Lectin-type oxidized LDL receptor-1 as a potential therapeutic target for cerebral cavernous malformations treatment.

Introduction: Cerebral cavernous malformations (CCMs) are pathologic lesions comprised of clusters of thin-walled capillaries characterized by abnormal proliferation, angiogenesis, and bleeding secondary to somatic or germline mutations in endothelial cells. CCMs can cause headaches, seizures and/or neurological defects. There is a clinical need to develop better tools to detect CCMs and follow their progression in conjunction with the current use of neuroimaging techniques. Here we present data supporting the utility of LOX-1 (lectin-type oxidized LDL receptor 1), a 50 kDa transmembrane protein implicated in endothelial cell dysfunction and ischemia, as a putative biomarker for CCM. Methods: CCM urine samples (n = 23) were collected from pediatric CCM patients. Matched healthy controls (n = 24) were collected from pediatric patients with either Chiari I malformation or fatty filum terminale, and otherwise normal findings. All samples were collected with patient/family consent and institutional review board approval.Samples were analyzed with Olink Proteomic Proximity Extension Assay (PEA). Differences in expression for 2,925 unique proteins were quantified between healthy control urine samples and CCM urine samples. The results were normalized, validated, and analyzed for demographic bias. In addition to urine samples, CCM tissue from patients was harvested and used to create primary cell lines for in vitro analysis of LOX-1 expression, in addition to immunofluorescence of lesional tissue excised at surgery. Results: ANOVA analysis of the CCM urine samples showed a statistically significant increase in LOX-1 compared to the control samples, with CCM patients exhibiting a > 5-fold increase in urinary expression. Corroborating these elevated levels of circulating marker, analysis of source tissue from surgically resected CCMs revealed that LOX-1 is increased in both CCM patient cavernoma primary cell lines and operative specimens. Conclusion: LOX-1 is involved with pathways implicated in CCM pathogenesis and our data here reveals that LOX-1 expression is significantly elevated in CCM patients as compared to matched healthy control individuals, including both source tissue from surgically excised CCMs and in analysis of samples collected from outside of the central nervous system, particularly urine. This proof-of-principle data suggests that LOX-1 may have potential utility as a target for CCM treatment and supports further investigation related to its potential mechanistic impact on CCM pathogenesis.

Monitoring SARS-CoV-2 IgA, IgM and IgG antibodies in dried blood and saliva samples using antibody proximity extension assays (AbPEA).

Using a modified proximity extension assay, total and immunoglobulin (Ig) class-specific anti-SARS-CoV-2 antibodies were sensitively and conveniently detected directly from ø1.2 mm discs cut from dried blood and saliva spots (DBS and DSS) without the need for elution. For total Ig detection, antigen probes were prepared by conjugating recombinant spike protein subunit 1 (S1-RBD) to a pair of oligonucleotides. To detect isotype-specific antibody reactivity, one antigen probe was replaced with oligonucleotide-conjugated antibodies specific for antibody isotypes. Binding of pairs of oligonucleotide-conjugated probes to antibodies in patient samples brings oligonucleotides in proximity. An added DNA polymerase uses a transient hybridization between the oligonucleotides to prime synthesis of a DNA strand, which serves as a DNA amplicon that is quantified by real-time PCR. The S1-RBD-specific IgG, IgM, and IgA antibodies in DBS samples collected over the course of a first and second vaccination exhibited kinetics consistent with previous reports. Both DBS and DSS collected from 42 individuals in the autumn of 2023 showed significant level of total S1-RBD antibodies with a correlation of R = 0.70. However, levels in DSS were generally 10 to 100-fold lower than in DBS. Anti-S1-RBD IgG and IgA in DSS demonstrated a correlation of R = 0.6.

Early prediction of spontaneous preterm birth before 34 gestational weeks based on a combination of inflammation-associated plasma proteins.

