Plasma Proteome Profiling Reveals Dynamics of Inflammatory and Lipid Homeostasis Markers after Roux-En-Y Gastric Bypass Surgery

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Sleeve-gastrectomy results in improved metabolism and a massive stress response of the liver proteome in a mouse model of metabolic dysfunction-associated steatohepatitis

Background

Bariatric surgery has been shown to improve the histopathological findings in patients with obesity and metabolic dysfunction-associated steatohepatitis, but there are also reports about non-responders or progressive disease after bariatric interventions. Therefore, it is of utmost importance to understand the pathophysiological processes in the liver after bariatric surgery.

Materials and methods

In the present study, 4 weeks old male C57/Bl6 mice were fed a Western Diet to induce metabolic dysfunction-associated steatohepatitis and sleeve-gastrectomy (SG), or sham operation in the pair-fed and ad libitum control group were performed. Mice were observed for two or eight weeks after surgery and metabolic assessment was performed throughout the experiment. Histopathology, flow cytometry and proteomic analyses were conducted to evaluate hepatic inflammation, liver metabolism and affected signaling pathways.

Results

Weight loss was higher, and metabolism significantly improved after SG. Two weeks after SG major inflammatory and regulatory disturbances in the liver were observed. The proportion of hepatic CD3+NK1.1+ cells were decreased, and proteins involved in apoptosis like Fas, Casp1 and Casp9 or in the acute phase response were upregulated in SG mice. These disturbances decreased in the long-term and we observed an increase of many proteins involved in lipid metabolism eight weeks following SG.

Conclusions

The rapid weight loss and decrease of hepatic fat after SG lead to a proinflammatory response in the liver in the early phase after surgery, which changes to a more moderate immune response in the long-term. We suggest a preoperative risk stratification and postoperative surveillance depending on the histopathological findings.

Apolipoprotein A-IV and its derived peptide, T55-121, improve glycemic control and increase energy expenditure

It is crucial to understand the glucose control within our bodies. Bariatric/metabolic surgeries, including laparoscopic sleeve gastrectomy (LSG) and Roux-en-Y gastric bypass (RYGB), provide an avenue for exploring the potential key factors involved in maintaining glucose homeostasis since these surgeries have shown promising results in improving glycemic control among patients with severe type 2 diabetes (T2D). For the first time, a markedly altered population of serum proteins in patients after LSG was discovered and analyzed through proteomics. Apolipoprotein A-IV (apoA-IV) was revealed to be increased dramatically in diabetic obese patients following LSG, and a similar effect was observed in patients after RYGB surgery. Moreover, recombinant apoA-IV protein treatment was proven to enhance insulin secretion in isolated human islets. These results showed that apoA-IV may play a crucial role in glycemic control in humans, potentially through enhancing insulin secretion in human islets. ApoA-IV was further shown to enhance energy expenditure and improve glucose tolerance in diabetic rodents, through stimulating glucose-dependent insulin secretion in pancreatic β cells, partially via Gαs-coupled GPCR/cAMP (G protein-coupled receptor/cyclic adenosine monophosphate) signaling. Furthermore, T55-121, truncated peptide 55-121 of apoA-IV, was discovered to mediate the function of apoA-IV. These collective findings contribute to our understanding of the relationship between apoA-IV and glycemic control, highlighting its potential as a biomarker or therapeutic target in managing and improving glucose regulation.

DAHEAN: A Danish nationwide study ensuring quality assurance through real-world data for suspected hereditary anemia patients

BACKGROUND

Hereditary anemias are a group of genetic diseases prevalent worldwide and pose a significant health burden on patients and societies. The clinical phenotype of hereditary anemias varies from compensated hemolysis to life-threatening anemia. They can be roughly categorized into three broad categories: hemoglobinopathies, membranopathies, and enzymopathies. Traditional therapeutic approaches like blood transfusions, iron chelation, and splenectomy are witnessing a paradigm shift with the advent of targeted treatments. However, access to these treatments remains limited due to lacking or imprecise diagnoses. The primary objective of the study is to establish accurate diagnoses for patients with hereditary anemias, enabling optimal management. As a secondary objective, the study aims to enhance our diagnostic capabilities.

RESULTS

The DAHEAN study is a nationwide cohort study that collects advanced phenotypic and genotypic data from patients suspected of having hereditary anemias from all pediatric and hematological departments in Denmark. The study deliberates monthly by a multidisciplinary anemia board involving experts from across Denmark. So far, fifty-seven patients have been thoroughly evaluated, and several have been given diagnoses not before seen in Denmark.

CONCLUSIONS

The DAHEAN study and infrastructure harness recent advancements in diagnostic tools to offer precise diagnoses and improved management strategies for patients with hereditary anemias.

Designing a single-arm phase 2 clinical trial of mitapivat for adult patients with erythrocyte membranopathies (SATISFY): a framework for interventional trials in rare anaemias - pilot study protocol

INTRODUCTION

Membranopathies encompass haemolytic disorders arising from genetic variants in erythrocyte membrane proteins, including hereditary spherocytosis and stomatocytosis. Congenital dyserythropoietic anaemia type II (CDA II) is associated with the SEC23B gene and can exhibit phenotypic similarities to membranopathies. Current treatment options for these conditions, apart from splenectomy, are primarily supportive. Mitapivat, a novel pyruvate kinase (PK) activator, has demonstrated efficacy in increasing haemoglobin levels and reducing haemolysis in patients with PK deficiency, thalassemia, sickle cell disease and a mouse model of hereditary spherocytosis.

