Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples

BACKGROUND

Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets.

RESULTS

As a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods.

CONCLUSIONS

Some normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performance, while quantile protein normalization provided worse results than those obtained with unnormalized data.

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.

Relevant proteins for the monitoring of engraftment phases after allogeneic hematopoietic stem cell transplantation

INTRODUCTION

Hematopoietic Stem Cell Transplant (HSCT) has been successfully used as standard therapy for hematological disorders. After conditioning therapy, patients undergoing allogeneic HSCT, present three different phases of engraftment: early pre-engraftment, early post-engraftment, and late engraftment. Severe complications are associated with morbidity, mortality, and malignancies in these phases, which include effects on the oral cavity.

OBJECTIVES

The changes in the salivary composition after HSCT may contribute to identifying relevant proteins that could map differences among the phases of diseases, driven for personalized diagnostics and therapy.

METHODS

Unstimulated whole saliva was collected from patients submitted to HSCT. The samples were submitted to trypsin digestion for a Mass spectrometry analysis. MaxQuant processed the Data analysis, and the relevant expressed proteins were subjected to pathway and network analyses.

RESULTS

Differences were observed in the most identified proteins, specifically in proteins involved with the regulation of body fluid levels and the mucosal immune response. The heatmap showed a list of proteins exclusively expressed during the different phases of HSCT: HBB, KNG1, HSPA, FGB, APOA1, PFN1, PRTN3, TMSB4X, YWHAZ, CAP1, ACTN1, CLU and ALDOA. Bioinformatics analysis implicated pathways involved in protein processing in the endoplasmic reticulum, complement and coagulation cascades, apoptosis signaling, and cholesterol metabolism.

CONCLUSION

The compositional changes in saliva reflected the three phases of HSCT and demonstrated the usefulness of proteomics and computational approaches as a revolutionary field in diagnostic methods.

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.

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Proteomics reveals liver-derived proteins systemically dysregulated in obesity.

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Obesity increases hepatic lipid metabolism via peroxisomal biogenesis.

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Obesity dysregulates secretory machinery and secreted proteins within the liver.

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Metabolic and immune proteins dysregulated in plasma of obese mice.

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Comparative proteomics of high-fat diet and monogenic (ob/ob) models of obesity.

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.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.

Relationship between the Plasma Proteome and Changes in Inflammatory Markers after Bariatric Surgery

Severe obesity is a disease associated with multiple adverse effects on health. Metabolic bariatric surgery (MBS) can have significant effects on multiple body systems and was shown to improve inflammatory markers in previous short-term follow-up studies. We evaluated associations between changes in inflammatory markers (CRP, IL6 and TNFα) and circulating proteins after MBS. Methods: Sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics was performed on plasma samples taken at baseline (pre-surgery) and 6 and 12 months after MBS, and concurrent analyses of inflammatory/metabolic parameters were carried out. The change in absolute abundances of those proteins, showing significant change at both 6 and 12 months, was tested for correlation with the absolute and percentage (%) change in inflammatory markers. Results: We found the following results: at 6 months, there was a correlation between %change in IL-6 and fold change in HSPA4 (rho = −0.659; p = 0.038) and in SERPINF1 (rho = 0.714, p = 0.020); at 12 months, there was a positive correlation between %change in IL-6 and fold change in the following proteins—LGALS3BP (rho = 0.700, p = 0.036), HSP90B1 (rho = 0.667; p = 0.05) and ACE (rho = 0.667, p = 0.05). We found significant inverse correlations at 12 months between %change in TNFα and the following proteins: EPHX2 and ACE (for both rho = −0.783, p = 0.013). We also found significant inverse correlations between %change in CRP at 12 months and SHBG (rho = −0.759, p = 0.029), L1CAM (rho = −0.904, p = 0.002) and AMBP (rho = −0.684, p = 0.042). Conclusion: Using SWATH-MS, we identified several proteins that are involved in the inflammatory response whose levels change in patients who achieve remission of T2DM after bariatric surgery in tandem with changes in IL6, TNFα and/or CRP. Future studies are needed to clarify the underlying mechanisms in how MBS decreases low-grade inflammation.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

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.

