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Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

To complement the conventional area under the ROC curve (AUC) which cannot fully describe the diagnostic accuracy of some non‐standard biomarkers, we introduce a transformed ROC curve and its associated transformed AUC (TAUC) in this article, and show that TAUC can relate the original improper biomarker to a proper biomarker after a non‐monotone transformation. We then provide nonparametric estimation of the non‐monotone transformation and TAUC, and establish their consistency and asymptotic normality. We conduct extensive simulation studies to assess the performance of the proposed TAUC method and compare with the traditional methods. Case studies on real biomedical data are provided to illustrate the proposed TAUC method. We are able to identify more important biomarkers that tend to escape the traditional screening method. [ABSTRACT FROM AUTHOR]

Background: Various biomarkers associated with sarcopenia have been identified. However, there is a scarcity of studies exploring and validating biomarkers in individuals with age-related sarcopenia. Aims: This study aimed to investigate the proteome and identify potential biomarkers for age-related sarcopenia. Methods: Proteomic analysis and experimental validation were conducted using plasma from hospitalized older adults. Sarcopenia diagnosis was based on the Asian Working Group for Sarcopenia 2019 criteria. Data-independent acquisition-based proteomics was performed on plasma from 60 participants, with 30 diagnosed with sarcopenia and 30 without sarcopenia. Differentially expressed proteins (DEPs) were selected and evaluated by Receiver Operating Characteristic (ROC) analysis. Biomarker candidates were further quantitatively validated by enzyme-linked immunosorbent assay (ELISA) utilizing plasma from 6 participants with sarcopenia and 6 without sarcopenia. Results: A total of 39 DEPs were identified and 12 DEPs were selected for ROC analysis. 8 DEPs were included for ELISA validation based on their predictive performance. Paraoxonase-3 (PON3) consistently showed down-regulation in the sarcopenic group across both methodologies. Insulin-like growth factor-binding protein-2 (IGFBP2) showed inconsistency in the sarcopenic group, with up-regulation observed in proteomic analysis but down-regulation in ELISA. Discussion: Decline in PON3 may result in an overload of oxidative stress in skeletal muscles and contribute to sarcopenia. Protein modifications of IGFBP2 might exhibit during sarcopenia pathogenesis. Conclusions: Plasma proteins are implicated in sarcopenia pathogenesis. PON3 is highlighted as a potential biomarker for patients with age-related sarcopenia. Further studies are imperative to gain an in-depth understanding of PON3 and IGFBP2. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor‐positive (HR+) human epidermal growth factor receptor 2‐negative (HER2−) breast cancer is the most common subtype. In this study, we aimed to search for a non‐invasive, specific, blood‐based biomarker for the early detection of luminal A breast cancer through proteomic studies. Methods: To explore new potential plasma biomarkers, we applied data‐independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer‐associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls. Results: The proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non‐breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3‐10, and IGHV3‐73 through multi‐model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91). Conclusions: The study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the proteome associated with psoriasis in large population cohorts to discover novel biomarkers that could guide therapy. Methods: We analyzed data from 54,306 participants enrolled in the UK Biobank Pharmacological Proteomics Project (UKB-PPP). We investigated the relationship between 2923 serum proteins and the risk of psoriasis using multivariate Cox regression models initially. This was complemented by two-sample Mendelian randomization (TSMR), Summary-data-based Mendelian Randomization (SMR), and coloc colocalization studies to identify genetic correlations with protein targets linked to psoriasis. A protein scoring system was created using the Cox proportional hazards model, and cumulative risk curves were generated to analyze psoriasis incidence variations. Results: Our study pinpointed 62 proteins significantly linked to the risk of developing psoriasis. Further analysis through TSMR narrowed these down to ten proteins with strong causal relationships to the disease. Additional deep-dive analyses such as SMR, colocalization, and differential expression studies highlighted four critical proteins (MMP12, PCSK9, PRSS8, and SCLY). We calculated a protein score based on the levels of these proteins, with higher scores correlating with increased risk of psoriasis. Conclusion: This study's integration of proteomic and genetic data from a European adult cohort provides compelling evidence of several proteins as viable predictive biomarkers and potential therapeutic targets for psoriasis, facilitating the advancement of targeted treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Plasma biomarkers have recently emerged for the diagnosis, assessment, and disease monitoring of Alzheimer's disease (AD), but have yet to be fully validated in preclinical AD. In addition to AD pathologic plasma biomarkers (amyloid-β (Aβ) and phosphorylated tau (p-tau) species), a proteomic panel can discriminate between symptomatic AD and cognitively unimpaired older adults in a dementia clinic population. Objective: Examine the added value of a plasma proteomic panel, validated in symptomatic AD, over standard AD pathologic plasma biomarkers and demographic and genetic (apolipoprotein (APOE) ɛ4 status) risk factors in detecting preclinical AD. Methods: 125 cognitively unimpaired older adults (mean age = 66 years) who completed Aβ PET and plasma draw were analyzed using multiple regression with Aβ PET status (positive versus negative) as the outcome to determine the best fit for predicting preclinical AD. Model 1 included age, education, and gender. Model 2 and 3 added predictors APOE ɛ4 status (carrier versus non-carrier) and AD pathologic blood biomarkers (Aβ42/40 ratio, p-tau181), respectively. Random forest modeling established the 5 proteomic markers from the proteomic panel that best predicted Aβ PET status, and these markers were added in Model 4. Results: The best model for predicting Aβ PET status included age, years of education, APOE ɛ4 status, Aβ42/40 ratio, and p-tau181. Adding the top 5 proteomic markers did not significantly improve the model. Conclusions: Proteomic markers in plasma did not add predictive value to standard AD pathologic plasma biomarkers in predicting preclinical AD in this sample. [ABSTRACT FROM AUTHOR]

