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

The 2024 Report on the Human Proteome from the HUPO Human Proteome Project

The Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify at least one isoform of every protein-coding gene and (2) to make proteomics an integral part of multiomics studies of human health and disease. The past year has seen major transitions for the HPP. neXtProt was retired as the official HPP knowledge base, UniProtKB became the reference proteome knowledge base, and Ensembl-GENCODE provides the reference protein target list. A function evidence FE1-5 scoring system has been developed for functional annotation of proteins, parallel to the PE1-5 UniProtKB/neXtProt scheme for evidence of protein expression. This report includes updates from neXtProt (version 2023-09) and UniProtKB release 2024_04, with protein expression detected (PE1) for 18138 of the 19411 GENCODE protein-coding genes (93%). The number of non-PE1 proteins ("missing proteins") is now 1273. The transition to GENCODE is a net reduction of 367 proteins (19,411 PE1-5 instead of 19,778 PE1-4 last year in neXtProt). We include reports from the Biology and Disease-driven HPP, the Human Protein Atlas, and the HPP Grand Challenge Project. We expect the new Functional Evidence FE1-5 scheme to energize the Grand Challenge Project for functional annotation of human proteins throughout the global proteomics community, including π-HuB in China.

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

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

Protocol for a randomised, double-blinded, controlled trial of youth with childhood-onset obesity treated with semaglutide 2.4 mg/week: the RESETTLE trial

INTRODUCTION

Childhood-onset obesity poses significant health risks, including early-onset type 2 diabetes, cardiovascular disease, and reduced quality of life. Hospital-based non-pharmacological obesity care can reduce childhood obesity, but 25% of children do not respond. Therefore, this study investigates the effect of the glucagon-like peptide-1 receptor agonist, semaglutide, as an add-on to hospital-based obesity care in youth who still have obesity following hospital-based obesity care as children. Furthermore, biomedical and psychosocial factors linked to treatment response will be investigated, alongside an exercise-based strategy to prevent weight regain and maintain a healthy body composition after semaglutide treatment.

METHODS AND ANALYSIS

This is an investigator-initiated, randomised, placebo-controlled, double-blind trial. We will enrol expectedly 180-270 young adults aged 18-28 years based on their previous response to a paediatric obesity management programme and their current body mass index (BMI). Participants are categorised into four groups: low treatment response (BMI SD score (SDS) reduction <0.10; BMI ≥30 kg/m2); medium treatment response (BMI SDS reduction >0.25; BMI ≥30 kg/m2); high treatment response (BMI SDS reduction >0.50; BMI <30 kg/m2) and a population-based reference group with normal weight development in childhood. Participants with BMI ≥30 kg/m2 are randomised 2:1 to subcutaneous injections of semaglutide 2.4 mg/week or placebo as an add-on to hospital-based obesity care for 68 weeks. The primary outcome is the change in BMI from randomisation to the end of treatment with semaglutide compared with placebo. Secondary endpoints are changes in weight and body composition.

ETHICS AND DISSEMINATION

The trial has been approved by the Danish Medicines Agency and the Ethical Committee of the Capital Region of Denmark (H-20039422). The trial will be conducted in accordance with the Declaration of Helsinki and follow the guidelines for Good Clinical Practice. Results will be presented at international scientific conferences and published in peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

EudraCT 2019-002274-31.

Integrative analysis of transcriptome and proteome wide association studies prioritized functional genes for obesity

BACKGROUND

Genome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses.

METHODS

We first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability.

RESULTS

We prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets.

CONCLUSION

Our study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.

Secreted protein TNA: a promising biomarker for understanding the adipose-bone axis and its impact on bone metabolism

BACKGROUND

Osteoporosis (OP) is a systemic bone disease characterized by reduced bone mass and deterioration of bone microstructure, leading to increased bone fragility. Platelets can take up and release cytokines, and a high platelet count has been associated with low bone density. Obesity is strongly associated with OP, and adipose tissue can influence platelet function by secreting adipokines. However, the biological relationship between these factors remains unclear.

METHODS

We conducted differential analysis to identify OP platelet-related plasma proteins. And, making comprehensive analysis, including functional enrichment, protein-protein interaction network analysis, and Friends analysis. The key protein, Tetranectin (TNA/CLEC3B), was identified through screening. Then, we analyzed TNA's potential roles in osteogenic and adipogenic differentiation using multiple RNA-seq data sets and validated its effect on osteoclast differentiation and bone resorption function through in vitro experiments.

RESULTS

Six OP-platelet-related proteins were identified via differential analysis. Then, we screened the key protein TNA, which was found to be highly expressed in adipose tissue. RNA-seq data suggested that TNA may promote early osteoblast differentiation. In vitro experiments showed that knockdown of TNA expression significantly increased the expression of osteoclast markers, thereby promoting osteoclast differentiation and bone resorption.