Background: In order to identify and possibly offer prophylactic treatment to women at risk for preterm birth (PTB), novel prediction models for PTB are needed. Our objective was to utilize high-sensitive plasma protein profiling to investigate whether early prediction of spontaneous PTB (sPTB) before 34 gestational weeks (gw) was possible in a low-risk population. Methods: A case-control study was conducted on 46 women with sPTB before 34 gw and 46 women with normal pregnancies and term deliveries. Prospectively collected plasma sampled at gw 11 (range 7-16) and gw 25 (range 23-30) was analyzed with a high-sensitivity Proximity Extension Assay for levels of 177 inflammation-associated proteins, and statistically processed with multivariate logistic regression analysis. Results: In the first trimester, higher levels of hepatocyte growth factor (HGF) were associated with sPTB <34 gw (OR 1.49 (1.03-2.15)). In the second trimester, higher levels of interleukin (IL)-10 (OR 2.15 (1.18-3.92)), IL-6 (OR 2.59 (1.34-4.99)), and the receptor activator of nuclear factor κB (RANK) (OR 2.18 (1.26-3.77)) were associated with sPTB <34 gw. The area under the curve for the prediction models including these proteins was 0.653 (0.534-0.759) in the first trimester and 0.854 (0.754-0.925) in the second trimester. Conclusion: A combination of inflammation-associated plasma proteins from the second trimester of pregnancy showed a good predictive ability regarding sPTB before 34 gw, suggesting it could be a valuable supplement for the assessment of the clinical risk of sPTB. However, although a high number (n=177) of plasma proteins were analyzed with a high-sensitivity method, the prediction of sPTB in the first trimester remains elusive.

Advances in aqueous humor proteomics for biomarker discovery and disease mechanisms exploration: a spotlight on primary open angle glaucoma.

Altered protein levels in the aqueous humor (AH) may be a valuable source of novel biomarkers in neurodegenerative retinal disease. The proximity of this body fluid to the disease focus, and its corresponding enrichment for tissue specific proteins, renders it an excellent matrix to study underlying molecular mechanisms. Novel proteomic methods accordingly hold large potential for insight into pathologies based on the composition of the AH proteome, including primary open angle glaucoma (POAG). Recent mass spectrometry-based studies use novel approaches to tackle the challenges arising from the combination of low available sample volume and protein concentration, thereby increasing proteome coverage. But despite significant improvements in mass spectrometry (MS), a different class of proteomic technologies is poised to majorly impact the analysis of ocular biofluids. Affinity proteomic workflows, having become available commercially recently, have started to complement data obtained by MS and likely will grow into a crucial tool for ophthalmological biomarker research. This review highlights corresponding approaches in proteome analysis of aqueous humor and discusses recent findings on alterations of the AH proteome in POAG.

Methods and clinical biomarker discovery for targeted proteomics using Olink technology.

PURPOSE: This paper is to offer insights for designing research utilizing Olink technology to identify biomarkers and potential therapeutic targets for disease treatment. EXPERIMENTAL DESIGN: We discusses the application of Olink technology in oncology, cardiovascular, respiratory and immune-related diseases, and Outlines the advantages and limitations of Olink technology. RESULTS: Olink technology simplifies the search for therapeutic targets, advances proteomics research, reveals the pathogenesis of diseases, and ultimately helps patients develop precision treatments. CONCLUSIONS: Although proteomics technology has been rapidly developed in recent years, each method has its own disadvantages, so in the future research, more methods should be selected for combined application to verify each other.

Proximity extension assay in cerebrospinal fluid identifies neurofilament light chain as biomarker of neurodegeneration in sporadic cerebral amyloid angiopathy.

BACKGROUND: Sporadic cerebral amyloid angiopathy (sCAA) is a disease characterised by the progressive deposition of the amyloid beta (Aß) in the cerebral vasculature, capable of causing a variety of symptoms, from (mild) cognitive impairment, to micro- and major haemorrhagic lesions. Modern diagnosis of sCAA relies on radiological detection of late-stage hallmarks of disease, complicating early diagnosis and potential interventions in disease progression. Our goal in this study was to identify and validate novel biomarkers for sCAA. METHODS: We performed a proximity extension assay (PEA) on cerebrospinal fluid (CSF) samples of sCAA/control participants (n = 34/51). Additionally, we attempted to validate the top candidate biomarker in CSF and serum samples (n = 38/26) in a largely overlapping validation cohort, through analysis with a targeted immunoassay. RESULTS: Thirteen proteins were differentially expressed through PEA, with top candidate NFL significantly increased in CSF of sCAA patients (p < 0.0001). Validation analyses using immunoassays revealed increased CSF and serum NFL levels in sCAA patients (both p < 0.0001) with good discrimination between sCAA and controls (AUC: 0.85; AUC: 0.79 respectively). Additionally, the CSF: serum NFL ratio was significantly elevated in sCAA (p = 0.002). DISCUSSION: Large-scale targeted proteomics screening of CSF of sCAA patients and controls identified thirteen biomarker candidates for sCAA. Orthogonal validation of NFL identified NFL in CSF and serum as biomarker, capable of differentiating between sCAA patients and controls.

