The differential plasma proteome of obese and overweight individuals undergoing a nutritional weight loss and maintenance intervention

Multiomics Picture of Obesity in Young Adults

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

Human cord plasma proteomic analysis reveals sexually dimorphic proteins associated with intrauterine growth restriction

Intrauterine growth restriction (IUGR) is associated with increased risk of cardiometabolic disease later in life and has been shown to affect female and male offspring differently, but the mechanisms remain unclear. The purpose of this study was to identify proteomic differences and metabolic risk markers in IUGR male and female neonates when compared to appropriate for gestational age (AGA) babies that will provide a better understanding of IUGR pathogenesis and its associated risks. Our results revealed alterations in IUGR cord plasma proteomes with most of the differentially abundant proteins implicated in peroxisome pathways. This effect was evident in females but not in males. Furthermore, we observed that catalase activity, a peroxisomal enzyme, was significantly increased in females (p < 0.05) but unchanged in males. Finally, we identified risk proteins associated with obesity, type-2 diabetes, and glucose intolerance such as EGF containing fibulin extracellular matrix protein 1 (EFEMP1), proprotein convertase subtilisin/kexin type 9 (PCSK9) and transforming growth factor beta receptor 3 (TGFBR3) proteins unique to females while coagulation factor IX (C9) and retinol binding protein 4 (RBP4) are unique in males. In conclusion, IUGR may display sexual dimorphism which may be associated with differences in lifelong risk for cardiometabolic disease between males and females.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

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.

Healthy lifestyle linked to innate immunity and lipoprotein metabolism: a cross-sectional comparison using untargeted proteomics

This study used untargeted proteomics to compare blood proteomic profiles in two groups of adults that differed widely in lifestyle habits. A total of 52 subjects in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females) participated in this cross-sectional study. Age, education level, marital status, and height did not differ significantly between LIFE and CON groups. The LIFE and CON groups differed markedly in body composition, physical activity patterns, dietary intake patterns, disease risk factor prevalence, blood measures of inflammation, triglycerides, HDL-cholesterol, glucose, and insulin, weight-adjusted leg/back and handgrip strength, and mood states. The proteomics analysis showed strong group differences for 39 of 725 proteins identified in dried blood spot samples. Of these, 18 were downregulated in the LIFE group and collectively indicated a lower innate immune activation signature. A total of 21 proteins were upregulated in the LIFE group and supported greater lipoprotein metabolism and HDL remodeling. Lifestyle-related habits and biomarkers were probed and the variance (> 50%) in proteomic profiles was best explained by group contrasts in indicators of adiposity. This cross-sectional study established that a relatively small number of proteins are associated with good lifestyle habits.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

Proteomics reveals unique plasma signatures in constitutional thinness

PURPOSE

Studying the plasma proteome of control versus constitutionally thin (CT) individuals, exposed to overfeeding, may give insights into weight-gain management, providing relevant information to the clinical entity of weight-gain resistant CT, and discovering new markers for the condition.

EXPERIMENTAL DESIGN

Untargeted protein relative quantification of 63 CT and normal-weight individuals was obtained in blood plasma at baseline, during and after an overfeeding challenge using mass spectrometry-based proteomics.

RESULTS

The plasma proteome of CT subjects presented limited specificity with respect to controls at baseline. Yet, CT showed lower levels of inflammatory C-reactive protein and larger levels of protective insulin-like growth factor-binding protein 2. Differences were more marked during and after overfeeding. CT plasma proteome showed larger magnitude and significance in response, suggesting enhanced "resilience" and more rapid adaptation to changes. Four proteins behaved similarly between CT and controls, while five were regulated in opposite fashion. Ten proteins were differential during overfeeding in CT only (including increased fatty acid-binding protein and glyceraldehyde-3-phosphate dehydrogenase, and decreased apolipoprotein C-II and transferrin receptor protein 1).

CONCLUSIONS AND CLINICAL RELEVANCE

This first proteomic profiling of a CT cohort reveals different plasma proteomes between CT subjects and controls in a longitudinal clinical trial. Our molecular observations further support that the resistance to weight gain in CT subjects appears predominantly biological.

CLINICALTRIALS

gov Identifier: NCT02004821.