Background: ST-elevation myocardial infarction (STEMI) and Takotsubo syndrome (TS) are two distinct cardiac conditions that both result in sudden loss of cardiac dysfunction and that are difficult to distinguish clinically. This study compared plasma protein changes in 24 women with STEMI and 12 women with TS in the acute phase (days 0–3 post symptom onset) and the stabilization phase (days 7, 14, and 30) to examine the molecular differences between these conditions. Methods: Plasma proteins from STEMI and TS patients were extracted during the acute and stabilization phases and analyzed via quantitative proteomics. Differential expression and functional significance were assessed. Data are accessible on ProteomeXchange, ID PXD051367. Results: During the acute phase, STEMI patients showed higher levels of myocardial inflammation and tissue damage proteins compared to TS patients, along with reduced tissue repair and anti-inflammatory proteins. In the stabilization phase, STEMI patients exhibited ongoing inflammation and disrupted lipid metabolism. Notably, ADIPOQ was consistently downregulated in STEMI patients in both phases. When comparing the acute to the stabilization phase, STEMI patients showed increased inflammatory proteins and decreased structural proteins. Conversely, TS patients showed increased proteins involved in inflammation and the regulatory response to counter excessive inflammation. Consistent protein changes between the acute and stabilization phases in both conditions, such as SAA2, CRP, SAA1, LBP, FGL1, AGT, MAN1A1, APOA4, COMP, and PCOLCE, suggest shared underlying pathophysiological mechanisms. Conclusions: This study presents protein changes in women with STEMI or TS and identifies ADIPOQ, SAA2, CRP, SAA1, LBP, FGL1, AGT, MAN1A1, APOA4, COMP, and PCOLCE as candidates for further exploration in both therapeutic and diagnostic contexts. [ABSTRACT FROM AUTHOR]

Background: The current landscape of acne therapeutics is notably lacking in targeted treatments, highlighting a critical need for the discovery of new drug targets to improve treatment outcomes. Objectives: This study aims to investigate the connections between proteomics and genetics in relation to acne across extensive population cohorts, aspiring to identify innovative preventive and therapeutic approaches. Methods: Employing a longitudinal cohort of 54,306 participants from the UK Biobank Pharmacological Proteomics Project (UKB-PPP), we performed an exhaustive evaluation of the associations between 2,923 serum proteins and acne risk. Initial multivariate Cox regression analyses assessed the relationship between protein expression levels and acne onset, followed by two-sample Mendelian Randomization (TSMR), Summary-data-based Mendelian Randomization (SMR), and colocalization to identify genetic correlations with potential protein targets. Results: Within the UKB cohort, we identified 19 proteins significantly associated with the risk of acne. Subsequent analysis using Two-Sample Mendelian Randomization (TSMR) refined this to two specific proteins: FSTL1 and ANXA5. Each one-standard deviation increase in the expression levels of FSTL1 and ANXA5 was associated with a 24% and 32% increase in acne incidence, respectively. These results were further validated by additional Summary-databased Mendelian Randomization (SMR) and differential expression analyses. Conclusions: Our comprehensive analysis of proteomic and genetic data from a European adult cohort provides compelling causal evidence that several proteins are promising targets for novel acne treatments. [ABSTRACT FROM AUTHOR]

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841–0.868] vs. 0.845 [0.829–0.860] vs 0.553 [0.528–0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843–0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854–0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD. [ABSTRACT FROM AUTHOR]

