Olink Proteomics-Based Exploration of Immuno-Oncology-Related Biomarkers Leading to Lung Adenocarcinoma Progression.

INTRODUCTION: It is crucial to investigate the distinct proteins that contribute to the advancement of lung cancer. MATERIAL AND METHODS: We analyzed the expression levels of 92 immuno-oncology-related proteins in 96 pairs of lung adenocarcinoma tissue samples using Olink proteomics. The differentially expressed proteins (DEPs) were successively screened in tumor and paraneoplastic groups, early and intermediate-late groups by a nonparametric rank sum test, and the distribution and expression levels of DEPs were determined by volcano and heat maps, etc., and the area under the curve was calculated. RESULTS: A total of 24 DEPs were identified in comparisons between tumor and paracancerous tissues. Among them, interleukin-8 (IL8) and chemokine (C-C motif) ligand 20 (CCL20) as potential markers for distinguishing tumor tissues. Through further screening, it was found that interleukin-6 (IL6) and vascular endothelial growth factor A (VEGFA) may be able to lead to tumor progression through the JaK-STAT signaling pathway, Toll-like receptor signaling pathway and PI3K/AKT signaling pathway. Interestingly, our study revealed a down-regulation of IL6 and VEGFA in tumor tissues compared to paracancerous tissues. CONCLUSIONS: IL8 + CCL20 (AUC: 0.7056) have the potential to differentiate tumor tissue from paracancerous tissue; IL6 + VEGFA (AUC: 0.7531) are important protein markers potentially responsible for tumor progression.

Plasma Olink Proteomics Reveals Novel Biomarkers for Prediction and Diagnosis in Dilated Cardiomyopathy with Heart Failure.

In this study, we utilized the Olink Cardiovascular III panel to compare the expression levels of 92 cardiovascular-related proteins between patients with dilated cardiomyopathy combined with heart failure (DCM-HF) (n = 20) and healthy normal people (Normal) (n = 18). The top five most significant proteins, including SPP1, IGFBP7, F11R, CHI3L1, and Plaur, were selected by Olink proteomics. These proteins were further validated using ELISA in plasma samples collected from an additional cohort. ELISA validation confirmed significant increases in SPP1, IGFBP7, F11R, CHI3L1, and Plaur in DCM-HF patients compared to healthy controls. GO and KEGG analysis indicated that NT-pro BNP, SPP1, IGFBP7, F11R, CHI3L1, Plaur, BLM hydrolase, CSTB, Gal-4, CCL15, CDH5, SR-PSOX, and CCL2 were associated with DCM-HF. Correlation analysis revealed that these 13 differentially expressed proteins have strong correlations with clinical indicators such as LVEF and NT-pro BNP, etc. Additionally, in the GEO-DCM data sets, the combined diagnostic value of these five core proteins AUC values of 0.959, 0.773, and 0.803, respectively indicating the predictive value of the five core proteins for DCM-HF. Our findings suggest that these proteins may be useful biomarkers for the diagnosis and prediction of DCM-HF, and further research is prompted to explore their potential as therapeutic targets.

Uncovering a neurological protein signature for severe COVID-19.

Coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked a global pandemic with severe complications and high morbidity rate. Neurological symptoms in COVID-19 patients, and neurological sequelae post COVID-19 recovery have been extensively reported. Yet, neurological molecular signature and signaling pathways that are affected in the central nervous system (CNS) of COVID-19 severe patients remain still unknown and need to be identified. Plasma samples from 49 severe COVID-19 patients, 50 mild COVID-19 patients, and 40 healthy controls were subjected to Olink proteomics analysis of 184 CNS-enriched proteins. By using a multi-approach bioinformatics analysis, we identified a 34-neurological protein signature for COVID-19 severity and unveiled dysregulated neurological pathways in severe cases. Here, we identified a new neurological protein signature for severe COVID-19 that was validated in different independent cohorts using blood and postmortem brain samples and shown to correlate with neurological diseases and pharmacological drugs. This protein signature could potentially aid the development of prognostic and diagnostic tools for neurological complications in post-COVID-19 convalescent patients with long term neurological sequelae.