Relationship between Serum Kallistatin and Afamin and Anthropometric Factors Associated with Obesity and of Being Overweight in Patients after Myocardial Infarction and without Myocardial Infarction

Extensive clinical and epidemiological evidence has linked obesity to a broad spectrum of cardiovascular disease (CVD), including coronary disease, heart failure, hypertension, cerebrovascular disease, atrial fibrillation, ventricular arrhythmias, and sudden death. In addition, increasing knowledge of regulatory peptides has allowed an assessment of their role in various non-communicable diseases, including CVD. The study assessed the concentration of kallistatin and afamin in the blood serum of patients after a myocardial infarction and without a cardiovascular event, and determined the relationship between the concentration of kallistatin and afamin and the anthropometric indicators of being overweight and of obesity in these groups. Serum kallistatin and afamin were quantified by ELISA tests in a cross-sectional study of 160 patients who were divided into two groups: study group (SG) (n = 80) and another with no cardiovascular event (CG) (n = 80). Serum kallistatin concentration was significantly higher in the SG (p < 0.001), while the level of afamin was significantly lower in this group (p < 0.001). In addition, a positive correlation was observed in the SG between the afamin concentration and the waist to hip ratio (WHR), lipid accumulation product (LAP) and the triglyceride glucose index (TyG index). In the CG, the concentration of kallistatin positively correlated with the LAP and TyG index, while the concentration of afamin positively correlated with all the examined parameters: body mass index (BMI), waist circumference (WC), hip circumference (HC), waist to hip ratio (WHtR), visceral adiposity index (VAI), LAP and TyG index. Serum kallistatin and afamin concentrations are associated with the anthropometric parameters related to being overweight and to obesity, especially to those describing the visceral distribution of adipose tissue and metabolic disorders related to excessive fatness.

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.

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/

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.

Proteomics of Human Milk: Definition of a Discovery Workflow for Clinical Research Studies

Milk is a complex biological fluid composed mainly of water, carbohydrates, lipids, proteins, and diverse bioactive factors. Human milk represents a unique tailored source of nutrients that adapts during lactation to the specific needs of the developing infant. Proteins in milk have been studied for decades, and proteomics, peptidomics, and glycoproteomics are the main approaches previously deployed to decipher the proteome of human milk. In the present work, we aimed at implementing a highly automated pipeline for the proteomic analysis of human milk with liquid chromatography mass spectrometry (MS). Commercial human milk samples were used to evaluate and optimize workflows. Centrifugation for defatting milk samples was assessed before and after reduction, alkylation, and enzymatic digestion of proteins, without and with presence of surfactants. Skimmed milk samples were analyzed using isobaric labeling-based quantitative MS on an Orbitrap Tribrid mass spectrometer. Sample fractionation using isoelectric focusing was also evaluated to more deeply profile the human milk proteome. Finally, the most appropriate workflow was transferred to a liquid handling workstation for automated sample preparation. In conclusion, we have defined and describe herein an efficient highly automated proteomic workflow for human milk sample analysis. It is compatible with clinical research, possibly allowing the analysis of sufficiently large cohorts of samples.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses.

Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637.

Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.

Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention

Inflammatory and cardiovascular biomarkers have been associated with obesity, but little is known about how they change upon dietary intervention and concomitant weight loss. Further, protein biomarkers might be useful for predicting weight loss in overweight and obese individuals. We performed secondary analyses in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized intervention trial that included healthy 609 adults (18–50 years old) with BMI 28–40 kg/m², to evaluate associations between circulating protein biomarkers and BMI at baseline, during a weight loss diet intervention, and to assess predictive potential of baseline blood proteins on weight loss. We analyzed 263 plasma proteins at baseline and 6 months into the intervention using the Olink Proteomics CVD II, CVD III and Inflammation arrays. BMI was assessed at baseline, after 3 and 6 months of dietary intervention. At baseline, 102 of the examined inflammatory and cardiovascular biomarkers were associated with BMI (>90% with successful replication in 1,584 overweight/obese individuals from a community-based cohort study) and 130 tracked with weight loss shedding light into the pathophysiology of obesity. However, out of 263 proteins analyzed at baseline, only fibroblast growth factor 21 (FGF-21) predicted weight loss, and none helped individualize dietary assignment.

Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition

In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Changes in Proteomic Profiles are Related to Changes in BMI and Fat Distribution During 10 Years of Aging

OBJECTIVE

This study investigated how changes in 84 proteins over a 10-year period of aging were related to changes in measures of body fat and distribution over the same period.

METHODS

Cardiovascular candidate proteins were measured using the proximal extension assay technique, along with BMI and waist-hip ratio (WHR), at ages 70, 75, and 80 in 1,016 participants of the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) cohort. Associations of changes in plasma protein levels, BMI, and WHR over time were analyzed using linear mixed models.

