogeneity, demonstrating that patients with HS tunnels have a different proteomic profile.

HS is a heterogenous disease in its clinical presentation, however, it is unknown if different morphological structures manifest in unique inflammatory signatures in HS skin and serum. We present a large-scale proteomic analysis demonstrating an unbiased clustering of HS disease into distinct subgroups. When subdivided by the presence of tunnels, there was a higher number of DEPs than when examining HS as a whole. HS had fewer DEPs compared to psoriasis and AD, which could reflect a smaller body surface area affected and the compartmentalization of immune response in HS. We demonstrate an interesting association between a morphological structure in the skin (tunnels) and serum biomarkers. Patients with draining tunnels had a significantly higher levels of neutrophil-related proteins in the serum (IL-17A, MPO, LCN2, CXCL8, EN-RAGE, DEFA1, MMP-9) compared to nondraining tunnels. This is consistent with the pus in draining tunnels being neutrophil mediated. Interestingly, HS patients with tunnels demonstrated enrichment of pathways related to ECM re-modelling and developmental biology. These signatures may explain the development of dermal tunnels and shift the paradigm from tunnels being an end-stage fibrotic feature of the disease to an active inflammatory structure. LCN2 is a protein secreted by granulocytes, neutrophils and keratinocytes. TNF-α is a potent inducer of LCN2 in granulocytes, while TNF-α and IL-17 has been shown to induce LCN2 production in keratinocytes (Wolk et al., 2017). Consistent with this, patients with tunnels had increased levels of IL-17A and TNF-α in the serum, which provides the direct mechanistic link for the increased levels of LCN2 in patients with tunnels. While some studies have shown that LCN2 is associated with obesity (Koiou et al., 2012, Mosialou et al., 2020, Wang et al., 2007) and could provide some cardiometabolic protection (Mosialou et al., 2020), a study of psoriasis patients did not find a correlation between BMI and LCN2 levels but did report an elevation of LCN2 in serum psoriasis patients compared to healthy controls (Kamata et al., 2012). Similarly, we did not find a significant difference in BMI between LCN2-low and LCN2-high patients in our cohort (p=0.34). In our study, LCN2 levels in the serum are correlated with neutrophilic markers and the number of tunnels, suggesting that the strong LCN2 signature associated with the disease activity and the presence of tunnels may supersede any differences related to the BMI. We believe that the elevated LCN2 levels were likely derived from the high inflammatory burden of the disease rather than BMI. This highlights the role of tunnels in disease activity. Patients with draining tunnels had significantly higher levels of ST2 protein compared to those with nondraining tunnels, suggesting that the inflammation extends beyond the skin and can potentially affect the cardiovascular health.

Furthermore, the presence of tunnels influences the time it takes to achieve HiSCR response in the PIONEER 2 study of adalimumab (Frew et al., 2019a, 2020b). Our data provides the molecular mechanism for why patients with tunnels have a different disease activity and respond differently to biologic therapy (Frew et al., 2020b). Differences in biomarkers between tunnels and non-tunnel samples define disease endotypes that could impact therapeutic choices. For example, we found higher levels of TNF in tunnel-positive individuals, and it has been shown that the presence of tunnels increases time to a clinical response to adalimumab (Frew et al., 2020b). We speculate that the high levels of TNF could require the use of high-dose TNF antibodies that have been shown to be effective in severe cases (Ghias et al., 2020). Our study provides evidence of how morphological structures could influence the molecular profile of HS patients. Identifying HS subsets (potentially based on the presence or absense of tunnels) can identify not just novel biomakeres specific to each disease subset, but potentially identify effective treatment unique to each HS subgroup.

Our findings demonstrate that the skin may be a driver of the neutrophilic inflammation in serum, as there was a proportional correlation between CSF3 mRNA in the skin and the neutrophil-related proteins in serum. We had previously reported increasing gradient of IL-17 from nonlesional to lesional skin (Navrazhina et al., 2020). IL-17 has been shown to stimulate granulopoiesis by inducing G-CSF (Hirai et al., 2012, Schwarzenberger et al., 2000, Schwarzenberger et al., 1998, Xu and Cao, 2010). It is plausible that IL-17, TNF-α, and IL-6 in HS skin may stimulate G-CSF production by fibroblasts, monocytes and endothelial cells, which in turn stimulates release of neutrophils from the bone marrow(Kaushansky, 2006, Xu and Cao, 2010). Potentially, G-CSF could affect cutaneous characteristics, as several individual case reports have reported psoriasiform cutaneous eruption in patients receiving G-CSF (Cho et al., 1998, Jang et al., 2017, Kavanaugh, 1996, Mössner et al., 2004).