METHODS AND ANALYSES

Safety and efficacy of mitapivat sulfate in adult patients with erythrocyte membranopathies (SATISFY) is a prospective, multicentre, single-arm phase two trial involving approximately 25 adult patients (≥18 years) diagnosed with a membranopathy or CDA II. During the 8-week dose escalation period, subjects will receive an initial dose of 50 mg mitapivat two times per day and may increase to 100 mg two times per day at week 4 based on the safety and changes in haemoglobin levels. Patients tolerating mitapivat well may be eligible to continue in two consecutive 24-week fixed dose periods.The primary objective of this study is to evaluate the safety of mitapivat, assessed through the occurrence of treatment-emergent adverse events. Secondary objectives include assessing the effects of mitapivat on haemoglobin levels, haemolysis, erythropoiesis, patient-reported outcome measures and spleen size.SATISFY aims to assess the safety and efficacy of mitapivat in adult patients with red blood cell membranopathies and CDA II, with the aim of establishing proof-of-concept in patients living with these rare conditions.

ETHICS AND DISSEMINATION

NCT05935202/CTIS:2023-503271-24-01. Findings will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov, NCT05935202. CTIS:2023-503271-24-01. Registered 07-July-2023. Protocol number: 2.1. https://clinicaltrials.gov/study/NCT05935202.

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Discovery of plasma proteins associated with ventricular fibrillation during first ST-elevation myocardial infarction via proteomics

AIMS

The underlying biological mechanisms of ventricular fibrillation (VF) during acute myocardial infarction are largely unknown. To our knowledge, this is the first proteomic study for this trait, with the aim to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI).

METHODS AND RESULTS

We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n = 110) and without VF (control, n = 120) before guided catheter insertion for primary percutaneous coronary intervention. The plasma proteome was investigated using mass spectrometry-based proteomics on plasma samples collected within 24 h of symptom onset, and one patient was excluded in quality control. In 229 STEMI patients {72% men, median age 62 years [interquartile range (IQR): 54-70]}, a median of 257 proteins (IQR: 244-281) were quantified per patient. A total of 26 proteins were associated with VF; these proteins were involved in several biological processes including blood coagulation, haemostasis, and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 [ACTBL2, fold change (FC) 2.25, P < 0.001, q = 0.023], and coagulation factor XIII-A (F13A1, FC 1.48, P < 0.001, q = 0.023). None of the proteins were correlated with anterior infarct location.

CONCLUSION

Ventricular fibrillation due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1), suggesting that they may represent novel underlying molecular VF mechanisms. Further research is needed to determine whether these proteins are predictive biomarkers or acute phase response proteins to VF during acute ischaemia.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

Serum afamin and its implications in adult growth hormone deficiency: a prospective GH-withdrawal study

Introduction

Adult growth hormone deficiency (AGHD) is associated with a high prevalence of metabolic syndrome (MS), which contributes to the unfavorable cardiovascular risk profile in these patients. Insulin like growth factor-1 (IGF-1) is a widely used biomarker, however it does not always reflect the cardiometabolic risk and has a poor relationship with clinical efficacy endpoints. Consequently, there is an unmet need for biomarkers to monitor responses to GH-replacement. Afamin is a hormone-like glycoprotein, expressed in the liver. Higher afamin levels are strongly associated with MS and insulin resistance (IR). Although both MS and IR are very common in AGHD, afamin has not been investigated in these patients.

Purpose

To investigate afamin as a potential biomarker in patients with AGHD.

Materials and methods

Participants included 20 AGHD patients (11 GH-substituted and 9 GH-unsubstituted) and 37 healthy controls. Subjects underwent routine laboratory examinations, anthropometric measurements, body composition analysis using multi-frequency bioelectrical impedance analysis (InBody720) and measurement of serum afamin concentrations. In GH-substituted subjects, GH-substitution was withdrawn for 2 months. Measurements were carried out right before GH-withdrawal, at the end of the 2-month withdrawal period, and 1 month after reinstituting GH-replacement therapy (GHRT).

Results

GH-unsubstituted patients demonstrated higher afamin levels compared to controls (p=0.03). Afamin positively correlated with skeletal muscle mass, bone mineral content, total body water, extracellular- and intracellular water content, insulin (all, p<0.01), HOMA-IR (p=0.01) and C-peptide (p=0.03) levels in AGHD but not in healthy controls. In GH-substituted patients 2-month of GH-withdrawal caused significant changes in body composition, including decreased fat-free mass, skeletal muscle mass, total body water, and intracellular water content (all, p<0.01); but these changes almost fully recovered 1 month after reinstituting GHRT. Unexpectedly, afamin levels decreased after GH-withdrawal (p=0.03) and increased with reinstitution (p<0.01). Changes of afamin levels during GH-withdrawal positively correlated with changes of HOMA-IR (r=0.80; p<0.01) and changes of insulin (r=0.71; p=0.02).

Conclusion

Higher afamin levels in unsubstituted AGHD patients might indicate severe metabolic dysregulation. Significant changes accompanying GH-withdrawal and reinstitution, along with strong correlations with measures of IR, suggest that afamin could be a promising biomarker to monitor GHRT-associated changes of insulin sensitivity.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.

