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

UNVEILING DEFECTS OF SECRETION MECHANISMS IN PARKINSON'S DISEASE

Neurodegenerative diseases are characterized by progressive dysfunction and loss of specific sets of neurons. While extensive research has focused on elucidating the genetic and epigenetic factors and molecular mechanisms underlying these disorders, emerging evidence highlights the critical role of secretion in the pathogenesis, possibly even onset, and progression of neurodegenerative diseases, suggesting the occurrence of non-cell-autonomous mechanisms. Secretion is a fundamental process that regulates intercellular communication, supports cellular homeostasis, and orchestrates various physiological functions in the body. Defective secretion can impair the release of neurotransmitters and other signaling molecules, disrupting synaptic transmission and compromising neuronal survival. It can also contribute to the accumulation, misfolding, and aggregation of disease-associated proteins, leading to neurotoxicity and neuronal dysfunction. In this review, we discuss the implications of defective secretion in the context of Parkinson's disease, emphasizing its role in protein aggregation, synaptic dysfunction, extracellular vesicle secretion and neuroinflammation. We propose a multiple-hit model whereby protein accumulation and secretory defects must be combined for the onset and progression of the disease.

An effective urobilin clearance strategy based on paramagnetic beads facilitates microscale proteomic analysis of urine

Urine provides an ideal source for disease biomarker discovery. High-adhesion contaminants such as urobilin, which are difficult to remove from urine, can severely interfere with urinary proteomic analysis. Here, we aimed to establish a strategy based on single-pot, solid-phase-enhanced sample preparation (SP3) technology to prepare samples for urinary proteomics analysis that almost completely eliminates the impact of urobilin. A systematic evaluation of the effects of two urinary protein precipitation methods, two types of protein lysis buffers, and different ratios of magnetic digestion beads on the identification and quantification of the microscale urinary proteome was conducted. Our results indicate that methanol-chloroform precipitation, coupled with efficient lysis facilitated by urea, and subsequent enzymatic digestion using a mix of hydrophilic and hydrophobic magnetic beads offers the best performance. Further applying this strategy to the urine of patients with benign prostatic hyperplasia, prostate cancer and healthy individuals, combined with a narrow window of data-independent acquisition, FGFR4, MYLK, ORM2, GOLM1, SPP1, CD55, CSF1, DLD and TIMP3 were identified as potential biomarkers to discriminate benign prostatic hyperplasia and prostate cancer patients.

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.

Exploration of potential biomarkers for early bladder cancer based on urine proteomics

Background

Bladder cancer is a common malignant tumor of the urinary system. The progression of the condition is associated with a poor prognosis, so it is necessary to identify new biomarkers to improve the diagnostic rate of bladder cancer.

Methods

In this study, 338 urine samples (144 bladder cancer, 123 healthy control, 32 cystitis, and 39 upper urinary tract cancer samples) were collected, among which 238 samples (discovery group) were analyzed by LC-MS. The urinary proteome characteristics of each group were compared with those of bladder cancer, and the differential proteins were defined by bioinformatics analysis. The pathways and functional enrichments were annotated. The selected proteins with the highest AUC score were used to construct a diagnostic panel. One hundred samples (validation group) were used to test the effect of the panel by ELISA.

Results

Compared with the healthy control, cystitis and upper urinary tract cancer samples, the number of differential proteins in the bladder cancer samples was 325, 158 and 473, respectively. The differentially expressed proteins were mainly related to lipid metabolism and iron metabolism and were involved in the proliferation, metabolism and necrosis of bladder cancer cells. The AUC of the panel of APOL1 and ITIH3 was 0.96 in the discovery group. ELISA detection showed an AUC of 0.92 in the validation group.

Conclusion

This study showed that urinary proteins can reflect the pathophysiological changes in bladder cancer and that important molecules can be used as biomarkers for bladder cancer screening. These findings will benefit the application of the urine proteome in clinical research.

