Proteomic Signatures Research Articles

Abstract B017: MicroRNA and proteomic analyses of plasma extracellular vesicles detect diffuse large B-cell lymphoma

Abstract Background: Liquid biopsy offers a non-invasive method for detection of non-Hodgkin lymphoma (NHL). Tissue biopsy, the standard method of NHL detection, causes patient discomfort and increases risk of infection. Extracellular vesicles (EVs) are secreted by cells as particles with cargos of nucleic acids and proteins enclosed in a lipid bilayer. The most common NHL subtype is diffuse large B-cell lymphoma (DLBCL). We analyzed plasma-derived EVs from DLBCL patients and matching healthy controls for microRNA (miRNA) and protein to identify potential liquid biopsy biomarkers. Approach: Plasma samples (1 mL) from DLBCL patients and healthy controls were eluted through Izon size-exclusion chromatography (SEC) to obtain EVs. The size distribution of EVs was characterized with nanoparticle tracking analysis. For miRNA analysis, SEC EVs from three DLBCL patients and three matching healthy controls underwent further selection by immunocapture and release according to NHL antigens CD19, CD20, CD22, and C37, with an EV microfluidic affinity purification (EV-MAP) chip. Western blot confirmed the presence of these NHL antigens in NHL cell lysate and NHL cell culture SEC EVs, with flow cytometry verifying expression on NHL cells. MiRNA from these immunocaptured EVs were then isolated and sequenced with Qiagen kits. MiRNA levels were assessed for fold change (FC) expression between cases and controls. Upregulated miRNAs from DLBCL patients were compiled with the miRNA Enrichment Analysis and Annotation Tool (miEAA). For proteomic analysis, SEC EVs from ten DLBCL patients and ten matching healthy controls were measured with label-free mass spectrometry and a custom peptide library. Protein levels were assessed for FC expression and compiled with the Mann-Whitney U test. Results: The size of EVs ranged from 50–300 nm. Exclusively different upregulated miRNAs (FC>2, p<0.05, FDR p<0.05) were found by selection with different antigens: None unique with CD19, one with CD20, eight with CD22, and one with CD37. MiR-320b was upregulated with CD20 (FC=52, p=0.0002, FDR p=0.02), and miR-3168 with CD37 (FC=906, p=0.0003, FDR p=0.02). With CD22, miR-190b-5p, let-7c-5p, miR-1-3p, miR-125b-5p, miR-133a-3p, miR-153-3p, miR- 184, and miR-206 were upregulated in DLBCL patients (FC>17, p<0.003, FDR p<0.05). The miEAA recognized all the upregulated CD22 miRNAs as overrepresented in B-cell lymphoma, except for miR-190b-5p. Proteomic analysis of EVs found 42 different exo-proteins with AUC>0.90 (p<0.05) and 12 different exo-proteins with AUC>0.95 (p<0.009) upregulated in DLBCL patients. Two upregulated exo-proteins (FC>2.81, p<0.0002, adjusted p<0.004), ADAM metallopeptidase with thrombospondin type 1 motif 13 (ADAMTS13) and von Willebrand Factor (vWF), achieved a perfect AUC=1 (p=0.00001) for all DLBCL patients and healthy controls. Conclusions: These results support the hypothesis that liquid biopsy of plasma EVs detect DLBCL. Upregulated miRNA and proteomic signatures via EVs indicate the presence of DLBCL for non-invasive NHL detection. Citation Format: Arthur A. Lee, Sagar Rayamajhi, Mengjia Hu, Harsh Pathak, Rajni V. Puri, Kamilla Isakova, Mei Han, Claire S. Berquist, Samuel G. Mackintosh, Stephanie D. Byrum, Dennis W. Province, Mohammod M. Rahman, Foyez Ahmmed, Leonidas E. Bantis, Malgorzata A. Witek, Steven A. Soper, Andrew K. Godwin, Haitham Abdelhakim. MicroRNA and proteomic analyses of plasma extracellular vesicles detect diffuse large B-cell lymphoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B017.

Plasma proteomic signature of risk and prognosis of frailty in the UK Biobank

Plasma proteomic signature of risk and prognosis of frailty in the UK Biobank

Abstract 4123872: Unsupervised clustering analysis of large-scale proteomics data identifies molecular phenogroups of heart failure in a population-based cohort

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.