The inflammatory proteome of hidradenitis suppurativa skin is more expansive than that of psoriasis vulgaris.

BACKGROUND: Although hidradenitis suppurativa (HS) shares some transcriptomic and cellular infiltrate features with psoriasis, their skin proteome remains unknown. OBJECTIVE: To define and compare inflammatory protein biomarkers of HS and psoriasis skin. METHODS: We assessed 92 inflammatory biomarkers in HS (n = 13), psoriasis (n = 11), and control skin (n = 11) using Olink high-throughput proteomics. We also correlated HS skin and blood biomarkers using proteomics and RNA sequencing. RESULTS: We identified 57 differentially expressed proteins (DEPs) in lesional psoriasis and 64 DEPs in lesional HS skin, compared to healthy controls. Both HS and psoriasis lesional skin demonstrated a significant upregulation of T helper 1 and T helper 17 proteins. Healthy-appearing perilesional HS skin had 63 DEPs compared to healthy controls. Nonlesional HS and psoriasis skin had 24 and 7 DEPs, respectively, compared to healthy controls. Tumor necrosis factor and 8 other proteins were significantly correlated with clinical severity in perilesional HS skin (2 cm from a nodule). LIMITATIONS: Inclusion of only moderate-to-severe patients and the cohort size. CONCLUSION: HS has a greater inflammatory profile and is more diffusely distributed compared with psoriasis. Proteins correlated with disease severity are potential disease mediators. Perilesional skin is comparably inflamed to lesional skin, suggesting the need to treat beyond skin nodules.

Single-cell transcriptomics combined with interstitial fluid proteomics defines cell type-specific immune regulation in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease, but its complex pathogenesis is only insufficiently understood, resulting in still limited treatment options. OBJECTIVE: We sought to characterize AD on both transcriptomic and proteomic levels in humans. METHODS: We used skin suction blistering, a painless and nonscarring procedure that can simultaneously sample skin cells and interstitial fluid. We then compared results with conventional biopsies. RESULTS: Suction blistering captured epidermal and most immune cells equally well as biopsies, except for mast cells and nonmigratory CD163+ macrophages that were only present in biopsy isolates. Using single-cell RNA sequencing, we found comparable transcriptional profiles of key inflammatory pathways between blister and biopsy AD, but suction blistering was superior in cell-specific resolution for high-abundance transcripts (KRT1/KRT10, KRT16/KRT6A, S100A8/S100A9), which showed some background signals in biopsy isolates. Compared with healthy controls, we found characteristic upregulation of AD-typical cytokines such as IL13 and IL22 in Th2 and Th22 cells, respectively, but we also discovered these mediators in proliferating T cells and natural killer T cells, that also expressed the antimicrobial cytokine IL26. Overall, not T cells, but myeloid cells were most strongly enriched in AD, and we found dendritic cell (CLEC7A, amphiregulin/AREG, EREG) and macrophage products (CCL13) among the top upregulated proteins in AD blister fluid proteomic analyses. CONCLUSION: These data show that by using cutting-edge technology, suction blistering offers several advantages over conventional biopsies, including better transcriptomic resolution of skin cells, combined with proteomic information from interstitial fluid, unraveling novel inflammatory players that shape the cellular and proteomic microenvironment of AD.

Combined Plasma Olink Proteomics and Transcriptomics Identifies CXCL1 and TNFRSF12A as Potential Predictive and Diagnostic Inflammatory Markers for Acute Kidney Injury.