Background: There is significant interest in developing alternatives to traditional blood transportation and separation methods, which often require centrifugation and cold storage to preserve specimen integrity. Here we provide new performance findings that characterize a novel device that separates whole blood via lateral flow then dries the isolated components for room temperature storage and transport. Methods: Untargeted proteomics was performed on non-small cell lung cancer (NSCLC) and normal healthy plasma applied to the device or prepared neat. Results: Significantly, proteomic profiles from the storage device were more reproducible than from neat plasma. Proteins depleted or absent in the device preparation were shown to be absorbed onto the device membrane through largely hydrophilic interactions. Use of the device did not impact proteins relevant to an NSCLC clinical immune classifier. The device was also evaluated for use in targeted proteomics experiments using multiple-reaction monitoring (MRM) mass spectrometry. Intra-specimen detection intensity for protein targets between neat and device preparations showed a strong correlation, and device variation was comparable to the neat after normalization. Inter-specimen measurements between the device and neat preparations were also highly concordant. Conclusions: These studies demonstrate that the lateral flow device is a viable blood separation and transportation tool for untargeted and targeted proteomics applications. [ABSTRACT FROM AUTHOR]

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer‐associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno‐Onc‐I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit‐link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early‐stage LC detection. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode. Methods: In this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID). Results: Five distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder’s trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms. Conclusion: This research points to a complex interplay between the plasma proteome and MDD’s clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools. Limitations: The study’s limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability. [ABSTRACT FROM AUTHOR]

Background Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics. Methods Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 nonsurvivors of SFTS. Validation was performed in a cohort of 154 patients with SFTS via enzyme-linked immunosorbent assay. We utilized the Drug-Gene Interaction Database to identify protein-drug interactions. Results Nonsurvivors exhibited 110 differentially expressed proteins as compared with survivors, with functional enrichment in the cell chemotaxis–related pathway. Thirteen differentially expressed proteins—including C-C motif chemokine 20 (CCL20), calcitonin gene–related peptide alpha, and pleiotrophin—were associated with multiple-organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P =.0004) and 0.9033 (P <.0001) in the discovery and validation cohorts, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 Food and Drug Administration–approved drugs targeting 37 death-related plasma proteins. Conclusions Distinct plasma proteomic profiles characterize SFTS cases with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis. [ABSTRACT FROM AUTHOR]

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Plasma protein has gained prominence in the non‐invasive predicting of lung cancer. We utilised Zeolite Zotero NaY‐based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3). Methods A total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm. Results For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). For task #2, nine proteins panel achieved an AUC of .88 (.80‒.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85‒.97). For task #3, 10 proteins panel showed an AUC of .88 (.74‒.96) for stage I, .92 (.84‒.97) for stage II, .88 (.76‒.96) for stage III and .99 (.98‒.99) for stage IV in the integration model. Conclusions This study comprehensively profiled the NaY‐based plasma proteome biomarker, laying the foundation for a high‐performance blood test for predicting multiple events in lung cancer. Key points Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome. The performance of NaY‐based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort. Furthermore, we explored the different patterns of plasma protein changes during the progression of lung cancer and used the explanations method to elucidate the roles of proteins in the multitask predictive model.

ABSTRACT Chronic obstructive pulmonary disease (COPD) is confirmed as an independent risk factor for the development of lung cancer. Although low‐dose CT screening significantly reduces the mortality rate of lung cancer, the misdiagnosis and missed diagnosis rates remain high in the COPD population. Additionally, several COPD patients are unable to undergo invasive histological examinations. Therefore, there is an urgent need for minimally invasive biomarkers to screen or diagnose lung cancer in COPD patients. In this study, peripheral blood samples were collected from COPD patients with and without lung cancer. Plasma exosomes (EVs) were extracted for proteomic analysis. Sixteen differentially expressed proteins (DEPs) were preliminarily selected via label‐free quantification (LFQ) proteomic technology and comprehensive bioinformatics analysis. Parallel reaction monitoring (PRM) targeted validation identified five candidate proteins associated with COPD with lung cancer. Compared to the COPD group, KRT1, KRT9, and KRT10 were significantly upregulated in the COPD with lung cancer group, while GPLD1 and TF were downregulated. The biomarkers identified in our study provide a foundation for non‐invasive screening and diagnosis of lung cancer in COPD patients and exploration of the mechanisms shared between COPD and lung cancer.