Plasma proteomics-based brain aging signature and incident dementia risk

AbstractInvestigating brain-enriched proteins with machine learning methods may enable a brain-specific understanding of brain aging and provide insights into the molecular mechanisms and pathological pathways of dementia. The study aims to analyze associations of brain-specific plasma proteomic aging signature with risks of incident dementia. In 45,429 dementia-free UK Biobank participants at baseline, we generated a brain-specific biological age using 63 brain-enriched plasma proteins with machine learning methods. The brain age gap was estimated, and Cox proportional hazards models were used to study the association with incident all-cause dementia, Alzheimer’s disease (AD), and vascular dementia. Per-unit increment in the brain age gap z-score was associated with significantly higher risks of all-cause dementia (hazard ratio [95% confidence interval], 1.67 [1.56–1.79], P < 0.001), AD (1.85 [1.66–2.08], P < 0.001), and vascular dementia (1.86 [1.55–2.24], P < 0.001), respectively. Notably, 2.1% of the study population exhibited extreme old brain aging defined as brain age gap z-score > 2, correlating with over threefold increased risks of all-cause dementia and vascular dementia (3.42 [2.25–5.20], P < 0.001, and 3.41 [1.05–11.13], P = 0.042, respectively), and fourfold increased risk of AD (4.45 [2.32–8.54], P < 0.001). The associations were stronger among participants with healthier lifestyle factors (all P-interaction < 0.05). These findings were corroborated by magnetic resonance imaging assessments showing that a higher brain age gap aligns global pathophysiology of dementia, including global and regional atrophy in gray matter, and white matter lesions (P < 0.001). The brain-specific proteomic age gap is a powerful biomarker of brain aging, indicative of dementia risk and neurodegeneration.

PATH-10. UTILIZATION OF TUMOR-DERIVED EXTRACELLULAR VESICLES FOR PATIENT STRATIFICATION AND BIOMARKER DISCOVERY

Abstract Liquid biopsy is poised to play an increasingly important role in clinical decision making by enabling earlier cancer detection, diagnosis, patient stratification, and treatment monitoring. Most approaches rely on detection of cell-free circulating tumor DNA or circulating tumor cells in the peripheral blood of cancer patients. However, there are several limitations to these approaches, including miniscule abundance, unfavorable signal to noise ratios, and limited insight into mechanisms driving disease. Extracellular vesicles (EVs) are an alternative liquid biopsy analyte comprising lipid membrane encapsulated particles carrying diverse protein, nucleic acid, and metabolite cargos. Blood plasma of cancer patients contains EVs derived directly from tumor tissue that serve as rich sources of insight into tumor biology and disease pathology. We have developed a clinically applicable, multi-omic EV subpopulation interrogation pipeline that robustly profiles tumor-derived EVs in biofluids. We have demonstrated the ability of the platform to enrich cancer markers and detect activation of pathways driving oncogenesis across several different cancers, including brain cancer. There is a particularly compelling need for liquid biopsy-based solutions in neuro-oncology. We have applied our EV-omic (EVO) pipeline in combination with machine learning to distinguish adult high-grade gliomas from benign brain tumor and healthy patients, using blood-based EV proteomic and transcriptomic signatures. Marker profiles found to be most important for patient stratification combine markers known to be relevant in tumor tissue along with novel features. Furthermore, IDH status and MGMT methylation metadata derived from matched patient tissue for select cases was used to generate unique blood-based profiles for glioma subtypes. Lastly, we show preliminary longitudinal data demonstrating the utility of EVs to track tumor changes post-surgical resection and post-treatment. This work demonstrates that EV-omics can add significant value in the neuro-oncology space.

BIOM-72. INTEGRATION OF ULTRASENSITIVE ELECTROLUMINESCENT IMMUNOASSAY AND CELL-FREE DNAMETHYLATION ANALYSIS FOR NON-INVASIVE MONITORING OF ADULT DIFFUSE GLIOMAS

Abstract Gliomas are highly aggressive brain tumors with nearly universal recurrence rate. Despite this, the ability to accurately predict and identify tumor recurrence relies solely on serial MRI imaging, which is marred by treatment effects such as radiation necrosis, and necessarily identifies progression retrospectively. It is believe that tumor undergo molecular changes upon tumor progression, however the resampling of tumors upon recurrence imposes significant risks. This highlights the need for novel non-invasive biomarkers capable of identifying tumor recurrence. Due to the low accuracies of individual biomarkers, we have proposed the use of an integrated, multi-platform approach to biomarker discovery. A cohort of 107 glioma plasma samples, including 30 pairs, underwent plasma proteomic analysis, consisting of a panel of serum proteins (FABP4, GFAP, NFL, Tau and MMP3,4 &7) quantified through ultrasensitive electrochemiluminescence multiplexed immunoassays, and plasma DNA methylation analysis, captured through cell-free methylated DNA immunoprecipitation and high-throughput sequencing. Unsupervised hierarchal clustering revealed robust separation of primary and recurrent tumors through plasma proteomics, associated with a distinct plasma methylation signature. NFL, Tau and MMP3 levels differed between primary and recurrent samples; pair-wise analysis revealed increased in NFL and Tau concentrations upon recurrence. Tau levels predicted outcome independent of WHO Grade and IDH status. A predictive generalized linear regression model created through the integration of the proteomic and methylation signatures allowed for the discrimination of primary and recurrent samples in 83% of cases. This work suggests that the combination of DNA methylation and plasma proteomics may improve the ability of these techniques for the serial monitoring of gliomas patients.

