Proteins In Plasma Samples Research Articles

Potential role of hemopexin in venous thromboembolism (VTE) mediated through effects on apoptosis-related proteins

ABSTRACT Objective: The limited understanding of venous thromboembolism (VTE) has hindered the development of novel treatment approaches. This study aimed to identify critical factors underlying VTE. Methods: The isobaric tags for relative and absolute quantitation (iTRAQ) method was applied to identify differentially expressed proteins in plasma samples from patients with VTE and healthy volunteers. Gene overexpression and knockdown techniques were used for cultured human umbilical vein endothelial cells (HUVECs) and VTE mouse models. Thrombosis and apoptosis related to the identified proteins were verified using qRT-PCR, western blot, and flow cytometry analyses. Results: iTRAQ analysis revealed significant upregulation of keratin 1, pro-platelet basic protein, and hemopexin (HPX) in the plasma of VTE patients. HPX was highly expressed in both plasma and GSE19151 and GSE48000 datasets. qRT-PCR and western blot results for HPX overexpressing HUVECs and VTE mouse models revealed that HPX positively regulated the expression of platelet endothelium aggregation receptor 1 and von Willebrand factor, but negatively regulated the expression of serpin family D member 1 and alpha-2-macroglobulin. Flow cytometry revealed that HPX exhibited pro-apoptotic activity in HPX overexpressing HUVECs. Furthermore, HPX elevated cleaved caspase-3 expression and inhibited B-cell lymphoma-2 expression in HUVECs and VTE mouse models. Conclusion: HPX exhibits possible prothrombotic activity by regulating thrombosis – apoptosis-related proteins.

A Comprehensive Proteomic and Bioinformatic Analysis of Human Spinal Cord Injury Plasma Identifies Proteins Associated with the Complement Cascade and Liver Function as Potential Prognostic Indicators of Neurological Outcome.

Spinal cord injury (SCI) is a major cause of disability, with complications postinjury often leading to lifelong health issues with the need for extensive treatment. Neurological outcome post-SCI can be variable and difficult to predict, particularly in incompletely injured patients. The identification of specific SCI biomarkers in blood may be able to improve prognostics in the field. This study has utilized proteomic and bioinformatic methodologies to investigate differentially expressed proteins in plasma samples across human SCI cohorts with the aim of identifying candidate prognostic biomarkers and biological pathway alterations that relate to neurological outcome. Blood samples were taken, following informed consent, from American Spinal Injury Association impairment scale (AIS) grade C "improvers" (those who experienced an AIS grade improvement) and "nonimprovers" (no AIS change) and AIS grade A and D at <2 weeks ("acute") and ∼3 months ("subacute") postinjury. The total protein concentration from each sample was extracted, with pooled samples being labeled and nonpooled samples treated with ProteoMiner™ beads. Samples were then analyzed using two 4-plex isobaric tag for relative and absolute quantification (iTRAQ) analyses and a label-free experiment for comparison before quantifying with mass spectrometry. Data are available via ProteomeXchange with identifiers PXD035025 and PXD035072 for the iTRAQ and label-free experiments, respectively. Proteomic datasets were analyzed using OpenMS (version 2.6.0). R (version 4.1.4) and, in particular, the R packages MSstats (version 4.0.1) and pathview (version 1.32.0) were used for downstream analysis. Proteins of interest identified from this analysis were further validated by enzyme-linked immunosorbent assay. The data demonstrated proteomic differences between the cohorts, with the results from the iTRAQ approach supporting those of the label-free analysis. A total of 79 and 87 differentially abundant proteins across AIS and longitudinal groups were identified from the iTRAQ and label-free analyses, respectively. Alpha-2-macroglobulin, retinol-binding protein 4 (RBP4), serum amyloid A1, peroxiredoxin 2 (PRX-2), apolipoprotein A1, and several immunoglobulins were identified as biologically relevant and differentially abundant, with potential as individual candidate prognostic biomarkers of neurological outcome. Bioinformatics analyses revealed that the majority of differentially abundant proteins were components of the complement cascade and most interacted directly with the liver. Many of the proteins of interest identified using proteomics were detected only in a single group and therefore have potential as binary (present or absent) biomarkers, RBP4 and PRX-2 in particular. Additional investigations into the chronology of these proteins and their levels in other tissues (cerebrospinal fluid in particular) are needed to better understand the underlying pathophysiology, including any potentially modifiable targets. Pathway analysis highlighted the complement cascade as being significant across groups of differential functional recovery.