Acute and chronic pain contributes significantly to the reduced quality of life in patients with HS (Savage et al., 2020). In this study, we explored the correlation between clinical parameters and the levels of neutrophilic proteins in the serum. Of particular interest is the correlation between LCN2 and IL-17A and reported pain levels in HS. The mechanisms of pain levels in HS have not been elucidated. Reports have suggested that neutrophil chemoattractant leukotriene B4 as well as the migration cascade of neutrophils themselves can lead to hyperalgesia and mechanical hyper nociception (Cunha et al., 2008, Levine et al., 1984). Consistent with this, animal studies have shown that both LCN2 and IL-17A are involved in mechanical hyperalgesia (Ebbinghaus et al., 2017, Jeon et al., 2013). Our data provides one plausible mechanism for the pain burden in HS.

A strength of our study is that we assessed a large panel of known biomarkers in an unbiased approach. Previous studies of HS serum demonstrated elevated levels of IL-17A, TNF-α, LCN2, and IL-6 (Jimenez-Gallo et al., 2017, Matusiak et al., 2009, Matusiak et al., 2017, Wolk et al., 2017). The clinical disease heterogeneity in HS complicates the identification of biomarkers of clinical response. All of the patients in our study were either untreated or had undergone a washout period, therefore eliminating these confounders in the analysis. Furthermore, given that changes in proteomic profiles are related to changes in BMI and fat distribution, we utilized BMI-matched healthy controls (Lind et al., 2020). Importantly, rather than focusing on known disease-associated cytokines, we sought to determine new disease associated biomarkers through use of a large biomarker panel and a hypothesis free approach.

The limitations of our work include a modest sample size (although comparable to other cohorts studied (Blok et al., 2016, Brunner et al., 2019, Wolk et al., 2017), use of controls that were older than the HS cohort and the relative limitation of the Olink platform where analysis is restricted to pre-grouped biomarker subsets. Future larger-scale studies are warranted to identify how other HS manifestations (abscesses vs nodule, draining vs non draining tunnel) impact serum proteome. This analysis is also limited to moderate and severe HS patients, and it would be desirable to study new on-set or mild HS patients in future studies. If serum biomarkers are also elevated in early-stage disease, these biomarkers may facilitate diagnosis and decrease the 5-14 year diagnostic delay experienced by HS patients (Jemec and Kimball, 2015). Given the cyclical nature of HS severity, marked by debilitating flare-ups and a remitting course, serum biomarkers become particularly crucial in order to assess disease activity and accurately diagnose, as well as measure therapeutic response.

In conclusion, we demonstrate that HS is a systemic, inflammatory condition associated with neutrophil-rich signature in serum and skin, with a significant serum-skin correlation of neutrophilic activity. We identify two HS subgroups corresponding with LCN2 protein levels in the serum and histologically confirmed presence of tunnels in affected skin.

MATERIALS&METHODS:

Patients:

The study was approved by the Institutional Review Board of the Rockefeller University and written informed consent was obtained. Twenty-two patients with Hurley stage II (n=15) and III (n=7) and nine BMI- matched healthy controls were included in this study (Supplementary Table 1). Exclusion criteria included HIV, Hepatitis B or C, current pregnancy or breastfeeding. Patients were required to undergo a washout period of five half-lives from previous systemic treatments including all oral antibiotic, retinoid and monoclonal antibody therapies.

Serum protein quantification:

Samples were centrifuged following collection, and serum was stored at −80°C. Samples were analyzed using the proteomic Olink Proseek multiplex assay (Uppsala, Sweden). 10 μl of serum was used for proximity extension assay which uses a real-time polymerase chain reaction to detect oligonucleotide-labeled antibody probe pairs to individual targets, as previously described (Assarsson et al., 2014, Bettoli et al., 2016). Samples were assessed using the Olink Inflammation (92 analytes), Cardiovascular II (92 analytes) and Cardiovascular III (92 analytes) and Cardiometabolic panel (92 analytes). All of the samples met the quality control for the Olink panels with the exception of one healthy control sample that did not meet the quality control for Cardiovascular II and Inflammation panels and was therefore excluded from the analysis of these two panels. Only samples that had detected expression of all the proteins in the panels were included in the heatmaps.