Changes in the Proteome Profile of People Achieving Remission of Type 2 Diabetes after Bariatric Surgery

Bariatric surgery (BS) results in metabolic pathway recalibration. We have identified potential biomarkers in plasma of people achieving type 2 diabetes mellitus (T2DM) remission after BS. Longitudinal analysis was performed on plasma from 10 individuals following Roux-en-Y gastric bypass (n = 7) or sleeve gastrectomy (n = 3). Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was done on samples taken at 4 months before (baseline) and 6 and 12 months after BS. Four hundred sixty-seven proteins were quantified by SWATH-MS. Principal component analysis resolved samples from distinct time points after selection of key discriminatory proteins: 25 proteins were differentially expressed between baseline and 6 months post-surgery; 39 proteins between baseline and 12 months. Eight proteins (SHBG, TF, PRG4, APOA4, LRG1, HSPA4, EPHX2 and PGLYRP) were significantly different to baseline at both 6 and 12 months post-surgery. The panel of proteins identified as consistently different included peptides related to insulin sensitivity (SHBG increase), systemic inflammation (TF and HSPA4-both decreased) and lipid metabolism (APOA4 decreased). We found significant changes in the proteome for eight proteins at 6- and 12-months post-BS, and several of these are key components in metabolic and inflammatory pathways. These may represent potential biomarkers of remission of T2DM.

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.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

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.

Plasma proteome alterations by MAPK inhibitors in BRAFV600-mutated metastatic cutaneous melanoma

Approximately half of metastatic cutaneous melanomas (CM) harbor a mutation in the BRAF protooncogene, upregulating the mitogen-activated protein kinase (MAPK)-pathway. The development of inhibitors targeting the MAPK pathway (MAPKi), i.e., BRAF- and MEK-inhibitors (BRAFi and MEKi), have substantially improved the survival in BRAFV600E/K-mutated stage IV metastatic CM. However, most patients develop resistance to treatment and no predictive biomarkers exist in practice. This study aimed at discovering plasma proteome changes during treatment MAPKi in patients with metastatic (stage IV) CM. Matched plasma samples before (pre) and during treatment (trm) from 23 patients with stage IV CM, treated with BRAF-inhibitors (BRAFi) alone or BRAF- and MEK- inhibitors combined (BRAFi and MEKi), were collected and analyzed with targeted proteomics by proximity extension assays. Additionally, plasma from 9 patients treated with BRAFi and MEKi was analyzed with in-depth high-resolution isoelectric focusing liquid-chromatography mass-spectrometry proteomics. Alterations of plasma proteins involved in granzyme and interferon gamma pathways were detected in patients treated with BRAFi, and cell adhesion-, neutrophil degranulation-, and proteolysis pathways in patients treated with BRAFi and MEKi. Several proteins were associated with progression-free survival after MAPKi treatment. We show that the majority of the altered plasma proteins were traceable to BRAFV600E-mutant metastatic CM tissue at mRNA level in 154 patients from the TCGA, further strengthening their involvement in tumoral response to treatment. This wide screen of plasma proteins unravels proteins that may serve as predictive and/or prognostic biomarkers of MAPKi treatment, opening a window of opportunity for plasma biomarker discovery in MAPKi-treatment of BRAFV600-mutant metastatic CM.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

Identification of Abnormal Proteins in Plasma from Gout Patients by LC-MS/MS

A high level of uric acid may cause hyperuricemia, which further develops into gout, eventually leading to chronic kidney disease. However, the pathogenic mechanism remains largely unknown. To investigate the cause and block the transformation of hyperuricemia to related diseases, it is important to discover the alterations in protein levels between gout patients and non-gout individuals. To date, human blood plasma is still the predominant matrices for clinical analysis. Due to the high abundance, the proteins of plasma samples have strong shielding effects on low abundance proteins, thus, the information on low abundance protein expression is always masked, while the low abundance proteins of human plasma are often of great significance for the diagnosis and treatment of diseases. Therefore, it is very important to separate and analyze the plasma proteins. High-performance liquid chromatography (LC) tandem mass spectrometry (MS)-based proteomics has been developed as a powerful tool to investigate changes in the human plasma proteome. Here, we used LC-MS/MS to detect the differential proteins in the plasmas from simple gout patients, gout with kidney damage patients, and non-gout individuals. We identified 32 obviously differential proteins between non-gout and gout subjects and 10 differential proteins between simple gout and gout with kidney damage patients. These differential proteins were further analyzed to characterize their localization and functions. Additionally, the correlation analysis showed multiple relationships between the abnormal plasma proteins and clinical biochemical indexes, particularly for the immune-inflammatory response proteins. Furthermore, inflammation factors gelsolin (GSN) were confirmed. Our results offer a view of plasma proteins for studying biomarkers of gout patients.