The Association between Proteomic Aging Clocks and the Risk of Cancer in Midlife Individuals.

To measure the aging process before a cancer diagnosis, we developed the first cancer-specific proteomic aging clock (CaPAC) and examined its association with cancer risk in the Atherosclerosis Risk in Communities (ARIC) and Multi-Ethnic Study of Atherosclerosis (MESA) studies. Using the SomaScan assay, ARIC measured 4,712 proteins in plasma samples collected in 1990-92 from 3,347 participants who developed cancer over follow-up until 2015 and 7,487 who remained cancer-free, all aged 46-70. We constructed CaPAC0 using elastic net regression among two-thirds randomly selected cancer-free participants (N=4,991, training set) and calculated age acceleration for CaPAC0 (CaPAA0) as residuals of CaPAC0 on chronological age in all remaining ARIC participants. We used multivariable-adjusted Cox proportional hazards regression to calculate hazard ratios (HRs) for the risk of overall, obesity-related, smoking-related, and the most common cancers (prostate, lung, breast, colorectal) with CaPAA0 using a case-cohort design. We replicated the analysis in 3,893 MESA participants aged 46-70 at Exam 1 (456 incident cancer). CaPAC0 was correlated with chronological age in ARIC and MESA (r=0.82 and 0.86, respectively). In both ARIC and MESA, CaPAA0 was significantly (p<0.05) associated with the risk of overall [HRs per 5-years=1.08 and 1.23, respectively], smoking-related [HRs=1.30 and 1.54, respectively], and lung cancers [HRs=1.54 and 1.94, respectively]. CaPAA0 was also significantly associated with colorectal cancer risk in ARIC [HR=1.31], but not in MESA. CaPAA0 was not associated with obesity-related, breast, or prostate cancers. CaPAA0 was associated with several types of cancer with the strongest association observed for lung cancer risk.

Extracellular acyl-CoA-binding protein as an independent biomarker of COVID-19 disease severity.

Factors leading to severe COVID-19 remain partially known. New biomarkers predicting COVID-19 severity that are also causally involved in disease pathogenesis could improve patient management and contribute to the development of innovative therapies. Autophagy, a cytosolic structure degradation pathway is involved in the maintenance of cellular homeostasis, degradation of intracellular pathogens and generation of energy for immune responses. Acyl-CoA binding protein (ACBP) is a key regulator of autophagy in the context of diabetes, obesity and anorexia. The objective of our work was to assess whether circulating ACBP levels are associated with COVID-19 severity, using proteomics data from the plasma of 903 COVID-19 patients. Somalogic proteomic analysis was used to detect 5000 proteins in plasma samples collected between March 2020 and August 2021 from hospitalized participants in the province of Quebec, Canada. Plasma samples from 903 COVID-19 patients collected during their admission during acute phase of COVID-19 and 295 hospitalized controls were assessed leading to 1198 interpretable proteomic profiles. Levels of anti-SARS-CoV-2 IgG were measured by ELISA and a cell-binding assay. The median age of the participants was 59 years, 46% were female, 65% had comorbidities. Plasma ACBP levels correlated with COVID-19 severity, in association with inflammation and anti-SARS-CoV-2 antibody levels, independently of sex or the presence of comorbidities. Samples collected during the second COVID-19 wave in Quebec had higher levels of plasma ACBP than during the first wave. Plasma ACBP levels were negatively correlated with biomarkers of T and NK cell responses interferon-γ, tumor necrosis factor-α and interleukin-21, independently of age, sex, and severity. Circulating ACBP levels can be considered a biomarker of COVID-19 severity linked to inflammation. The contribution of extracellular ACBP to immunometabolic responses during viral infection should be further studied.