Longitudinal plasma proteome profiling reveals the diversity of biomarkers for diagnosis and cetuximab therapy response of colorectal cancer

Cetuximab therapy is the major treatment for colorectal cancer (CRC), but drug resistance limits its effectiveness. Here, we perform longitudinal and deep proteomic profiling of 641 plasma samples originated from 147 CRC patients (CRCs) undergoing cetuximab therapy with multi-course treatment, and 90 healthy controls (HCs). COL12A1, THBS2, S100A8, and S100A9 are screened as potential proteins to distinguish CRCs from HCs both in plasma and tissue validation cohorts. We identify the potential biomarkers (RRAS2, MMP8, FBLN1, RPTOR, and IMPDH2) for the initial response prediction. In a longitudinal setting, we identify two clusters with distinct fluctuations and construct the model with high accuracy to predict the longitudinal response, further validated in the independent cohort. This study reveals the heterogeneity of different biomarkers for tumor diagnosis, the initial and longitudinal response prediction respectively in the first course and multi-course cetuximab treatment, may ultimately be useful in monitoring and intervention strategies for CRC.

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.

Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome

Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.

Cardioprotective efficacy of Xin-shu-bao tablet in heart failure with reduced ejection fraction by modulating THBD/ARRB1/FGF1/STIM1 signaling

Traditional Chinese medicine offer unique advantages in mitigating and preventing early or intermediate stage for treating heart failure (HF). The purpose of this study was to assess the in vivo therapeutic efficacy of Xin-shu-bao (XSB) at different stages of HF following induction of a myocardial infarction (MI) in mice and use mass spectrometry-based proteomics to identify potential therapeutic targets for different stages of HF based on the molecular changes following XSB treatment. XSB had high cardioprotective efficacy in the pre-HF with reduced ejection fraction (HFrEF) stages, but had a weak or no effect in the post-HFrEF stages. This was supported by echocardiographic measurements showing that XSB decreased ejection fraction and fractional shortening in HF. XSB administration improved cardiac function in the pre- and post-HFrEF mouse model, ameliorated deleterious changes to the morphology and subcellular structure of cardiomyocytes, and reduced cardiac fibrosis. Proteomics analysis showed that XSB intervention exclusively targeted thrombomodulin (THBD) and stromal interaction molecule 1 (STIM1) proteins when administered to the mice for both 8 and 6 weeks. Furthermore, XSB intervention for 8, 6, and 4 weeks after MI induction increased the expression of fibroblast growth factor 1 (FGF1) and decreased arrestin β1 (ARRB1), which are classic biomarkers of cardiac fibroblast transformation and collagen synthesis, respectively. Overall, the study suggests that early intervention with XSB could be an effective strategy for preventing HFrEF and highlights potential therapeutic targets for further investigation into HFrEF remediation strategies.

Long-term effect of sleeve gastrectomy surgery on Hormonal Profile, Semen Parameters and sexual functions of obese infertile men; a prospective observational study

BACKGROUND

The effect of bariatric surgery on impaired semen parameters, hormonal profile and sexual function remains controversial to some extent.

THE CONTEXT AND PURPOSE OF THE STUDY

To look at the long-term effects of sleeve gastrectomy on hormonal profiles, sperm parameters, and sexual function in infertile men with severe obesity. This prospective study included fifty-four obese patients with primary or secondary infertility who were scheduled for sleeve gastrectomy between February 2018 and March 2021. All participants were given a sperm analysis and a serum hormone profile before, 12, and 18 months after surgery. We used the International Index of Erectile Function questionnaire to assess sexual function.

RESULTS

There was a significant correlation between weight loss after sleeve gastrectomy and improvement in lipid profile (p < 0.05). No significant detectable effect of post-gastrectomy weight loss on patients with diabetes mellitus, hypertension, or obstructive sleep apnea. As regards the hormonal profile, sex hormone binding globulin, total and free testosterone improved significantly after 12- and 18-months following sleeve gastrectomy. There was a significant increase in sperm count and total sperm number during the follow-up after sleeve gastrectomy (p < 0.05), however, there were no significant changes in other semen parameters. Concerning sexual function, sexual desire, erectile function, and satisfaction improved significantly at 12 and 18 months after surgery.

CONCLUSION

Weight loss through sleeve gastrectomy surgery significantly improves testosterone deficiency, sexual performance, and Sperm count in obese infertile men.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Impact of Bariatric Surgery on the Stability of the Genetic Material, Oxidation, and Repair of DNA and Telomere Lengths

Obesity causes genetic instability, which plays a key-role in the etiology of cancer and aging. We investigated the impact of bariatric surgery (BS) on DNA repair, oxidative DNA damage, telomere lengths, alterations of antioxidant enzymes and, selected proteins which reflect inflammation. The study was realized with BS patients (n = 35). DNA damage, base oxidation, BER, and NER were measured before and 1 month and 6 months after surgery with the single-cell gel electrophoresis technique. SOD and GPx were quantified spectrophotometrically, malondealdehyde (MDA) was quantified by HPLC. Telomere lengths were determined with qPCR, and plasma proteome profiling was performed with high-resolution mass spectrophotometry. Six months after the operations, reduction of body weight by 27.5% was observed. DNA damage decreased after this period, this effect was paralleled by reduced formation of oxidized DNA bases, a decline in the MDA levels and of BER and NER, and an increase in the telomere lengths. The activities of antioxidant enzymes were not altered. Clear downregulation of certain proteins (CRP, SAA1) which reflect inflammation and cancer risks was observed. Our findings show that BS causes reduced oxidative damage of DNA bases, possibly as a consequence of reduction of inflammation and lipid peroxidation, and indicate that the surgery has beneficial long-term health effects.

Updates on the study of lysosomal protein dynamics: possibilities for the clinic

INTRODUCTION

The lysosome is the main degradative organelle of almost all mammalian cells, fulfilling important functions in macromolecule recycling, metabolism, and signaling. Lysosomal dysfunction is connected to a continuously growing number of pathologic conditions, and lysosomal proteins present potential biomarkers for a variety of diseases. Therefore, there is an increasing interest in their analysis in patient samples.