Acute kidney injury (AKI) poses a significant global public health challenge. Current methods for detecting AKI rely on monitoring changes in serum creatinine (Scr), blood urea nitrogen (BUN), urinary output and some commonly employed biomarkers. However, these indicators are usually neither specific nor sensitive to AKI, especially in cases of mild kidney injury. AKI is accompanied by severe inflammatory reactions, resulting in the upregulation of numerous inflammation-associated proteins in the plasma. Plasma biomarkers are a noninvasive method for detecting kidney injury, and to date, plasma inflammation-associated cytokines have not been adequately studied in AKI patients. The objective of our research was to identify novel inflammatory biomarkers for AKI. We utilized Olink proteomics to analyze the alterations in plasma inflammation-related proteins in the serum of healthy mice (n = 2) or mice treated with cisplatin (n = 6). Additionally, transcriptome datasets for the lipopolysaccharide (LPS), cisplatin, and ischemia‒reperfusion injury (IRI) groups were obtained from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We calculated the intersection of differentially expressed proteins (DEPs) and genes (DEGs) from both datasets. In the Olink proteomics analysis, the AKI group had significantly greater levels of 11 DEPs than did the control group. In addition, 56 common upregulated DEGs were obtained from the transcriptome dataset. The expression of CXCL1 and TNFRSF12A overlapped across all the datasets. The transcription and protein expression levels of CXCL1 and TNFRSF12A were detected in vivo. The gene and protein levels of CXCL1 and TNFRSF12A were significantly increased in different AKI mouse models and clinical patients, suggesting that these genes and proteins could be potential specific biomarkers for the identification of AKI.

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

BACKGROUND: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS: Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS: HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

Comparative Proteomics Analysis Reveals Distinct Molecular Phenotype and Biomarkers in Patients with Erythrodermic Atopic Dermatitis and Erythrodermic Psoriasis.

Erythrodermic atopic dermatitis (EAD) and erythrodermic psoriasis (EP) are rare yet debilitating inflammatory skin disorders that propose challenges in diagnosis and discovering effective therapeutic targets. Despite their clinical and histological similarities, the underlying molecular mechanisms and systemic biomarkers of these diseases are substantially unclear. In this study, we sought to investigate the differential serum proteome of EP and EAD patients and identify biomarkers for these two subtypes of erythroderma. We recruited 14 EAD patients, 14 EP patients and 14 healthy controls. Serum samples were collected and analyzed using the Olink high-throughput platform to assess the levels of 269 inflammation-/immune response-/cardiovascular-related biomarkers. Both EAD and EP patients exhibited enhanced immune activation and dysregulated cardiovascular profiles compared to healthy controls. EAD demonstrated a more pronounced inflammation tone, characterized by Th1/Th2/Th22/IL-1-dominant patterns, as well as increased TNF superfamily, Th17, and apoptosis markers. Conversely, EP displayed inflammation with Th1/Th17/TNF-skewing and mild Th2 upregulation, along with notable increases in epidermal-development markers. Disease severity in EAD was strongly correlated with apoptosis/Th2 markers, while correlated with Th17 markers in EP. Furthermore, a panel of eight markers (IL-17A/IL-17C/PI3/CCL20/SH2D1A/SIRT2/DFFA/IL-13) was identified that effectively discriminated between EP and EAD, with an Area Under the Curve greater than 0.8. Our study comprehensively characterizes the circulating molecular profiles in EAD and EP patients, providing insights into the similarities and complexities of their inflammation phenotypes. The identified serum biomarkers have the potential to differentiate between EP and EAD, which could aid in the diagnosis and guiding tailored therapeutics.

Validation of elevated levels of interleukin-8 in the cerebrospinal fluid, and discovery of new biomarkers in patients with GBS and CIDP using a proximity extension assay.