Identification of extracellular matrix proteins in plasma as a potential biomarker for intervertebral disc degeneration.

Recently, there has been significant focus on extracellular matrix proteolysis due to its importance in the pathological progression of intervertebral disc degeneration (IVDD). The present study investigates the circulating levels of extracellular matrix proteins in the plasma of IVDD and determines their potential relevance as biomarkers in disc degeneration. Global proteomic analysis was performed in the plasma samples of 10 healthy volunteers (HV) and 10 diseased subjects (DS) after depletion of highly abundant proteins such as albumin and IgG. We identified 144 and 135 matrix-associated proteins in plasma samples from healthy volunteers (HV) and patients with disc degeneration (DS), respectively. Among these, 49 of the matrix-associated proteins were identical to the proteins found in intervertebral disc (IVD) tissues retrieved from the in-house library. Applying stringent parameters, we selected 28 proteins, with 26 present in DS and 21 in HV. 19 proteins were found common between the groups, two of which-aggrecan (ACAN) and fibulin 1 (FBLN1) - showed statistically significant differences. Specifically, ACAN was up-regulated and FBLN1 was down-regulated in the DS-plasma. In particular, DS-plasma exhibited specific expression of collagen type 2a1 (COL2A1), native to the nucleus pulposus. The distinct presence of collagen type 2a1 and the elevated expression of aggrecan in IVDD plasma may serve as the basis for the development of a potential biomarker for monitoring the progression of disc degeneration.

Protein biomarker signatures of preeclampsia - a longitudinal 5000-multiplex proteomics study

We aimed to explore novel biomarker candidates and biomarker signatures of late-onset preeclampsia (LOPE) by profiling samples collected in a longitudinal discovery cohort with a high-throughput proteomics platform. Using the Somalogic 5000-plex platform, we analyzed proteins in plasma samples collected at three visits (gestational weeks (GW) 12–19, 20–26 and 28–34 in 35 women with LOPE (birth ≥ 34 GW) and 70 healthy pregnant women). To identify biomarker signatures, we combined Elastic Net with Stability Selection for stable variable selection and validated their predictive performance in a validation cohort. The biomarker signature with the highest predictive performance (AUC 0.88 (95% CI 0.85–0.97)) was identified in the last trimester of pregnancy (GW 28–34) and included the Fatty acid amid hydrolase 2 (FAAH2), HtrA serine peptidase 1 (HTRA1) and Interleukin-17 receptor C (IL17RC) together with sFLT1 and maternal age, BMI and nulliparity. Our biomarker signature showed increased or similar predictive performance to the sFLT1/PGF-ratio within our data set, and we were able to validate the biomarker signature in a validation cohort (AUC ≥ 0.90). Further validation of these candidates should be performed using another protein quantification platform in an independent cohort where the negative and positive predictive values can be validly calculated.

Plasma Inflammatory Proteome Profile in a Cohort of Patients with Recurrent Vulvovaginal Candidiasis in Kenya.

Vulvovaginal candidiasis (VVC) affects up to 75% of women at least once during their lifetime, and up to 8% of women suffer from frequent recurrent episodes of VVC (RVVC). A lack of a protective host response underlies vaginal Candida infections, while a dysregulated hyperinflammatory response may drive RVVC. This study aimed to investigate the systemic inflammatory protein profile in women with RVVC in an African population, considering the potential influence of hormonal contraceptive use on systemic inflammation. Using multiplex Proximity Extension Assay technology, we measured 92 circulatory inflammatory proteins in plasma samples from 158 RVVC patients and 92 asymptomatic women (controls). Hormonal contraceptive use was not found to have a statistically significant correlation with a systemic inflammatory protein profile in either RVVC patients or the asymptomatic women. RVVC women had lower circulating Fibroblast Growth Factor 21 (FGF-21) concentrations compared with healthy controls (adjusted p value = 0.028). Reduced concentrations of FGF-21 may be linked to the immune pathology observed in RVVC cases through IL-1β. This study may help to identify new biomarkers for the diagnosis and future development of novel immunomodulatory treatments for RVVC.