BIOM-08. LEVERAGING CSF PROTEOMICS TO OPTIMIZE CNS LYMPHOMA MANAGEMENT

Abstract INTRODUCTION Despite exquisite responses to initial therapy for CNS lymphoma (CNSL), relapse is common and overall responses are disappointing compared to other non-CNS diffuse large B-cell lymphomas. Incorporation of multi-agent intraventricular chemotherapy (MAIVC) alongside systemic therapy is novel. Identification of minimally-invasive biomarkers of response to MAIVC are necessary but lacking. The objectives of this study were to 1) identify baseline cerebrospinal fluid (CSF)-based proteomic predictive biomarkers of MAIVC treatment response and 2) identify tumor burden markers for longitudinal monitoring of MAIVC treatment response in CNSL. METHODS A cohort of patients with primary or secondary CNSL that were treated with MAIVC at Penn State Health (2015-2021) was included. Each MAIVC cycle was administered via Ommaya reservoir and included a 2-drug combination comprised of methotrexate, rituximab, cytarabine, etoposide, topotecan, gemcitabine, or thiotepa. Patient-matched CSF was sampled at pre- and post-therapeutic endpoints and profiled using shotgun proteomics. Patients were grouped by treatment response status. Early responders were defined as patients that achieved malignant cell-free CSF within 6 MAIVC cycles, whereas never responders were those patients that i) did not achieve malignant cell-free CSF by endpoint or ii) required salvage surgery or radiation during MAIVC. RESULTS 59 patients were included (21 primary; 38 secondary CNSL) with a median age of 66 years (range 25-90) and median number of MAIVC cycles of 8 (range 2-23). A proteomic-based classifier, comprised of SGCE, LCP1 and AGRN, discriminated between early and never responders with an AUROC of 0.95. Comparison of CSF at baseline (<3 cycles MAIVC) vs. post-treatment (>12 cycles MAIVC) identified H3F3A, YWHAE, LCP1, CA1 and GAPDH as tumor burden biomarkers. CONCLUSION Here we developed a proteomic signature capable of predicting CNSL patient response to MAIVC, with potential to improve clinical decision making. Furthermore, the biomarkers associated with tumor burden represent important indicators of treatment response. Ongoing efforts are underway to evaluate these candidates as actionable therapeutic targets.

Feasibility of plasma proteomics in patients with immune thrombocytopenia.

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 etal. 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.

Bioenergetic shift and proteomic signature induced by lentiviral-transduction of GFP-based biosensors

Fluorescent proteins (FPs) stand as pivotal tools extensively employed across diverse biological research endeavors in various model systems. However, long-standing concerns surround their use due to the numerous side effects associated with their expression. Recent investigations have brought to light the significance of hydrogen peroxide (H2O2) that is associated with the maturation process of green fluorescent protein (GFP) fluorophores. The structural and functional impairments associated with GFP expression are possibly linked to this amount of H2O2. In this study, we assess the impact of the GFP-based HyPer7 biosensor on cellular homeostasis and proteome changes, aiming to identify potential risks related to oxidative stress responses that potentially risks the application of such tools. Cells expressing genome-integrated HyPer7 demonstrated altered mitochondrial membrane potential (MMP), which was alleviated by the addition of antioxidants or culturing cells at physiological normoxia (5 kPa O2). Additionally, HyPer7-expressing cells also exhibited significant impairment in mitochondrial oxidative respiration, suggesting broader mitochondrial dysfunction. Through untargeted proteomics analysis, we identified 26 proteins exhibiting differential expression in HyPer7-expressing cells compared to respective control cells. Functional annotation analysis showed that the list of the delineated proteins is associated with cellular responses to stress and the regulation of antioxidant mechanisms. Our findings underscore the significance of caution and validation in ensuring a thorough comprehension of cellular responses when using fluorescent protein-based tools, thereby enhancing the reliability of the results.