AREAS COVERED

We provide an overview of OMICs studies which identified lysosomal proteins as potential biomarkers for pathological conditions, covering proteomics, genomics, and transcriptomics approaches, identified through PubMed searches. With respect to discovery proteomics analyses, mainly lysosomal luminal and associated proteins were detected, while membrane proteins were found less frequently. Comprehensive coverage of the lysosomal proteome was only achieved by ultra-deep-coverage studies, but targeted approaches allowed for the reproducible quantification of lysosomal proteins in diverse sample types.

EXPERT OPINION

The low abundance of lysosomal proteins complicates their reproducible analysis in patient samples. Whole proteome shotgun analyses fail in many instances to cover the lysosomal proteome, which is due to under-sampling and/or a lack of sensitivity. With the current state of the art, targeted proteomics assays provide the best performance for the characterization of lysosomal proteins in patient samples.

Mass spectrometry-based analysis on the impact of whole blood donation on the global plasma proteome

BACKGROUND

Biomonitoring may provide important insights into the impact of a whole blood donation for individual blood donors.

STUDY DESIGN AND METHODS

Here, we used unbiased mass spectrometry (MS)-based proteomics to assess longitudinal changes in the global plasma proteome, after a single blood donation for new and regular donors. Subsequently, we compared plasma proteomes of 76 male and female whole blood donors, that were grouped based on their ferritin and hemoglobin (Hb) levels.

RESULTS

The longitudinal analysis showed limited changes in the plasma proteomes of new and regular donors after a whole blood donation during a 180-day follow-up period, apart from a significant short-term decrease in fibronectin. No differences were observed in the plasma proteomes of donors with high versus low Hb and/or ferritin levels. Plasma proteins with the highest variation between and within donors included pregnancy zone protein, which was associated with sex, Alfa 1-antitrypsin which was associated with the allelic variation, and Immunoglobulin D. Coexpression analysis revealed clustering of proteins that are associated with platelet, red cell, and neutrophil signatures as well as with the complement system and immune responses, including a prominent correlating cluster of immunoglobulin M (IgM), immunoglobulin J chain (JCHAIN), and CD5 antigen-like (CD5L).

DISCUSSION

Overall, our proteomic approach shows that whole blood donation has a limited impact on the plasma proteins measured. Our findings suggest that plasma profiling can be successfully employed to consistently detect proteins and protein complexes that reflect the functionality and integrity of platelets, red blood cells, and immune cells in blood donors and thus highlights its potential use for donor health monitoring.

High-Throughput and In-Depth Proteomic Profiling of 5 μL Plasma and Serum Using TMTpro 16-Plex

High-throughput and in-depth proteomic analysis of plasma and serum samples remains challenging due to the presence of multiple high-abundance proteins. Here, we provide a detailed protocol for proteomic analysis of serum and plasma specimens using a high-abundance protein depletion kit and TMTpro 16-plex reagents. This method requires only 5 μL serum or plasma, identifying and quantifying about 1000 proteins. A batch of 16 samples can be processed in 36 h. On average, each sample consumes about 1.5 h of mass spectrometer instrument time. Overall, our method can identify proteins across six orders of magnitude with high reproducibility (CV < 20%) using a shorter instrument time and less sample volume compared to existing methods. Thus, the method is suitable to be applied to large-scale proteomic studies.

Synthesis of Surface-Functionalized Molybdenum Disulfide Nanomaterials for Efficient Adsorption and Deep Profiling of the Human Plasma Proteome by Data-Independent Acquisition

Blood is one of the most important clinical samples for protein biomarker discovery, as it provides rich physiological and pathological information and is easy to obtain with low invasiveness. However, the discovery of protein biomarkers in the blood by mass spectrometry (MS)-based proteomic strategies has been shown to be highly challenging due to the particularly large concentration range of proteins and the strong interference by the high-abundant proteins in the blood. Therefore, developing sensitive methods for low-abundant biomarker protein identification is a key issue that has received great attention. Here, we report the synthesis and characterization of surface-functionalized magnetic molybdenum disulfide (MoS₂) for the large-scale adsorption of low-abundant plasma proteins and deep profiling by MS. MoS₂ nanomaterials resulted in the coverage of more than 3400 proteins (including a single-peptide hit) in a single LC-MS analysis without peptide prefractionation using pooled plasma samples, which were five times more than those obtained by the direct analysis of the plasma proteome. A detection limit in the low ng L^-1 range was obtained, which is rare compared with previous reports.

Targeted proteomics on its way to discovery

For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes. This article is protected by copyright. All rights reserved.

Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson's disease

Parkinson's disease (PD) is a growing burden worldwide, and there is no reliable biomarker used in clinical routines to date. Cerebrospinal fluid (CSF) is routinely collected in patients with neurological symptoms and should closely reflect alterations in PD patients' brains. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomics workflow for CSF proteome profiling. From two independent cohorts with over 200 individuals, our workflow reproducibly quantifies over 1,700 proteins from minimal CSF amounts. Machine learning determines OMD, CD44, VGF, PRL, and MAN2B1 to be altered in PD patients or to significantly correlate with clinical scores. We also uncover signatures of enhanced neuroinflammation in LRRK2 G2019S carriers, as indicated by increased levels of CTSS, PLD4, and HLA proteins. A comparison with our previously acquired urinary proteomes reveals a large overlap in PD-associated changes, including lysosomal proteins, opening up new avenues to improve our understanding of PD pathogenesis.