A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans.

Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a 'high impact' SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.

Network Analysis Reveals Protein Modules Associated with Childhood Respiratory Diseases.

The first year of life is a period of rapid immune development that can impact health trajectories and the risk of developing respiratory-related diseases, such as asthma, recurrent infections, and eczema. However, the biology underlying subsequent disease development remains unknown. Using weighted gene correlation network analysis (WGCNA), we derived modules of highly correlated immune-related proteins in plasma samples from children at age 1 year (N=294) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART). We applied regression analyses to assess relationships between protein modules and development of childhood respiratory diseases up to age 6 years. We then characterized genomic, environmental, and metabolomic factors associated with modules. WGCNA identified four protein modules at age 1 year associated with incidence of childhood asthma and/or recurrent wheeze (Padj range: 0.02-0.03), respiratory infections (Padj range: 6.3×10-9-2.9×10-6), and eczema (Padj=0.01) by age 6 years; three modules were associated with at least one environmental exposure (Padj range: 2.8×10-10-0.03) and disrupted metabolomic pathway(s) (Padj range: 2.8×10-6-0.04). No genome-wide SNPs were identified as significant genetic risk factors for any protein module. Relationships between protein modules with clinical, environmental, and 'omic factors were temporally sensitive and could not be recapitulated in protein profiles at age 6 years. These findings suggested protein profiles as early as age 1 year predicted development of respiratory-related diseases through age 6 and were associated with changes in pathways related to amino acid and energy metabolism. These may inform new strategies to identify vulnerable individuals based on immune protein profiling.

Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases.

JOURNAL/nrgr/04.03/01300535-202508000-00027/figure1/v/2024-09-30T120553Z/r/image-tiff Several promising plasma biomarker proteins, such as amyloid-β (Aβ), tau, neurofilament light chain, and glial fibrillary acidic protein, are widely used for the diagnosis of neurodegenerative diseases. However, little is known about the long-term stability of these biomarker proteins in plasma samples stored at -80°C. We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort. Plasma samples from 229 cognitively unimpaired individuals, encompassing healthy controls and those experiencing subjective cognitive decline, as well as 99 patients with cognitive impairment, comprising those with mild cognitive impairment and dementia, were acquired from the Sino Longitudinal Study on Cognitive Decline project. These samples were stored at -80°C for up to 6 years before being used in this study. Our results showed that plasma levels of Aβ42, Aβ40, neurofilament light chain, and glial fibrillary acidic protein were not significantly correlated with sample storage time. However, the level of total tau showed a negative correlation with sample storage time. Notably, in individuals without cognitive impairment, plasma levels of total protein and tau phosphorylated protein threonine 181 (p-tau181)also showed a negative correlation with sample storage time. This was not observed in individuals with cognitive impairment. Consequently, we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time. Therefore, caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases, such as Alzheimer's disease. Furthermore, in cohort studies, it is important to consider the impact of storage time on the overall results.

Circulating microRNA Profiles Identify a Patient Subgroup with High Inflammation and Severe Symptoms in Schizophrenia Experiencing Acute Psychosis.