Serum protein profiling reveals an inflammation signature as a predictor of early breast cancer survival.

BACKGROUND: Breast cancers exhibit considerable heterogeneity in their biology, immunology, and prognosis. Currently, no validated, serum protein-based tools are available to evaluate the prognosis of patients with early breast cancer. METHODS: The study population consisted of 521 early-stage breast cancer patients with a median follow-up of 8.9 years. Additionally, 61 patients with breast fibroadenoma or atypical ductal hyperplasia were included as controls. We used a proximity extension assay to measure the preoperative serum levels of 92 proteins associated with inflammatory and immune response processes. The invasive cancers were randomly split into discovery (n = 413) and validation (n = 108) cohorts for the statistical analyses. RESULTS: Using LASSO regression, we identified a nine-protein signature (CCL8, CCL23, CCL28, CSCL10, S100A12, IL10, IL10RB, STAMPB2, and TNFß) that predicted various survival endpoints more accurately than traditional prognostic factors. In the time-dependent analyses, the prognostic power of the model remained rather stable over time. We also developed and validated a 17-protein model with the potential to differentiate benign breast lesions from malignant lesions (Wilcoxon p < 2.2*10- 16; AUC 0.94). CONCLUSIONS: Inflammation and immunity-related serum proteins have the potential to rise above the classical prognostic factors of early-stage breast cancer. They may also help to distinguish benign from malignant breast lesions.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer.

BACKGROUND: Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis. METHODS: We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models. RESULTS: In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively. CONCLUSIONS: This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Search for cerebrospinal fluid biomarkers in patients with major psychiatric disorders: Multiplex immunoassay findings and proximity extension assay prospects.

Multiplex immunoassays have been developed to detect multiple proteins simultaneously and are used to search for biomarkers, including those present in major psychiatric disorders. This study aimed to review multiplex immunoassay studies on cerebrospinal fluid (CSF) biomarkers in patients with schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD) and examine future research directions using improved proteomic techniques. According to the results of previous multiplex immunoassay studies, increased CSF IFN-ß, IL-8, MCP-2, MMP-2, PAI-1, sICAM-1, and sVCAM-1 and decreased CSF ACE, APP, fibrinogen, and GDNF were observed in patients with schizophrenia, while CSF HGF and S100B were positively correlated with psychotic symptom and CSF IL-11, IL-29/IFN-λ1, and TSLP were negatively correlated. Increased CSF IFN-ß and IL-1ß and decreased CSF Aß42, APP, IL-6, and NCAM-1 were observed, while CSF S100B was positively correlated with manic symptom in patients with BD. Increased CSF IL-4, MCP-1, MIP-1ß, and MMP-2 were observed in patients with MDD, while CSF HGF and MMP-2 were positively correlated with depressive symptom and CSF IL-15 and MCP-1 were negatively correlated. However, signal cross-talk and cross-reactivity problems have been observed in previous studies using multiplex immunoassay. The proximity extension assay can be used to overcome cross-reactivity and enable ultrasensitive multiplexed detection and quantification of more than 1000 target proteins. However, proteomic studies using proximity extension assay technology in patients with schizophrenia, BD, or MDD are still scarce. Therefore, future high-quality proteomic studies are required to identify CSF biomarkers for larger sets of target proteins in patients with major psychiatric disorders.

Identification of novel protein biomarkers from the blood and urine for the early diagnosis of bladder cancer via proximity extension analysis.