Skin mRNA quantification:

RNA from frozen skin biopsies was isolated using miRNAeasy Mini Kit (Qiagen) and DNA was removed using on-column DNase digestion from the RNase-free DNase Set (Qiagen). RNA-sequencing (RNA) seq was performed using NovaSeq 6000 (Illumina, San Diego, CA) and analysis was conducted as previously described (Suárez-Fariñas et al., 2015, Visvanathan et al., 2019). RT-PCR was used to assess CSF3 expression in LS skin, and expression of CSF3 mRNA in the skin was normalized to house-keeping gene hARP as previously described (Navrazhina et al., 2020). Probes used were TaqMan Gene Expression Assay CSF3 (Hs00738432_g1) and hARP (AID1UP5) from Thermo Fisher Scientific.

Statistical Analysis:

Statistical analysis was performed in R language (R-project.org, R Foundation, Vienna, Austria) using publicly available Bioconductor Project packages (www.bioconductor.org; Bioconductor Core Team, Buffalo, New York). Quality control of OLINK data was accomplished using OLINK’s standard quality control (QC) pipeline (Lind et al., 2015). One healthy control sample did not pass the quality control for Cardiovascular II and Inflammatory panel quantification, therefore was excluded from the analysis in both panels. The Olink platform presents data in Normalized Protein expression (NPX) arbitrary Log2 scaled units. Protein expression profiles were modeled with linear models using the limma framework as previously described (Brunner et al., 2017). This model considers disease state and tunnel-status as fixed factors, while random effect related to the subjects was included in the model (Brunner et al., 2017, He et al., 2020). Least squared means and comparisons for protein profiles among different groups were estimated and hypothesis testing was performed under the general framework for linear models in the limma package. A sensitivity analysis for the smoking status was implemented, demonstrating no imputation-related departures from conclusions reached. Differentially expressed proteins were defined as those with Fold Change abs(FCH) ≥ 1.2, and p-value of ≤ 0.05, consistent with previous studies (Brunner et al., 2017, He et al., 2020) Correlation between messenger RNA (mRNA) levels in the skin, protein levels in the serum, and clinical parameters were evaluated using Pearson correlations on log2-transformed expression values.

Pathway analysis:

Gene set enrichment analysis was performed using the eXploring Genomic Relations (accessed 11/15/2020) (Fang et al., 2016) for biological processes pathways including KEGG (Kanehisa et al., 2010), BioCarta (Croft et al., 2014), REACTOME (Croft et al., 2014) and Gene Ontology (2019). False Discovery Rate (FDR) cutoff at 0.05 was used to identify significant enrichment. Statistical overrepresentation test was performed using PANTHER GO cellular component complete analysis (Mi et al., 2019). Significance was defined as FDR <0.05.

Supplementary Material

1

Supplementary Table 1: Demographic characteristics of participants

Supplementary Figure 1: HS serum is distinct from healthy controls. Unsupervised hierarchical clustering of all proteins comparing HS and heathy volunteer (HV) controls. Fold changes are shown relative to healthy controls; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001

ACKNOWLEDGEMENTS:

We acknowledge members of the Krueger laboratory for their thoughtful discussions throughout this project and manuscript preparation.

Funding:

K.N. was supported by a MSTP grant from the National Institute of General Medical Sciences of the NIH under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program. J.W.F. and J.G.K. were supported in part by grant # UL1 TR001866 from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program. J.W.F. was supported by the Shapiro-Silverberg Fund for the Advancement of Translational Research and the Hidradenitis Suppurativa Foundation Danby Grant.

CONFLICT OF INTEREST:

JGK has received research support (grants paid to institution) from AbbVie, Amgen, BMS, Boehringer, EMD Serono, Innovaderm, Kineta, LEO Pharma, Novan, Novartis, Paraxel, Pfizer, Regeneron, and Vitae and personal fees from AbbVie, Acros, Allergan, Aurigne, BiogenIdec, Boehringer, Escalier, Janssen, Lilly, Novartis, Pfizer, Roche, and Valeant.. JWF has conducted advisory work for Janssen, Boehringer-Ingelheim, Pfizer, Kyowa Kirin, LEO Pharms, Regeneron and UCB, participated in trials for UCB and received research support from Ortho Dermatologics. The other authors declare they have no relevant conflicts of interest.

