Comprehensive Analysis of Individual Variation in the Urinary Proteome Revealed Significant Gender Differences

Brain-body mechanisms contribute to sexual dimorphism in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is the most common form of human motor neuron disease. It is characterized by the progressive degeneration of upper and lower motor neurons, leading to generalized motor weakness and, ultimately, respiratory paralysis and death within 3-5 years. The disease is shaped by genetics, age, sex and environmental stressors, but no cure or routine biomarkers exist for the disease. Male individuals have a higher propensity to develop ALS, and a different manifestation of the disease phenotype, than female individuals. However, the mechanisms underlying these sex differences remain a mystery. In this Review, we summarize the epidemiology of ALS, examine the sexually dimorphic presentation of the disease and highlight the genetic variants and molecular pathways that might contribute to sex differences in humans and animal models of ALS. We advance the idea that sexual dimorphism in ALS arises from the interactions between the CNS and peripheral organs, involving vascular, metabolic, endocrine, musculoskeletal and immune systems, which are strikingly different between male and female individuals. Finally, we review the response to treatments in ALS and discuss the potential to implement future personalized therapeutic strategies for the disease.

A Pilot Urinary Proteome Study Reveals Widespread Influences of Circadian Rhythm Disruption by Sleep Deprivation

It is widely accepted that circadian rhythm disruption caused short- or long-term adverse effects on health. Although many previous studies have focused on exploration of the molecular mechanisms, there is no rapid, convenient, and non-invasive method to reveal the influence on health after circadian rhythm disruption. Here, we performed a high-resolution mass spectrometry-based data-independent acquisition (DIA) quantitative urinary proteomic approach in order to explore whether urine could reveal stress changes to those brought about by circadian rhythm disruption after sleep deprivation. After sleep deprivation, the subjects showed a significant increase in both systolic and diastolic blood pressure compared with routine sleep. More than 2000 proteins were quantified and they contained specific proteins for various organs throughout the body. And a total of 177 significantly up-regulated proteins and 68 significantly down-regulated proteins were obtained after sleep deprivation. These differentially expressed proteins (DEPs) were associated with multiple organs and pathways, which reflected widespread influences of sleep deprivation. Besides, machine learning identified a panel of five DEPs (CD300A, SCAMP3, TXN2, EFEMP1, and MYH11) that can effectively discriminate circadian rhythm disruption. Taken together, our results validate the value of urinary proteome in predicting and diagnosing the changes by circadian rhythm disruption.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort

The current study aims to develop and validate machine learning (ML) models for the prediction of cancer status by the non-invasive urinary proteomic in a population-based cohort. In this retrospective study, urinary proteome profiles in 804 cases from the FLEMENGHO cohort were measured by mass spectrometry. After feature selection by LASSO on both clinical variables and urinary proteome profile, benchmark models by clinical variables were built with six different ML algorithms. Proteome-based models and combined models were built and compared with the benchmark models. The models' performance, i.e. area under the curve (AUC) was compared by Delong method. The 95% confidence interval was estimated by the bootstrapping method. The best-performing model was explained by Shapley Additive Explanations (SHAP) method. The predictive role of proteome biomarkers in longitudinal cancer diagnosis was also explored. A clinical model, based on age, blood sugar and blood lipid profile, yielded the best AUC of 0.75 (0.68-0.82), with 0.80 (0.72-0.91) for the proteome model based on 13 selected biomarkers and 0.83 (0.77-0.90) for the combined model (P=0.01 for comparison with clinical model). SHAP on the support vector machine in the combined setting showed that except for age, proteome biomarkers contribute to the final prediction of the model. After adjusting with clinical factors, three proteome biomarkers are independent risk factors for longitudinal cancer development. Urinary proteome profiling, together with fine-tuned machine learning algorithms, demonstrates the predictive potential for cancer diagnosis transparently.

Acute exposure of microwave impairs attention process by activating microglial inflammation

BACKGROUND

Attention provides the foundation for cognitions, which was shown to be affected by microwave (MW) radiation. With the ubiquitous of microwaves, public concerns regarding the impact of MW radiation on attention has hence been increased. Our study aims to investigate the potential effect and mechanism of acute microwave exposure on attention.

RESULTS

We identified obvious impairment of attention in mice by the five-choice serial reaction time (5-CSRT) task. Proteomic analysis of the cerebrospinal fluid (CSF) revealed neuroinflammation and microglial activation potentially due to acute MW exposure. Moreover, biochemical analysis further confirmed microglial activation in the prefrontal cortex (PFC) of mice subjected to acute MW exposure. Finally, minocycline, a commercially available anti-inflammatory compound, attenuated neuroinflammation, inhibited the upregulation of N-methyl-D-aspartic acid receptor (NMDAR) including NR2A and NR2B, and also accelerated the attentional recovery after MW exposure.

CONCLUSIONS

We believe that microglial activation and NMDAR upregulation likely contribute to inattention induced by acute MW exposure, and we found that minocycline may be effective in preventing such process.

Comparison of clean catch and bag urine using LC-MS/MS proteomics in infants

BACKGROUND

Urinary proteomics identifies the totality of urinary proteins and can therefore help in getting an early and precise diagnosis of various pathological processes in the kidneys. In infants, non-invasive urine collection is most commonly accomplished with a urine bag or clean catch. The influence of those two collection methods on urinary proteomics was assessed in this study.

METHODS

Thirty-two urine samples were collected in infants using urine bag and clean catch within 24 h. Nine boys and seven girls with a mean age of 4.3 ± 2.9 months were included (5 × post-pyelonephritis, 10 × non-kidney disease, 1 × chronic kidney disease (CKD)). Liquid chromatography-mass spectrometry (LC-MS/MS) was performed in data-independent acquisition (DIA) mode. Protein identification and quantification were achieved using Spectronaut.