Plasma proteomic signatures of liver steatosis and fibrosis in people living with HIV: a cross-sectional study

Insights into the mechanisms driving metabolic dysfunction-associated steatotic liver disease (MASLD) in people living with HIV (PLHIV) remain limited. Plasma proteomics holds promise for biomarker discovery and the elucidation of biological mechanisms. We performed cross-sectional analyses on data from 1036 virally suppressed PLHIV using antiretroviral treatment (ART) from the Dutch multi-centre 2000HIV cohort. Participants underwent transient elastography to assess liver steatosis (controlled attenuation parameter (CAP)≥263dB/m) and -fibrosis (liver stiffness measurement (LSM)≥7.0kPa). Plasma protein concentrations (n=2367) (Olink® Explore Panel) were compared between PLHIV with vs. without liver steatosis and PLHIV with vs. without fibrosis. Enriched pathways (using GO, KEGG and Reactome libraries) and correlations with clinical characteristics wereassessed, and analyses were stratified by BMI category. In addition, concentrations of 242 proteins werecompared between individuals ("controls") with and without liver steatosis (ratio of methylene:methylene and water >5.6% on magnetic resonance spectroscopy) from a separate cohort (300-OB), all having a BMI >26kg/m2. Steatosis and fibrosis were associated with 67/2367 (2.2%) and 17/2367 (0.7%) differentially expressed proteins (DEP), respectively, enriched in mostly metabolic pathways. Immunoglobulin superfamily member 9 (IGSF9) was amongst the top DEP associated with both steatosis and fibrosis. Stratifying by BMI revealed 8/2367 DEP associated with steatosis in lean- and 12/2367 DEP in overweight/obese individuals, with two shared DEP (IGSF9 and GHR). Conversely, protein signatures of overweight/obese PLHIV (32/242 DEP) and overweight/obese HIV-uninfected individuals (32/242 DEP) exhibited substantial overlap with 16 shared DEP. Notably, DEP correlated with HIV characteristics in lean individuals but not in overweight/obese PLHIV. Lean and overweight/obese PLHIV exhibit distinct proteomic signatures associated with liver steatosis, with the former being more strongly correlated with HIV-specific factors and ART. In addition, we identified a protein, IGSF9, strongly related to liver fibrosis and steatosis across BMI categories. The 2000HIV study is funded by ViiV Healthcare.

Proteomic analysis of APOEε4 carriers implicates lipid metabolism, complement and lymphocyte signaling in cognitive resilience

BackgroundApolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer’s disease (AD). This case-cohort study used targeted plasma biomarkers and large-scale proteomics to examine the biological mechanisms that allow some APOEε4 carriers to maintain normal cognitive functioning in older adulthood.MethodsAPOEε4 carriers and APOEε3 homozygotes enrolled in the Women’s Health Initiative Memory Study (WHIMS) from 1996 to 1999 were classified as resilient if they remained cognitively unimpaired beyond age 80, and as non-resilient if they developed cognitive impairment before or at age 80. AD pathology (Aß42/40) and neurodegeneration (NfL, tau) biomarkers, as well as 1007 proteins (Olink) were quantified in blood collected at study enrollment (on average 14 years prior) when participants were cognitively normal. We identified plasma proteins that distinguished between resilient and non-resilient APOEε4 carriers, examined whether these associations generalized to APOEε3 homozygotes, and replicated these findings in the UK Biobank.ResultsA total of 1610 participants were included (baseline age: 71.3 [3.8 SD] years; all White; 42% APOEε4 carriers). Compared to resilient APOEε4 carriers, non-resilient APOEε4 carriers had lower Aß42/40/tau ratio and greater NfL at baseline. Proteomic analyses identified four proteins differentially expressed between resilient and non-resilient APOEε4 carriers at an FDR-corrected P < 0.05. While one of the candidate proteins, a marker of neuronal injury (NfL), also distinguished resilient from non-resilient APOEε3 homozygotes, the other three proteins, known to be involved in lipid metabolism (ANGPTL4) and immune signaling (PTX3, NCR1), only predicted resilient vs. non-resilient status among APOEε4 carriers (protein*genotype interaction-P < 0.05). Three of these four proteins also predicted 14-year dementia risk among APOEε4 carriers in the UK Biobank validation sample (N = 9420). While the candidate proteins showed little to no association with targeted biomarkers of AD pathology, protein network and enrichment analyses suggested that natural killer (NK) cell and T lymphocyte signaling (via PKC-θ) distinguished resilient from non-resilient APOEε4 carriers.ConclusionsWe identified and replicated a plasma proteomic signature associated with cognitive resilience among APOEε4 carriers. These proteins implicate specific immune processes in the preservation of cognitive status despite elevated genetic risk for AD. Future studies in diverse cohorts will be needed to assess the generalizability of these results.