Rationale: Distinguishing connective tissue disease–associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. Objectives: To identify proteins that separate and classify patients with CTD-ILD and those with IPF. Methods: Four registries with 1,247 patients with IPF and 352 patients with CTD-ILD were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using recursive feature elimination to construct a proteomic classifier. Multiple machine learning models, including support vector machine, LASSO (least absolute shrinkage and selection operator) regression, random forest, and imbalanced Random Forest, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. Measurements and Main Results: A classifier with 37 proteins (proteomic classifier 37 [PC37]) was enriched in the biological process of bronchiole development and smooth muscle proliferation and immune responses. Four machine learning models used PC37 with sex and age score to generate continuous classification values. Receiver operating characteristic curve analyses of these scores demonstrated consistent areas under the curve of 0.85–0.90 in the test cohort and 0.94–0.96 in the single-sample dataset. Binary classification demonstrated 78.6–80.4% sensitivity and 76–84.4% specificity in the test cohort and 93.5–96.1% sensitivity and 69.5–77.6% specificity in the single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194 of 248) accuracy in the test cohort and 82.9% (208 of 251) in the single-sample classification dataset. Conclusions: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Many of the identified proteins are involved in immune pathways. We further developed a novel approach for single-sample classification, which could facilitate honing the differential diagnosis of ILD in challenging cases and improve clinical decision making. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer limited information about the disease process and accurate diagnosis depends on the presence of a substantial accumulation of amyloid deposition which significantly impedes the early screening of AD. In this study, we have combined plasma proteomics with an ensemble learning model (CatBoost) to develop a cost‐effective and non‐invasive diagnostic method for AD. A longitudinal panel has been identified that can serve as reliable biomarkers across the entire progression of AD. Simultaneously, we have developed a neural network algorithm that utilizes plasma proteins to detect stages of Alzheimer's disease. Based on the developed longitudinal panel, the CatBoost model achieved an area under the operating curve of at least 0.90 in distinguishing mild cognitive impairment from cognitively normal. The neural network model was utilized for the detection of three stages of AD, and the results demonstrated that the neural network model exhibited an accuracy as high as 0.83, surpassing that of the traditional machine learning model. [ABSTRACT FROM AUTHOR]

Introduction: The exposome concept includes nutrition as it significantly influences human health, impacting the onset and progression of diseases. Gluten-containing wheat products are an essential source of energy for the world's population. However, a rising number of non-celiac healthy individuals tend to reduce or completely avoid gluten-containing cereals for health reasons. Aim and Methods: This prospective interventional human study aimed to investigate whether short-term gluten avoidance improves cardiovascular endpoints and quality of life (QoL) in healthy volunteers. A cohort of 27 participants followed a strict gluten-free diet (GFD) for four weeks. Endothelial function measured by flow-mediated vasodilation (FMD), blood testing, plasma proteomics (Olink®) and QoL as measured by the World Health Organisation Quality-of-Life (WHOQOL) survey were investigated. Results: GFD resulted in decreased leucocyte count and C-reactive protein levels along with a trend of reduced inflammation biomarkers determined by plasma proteomics. A positive trend indicated improvement in FMD, whereas other cardiovascular endpoints remained unchanged. In addition, no improvement in QoL was observed. Conclusion: In healthy individuals, a short-term GFD demonstrated anti-inflammatory effects but did not result in overall cardiovascular improvement or enhanced quality of life. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) prognosis is determined by the disease stage with low survival rates for advanced stages. Current CRC screening programs are mainly using colonoscopy, limited by its invasiveness and high cost. Therefore, non-invasive, cost-effective, and accurate alternatives are urgently needed. Objective and design: This retrospective multi-center plasma proteomics study was performed to identify potential blood-based biomarkers in 36 CRC patients and 26 healthy volunteers by high-resolution mass spectrometry proteomics followed by the validation in an independent CRC cohort (60 CRC patients and 44 healthy subjects) of identified selected biomarkers. Results: Among the 322 identified plasma proteins, 37 were changed between CRC patients and healthy volunteers and were associated with the complement cascade, cholesterol metabolism, and SERPIN family members. Increased levels in CRC patients of the complement proteins C1QB, C4B, and C5 as well as pro-inflammatory proteins, lipopolysaccharide-binding protein (LBP) and serum amyloid A4, constitutive (SAA4) were revealed for first time. Importantly, increased level of C5 was verified in an independent validation CRC cohort. Increased C4B and C8A levels were correlated with cancer-associated inflammation and CRC progression, while cancer-associated inflammation was linked to the acute-phase reactant leucine-rich alpha-2-glycoprotein 1 (LRG1) and ceruloplasmin. Moreover, a 4-protein signature including C4B, C8A, apolipoprotein C2 (APO) C2, and immunoglobulin heavy constant gamma 2 was changed between early and late CRC stages. Conclusion: Our results suggest that C5 could be a potential biomarker for CRC diagnosis. Further validation studies will aid the application of these new potential biomarkers to improve CRC diagnosis and patient care. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis as a fatal neurodegenerative disease currently lacks effective therapeutic agents. Thus, finding new therapeutic targets to drive disease treatment is necessary. In this study, we utilized brain and plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis to identify potential drug targets for amyotrophic lateral sclerosis. Additionally, we validated our results externally using other datasets. We also used Bayesian co-localization analysis and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Mendelian randomization analysis indicated that elevated levels of ANO5 (OR = 1.30; 95 % CI, 1.14–1.49; P = 1.52E-04), SCFD1 (OR = 3.82; 95 % CI, 2.39–6.10; P = 2.19E-08), and SIGLEC9 (OR = 1.05; 95% CI, 1.03–1.07; P = 4.71E-05) are associated with an increased risk of amyotrophic lateral sclerosis, with external validation supporting these findings. Co-localization analysis confirmed that ANO5, SCFD1, and SIGLEC9 (coloc.abf-PPH4 = 0.848, 0.984, and 0.945, respectively) shared the same variant with amyotrophic lateral sclerosis, further substantiating potential role of these proteins as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of amyotrophic lateral sclerosis. Our findings suggested that elevated levels of ANO5, SCFD1, and SIGLEC9 are connected with an increased risk of amyotrophic lateral sclerosis and might be promising therapeutic targets. However, further exploration is necessary to fully understand the underlying mechanisms involved.