ABBREVIATIONS:

Abs

Absolute Value

AD

Atopic Dermatitis

BMI

Body Mass Index

CSF-3/G-CSF

Colony Stimulating Factor 3/Granulocyte Colony-Stimulating Factor

CTSD

Cathepsin D

HS

Hidradenitis Suppurativa

HV

Healthy Volunteer

IL

Interleukin

LCN2

Lipocalin-2

MMP

Matrix Metalloproteinase

Footnotes

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DATA AVAILABILITY:

The datasets related to this article can be found at http://dx.doi.org/10.17632/jk7bb355tr and https://data.mendeley.com/datasets/jk7bb355tr/2 hosted at Mendeley. All other supporting data is available by the upon written request to the corresponding author.

REFERENCES:

  1. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res 2019;47(D1):D330–d8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Assan F, Gottlieb J, Tubach F, Lebbah S, Guigue N, Hickman G, et al. Anti-Saccharomyces cerevisiae IgG and IgA antibodies are associated with systemic inflammation and advanced disease in hidradenitis suppurativa. J Allergy Clin Immunol 2020;146(2):452–5.e5. [DOI] [PubMed] [Google Scholar]
  3. Assarsson E, Lundberg M, Holmquist G, Björkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One 2014;9(4):e95192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bell EJ, Decker PA, Tsai MY, Pankow JS, Hanson NQ, Wassel CL, et al. Hepatocyte growth factor is associated with progression of atherosclerosis: The Multi-Ethnic Study of Atherosclerosis (MESA). Atherosclerosis 2018;272:162–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bettoli V, Join-Lambert O, Nassif A. Antibiotic Treatment of Hidradenitis Suppurativa. Dermatol Clin 2016;34(1):81–9. [DOI] [PubMed] [Google Scholar]
  6. Blok JL, Li K, Brodmerkel C, Horvátovich P, Jonkman MF, Horváth B. Ustekinumab in hidradenitis suppurativa: clinical results and a search for potential biomarkers in serum. Br J Dermatol 2016;174(4):839–46. [DOI] [PubMed] [Google Scholar]
  7. Brunner PM, He H, Pavel AB, Czarnowicki T, Lefferdink R, Erickson T, et al. The blood proteomic signature of early-onset pediatric atopic dermatitis shows systemic inflammation and is distinct from adult long-standing disease. J Am Acad Dermatol 2019;81(2):510–9. [DOI] [PubMed] [Google Scholar]
  8. Brunner PM, Suárez-Fariñas M, He H, Malik K, Wen HC, Gonzalez J, et al. The atopic dermatitis blood signature is characterized by increases in inflammatory and cardiovascular risk proteins. Sci Rep 2017;7(1):8707. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Byrd AS, Carmona-Rivera C, O’Neil LJ, Carlucci PM, Cisar C, Rosenberg AZ, et al. Neutrophil extracellular traps, B cells, and type I interferons contribute to immune dysregulation in hidradenitis suppurativa. Sci Transl Med 2019;11(508). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Byrd AS, Kerns ML, Williams DW, Zarif JC, Rosenberg AZ, Delsante M, et al. Collagen deposition in chronic hidradenitis suppurativa: potential role for CD163(+) macrophages. Br J Dermatol 2018;179(3):792–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chassaing B, Srinivasan G, Delgado MA, Young AN, Gewirtz AT, Vijay-Kumar M. Fecal lipocalin 2, a sensitive and broadly dynamic non-invasive biomarker for intestinal inflammation. PLoS One 2012;7(9):e44328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cho SG, Park YM, Moon H, Kim KM, Bae SS, Kim GB, et al. Psoriasiform eruption triggered by recombinant granulocyte-macrophage colony stimulating factor (rGM-CSF) and exacerbated by granulocyte colony stimulating factor (rG-CSF) in a patient with breast cancer. J Korean Med Sci 1998;13(6):685–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res 2014;42(Database issue):D472–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cunha TM, Verri WA Jr., Schivo IR, Napimoga MH, Parada CA, Poole S, et al. Crucial role of neutrophils in the development of mechanical inflammatory hypernociception. J Leukoc Biol 2008;83(4):824–32. [DOI] [PubMed] [Google Scholar]