CONCLUSIONS

We identified TNA as a secreted protein that inhibits osteoclast differentiation and bone resorption. While, it potentially promoted early osteoblast differentiation from bioinformatic results. TNA may play a role in bone metabolism through the adipose-bone axis.

A Multiplexed Quantitative Proteomics Approach to the Human Plasma Protein Signature

Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

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

An enhanced label-free proteomics approach for deep-diving into equine plasma proteome, including the discovery of protein biomarkers for strenuous exercise

Plasma proteins have been a valuable source of biomarkers for clinical uses and for monitoring of the illicit use of prohibited substances or practices in equine sports. We have previously reported the first use of label-free proteomics in profiling equine plasma proteome. This study aimed to refine the method by systematically evaluating various plasma fractionation methods and the use of narrower precursor mass ranges in data-independent acquisition (DIA) mass spectrometry (MS). Tandem fractionations of equine plasma with octanoic acid precipitation followed by solid-phase extraction (SPE) with C4 cartridges provided the largest increase in the number of new proteins identified. The use of two narrow precursor mass ranges of m/z 400-600 and 600-800 in DIA not only identified most proteins detectable by using a single mass range of m/z 350-1500 but also identified ~27% more proteins. The improved method was applied to analyse the plasma proteome of 'postrace' samples which, unlike other samples, had been collected from racehorses soon after racing. Multivariate data analysis has identified upregulation of 14 proteins and downregulation of six proteins in postrace plasma compared with the non-postrace plasma samples. Literature review of these proteins has provided evidence of exercise-induced haemolysis and changes in antioxidant enzyme activities, kinin system, insulin signalling and energy metabolism after strenuous exercise. The improved method has enabled a deeper profiling of the equine plasma proteome and identified the proteins associated with normal physiological changes after racing which are potential confounding factors in the development of a biomarker approach for doping control.

The impact of short-term eucaloric low- and high-carbohydrate diets on liver triacylglycerol content in males with overweight and obesity: a randomized crossover study

BACKGROUND

Intrahepatic triacylglycerol (liver TG) content is associated with hepatic insulin resistance and dyslipidemia. Liver TG content can be modulated within days under hypocaloric conditions.

OBJECTIVES

We hypothesized that 4 d of eucaloric low-carbohydrate/high-fat (LC) intake would decrease liver TG content, whereas a high-carbohydrate/low-fat (HC) intake would increase liver TG content, and further that alterations in liver TG would be linked to dynamic changes in hepatic glucose and lipid metabolism.

METHODS

A randomized crossover trial in males with 4 d + 4 d of LC and HC, respectively, with ≥2 wk of washout. 1H-magnetic resonance spectroscopy (1H-MRS) was used to measure liver TG content, with metabolic testing before and after intake of an LC diet (11E% carbohydrate corresponding to 102 ± 12 {mean ± standard deviation [SD]) g/d, 70E% fat} and an HC diet (65E% carbohydrate corresponding to 537 ± 56 g/d, 16E% fat). Stable [6,6-2H2]-glucose and [1,1,2,3,3-D5]-glycerol tracer infusions combined with hyperinsulinemic-euglycemic clamps and indirect calorimetry were used to measure rates of hepatic glucose production and lipolysis, whole-body insulin sensitivity and substrate oxidation.

RESULTS

Eleven normoglycemic males with overweight or obesity (BMI 31.6 ± 3.7 kg/m2) completed both diets. The LC diet reduced liver TG content by 35.3% (95% confidence interval: -46.6, -24.1) from 4.9% [2.4-11.0] (median interquartile range) to 2.9% [1.4-6.9], whereas there was no change after the HC diet. After the LC diet, fasting whole-body fat oxidation and plasma beta-hydroxybutyrate concentration increased, whereas markers of de novo lipogenesis (DNL) diminished. Fasting plasma TG and insulin concentrations were lowered and the hepatic insulin sensitivity index increased after LC. Peripheral glucose disposal was unchanged.

CONCLUSIONS

Reduced carbohydrate and increased fat intake for 4 d induced a marked reduction in liver TG content and increased hepatic insulin sensitivity. Increased rates of fat oxidation and ketogenesis combined with lower rates of DNL are suggested to be responsible for lowering liver TG. This trial was registered at clinicaltrials.gov as NCT04581421.

Proteomic profile and predictive markers of outcome in patients with subarachnoid hemorrhage

BACKGROUND

The molecular mechanisms underlying development of posthemorrhagic hydrocephalus (PHH) following subarachnoid hemorrhage (SAH) remain incompletely understood. Consequently, treatment strategies tailored towards the individual patient remain limited. This study aimed to identify proteomic cerebrospinal fluid (CSF) biomarkers capable of predicting shunt dependency and functional outcome in patients with SAH in order to improve informed clinical decision making.