Background and aimsUltrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.MethodWe examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (R2).ResultThe mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R2 (0.261, 0.228–0.294 and 0.260, 0.226–0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.ConclusionPlasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.

Summary: Background: Passive administration of SARS-CoV-2 neutralizing monoclonal antibodies (mAbs), such as CAS + IMD (Casirivimab + Imdevimab) antibody cocktail demonstrated beneficial effects on clinical outcomes in hospitalized patients with COVID-19 who were seronegative at baseline and outpatients. However, little is known about their impact on the host immunophenotypes. Methods: We conducted an immunoprofiling study in 46 patients from a single site of a multi-site trial of CAS + IMD in hospitalized patients. We collected longitudinal samples during October 2020 ∼ April 2021, prior to the emergence of the Delta and Omicron variants and the use of COVID-19 vaccines. All collected samples were analyzed without exclusion and post-hoc statistical analysis was performed. We examined the dynamic interplay of CAS + IMD with host immunity applying dimensional reduction approach on plasma proteomics and high dimensional flow cytometry data. Findings: Using an unbiased clustering method, we identified unique immunophenotypes associated with acute inflammation and disease resolution. Compared to placebo group, administration of CAS + IMD accelerated the transition from an acute inflammatory immunophenotype, to a less inflammatory or “resolving” immunophenotype, as characterized by reduced tissue injury, proinflammatory markers and restored lymphocyte/monocyte imbalance independent of baseline serostatus. Moreover, CAS + IMD did not impair the magnitude or the quality of host T cell immunity against SARS-CoV-2 spike protein. Interpretation: Our results identified immunophenotypic changes indicative of a possible SARS-CoV-2 neutralizing antibodies-induced anti-inflammatory effect, without an evident impairment of cellular antiviral immunity, suggesting that further studies of Mabs effects on SAS-CoV-2 or other viral mediated inflammation are warranted. Funding: Regeneron Pharmaceuticals Inc and federal funds from the Department of Health and Human Services; Administration for Strategic Preparedness and Response; Biomedical Advanced Research and Development Authority, under OT number: HHSO100201700020C.

ObjectivesThis study aimed to explore the relationship between plasma proteome and the clinical features of Major Depressive Disorder (MDD) during treatment of acute episode.MethodsIn this longitudinal observational study, 26 patients hospitalized for moderate to severe MDD were analyzed. The study utilized Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS) alongside clinical metrics, including symptomatology derived from the Montgomery-Åsberg Depression Rating Scale (MADRS). Plasma protein analysis was conducted at the onset of acute depression and 6 weeks into treatment. Analytical methods comprised of Linear Models for Microarray Data (LIMMA), Weighted Correlation Network Analysis (WGCNA), Generalized Linear Models, Random Forests, and The Database for Annotation, Visualization and Integrated Discovery (DAVID).ResultsFive distinct plasma protein modules were identified, correlating with specific biological processes, and uniquely associated with symptom presentation, the disorder’s trajectory, and treatment response. A module rich in proteins related to adaptive immunity was correlated with the manifestation of somatic syndrome, treatment response, and inversely associated with achieving remission. A module associated with cell adhesion was linked to affective symptoms and avolition, and played a role in the initial episodes and treatment response. Another module, characterized by proteins involved in blood coagulation and lipid transport, exhibited negative correlations with a variety of MDD symptoms and was predominantly associated with the manifestation of psychotic symptoms.ConclusionThis research points to a complex interplay between the plasma proteome and MDD’s clinical presentation, suggesting that somatic, affective, and psychotic symptoms may represent distinct endophenotypic manifestations of MDD. These insights hold potential for advancing targeted therapeutic strategies and diagnostic tools.LimitationsThe study’s limited sample size and its naturalistic design, encompassing diverse treatment modalities, present methodological constraints. Furthermore, the analysis focused on peripheral blood proteins, with potential implications for interpretability.