Schizophrenia is a complex and heterogenous psychiatric disorder. This study aimed to demonstrate the potential of circulating microRNAs (miRNAs) as a clinical biomarker to stratify schizophrenia patients and to enhance understandings of their heterogenous pathophysiology. We measured levels of 179 miRNA and 378 proteins in plasma samples of schizophrenia patients experiencing acute psychosis and obtained their Positive and Negative Syndrome Scale (PANSS) scores. The plasma miRNA profile revealed three subgroups of schizophrenia patients, where one subgroup tended to have higher scores of all the PANSS subscales compared to the other subgroups. The subgroup with high PANSS scores had four distinctively downregulated miRNAs, which enriched 'Immune Response' according to miRNA set enrichment analysis and were reported to negatively regulate IL-1β, IL-6, and TNFα. The same subgroup had 22 distinctively upregulated proteins, which enriched 'Cytokine-cytokine receptor interaction' according to protein set enrichment analysis, and all the mapped proteins were pro-inflammatory cytokines. Hence, the subgroup is inferred to have comparatively high inflammation within schizophrenia. In conclusion, miRNAs are a potential biomarker that reflects both disease symptoms and molecular pathophysiology, and identify a patient subgroup with high inflammation. These findings provide insights for the precision medicinal strategies for anti-inflammatory treatments in the high-inflammation subgroup of schizophrenia.

Nontargeted Plasma Proteomic Analysis of Renal Disease and Pulmonary Hypertension in Patients with Sickle Cell Disease.

Sickle cell disease (SCD) is characterized by red blood cell sickling, vaso-occlusion, hemolytic anemia, damage to multiple organ systems, and, as a result, shortened life expectancy. Sickle cell disease nephropathy (SCDN) and pulmonary hypertension (pHTN) are common and frequently co-occurring complications of SCD; both are associated with markedly accelerated mortality. To identify candidate circulating biomarkers of SCDN and pHTN, we used mass spectrometry to quantify the relative abundance of >1000 proteins in plasma samples from 189 adults with SCD from the Outcome Modifying Genes in SCD (OMG-SCD) cohort (ProteomeXchange identifier PXD048716). Forty-four proteins were differentially abundant in SCDN, most significantly cystatin-C and collagen α-1(XVIII) chain (COIA1), and 55 proteins were dysregulated in patients with SCDN and pHTN, most significantly insulin-like growth factor-binding protein 6 (IBP6). Network analysis identified a module of 133 coregulated proteins significantly associated with SCDN, that was enriched for extracellular matrix proteins, insulin-like growth factor binding proteins, cell adhesion proteins, EGF-like calcium binding proteins, and several cadherin family members. Collectively, these data provide a comprehensive understanding of plasma protein changes in SCDN and pHTN which validate numerous studies of chronic kidney disease and suggest shared profiles of protein disruption in kidney dysfunction and pHTN among SCD patients.

Predicting the efficacy of glucocorticoids in pediatric primary immune thrombocytopenia using plasma proteomics.

Primary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder among children. While glucocorticoids are the primary first-line treatment for ITP treatment, they prove ineffective in certain patients. The challenge of identifying biomarkers capable of early prediction regarding the response to glucocorticoid therapy in ITP persists. This study aimed to identify ideal biomarkers for predicting glucocorticoid efficacy in patients with ITP using plasma proteomics. A four-dimensional data-independent acquisition approach was performed to determine the differentially expressed proteins in plasma samples collected from glucocorticoid-sensitive (GCS) (n=18) and glucocorticoid-resistant (GCR) (n=17) children with ITP treated with prednisone. The significantly differentially expressed proteins were selected for enzyme-linked immunosorbent assay validation in a cohort conprising 65 samples(30 healthy controls, 18 GCS and 17 GCR children with ITP). Receiver operating characteristics curves, calibration curves, and clinical decision curve analysis were used to determine the diagnostic efficacy of this method. 47 differentially expressed proteins (36 up-regulated and 11 down-regulated) were identified in the GCR group compared with the GCS group. The significantly differentially expressed proteins myosin heavy chain 9 (MYH9) and fetuin B (FETUB) were selected for enzyme-linked immunosorbent assay validation. The validation results were consistent with the proteomics analyses. Compared with the GCS group, the GCR group exhibited a significantly reduced the plasma concentration of MYH9 and elevated the plasma concentration of FETUB. Furthermore, the receiver operating characteristics curves, calibration curves, and clinical decision curve analysis demonstrated good diagnostic efficacy of these validated biomarkers. This study contributes to the establishment of objective biological indicators for precision therapy in children with ITP. More importantly, the proteins MYH9 and FETUB hold potential as a foundation for making informed decisions regarding alternative treatments for drugresistant patients, thereby preventing treatment delays.