BACKGROUND: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC. METHODS: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique. Differential protein expression was then evaluated by univariate analysis (p < 0.05). The expression of the selected potential marker was further verified in BC and adjacent tissues by immunohistochemistry (IHC) and single-cell sequencing. A model was constructed to differentiate BC from HC by LASSO regression and compared to the detection capability of FISH. RESULTS: The univariate analysis revealed significant differences in the expression levels of 40 proteins in the serum (p < 0.05) and 17 proteins in the urine (p < 0.05) between BC patients and HC. Six proteins (AREG, RET, WFDC2, FGFBP1, ESM-1, and PVRL4) were selected as potential BC biomarkers, and their expression was evaluated at the protein and transcriptome levels by IHC and single-cell sequencing, respectively. A diagnostic model (a signature) consisting of 14 protein markers (11 in serum and three in urine) was also established using LASSO regression to distinguish between BC patients and HC (area under the curve = 0.91, PPV = 0.91, sensitivity = 0.87, and specificity = 0.82). Our model showed better diagnostic efficacy than FISH, especially for early-stage, small, and low-grade BC. CONCLUSION: Using the PEA method, we identified a panel of potential protein markers in the serum and urine of BC patients. These proteins are associated with the development of BC. A total of 14 of these proteins can be used to detect early-stage, small, low-grade BC. Thus, these markers are promising for clinical translation to improve the prognosis of BC patients.

Proteomics on human cerebral cavernous malformations reveals novel biomarkers in neurovascular dysfunction for the disease pathology.

BACKGROUND: Cerebral cavernous malformation (CCM) is a disease associated with an elevated risk of focal neurological deficits, seizures, and hemorrhagic stroke. The disease has an inflammatory profile and improved knowledge of CCM pathology mechanisms and exploration of candidate biomarkers will enable new non-invasive treatments. METHODS: We analyzed protein signatures in human CCM tissue samples by using a highly specific and sensitive multiplexing technique, proximity extension assay. FINDINGS: Data analysis revealed CCM specific proteins involved in endothelial dysfunction/inflammation/activation, leukocyte infiltration/chemotaxis, hemostasis, extracellular matrix dysfunction, astrocyte and microglial cell activation. Biomarker expression profiles matched bleeding status, especially with higher levels of inflammatory markers and activated astrocytes in ruptured than non-ruptured samples, some of these biomarkers are secreted into blood or urine. Furthermore, analysis was also done in a spatially resolving manner by separating the lesion area from the surrounding brain tissue. Our spatial studies revealed that although appearing histologically normal, the CCM border areas were pathological when compared to control brain tissues. Moreover, the functional relevance of CD93, ICAM-1 and MMP9, markers related to endothelial cell activation and extracellular matrix was validated by a murine pre-clinical CCM model. INTERPRETATION: Here we present a novel strategy for proteomics analysis on human CCMs, offering a possibility for high-throughput protein screening acquiring data on the local environment in the brain. Our data presented here describe CCM relevant brain proteins and specifically those which are secreted can serve the need of circulating CCM biomarkers to predict cavernoma's risk of bleeding.

Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data.

BACKGROUND: Concurrent chemo-radiotherapy (CCRT) is the preferred non-surgical treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Unfortunately, some patients respond poorly, which leads to inappropriate or excessive treatment and affects patient survival. To accurately predict the response of ESCC patients to CCRT, we developed classification models based on the clinical, serum proteomic and radiomic data. METHODS: A total of 138 ESCC patients receiving CCRT were enrolled in this study and randomly split into a training cohort (n = 92) and a test cohort (n = 46). All patients were classified into either complete response (CR) or incomplete response (non-CR) groups according to RECIST1.1. Radiomic features were extracted by 3Dslicer. Serum proteomic data was obtained by Olink proximity extension assay. The logistic regression model with elastic-net penalty and the R-package "rms" v6.2-0 were applied to construct classification and nomogram models, respectively. The area under the receiver operating characteristic curves (AUC) was used to evaluate the predictive performance of the models. RESULTS: Seven classification models based on multi-omics data were constructed, of which Model-COR, which integrates five clinical, five serum proteomic, and seven radiomic features, achieved the best predictive performance on the test cohort (AUC = 0.8357, 95 % CI: 0.7158-0.9556). Meanwhile, patients predicted to be CR by Model-COR showed significantly longer overall survival than those predicted to be non-CR in both cohorts (Log-rank P = 0.0014 and 0.027, respectively). Furthermore, two nomogram models based on multi-omics data also performed well in predicting response to CCRT (AUC = 0.8398 and 0.8483, respectively). CONCLUSION: We developed and validated a multi-omics based classification model and two nomogram models for predicting the response of ESCC patients to CCRT, which achieved the best prediction performance by integrating clinical, serum Olink proteomic, and radiomic data. These models could be useful for personalized treatment decisions and more precise clinical radiotherapy and chemotherapy for ESCC patients.