METHODS

Ventricular CSF samples were collected twice from 23 patients with SAH who required external ventricular drain (EVD) insertion (12 patients with successful EVD weaning, 11 patients in need of permanent CSF shunting due to development of PHH). The paired CSF samples were collected acutely after ictus and later upon EVD removal. Cisternal CSF samples were collected from 10 healthy control subjects undergoing vascular clipping of an unruptured aneurysm. All CSF samples were subjected to mass spectrometry-based proteomics analysis. Proteomic biomarkers were quantified using area under the curve (AUC) estimates from a receiver operating curve (ROC).

RESULTS

CSF from patients with SAH displayed a distinct proteomic profile in comparison to that of healthy control subjects. The CSF collected acutely after ictus from patients with SAH was moreover distinct from that collected weeks later but appeared similar in the weaned and shunted patient groups. Sixteen unique proteins were identified as potential predictors of shunt dependency, while three proteins were identified as potential predictors of functional outcome assessed six months after ictus with the modified Rankin Scale.

CONCLUSIONS

We here identified several potential proteomic biomarkers in CSF from patients with SAH capable of predicting (i) shunt dependency and thus development of PHH and (ii) the functional outcome assessed six months after ictus. These proteomic biomarkers may have the potential to aid clinical decision making by predicting shunt dependency and functional outcome following SAH.

Trigeminal ganglion neurons are directly activated by influx of CSF solutes in a migraine model

Classical migraine patients experience aura, which is transient neurological deficits associated with cortical spreading depression (CSD), preceding headache attacks. It is not currently understood how a pathological event in cortex can affect peripheral sensory neurons. In this study, we show that cerebrospinal fluid (CSF) flows into the trigeminal ganglion, establishing nonsynaptic signaling between brain and trigeminal cells. After CSD, ~11% of the CSF proteome is altered, with up-regulation of proteins that directly activate receptors in the trigeminal ganglion. CSF collected from animals exposed to CSD activates trigeminal neurons in naïve mice in part by CSF-borne calcitonin gene-related peptide (CGRP). We identify a communication pathway between the central and peripheral nervous system that might explain the relationship between migrainous aura and headache.

Variant mapping using mass spectrometry-based proteotyping as a diagnostic tool in von Willebrand disease

BACKGROUND

von Willebrand disease (VWD) is the most common inherited bleeding disorder, characterized by either partial or complete von Willebrand factor (VWF) deficiency or by the occurrence of VWF proteoforms of altered functionality. The gene encoding VWF is highly polymorphic, giving rise to a variety of proteoforms with varying plasma concentrations and clinical significance.

OBJECTIVES

To address this complexity, we translated genomic variation in VWF to corresponding VWF proteoforms circulating in blood.

METHODS

VWF was characterized in VWD patients (n = 64) participating in the Willebrand in the Netherlands study by conventional laboratory testing, DNA sequencing and complementary discovery, and targeted mass spectrometry-based plasma proteomic strategies.

RESULTS

Unbiased plasma profiling combined with immune enrichment of VWF verified VWF and its binding partner factor VIII as key determinants of VWD and revealed a remarkable heterogeneity in VWF amino acid sequence coverage among patients. Subsequent VWF proteotyping enabled identification of both polymorphisms (eg, p.Thr789Ala, p.Gln852Arg, and p.Thr1381Ala), as well as pathogenic variants (n = 16) along with their corresponding canonical sequences. Targeted proteomics using stable isotope-labeled peptides confirmed unbiased proteotyping for 5 selected variants and suggested differential proteoform quantities in plasma. The variant-to-wild-type peptide ratio was determined in 6 type 2B patients heterozygous for p.Arg1306Trp, confirming the relatively low proteoform concentration of the pathogenic variant. The elevated VWF propeptide/VWF ratio indicated increased clearance of specific VWF proteoforms.

CONCLUSION

This study highlights how VWF proteotyping from plasma could be the first step to bridge the gap between genotyping and functional testing in VWD.

Altered proteome profiles related to visceral adiposity may mediate the favorable effect of green Mediterranean diet: the DIRECT-PLUS trial

OBJECTIVE

The objective of this study was to explore the effects of a green Mediterranean (green-MED) diet, which is high in dietary polyphenols and green plant-based protein and low in red/processed meat, on cardiovascular disease and inflammation-related circulating proteins and their associations with cardiometabolic risk parameters.

METHODS

In the 18-month weight loss trial Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS), 294 participants with abdominal obesity were randomized to basic healthy dietary guidelines, Mediterranean (MED), or green-MED diets. Both isocaloric MED diet groups consumed walnuts (28 g/day), and the green-MED diet group also consumed green tea (3-4 cups/day) and green shakes (Mankai plant shake, 500 mL/day) and avoided red/processed meat. Proteome panels were measured at three time points using Olink CVDII.