Background: Biomarkers for diagnosis of inflammatory neuropathies, assessment of prognosis, and treatment response are lacking. Methods: CSF and EDTA plasma from patients with Guillain-Barré syndrome (GBS), chronic inflammatory demyelinating polyneuropathy (CIDP), healthy controls (HC) and disease controls were analyzed with Olink multiplex proximity extension assay (PEA) from two independent cohorts. Levels of interleukin-8 (IL8) were further analyzed with ELISA in patients with GBS, CIDP, paraproteinemia-related demyelinating polyneuropathy (PDN), multifocal motor neuropathy (MMN), HC and disease controls. ROC analysis was performed. Outcome was measured with the GBS-disability score (GBS-ds) or Inflammatory Neuropathy Cause and Treatment (INCAT) score. Results: In CSF, multiplex PEA analysis revealed up-regulation of IL8 in GBS compared to CIDP and HC respectively, and CIDP compared to HC. In addition, levels of IL2RA were upregulated in GBS compared to both HC and CIDP, SELE in GBS compared to HC, and ITGAM, IL6, and NRP1 in GBS compared to CIDP. In plasma, levels of MMP3, THBD and ITGAM were upregulated in CIDP compared to HC. Validation of multiplex IL8 results using ELISA, revealed increased levels of IL8 in CSF in patients with GBS and CIDP versus HC and non-inflammatory polyneuropathies (NIP) respectively, as well as in PDN versus NIP and HC. Levels of IL8 in CSF correlated with impairment in the acute phase of GBS as well as outcome at 6-months follow up. Conclusion: IL8 in CSF is validated as a diagnostic biomarker in GBS and CIDP, and a prognostic biomarker in GBS. Multiplex PEA hereby identifies several potential biomarkers in GBS and CIDP.

Plasma protein profiling reveals dynamic immunomodulatory changes in multiple sclerosis patients during pregnancy.

Multiple sclerosis (MS) is a chronic autoimmune neuroinflammatory and neurodegenerative disorder of the central nervous system. Pregnancy represents a natural modulation of the disease course, where the relapse rate decreases, especially in the 3rd trimester, followed by a transient exacerbation after delivery. Although the exact mechanisms behind the pregnancy-induced modulation are yet to be deciphered, it is likely that the immune tolerance established during pregnancy is involved. In this study, we used the highly sensitive and specific proximity extension assay technology to perform protein profiling analysis of 92 inflammation-related proteins in MS patients (n=15) and healthy controls (n=10), longitudinally sampled before, during, and after pregnancy. Differential expression analysis was performed using linear models and p-values were adjusted for false discovery rate due to multiple comparisons. Our findings reveal gradual dynamic changes in plasma proteins that are most prominent during the 3rd trimester while reverting post-partum. Thus, this pattern reflects the disease activity of MS during pregnancy. Among the differentially expressed proteins in pregnancy, several proteins with known immunoregulatory properties were upregulated, such as PD-L1, LIF-R, TGF-ß1, and CCL28. On the other hand, inflammatory chemokines such as CCL8, CCL13, and CXCL5, as well as members of the tumor necrosis factor family, TRANCE and TWEAK, were downregulated. Further in-depth studies will reveal if these proteins can serve as biomarkers in MS and whether they are mechanistically involved in the disease amelioration and worsening. A deeper understanding of the mechanisms involved may identify new treatment strategies mimicking the pregnancy milieu.

Identification of Robust Protein Associations With COVID-19 Disease Based on Five Clinical Studies.

Multiple studies have investigated the role of blood circulating proteins in COVID-19 disease using the Olink affinity proteomics platform. However, study inclusion criteria and sample collection conditions varied between studies, leading to sometimes incongruent associations. To identify the most robust protein markers of the disease and the underlying pathways that are relevant under all conditions, it is essential to identify proteins that replicate most widely. Here we combined the Olink proteomics profiles of two newly recruited COVID-19 studies (N=68 and N=98) with those of three previously published COVID-19 studies (N=383, N=83, N=57). For these studies, three Olink panels (Inflammation and Cardiovascular II & III) with 253 unique proteins were compared. Case/control analysis revealed thirteen proteins (CCL16, CCL7, CXCL10, CCL8, LGALS9, CXCL11, IL1RN, CCL2, CD274, IL6, IL18, MERTK, IFNγ, and IL18R1) that were differentially expressed in COVID-19 patients in all five studies. Except CCL16, which was higher in controls, all proteins were overexpressed in COVID-19 patients. Pathway analysis revealed concordant trends across all studies with pathways related to cytokine-cytokine interaction, IL18 signaling, fluid shear stress and rheumatoid arthritis. Our results reaffirm previous findings related to a COVID-19 cytokine storm syndrome. Cross-study robustness of COVID-19 specific protein expression profiles support the utility of affinity proteomics as a tool and for the identification of potential therapeutic targets.