RESULTS

A total of 1454 urinary proteins were detected. Albumin and α-1-microglobulin were detected the most. The 18 top-abundant proteins accounted for 50% of total abundance. The number of proteins was slightly, but insignificantly higher in clean catch (957 ± 245) than in bag urine (876 ± 255). The median intensity was 1.2 × higher in the clean catch. Overall, differential detection of proteins was 29% between the collection methods; however, it diminished to 3% in the 96 top-abundant proteins. Pearson's correlation coefficient was 0.81 ± 0.11, demonstrating a high intraindividual correlation. A principal component analysis and a heat map showed clustering according to diagnoses and patients rather than to the collection method.

CONCLUSION

Urinary proteomics shows a high correlation with minor variation in low-abundant proteins between the two urine collection methods. The biological characteristics overrule this variation. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information.

Inoculum size-insensitive susceptibility determination of urine sample based on in-situ measurement of inducible enzyme activity after 20 min of antibiotic exposure

BACKGROUND

The empirical antibiotic therapies for bacterial infections cause the emergence and propagation of multi-drug resistant bacteria, which not only impair the effectiveness of existing antibiotics but also raise healthcare costs. To reduce the empirical treatments, rapid antimicrobial susceptibility testing (AST) of causative microorganisms in clinical samples should be conducted for prescribing evidence-based antibiotics. However, most of culture-based ASTs suffer from inoculum effect and lack differentiation of target pathogen and commensals, hampering their adoption for evidence-based antibiotic prescription. Therefore, rapid ASTs which can specifically determine pathogens' susceptibilities, regardless of the bacterial load in clinical samples, are in urgent need.

RESULTS

We present a pathogen-specific and inoculum size-insensitive AST to achieve the reliable susceptibility determination on Escherichia coli (E. coli) in urine samples. The developed AST is featured with an 1 h sample-to-result workflow in a filter, termed on-filter AST. The AST results can be obtained by using an inducible enzymatic assay to in-situ measure the cell response of E. coli collected from urine after 20 min of antibiotic exposure. The calculated detection limit of our AST (1.95 × 104 CFU/mL) is much lower than the diagnosis threshold of urinary tract infections. The specific expression of the inducible enzyme enables on-filter AST to correctly profile the susceptibilities of target pathogen to multi-type antibiotics without the interference from commensals. We performed the on-filter AST on 1 mL urine samples with bacterial loads varying from 105 CFU/mL to 107 CFU/mL and compared the results to that of standard method, demonstrating its insensitivity to inoculum size.

SIGNIFICANCE

The developed AST is demonstrated to be of high sensitivity, specificity, and insensitive to inoculum size. With further developments for additional bacteria and clinical validation, on-filter AST is promising as a rapid and reliable surrogate of culture-based AST to promote the evidence-based prescription at the first visit and minimize the emergency of new multi-drug resistant microorganisms.

Urinary biomarkers for amyotrophic lateral sclerosis: candidates, opportunities and considerations

Amyotrophic lateral sclerosis is a relentless neurodegenerative disease that is mostly fatal within 3-5 years and is diagnosed on evidence of progressive upper and lower motor neuron degeneration. Around 15% of those with amyotrophic lateral sclerosis also have frontotemporal degeneration, and gene mutations account for ∼10%. Amyotrophic lateral sclerosis is a variable heterogeneous disease, and it is becoming increasingly clear that numerous different disease processes culminate in the final degeneration of motor neurons. There is a profound need to clearly articulate and measure pathological process that occurs. Such information is needed to tailor treatments to individuals with amyotrophic lateral sclerosis according to an individual's pathological fingerprint. For new candidate therapies, there is also a need for methods to select patients according to expected treatment outcomes and measure the success, or not, of treatments. Biomarkers are essential tools to fulfil these needs, and urine is a rich source for candidate biofluid biomarkers. This review will describe promising candidate urinary biomarkers of amyotrophic lateral sclerosis and other possible urinary candidates in future areas of investigation as well as the limitations of urinary biomarkers.

Multiomic Approaches for Cancer Biomarker Discovery in Liquid Biopsies: Advances and Challenges

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.

Urinary proteome profiles associated with cognitive decline in community elderly residents—A pilot study

Non-invasive and simple methods enabling easy identification of individuals at high risk of cognitive decline are needed as preventive measures against dementia. This pilot study aimed to explore protein biomarkers that can predict cognitive decline using urine, which can be collected non-invasively. Study subjects were selected from participants in a cohort study of middle-aged and older community-dwelling adults who underwent cognitive testing using the Mini-Mental State Examination and provided spot urine samples at two time points with an interval of approximately 5 years. Seven participants whose cognitive function declined 4 or more points from baseline (Group D) and 7 sex- and age-matched participants whose cognitive function remained within the normal range during the same period (Group M) were selected. Urinary proteomics using mass spectrometry was performed and discriminant models were created using orthogonal partial least squares-discriminant analysis (OPLS-DA). OPLS-DA yielded two models that significantly discriminated between the two groups at baseline and follow-up. Both models had ORM1, ORM2, and SERPINA3 in common. A further OPLS-DA model using baseline ORM1, ORM2, and SERPINA3 data showed similar predictive performance for data at follow-up as it did for baseline data (sensitivity: 0.85, specificity: 0.85), with the receiver operating characteristic curve analysis yielding an area under the curve of 0.878. This prospective study demonstrated the potential for using urine to identify biomarkers of cognitive decline.