Cell-Mediated Proteomics, and Serological and Mucosal Humoral Immune Responses after Seasonal Influenza Immunization: Characterization of Serological Responders and Non-Responders.

Immunization against influenza through vaccination is the most effective method with which to prevent infection. To assess protection after immunization, analysing humoral response with a hemagglutinin inhibition assay is the gold standard, but cell-mediated immune response has been shown to better correlate with protection in the elderly. Our aim was to explore the influenza-specific cell-mediated and mucosal humoral responses in serologically defined responders and non-responders. We analysed sera for total immunoglobulins (Ig) A, G, and M and nasal swab samples for influenza-specific IgA. Peripheral blood mononuclear cells were stimulated with trivalent influenza vaccine VaxiGripTetra, and supernatants were analysed for influenza-specific responses with the Olink Immune-Oncology panel using a proximity extension assay. We included 73 individuals, of which 69 completed the study with follow-up sampling at one and six months post-vaccination. Of the 73, 51 (70%) were found to be serological responders and 22 (30%) were non-responders. We did not find any significant differences in sex or mucosal humoral response between responders and non-responders; however, a higher IFNγ/IL-10 ratio in individuals ≤65 years of age indicates an enhanced cell-mediated immune response in this age group. Characteristics of the non-responders were found to be higher levels of IgM, Granzyme B and Interleukin 12, and lower levels of C-X-C motif chemokine 13 compared with those of the responders. In conclusion, our results did not show any correlation between serological response and age. Furthermore, the majority of influenza-specific cell-mediated immune markers did not differ between responders and non-responders; the immune marker profile of the non-responders and its contribution to protection is of interest but needs to be further explored.

Exploring candidate biomarkers for rheumatoid arthritis through cardiovascular and cardiometabolic serum proteome profiling.

Introduction: RA patients are at higher risk of cardiovascular disease, influenced by therapies. Studying their cardiovascular and cardiometabolic proteome can unveil biomarkers and insights into related biological pathways. Methods: This study included two cohorts of RA patients: newly diagnosed individuals (n=25) and those with established RA (disease duration >25 years, n=25). Both cohorts were age and sex-matched with a control group (n=25). Additionally, a longitudinal investigation was conducted on a cohort of 25 RA patients treated with methotrexate and another cohort of 25 RA patients treated with tofacitinib for 6 months. Clinical and analytical variables were recorded, and serum profiling of 184 proteins was performed using the Olink technology platform. Results: RA patients exhibited elevated levels of 75 proteins that might be associated with cardiovascular disease. In addition, 24 proteins were increased in RA patients with established disease. Twenty proteins were commonly altered in both cohorts of RA patients. Among these, elevated levels of CTSL1, SORT1, SAA4, TNFRSF10A, ST6GAL1 and CCL18 discriminated RA patients and HDs with high specificity and sensitivity. Methotrexate treatment significantly reduced the levels of 13 proteins, while tofacitinib therapy modulated the expression of 10 proteins. These reductions were associated with a decrease in DAS28. Baseline levels of SAA4 and high levels of BNP were associated to the non-response to methotrexate. Changes in IL6 levels were specifically linked to the response to methotrexate. Regarding tofacitinib, differences in baseline levels of LOX1 and CNDP1 were noted between non-responder and responder RA patients. In addition, response to tofacitinib correlated with changes in SAA4 and TIMD4 levels. Conclusion: In summary, this study pinpoints molecular changes linked to cardiovascular disease in RA and proposes candidate protein biomarkers for distinguishing RA patients from healthy individuals. It also highlights how methotrexate and tofacitinib impact these proteins, with distinct alterations corresponding to each drug's response, identifying potential candidates, as SAA4, for the response to these therapies.

Proximity extension assay proteomics and renal single cell transcriptomics uncover novel urinary biomarkers for active lupus nephritis.