Plasma proteomic changes in response to surgical trauma and a novel transdermal analgesic treatment in dogs

Assessment of pain responses and inflammation during animal surgery is difficult because traditional methods, such as visual analogue scores, are not applicable while under anaesthesia. Acute phase proteins (APPs), such as C-reactive protein and haptoglobin, that are typically monitored in veterinary research, do not show a significant change until at least 2 h post-surgery and therefore, immediate pathophysiological changes are uncertain. The current study used sequential window acquisition of all theoretical mass spectra (SWATH-MS) to investigate plasma proteome changes that occur immediately following surgery in dogs and also to assess the efficacy of a novel transdermal ketoprofen (TK) formulation. Castration was chosen as surgical model in this study. The procedure was performed on twelve dogs (n = 6 in two groups) and blood samples were collected at 0 h, 1 and 2 h after surgery for proteomic analysis. Following surgery, there was a general downregulation of proteins, including complement C- 3, complement factor B, complement factor D, transthyretin, and proteins associated with lipid, cholesterol, and glucose metabolisms, reflecting the systemic response to surgical trauma. Many of these changes were diminished in the transdermal group (TD) since ketoprofen, a non-steroidal anti-inflammatory drug (NSAID), inhibits prostanoids and the associated chemotactic neutrophil migration to site of tissue injury. SIGNIFICANCE: SWATH-MS Proteomic analysis revealed significant changes in plasma proteins, predominantly involved in early acute phase and inflammatory response at 1 & 2 h after surgery in castrated dogs. Pre-operative application of transdermal ketoprofen formulation had reduced the systemic immune response, which was confirmed by negligible alteration of proteins in transdermal treated group. A key outcome of this experiment was studying the efficacy of a novel transdermal NSAID formulation in dogs.

A knowledge graph to interpret clinical proteomics data

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

Plasma proteome profiling combined with clinical and genetic features reveals the pathophysiological characteristics of β-thalassemia

The phenotype of β-thalassemia underlies multigene interactions, making clinical stratification complicated. An increasing number of genetic modifiers affecting the disease severity have been identified, but are still unable to meet the demand of precision diagnosis. Here, we systematically conducted a comparative plasma proteomic profiling on patients with β-thalassemia and healthy controls. Among 246 dysregulated proteins, 13 core protein signatures with excellent biomarker potential are proposed. The combination of proteome and patients' clinical data revealed patients with codons 41/42 -TTCT mutations have an elevated risk of higher iron burden, dysplasia, and osteoporosis than patients with other genotypes. Notably, 85 proteins correlating to fetal hemoglobin (Hb F) were identified, among which the abundance of 27 proteins may affect the transfusion burden in patients with β-thalassemia. The current study thus provides protein signatures as potential diagnostic biomarkers or therapeutic clues for β-thalassemia.

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246 dysregulated proteins are detected in plasma of patients with β-thalassemia

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13 potential biomarkers and 27 proteins related to disease progression are found

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Variations in plasma proteome reveal the disease pathophysiological characteristics

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Codons 41/42 -TTCT carriers have higher ferritin levels compared to non-carriers

Rewiring of the Liver Transcriptome across Multiple Time-Scales Is Associated with the Weight Loss-Independent Resolution of NAFLD Following RYGB

Roux-en-Y gastric bypass (RYGB) surgery potently improves obesity and a myriad of obesity-associated co-morbidities including type 2 diabetes and non-alcoholic fatty liver disease (NAFLD). Time-series omics data are increasingly being utilized to provide insight into the mechanistic underpinnings that correspond to metabolic adaptations in RYGB. However, the conventional computational biology methods used to interpret these temporal multi-dimensional datasets have been generally limited to pathway enrichment analysis (PEA) of isolated pair-wise comparisons based on either experimental condition or time point, neither of which adequately capture responses to perturbations that span multiple time scales. To address this, we have developed a novel graph network-based analysis workflow designed to identify modules enriched with biomolecules that share common dynamic profiles, where the network is constructed from all known biological interactions available through the Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. This methodology was applied to time-series RNAseq transcriptomics data collected on rodent liver samples following RYGB, and those of sham-operated and weight-matched control groups, to elucidate the molecular pathways involved in the improvement of as NAFLD. We report several network modules exhibiting a statistically significant enrichment of genes whose expression trends capture acute-phase as well as long term physiological responses to RYGB in a single analysis. Of note, we found the HIF1 and P53 signaling cascades to be associated with the immediate and the long-term response to RYGB, respectively. The discovery of less intuitive network modules that may have gone overlooked with conventional PEA techniques provides a framework for identifying novel drug targets for NAFLD and other metabolic syndrome co-morbidities.

Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions

SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies.

Integrated Liver and Plasma Proteomics in Obese Mice Reveals Complex Metabolic Regulation

Obesity leads to the development of nonalcoholic fatty liver disease (NAFLD) and associated alterations to the plasma proteome. To elucidate the underlying changes associated with obesity, we performed liquid chromatography-tandem mass spectrometry in the liver and plasma of obese leptin-deficient ob/ob mice and integrated these data with publicly available transcriptomic and proteomic datasets of obesity and metabolic diseases in preclinical and clinical cohorts. We quantified 7173 and 555 proteins in the liver and plasma proteomes, respectively. The abundance of proteins related to fatty acid metabolism were increased, alongside peroxisomal proliferation in ob/ob liver. Putatively secreted proteins and the secretory machinery were also dysregulated in the liver, which was mirrored by a substantial alteration of the plasma proteome. Greater than 50% of the plasma proteins were differentially regulated, including NAFLD biomarkers, lipoproteins, the 20S proteasome, and the complement and coagulation cascades of the immune system. Integration of the liver and plasma proteomes identified proteins that were concomitantly regulated in the liver and plasma in obesity, suggesting that the systemic abundance of these plasma proteins is regulated by secretion from the liver. Many of these proteins are systemically regulated during type 2 diabetes and/or NAFLD in humans, indicating the clinical importance of liver-plasma cross talk and the relevance of our investigations in ob/ob mice. Together, these analyses yield a comprehensive insight into obesity and provide an extensive resource for obesity research in a prevailing model organism.