Urinary complement proteins in IgA nephropathy progression from a relative quantitative proteomic analysis

Aim

IgA nephropathy (IgAN) is one of the leading causes of end-stage renal disease (ESRD). Urine testing is a non-invasive way to track the biomarkers used for measuring renal injury. This study aimed to analyse urinary complement proteins during IgAN progression using quantitative proteomics.

Methods

In the discovery phase, we analysed 22 IgAN patients who were divided into three groups (IgAN 1-3) according to their estimated glomerular filtration rate (eGFR). Eight patients with primary membranous nephropathy (pMN) were used as controls. Isobaric tags for relative and absolute quantitation (iTRAQ) labelling, coupled with liquid chromatography-tandem mass spectrometry, was used to analyse global urinary protein expression. In the validation phase, western blotting and parallel reaction monitoring (PRM) were used to verify the iTRAQ results in an independent cohort (N = 64).

Results

In the discovery phase, 747 proteins were identified in the urine of IgAN and pMN patients. There were different urine protein profiles in IgAN and pMN patients, and the bioinformatics analysis revealed that the complement and coagulation pathways were most activated. We identified a total of 27 urinary complement proteins related to IgAN. The relative abundance of C3, the membrane attack complex (MAC), the complement regulatory proteins of the alternative pathway (AP), and MBL (mannose-binding lectin) and MASP1 (MBL associated serine protease 2) in the lectin pathway (LP) increased during IgAN progression. This was especially true for MAC, which was found to be involved prominently in disease progression. Alpha-N-acetylglucosaminidase (NAGLU) and α-galactosidase A (GLA) were validated by western blot and the results were consistent with the iTRAQ results. Ten proteins were validated in a PRM analysis, and these results were also consistent with the iTRAQ results. Complement factor B (CFB) and complement component C8 alpha chain (C8A) both increased with the progression of IgAN. The combination of CFB and mucosal addressin cell adhesion molecule-1 (MAdCAM-1) also showed potential as a urinary biomarker for monitoring IgAN development.

Conclusion

There were abundant complement components in the urine of IgAN patients, indicating that the activation of AP and LP is involved in IgAN progression. Urinary complement proteins may be used as biomarkers for evaluating IgAN progression in the future.

A Pilot Study on the Urine Proteome of Cats Fed a High-Protein Complete Diet, Supplemented with or without Arginine, Ornithine or Zeolite

Proteome analyses can be used to detect biomarkers for the healthy and diseased organism. However, data in cats are scarce, and no information is available on the potential impact of nutritional interventions on the feline urine proteome. In the present study, a label-free shotgun proteomics approach was performed to investigate the urinary proteins of four healthy adult cats. Each animal received a high-protein complete diet without (w/o) or with supplements that could affect the protein metabolism: arginine (+100% compared to the arginine concentration in the w/o diet), ornithine (+200% compared to the arginine concentration in the w/o diet) or zeolite (0.375 g/kg body weight/day). Our results demonstrate a huge number of proteins in the urine of cats (516 ± 49, 512 ± 39, 399 ± 149 and 455 ± 134 in the w/o, arginine, ornithine and zeolite group, respectively), which are associated with several biological processes. In addition, up- and downregulated urinary proteins could be detected in the dietary supplementation periods. Overall, the present pilot study provides basic data on the urine proteome of healthy adult cats. With increasing information, the numerousness of urinary proteins implies the potential to identify biomarkers and metabolic pathways in the feline organism.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Urine Proteomics Reveals Sex-Specific Response to Total Pancreatectomy With Islet Autotransplantation

OBJECTIVES

Total pancreatectomy with islet autotransplantation (TPIAT) is a surgical option for refractory chronic pancreatitis-related pain. Despite the known clinical implications of TPIAT, the molecular effects remain poorly investigated. We performed the first hypothesis-generating study of the urinary proteome before and after TPIAT.

METHODS

Twenty-two patients eligible for TPIAT were prospectively enrolled. Urine samples were collected the week before and 12 to 18 months after TPIAT. The urine samples were prepared for bottom-up label-free quantitative proteomics using the "MStern" protocol.

RESULTS

Using 17 paired samples, we identified 2477 urinary proteins, of which 301 were significantly changed post-TPIAT versus pre-TPIAT. Our quantitative analysis revealed that the molecular response to TPIAT was highly sex-specific, with pronounced sex differences pre-TPIAT but minimal differences afterward. Comparing post-TPIAT versus pre-TPIAT, we found changes in cell-cell adhesion, intracellular vacuoles, and immune response proteins. After surgery, immunoglobulins, complement proteins, and cathepsins were increased, findings that may reflect glomerular damage. Finally, we identified both known and novel markers for immunoglobulin A nephropathy after 1 patient developed the disease 2 years after TPIAT.

CONCLUSIONS

We found distinct changes in the urinary proteomic profile after TPIAT and the response to TPIAT is highly sex-specific.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Sex discrimination from urine traces for forensic purposes using attenuated total reflectance Fourier transform infrared spectroscopy and multivariate data analysis

The characteristics of ATR FT-IR spectroscopy are extremely attractive and escalating popularity in the field of body fluid analysis owing to its non-destructive, rapid, and reliable nature. Herein, the present study establishes that how ATR FT-IR spectroscopy could be utilized as a non-destructive, non-invasive, and confirmatory technique for sex discrimination from dry urine traces. Traces of body fluids are of paramount importance to criminal investigations as a major source of individualization by DNA profiling. However, the significance of DNA profiling from urine traces is highly diminished due to the small amount of DNA in urine traces. For that reason, the sex discrimination between the male and female donors is sorely desirable. In this study, ATR FT-IR spectroscopy in combination with partial least squares-discriminant analysis (PLS-DA) model unequivocally demonstrated the successful sex discrimination of an individual from dry traces of urine with 95.3% accuracy. PCA-Linear Discriminant Analysis (LDA) approach provided 85.2% of accuracy; however, PCA could not provide the sufficient findings for the discrimination of male and female urine spectra. The validation study was conducted and obtained 0% rates of false-positive and negative assignments. Additionally, this study also attended to assess the influence of substrates on the analysis of urine traces and results have been discussed.