OBJECTIVE: To identify urinary biomarkers that can distinguish active renal involvement in Lupus Nephritis (LN), a severe manifestation of systemic lupus erythematosus (SLE). METHODS: Urine from 117 subjects, comprised of inactive SLE, active non-renal lupus, active LN, and healthy controls, were subjected to Proximity Extension Assay (PEA) based comprehensive proteomics followed by ELISA validation in an independent, ethnically diverse cohort. Proteomic data is also cross-referenced to renal transcriptomic data to elucidate cellular origins of biomarkers. RESULTS: Systems biology analyses revealed progressive activation of cytokine signaling, chemokine activity and coagulation pathways, with worsening renal disease. In addition to validating 30 previously reported biomarkers, this study uncovers several novel candidates. Following ELISA validation in an independent cohort of different ethnicity, the six most discriminatory biomarkers for active LN were urinary ICAM-2, FABP4, FASLG, IGFBP-2, SELE and TNFSF13B/BAFF, with ROC AUC ≥80%, with most correlating strongly with clinical disease activity. Transcriptomic analyses of LN kidneys mapped the likely origin of these proteins to intra-renal myeloid cells (CXCL16, IL-1RT2, TNFSF13B/BAFF), T/NK cells (FASLG), leukocytes (ICAM2) and endothelial cells (SELE). CONCLUSION: In addition to confirming the diagnostic potential of urine ALCAM, CD163, MCP1, SELL, ICAM1, VCAM1, NGAL and TWEAK for active LN, this study adds urine ICAM-2, FABP4, FASLG, IGFBP-2, SELE, and TNFSF13B/BAFF as additional markers that warrant systematic validation in larger cross-sectional and longitudinal cohorts.

Dimethyl fumarate treatment in relapsing remitting MS changes the inflammatory CSF protein profile by a prominent decrease in T-helper 1 immunity.

BACKGROUND: Dimethyl fumarate (DMF) is a common treatment for multiple sclerosis (MS), but its mechanisms of action are not fully understood. Targeted proteomics offers insights into effects of DMF and biomarkers for treatment responses. OBJECTIVES: To assess influence of DMF on inflammation- and neuro-associated proteins in plasma and cerebrospinal fluid (CSF) in MS and to reveal biomarkers for predicting treatment responses. METHODS: Using the high-sensitivity and high-specificity method of proximity extension assay (PEA), we measured 182 inflammation- and neuro-associated proteins in paired plasma (n = 28) and CSF (n = 12) samples before and after one year of DMF treatment. Disease activity was evaluated through clinical examination and MRI. Statistical tests, network analysis, and regression models were used. RESULTS: Several proteins including T-helper 1 (Th1)-associated proteins (CXCL10, CXCL11, granzyme A, IL-12p70, lymphotoxin-alpha) were consistently decreased in CSF, while IL-7 was increased after one year of treatment. The changes in plasma protein levels did not follow the same pattern as in CSF. Logistic regression models identified potential biomarker candidates (including plexins and neurotrophins) for prediction of treatment response. CONCLUSIONS: DMF treatment induced prominent changes in CSF proteins, consistently reducing Th1-associated pro-inflammatory proteins. Neurodegeneration-related CSF proteins were able to predict treatment response. Protein biomarkers hold promise for personalized medicine.

Identification of Plasma Inflammatory Markers of Adolescent Depression Using the Olink Proteomics Platform.

Purpose: The quality of life of worldwide adolescents has been seriously affected by depression. Notably, the inflammatory response is closely associated with the pathophysiology of depression. The present study applied a novel targeted proteomics technology, Olink proximity extension assay (PEA), to profile circulating immune-related proteins in adolescents with depression. Methods: In the present study, the expression levels of 92 inflammation-related proteins were compared between adolescents with depression (ADs) (n=15) and healthy controls (HCs) (n=15), using the OLINK PEA inflammation panel. We further validated 5 top proteins that were identified through KEGG and GO analyses between 40 HCs and 50 ADs, including CCL4, CXCL5, CXCL6, CXCL11, and IL-18 using enzyme linked immunosorbent assay (ELISA). Results: We identified 13 differentially expressed proteins between the two cohorts, including 5 up-regulated and 8 down-regulated proteins. Among them, the TRAIL protein levels were significantly negatively correlated with the HAMA-14 score (r=-0.538, p= 0.038), and the levels of transforming growth factor α (TGF-α) were significantly associated with a change in appetite (r = -0.658, p = 0.008). After validation by ELISA, CCL4, CXCL5, CXCL11, and IL-18 showed significant changes between ADs and HCs (p < 0.05), while CXCL6 showed an up-regulated tendency in ADs (p=0.0673). The pooled diagnostic efficacy (area under the curve [AUC]) of these five inflammation markers in clinical diagnosis for adolescent depression was 0.819 (95% CI: 0.735-0.904). Conclusion: We report a number of inflammation-related plasma biomarkers, which uncover a potential involvement of chemokines, cytokines, and cytokine receptors in adolescent depression. Their roles in the pathophysiology of depression need to be further elucidated.