VGF modifications related to nigrostriatal dopaminergic neurodegeneration induced by the pesticide fipronil in adult male rats

BACKGROUND

Dopamine is reduced in the brain of rats treated with fipronil, a broad-spectrum insecticide. VGF (no acronym) is a neurotrophin-inducible protein expressed as the 75 kDa form (precursor or pro-VGF) or its truncated peptides. VGF immunostaining has been revealed using an antibody against the C-terminal nonapeptide of the rat pro-VGF in the nerve terminals of the rat substantia nigra, where it was reduced after 6-hydroxydopamine treatment. It is unknown whether pro-VGF and/or its shortened peptides are present in these neurons. Therefore, the aim of this study was first to determine which types of VGF are expressed in the normal substantia nigra (and striatum) and then to determine VGF modulations and whether they occur in parallel with locomotor changes after fipronil injection.

METHODS

Rats were divided into two groups that received a unilateral intranigral infusion of either fipronil (25 µg) diluted in dimethyl sulfoxide (DMSO) or DMSO alone, and then were tested for locomotor activity. An untreated group of rats (n=4) was used for identification of the VGF fragments using high performance liquid chromatography-mass spectrometry and western blot, while changes in treated groups (fipronil vs DMSO, each n=6) were investigated by immunohistochemistry using an antibody against the rat pro-VGF C-terminal nonapeptide in parallel with the anti-tyrosine hydroxylase antibody.

RESULTS

In untreated rats, the VGF C-terminal antibody identified mostly a 75 kDa band in the substantia nigra and striatum, supporting the finding of high-resolution mass spectrometry, which revealed fragments covering the majority of the pro-VGF sequence. Furthermore, several shortened VGF C-terminal forms (varying from 10 to 55 kDa) were also found by western blot, while high-resolution mass spectrometry revealed a C-terminal peptide overlapping the immunogen used to create the VGF antibody in both substantia nigra and striatum. In the substantia nigra of fipronil-treated rats, immunostaining for tyrosine hydroxylase and VGF was reduced compared to DMSO-treated rat group, and this was related with significant changes in locomotor activity.

CONCLUSION

Fipronil has the ability to modulate the production of pro-VGF and/or its C-terminal truncated peptides in the nigrostriatal system indicating its intimate interaction with the dopaminergic neurotransmission and implying a potential function in modulating locomotor activity.

A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells

Parkinson´s disease (PD) is a common neurodegenerative movement disorder and leucine-rich repeat kinase 2 (LRRK2) is a promising therapeutic target for disease intervention. However, the ability to stratify patients who will benefit from such treatment modalities based on shared etiology is critical for the success of disease-modifying therapies. Ciliary and centrosomal alterations are commonly associated with pathogenic LRRK2 kinase activity and can be detected in many cell types. We previously found centrosomal deficits in immortalized lymphocytes from G2019S-LRRK2 PD patients. Here, to investigate whether such deficits may serve as a potential blood biomarker for PD which is susceptible to LRKK2 inhibitor treatment, we characterized patient-derived cells from distinct PD cohorts. We report centrosomal alterations in peripheral cells from a subset of early-stage idiopathic PD patients which is mitigated by LRRK2 kinase inhibition, supporting a role for aberrant LRRK2 activity in idiopathic PD. Centrosomal defects are detected in R1441G-LRRK2 and G2019S-LRRK2 PD patients and in non-manifesting LRRK2 mutation carriers, indicating that they accumulate prior to a clinical PD diagnosis. They are present in immortalized cells as well as in primary lymphocytes from peripheral blood. These findings indicate that analysis of centrosomal defects as a blood-based patient stratification biomarker may help nominate idiopathic PD patients who will benefit from LRRK2-related therapeutics.

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N = 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N = 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

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

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

Evaluation of α-synuclein in CNS-originating extracellular vesicles for Parkinsonian disorders: A systematic review and meta-analysis

BACKGROUND & AIMS

Parkinsonian disorders, such as Parkinson's disease (PD), multiple system atrophy (MSA), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), share early motor symptoms but have distinct pathophysiology. As a result, accurate premortem diagnosis is challenging for neurologists, hindering efforts for disease-modifying therapeutic discovery. Extracellular vesicles (EVs) contain cell-state-specific biomolecules and can cross the blood-brain barrier to the peripheral circulation, providing a unique central nervous system (CNS) insight. This meta-analysis evaluated blood-isolated neuronal and oligodendroglial EVs (nEVs and oEVs) α-synuclein levels in Parkinsonian disorders.

METHODS

Following PRISMA guidelines, the meta-analysis included 13 studies. An inverse-variance random-effects model quantified effect size (SMD), QUADAS-2 assessed risk of bias and publication bias was evaluated. Demographic and clinical variables were collected for meta-regression.