Differential expression of aerobic oxidative metabolism-related proteins in diabetic urinary exosomes

BACKGROUND

As a metabolic disease, any abnormality in the aerobic oxidation pathway of glucose may lead to the occurrence of diabetes. This study aimed to investigate the changes in proteins related to aerobic oxidative metabolism in urinary exosomes of diabetic patients and normal controls of different ages, and to further verify their correlation with the pathogenesis of diabetes.

METHODS

Samples were collected, and proteomic information of urinary exosomes was collected by LC-MS/MS. ELISA was used to further detect the expression of aerobic and oxidative metabolism-related proteins in urinary exosomes of diabetic patients and normal controls of different ages, and to draw receiver operating characteristic (ROC) curve to evaluate its value in diabetes monitoring.

RESULTS

A total of 17 proteins involved in aerobic oxidative metabolism of glucose were identified in urinary exosome proteins. Compared with normal control, the expressions of PFKM, GAPDH, ACO2 and MDH2 in diabetic patients were decreased, and the expression of IDH3G was increased. The concentrations of PFKM, GAPDH and ACO2 in urinary exosomes were linearly correlated with the expression of MDH2 (P<0.05). These four proteins vary with age, with the maximum concentration in the 45-59 age group. PFKM, GAPDH, ACO2, and MDH2 in urinary exosomes have certain monitoring value. When used in combination, the AUC was 0.840 (95% CI 0.764-0.915).

CONCLUSIONS

In diabetic patients, aerobic oxidative metabolism is reduced, and the expression of aerobic oxidative metabolism-related proteins PFKM, GAPDH, ACO2, and MDH2 in urinary exosomes is reduced, which may become potential biomarkers for monitoring changes in diabetes.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic-peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

Qualitative and quantitative proteomic and metaproteomic analyses of healthy human urine sediment

PURPOSE

The healthy human urine sediment proteome and metaproteome are investigated, to shed light on the variations of urine sediment proteins and metaproteins associated with sex and age.

EXPERIMENTAL DESIGN

Urine sediment samples are collected from 19 healthy subjects. Protein identification and quantification are performed by liquid chromatography coupled high-resolution mass spectrometry.

RESULTS

A total of 2736 human proteins were identified, which were primarily associated with inflammatory response and energy metabolism. For the metaproteome, 65 genera were identified that were primarily involved in translation and carbohydrate metabolic processes. The median biological coefficient variation of the proteome/metaproteome of human urine sediment was 0.5/0.72, similar to the proteome of human urine supernatant. In addition, sex and age were observed to affect the proteome and metaproteome of healthy human urine sediment.

CONCLUSION AND CLINICAL RELEVANCE

The healthy human urine sediment were characterized, indicating that urine sediment might represent an alternative resource for disease research in addition to urine supernatant, but the influence of sex and age must be considered in the study design process.

Characterization of the Human Eccrine Sweat Proteome-A Focus on the Biological Variability of Individual Sweat Protein Profiles

The potential of eccrine sweat as a bio-fluid of interest for diagnosis and personalized therapy has not yet been fully evaluated, due to the lack of in-depth sweat characterization studies. Thanks to recent developments in omics, together with the availability of accredited sweat collection methods, the analysis of human sweat may now be envisioned as a standardized, non-invasive test for individualized monitoring and personalized medicine. Here, we characterized individual sweat samples, collected from 28 healthy adult volunteers under the most standardized sampling methodology, by applying optimized shotgun proteomics. The thorough characterization of the sweat proteome allowed the identification of 983 unique proteins from which 344 were identified across all samples. Annotation-wise, the study of the sweat proteome unveiled the over-representation of newly addressed actin dynamics, oxidative stress and proteasome-related functions, in addition to well-described proteolysis and anti-microbial immunity. The sweat proteome composition correlated with the inter-individual variability of sweat secretion parameters. In addition, both gender-exclusive proteins and gender-specific protein abundances were highlighted, despite the high similarity between human female and male sweat proteomes. In conclusion, standardized sample collection coupled with optimized shotgun proteomics significantly improved the depth of sweat proteome coverage, far beyond previous similar studies. The identified proteins were involved in many diverse biological processes and molecular functions, indicating the potential of this bio-fluid as a valuable biological matrix for further studies. Addressing sweat variability, our results prove the proteomic profiling of sweat to be a promising bio-fluid analysis for individualized, non-invasive monitoring and personalized medicine.

Longitudinal stability of urinary extracellular vesicle protein patterns within and between individuals

The protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-invasive biomarker source. However, little is known about the consistency and variability of urinary EV proteins within and between individuals over a longer time-period. Here, we evaluated the stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using data-independent-acquisition mass spectrometry. The 1802 identified proteins had a high correlation amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded person-specific profiles. The core EV-protein-interaction network of 516 proteins detected in all measured samples revealed sub-clusters involved in the biological processes of G-protein signaling, cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specific expression patterns were detected in the urinary EV proteome. Our findings indicate that the urinary EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period, further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.

Variability of multi-omics profiles in a population-based child cohort

BACKGROUND

Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood.

METHODS

We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability.

RESULTS

All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability.

CONCLUSIONS

Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both.