The Identification of Proteomic Signatures Associated with Alkaline Tolerance in the Skin Mucus of Crucian Carp (Carassius auratus)

The skin is covered by a protective mucus layer, which is essential to the innate defense mechanism of fish. Investigating the response of skin mucus to various toxic stresses is crucial for enhancing its ability to tackle environmental challenges and developing strategies to mitigate toxic effects. Alkalinity stress assays (50 mmol/L NaHCO3) were conducted on crucian carp (Carassius auratus) from Lake Dali Nur (pH = 9.6) and Ping Xiang red crucian carp from freshwater (pH = 7) over 7 days. The expression of skin mucous proteins was analyzed using the liquid chromatography (LC)-spectrometry (MS)/MS Analysis-Data-independent acquisition (DIA) mode. A total of 12,537 proteins were identified across 20 samples from four groups, with 12,025 quantified. In the alkaline water population, high alkali stress resulted in the up-regulation of 139 proteins and the down-regulation of 500 proteins. In contrast, the freshwater population showed an increase in 112 proteins and a decrease in 120; both populations had a total of 23 genes up-regulated and 21 down-regulated. The protein regulatory network for the alkaline water group included 3146 pairwise interactions among 464 nodes, with only 20 being differentially expressed proteins. Conversely, the freshwater group’s network comprised just 1027 specific interactions across 337 nodes, with 6 corresponding to differentially expressed proteins. A common protein regulatory network responding to high alkali stress was extracted and visualized for both populations. Based on their regulatory relationships and expression levels, these proteins are hypothesized to play similar roles under high alkali stress. Notably, the alpha-globin fragment and keratin type I cytoskeletal 13-like proteins showed markedly up-regulated expression, with the alpha-globin fragment increasing nearly a thousandfold from an extremely low level. This suggests it could serve as a potential biomarker for alkali tolerance, warranting further investigation.

Protein quantitative trait loci identifies genetic biomarkers of proteins associated with measures of myocardial fibrosis in new-onset, treatment naive rheumatoid arthritis

Abstract Introduction The excess risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) compared to the general population is attributed to chronic inflammation. Identifying the underlying mechanisms and shared inflammatory pathways more precisely would enable tailored treatment. Purpose To identify genetic variants associated with the cardio-metabolic-inflammatory proteomic signature in patients with early, treatment-naïve RA and low CVD risk. Methods The Coronary Artery Disease Evaluation in RA (CADERA) study was a bolt-on to a RCT of patients with early, treatment-naïve RA and low CVD risk profile (maximum of 1 traditional CVD risk factor and no diabetes mellitus). Normalised expression of 334 proteins from 75 CADERA patients who underwent cardiovascular magnetic resonance imaging (CMR) was measured using the Olink Proximity Extension Immunoassay across Inflammation, Cardiovascular II, III and Cardiometabolic panels. Genome-wide single nucleotide polymorphism (SNP) data were generated using the Illumina Global Screening Array and was imputed and checked for data quality using standard approaches. Proteins associated with CMR parameters were identified using Bayesian mixed effects linear regression. Protein quantitative trait locus (pQTL) analysis was then performed using linear regression models adjusted for age, gender, and systolic blood pressure. Cis-pQTLs were defined as SNPs within 1Mb of the protein-encoding gene, and significance threshold was set at 1e-5. Due to sample size, trans-pQTLs were not considered in this analysis. Linkage disequilibrium (LD) clumping was performed using PLINK software to identify index SNPs defined as the most significant variant in LD (r2 &amp;lt;0.5) within a region of +/- 250Kb. Results Following quality control 318 proteins and 5,971,781 SNPs were available for analysis in 75 CADERA patients. The median age was 51 years [inter-quartile range (IQR) 40 - 61] with 52/75 (69%) females, 40/75 (53%) current/ex-smokers, and median body mass index (BMI) 26.4kg/m2 (IQR 23.3 - 28). Participants had a median RA symptom duration of 21.9 weeks (IQR 14.4 - 32.7). Only 8/75 (10%) reported a history of hypertension, hypercholesteroaemia or family history of CVD. 54 out of 318 proteins were associated with CMR measures of myocardial fibrosis and/or vascular stiffness. 460 cis-pQTLs were identified for 20 out of the 318 proteins. LD clumping revealed 40 cis-pQTLs independently associated with these 20 proteins. Of these, NOTCH3 (rs34763630) was positively associated with extracellular volume, and SELPLG (rs7980427) was negatively associated with late gadolinium enhancement as detected on CMR (table 1). No other proteins with genetic support were associated with CMR measures. Conclusion This study for the first time, implicates genetic markers associated with two proteins as putative candidate biomarkers of CMR-identified myocardial fibrosis in new-onset RA that now warrant validation in an independent cohort.