BIOM-08. SINGLE-MOLECULE SYSTEMS FOR DETECTION OF PLASMA CIRCULATING ONCOPROTEINS IN DIFFUSE MIDLINE GLIOMA

Abstract Diffuse midline glioma (DMG) is a high-risk pediatric brain tumor that has primarily been diagnosed and monitored via medical imaging (MRI) due to risks associated with re-biopsying midline brain structures. Recent advances in molecular diagnostics have improved classification and molecular characterization of DMG; these tumors are in dire need of non-invasive, molecular-based diagnostic tools to supplement serial assessments of radiographic response. Our group recently developed a single-molecule imaging platform, Epigenetics of Plasma Isolated Nucleosomes (EPINUC), to profile histone modification patterns in cell-free tumor DNA (ctDNA) from plasma of cancer patients. We developed a novel strategy for single-molecule detection of the histone mutation H3K27M, a key DMG genetic driver, by capturing mutant nucleosomes on a PEG-streptavidin coated surface and detecting them with fluorescently labeled antibodies targeting histone modifications enriched on these mutant nucleosomes. We applied this method to 46 H3K27M and 10 H3 wildtype DMG samples and found significantly higher signal in K27M samples. Using this data, we generated a predictive model of H3 status with an AUC of 0.81, sensitivity 90%, specificity 71%, and precision 73%. This test was found to be highly reproducible and rapid (60 minutes) and required less than 100 uL of plasma. We also analyzed mutant p53 protein in plasma samples from 11 p53-mutant and 4 p53-wildtype DMG patients, seeing significant separation between the two groups. We overlayed H3K27M and p53 cell-free protein results with H3.3K27M and TP53 ctDNA variant allele fraction (VAF) by digital droplet PCR (ddPCR) in serial plasma samples from n = 3 DMG patients undergoing experimental therapy. These data highlighted unique patterns of disease response by (1) MRI, (2) plasma tumor protein, and (3) plasma ctDNA. Overall, these results demonstrate the clinical potential of our platform for non-invasive and accurate detection and monitoring of levels of key oncoproteins in plasma of DMG patients.

Spectral library search for improved TMTpro labelled peptide assignment in human plasma proteomics.

Clinical biomarker discovery is often based on the analysis of human plasma samples. However, the high dynamic range and complexity of plasma pose significant challenges to mass spectrometry-based proteomics. Current methods for improving protein identifications require laborious pre-analytical sample preparation. In this study, we developed and evaluated a TMTpro-specific spectral library for improved protein identification in human plasma proteomics. The library was constructed by LC-MS/MS analysis of highly fractionated TMTpro-tagged human plasma, human cell lysates, and relevant arterial tissues. The library was curated using several quality filters to ensure reliable peptide identifications. Our results show that spectral library searching using the TMTpro spectral library improves the identification of proteins in plasma samples compared to conventional sequence database searching. Protein identifications made by the spectral library search engine demonstrated a high degree of complementarity with the sequence database search engine, indicating the feasibility of increasing the number of protein identifications without additional pre-analytical sample preparation. The TMTpro-specific spectral library provides a resource for future plasma proteomics research and optimization of search algorithms for greater accuracy and speed in protein identifications in human plasma proteomics, and is made publicly available to the research community via ProteomeXchange with identifier PXD042546.

Uncovering Novel Biomarkers of Inflammation as Potential Screening Targets of Disease Risk in Healthcare Shift Workers: A Pilot Study.