Inflammation-related plasma protein levels and association with adiposity measurements in young adults

Obesity-related inflammation is associated with cardiovascular, metabolic, and pulmonary diseases. The aim of this study was to demonstrate associations between adiposity measurements and levels of inflammation-related plasma proteins in a population of young adults. Subjects from a population-based birth cohort with a mean age of 22.5 years were included in the study population (n = 2074). Protein levels were analyzed using the Olink Proseek Multiplex Inflammation panel. Percentage body fat (%BF) and visceral fat rating (VFR) measurements were collected using Tanita MC 780 body composition monitor. Linear regression of standardized values was used to investigate associations. Potential effect modifications by sex and BMI category were assessed. Of 71 investigated proteins, 54 were significantly associated with all adiposity measurements [%BF, body mass index (BMI), VFR and waist circumference]. Among proteins associated with %BF, seven showed a larger or unique association in overweight/obese subjects and three showed a significant effect modification by sex. Fourteen proteins more strongly associated with VFR in females compared to males. Adipose-associated systemic inflammation was observed in this young adult population. Sex and adiposity localization influenced some of the associations. Our results highlight specific proteins as suitable biomarkers related to adiposity.

AMPAR Auxiliary Protein SHISA6 Facilitates Purkinje Cell Synaptic Excitability and Procedural Memory Formation

The majority of excitatory postsynaptic currents in the brain are gated through AMPA-type glutamate receptors, the kinetics and trafficking of which can be modulated by auxiliary proteins. It remains to be elucidated whether and how auxiliary proteins can modulate synaptic function to contribute to procedural memory formation. In this study, we report that the AMPA-type glutamate receptor (AMPAR) auxiliary protein SHISA6 (CKAMP52) is expressed in cerebellar Purkinje cells, where it co-localizes with GluA2-containing AMPARs. The absence of SHISA6 in Purkinje cells results in severe impairments in the adaptation of the vestibulo-ocular reflex and eyeblink conditioning. The physiological abnormalities include decreased presence of AMPARs in synaptosomes, impaired excitatory transmission, increased deactivation of AMPA receptors, and reduced induction of long-term potentiation at Purkinje cell synapses. Our data indicate that Purkinje cells require SHISA6-dependent modification of AMPAR function in order to facilitate cerebellar, procedural memory formation.

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SHISA6 is prominently expressed in Purkinje cells in close association with AMPARs

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SHISA6 absence in Purkinje cells results in impaired procedural memory formation

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Purkinje cell synaptic baseline excitatory transmission is facilitated by SHISA6

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Purkinje cell AMPAR kinetics are modulated by SHISA6

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk

Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain-containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.

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.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

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.

Applications of Omics Technologies for Three-Dimensional In Vitro Disease Models

Omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, multiomics, and integrated modalities, have greatly contributed to our understanding of various diseases by enabling researchers to probe the molecular wiring of cellular systems in a high-throughput and precise manner. With the development of tissue-engineered three-dimensional (3D) in vitro disease models, such as organoids and spheroids, there is potential of integrating omics technologies with 3D disease models to elucidate the complex links between genotype and phenotype. These 3D disease models have been used to model cancer, infectious disease, toxicity, neurological disorders, and others. In this review, we provide an overview of omics technologies, highlight current and emerging studies, discuss the associated experimental design considerations, barriers and challenges of omics technologies, and provide an outlook on the future applications of omics technologies with 3D models. Overall, this review aims to provide a valuable resource for tissue engineers seeking to leverage omics technologies for diving deeper into biological discovery. Impact statement With the emergence of three-dimensional (3D) in vitro disease models, tissue engineers are increasingly interested to investigate these systems to address biological questions related to disease mechanism, drug target discovery, therapy resistance, and more. Omics technologies are a powerful and high-throughput approach, but their application for 3D disease models is not maximally utilized. This review illustrates the achievements and potential of using omics technologies to leverage the full potential of 3D in vitro disease models. This will improve the quality of such models, advance our understanding of disease, and contribute to therapy development.