RESULTS

The meta-analysis included 1,565 patients with PD, 206 with MSA, 21 with DLB, 172 with PSP, 152 with CBS and 967 healthy controls (HCs). Findings suggest that combined concentrations of nEVs and oEVs α-syn is higher in patients with PD compared to HCs (SMD = 0.21, p = 0.021), while nEVs α-syn is lower in patients with PSP and CBS compared to patients with PD (SMD = -1.04, p = 0.0017) or HCs (SMD = -0.41, p < 0.001). Additionally, α-syn in nEVs and/or oEVs did not significantly differ in patients with PD vs. MSA, contradicting the literature. Meta-regressions show that demographic and clinical factors were not significant predictors of nEVs or oEVs α-syn concentrations.

CONCLUSION

The results highlight the need for standardized procedures and independent validations in biomarker studies and the development of improved biomarkers for distinguishing Parkinsonian disorders.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

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.

Urine-HILIC: Automated Sample Preparation for Bottom-Up Urinary Proteome Profiling in Clinical Proteomics

Urine provides a diverse source of information related to a patient's health status and is ideal for clinical proteomics due to its ease of collection. To date, most methods for the preparation of urine samples lack the throughput required to analyze large clinical cohorts. To this end, we developed a novel workflow, urine-HILIC (uHLC), based on an on-bead protein capture, clean-up, and digestion without the need for bottleneck processing steps such as protein precipitation or centrifugation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample was subjected to automated sample preparation in a KingFisher™ Flex magnetic handling station using the novel approach based on MagReSyn® HILIC microspheres. For benchmarking, the pooled sample was also prepared using a published protocol based on an on-membrane (OM) protein capture and digestion workflow. Peptides were analyzed by LCMS in data-independent acquisition (DIA) mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. The data were searched in Spectronaut™ 17. Both workflows showed similar peptide and protein identifications in the pooled sample. The uHLC workflow was easier to set up and complete, having less hands-on time than the OM method, with fewer manual processing steps. Lower peptide and protein coefficient of variation was observed in the uHLC technical replicates. Following statistical analysis, candidate protein markers were filtered, at ≥8.35-fold change in abundance, ≥2 unique peptides and ≤1% false discovery rate, and revealed 121 significant, differentially abundant proteins, some of which have known associations with kidney injury. The pilot data derived using this novel workflow provide information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

Urine proteomic analysis of the rat e-cigarette model

Background

We were curious if the urinary proteome could reflect the effects of e-cigarettes on the organism.

Methods

Urine samples were collected from a rat e-cigarette model before, during, and after two weeks of e-cigarette smoking. Urine proteomes before and after smoking of each rat were compared individually, while the control group was set up to rule out differences caused by rat growth and development.

Results

Fetuin-B, a biomarker of chronic obstructive pulmonary disease (COPD), and annexin A2, which is recognized as a multiple tumour marker, were identified as differential proteins in five out of six smoking rats on day 3. To our surprise, odourant-binding proteins expressed in the olfactory epithelium were also found and were significantly upregulated. Pathways enriched by the differential proteins include the apelin signalling pathway, folate biosynthesis pathway, arachidonic acid metabolism, chemical carcinogenesis-DNA adducts and chemical carcinogenesis-reactive oxygen species. They have been reported to be associated with immune system, cardiovascular system, respiratory system, etc.

Conclusions

Urinary proteome could reflect the effects of e-cigarettes in rats.

Mass Spectrometric Analysis of Urinary N-Glycosylation Changes in Patients with Parkinson's Disease