Shift work, experienced by nearly 30% of the U.S. workforce, is hazardous to health and has become a pervasive labor practice in the healthcare sector worldwide. It increases the risk of stroke, diabetes, cancer, and cardiovascular disease. Nonetheless, specific screening targets for shift workers still need to be defined. In this study, we have begun uncovering these targets as specific low-grade systemic inflammation markers and functional endotoxin-elicited responses that may foreshadow disease risk in shift workers. One hundred four participants (normothermic and normotensive) were healthy, non-smoking, and drug- and medication-free volunteers recruited from Atlanta area hospitals and medical schools. We assessed the concentration of three proteins in plasma samples from day workers and shift workers (lipopolysaccharide-binding protein, IL-10, and TNF-α), and the relationship between these baseline biomarkers and their response to an ex-vivo endotoxin challenge. We show that shift work increases low-grade systemic inflammation and disrupts discrete endotoxin responses. As shift work exposure increases, the correlation between low-grade systemic inflammation markers and their endotoxin responses was disrupted; this effect was more robust for TNF-α than for IL-10. With increased shift work exposure, these events, alone or combined, represent potential systemic and functional signals that may be harnessed to develop screening tools to identify at-risk individuals.

P157 Distinct differences in complement perturbation between scleroderma-related interstitial lung disease with and without emphysema

Abstract Background/Aims Aberrant complement activation is associated with autoimmune diseases including systemic sclerosis (SSc). As a leading cause of death in SSc, interstitial lung disease (ILD) can coexist with emphysema in non-smoking patients, worsening prognosis. It has been suggested that the airway destruction observed in SSc patients may be an exaggerated inflammatory response related to increased complement activation. Methods We analysed 10 complement proteins in plasma samples of 16 non-smoking SSc patients with emphysema (SSc-Emp), 8 SSc no-ILD, 8 SSc-ILD patients and 8 healthy controls (HC) (Table.1) selected from 1800 SSc patients under active follow up at our centre. The extent and distribution of emphysema was evaluated on high resolution computed tomography (HRCT) and enzyme-linked immunosorbent assays (ELISA) were performed for C1q, MASP-2, Factor B, Factor Bb, Factor H, C3, C3a, C4, C5, C5a and TCC (terminal complement complex). One-way ANOVA and post-hoc Tukey test were used for analysis. Results HRCT confirmed that amongst the emphysema cohort, there was a spectrum of mild to severe destruction including both paraseptal and centrilobular emphysema. All 16 SSc-Emp patients had ILD and 9 of the 16 patients demonstrated perivascular emphysema not previously described in SSc. The SSc-ILD group was characterised by extensive lung fibrosis on CT, mean FVC 71% predicted. Across the four groups, C1q was significantly reduced in the SSc-ILD cohort (SSc-Emp 950±404µg/mL, SSc-ILD 355±116µg/mL, SSc-no ILD 1087±206µg/mL, HC 858±210µg/mL, p= 0.0002). In contrast, the ratio of C3a/C3 was significantly increased in the SSc-ILD group compared with the SSc-Emp group (SSc-ILD 0.12±0.07 vs SSc-Emp 0.06±0.03, p= 0.0283). The ratio of Bb/B was significantly increased in the SSc no-ILD group compared with all other groups (SSc-Emp 1.57±0.80µg/mL, SSc-ILD 1.27±0.56µg/mL, SSc-no ILD 3.86±1.12µg/mL, HC 1.89±0.75µg/mL, p&amp;lt; 0.0001). There was a trend towards an increased ratio of C5a/C5 in SSc-ILD and SSc-Emp compared with HC and reduced MASP2 in the SSc groups compared with HC. Conclusion This data demonstrates dysregulated complement levels in three SSc subgroups. Notably, the perturbed C1q and C3a:C3 pathways in SSc-ILD are reversed in SSc-emphysema, supporting complement activation as part of the divergent tissue remodelling responses in these distinct SSc lung phenotypes. Disclosure C. Beesley: Grants/research support; Versus Arthritis [grant number 558800]. A. Cole: None. N. Goldman: None. J. Barnett: None. D. Abraham: None. C. Denton: None. R. Mageed: None. V. Ong: None.