Comprehensive analysis of immunoglobulin and clinical variables identifies functional linkages and diagnostic indicators associated with Behcet's disease patients receiving immunomodulatory treatment

BACKGROUND

Behcet's disease (BD) is a relapsing systemic vascular autoimmune/inflammatory disease. Despite much effort to investigate BD, there are virtually no unique laboratory markers identified to help in the diagnosis of BD, and the pathogenesis is largely unknown. The aim of this work is to explore interactions between different clinical variables by correlation analysis to determine associations between the functional linkages of different paired variables and potential diagnostic biomarkers of BD.

METHODS

We measured the immunoglobulin proteome (IgG, IgG1-4, IgA, IgA1-2) and 29 clinical variables in 66 healthy controls and 63 patients with BD. We performed a comprehensive clinical variable linkage analysis and defined the physiological, pathological and pharmacological linkages based on the correlations of all variables in healthy controls and BD patients without and with immunomodulatory therapy. We further calculated relative changes between variables derived from comprehensive linkage analysis for better indications in the clinic. The potential indicators were validated in a validation set with 76 patients with BD, 30 healthy controls, 18 patients with Takayasu arteritis and 18 patients with ANCA-associated vasculitis.

RESULTS

In this study, the variables identified were found to act in synergy rather than alone in BD patients under physiological, pathological and pharmacological conditions. Immunity and inflammation can be suppressed by corticosteroids and immunosuppressants, and integrative analysis of granulocytes, platelets and related variables is likely to provide a more comprehensive understanding of disease activity, thrombotic potential and ultimately potential tissue damage. We determined that total protein/mean corpuscular hemoglobin and total protein/mean corpuscular hemoglobin levels, total protein/mean corpuscular volume, and plateletcrit/monocyte counts were significantly increased in BD compared with controls (P < 0.05, in both the discovery and validation sets), which helped in distinguishing BD patients from healthy and vasculitis controls. Chronic anemia in BD combined with increased total protein contributed to higher levels of these biomarkers, and the interactions between platelets and monocytes may be linked to vascular involvement.

CONCLUSIONS

All these results demonstrate the utility of our approach in elucidating the pathogenesis and in identifying novel biomarkers for autoimmune diseases in the future.

Does Proteomic Mirror Reflect Clinical Characteristics of Obesity?

Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of “cheap calories” are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy.

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.

Glycoproteoform Profiles of Individual Patients' Plasma Alpha-1-Antichymotrypsin are Unique and Extensively Remodeled Following a Septic Episode

Sepsis and septic shock remain the leading causes of death in intensive care units (ICUs), yet the pathogenesis originating from the inflammatory response during sepsis remains ambiguous. Acute-phase proteins are typically highly glycosylated, and the nature of the glycans have been linked to the incidence and severity of such inflammatory responses. To further build upon these findings we here monitored, the longitudinal changes in the plasma proteome and, in molecular detail, glycoproteoform profiles of alpha-1-antichymotrypsin (AACT) extracted from plasma of ten individual septic patients. For each patient we included four different time-points, including post-operative (before sepsis) and following discharge from the ICU. We isolated AACT from plasma depleted for albumin, IgG and serotransferrin and used high-resolution native mass spectrometry to qualitatively and quantitatively monitor the multifaceted glycan microheterogeneity of desialylated AACT, which allowed us to monitor how changes in the glycoproteoform profiles reflected the patient's physiological state. Although we observed a general trend in the remodeling of the AACT glycoproteoform profiles, e.g. increased fucosylation and branching/LacNAc elongation, each patient exhibited unique features and responses, providing a resilient proof-of-concept for the importance of personalized longitudinal glycoproteoform profiling. Importantly, we observed that the AACT glycoproteoform changes induced by sepsis did not readily subside after discharge from ICU.