Protein profiling in plasma for biomarkers of seizure.

PURPOSE: A biochemical way to measure seizures would greatly benefit epilepsy research and clinical follow-up. Short-term biomarkers like lactate exist, and interest in biomarkers representative of longer-term seizure burden is growing. In this exploratory study, we aimed to identify markers in blood plasma that differentiate persons with recent seizures from persons with epilepsy and long-standing seizure freedom. METHODS: A proteomic analysis was performed on plasma samples of 120 persons with seizures using the Olink Neuro-exploratory panel. Participants were selected from a regional biobank study in Västra Götaland (Sweden) and categorized into two groups: recent seizure and seizure-free. The panel contained 92 proteins linked to neurological diseases and processes, and levels of these proteins were compared between the patient groups to identify potential markers of seizure activity. RESULTS: We identified significant differences in protein levels between the recent seizure and seizure-free patient groups for Cadherin-15 [(CDH15; p = 0.008)], Latent transforming growth factor beta-binding protein 3 [(LTBP3; p = 0.002)], Phosphoethanolamine/phosphocholine phosphatase 1 [(PHOSPHO1; p = 0.011)], and Progestagen associated endometrial protein [(PAEP; p = 0.0005)]. CONCLUSION: The findings in this study present CDH15, LTBP3, PHOSPHO1 and PAEP as candidate markers of seizure activity. Further confirmatory studies are needed.

Identification of diagnostic and prognostic biomarkers of PD using a multiplex proteomics approach.

Given the complexity of Parkinson's disease (PD), achieving acceptable diagnostic and prognostic accuracy will require the support of a panel of diverse biomarkers. We used Proximity extension assays to measure a panel of 92 proteins in CSF of 120 newly diagnosed PD patients and 45 control subjects without neurological disease. From 75 proteins detectable in the CSF of >90% of the subjects, regularized regression analysis identified four proteins (ß-NGF, CD38, tau and NCAN) as downregulated in newly diagnosed PD patients (age at diagnosis 67.2 ± 9.4 years) compared to controls (age 65.4 ± 10.9 years). Higher tau (ß -0.82 transformed MMSE points/year, 95% CI -1.37 to -0.27, P = 0.005) was also linked to faster cognitive decline over the first ten years after PD diagnosis. These findings provide insights into multiple aspects of PD pathophysiology and may serve as the foundation for identifying new biomarkers and therapeutic targets.

Large-scale proximity extension assay reveals CSF midkine and DOPA decarboxylase as supportive diagnostic biomarkers for Parkinson's disease.

BACKGROUND: There is a need for biomarkers to support an accurate diagnosis of Parkinson's disease (PD). Cerebrospinal fluid (CSF) has been a successful biofluid for finding neurodegenerative biomarkers, and modern highly sensitive multiplexing methods offer the possibility to perform discovery studies. Using a large-scale multiplex proximity extension assay (PEA) approach, we aimed to discover novel diagnostic protein biomarkers allowing accurate discrimination of PD from both controls and atypical Parkinsonian disorders (APD). METHODS: CSF from patients with PD, corticobasal syndrome (CBS), progressive supranuclear palsy (PSP), multiple system atrophy and controls, were analysed with Olink PEA panels. Three cohorts were used in this study, comprising 192, 88 and 36 cases, respectively. All samples were run on the Cardiovascular II, Oncology II and Metabolism PEA panels. RESULTS: Our analysis revealed that 26 and 39 proteins were differentially expressed in the CSF of test and validation PD cohorts, respectively, compared to controls. Among them, 6 proteins were changed in both cohorts. Midkine (MK) was increased in PD with the strongest effect size and results were validated with ELISA. Another most increased protein in PD, DOPA decarboxylase (DDC), which catalyses the decarboxylation of DOPA (L-3,4-dihydroxyphenylalanine) to dopamine, was strongly correlated with dopaminergic treatment. Moreover, Kallikrein 10 was specifically changed in APD compared with both PD and controls, but unchanged between PD and controls. Wnt inhibitory factor 1 was consistently downregulated in CBS and PSP patients in two independent cohorts. CONCLUSIONS: Using the large-scale PEA approach, we have identified potential novel PD diagnostic biomarkers, most notably MK and DDC, in the CSF of PD patients.