Urinary proteomics for kidney dysfunction: insights and trends

Introduction: Kidney dysfunction poses a high burden on patients and health care systems. Early detection and accurate prediction of kidney disease progression remains a major challenge. Compared to existing clinical parameters, urinary proteomics has the potential to reveal molecular alterations within the kidney that may alter its function before the onset of clinical symptoms. Thus, urinary proteomics has greater prognostic potential for assessment of kidney dysfunction progression.Areas covered: Advances in urinary proteomics for major causes of kidney dysfunction are discussed. The application of urinary extracellular vesicles for studying kidney dysfunction are discussed. Technological advances in urinary proteomics are discussed. The literature was identified using a database search for titles containing 'proteom*' and 'urin*' and published within the past 5 years. Retrieved literature was manually filtered to retain kidney dysfunctions-related studies.Expert opinion: Despite major advances, diagnosis by urinary proteomics has not been fully applied in any clinical settings. This could be attributed to the complex nature of kidney diseases, in addition to the constraints on study power and feasibility of incorporating mass spectrometry techniques in daily routine analysis. Nevertheless, we are confident that advances in urinary proteomics will soon provide superior insights into kidney disease beyond existing clinical parameters.

Early disease biomarkers can be found using animal models urine proteomics

INTRODUCTION

Early disease detection is a prerequisite for early intervention. Urine is not subjected to homeostatic control, and therefore, it accumulates very early changes associated with disease processes, some of which may be used as biomarkers. Animal models must be used to identify urinary changes associated with very early stages of diseases to avoid potential interfering factors and obtain urine samples at a sufficiently early time point before pathological or clinical manifestations occur.

AREAS COVERED

We reviewed recent (from 2009-2020) urine proteome studies using animal models of many diseases. We focused on early changes in urine proteome of animal models, particularly changes occurring prior to alterations in blood tests, light microscopy observations and clinical manifestations. Additional studies relevant to the topic were also extracted from the references of the cited papers. Changes in the urine proteome at different disease stages and the ability of the urine proteome to differentiate among different animal models are also discussed in this review.

EXPERT COMMENTARY

Urine proteomes of animal models may reflect early changes that occur even before changes in blood parameters, light microscopy observations and clinical manifestations, suggesting the potential use of urinary biomarkers for the very early detection of human diseases.

Proteotyping of knockout mouse strains reveals sex- and strain-specific signatures in blood plasma

We proteotyped blood plasma from 30 mouse knockout strains and corresponding wild-type mice from the International Mouse Phenotyping Consortium. We used targeted proteomics with internal standards to quantify 375 proteins in 218 samples. Our results provide insights into the manifested effects of each gene knockout at the plasma proteome level. We first investigated possible contamination by erythrocytes during sample preparation and labeled, in one case, up to 11 differential proteins as erythrocyte originated. Second, we showed that differences in baseline protein abundance between female and male mice were evident in all mice, emphasizing the necessity to include both sexes in basic research, target discovery, and preclinical effect and safety studies. Next, we identified the protein signature of each gene knockout and performed functional analyses for all knockout strains. Further, to demonstrate how proteome analysis identifies the effect of gene deficiency beyond traditional phenotyping tests, we provide in-depth analysis of two strains, C8a-/- and Npc2+/-. The proteins encoded by these genes are well-characterized providing good validation of our method in homozygous and heterozygous knockout mice. Ig alpha chain C region, a poorly characterized protein, was among the differentiating proteins in C8a-/-. In Npc2+/- mice, where histopathology and traditional tests failed to differentiate heterozygous from wild-type mice, our data showed significant difference in various lysosomal storage disease-related proteins. Our results demonstrate how to combine absolute quantitative proteomics with mouse gene knockout strategies to systematically study the effect of protein absence. The approach used here for blood plasma is applicable to all tissue protein extracts.

Alzheimer-Associated Neuronal Thread Protein: Research Course and Prospects for the Future

Alzheimer's disease (AD) is the leading cause of dementia. With aging societies, the prevalence of AD is increasing dramatically worldwide. The onset of AD is often not identified, and currently no available treatments are capable of stopping the disease process and its effect on cognitive decline. Thus, well-validated biomarkers of the preclinical stages of AD are needed. Alzheimer-associated neuronal thread protein (AD7c-NTP) is a member of the neuronal thread protein family and has a molecular weight of approximately 41 kD. AD7c-NTP has been identified as a biomarker for its specifically elevated levels in putative brain domains, cerebrospinal fluid (CSF), and the urine of AD and mild cognitive impairment (MCI) patients. Since the urine test is non-invasive, easy to perform, and patients accept it more easily than other methods, the urinary AD7c-NTP concentration has been recommended as a practical diagnostic tool for diagnosing AD and MCI. AD7c-NTP has undergone nearly 25 years of research course from its initial discovery to pathological verification, multi-center clinical evaluation, improvement of detection methods, epidemiological investigation, and combined application with other biomarkers. However, as a fluid biomarker, AD7c-NTP can be detected in urine instead of the traditional biomarker sources-CSF or blood, which has made the use of AD7c-NTP as a biomarker controversial. In this article, we review the research course of AD7c-NTP and suggest directions for future research.