Ethical Principles, Constraints and Opportunities in Clinical Proteomics

Recent advances in mass spectrometry (MS)-based proteomics have vastly increased the quality and scope of biological information that can be derived from human samples. These advances have rendered current workflows increasingly applicable in biomedical and clinical contexts. As proteomics is poised to take an important role in the clinic, associated ethical responsibilities increase in tandem with impacts on the health, privacy, and wellbeing of individuals. We conducted and here report a systematic literature review of ethical issues in clinical proteomics. We add our perspectives from a background of bioethics, the results of our accompanying paper extracting individual-sensitive results from patient samples, and the literature addressing similar issues in genomics. The spectrum of potential issues ranges from patient re-identification to incidental findings of clinical significance. The latter can be divided into actionable and unactionable findings. Some of these have the potential to be employed in discriminatory or privacy-infringing ways. However, incidental findings may also have great positive potential. A plasma proteome profile, for instance, could inform on the general health or disease status of an individual regardless of the narrow diagnostic question that prompted it. We suggest that early discussion of ethical issues in clinical proteomics can ensure that eventual healthcare practices and regulations reflect the considered judgment of the community and anticipate opportunities and problems that may arise as the technology matures.

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.

Identification-Free Control over the Precursor Isotopic Mass Misassignment in Orbitrap-Based Proteomics

Selection of a precursor ion from a peptide isotopic cluster to obtain a fragmentation mass spectrum is a crucial step in data-dependent proteome analysis. However, the monoisotopic mass assignment performed in this step is often an issue confronted by the data acquisition software of hybrid Orbitrap FTMS that is most widely used in proteomics. To address the problem, many data processing tools, such as raw data converters and search engines, have optional accounting for the precursor mass shift due to the isotopic error. These solutions require additional data preprocessing steps and lead to an increase in the search space, thus making the analysis longer and/or less reliable. In this work, we processed 100 Orbitrap-based LC-MS/MS runs from 10 publicly available data sets to examine the rate of precursor isotope misassignment. The effect from taking the isotope error into account during the search on the number of identified peptides varied in a wide range from 0 to 33%. Thus, it may be tempting to spend extra time before or during a search to account for the mass assignment issue. Alternatively, this effect can be predicted a priori using an identification-free metric, which can be a part of data quality control software. Based on the results obtained in this work, we propose such a metric be further added into the visual and intuitive quality control software, viQC, developed previously and available at https://github.com/lisavetasol/viQC. It takes about a minute to calculate and plot nine quality metrics, including the proposed one for typical proteome analysis.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

A high-fat diet delays plasmin generation in a thrombomodulin-dependent manner in mice

Obesity is a prevalent prothrombotic risk factor marked by enhanced fibrin formation and suppressed fibrinolysis. Fibrin both promotes thrombotic events and drives obesity pathophysiology, but a lack of essential analytical tools has left fibrinolytic mechanisms affected by obesity poorly defined. Using a plasmin-specific fluorogenic substrate, we developed a plasmin generation (PG) assay for mouse plasma that is sensitive to tissue plasminogen activator, α2-antiplasmin, active plasminogen activator inhibitor (PAI-1), and fibrin formation, but not fibrin crosslinking. Compared with plasmas from mice fed a control diet, plasmas from mice fed a high-fat diet (HFD) showed delayed PG and reduced PG velocity. Concurrent to impaired PG, HFD also enhanced thrombin generation (TG). The collective impact of abnormal TG and PG in HFD-fed mice produced normal fibrin formation kinetics but delayed fibrinolysis. Functional and proteomic analyses determined that delayed PG in HFD-fed mice was not due to altered levels of plasminogen, α2-antiplasmin, or fibrinogen. Changes in PG were also not explained by elevated PAI-1 because active PAI-1 concentrations required to inhibit the PG assay were 100-fold higher than circulating concentrations in mice. HFD-fed mice had increased circulating thrombomodulin, and inhibiting thrombomodulin or thrombin-activatable fibrinolysis inhibitor (TAFI) normalized PG, revealing a thrombomodulin- and TAFI-dependent antifibrinolytic mechanism. Integrating kinetic parameters to calculate the metric of TG/PG ratio revealed a quantifiable net shift toward a prothrombotic phenotype in HFD-fed mice. Integrating TG and PG measurements may define a prothrombotic risk factor in diet-induced obesity.