ITP is an acquired autoimmune bleeding disorder characterized by an isolated thrombocytopenia. The pathophysiology is highly multifactorial and involves antibody‐ and/or cytotoxic T cell‐mediated killing of platelets and disruption of megakaryocyte function hampering platelet production. ITP remains a diagnosis of exclusion, and due to the high degree of variability between patients, it remains challenging to predict disease courses and responses to therapeutic agents. Hence, diagnostic and therapeutic laboratory biomarkers are highly warranted. To address this issue, in their paper, Jiang and colleagues have performed plasma proteomics in ITP patients (n = 40), in comparison to patients with thrombocytopenia due to other causes than ITP (non‐ITP thrombocytopenia, n = 19) and healthy controls (n = 18). The data underscore that patients with ITP have a distinct plasma proteomic signature compared to non‐ITP thrombocytopenia patients and healthy individuals. The findings should be further validated and investigated but suggest that the application of plasma proteomics is feasible and promising with respect to the search for potential biomarkers in patients with ITP.Commentary on: Jiang et al. Targeted proteomics profiling reveals valuable biomarkers in the diagnosis of primary immune thrombocytopenia. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19760 [ABSTRACT FROM AUTHOR]

Introduction: Insomnia, a common clinical disorder, significantly impacts the physical and mental well-being of patients. Currently, available hypnotic medications are unsatisfactory due to adverse reactions and dependency, necessitating the identification of new drug targets for the treatment of insomnia. Methods: In this study, we utilized 734 plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis, with insomnia as the outcome variable, to identify potential drug targets for insomnia. Additionally, we validated our results externally using other datasets. Sensitivity analyses entailed reverse Mendelian randomization analysis, Bayesian co-localization analysis, and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Results: Mendelian randomization analysis indicated that elevated levels of TGFBI (OR = 1.01; 95% CI, 1.01-1.02) and PAM ((OR = 1.01; 95% CI, 1.01-1.02) in plasma are associated with an increased risk of insomnia, with external validation supporting these findings. Moreover, there was no evidence of reverse causality for these two proteins. Co-localization analysis confirmed that PAM (coloc.abf- PPH4 = 0.823) shared the same variant with insomnia, further substantiating its potential role as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of insomnia. Conclusion: Overall, our findings suggested that elevated plasma levels of TGFBI and PAM are connected with an increased risk of insomnia and might be promising therapeutic targets, particularly PAM. However, further exploration is necessary to fully understand the underlying mechanisms involved. [ABSTRACT FROM AUTHOR]

Introduction: Bloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine. Methods: In this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting highabundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose. Results and discussion: Among all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies. [ABSTRACT FROM AUTHOR]

Despite great scientific efforts, deep understanding of coronavirus-19 disease (COVID-19) immunopathology and clinical biomarkers remains a challenge. Pre-existing comorbidities increase the mortality rate and aggravate the exacerbated immune response against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, which can result in more severe symptoms as well as long-COVID and post-COVID complications. In this study, we applied proteomics analysis of plasma samples from 28 patients with SARS-CoV-2, with and without pre-existing comorbidities, as well as their corresponding controls to determine the systemic protein changes caused by the SARS-CoV-2 infection. As a result, the protein signature shared amongst COVID-19 patients with comorbidities was revealed to be characterized by alterations in the coagulation and complement pathways, acute-phase response proteins, tissue damage and remodeling, as well as cholesterol metabolism. These altered proteins may play a relevant role in COVID-19 pathophysiology. Moreover, several novel potential biomarkers for early diagnosis of the SARS-CoV-2 infection were detected, such as increased levels of keratin K22E, extracellular matrix protein-1 (ECM1), and acute-phase response protein α-2-antiplasmin (A2AP). Importantly, elevated A2AP may contribute to persistent clotting complications associated with the long-COVID syndrome in patients with comorbidities. This study provides new insights into COVID-19 pathogenesis and proposes novel potential biomarkers for early diagnosis that could be facilitated for clinical application by further validation studies. [ABSTRACT FROM AUTHOR]

The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment. [ABSTRACT FROM AUTHOR]

Background: There is significant interest in developing alternatives to traditional blood transportation and separation methods, which often require centrifugation and cold storage to preserve specimen integrity. Here we provide new performance findings that characterize a novel device that separates whole blood via lateral flow then dries the isolated components for room temperature storage and transport. Methods: Untargeted proteomics was performed on non-small cell lung cancer (NSCLC) and normal healthy plasma applied to the device or prepared neat. Results: Significantly, proteomic profiles from the storage device were more reproducible than from neat plasma. Proteins depleted or absent in the device preparation were shown to be absorbed onto the device membrane through largely hydrophilic interactions. Use of the device did not impact proteins relevant to an NSCLC clinical immune classifier. The device was also evaluated for use in targeted proteomics experiments using multiple-reaction monitoring (MRM) mass spectrometry. Intra-specimen detection intensity for protein targets between neat and device preparations showed a strong correlation, and device variation was comparable to the neat after normalization. Inter-specimen measurements between the device and neat preparations were also highly concordant. Conclusions: These studies demonstrate that the lateral flow device is a viable blood separation and transportation tool for untargeted and targeted proteomics applications.