Urine is thought to provide earlier and more sensitive molecular changes for biomarker discovery than blood. Numerous glycoproteins, peptides, and free glycans are present in urine through glomerular filtration of plasma, cell shedding, apoptosis, proteolytic cleavage, and exosome secretion. Urine biomarkers have enormous diagnostic potential, and the use of these biomarkers is a long-standing practice. The discovery of non-urological disease biomarkers from urine is also gaining attention due to its non-invasive sample collection and ease of analysis. Abnormal protein glycosylation in plasma or cerebrospinal fluid has been associated with Parkinson's disease, however, whether urine with Parkinson's disease has characteristic glycosylation remains to be explored. Here, we use mass spectrometry-based glycomics and glycoproteomics approaches to analyze urine samples for glycans, glycosites, and intact glycopeptides of urine samples. Reduced abundance of N-glycans was detected at the level of total glycans as well as specific glycosites of glycopeptides. The most abundant N-glycan in urine is S(6)1H5N4F1; S(6)2H5N4 and N4H4F1 are highly present in serum and urine, and 10 biantennary galactosylated N-glycans in the urine of PD patients were significantly decreased. The downregulation of sialylation may be due to the reduction of ST3GAL2. Site-specific N-glycosylation analysis revealed that AMBP, UMOD, and RNase1 have PD-specific N-glycosylation sites. GO and KEGG analysis revealed that N-glycosylation changes may provide clues to identify disease-specific glycosylation biomarkers in Parkinson's disease.

A Multiplexed Urinary Biomarker Panel Has Potential for Alzheimer's Disease Diagnosis Using Targeted Proteomics and Machine Learning

As disease-modifying therapies are now available for Alzheimer's disease (AD), accessible, accurate and affordable biomarkers to support diagnosis are urgently needed. We sought to develop a mass spectrometry-based urine test as a high-throughput screening tool for diagnosing AD. We collected urine from a discovery cohort (n = 11) of well-characterised individuals with AD (n = 6) and their asymptomatic, CSF biomarker-negative study partners (n = 5) and used untargeted proteomics for biomarker discovery. Protein biomarkers identified were taken forward to develop a high-throughput, multiplexed and targeted proteomic assay which was tested on an independent cohort (n = 21). The panel of proteins identified are known to be involved in AD pathogenesis. In comparing AD and controls, a panel of proteins including MIEN1, TNFB, VCAM1, REG1B and ABCA7 had a classification accuracy of 86%. These proteins have been previously implicated in AD pathogenesis. This suggests that urine-targeted mass spectrometry has potential utility as a diagnostic screening tool in AD.

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Brain-Biomarker Changes in Body Fluids of Patients with Parkinson's Disease

Parkinson's disease (PD) is an incurable neurodegenerative disease that is rarely diagnosed at an early stage. Although the understanding of PD-related mechanisms has greatly improved over the last decade, the diagnosis of PD is still based on neurological examination through the identification of motor symptoms, including bradykinesia, rigidity, postural instability, and resting tremor. The early phase of PD is characterized by subtle symptoms with a misdiagnosis rate of approximately 16-20%. The difficulty in recognizing early PD has implications for the potential use of novel therapeutic approaches. For this reason, it is important to discover PD brain biomarkers that can indicate early dopaminergic dysfunction through their changes in body fluids, such as saliva, urine, blood, or cerebrospinal fluid (CSF). For the CFS-based test, the invasiveness of sampling is a major limitation, whereas the other body fluids are easier to obtain and could also allow population screening. Following the identification of the crucial role of alpha-synuclein (α-syn) in the pathology of PD, a very large number of studies have summarized its changes in body fluids. However, methodological problems have led to the poor diagnostic/prognostic value of this protein and alternative biomarkers are currently being investigated. The aim of this paper is therefore to summarize studies on protein biomarkers that are alternatives to α-syn, particularly those that change in nigrostriatal areas and in biofluids, with a focus on blood, and, eventually, saliva and urine.

Data-independent acquisition-based mass spectrometry(DIA-MS) for quantitative analysis of patients with chronic hepatitis B

Chronic hepatitis B is a significant public health problem and complex pathologic process, and unraveling the underlying mechanisms and pathophysiology is of great significance. Data independent acquisition mass spectrometry (DIA-MS) is a label-free quantitative proteomics method that has been successfully applied to the study of a wide range of diseases. The aim of this study was to apply DIA-MS for proteomic analysis of patients with chronic hepatitis B. We performed comprehensive proteomics analysis of protein expression in serum samples from HBV patients and healthy controls by using DIA-MS. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein network analysis were performed on differentially expressed proteins and were further combined with literature analysis. We successfully identified a total of 3786 serum proteins with a high quantitative performance from serum samples in this study. We identified 310 differentially expressed proteins (DEPs) (fold change > 1.5 and P value < 0.05 as the criteria for a significant difference) between HBV and healthy samples. A total of 242 upregulated proteins and 68 downregulated proteins were among the DEPs. Some protein expression levels were significantly elevated or decreased in patients with chronic hepatitis B, indicating a relation to chronic liver disease, which should be further investigated.