RESULTS

Changes in 19 and 16 proteins were significantly associated with changes in BMI and WHR, respectively (P < 0.00059), over the investigated 10-year period. Leptin and fatty acid-binding protein 4 were among the proteins most strongly associated with changes in both BMI and WHR. Four of the proteins significantly tracked with change in BMI (P < 0.00059) but not WHR (P > 0.05): endothelial cell-specific molecule 1, pentraxin-related protein PTX3, ST2 protein (also known as interleukin-1 receptor-like 1), and spondin-1. Five proteins tracked with change in WHR (P < 0.00059) but not BMI (P > 0.05): caspase-8, cathepsin L1, oxidized low-density lipoprotein receptor 1, interleukin-6 receptor subunit alpha, and C-C motif chemokine 20.

CONCLUSIONS

This is the first large longitudinal study of how changes in plasma protein signatures are associated with changes in measures of body fat and distribution over 10 years of aging.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Serum proteome changes and accelerated reduction of fat mass after laparoscopic gastric plication in morbidly obese patients

Laparoscopic Gastric Plication (LGP) is a relatively new bariatric surgical procedure which no part of the stomach is removed. It is not clearly understood how LGP leads to fatty tissue reduction. We aimed to investigate the impact of LGP on serum proteome and understand molecular mechanisms of LGP-induced weight loss post-surgery. A Prospective observational study of 16 obese individuals who underwent LGP was performed. A Label-free quantitative shotgun proteomics approach was used to compare serum proteome of subjects before surgery with serum of the same individuals 1 to 2 months post-surgery (T1) and 4 to 5 months post-surgery (T2). The proteome analysis revealed that 48 proteins were differentially regulated between pre-surgery and T1, and seven proteins between pre-surgery and T2 of which six proteins were shared between the two timepoints. Among differentially regulated proteins, four proteins (SRGN, FETUB, LCP1 and CFP) have not previously been described in the context of BMI/weight loss. Despite few differences following LGP, most regulated serum proteins are in accordance with alternative weight loss procedures. Pathway analysis revealed changes to lipid- and inflammatory pathways, including PPARα/RXRα, LXR/RXR and FXR/RXR activation, especially at T1. At T2, the pathways related to inflammation and immune system are most affected. SIGNIFICANCE: Among the available clinical therapies for morbid obesity, bariatric surgery is considered as the most effective approach to achieve long-term weight loss, alongside a significant improvement in metabolic syndrome. However, very little is known about the underlying mechanism associated with significant weight loss post-surgery. Understanding such mechanisms could lead to development of safer non-surgical weight loss approaches. We here present the first analysis of the impact of LGP on the serum proteome, to bring new insights into the underlying molecular mechanism. Our findings indicate that LGP has a comprehensive systemic effect based on the blood serum proteome profile which might account for accelerated reduction of fat mass after surgery, thus, food restriction is not the only reason for weight loss following this unique surgical approach. As secretory regions of the stomach are preserved in LGP and it is associated with minimal physiological and anatomical changes, the findings are of high importance in the field of bariatric surgery and weight loss.

Plasma Protein and MicroRNA Biomarkers of Insulin Resistance: A Network-Based Integrative -Omics Analysis

Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma.

Proteomic Analysis of Human Plasma during Intermittent Fasting

Intermittent fasting (IF) increases lifespan and decreases metabolic disease phenotypes and cancer risk in model organisms, but the health benefits of IF in humans are less clear. Human plasma derived from clinical trials is one of the most difficult sample sets to analyze using mass spectrometry-based proteomics due to the extensive sample preparation required and the need to process many samples to achieve statistical significance. Here, we describe an optimized and accessible device (Spin96) to accommodate up to 96 StageTips, a widely used sample preparation medium enabling efficient and consistent processing of samples prior to LC-MS/MS. We have applied this device to the analysis of human plasma from a clinical trial of IF. In this longitudinal study employing 8-weeks IF, we identified significant abundance differences induced by the IF intervention, including increased apolipoprotein A4 (APOA4) and decreased apolipoprotein C2 (APOC2) and C3 (APOC3). These changes correlated with a significant decrease in plasma triglycerides after the IF intervention. Given that these proteins have a role in regulating apolipoprotein particle metabolism, we propose that IF had a positive effect on lipid metabolism through modulation of HDL particle size and function. In addition, we applied a novel human protein variant database to detect common protein variants across the participants. We show that consistent detection of clinically relevant peptides derived from both alleles of many proteins is possible, including some that are associated with human metabolic phenotypes. Together, these findings illustrate the power of accessible workflows for proteomics analysis of clinical samples to yield significant biological insight.