Integrated plasma proteomics and myocardial tissue transcriptomics reveal elevated fibrosis markers in arrhythmogenic cardiomyopathy patients

Abstract Background Arrhythmogenic cardiomyopathy (ACM) is characterized by myocardial fibrofatty replacement and ventricular arrhythmias, with potential progression to heart failure. This study aims to identify ACM-specific biomarkers for improved prognosis and possible treatment strategies. Methods RNA sequencing and transcriptome analyses were performed in cardiomyocytes of 10 ACM patients, 10 dilated cardiomyopathy patients and 10 healthy controls. Analyses were performed using liquid chromatography and mass spectrometry. Additionally, plasma protein expression was assessed in 38 ACM patients and 24 healthy controls using the Proximity Extension Assay (Olink®). A standardized bicycle exercise test was performed in 11 ACM patients and controls and blood was obtained before and after exercise. Results Myocardial tissue transcriptomics identified several upregulated molecules associated with extracellular matrix formation and immune response in ACM. Plasma protein sequencing revealed upregulation of proteins involved in metabolic pathways, inflammation, and fibrosis. Integration of these analyses identified key soluble profibrotic markers such as Fibulin-3, Endostatin and C-X-C motif chemokine 10 (CXCL10) in plasma of ACM patients. After exercise, Fibulin-3 levels increased in ACM patients compared to controls. These findings were further validated using enzyme-linked immunosorbent assay (ELISA). Conclusion This comprehensive analysis revealed distinct proteomic and transcriptomic signatures in ACM patients' plasma and cardiomyocytes. The integrative analysis identified several upregulated profibrotic markers in ACM. Our Findings may advance diagnostic and prognostic strategies and offer potential therapeutic targets.

Urinary proteomic signature of mineralocorticoid receptor antagonism by spironolactone: evidence from the HOMAGE trial

Abstract Background Heart failure (HF) is characterised by collagen deposition. Urinary proteomic profiling (UPP) detects many sequenced peptides, for &amp;gt;70% derived from collagens. Purpose UPP and serum fibrosis markers were analysed together in patients at risk of HF with as objective to generate insights into the antifibrotic action of spironolactone. Methods In the open-label HOMAGE trial, patients were randomised to usual therapy combined or not with spironolactone 25-50 mg/d and followed for 9 months (n=290; 23.8% women; median age: 73 years). UPP was done by capillary electrophoresis coupled with mass spectrometry; 1498 urinary peptides with detectable signal in ≥30% of patients were analysed. Serum markers of COL1A1 synthesis (PICP and PICP/CITP) were measured. After rank normalisation of the biomarker distributions, between-group differences in their changes were assessed by multivariable-adjusted models. Correlations between the changes in urinary peptides and in serum PICP and PICP/CITP were compared between groups using Fisher Z transform. Results Among all the urinary peptides, only the changes in collagen fragments remained significantly different (p&amp;lt;0.05) between randomisation groups after accounting for baseline levels, covariables and multiple testing. Compared to the control group, spironolactone reduced 16 of 27 collagen-derived urinary peptides. From baseline to 9 months, serum PICP and the PICP/CITP ratio decreased from 79.0 to 75.4 µg/L and from 21.3 to 18.3, respectively (p≤0.0129), reflecting decreased COL1A1 synthesis. Spironolactone did not affect the correlations between changes in urinary COL1A1 fragments and the serum fibrosis markers. Conclusions Spironolactone downregulated urinary collagen-derived peptides, probably by shrinking the body-wide pool of collagens. Spironolactone did not affect the interaction between urinary and serum fibrosis markers, suggesting that collagen scaffolding is maintained, thereby explaining why some urinary collagen fragments increased. Combining urinary and serum fibrosis biomarkers opens new avenues for discovery of antifibrotic drugs and refines insight in the action of antifibrotic drugs.Figure 1Figure 2

OSTEO18, a novel urinary proteomic signature, associated with osteoporosis in heart transplant recipients