Proteomic Analysis of Whole Blood Using Volumetric Absorptive Microsampling for Precision Medicine Biomarker Studies

Microsampling of patient blood promises several benefits over conventional phlebotomy practices to facilitate precision medicine studies. These include at-home patient blood collection, supporting telehealth monitoring, minimal postcollection processing, and compatibility with nonrefrigerated transport and storage. However, for proteomic biomarker studies, mass spectrometry of whole blood has generally been avoided in favor of using plasma or serum obtained from venepuncture. We evaluated the use of a volumetric absorptive microsampling (VAMS) device as a sample preparation matrix to enable LC-MS proteomic analyses of dried whole blood. We demonstrated the detection and robust quantitation of up to 1600 proteins from single-shot shotgun-LC-MS analysis of dried whole blood, greatly enhancing proteome depth compared with conventional single-shot LC-MS analyses of undepleted plasma. Some proteins not previously reported in blood were detected using this approach. Various washing reagents were used to demonstrate that proteins can be preferentially removed from VAMS devices prior to downstream analyses. We provide a demonstration that archival frozen blood cell pellets housed under long-term storage (exceeding 5 years) are compatible with VAMS to enable quantitation of potential biomarker proteins from biobank repositories. These demonstrations are important steps in establishing viable analysis workflows to underpin large-scale precision medicine studies. Data are available via ProteomeXchange with the identifier PXD028605.

A Bioinformatics Approach to Mine the Microbial Proteomic Profile of COVID-19 Mass Spectrometry Data

Mass spectrometry (MS) is one of the key technologies used in proteomics. The majority of studies carried out using proteomics have focused on identifying proteins in biological samples such as human plasma to pin down prognostic or diagnostic biomarkers associated with particular conditions or diseases. This study aims to quantify microbial (viral and bacterial) proteins in healthy human plasma. MS data of healthy human plasma were searched against the complete proteomes of all available viruses and bacteria. With this baseline established, the same strategy was applied to characterize the metaproteomic profile of different SARS-CoV-2 disease stages in the plasma of patients. Two SARS-CoV-2 proteins were detected with a high confidence and could serve as the early markers of SARS-CoV-2 infection. The complete bacterial and viral protein content in SARS-CoV-2 samples was compared for the different disease stages. The number of viral proteins was found to increase significantly with the progression of the infection, at the expense of bacterial proteins. This strategy can be extended to aid in the development of early diagnostic tests for other infectious diseases based on the presence of microbial biomarkers in human plasma samples.

Proteome-wide associations with short- and long-term weight loss and regain after Roux-en-Y gastric bypass surgery

OBJECTIVE

Gastric bypass surgery results in long-term weight loss. Small studies have examined protein changes during rapid weight loss (up to 1 or 2 years post surgery). This study tested whether short-term changes were maintained after 12 years.

METHODS

A 12-year follow-up, protein-wide association study of 1,297 SomaLogic aptamer-based plasma proteins compared short- (2-year) and long-term (12-year) protein changes in 234 individuals who had gastric bypass surgery with 144 nonintervened individuals with severe obesity.

RESULTS

There were 51 replicated 12-year protein changes that differed between the surgery and nonsurgery groups. Adjusting for change in BMI, only 12 proteins remained significant, suggesting that BMI change was the primary reason for most protein changes and not non-BMI-related surgical effects. Protein changes were related to BMI changes during both weight-loss and weight-regain periods. The significant proteins were associated primarily with lipid, uric acid, or resting energy expenditure clinical variables and metabolic pathways. Eight protein changes were associated with 12-year diabetes remission, including apolipoprotein M, sex hormone binding globulin, and adiponectin (p < 3.5 × 10^-5 ).

CONCLUSIONS

This study showed that most short-term postsurgical changes in proteins were maintained at 12 years. Systemic protection pathways, including inflammation, complement, lipid, and adipocyte pathways, were related to the long-term benefits of gastric bypass surgery.

Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies-A TMT-Based LC-MSMS Proteome Investigation

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.

Remission of Diabetes Following Bariatric Surgery: Plasma Proteomic Profiles

Bariatric surgery restores glucose tolerance in many, but not all, severely obese subjects with type 2 diabetes (T2D). We aimed to evaluate the plasma protein profiles associated with the T2D remission after obesity surgery. We recruited seventeen women with severe obesity submitted to bariatric procedures, including six non-diabetic patients and eleven patients with T2D. After surgery, diabetes remitted in 7 of the 11 patients with T2D. Plasma protein profiles at baseline and 6 months after bariatric surgery were analyzed by two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight coupled to mass spectrometry (MALDI-TOF/TOF MS). Remission of T2D following bariatric procedures was associated with changes in alpha-1-antichymotrypsin (SERPINA 3, p < 0.05), alpha-2-macroglobulin (A2M, p < 0.005), ceruloplasmin (CP, p < 0.05), fibrinogen beta chain (FBG, p < 0.05), fibrinogen gamma chain (FGG, p < 0.05), gelsolin (GSN, p < 0.05), prothrombin (F2, p < 0.05), and serum amyloid p-component (APCS, p < 0.05). The resolution of diabetes after bariatric surgery is associated with specific changes in the plasma proteomic profiles of proteins involved in acute-phase response, fibrinolysis, platelet degranulation, and blood coagulation, providing a pathophysiological basis for the study of their potential use as biomarkers of the surgical remission of T2D in a larger series of severely obese patients.