RESULTS

At baseline, a dominant protein cluster was significantly related to higher phenotypic cardiometabolic risk parameters, with the strongest associations attributed to magnetic resonance imaging-assessed visceral adiposity (false discovery rate of 5%). Overall, after 6 months of intervention, both the MED and green-MED diets induced improvements in cardiovascular disease and proinflammatory risk proteins (p < 0.05, vs. healthy dietary guidelines), with the green-MED diet leading to more pronounced beneficial changes, largely driven by dominant proinflammatory proteins (IL-1 receptor antagonist protein, IL-16, IL-18, thrombospondin-2, leptin, prostasin, galectin-9, and fibroblast growth factor 21; adjusted for age, sex, and weight loss; p < 0.05). After 18 months, proteomics cluster changes presented the strongest correlations with visceral adiposity reduction.

CONCLUSIONS

Proteomics clusters may enhance our understanding of the favorable effect of a green-MED diet that is enriched with polyphenols and low in red/processed meat on visceral adiposity and cardiometabolic risk.

Profound perturbations are found in the proteome and metabolome in children with obesity after weight loss intervention

Background and aims

The mechanisms occur in children with obesity after lifestyle intervention remain poorly explained. Here, we investigated the serum proteomes and metabolomes of children with obesity who had undergone 30 days of weight loss intervention.

Methods and results

Serum samples and clinical parameters were collected before and after lifestyle alteration interventions. Proteomic and metabolomic profiling was used to identify the differentially expressed proteins and differentially abundant metabolites in response to weight loss intervention. Lifestyle alteration interventions significantly decreased BMI, waist circumference, hip circumference and body fat, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high non-HDL cholesterol, but not TG and high-density lipoprotein cholesterol (HDL), in children with obesity. By comparing the multiomics data, we identified 43 proteins and 165 metabolites that were significantly differentially expressed in children with obesity before and after lifestyle alteration interventions. Using integrated -omics analysis, we obtained 7 KEGG pathways that were organically integrated based on the correlations between differentially expressed proteins (DEPs) and metabolites (DMs). Further interaction analysis identified 7 proteins as candidate DEPs and 9 metabolites as candidate DMs. Interestingly, we found that some of these candidate DEPs and candidate DMs were significantly correlated with clinical parameters.

Conclusion

Our results provide valuable proteome and metabolome data resources for better understanding weight loss-associated responses in children with obesity. In addition, we analyzed the number of significantly differentially expressed proteins and metabolites, shed new light on weight loss pathogenesis in children with obesity, and added potential therapeutic agents for obese children.

Harnessing the power of proteomics in precision diabetes medicine

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

Screening of novel biomarkers for acute kidney transplant rejection using DIA-MS based proteomics

BACKGROUND

Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection.

METHODS

The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA).

RESULTS

A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis.

CONCLUSIONS

Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.

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.

Systemic proteome adaptions to 7-day complete caloric restriction in humans

Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.

The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy

Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis

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

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

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

A uniform data processing pipeline enables harmonized nanoparticle protein corona analysis across proteomics core facilities

Protein corona, a layer of biomolecules primarily comprising proteins, forms dynamically on nanoparticles in biological fluids and is crucial for predicting nanomedicine safety and efficacy. The protein composition of the corona layer is typically analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Our recent study, involving identical samples analyzed by 17 proteomics facilities, highlighted significant data variability, with only 1.8% of proteins consistently identified across these centers. Here, we implement an aggregated database search unifying parameters such as variable modifications, enzyme specificity, number of allowed missed cleavages and a stringent 1% false discovery rate at the protein and peptide levels. Such uniform search dramatically harmonizes the proteomics data, increasing the reproducibility and the percentage of consistency-identified unique proteins across distinct cores. Specifically, out of the 717 quantified proteins, 253 (35.3%) are shared among the top 5 facilities (and 16.2% among top 11 facilities). Furthermore, we note that reduction and alkylation are important steps in protein corona sample processing and as expected, omitting these steps reduces the number of total quantified peptides by around 20%. These findings underscore the need for standardized procedures in protein corona analysis, which is vital for advancing clinical applications of nanoscale biotechnologies.

On the excessive use of coefficient of variation as a metric of quantitation quality in proteomics

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

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.

Ventricular CSF proteomic profiles and predictors of surgical treatment outcome in chronic hydrocephalus

BACKGROUND

By applying an unbiased proteomic approach, we aimed to search for cerebrospinal fluid (CSF) protein biomarkers distinguishing between obstructive and communicating hydrocephalus in order to improve appropriate surgical selection for endoscopic third ventriculostomy vs. shunt implants. Our second study purpose was to look for potential CSF biomarkers distinguishing between patients with adult chronic hydrocephalus benefitting from surgery (responders) vs. those who did not (non-responders).