  15. Ebbinghaus M, Natura G, Segond von Banchet G, Hensellek S, Böttcher M, Hoffmann B, et al. Interleukin-17A is involved in mechanical hyperalgesia but not in the severity of murine antigen-induced arthritis. Sci Rep 2017;7(1):10334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fang H, Knezevic B, Burnham KL, Knight JC. XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits. Genome Med 2016;8(1):129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Frew JW, Jiang CS, Singh N, Grand D, Navrazhina K, Vaughan R, et al. Clinical response rates, placebo response rates, and significantly associated covariates are dependent on choice of outcome measure in hidradenitis suppurativa: A post hoc analysis of PIONEER 1 and 2 individual patient data. J Am Acad Dermatol 2019a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Frew JW, Jiang CS, Singh N, Grand D, Navrazhina K, Vaughan R, et al. Clinical response rates, placebo response rates, and significantly associated covariates are dependent on choice of outcome measure in hidradenitis suppurativa: A post hoc analysis of PIONEER 1 and 2 individual patient data. J Am Acad Dermatol 2020a;82(5):1150–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Frew JW, Jiang CS, Singh N, Grand D, Navrazhina K, Vaughan R, et al. Dermal Tunnels Influence Time to Clinical Response and Family History Influences Time to Loss of Clinical Response in Hidradenitis Suppurativa Patients Treated with Adalimumab. Clin Exp Dermatol 2020b. [DOI] [PubMed] [Google Scholar]
  20. Frew JW, Navrazhina K, Byrd AS, Garg A, Ingram JR, Kirby JS, et al. Defining lesional, perilesional and unaffected skin in hidradenitis suppurativa: proposed recommendations for clinical trials and translational research studies. Br J Dermatol 2019b;181(6):1339–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Furze RC, Rankin SM. Neutrophil mobilization and clearance in the bone marrow. Immunology 2008;125(3):281–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ghias MH, Johnston AD, Kutner AJ, Micheletti RG, Hosgood HD, Cohen SR. High-dose, high-frequency infliximab: A novel treatment paradigm for hidradenitis suppurativa. J Am Acad Dermatol 2020;82(5):1094–101. [DOI] [PubMed] [Google Scholar]
  23. Glickman JW, Dubin C, Renert-Yuval Y, Dahabreh D, Kimmel GW, Auyeung K, et al. Cross-sectional study of blood biomarkers of patients with moderate to severe alopecia areata reveals systemic immune and cardiovascular biomarker dysregulation. J Am Acad Dermatol 2020. [DOI] [PubMed] [Google Scholar]
  24. Gudjonsson JE, Tsoi LC, Ma F, Billi AC, van Straalen KR, Vossen AR, et al. Contribution of plasma cells and B-cells to hidradenitis suppurativa pathogenesis. JCI Insight 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. He H, Del Duca E, Diaz A, Kim HJ, Gay-Mimbrera J, Zhang N, et al. Mild atopic dermatitis lacks systemic inflammation and shows reduced nonlesional skin abnormalities. J Allergy Clin Immunol 2020. [DOI] [PubMed] [Google Scholar]
  26. Hirai Y, Iyoda M, Shibata T, Kuno Y, Kawaguchi M, Hizawa N, et al. IL-17A stimulates granulocyte colony-stimulating factor production via ERK1/2 but not p38 or JNK in human renal proximal tubular epithelial cells. Am J Physiol Renal Physiol 2012;302(2):F244–50. [DOI] [PubMed] [Google Scholar]
  27. Hoffman LK, Tomalin LE, Schultz G, Howell MD, Anandasabapathy N, Alavi A, et al. Integrating the skin and blood transcriptomes and serum proteome in hidradenitis suppurativa reveals complement dysregulation and a plasma cell signature. PLoS One 2018;13(9):e0203672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jang MS, Park JB, Kim JH, Yang MH, Lee KH, Han SH, et al. Granulocyte Colony-Stimulating Factor-Induced Psoriasiform Dermatitis Improved by Narrowband Ultraviolet B. Ann Dermatol 2017;29(2):232–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jemec GB, Heidenheim M, Nielsen NH. The prevalence of hidradenitis suppurativa and its potential precursor lesions. J Am Acad Dermatol 1996;35(2 Pt 1):191–4. [DOI] [PubMed] [Google Scholar]