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.

IntroductionBloodwork is a widely used diagnostic tool in veterinary medicine, as diagnosis and therapeutic interventions often rely on blood biomarkers. However, biomarkers available in veterinary medicine often lack sensitivity or specificity. Mass spectrometry-based proteomics technology has been extensively used in the analysis of biological fluids. It offers excellent potential for a more comprehensive characterization of the plasma proteome in veterinary medicine.MethodsIn this study, we aimed to identify and quantify plasma proteins in a cohort of healthy dogs and compare two techniques for depleting high-abundance plasma proteins to enable the detection of lower-abundance proteins via label-free quantification liquid chromatography-mass spectrometry. We utilized surplus lithium-heparin plasma from 30 healthy dogs, subdivided into five groups of pooled plasma from 6 randomly selected individuals each. Firstly, we used a commercial kit to deplete high-abundance plasma proteins. Secondly, we employed an in-house method to remove albumin using Blue-Sepharose.Results and discussionAmong all the samples, some of the most abundant proteins identified were apolipoprotein A and B, albumin, alpha-2-macroglobulin, fibrinogen beta chain, fibronectin, complement C3, serotransferrin, and coagulation factor V. However, neither of the depletion techniques achieved significant depletion of highly abundant proteins. Despite this limitation, we could detect and quantify many clinically relevant proteins. Determining the healthy canine proteome is a crucial first step in establishing a reference proteome for canine plasma. After enrichment, this reference proteome can later be utilized to identify protein markers associated with different diseases, thereby contributing to the diagnosis and prognosis of various pathologies.

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Integrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases. The purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments. We conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis -protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls). Overall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis -pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis -pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD. Integrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD. [Display omitted] [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The euglycaemic–hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. Methods: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). Results: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. Conclusions/interpretation: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences. [ABSTRACT FROM AUTHOR]

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the “masking” effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.

Malaria in pregnancy (MiP) is a public health problem in malaria-endemic areas, contributing to detrimental outcomes for both mother and fetus. Primigravida and second-time mothers are most affected by severe anemia complications and babies with low birth weight compared to multigravida women. Infected erythrocytes (IE) reach the placenta, activating the immune response by placental monocyte infiltration and inflammation. However, specific markers of MiP result in poor outcomes, such as low birth weight, and intrauterine growth restriction for babies and maternal anemia in women infected with Plasmodium falciparum are limited. In this study, we identified the plasma proteome signature of a mouse model infected with Plasmodium berghei ANKA and pregnant women infected with Plasmodium falciparum infection using quantitative mass spectrometry-based proteomics. A total of 279 and 249 proteins were quantified in murine and human plasma samples, of which 28% and 30% were regulated proteins, respectively. Most of the regulated proteins in both organisms are involved in complement system activation during malaria in pregnancy. CBA anaphylatoxin assay confirmed the complement system activation by the increase in C3a and C4a anaphylatoxins in the infected plasma compared to non-infected plasma. Moreover, correlation analysis showed the association between complement system activation and reduced head circumference in newborns from Pf-infected mothers. The data obtained in this study highlight the correlation between the complement system and immune and newborn outcomes resulting from malaria in pregnancy. [ABSTRACT FROM AUTHOR]

therefore important to deeply characterize the human nutritional responses. Objective: Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers. Methods: We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters. Results: Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes. Conclusion: Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system. [ABSTRACT FROM AUTHOR]