Rapid Sample Preparation Workflow for Serum Sample Analysis with Different Mass Spectrometry Acquisition Strategies

Fast, robust, and high-throughput mass spectrometry-based serum proteomic pipelines have great potential to yield information for biomarker discovery and daily clinical practice. Here, we developed a simple and rapid sample preparation (RSP) workflow by reducing the classical pretreatment time from overnight to less than 1.5 h in an ordinary system. In HeLa cell lysates and serum samples, the number of proteins and tryptic peptides generated using the RSP was comparable to that generated using conventional methods. For fast scanning of the serum proteome, the RSP-supported pipeline could complete a test in less than 2 h with 30 min of LC-MS/MS analysis. Nearly 390 proteins spanning 8 magnitudes of abundance range were identified with high reproducibility, containing over 90 cancer-associated proteins and over 50 FDA-approved biomarkers. For fast assay development, eight candidate biomarker peptides for cardiovascular disease (CVD) were quantified by MRM with high accuracy (CV% <10). After a simple highly abundant protein removal, a deep serum proteome of over 1400 proteins was reached. By analyzing the depleted serum in DIA acquisition mode, over 700 proteins were quantified. The differentially expressed proteins could help us unambiguously distinguish the serum samples from healthy people and patients with pancreatic cancer (PC). Potential biomarkers for PC were also found. The new RSP method, which is rapid and simple, meets the demands of both deep mining and fast analysis of serum proteins. We believe that it will be widely used in serum protein studies and accelerate the transformation from biomarker discovery to clinical application.

Deciphering the Plasma Proteome of Type 2 Diabetes

With an estimated prevalence of 463 million affected, type 2 diabetes represents a major challenge to health care systems worldwide. Analyzing the plasma proteomes of individuals with type 2 diabetes may illuminate hitherto unknown functional mechanisms underlying disease pathology. We assessed the associations between type 2 diabetes and >1,000 plasma proteins in the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 993, 110 cases), with subsequent replication in the third wave of the Nord-Trøndelag Health Study (HUNT3) cohort (n = 940, 149 cases). We computed logistic regression models adjusted for age, sex, BMI, smoking status, and hypertension. Additionally, we investigated associations with incident type 2 diabetes and performed two-sample bidirectional Mendelian randomization (MR) analysis to prioritize our results. Association analysis of prevalent type 2 diabetes revealed 24 replicated proteins, of which 8 are novel. Proteins showing association with incident type 2 diabetes were aminoacylase-1, growth hormone receptor, and insulin-like growth factor-binding protein 2. Aminoacylase-1 was associated with both prevalent and incident type 2 diabetes. MR analysis yielded nominally significant causal effects of type 2 diabetes on cathepsin Z and rennin, both known to have roles in the pathophysiological pathways of cardiovascular disease, and of sex hormone-binding globulin on type 2 diabetes. In conclusion, our high-throughput proteomics study replicated previously reported type 2 diabetes-protein associations and identified new candidate proteins possibly involved in the pathogenesis of type 2 diabetes.

Multiplatform Approach for Plasma Proteomics: Complementarity of Olink Proximity Extension Assay Technology to Mass Spectrometry-Based Protein Profiling

The plasma proteome is the ultimate target for biomarker discovery. It stores an endless amount of information on the pathophysiological status of a living organism, which is, however, still difficult to comprehensively access. The high complexity of the plasma proteome can be addressed by either a system-wide and unbiased tool such as mass spectrometry (LC-MS/MS) or a highly sensitive targeted immunoassay such as the proximity extension assay (PEA). To address relevant differences and important shared characteristics, we tested the performance of LC-MS/MS in the data-dependent and data-independent acquisition modes and Olink PEA to measure circulating plasma proteins in 173 human plasma samples from a Southern German population-based cohort. We demonstrated the measurement of more than 300 proteins with both LC-MS/MS approaches applied, mainly including high-abundance plasma proteins. By the use of the PEA technology, we measured 728 plasma proteins, covering a broad dynamic range with high sensitivity down to pg/mL concentrations. Then, we quantified 35 overlapping proteins with all three analytical platforms, verifying the reproducibility of data distributions, measurement correlation, and gender-based differential expression. Our work highlights the limitations and the advantages of both targeted and untargeted approaches and proves their complementary strengths. We demonstrated a significant gain in proteome coverage depth and subsequent biological insight by a combination of platforms-a promising approach for future biomarker and mechanistic studies.