METHODS

Ventricular CSF samples were collected from 62 patients with communicating hydrocephalus and 28 patients with obstructive hydrocephalus. CSF was collected in relation to the patients' surgical treatment. As a control group, CSF was collected from ten patients with unruptured aneurysm undergoing preventive surgery (vascular clipping).

RESULTS

Mass spectrometry-based proteomic analysis of the samples identified 1251 unique proteins. No proteins differed significantly between the communicating hydrocephalus group and the obstructive hydrocephalus group. Four proteins were found to be significantly less abundant in CSF from communicating hydrocephalus patients compared to control subjects. A PCA plot revealed similar proteomic CSF profiles of obstructive and communicating hydrocephalus and control samples. For obstructive hydrocephalus, ten proteins were found to predict responders from non-responders.

CONCLUSION

Here, we show that the proteomic profile of ventricular CSF from patients with hydrocephalus differs slightly from control subjects. Furthermore, we find ten predictors of response to surgical outcome (endoscopic third ventriculostomy or ventriculo-peritoneal shunt) in patients with obstructive hydrocephalus.

Foresight in clinical proteomics: current status, ethical considerations, and future perspectives

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

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.

Conventional and genetic associations of adiposity with 1463 proteins in relatively lean Chinese adults

Adiposity is associated with multiple diseases and traits, but little is known about the causal relevance and mechanisms underlying these associations. Large-scale proteomic profiling, especially when integrated with genetic data, can clarify mechanisms linking adiposity with disease outcomes. We examined the associations of adiposity with plasma levels of 1463 proteins in 3977 Chinese adults, using measured and genetically-instrumented BMI. We further used two-sample bi-directional MR analyses to assess if certain proteins influenced adiposity, along with other (e.g. enrichment) analyses to clarify possible mechanisms underlying the observed associations. Overall, the mean (SD) baseline BMI was 23.9 (3.3) kg/m2, with only 6% being obese (i.e. BMI ≥ 30 kg/m2). Measured and genetically-instrumented BMI was significantly associated at FDR < 0.05 with levels of 1096 (positive/inverse: 826/270) and 307 (positive/inverse: 270/37) proteins, respectively, with FABP4, LEP, IL1RN, LSP1, GOLM2, TNFRSF6B, and ADAMTS15 showing the strongest positive and PON3, NCAN, LEPR, IGFBP2 and MOG showing the strongest inverse genetic associations. These associations were largely linear, in adiposity-to-protein direction, and replicated (> 90%) in Europeans of UKB (mean BMI 27.4 kg/m2). Enrichment analyses of the top > 50 BMI-associated proteins demonstrated their involvement in atherosclerosis, lipid metabolism, tumour progression and inflammation. Two-sample bi-directional MR analyses using cis-pQTLs identified in CKB GWAS found eight proteins (ITIH3, LRP11, SCAMP3, NUDT5, OGN, EFEMP1, TXNDC15, PRDX6) significantly affect levels of BMI, with NUDT5 also showing bi-directional association. The findings among relatively lean Chinese adults identified novel pathways by which adiposity may increase disease risks and novel potential targets for treatment of obesity and obesity-related diseases.

Proteomic Profiling Identifies Distinct Regulation of Proteins in Obese Diabetic Patients Treated with Metformin

Background: Obesity and type 2 diabetes mellitus (T2DM) are characterized by underlying low-grade chronic inflammation. Metformin has been used as the first line of therapy in T2DM as it decreases hepatic glucose production and glucose intestinal absorption, enhances insulin sensitivity and weight loss, and is known to ameliorate inflammation. The mechanisms through which metformin exerts its effect remain unclear. Proteomics has emerged as a unique approach to explore the biological changes associated with diseases, including T2DM. It provides insight into the circulating biomarkers/mediators which could be utilized for disease screening, diagnosis, and prognosis. Methods: This study evaluated the proteomic changes in obese (Ob), obese diabetics (OD), and obese diabetic patients on metformin (ODM) using a 2D DIGE MALDI-TOF mass spectrometric approach. Results: Significant changes in sixteen plasma proteins (15 up and 1 down, ANOVA, p ≤ 0.05; fold change ≥ 1.5) were observed in the ODM group when compared to the Ob and OD groups. Bioinformatic network pathway analysis revealed that the majority of these altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. Conclusions: Our study provides important information about the possible biomarkers altered by metformin treatment in obese patients with and without T2DM. These altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. The presented proteomic profiling approach may help in identifying potential biomarkers/mediators affected by metformin treatment in T2DM and inform the understanding of metformin's mechanisms of action.

