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BACKGROUND: Infectious diseases are a major cause of mortality in spite of existing public health, anti-microbial and vaccine interventions. We aimed to define plasma proteomic associates of infection mortality and then apply Mendelian randomisation (MR) to yield biomarkers that may be causally associated. METHODS: We used UK Biobank plasma proteomic data to associate 2923 plasma proteins with infection mortality before 31st December 2019 (240 events in 52,520 participants). Since many plasma proteins also predict non-infection mortality, we focussed on those associated with >1.5-fold risk of infection mortality in an analysis excluding survivors. Protein quantitative trait scores (pQTS) were then used to identify whether genetically predicted protein levels also associated with infection mortality. To conduct Two Sample MR, we performed a genome-wide association study (GWAS) of infection mortality using UK Biobank participants without plasma proteomic data (n = 363,953 including 984 infection deaths). FINDINGS: After adjusting for clinical risk factors, 1142 plasma proteins were associated with risk of infection mortality (false discovery rate <0.05). 259 proteins were associated with >1.5-fold increased risk of infection versus non-infection mortality. Of these, we identified genetically predicted increasing MERTK concentration was associated with increased risk of infection mortality. MR supported a causal association between increasing plasma MERTK protein and infection mortality (odds ratio 1.46 per unit; 95% CI 1.15- 1.85; p = 0.002). CONCLUSION: Plasma MERTK is causally associated with infection mortality and warrants exploration as a potential therapeutic target.

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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 âˆ¼ 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.

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Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

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This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.

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Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.

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AIM: To provide a novel perspective on the pathogenesis of acute myocardial infarction (AMI) patients with respect to glutamic oxaloacetic transaminase (GOT). METHODS: The plasma proteome of 20 patients with AMI were matched for age and sex and compared with 10 healthy individuals. We analyzed the mass spectrum data and compared the signal intensity of the corresponding peptides which related to their corresponding proteins. A sample-specific protein database was constructed and a quality control analysis was conducted to screen out the key regulatory proteins under specific experimental conditions. The data from 37 new AMI patients and 13 healthy adults were subjected to parallel reaction monitoring (PRM) to verify the target proteins found. Finally, the survival status of the key genes (> 1.5-fold) in the PPI were analyzed. RESULTS: 2589 and 2162 proteins were identified and quantified, respectively, and 143 differentially expressed proteins (DEPs) (≥1.5-fold) were found between the AMI and control groups. Of these 90 and 53 were significantly up-regulated and down-regulated, respectively. Gene ontology, KEGG enrichment, protein domain and cluster analysis as well as PPI networks of the DEPs revealed a central role of acute inflammatory response processes in patients with AMI. A cluster of proteins were found to be related to cysteine, methionine, arginine, proline, phenylalanine and propanoate metabolism as well as the cAMP signaling pathway. PPI network analysis showed CHI3L1, COPB2, GOT2, MB, CYCS, GOT1, CKM, SAA1 and PRKCD and RPS3 were in key positions, but only MB, CKM, GOT1, PRKCD, CYCS and GOT2 were found in a cluster. PRM verified the high levels of MB, CKM, GOT1 and GOT2 in 37 AMI patients but there was no statistical difference in the survival status for patients with either high or low expression levels of these proteins. CONCLUSIONS: Our findings showed that acute inflammatory response processes play a central role in patients with AMI. Cysteine and methionine metabolism was also activated, in which GOT1 and GOT2 were key proteins. These pathways might be potential targets for diagnosis and novel therapies to improve the poor outcomes observed in patients with heart failure.

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Plasma proteomics offers high potential for biomarker discovery, as plasma is collected through a minimally invasive procedure and constitutes the most complex human-derived proteome. However, the wide dynamic range poses a significant challenge. Here, we propose a semi-automated method based on the use of multiple single chain variable fragment antibodies, each enriching for peptides found in up to a few hundred proteins. This approach allows for the analysis of a complementary fraction compared to full proteome analysis. Proteins from pooled plasma were extracted and digested before testing the performance of 29 different antibodies with the aim of reproducibly maximizing peptide enrichment. Our results demonstrate the enrichment of 3662 peptides not detected in neat plasma or negative controls. Moreover, most antibodies were able to enrich for at least 155 peptides across different levels of abundance in plasma. To further reduce analysis time, a combination of antibodies was used in a multiplexed setting. Repeated sample analyses showed low coefficients of variation, and the method is flexible in terms of affinity binders. It does not impose drastic increases in instrument time, thus showing excellent potential for usage in large scale discovery projects.

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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.

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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.

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Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

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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.

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The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual's humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, we incorporated high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, we combined de novo searching and BLAST alignment. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, we determined the uniqueness of tryptic VR peptides and their suitability as antibody surrogate. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.

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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.

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This technical note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis, to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion, a technique that streamlines the process by combining purification and digestion steps, thereby reducing sample loss and improving efficiency. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cells and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24-minute active gradient. In 200ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45-minute run covers ~90% of the expressed proteome. In plasma samples including naive, depleted, perchloric acid precipitated, and Seer nanoparticle captured, all with a 24-minute gradient length, we identified 87, 108, 96 and 137 out of 216 FDA approved circulating protein biomarkers, respectively. This complete workflow allows for large swaths of the proteome to be identified and is compatible across diverse sample types.

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Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 robot that combines sample digestion, cleanup, and loading on Evotips in a fully automated manner, allowing the processing of up to 192 samples in 6 h. Analysis of 192 automated HeLa cell sample preparations consistently identified ∼8000 protein groups and ∼130,000 peptide precursors with an 11.5 min active liquid chromatography gradient with the Evosep One and narrow-window data-independent acquisition (nDIA) with the Orbitrap Astral mass spectrometer providing a throughput of 100 samples per day. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic titanium-immobilized metal ion affinity chromatography beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated digestion and Evotip loading workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.

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Using proteomics and complexome profiling, we evaluated in a year-long study longitudinal variations in the plasma proteome of kidney failure patients, prior to and after a kidney transplantation. The post-transplant period was complicated by bacterial infections, resulting in dramatic changes in the proteome, attributed to an acute phase response (APR). As positive acute phase proteins (APPs), being elevated upon inflammation, we observed the well-described C-reactive protein and Serum Amyloid A (SAA), but also Fibrinogen, Haptoglobin, Leucine-rich alpha-2-glycoprotein, Lipopolysaccharide-binding protein, Alpha-1-antitrypsin, Alpha-1-antichymotrypsin, S100, and CD14. As negative APPs, being downregulated upon inflammation, we identified the well-documented Serotransferrin and Transthyretin, but added Kallistatin, Heparin cofactor 2, and interalpha-trypsin inhibitor heavy chain H1 and H2 (ITIH1, ITIH2). For the patient with the most severe APR, we performed plasma complexome profiling by SEC-LC-MS on all longitudinal samples. We observed that several plasma proteins displaying alike concentration patterns coelute and form macromolecular complexes. By complexome profiling, we expose how SAA1 and SAA2 become incorporated into high-density lipid particles, replacing largely Apolipoprotein (APO)A1 and APOA4. Overall, our data highlight that the combination of in-depth longitudinal plasma proteome and complexome profiling can shed further light on correlated variations in the abundance of several plasma proteins upon inflammatory events.

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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.

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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.

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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 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 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.

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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.