  30. Jemec GB, Kimball AB. Hidradenitis suppurativa: Epidemiology and scope of the problem. J Am Acad Dermatol 2015;73(5 Suppl 1):S4–7. [DOI] [PubMed] [Google Scholar]
  31. Jeon S, Jha MK, Ock J, Seo J, Jin M, Cho H, et al. Role of lipocalin-2-chemokine axis in the development of neuropathic pain following peripheral nerve injury. J Biol Chem 2013;288(33):24116–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jimenez-Gallo D, de la Varga-Martinez R, Ossorio-Garcia L, Albarran-Planelles C, Rodriguez C, Linares-Barrios M. The Clinical Significance of Increased Serum Proinflammatory Cytokines, C-Reactive Protein, and Erythrocyte Sedimentation Rate in Patients with Hidradenitis Suppurativa. Mediators Inflamm 2017;2017:2450401. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kamata M, Tada Y, Tatsuta A, Kawashima T, Shibata S, Mitsui H, et al. Serum lipocalin-2 levels are increased in patients with psoriasis. Clin Exp Dermatol 2012;37(3):296–9. [DOI] [PubMed] [Google Scholar]
  34. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2010;38(Database issue):D355–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kaushansky K Lineage-specific hematopoietic growth factors. N Engl J Med 2006;354(19):2034–45. [DOI] [PubMed] [Google Scholar]
  36. Kavanaugh A Flare of psoriasis and psoriatic arthritis following treatment with granulocyte colony-stimulating factor. Am J Med 1996;101(5):567–8. [DOI] [PubMed] [Google Scholar]
  37. Kelly G, Hughes R, McGarry T, van den Born M, Adamzik K, Fitzgerald R, et al. Dysregulated cytokine expression in lesional and nonlesional skin in hidradenitis suppurativa. Br J Dermatol 2015;173(6):1431–9. [DOI] [PubMed] [Google Scholar]
  38. Kim J, Tomalin L, Lee J, Fitz LJ, Berstein G, Correa-da Rosa J, et al. Reduction of Inflammatory and Cardiovascular Proteins in the Blood of Patients with Psoriasis: Differential Responses between Tofacitinib and Etanercept after 4 Weeks of Treatment. J Invest Dermatol 2018;138(2):273–81. [DOI] [PubMed] [Google Scholar]
  39. Koiou E, Tziomalos K, Katsikis I, Kandaraki EA, Kalaitzakis E, Delkos D, et al. Weight loss significantly reduces serum lipocalin-2 levels in overweight and obese women with polycystic ovary syndrome. Gynecol Endocrinol 2012;28(1):20–4. [DOI] [PubMed] [Google Scholar]
  40. Kurek A, Johanne Peters EM, Sabat R, Sterry W, Schneider-Burrus S. Depression is a frequent co-morbidity in patients with acne inversa. J Dtsch Dermatol Ges 2013;11(8):743–9, –50. [DOI] [PubMed] [Google Scholar]
  41. Levine JD, Lau W, Kwiat G, Goetzl EJ. Leukotriene B4 produces hyperalgesia that is dependent on polymorphonuclear leukocytes. Science 1984;225(4663):743–5. [DOI] [PubMed] [Google Scholar]
  42. Lima AL, Karl I, Giner T, Poppe H, Schmidt M, Presser D, et al. Keratinocytes and neutrophils are important sources of proinflammatory molecules in hidradenitis suppurativa. Br J Dermatol 2016;174(3):514–21. [DOI] [PubMed] [Google Scholar]
  43. Lind L, Ärnlöv J, Lindahl B, Siegbahn A, Sundström J, Ingelsson E. Use of a proximity extension assay proteomics chip to discover new biomarkers for human atherosclerosis. Atherosclerosis 2015;242(1):205–10. [DOI] [PubMed] [Google Scholar]