Abstract Background Immunosuppressive treatment in heart transplant (HTx) recipient causes osteoporosis. The urinary proteomic profile (UPP) includes peptide fragments derived from the bone extracellular matrix. Purpose Study aims were to develop and validate a multidimensional UPP biomarker for osteoporosis in HTx patients from single sequenced urinary peptides identifying the parent proteins. Methods A single-center HTx cohort was analyzed. Urine samples were measured by capillary electrophoresis coupled with mass spectrometry. Cases with osteoporosis and matching controls were randomly selected from all available 389 patients. In derivation case-control dataset, 1576 sequenced peptides detectable in ≥30% of patients. Applying statistical analysis on these, an 18-peptide multidimensional osteoporosis UPP biomarker (OSTEO18) was generated by support vector modeling. The 2 replication datasets included 118 and 94 patients. For further validation, the whole cohort was analyzed. Statistical methods included logistic regression and receiver operating characteristic curve (ROC) analysis. Results In derivation dataset, the AUC, sensitivity and specificity of OSTEO18 were 0.83 (95% CI: 0.76-0.90), 74.3% and 87.1%, respectively. In replication datasets, results were confirmatory. In the whole cohort (154 osteoporotic patients [39.6%]), the ORs for osteoporosis increased (p &amp;lt; 0.0001) across OSTEO18 quartiles from 0.39 (95% CI: 0.25-0.61) to 3.14 (2.08-4.75). With full adjustment for known osteoporosis risk factors, OSTEO18 improved AUC from 0.708 to 0.786 (p = 0.0003) for OSTEO18 categorized (optimized threshold: 0.095) and to 0.784 (p = 0.0004) for OSTEO18 as continuously distributed classifier. Conclusion OSTEO18 is a clinically meaningful novel biomarker indicative of osteoporosis in HTx recipients and is being certified as in-vitro diagnostic.

Insights into type 2 diabetes mellitus in atrial fibrillation patients: a proteomics approach

Abstract Introduction Atrial fibrillation (AF) and Type 2 Diabetes Mellitus (T2DM) are two closely interconnected conditions, sharing a complex relationship within cardiac pathophysiology. Common risk factors such as obesity, hypertension, inflammation, and endothelial dysfunction contribute to their coexistence. T2DM is known to drive cardiac fibrosis and promote electrical and structural remodeling of the heart, creating a substrate conducive to AF onset, while AF in T2DM patients increases the risk of adverse cardiovascular outcomes, such as stroke. Understanding this complex relationship is pivotal for improving the therapeutic and preventive strategies for these patients. This study aims to examine the proteomic differences between AF patients with and without T2DM, providing novel insights into pathophysiological mechanisms and identifying novel therapeutic targets and biomarkers. Methods In total, 14 AF patients undergoing open-heart surgery, comprising of 6 T2DM individuals and 8 non-T2DM individuals have been included. The mean age of the T2DM patients was 69.5 ± 10 years, while the non-T2DM was 69.3 ± 8.2 and BMI was 29.2 ± 2.9 and 29.5 ± 3 respectively. Right atrial appendages (RAA) from these patients were obtained in an RNA stabilization solution during open-heart surgery and processed using the phenol-based method for protein extraction. The proteins were digested with trypsin to generate peptides, followed by an additional clean-up step using C18 pipette tips. The prepared peptide samples were analyzed by employing a label-free-based quantitative (LFQ) approach, using a C18 (75cmx75cm) column. Proteomic analysis was performed using a Nano liquid chromatography system coupled to tandem mass spectrometer (nano LC-MS/MS), followed by a comprehensive bioinformatics analysis, using appropriate software. Results A total of 1548 proteins were identified, of which 899 could be quantified using LFQ analysis. Applying strict significance criteria, including FDR &amp;lt;0.1 and Abundance ratio (AR)&amp;gt;2 or &amp;lt;0.5, we identified 100 differential expressed proteins, with 21 upregulated and 79 downregulated in T2DM (Fig.1). Most notably, DPP9 and ATP1A2 were overexpressed in hearts of AF patients with T2DM (AR=100), while INPPL1 and ARID5A were underexpressed (AR=0.01), thereby representing attractive therapeutic targets. In pathway enrichment analysis (Fig.2), the dysregulated proteins participate in noteworthy pathways, including Non-alcoholic fatty liver disease (NAFLD), Type II diabetes mellitus, and Insulin signaling pathway. Conclusion Given the increased morbidity associated with the coexistence of AF and T2DM, there is a pressing need for novel therapeutic strategies tailored to this patient population. While AF and T2DM share certain molecular characteristics in the heart, this study emphasizes the necessity for further evaluation and validation of the distinct proteomic signatures identified.Fig.1:Volcano PlotFig.2:Protein Enrichment Network

Unraveling the proteomic signatures of coronary artery disease and hypercholesterolemia.