A transcriptomic and proteomic atlas of obesity and type 2 diabetes in cynomolgus monkeys

Obesity and type 2 diabetes (T2D) remain major global healthcare challenges, and developing therapeutics necessitates using nonhuman primate models. Here, we present a transcriptomic and proteomic atlas of all the major organs of cynomolgus monkeys with spontaneous obesity or T2D in comparison to healthy controls. Molecular changes occur predominantly in the adipose tissues of individuals with obesity, while extensive expression perturbations among T2D individuals are observed in many tissues such as the liver and kidney. Immune-response-related pathways are upregulated in obesity and T2D, whereas metabolism and mitochondrial pathways are downregulated. Moreover, we highlight some potential therapeutic targets, including SLC2A1 and PCSK1 in obesity as well as SLC30A8 and SLC2A2 in T2D. Our study provides a resource for exploring the complex molecular mechanism of obesity and T2D and developing therapies for these diseases, with limitations including lack of hypothalamus, isolated islets of Langerhans, longitudinal data, and body fat percentage.

Identification of adipose tissue transcriptomic memory of anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a complex debilitating disease characterized by intense fear of weight gain and excessive exercise. It is the deadliest of any psychiatric disorder with a high rate of recidivism, yet its pathophysiology is unclear. The Activity-Based Anorexia (ABA) paradigm is a widely accepted mouse model of AN that recapitulates hypophagia and hyperactivity despite reduced body weight, however, not the chronicity.

METHODS

Here, we modified the prototypical ABA paradigm to increase the time to lose 25% of baseline body weight from less than 7 days to more than 2 weeks. We used this paradigm to identify persistently altered genes after weight restoration that represent a transcriptomic memory of under-nutrition and may contribute to AN relapse using RNA sequencing. We focused on adipose tissue as it was identified as a major location of transcriptomic memory of over-nutririon.

RESULTS

We identified 300 dysregulated genes that were refractory to weight restroration after ABA, including Calm2 and Vps13d, which could be potential global regulators of transcriptomic memory in both chronic over- and under-nutrition.

CONCLUSION

We demonstrated the presence of peristent changes in the adipose tissue transcriptome in the ABA mice after weight restoration. Despite being on the opposite spectrum of weight perturbations, majority of the transcriptomic memory genes of under- and over-nutrition did not overlap, suggestive of the different mechanisms involved in these extreme nutritional statuses.

Obesity accelerates age defects in B cells, and weight loss improves B cell function

BACKGROUND

We have previously shown that obesity accelerates age-associated defects in B cell function and antibody production leading to decreased secretion of protective antibodies and increased autoimmunity. We wanted to evaluate if obese adults enrolled in a voluntary weight reduction program had higher protective and lower autoimmune antibody responses similar to those observed in lean adults.

METHODS

Experiments were performed using blood isolated from an established cohort of female lean adult and elderly individuals, as well as from the blood of female adults with obesity, before and after a voluntary weight reduction program in which their Body Mass Index (BMI) was reduced 10-34% in 12 months. All participants were vaccinated with the Trivalent Inactivated Influenza vaccine. Serum samples were evaluated for the presence of pro-inflammatory cytokines and adipokines, vaccine-specific antibodies and autoimmune antibodies. We evaluated the composition of the B cell pool by flow cytometry, the expression of RNA for class switch transcription factors and pro-inflammatory markers by qPCR, the in vitro secretion of pro- and anti-inflammatory cytokines and their capacity to induce pro-inflammatory T cells.

RESULTS

Obesity, similar to aging, induced increased serum levels of pro-inflammatory cytokines and autoimmune antibodies, while vaccine-specific antibodies were reduced. In agreement with the serum results, the B cell pool of obese adults and elderly individuals was enriched in pro-inflammatory B cell subsets and was characterized by higher expression of markers associated with cell senescence, higher levels of T-bet, the transcription factor for autoimmune antibodies and lower levels of E47, the transcription factor associated with protective responses to the influenza vaccine. B cells from obese adults and elderly individuals were also able to secrete inflammatory cytokines and support the generation of inflammatory T cells. All these pro-inflammatory characteristics of B cells from obese individuals were significantly attenuated, but not completely reversed, by weight loss.

CONCLUSIONS

Although the results from our small observational study show that obesity-induced dysfunctional B cell responses, similar to those occurring during aging, are ameliorated in some but not all obese individuals after weight loss, the effects of body weight loss on mechanistic pathways are largely missing and deserve further investigation.

The Impact of Acute Nutritional Interventions on the Plasma Proteome

CONTEXT

Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses.

OBJECTIVE

Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers.

METHODS

We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters.

RESULTS

Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes.

CONCLUSION

Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.

Vacuum Insulated Probe Heated Electrospray Ionization Source Enhances Microflow Rate Chromatography Signals in the Bruker timsTOF Mass Spectrometer

By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide molecular ions by the electrospray source. To maximize the transfer of peptides from the liquid to gaseous phase and allow molecular ions to enter the mass spectrometer at microspray flow rates, an efficient electrospray process is required. Here we describe the superior performance of newly design vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled to a Bruker timsTOF PRO mass spectrometer operated in microspray mode. VIP-HESI significantly improves chromatography signals in comparison to electrospray ionization (ESI) and nanospray ionization using the captivespray (CS) source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient of variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that the Slice-PASEF VIP-HESI mode is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with microflow rate chromatography achieves a higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications. Data and spectral libraries are available via ProteomeXchange (PXD040497).