  44. Lind L, Figarska S, Sundström J, Fall T, Ärnlöv J, Ingelsson E. Changes in Proteomic Profiles are Related to Changes in BMI and Fat Distribution During 10 Years of Aging. Obesity (Silver Spring) 2020;28(1):178–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lowe MM, Naik HB, Clancy S, Pauli M, Smith KM, Bi Y, et al. Immunopathogenesis of hidradenitis suppurativa and response to anti-TNFα therapy. JCI Insight 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Matusiak L, Bieniek A, Szepietowski JC. Increased serum tumour necrosis factor-alpha in hidradenitis suppurativa patients: is there a basis for treatment with anti-tumour necrosis factor-alpha agents? Acta Derm Venereol 2009;89(6):601–3. [DOI] [PubMed] [Google Scholar]
  47. Matusiak L, Bieniek A, Szepietowski JC. Psychophysical aspects of hidradenitis suppurativa. Acta Derm Venereol 2010;90(3):264–8. [DOI] [PubMed] [Google Scholar]
  48. Matusiak Ł, Szczęh J, Bieniek A, Nowicka-Suszko D, Szepietowski JC. Increased interleukin (IL)-17 serum levels in patients with hidradenitis suppurativa: Implications for treatment with anti-IL-17 agents. J Am Acad Dermatol 2017;76(4):670–5. [DOI] [PubMed] [Google Scholar]
  49. Mi H, Muruganujan A, Huang X, Ebert D, Mills C, Guo X, et al. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat Protoc 2019;14(3):703–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Miller IM, Ellervik C, Vinding GR, Zarchi K, Ibler KS, Knudsen KM, et al. Association of metabolic syndrome and hidradenitis suppurativa. JAMA Dermatol 2014;150(12):1273–80. [DOI] [PubMed] [Google Scholar]
  51. Mosialou I, Shikhel S, Luo N, Petropoulou PI, Panitsas K, Bisikirska B, et al. Lipocalin-2 counteracts metabolic dysregulation in obesity and diabetes. J Exp Med 2020;217(10). [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Mössner R, Beckmann I, Hallermann C, Neumann C, Reich K. Granulocyte colony-stimulating-factor-induced psoriasiform dermatitis resembles psoriasis with regard to abnormal cytokine expression and epidermal activation. Exp Dermatol 2004;13(6):340–6. [DOI] [PubMed] [Google Scholar]
  53. Navrazhina K, Frew JW, Krueger JG. Interleukin 17C is elevated in lesional tissue of hidradenitis suppurativa. Br J Dermatol 2020;182(4):1045–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Reddy S, Strunk A, Jemec GBE, Garg A. Incidence of Myocardial Infarction and Cerebrovascular Accident in Patients With Hidradenitis Suppurativa. JAMA Dermatol 2020;156(1):65–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Sabat R, Chanwangpong A, Schneider-Burrus S, Metternich D, Kokolakis G, Kurek A, et al. Increased prevalence of metabolic syndrome in patients with acne inversa. PLoS One 2012;7(2):e31810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Sabat R, Jemec GBE, Matusiak Ł, Kimball AB, Prens E, Wolk K. Hidradenitis suppurativa. Nat Rev Dis Primers 2020;6(1):18. [DOI] [PubMed] [Google Scholar]
  57. Sanchez J, Le Jan S, Muller C, François C, Renard Y, Durlach A, et al. Matrix remodelling and MMP expression/activation are associated with hidradenitis suppurativa skin inflammation. Exp Dermatol 2019;28(5):593–600. [DOI] [PubMed] [Google Scholar]
  58. Savage KT, Singh V, Patel ZS, Yannuzzi CA, McKenzie-Brown AM, Lowes MA, et al. Pain management in hidradenitis suppurativa and a proposed treatment algorithm. J Am Acad Dermatol 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Schroll A, Eller K, Feistritzer C, Nairz M, Sonnweber T, Moser PA, et al. Lipocalin-2 ameliorates granulocyte functionality. Eur J Immunol 2012;42(12):3346–57. [DOI] [PubMed] [Google Scholar]
  60. Schwarzenberger P, Huang W, Ye P, Oliver P, Manuel M, Zhang Z, et al. Requirement of endogenous stem cell factor and granulocyte-colony-stimulating factor for IL-17-mediated granulopoiesis. J Immunol 2000;164(9):4783–9. [DOI] [PubMed] [Google Scholar]
  61. Schwarzenberger P, La Russa V, Miller A, Ye P, Huang W, Zieske A, et al. IL-17 stimulates granulopoiesis in mice: use of an alternate, novel gene therapy-derived method for in vivo evaluation of cytokines. J Immunol 1998;161(11):6383–9. [PubMed] [Google Scholar]