Analyzing Cerebrospinal Fluid Proteomes to Characterize Central Nervous System Disorders: A Highly Automated Mass Spectrometry-Based Pipeline for Biomarker Discovery

Over the past decade, liquid chromatography tandem mass spectrometry (LC MS/MS)-based workflows become standard for biomarker discovery in proteomics. These medium- to high-throughput (in terms of protein content) profiling approaches have been applied to clinical research. As a result, human proteomes have been characterized to a greater extent than ever before. However, proteomics in clinical research and biomarker discovery studies has generally been performed with small cohorts of subjects (or pooled samples from larger cohorts). This is problematic, as when aiming to identify novel biomarkers, small studies suffer from inherent and important limitations, as a result of the reduced biological diversity and representativity of human populations. Consequently, larger-scale proteomics will be key to delivering robust biomarker candidates and enabling translation to clinical practice.Cerebrospinal fluid (CSF) is a highly clinically relevant body fluid, and an important source of potential biomarkers for brain-associated damage, such as that induced by traumatic brain injury and stroke, and brain diseases, such as Alzheimer's disease and Parkinson's disease. We have developed a scalable automated proteomic pipeline (ASAP²) for biomarker discovery. This workflow is compatible with larger clinical research studies in terms of sample size, while still allowing several hundred proteins to be measured in CSF by MS. In this chapter, we describe the whole proteomic workflow to analyze human CSF. We further illustrate our protocol with some examples from an analysis of hundreds of human CSF samples, in the specific context of biomarker discovery to characterize central nervous system disorders.

Comparative Proteomic Analysis of Visceral Adipose Tissue in Morbidly Obese and Normal Weight Chinese Women

OBJECTIVE

Visceral adipose tissue (VAT) plays a central role in the balance of energy metabolism. The objective of this study was to investigate the differentially expressed proteins in VAT between morbidly obese (BMI >35 kg/m²) and normal weight Chinese women.

METHOD

Nine morbidly obese women and 8 normal weight women as controls were enrolled. Abdominal VAT was excised and analyzed by label-free one-dimensional liquid chromatography tandem mass spectrometry (1D-LC-MS/MS). Differentially expressed VAT proteins were further analyzed with Gene Ontology (GO) analysis and Ingenuity Pathway Analysis (IPA). Masson's trichrome staining and CD68 immunohistochemical staining of VAT were conducted in all subjects.

RESULT

A total of 124 differentially expressed proteins were found with a ≥2-fold difference. Forty-one proteins were upregulated, and 83 proteins were downregulated in obese individuals. These altered VAT proteins were involved in the attenuation of the liver X receptor/retinoid X receptor (LXR/RXR) signaling pathway and the activation of the acute-phase response process. Three proteins (ACSL1, HADH, and UCHL1) were validated by western blotting using the same set of VAT samples from 6 morbidly obese and 7 normal weight patients, and the results indicated that the magnitude and direction of the protein changes were in accordance with the proteomic analysis. Masson's trichrome staining and CD68 immunohistochemical staining demonstrated that there was much more collagen fiber deposition and CD68-positive macrophages in the VAT of morbidly obese patients, suggesting extensive fiber deposition and macrophage infiltration.

CONCLUSION

A number of differentially expressed proteins were identified in VAT between morbidly obese and normal weight Chinese females. These differential proteins could be potential candidates in addressing the role of VAT in the development of obesity.

Obesity shows preserved plasma proteome in large independent clinical cohorts

Holistic human proteome maps are expected to complement comprehensive profile assessment of health and disease phenotypes. However, methodologies to analyze proteomes in human tissue or body fluid samples at relevant scale and performance are still limited in clinical research. Their deployment and demonstration in large enough human populations are even sparser. In the present study, we have characterized and compared the plasma proteomes of two large independent cohorts of obese and overweight individuals using shotgun mass spectrometry (MS)-based proteomics. Herein, we showed, in both populations from different continents of about 500 individuals each, the concordance of plasma protein MS measurements in terms of variability, gender-specificity, and age-relationship. Additionally, we replicated several known and new associations between proteins, clinical and molecular variables, such as insulin and glucose concentrations. In conclusion, our MS-based analyses of plasma samples from independent human cohorts proved the practical feasibility and efficiency of a large and unified discovery/replication approach in proteomics, which was also recently coined “rectangular” design.

Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity

Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation techniques, such as high-resolution two-dimensional gel electrophoresis or liquid chromatography in combination with mass spectrometry, for high-throughput protein identification. These studies have applied proteomics to comprehensive biochemical profiling and comparison studies while using different tissues and biological fluids from patients to demonstrate the physiological or pathological adaptations within their proteomes. Further investigations into these proteome-wide alterations will enable us to not only understand the disease pathophysiology, but also to determine signature proteins that can serve as biomarkers for obesity and related diseases. This review examines the different proteomic techniques used to study human obesity and discusses its successful applications along with its technical limitations.

A proteomic signature that reflects pancreatic beta-cell function

AIM

Proteomics has the potential to enhance early identification of beta-cell dysfunction, in conjunction with monitoring the various stages of type 2 diabetes onset. The most routine method of assessing pancreatic beta-cell function is an oral glucose tolerance test, however this method is time consuming and carries a participant burden. The objectives of this research were to identify protein signatures and pathways related to pancreatic beta-cell function in fasting blood samples.

METHODS

Beta-cell function measures were calculated for MECHE study participants who completed an oral glucose tolerance test and had proteomic data (n = 100). Information on 1,129 protein levels was obtained using the SOMAscan assay. Receiver operating characteristic curves were used to assess discriminatory ability of proteins of interest. Subsequent in vitro experiments were performed using the BRIN-BD11 pancreatic beta-cell line. Replication of findings were achieved in a second human cohort where possible.

RESULTS

Twenty-two proteins measured by aptamer technology were significantly associated with beta-cell function/HOMA-IR while 17 proteins were significantly associated with the disposition index (p ≤ 0.01). Receiver operator characteristic curves determined the protein panels to have excellent discrimination between low and high beta-cell function. Linear regression analysis determined that beta-endorphin and IL-17F have strong associations with beta-cell function/HOMA-IR, β = 0.039 (p = 0.005) and β = -0.027 (p = 0.013) respectively. Calcineurin and CRTAM were strongly associated with the disposition index (β = 0.005 and β = 0.005 respectively, p = 0.012). In vitro experiments confirmed that IL-17F modulated insulin secretion in the BRIN-BD11 cell line, with the lower concentration of 10 ng/mL significantly increasing glucose stimulated insulin secretion (p = 0.043).

CONCLUSIONS

Early detection of compromised beta-cell function could allow for implementation of nutritional and lifestyle interventions before progression to type 2 diabetes.

Alzheimer disease pathology and the cerebrospinal fluid proteome

BACKGROUND

Altered proteome profiles have been reported in both postmortem brain tissues and body fluids of subjects with Alzheimer disease (AD), but their broad relationships with AD pathology, amyloid pathology, and tau-related neurodegeneration have not yet been fully explored. Using a robust automated MS-based proteomic biomarker discovery workflow, we measured cerebrospinal fluid (CSF) proteomes to explore their association with well-established markers of core AD pathology.

METHODS

Cross-sectional analysis was performed on CSF collected from 120 older community-dwelling adults with normal (n = 48) or impaired cognition (n = 72). LC-MS quantified hundreds of proteins in the CSF. CSF concentrations of β-amyloid 1-42 (Aβ1-42), tau, and tau phosphorylated at threonine 181 (P-tau181) were determined with immunoassays. First, we explored proteins relevant to biomarker-defined AD. Then, correlation analysis of CSF proteins with CSF markers of amyloid pathology, neuronal injury, and tau hyperphosphorylation (i.e., Aβ1-42, tau, P-tau181) was performed using Pearson's correlation coefficient and Bonferroni correction for multiple comparisons.

RESULTS

We quantified 790 proteins in CSF samples with MS. Four CSF proteins showed an association with CSF Aβ1-42 levels (p value ≤ 0.05 with correlation coefficient (R) ≥ 0.38). We identified 50 additional CSF proteins associated with CSF tau and 46 proteins associated with CSF P-tau181 (p value ≤ 0.05 with R ≥ 0.37). The majority of those proteins that showed such associations were brain-enriched proteins. Gene Ontology annotation revealed an enrichment for synaptic proteins and proteins originating from reelin-producing cells and the myelin sheath.

CONCLUSIONS

We used an MS-based proteomic workflow to profile the CSF proteome in relation to cerebral AD pathology. We report strong evidence of previously reported CSF proteins and several novel CSF proteins specifically associated with amyloid pathology or neuronal injury and tau hyperphosphorylation.