Atherosclerosis, a leading cause of coronary artery disease (CAD), is heavily influenced by hypercholesterolemia (HC). Proteomics research has shown promise in identifying biological markers for CAD diagnosis and prognosis. This cross-sectional study aimed to identify novel biomarkers for detecting HC and CAD. Through the analysis of proteome data from healthy controls (n=45) and patients diagnosed with HC (n=51) or CAD (n=32), distinct protein patterns associated with each condition were identified. Significant alterations in protein levels were identified with a false discovery rate (FDR)-corrected q-value of <0.05. Subsequent receiver operating characteristic (ROC) analysis, with an area under the curve (AUC) greater than 0.75, was conducted. CAD patients exhibited significantly increased levels of the cholesterol-metabolizing protein PCSK9 and varied levels of the angiogenesis-related protein SDF-1 compared to controls. In pairwise comparisons among the study groups, 65 proteins showed significant differential expression. Notably, 14 of these proteins had significant correlations with blood cholesterol levels. Additionally, 22 of the identified proteins were associated with CAD or HC pathways, with nine proteins being common to both conditions (Apo E, Apo E3, MMP-3, PCSK9, SDF-1, Apo B, PAFAH, HSP 60, and TAK1-TAB1). Nevertheless, this is an exploratory study, and validation studies are needed to confirm these potential protein biomarkers for CAD in the context of HC.

Loss of CLN3 in microglia leads to impaired lipid metabolism and myelin turnover

Loss-of-function mutations in CLN3 cause juvenile Batten disease, featuring neurodegeneration and early-stage neuroinflammation. How loss of CLN3 function leads to early neuroinflammation is not yet understood. Here, we have comprehensively studied microglia from Cln3∆ex7/8 mice, a genetically accurate disease model. Loss of CLN3 function in microglia leads to lysosomal storage material accumulation and abnormal morphology of subcellular organelles. Moreover, pathological proteomic signatures are indicative of defects in lysosomal function and abnormal lipid metabolism. Consistent with these findings, CLN3-deficient microglia are unable to efficiently turnover myelin and metabolize the associated lipids, showing defects in lipid droplet formation and cholesterol accumulation. Accordingly, we also observe impaired myelin integrity in aged Cln3∆ex7/8 mouse brain. Autophagy inducers and cholesterol-lowering drugs correct the observed microglial phenotypes. Taken together, these data implicate a cell-autonomous defect in CLN3-deficient microglia that impacts their ability to support neuronal cell health, suggesting microglial targeted therapies should be considered for CLN3 disease.

Proteomic Signatures of Right Ventricular Outcomes in Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a disease of progressive right ventricular (RV) failure with high morbidity and mortality. Our goal is to investigate proteomic features and pathways associated with RV-focused outcomes including mortality, RV dilation, and NT-proBNP (N-terminal pro-B-type natriuretic peptide) in PAH. Participants in a single-institution cohort with 3 years of follow-up underwent proteomic profiling of their plasma using 7288 aptamers (targeting 6467 unique human proteins). Partial least squares discriminant analysis was performed to assess global protein variation associated with mortality, RV dilation, and NT-proBNP levels. Differentially abundant proteins and enriched pathways associated with outcomes were identified following baseline adjustments. RV vulnerability models estimated associations for individuals with similar afterload following adjustment for pulmonary vascular resistance. A total of 117 participants with PAH were included. Partial least squares discriminant analysis of the proteome showed clear separation between survivors and nonsurvivors, participants with dilated versus nondilated RVs, and across NT-proBNP levels. Proteins and pathways involving the ECM (extracellular matrix) were upregulated in participants who died during follow-up, those with severe RV dilation, and those with higher levels of NT-proBNP. Pulmonary vascular resistance adjustment reinforced the importance of ECM proteins in the association with RV vulnerability, independent of afterload. These findings were confirmed in independent PAH cohorts with available plasma proteomics and RV tissue gene and protein expression. Distinct plasma proteomic profiles are associated with mortality, RV dilation, and NT-proBNP in PAH. Proteins and pathways governing tissue remodeling are strongly associated with poor outcomes, may mediate RV vulnerability to right heart failure, and represent promising candidates as biomarkers and potential therapeutic targets.