  62. Semerad CL, Liu F, Gregory AD, Stumpf K, Link DC. G-CSF is an essential regulator of neutrophil trafficking from the bone marrow to the blood. Immunity 2002;17(4):413–23. [DOI] [PubMed] [Google Scholar]
  63. Suárez-Fariñas M, Ungar B, Correa da Rosa J, Ewald DA, Rozenblit M, Gonzalez J, et al. RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications. J Allergy Clin Immunol 2015;135(5):1218–27. [DOI] [PubMed] [Google Scholar]
  64. Thomi R, Schlapbach C, Yawalkar N, Simon D, Yerly D, Hunger RE. Elevated levels of the antimicrobial peptide LL-37 in hidradenitis suppurativa are associated with a Th1/Th17 immune response. Exp Dermatol 2018;27(2):172–7. [DOI] [PubMed] [Google Scholar]
  65. Thorsvik S, Damås JK, Granlund AV, Flo TH, Bergh K, Østvik AE, et al. Fecal neutrophil gelatinase-associated lipocalin as a biomarker for inflammatory bowel disease. J Gastroenterol Hepatol 2017;32(1):128–35. [DOI] [PubMed] [Google Scholar]
  66. van der Zee HH, de Ruiter L, van den Broecke DG, Dik WA, Laman JD, Prens EP. Elevated levels of tumour necrosis factor (TNF)-α, interleukin (ILJ-1β and IL-10 in hidradenitis suppurativa skin: a rationale for targeting TNF-α and IL-1β. Br J Dermatol 2011;164(6):1292–8. [DOI] [PubMed] [Google Scholar]
  67. van der Zee HH, de Winter K, van der Woude CJ, Prens EP. The prevalence of hidradenitis suppurativa in 1093 patients with inflammatory bowel disease. Br J Dermatol 2014;171(3):673–5. [DOI] [PubMed] [Google Scholar]
  68. Villacorta H, Maisel AS. Soluble ST2 Testing: A Promising Biomarker in the Management of Heart Failure. Arq Bras Cardiol 2016;106(2):145–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Visvanathan S, Baum P, Vinisko R, Schmid R, Flack M, Lalovic B, et al. Psoriatic skin molecular and histopathologic profiles after treatment with risankizumab versus ustekinumab. J Allergy Clin Immunol 2019;143(6):2158–69. [DOI] [PubMed] [Google Scholar]
  70. Vossen A, van der Zee HH, Tsoi LC, Xing X, Devalaraja M, Gudjonsson JE, et al. Novel cytokine and chemokine markers of hidradenitis suppurativa reflect chronic inflammation and itch. Allergy 2019;74(3):631–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wang Y, Lam KS, Kraegen EW, Sweeney G, Zhang J, Tso AW, et al. Lipocalin-2 is an inflammatory marker closely associated with obesity, insulin resistance, and hyperglycemia in humans. Clin Chem 2007;53(1):34–41. [DOI] [PubMed] [Google Scholar]
  72. Wolk K, Frambach Y, Jacobi A, Wilsmann-Theis D, Phillipp S, Witte-Händel E, et al. Increased levels of lipocalin 2 in palmoplantar pustular psoriasis. J Dermatol Sci 2018;90(1):68–74. [DOI] [PubMed] [Google Scholar]
  73. Wolk K, Warszawska K, Hoeflich C, Witte E, Schneider-Burrus S, Witte K, et al. Deficiency of IL-22 contributes to a chronic inflammatory disease: pathogenetic mechanisms in acne inversa. J Immunol 2011;186(2):1228–39. [DOI] [PubMed] [Google Scholar]
  74. Wolk K, Wenzel J, Tsaousi A, Witte-Handel E, Babel N, Zelenak C, et al. Lipocalin-2 is expressed by activated granulocytes and keratinocytes in affected skin and reflects disease activity in acne inversa/hidradenitis suppurativa. Br J Dermatol 2017;177(5):1385–93. [DOI] [PubMed] [Google Scholar]
  75. Xu S, Cao X. Interleukin-17 and its expanding biological functions. Cell Mol Immunol 2010;7(3):164–74. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

Supplementary Table 1: Demographic characteristics of participants

Supplementary Figure 1: HS serum is distinct from healthy controls. Unsupervised hierarchical clustering of all proteins comparing HS and heathy volunteer (HV) controls. Fold changes are shown relative to healthy controls; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001

NIHMS1685829-supplement-1.pdf (754.1KB, pdf)

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