Lithium preserves peritoneal membrane integrity by suppressing mesothelial cell αB-crystallin

Life-saving renal replacement therapy by peritoneal dialysis (PD) is limited in use and duration by progressive impairment of peritoneal membrane integrity and homeostasis. Preservation of peritoneal membrane integrity during chronic PD remains an urgent but long unmet medical need. PD therapy failure results from peritoneal fibrosis and angiogenesis caused by hypertonic PD fluid (PDF)-induced mesothelial cytotoxicity. However, the pathophysiological mechanisms involved are incompletely understood, limiting identification of therapeutic targets. We report that addition of lithium chloride (LiCl) to PDF is a translatable intervention to counteract PDF-induced mesothelial cell death, peritoneal membrane fibrosis, and angiogenesis. LiCl improved mesothelial cell survival in a dose-dependent manner. Combined transcriptomic and proteomic characterization of icodextrin-based PDF-induced mesothelial cell injury identified αB-crystallin as the mesothelial cell protein most consistently counter-regulated by LiCl. In vitro and in vivo overexpression of αB-crystallin triggered a fibrotic phenotype and PDF-like up-regulation of vascular endothelial growth factor (VEGF), CD31-positive cells, and TGF-β-independent activation of TGF-β-regulated targets. In contrast, αB-crystallin knockdown decreased VEGF expression and early mesothelial-to-mesenchymal transition. LiCl reduced VEGF release and counteracted fibrosis- and angiogenesis-associated processes. αB-crystallin in patient-derived mesothelial cells was specifically up-regulated in response to PDF and increased in peritoneal mesothelial cells from biopsies from pediatric patients undergoing PD, correlating with markers of angiogenesis and fibrosis. LiCl-supplemented PDF promoted morphological preservation of mesothelial cells and the submesothelial zone in a mouse model of chronic PD. Thus, repurposing LiCl as a cytoprotective PDF additive may offer a translatable therapeutic strategy to combat peritoneal membrane deterioration during PD therapy.

Artificial intelligence for proteomics and biomarker discovery

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.

High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion

A deeper understanding of COVID-19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)-based proteomics to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system-wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance.

Molecular Aspects of Lifestyle and Environmental Effects in Patients With Diabetes: JACC Focus Seminar

Diabetes is characterized as an integrated condition of dysregulated metabolism across multiple tissues, with well-established consequences on the cardiovascular system. Recent advances in precision phenotyping in biofluids and tissues in large human observational and interventional studies have afforded a unique opportunity to translate seminal findings in models and cellular systems to patients at risk for diabetes and its complications. Specifically, techniques to assay metabolites, proteins, and transcripts, alongside more recent assessment of the gut microbiome, underscore the complexity of diabetes in patients, suggesting avenues for precision phenotyping of risk, response to intervention, and potentially novel therapies. In addition, the influence of external factors and inputs (eg, activity, diet, medical therapies) on each domain of molecular characterization has gained prominence toward better understanding their role in prevention. Here, the authors provide a broad overview of the role of several of these molecular domains in human translational investigation in diabetes.

Human plasma proteomic profiles indicative of cardiorespiratory fitness

Maximal oxygen uptake (VO₂max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO₂max, and of increases of VO₂max in response to exercise training (ΔVO₂max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO₂max and ΔVO₂max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual's ΔVO₂max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans.

Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Owing to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human data were used to create a protein-protein interaction network based on the causative genes. Network evaluation as a combination of topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes, such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, indicating that there is scope to further classify conditions currently described under the same umbrella-term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

Common and Novel Markers for Measuring Inflammation and Oxidative Stress Ex Vivo in Research and Clinical Practice-Which to Use Regarding Disease Outcomes?

Many chronic conditions such as cancer, chronic obstructive pulmonary disease, type-2 diabetes, obesity, peripheral/coronary artery disease and auto-immune diseases are associated with low-grade inflammation. Closely related to inflammation is oxidative stress (OS), which can be either causal or secondary to inflammation. While a low level of OS is physiological, chronically increased OS is deleterious. Therefore, valid biomarkers of these signalling pathways may enable detection and following progression of OS/inflammation as well as to evaluate treatment efficacy. Such biomarkers should be stable and obtainable through non-invasive methods and their determination should be affordable and easy. The most frequently used inflammatory markers include acute-phase proteins, essentially CRP, serum amyloid A, fibrinogen and procalcitonin, and cytokines, predominantly TNFα, interleukins 1β, 6, 8, 10 and 12 and their receptors and IFNγ. Some cytokines appear to be disease-specific. Conversely, OS-being ubiquitous-and its biomarkers appear less disease or tissue-specific. These include lipid peroxidation products, e.g., F2-isoprostanes and malondialdehyde, DNA breakdown products (e.g., 8-OH-dG), protein adducts (e.g., carbonylated proteins), or antioxidant status. More novel markers include also -omics related ones, as well as non-invasive, questionnaire-based measures, such as the dietary inflammatory-index (DII), but their link to biological responses may be variable. Nevertheless, many of these markers have been clearly related to a number of diseases. However, their use in clinical practice is often limited, due to lacking analytical or clinical validation, or technical challenges. In this review, we strive to highlight frequently employed and useful markers of inflammation-related OS, including novel promising markers.