Positional proteomics: is the technology ready to study clinical cohorts?

INTRODUCTION

Positional proteomics provides proteome-wide information on protein termini and their modifications, uniquely enabling unambiguous identification of site-specific, limited proteolysis. Such proteolytic cleavage irreversibly modifies protein sequences resulting in new proteoforms with distinct protease-generated neo-N and C-termini and altered localization and activity. Misregulated proteolysis is implicated in a wide variety of human diseases. Protein termini, therefore, constitute a huge, largely unexplored source of specific analytes that provides a deep view into the functional proteome and a treasure trove for biomarkers.

AREAS COVERED

We briefly review principal approaches to define protein termini and discuss recent advances in method development. We further highlight the potential of positional proteomics to identify and trace specific proteoforms, with a focus on proteolytic processes altered in disease. Lastly, we discuss current challenges and potential for applying positional proteomics in biomarker and pre-clinical research.

EXPERT OPINION

Recent developments in positional proteomics have provided significant advances in sensitivity and throughput. In-depth analysis of proteolytic processes in clinical cohorts thus appears feasible in the near future. We argue that this will provide insights into the functional state of the proteome and offer new opportunities to utilize proteolytic processes altered or targeted in disease as specific diagnostic, prognostic and companion biomarkers.

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.

Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

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

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

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity

INTRODUCTION

Obesity is a multifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not a single country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without a comprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes.

OBJECTIVE

The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how a multi-omics approach can be fundamental for the implementation of precision obesity management.

RESULTS

The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity.

CONCLUSION

Integrating multi-omics approaches will enable a paradigm shift from the one size fits all approach towards precision obesity management, i.e. (1) precision prevention of the onset of obesity, (2) precision medicine and tailored treatment of obesity, and (3) precision risk reduction and prevention of secondary diseases related to obesity.

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

BACKGROUND

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

STUDY DESIGN AND METHODS

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

RESULTS

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

DISCUSSION

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

Vacuum Insulated Probe Heated ElectroSpray Ionization source (VIP-HESI) enhances micro flow rate chromatography signals in the Bruker timsTOF mass spectrometer

By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide ions by the electrospray source. To maximize the transfer of peptides from liquid to a gaseous phase to allow molecular ions to enter the mass spectrometer at micro-spray flow rates, an efficient electrospray process is required. Here we describe superior performance of new Vacuum-Insulated-Probe-Heated-ElectroSpray-Ionization source (VIP-HESI) coupled with micro-spray flow rate chromatography and Bruker timsTOF PRO mass spectrometer. VIP-HESI significantly improves chromatography signals in comparison to nano-spray ionization using the CaptiveSpray source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient-of-variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that Slice-PASEF mode with VIP-HESI setup is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with micro-flow-rate chromatography achieves higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications.

Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.

Validation of disease-specific biomarkers for the early detection of bronchopulmonary dysplasia

OBJECTIVE

To demonstrate and validate the improvement of current risk stratification for bronchopulmonary dysplasia (BPD) early after birth by plasma protein markers (sialic acid-binding Ig-like lectin 14 (SIGLEC-14), basal cell adhesion molecule (BCAM), angiopoietin-like 3 protein (ANGPTL-3)) in extremely premature infants.

METHODS AND RESULTS

Proteome screening in first-week-of-life plasma samples of n = 52 preterm infants <32 weeks gestational age (GA) on two proteomic platforms (SomaLogic^®, Olink-Proteomics^®) confirmed three biomarkers with significant predictive power: BCAM, SIGLEC-14, and ANGPTL-3. We demonstrate high sensitivity (0.92) and specificity (0.86) under consideration of GA, show the proteins' critical contribution to the predictive power of known clinical risk factors, e.g., birth weight and GA, and predicted the duration of mechanical ventilation, oxygen supplementation, as well as neonatal intensive care stay. We confirmed significant predictive power for BPD cases when switching to a clinically applicable method (enzyme-linked immunosorbent assay) in an independent sample set (n = 25, p < 0.001) and demonstrated disease specificity in different cohorts of neonatal and adult lung disease.

CONCLUSION

While successfully addressing typical challenges of clinical biomarker studies, we demonstrated the potential of BCAM, SIGLEC-14, and ANGPTL-3 to inform future clinical decision making in the preterm infant at risk for BPD.

TRIAL REGISTRATION

Deutsches Register Klinische Studien (DRKS) No. 00004600; https://www.drks.de .