[Display omitted] Systemic mastocytosis (SM) is a heterogeneous group of mast cell–driven diseases diagnosed by bone marrow sampling. However, there are a limited number of available blood disease biomarkers. Our aim was to identify mast cell–derived proteins that could potentially serve as blood biomarkers for indolent and advanced forms of SM. We performed a plasma proteomics screening coupled with single-cell transcriptomic analysis in SM patients and healthy subjects. Plasma proteomics screening identified 19 proteins upregulated in indolent disease compared to healthy, and 16 proteins in advanced disease compared to indolent. Among these, 5 proteins, CCL19, CCL23, CXCL13, IL-10, and IL-12Rβ1, were higher in indolent relative to healthy and in advanced disease compared to indolent. Single-cell RNA sequencing demonstrated that CCL23, IL-10, and IL-6 were selectively produced by mast cells. Notably, plasma CCL23 levels correlated positively with known markers of SM disease severity, namely tryptase levels, percentage bone marrow mast cell infiltration, and IL-6. CCL23 is produced predominantly by mast cells in SM, and CCL23 plasma levels are associated with disease severity, correlating positively with established markers of disease burden, thus suggesting that CCL23 is a specific SM biomarker. In addition, the combination of CCL19, CCL23, CXCL13, IL-10, and IL-12Rβ1 may be useful for defining disease stage. [ABSTRACT FROM AUTHOR]

Summary: Background: Biomarkers predicting the outcome of HIV-1 virus control in natural infection and after therapeutic interventions in HIV-1 cure trials remain poorly defined. The BCN02 trial (NCT02616874), combined a T-cell vaccine with romidepsin (RMD), a cancer-drug that was used to promote HIV-1 latency reversal and which has also been shown to have beneficial effects on neurofunction. We conducted longitudinal plasma proteomics analyses in trial participants to define biomarkers associated with virus control during monitored antiretroviral pause (MAP) and to identify novel therapeutic targets that can improve future cure strategies. Methods: BCN02 was a phase I, open-label, single-arm clinical trial in early-treated, HIV infected individuals. Longitudinal plasma proteomes were analyzed in 11 BCN02 participants, including 8 participants that showed a rapid HIV-1 plasma rebound during a monitored antiretroviral pause (MAP-NC, ‘non-controllers’) and 3 that remained off ART with sustained plasma viremia

Despite great scientific efforts, deep understanding of coronavirus-19 disease (COVID-19) immunopathology and clinical biomarkers remains a challenge. Pre-existing comorbidities increase the mortality rate and aggravate the exacerbated immune response against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, which can result in more severe symptoms as well as long-COVID and post-COVID complications. In this study, we applied proteomics analysis of plasma samples from 28 patients with SARS-CoV-2, with and without pre-existing comorbidities, as well as their corresponding controls to determine the systemic protein changes caused by the SARS-CoV-2 infection. As a result, the protein signature shared amongst COVID-19 patients with comorbidities was revealed to be characterized by alterations in the coagulation and complement pathways, acute-phase response proteins, tissue damage and remodeling, as well as cholesterol metabolism. These altered proteins may play a relevant role in COVID-19 pathophysiology. Moreover, several novel potential biomarkers for early diagnosis of the SARS-CoV-2 infection were detected, such as increased levels of keratin K22E, extracellular matrix protein-1 (ECM1), and acute-phase response protein α-2-antiplasmin (A2AP). Importantly, elevated A2AP may contribute to persistent clotting complications associated with the long-COVID syndrome in patients with comorbidities. This study provides new insights into COVID-19 pathogenesis and proposes novel potential biomarkers for early diagnosis that could be facilitated for clinical application by further validation studies.

Sepsis is an extreme host response to infection that leads to loss of organ function and cardiovascular integrity. Mortality from sepsis is on the rise. Despite more than three decades of research and clinical trials, specific diagnostic and therapeutic strategies for sepsis are still absent. The use of LFQ- and TMT-based quantitative proteomics is reported here to study the plasma proteome in five mouse models of sepsis. A knowledge-based interpretation of the data reveals a protein network with extensive connectivity through documented functional or physical interactions. The individual proteins in the network all have a documented role in sepsis and are known to be extracellular. The changes in protein abundance observed in the mouse models of sepsis have for the most part the same directionality (increased or decreased abundance) as reported in the literature for human sepsis. This network has been named the Plasma Proteome Signature of Sepsis (PPSS). The PPSS is a quantifiable molecular readout that can supplant the current symptom-based approach used to diagnose sepsis. This type of molecular interpretation of sepsis, its progression, and its response to therapeutic intervention are an important step in advancing our understanding of sepsis, and for discovering and evaluating new therapeutic strategies.

Introduction: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive bloodbased biomarkers for CRC early detection and prognosis are urgently needed. Methods: To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few mL of plasma sample. Results: Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon g (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion: Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC. [ABSTRACT FROM AUTHOR]

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia. [ABSTRACT FROM AUTHOR]

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications. [Display omitted] • Plasma proteomics analysis based on protein corona on zeolite NaY was first proposed. • This method required only one type of particle, zeolite NaY. • NaY significantly enhanced the detection of low abundance plasma proteins. • Approximately 4000 proteins can be stably detected in a single-run LC-MS/MS. • A dynamic range of 8 orders of magnitude can be covered with sensitivity up to pg/mL. [ABSTRACT FROM AUTHOR]