Urinary proteome profiling for stratifying patients with familial Parkinson's disease

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non-invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non-carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD-associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.

High-throughput, in-depth and estimated absolute quantification of plasma proteome using data-independent acquisition/mass spectrometry ("HIAP-DIA")

Mass spectrometry-based plasma proteomics has been demonstrated to be a useful tool capable of quantifying hundreds of proteins in a single LC-MS/MS experiment, for biomarker discovery or elucidation of disease mechanisms. We developed a novel data-independent acquisition (DIA)/MS-based workflow for high-throughput, in-depth and estimated absolute quantification of plasma proteins (termed HIAP-DIA), without depleting high-abundant proteins, in a single-shot experiment. In HIAP-DIA workflow, we generated an ultra-deep cumulative undepleted and depleted spectral library which contained 55,157 peptides and 5,328 proteins, optimized column length (50 cm) and gradient (90 min) of liquid chromatography instrumentation, optimized 50 DIA segments with average isolation window 17 Th, and selected reference proteins for estimated absolute quantification of all plasma proteins. A total of 606 proteins were quantified in triplicate, and 427 proteins were quantified with CV <20% in plasma proteome. R-squared value of overlapped 208 endogenous PQ500 estimated protein amounts from HIAP-DIA and absolute quantification with internal standards was 0.82, indicating high quantification accuracy of HIAP-DIA. As a pilot study, the HIAP-DIA approach described here was applied to a myelodysplastic syndromes (MDS) disease cohort. We achieved absolute quantification of 789 plasma proteins in 22 clinical plasma samples, spanning less than six orders of magnitude with quantification limit 10-20 ng/mL, and discovered 95 differentially expressed proteins providing insights into MDS pathophysiology.

Factors Affecting Metabolic Outcomes Post Bariatric Surgery: Role of Adipose Tissue

Obesity is an ever-growing public health crisis, and bariatric surgery (BS) has become a valuable tool in ameliorating obesity, along with comorbid conditions such as diabetes, dyslipidemia and hypertension. BS techniques have come a long way, leading to impressive improvements in the health of the majority of patients. Unfortunately, not every patient responds optimally to BS and there is no method that is sufficient to pre-operatively predict who will receive maximum benefit from this surgical intervention. This review focuses on the adipose tissue characteristics and related parameters that may affect outcomes, as well as the potential influences of insulin resistance, BMI, age, psychologic and genetic factors. Understanding the role of these factors may help predict who will benefit the most from BS.

Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice

Performing large-scale plasma proteome profiling is challenging due to limitations imposed by lengthy preparation and instrument time. We present a fully automated multiplexed proteome profiling platform (AutoMP3) using the Hamilton Vantage liquid handling robot capable of preparing hundreds to thousands of samples. To maximize protein depth in single-shot runs, we combined 16-plex Tandem Mass Tags (TMTpro) with high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro) and real-time search (RTS). We quantified over 40 proteins/min/sample, doubling the previously published rates. We applied AutoMP3 to investigate the naked mole-rat plasma proteome both as a function of the circadian cycle and in response to ultraviolet (UV) treatment. In keeping with the lack of synchronized circadian rhythms in naked mole-rats, we find few circadian patterns in plasma proteins over the course of 48 h. Furthermore, we quantify many disparate changes between mice and naked mole-rats at both 48 h and one week after UV exposure. These species differences in plasma protein temporal responses could contribute to the pronounced cancer resistance observed in naked mole-rats. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD022891.

Extending the Depth of Human Plasma Proteome Coverage Using Simple Fractionation Techniques

Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.

Plasma Proteomes Can Be Reidentifiable and Potentially Contain Personally Sensitive and Incidental Findings

The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual-beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of-and argue for the desirability for-ethical and human-rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.

Plasma proteome profiles treatment efficacy of incretin dual agonism in diet-induced obese female and male mice

AIMS

Unimolecular peptides targeting the receptors for glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) (GLP-1/GIP co-agonist) have been shown to outperform each single peptide in the treatment of obesity and cardiometabolic disease in preclinical and clinical trials. By combining physiological treatment endpoints with plasma proteomic profiling (PPP), we aimed to identify biomarkers to advance non-invasive metabolic monitoring of compound treatment success and exploration of ulterior treatment effects on an individual basis.

MATERIALS AND METHODS

We performed metabolic phenotyping along with PPP in body weight-matched male and female diet-induced obese (DIO) mice treated for 21 days with phosphate-buffered saline, single GIP and GLP-1 mono-agonists, or a GLP-1/GIP co-agonist.

RESULTS

GLP-1R/GIPR co-agonism improved obesity, glucose intolerance, non-alcoholic fatty liver disease (NAFLD) and dyslipidaemia with superior efficacy in both male and female mice compared with mono-agonist treatments. PPP revealed broader changes of plasma proteins after GLP-1/GIP co-agonist compared with mono-agonist treatments in both sexes, including established and potential novel biomarkers for systemic inflammation, NAFLD and atherosclerosis. Subtle sex-specific differences have been observed in metabolic phenotyping and PPP.

CONCLUSIONS

We herein show that a recently developed unimolecular GLP-1/GIP co-agonist is more efficient in improving metabolic disease than either mono-agonist in both sexes. PPP led to the identification of a sex-independent protein panel with the potential to monitor non-invasively the treatment efficacies on metabolic function of this clinically advancing GLP-1/GIP co-agonist.