The role of PKM2 nuclear translocation in the constant activation of the NF-κB signaling pathway in cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) play critical roles in cancer progression by regulating tumor cell proliferation, angiogenesis, and metastasis. Recent studies demonstrated that CAFs induce inhibitory immune cell infiltration and chemotherapy resistance in gastric cancer by activating the NF-κB signaling pathway to secrete IL6, IL8, and other inflammatory factors. Inhibition of the NF-κB signaling pathway in CAFs might be a potential therapeutic strategy in gastric cancer. However, how the NF-κB pathway is activated in CAFs remains unclear. We showed that mesenchymal stem cells (MSCs) differentiated into CAFs, induced by the exosomes derived from gastric cancer cells. During the process of differentiation from MSCs into CAFs, we showed that nuclear PKM2 expression was continuously upregulated and associated with NF-κB P65 acetylation, contributing to P65 nuclear retention in CAFs and constant transcription of IL-6, IL-8, and other inflammatory factors, thus promoting gastric cancer cell proliferation. We showed that NF-κB P65 acetylation was induced by P300. We showed that nuclear PKM2 was derived from exosomes of gastric cancer cell lines and the positive feedback loop induced by PKM2-P65 combination. It is also proved that P300 inhibitors can inhibit tumor proliferation in an AGS subcutaneous xenograft tumor model. Our study showed that gastric cancer cells influence the continuous activation of the NF-κB signaling pathway in CAFs by secreting gastric cancer exosomes containing PKM2, thus inducing abnormal metabolism and inflammation activation. This study provides a new therapeutic target for CAF normalization or deactivation strategies.

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

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

Proteomic analysis of urine reveals biomarkers for the diagnosis and phenotyping of abdominal-type Henoch-Schonlein purpura

BACKGROUND

Abdominal-type Henoch-Schonlein purpura (HSP) is a common refractory disease in children. Currently, no specific diagnostic biomarker is available for HSP.

METHODS

Children with abdominal type HSP were first diagnosed with three syndromes using Chinese traditional medicine. The urinary proteomes among the three syndromes of patients with abdominal type HSP and healthy controls were compared using two label-free proteomics quantifications, including data-dependent acquisition and data-independent acquisition.

RESULTS

For the comparison between patients with abdominal type HSP and healthy children, a total of 75 differential urinary proteins were identified by determining the overlap of the two experiments. The ingenuity pathway analysis (IPA) analysis showed that these differential proteins were correlated with the pathogenesis of abdominal type HSP. Of these, 37 proteins were distributed in 13 solid tissues as tissue-enriched proteins. Monitoring changes in these proteins might help us detect uncommon clinical manifestations of HSP. Patients with abdominal type HSP can be further distinguished into three syndromes based on the urine proteome. Finally, a panel of six urinary proteins (P25774, P09417, Q7Z5L0, P60900, P14550 and P09668) was constructed for both the diagnosis and phenotyping of abdominal type HSP.

CONCLUSIONS

Urinary protein biomarkers for the diagnosis and phenotyping of abdominal type HSP were identified, which will contribute to the personalized treatment of patients with abdominal type HSP.

Label-Free Liquid Chromatography-Mass Spectrometry Proteomic Analysis of Urinary Identification in Diabetic Vascular Dementia in a Han Chinese Population

Objective: This study aimed to identify potential diagnostic biomarkers of diabetic vascular dementia (DVD) and unravel the underlying mechanisms using mass spectrometry (MS).

Methods: Label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic analysis was applied to urine samples from four groups, including 14 patients with vascular dementia (VD), 22 patients with type 2 diabetes mellitus (T2DM), 12 patients with DVD, and 21 normal controls (NCs). Searching the MS data by Proteome Discoverer software (ThermoFisher Scientific; Waltham, MA, USA), protein abundances were analyzed qualitatively and quantitatively and compared between these groups. Combining bioinformatics analysis using Gene Ontology (GO), pathway crosstalk analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI) network analysis using STRING, and literature searching, the differentially expressed proteins (DEPs) of DVD can be comprehensively judged and were further quantified by receiver operating characteristic (ROC) curve methods.

Results: The proteomic findings showed quantitative changes in patients with DVD compared to patients with NC, T2DM, and VD groups; among 4,744 identified urine proteins, 1,222, 1,152, and 1,180 proteins displayed quantitative changes unique to DVD vs. NC, T2DM, and VD, respectively, including 481 overlapped common DEPs. Then, nine unique proteins [including HP, SERPIND, ATP5PB, VNN2, ALDH3A1, U2AF2, C6, A0A5C2GRG5 (no name), and A0A5C2FZ29 (no name)] and two composite markers (CM) (A0A5C2GRG5+U2AF2 and U2AF2+C6) were confirmed by a ROC curve method.

Conclusion: This study provided an insight into the potential pathogenesis of DVD and elucidated a method for early detection.

SWATH-MS Protocols in Human Diseases

Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).

Proteomic Profiling of the Human Tissue and Biological Fluid Proteome

In-depth analysis of the human genome sequence has led to the annotation of approximately 20,000 human protein-coding genes. Although mass spectrometry (MS)-based workflows have made a great headway in achieving near genome-wide coverage, an equivalent complete map of the human proteome remains elusive. Delineating the spatial distribution of all human proteins at the organ, tissue, and cellular level can offer insight into health and disease and represents an excellent reference for the discovery of biomarkers and therapeutic targets. Here, we performed label-free liquid chromatography coupled to tandem MS (LC-MS/MS) to profile the normal human proteome. In total, we analyzed 117 samples from 46 normal tissues and organs at autopsy. Our high-resolution MS approach allowed for the quantification of 10,438 unique proteins. In order to expand our coverage of the human proteome, we combined our previously published biological fluid proteomic data from healthy individuals. We considered data from seven biological fluids, including urine, cerebrospinal fluid, synovial fluid, seminal plasma, sweat, cervical vaginal fluid, and nipple aspirate fluid. Overall, we generated tandem mass spectra corresponding to 13,028 unique human protein-coding genes. Although our analysis did not accomplish complete proteome coverage, it should be an important complementary resource for future biomarker discovery.