COVID-19: Nanomedicine Uncovers Blood-Clot Mystery

Further complications associated with infection by severe acute respiratory syndrome coronavirus 2 (a.k.a. SARS-CoV-2) continue to be reported. Very recent findings reveal that 20-30% of patients at high risk of mortality from COVID-19 infection experience blood clotting that leads to stroke and sudden death. Timely assessment of the severity of blood clotting will be of enormous help to clinicians in determining the right blood-thinning medications to prevent stroke or other life-threatening consequences. Therefore, rapid identification of blood-clotting-related proteins in the plasma of COVID-19 patients would save many lives. Several nanotechnology-based approaches are being developed to diagnose patients at high risk of death due to complications from COVID-19 infections, including blood clots. This Perspective outlines (i) the significant potential of nanomedicine in assessing the risk of blood clotting and its severity in SARS-CoV-2 infected patients and (ii) its synergistic roles with advanced mass-spectrometry-based proteomics approaches in identifying the important protein patterns that are involved in the occurrence and progression of this disease. The combination of such powerful tools might help us understand the clotting phenomenon and pave the way for development of new diagnostics and therapeutics in the fight against COVID-19.

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

PURPOSE OF THE REVIEW

Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D).

RECENT FINDINGS

Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

Genetic and nongenetic profiling of milk β-hydroxybutyrate and acetone and their associations with ketosis in Holstein cows

Ketosis is a metabolic disorder of increasing importance in high-yielding dairy cows, but accurate population-wide binary health trait recording is difficult to implement. Against this background, proper Gaussian indicator traits, which can be routinely measured in milk, are needed. Consequently, we focused on the ketone bodies acetone and β-hydroxybutyrate (BHB), measured via Fourier-transform infrared spectroscopy (FTIR) in milk. In the present study, 62,568 Holstein cows from large-scale German co-operator herds were phenotyped for clinical ketosis (KET) according to a veterinarian diagnosis key. A sub-sample of 16,861 cows additionally had first test-day observations for FTIR acetone and BHB. Associations between FTIR acetone and BHB with KET and with test-day traits were studied phenotypically and quantitative genetically. Furthermore, we estimated SNP marker effects for acetone and BHB (application of genome-wide association studies) based on 40,828 SNP markers from 4,384 genotyped cows, and studied potential candidate genes influencing body fat mobilization. Generalized linear mixed models were applied to infer the influence of binary KET on Gaussian-distributed acetone and BHB (definition of an identity link function), and vice versa, such as the influence of acetone and BHB on KET (definition of a logit link function). Additionally, linear models were applied to study associations between BHB, acetone and test-day traits (milk yield, fat percentage, protein percentage, fat-to-protein ratio and somatic cell score) from the first test-day after calving. An increasing KET incidence was statistically significant associated with increasing FTIR acetone and BHB milk concentrations. Acetone and BHB concentrations were positively associated with fat percentage, fat-to-protein ratio and somatic cell score. Bivariate linear animal models were applied to estimate genetic (co)variance components for KET, acetone, BHB and test-day traits within parities 1 to 3, and considering all parities simultaneously in repeatability models. Pedigree-based heritabilities were quite small (i.e., in the range from 0.01 in parity 3 to 0.07 in parity 1 for acetone, and from 0.03-0.04 for BHB). Heritabilites from repeatability models were 0.05 for acetone, and 0.03 for BHB. Genetic correlations between acetone and BHB were moderate to large within parities and considering all parities simultaneously (0.69-0.98). Genetic correlations between acetone and BHB with KET from different parities ranged from 0.71 to 0.99. Genetic correlations between acetone across parities, and between BHB across parities, ranged from 0.55 to 0.66. Genetic correlations between KET, acetone, and BHB with fat-to-protein ratio and with fat percentage were large and positive, but negative with milk yield. In genome-wide association studies, we identified SNP on BTA 4, 10, 11, and 29 significantly influencing acetone, and on BTA 1 and 16 significantly influencing BHB. The identified potential candidate genes NRXN3, ACOXL, BCL2L11, HIBADH, KCNJ1, and PRG4 are involved in lipid and glucose metabolism pathways.