IMPACT

The urgent need for biomarkers that enable early decision making and personalized monitoring strategies in preterm infants with BPD is challenged by targeted marker analyses, cohort size, and disease heterogeneity. We demonstrate the potential of the plasma proteins BCAM, SIGLEC-14, and ANGPTL-3 to identify infants with BPD early after birth while improving the predictive power of clinical variables, confirming the robustness toward proteome assays and proving disease specificity. Our comprehensive analysis enables a phase-III clinical trial that allows full implementation of the biomarkers into clinical routine to enable early risk stratification in preterms with BPD.

Differential proteomic profile of lumbar and ventricular cerebrospinal fluid

BACKGROUND

Pathological cerebral conditions may manifest in altered composition of the cerebrospinal fluid (CSF). Although diagnostic CSF analysis seeks to establish pathological disturbances in the brain proper, CSF is generally sampled from the lumbar compartment for reasons of technical ease and ethical considerations. We here aimed to compare the molecular composition of CSF obtained from the ventricular versus the lumbar CSF compartments to establish a relevance for employing lumbar CSF as a proxy for the CSF bathing the brain tissue.

METHODS

CSF was collected from 46 patients with idiopathic normal pressure hydrocephalus (iNPH) patients during their diagnostic workup (lumbar samples) and in connection with their subsequent CSF diversion shunt surgery (ventricular samples). The mass-spectrometry-based proteomic profile was determined in these samples and in addition, selected biomarkers were quantified with ELISA (S100B, neurofilament light (NfL), amyloid-β (Aβ₄₀, Aβ₄₂), and total tau (T-tau) and phosphorylated tau (P-tau) forms). The latter analysis was extended to include paired porcine samples obtained from the lumbar compartment and the cerebromedullary cistern closely related to the ventricles.

RESULTS

In total 1231 proteins were detected in the human CSF. Of these, 216 distributed equally in the two CSF compartments, whereas 22 were preferentially (or solely) present in the ventricular CSF and four in the lumbar CSF. The selected biomarkers of neurodegeneration and Alzheimer's disease displayed differential distribution, some with higher (S100B, T-tau, and P-tau) and some with lower (NfL, Aβ₄₀, Aβ₄₂) levels in the ventricular compartment. In the porcine samples, all biomarkers were most abundant in the lumbar CSF.

CONCLUSIONS

The overall proteomic profile differs between the ventricular and the lumbar CSF compartments, and so does the distribution of clinically employed biomarkers. However, for a range of CSF proteins and biomarkers, one can reliably employ lumbar CSF as a proxy for ventricular CSF if or a lumbar/cranial index for the particular molecule has been established. It is therefore important to verify the compartmental preference of the proteins or biomarkers of interest prior to extrapolating from lumbar CSF to that of the ventricular fluid bordering the brain.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

The inflammatory proteome, obesity, and medical weight loss and regain in humans

OBJECTIVE

Weight regain occurs after medical weight loss via mechanisms of post-weight-loss "metabolic adaptation." The relationship of inflammatory proteins with weight loss/regain was studied to determine a role for inflammation in metabolic adaptation.

METHODS

Seventy-four proteins central to inflammation and immune regulation (Olink) were analyzed in plasma from up to 490 participants in a trial of medical weight-loss maintenance. Cross-sectional and longitudinal associations of proteins with weight were measured using linear and mixed effects regression models and t testing, with replication in the Framingham Heart Study.

RESULTS

Broad changes in the inflammatory proteome were observed among the study cohort (60% women, 35% African American) with initial weight loss of ≈8 kg from a median 94 kg at study entry (33/74 proteins; 7 increased; 26 decreased), many of which tracked with weight regain of median ≈2 kg over the next 30 months. Ten proteins were associated with different rates of weight regain, some specifying pathways of chemotaxis and innate immune responses. Several of the observed protein associations were also linked to prevalent obesity in the Framingham Heart Study.

CONCLUSIONS

Broad changes in the inflammatory proteome track with changes in weight and may identify specific pathways that modify patterns of weight regain.

The alternative proteome in neurobiology

Translation involves the biosynthesis of a protein sequence following the decoding of the genetic information embedded in a messenger RNA (mRNA). Typically, the eukaryotic mRNA was considered to be inherently monocistronic, but this paradigm is not in agreement with the translational landscape of cells, tissues, and organs. Recent ribosome sequencing (Ribo-seq) and proteomics studies show that, in addition to currently annotated reference proteins (RefProt), other proteins termed alternative proteins (AltProts), and microproteins are encoded in regions of mRNAs thought to be untranslated or in transcripts annotated as non-coding. This experimental evidence expands the repertoire of functional proteins within a cell and potentially provides important information on biological processes. This review explores the hitherto overlooked alternative proteome in neurobiology and considers the role of AltProts in pathological and healthy neuromolecular processes.