Plasma Proteomics Unveil Novel Immune Signatures and Biomarkers upon SARS-CoV-2 Infection

Several elements have an impact on COVID-19, including comorbidities, age and sex. To determine the protein profile changes in peripheral blood caused by a SARS-CoV-2 infection, a proximity extension assay was used to quantify 1387 proteins in plasma samples among 28 Finnish patients with COVID-19 with and without comorbidities and their controls. Key immune signatures, including CD4 and CD28, were changed in patients with comorbidities. Importantly, several unreported elevated proteins in patients with COVID-19, such as RBP2 and BST2, which show anti-microbial activity, along with proteins involved in extracellular matrix remodeling, including MATN2 and COL6A3, were identified. RNF41 was downregulated in patients compared to healthy controls. Our study demonstrates that SARS-CoV-2 infection causes distinct plasma protein changes in the presence of comorbidities despite the interpatient heterogeneity, and several novel potential biomarkers associated with a SARS-CoV-2 infection alone and in the presence of comorbidities were identified. Protein changes linked to the generation of SARS-CoV-2-specific antibodies, long-term effects and potential association with post-COVID-19 condition were revealed. Further study to characterize the identified plasma protein changes from larger cohorts with more diverse ethnicities of patients with COVID-19 combined with functional studies will facilitate the identification of novel diagnostic, prognostic biomarkers and potential therapeutic targets for patients with COVID-19.

Plasma proteome landscape and impact of the circulatory proteome on clinical outcomes in metastatic prostate cancer.

237 Background: We performed plasma-based high-plex proteomic profiling for identifying classifiers of clinical outcomes in metastatic prostate cancer (PC). Olink Explore NGS-based proteome profiling platform was used for high-precision analysis of 736 cancer associated plasma proteins in plasma samples from non-metastatic stage prostate cancer (PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate resistant PC (mCRPC) states. Methods: Plasma was collected prospectively in a cohort of 108 PC patients (24 with non-metastatic PC; 28 mHSPC; 56 mCRPC of which 37 patients were collected before starting any mCRPC treatments). Proteomic data were generated with Proximity Extension Assay (PEA) on the Olink platform from 100 µL plasma per sample. Levels of 736 cancer-associated protein assays were denoted as normalized protein expression (NPX) units through a QC and normalization process developed and provided by Olink. Data generation of NPX consists of normalization to the extension control, log2 -transformation, and level adjustment using the plate control (plasma sample). Temporal trends of differentially expressed assays in non-metastatic PC, mHSPC and mCRPC states were identified using linear mixed effects model (FDR with Benjamini-Hochberg (BH) adjustment; q-value&lt;0.05, R version 4.1.2.). Clinical outcomes included in mCRPC state overall survival (calculated as time from turning mCRPC to death) and in mHSPC early failure of ADT-based therapies defined as progression within 12.5 months. Cox proportional hazard regression was performed for proteins associated with mHSPC and mCRPC states and clinical endpoint of interest. Results: After BH adjustments, 105 protein assays were differentially expressed across non-metastatic, mHSPC and mCRPC states of which 73 assays differed between non-metastatic and metastatic states (q&lt;0.05). 83/105 assays differed between mHSPC and mCRPC states (q&lt;0.05). Of the 83 plasma proteins, 77 were over-expressed in mCRPC. 19/37 mCRPC patients who had collections performed before mCRPC treatments had died. The median time to death was 29 months (Range: 1.9-119 mths). After adjustment for serum Alkaline phosphatase (ALP) levels in these 37 mCRPC patients 32/77 were significantly associated with overall survival. After performing an enrichment analysis the oxidative phosphorylation pathway with specific proteins assays (IMMT, COX5B and FXN, p = 5.1e-4, FDR = 2.55 e-2) were significantly overexpressed in patients with poor survival. Conclusions: A global plasma proteomic profiling of cancer related proteins revealed significant differences in expression in different states of cancer progression. Overexpressed proteins related to oxidative phosphorylation pathway in mCRPC in specific are associated with poor survival.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.