Omics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention

PURPOSE OF REVIEW

Omics-based technologies were suggested to provide an advanced understanding of obesity etiology and its metabolic consequences. This review highlights the recent developments in "omics"-based research aimed to identify obesity-related biomarkers.

RECENT FINDINGS

Recent advances in obesity and metabolism research increasingly rely on new technologies to identify mechanisms in the development of obesity using various "omics" platforms. Genetic and epigenetic biomarkers that translate into changes in transcriptome, proteome, and metabolome could serve as targets for obesity prevention. Despite a number of promising candidate biomarkers, there is an increased demand for larger prospective cohort studies to validate findings and determine biomarker reproducibility before they can find applications in primary care and public health. "Omics" biomarkers have advanced our knowledge on the etiology of obesity and its links with chronic diseases. They bring substantial promise in identifying effective public health strategies that pave the way towards patient stratification and precision prevention.

Protocol for parallel proteomic and metabolomic analysis of mouse intervertebral disc tissues

The comprehensiveness of data collected by "omics" modalities has demonstrated the ability to drastically transform our understanding of the molecular mechanisms of chronic, complex diseases such as musculoskeletal pathologies, how biomarkers are identified, and how therapeutic targets are developed. Standardization of protocols will enable comparisons between findings reported by multiple research groups and move the application of these technologies forward. Herein, we describe a protocol for parallel proteomic and metabolomic analysis of mouse intervertebral disc (IVD) tissues, building from the combined expertise of our collaborative team. This protocol covers dissection of murine IVD tissues, sample isolation, and data analysis for both proteomics and metabolomics applications. The protocol presented below was optimized to maximize the utility of a mouse model for "omics" applications, accounting for the challenges associated with the small starting quantity of sample due to small tissue size as well as the extracellular matrix-rich nature of the tissue.

Genetics meets proteomics: perspectives for large population-based studies

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.

Differentially expressed serum proteins from obese Wistar rats as a risk factor for obesity-induced diseases

Obesity is a chronic disease that negatively affects life expectancy through its association with life-threatening diseases such as cancer and cardiovascular diseases. Expression proteomics combined with in silico interaction studies are used to uncover potential biomarkers and the pathways that promote obesity-related complications. These biomarkers can either aid in the development of personalized therapies or identify individuals at risk of developing obesity-related diseases. To determine the serum protein changes, Wistar rats were fed standard chow (low fat, LF), or chow formulated high fat (HF) diets (HF1, HF2 and HF3) for 8 and 42 weeks to induce obesity. Serum samples were collected from lean and obese rats at these time points. The serum samples were precipitated using trichloroacetic acid (TCA)/acetone and analyzed by 2-Dimensional SDS-PAGE. Serum protein profiles were examined using mass spectrometry (MS)-based proteomics and validated by western blotting. Protein-protein interactions among the selected proteins were studied in silico using bioinformatics tools. Several proteins showed differences in expression among the three HF diets when compared to the LF diet, and only proteins with ≥ twofold expression levels were considered differentially expressed. Apolipoprotein-AIV (APOA4), C-reactive protein (CRP), and alpha 2-HS glycoprotein (AHSG) showed differential expression at both 8 and 42 weeks, whereas alpha 1 macroglobulin (AMBP) was differentially expressed only at 8 weeks. Network analysis revealed some interactions among the proteins, an indication that these proteins might interactively play a crucial role in development of obesity-induced diseases. These data show the variation in the expression of serum proteins during acute and chronic exposure to high fat diet. Based on the expression and the in-silico interaction these proteins warrant further investigation for their role in obesity development.

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.