Urinary Apolipoprotein C3 Is a Potential Biomarker for Alzheimer's Disease

INTRODUCTION

Biomarkers of Alzheimer's disease (AD) that can easily be measured in routine health checkups are desirable. Urine is a source of biomarkers that can be collected easily and noninvasively. We previously reported on the comprehensive profile of the urinary proteome of AD patients and identified proteins estimated to be significantly increased or decreased in AD patients by a label-free quantification method. The present study aimed to validate urinary levels of proteins that significantly differed between AD and control samples from our proteomics study (i.e., apolipoprotein C3 [ApoC3], insulin-like growth factor-binding protein 3 [Igfbp3], and apolipoprotein D [ApoD]).

METHODS

Enzyme-linked immunosorbent assays (ELISAs) were performed using urine samples from the same patient and control groups analyzed in the previous proteomics study (18 AD and 18 controls, set 1) and urine samples from an independent group of AD patients and controls (13 AD, 5 mild cognitive impairment [MCI], and 32 controls) from the National Center for Geriatrics and Gerontology Biobank (set 2).

RESULTS

In set 1, the crude urinary levels of ApoD, Igfbp3, and creatinine-adjusted ApoD were significantly higher in the AD group relative to the control group (p = 0.003, p = 0.002, and p = 0.019, respectively), consistent with our previous proteomics results. In set 2, however, the crude urinary levels of Igfbp3 were significantly lower in the AD+MCI group than in the control group (p = 0.028), and the levels of ApoD and ApoC3 did not differ significantly compared to the control group. Combined analysis of all samples revealed creatinine-adjusted ApoC3 levels to be significantly higher in the AD+MCI group (p = 0.015) and the AD-only group (p = 0.011) relative to the control group.

CONCLUSION

ApoC3 may be a potential biomarker for AD, as validated by ELISA. Further analysis of ApoC3 as a urinary biomarker for AD is warranted.

Investigation of Plasma Metabolic and Lipidomic Characteristics of a Chinese Cohort and a Pilot Study of Renal Cell Carcinoma Biomarker

Plasma metabolomics and lipidomics have been commonly used for biomarker discovery. Studies in white and Japanese populations suggested that gender and age can affect circulating plasma metabolite profiles; however, the metabolomics characteristics in Chinese population has not been surveyed. In our study, we applied liquid chromatography-mass spectrometry-based approach to analyze Chinese plasma metabolome and lipidome in a cohort of 534 healthy adults (aging from 15 to 79). Fatty-acid metabolism was found to be gender- and age-dependent in Chinese, similar with metabolomics characteristics in Japanese and white populations. Differently, lipids, such as TGs and DGs, were found to be gender-independent in Chinese population. Moreover, nicotinate and nicotinamide metabolism was found to be specifically age-related in Chinese. The application of plasma metabolome and lipidome for renal cell carcinoma diagnosis (143 RCC patients and 34 benign kidney tumor patients) showed good accuracy, with an area under the curve (AUC) of 0.971 for distinction from healthy control, and 0.839 for distinction from the benign. Bile acid metabolism was found to be related to RCC probably combination with intestinal microflora. Definition of the variation and characteristics of Chinese normal plasma metabolome and lipidome might provide a basis for disease biomarker analysis.

An in-depth Comparison of the Pediatric and Adult Urinary N-glycomes

We performed an in-depth characterization and comparison of the pediatric and adult urinary glycomes using a nanoLC-MS/MS based glycomics method, which included normal healthy pediatric (1-10 years, n = 21) and adult (21-50 years, n = 22) individuals. A total of 116 N-glycan compositions were identified, and 46 of them could be reproducibly quantified. We performed quantitative comparisons of the 46 glycan compositions between different age and sex groups. The results showed significant quantitative changes between the pediatric and adult cohorts. The pediatric urinary N-glycome was found to contain a higher level of high-mannose (HM), asialylated/afucosylated glycans (excluding HM), neutral fucosylated and agalactosylated glycans, and a lower level of trisialylated glycans compared with the adult. We further analyzed gender-associated glycan changes in the pediatric and adult group, respectively. In the pediatric group, there was almost no difference of glycan levels between males and females. In adult, the majority of glycans were more abundant in males than females, except the high-mannose and tetrasialylated glycans. These findings highlight the importance to consider age-matching and adult sex-matching for urinary glycan studies. The identified normal pediatric and adult urinary glycomes can serve as a baseline reference for comparisons to other disease states affected by glycosylation.

Proteomic profiling analysis of postmenopausal osteoporosis and osteopenia identifies potential proteins associated with low bone mineral density

Postmenopausal osteoporosis (PMOP) is a major global public health concern and older women are more susceptible to experiencing fragility fractures. Our study investigated the associations between circulating proteins with bone mineral density (BMD) in postmenopausal women with or without low BMD (osteoporosis and osteopenia) using a tandem mass tag (TMT) labeling proteomic experiment and parallel reaction monitoring testing. Across all plasma samples, we quantitatively measured 1,092 proteins, and the OP and normal control (NC) samples were differentiated by principal component analysis and a partial least squares-discrimination analysis model based on the protein profiling data. The differentially abundant proteins between the low BMD and NC samples mostly exhibited binding, molecular function regulator, transporter and molecular transducer activity, and were involved in metabolic and cellular processes, stimulus response, biological regulation, immune system processes and so forth. TMT analysis and RRM validation indicated that the expression of protein Lysozyme C (P61626) was negatively related to BMD, while the expression of proteins Glucosidase (A0A024R592) and Protein disulfideisomerase A5 (Q14554) was positively related to BMD values. Collectively, our results suggest that postmenopausal women with low BMD have a different proteomic profile or signature. Protein alterations may play an important role in the pathogenesis of PMOP, and they may act as novel biomarkers and targets of therapeutic agents for this disease.

Urinary biomarker discovery in gliomas using mass spectrometry-based clinical proteomics

Background

Gliomas are the most common primary malignant brain tumors and have a poor prognosis. Early detection of gliomas is crucial to improve patient outcomes. Urine accumulates systematic body changes and thus serves as an excellent early biomarker source.

Methods

At the biomarker discovery phase, we performed a self-controlled proteomics analysis by comparing urine samples collected from five glioma patients at the time of tumor diagnosis and after surgical removal of the tumor. At the biomarker validation phase, we further validated some promising proteins using parallel reaction monitoring (PRM)-based targeted proteomics in another cohort, including glioma, meningioma, and moyamoya disease patients as well as healthy controls.

Results

Using label-free proteome quantitation (LFQ), we identified twenty-seven urinary proteins that were significantly changed after tumor resection, many of which have been previously associated with gliomas. The functions of these proteins were significantly enriched in the autophagy and angiogenesis, which are associated with glioma development. After targeted proteomics validation, we identified a biomarker panel (AACT, TSP4, MDHM, CALR, LEG1, and AHSG) with an area under the curve (AUC) value of 0.958 for the detection of gliomas. Interestingly, AACT, LEG1, and AHSG are also potential cerebrospinal fluid or blood biomarkers of gliomas.

Conclusions

Using LFQ and PRM proteome quantification, we identified candidate urinary protein biomarkers with the potential to detect gliomas. This study will also provide clues for future biomarker studies involving brain diseases.

Identification of marker proteins of muscular dystrophy in the urine proteome from the mdx-4cv model of dystrophinopathy

Since the protein constituents of urine present a dynamic proteome that can reflect a variety of disease-related alterations in the body, the mass spectrometric survey of proteome-wide changes in urine promises new insights into pathogenic mechanisms. Urine can be investigated in a completely non-invasive way and provides valuable biomedical information on body-wide changes. In this report, we have focused on the urine proteome in X-linked muscular dystrophy using the established mdx-4cv mouse model of dystrophinopathy. In order to avoid potential artefacts due to the manipulation of the biofluid proteome prior to mass spectrometry, crude urine specimens were analyzed without the prior usage of centrifugation steps or concentration procedures. Comparative proteomics revealed 21 increased and 8 decreased proteins out of 870 identified urinary proteoforms using 50 μl of biofluid per investigated sample, i.e. 14 wild type versus 14 mdx-4cv specimens. Promising marker proteins that were almost exclusively found in mdx-4cv urine included nidogen, parvalbumin and titin. Interestingly, the mass spectrometric identification of urine-associated titin revealed a wide spread of peptides over the sequence of this giant muscle protein. The newly established urinomic signature of dystrophinopathy might be helpful for the design of non-invasive assays to improve diagnosis, prognosis, therapy-monitoring and evaluation of potential harmful side effects of novel treatments in the field of muscular dystrophy research.

Proteomics and Metabolomics in Kidney Disease, including Insights into Etiology, Treatment, and Prevention

In this review of the application of proteomics and metabolomics to kidney disease research, we review key concepts, highlight illustrative examples, and outline future directions. The proteome and metabolome reflect the influence of environmental exposures in addition to genetic coding. Circulating levels of proteins and metabolites are dynamic and modifiable, and thus amenable to therapeutic targeting. Design and analytic considerations in proteomics and metabolomics studies should be tailored to the investigator's goals. For the identification of clinical biomarkers, adjustment for all potential confounding variables, particularly GFR, and strict significance thresholds are warranted. However, this approach has the potential to obscure biologic signals and can be overly conservative given the high degree of intercorrelation within the proteome and metabolome. Mass spectrometry, often coupled to up-front chromatographic separation techniques, is a major workhorse in both proteomics and metabolomics. High-throughput antibody- and aptamer-based proteomic platforms have emerged as additional, powerful approaches to assay the proteome. As the breadth of coverage for these methodologies continues to expand, machine learning tools and pathway analyses can help select the molecules of greatest interest and categorize them in distinct biologic themes. Studies to date have already made a substantial effect, for example elucidating target antigens in membranous nephropathy, identifying a signature of urinary peptides that adds prognostic information to urinary albumin in CKD, implicating circulating inflammatory proteins as potential mediators of diabetic nephropathy, demonstrating the key role of the microbiome in the uremic milieu, and highlighting kidney bioenergetics as a modifiable factor in AKI. Additional studies are required to replicate and expand on these findings in independent cohorts. Further, more work is needed to understand the longitudinal trajectory of select protein and metabolite markers, perform transomics analyses within merged datasets, and incorporate more kidney tissue-based investigation.

Using the tools of proteomics to understand the pathogenesis of idiopathic inflammatory myopathies

PURPOSE OF REVIEW

One of the most important advances in medical research over the past 20 years has been the emergence of technologies to assess complex biological processes on a global scale. Although a great deal of attention has been given to genome-scale genetics and genomics technologies, the utility of studying the proteome in a comprehensive way is sometimes under-appreciated. In this review, we discuss recent advances in proteomics as applied to dermatomyositis/polymyositis as well as findings from other inflammatory diseases that may enlighten our understanding of dermatomyositis/polymyositis.

RECENT FINDINGS

Proteomic approaches have been used to investigate basic mechanisms contributing to lung and skin disease in dermatomyositis/polymyositis as well as to the muscle disease itself. In addition, proteomic approaches have been used to identify autoantibodies targeting the endothelium in juvenile dermatomyositis. Studies from other inflammatory diseases have shown the promise of using proteomics to characterize the composition of immune complexes and the protein cargoes of exosomes.

SUMMARY

There are many relevant scientific and clinical questions in dermatomyositis/polymyositis that can be addressed using proteomics approaches. Careful attention to both methodology and analytic approaches are required to obtain useful and reproducible data.