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.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Discovery of Oogenesis Biomarkers from Mouse Oocytes Using a Single-Cell Proteomics Approach

We established an efficient and simplified single-cell proteomics (ES-SCP) workflow to realize proteomics profiling at the single-oocyte level. With the ES-SCP workflow, we constructed a deep coverage proteome library during oocyte maturation, which contained more than 6000 protein groups, and identified and quantified more than 4000 protein groups from a pool of only 15 oocytes at germinal vesicle (GV), GV breakdown (GVBD), and metaphase II (MII) stages. More than 1500 protein groups can be identified from single oocytes. We found that marker proteins including maternal factors and mRNA regulators, such as ZAR1, TLE6, and BTG4, showed significant variations in abundance during oocyte maturation, and it was discovered that maternal mRNA degradation was indispensable during oocyte maturation. Proteomics analysis from single oocytes revealed that changes in antioxidant factors, maternal factors, mRNA stabilization, and energy metabolism were the factors that affect the oocyte quality during ovary aging. Our data laid the foundation for future innovations in assisted reproduction.

Elevated urine BMP phospholipids in LRRK2 and VPS35 mutation carriers with and without Parkinson's disease

Elevated urine bis(monoacylglycerol)phosphate (BMP) levels have been found in gain-of-kinase function LRRK2 G2019S mutation carriers. Here, we have expanded urine BMP analysis to other Parkinson's disease (PD) associated mutations and found them to be consistently elevated in carriers of LRRK2 G2019S and R1441G/C as well as VPS35 D620N mutations. Urine BMP levels are promising biomarkers for patient stratification and potentially target engagement in clinical trials of emerging targeted PD therapies.

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.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Quantitative proteomics identifies and validates urinary biomarkers of rhabdomyosarcoma in children

BACKGROUND

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma with poor prognosis in children. The 5-year survival rate for early RMS has improved, whereas it remains unsatisfactory for advanced patients. Urine can rapidly reflect changes in the body and identify low-abundance proteins. Early screening of tumor markers through urine in RMS allows for earlier treatment, which is associated with better outcomes.

METHODS

RMS patients under 18 years old, including those newly diagnosed and after surgery, were enrolled. Urine samples were collected at the time points of admission and after four cycles of chemotherapy during follow-up. Then, a two-stage workflow was established. (1) In the discovery stage, differential proteins (DPs) were initially identified in 43 RMS patients and 12 healthy controls (HCs) using a data-independent acquisition method. (2) In the verification stage, DPs were further verified as biomarkers in 54 RMS patients and 25 HCs using parallel reaction monitoring analysis. Furthermore, a receiver operating characteristic (ROC) curve was used to construct the protein panels for the diagnosis of RMS. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software were used to perform bioinformatics analysis.

RESULTS

A total of 251 proteins were significantly altered in the discovery stage, most of which were enriched in the head, neck and urogenital tract, consistent with the most common sites of RMS. The most overrepresented biological processes from GO analysis included immunity, inflammation, tumor invasion and neuronal damage. Pathways engaging the identified proteins revealed 33 common pathways, including WNT/β-catenin signaling and PI3K/AKT signaling. Finally, 39 proteins were confirmed as urinary biomarkers for RMS, and a diagnostic panel composed of 5 candidate proteins (EPS8L2, SPARC, HLA-DRB1, ACAN, and CILP) was constructed for the early screening of RMS (AUC: 0.79, 95%CI = 0.66 ~ 0.92).

CONCLUSIONS

These findings provide novel biomarkers in urine that are easy to translate into clinical diagnosis of RMS and illustrate the value of global and targeted urine proteomics to identify and qualify candidate biomarkers for noninvasive molecular diagnosis.

Secretion of VGF relies on the interplay between LRRK2 and post-Golgi v-SNAREs

The neuropeptide VGF was recently proposed as a neurodegeneration biomarker. The Parkinson's disease-related protein leucine-rich repeat kinase 2 (LRRK2) regulates endolysosomal dynamics, a process that involves SNARE-mediated membrane fusion and could regulate secretion. Here we investigate potential biochemical and functional links between LRRK2 and v-SNAREs. We find that LRRK2 directly interacts with the v-SNAREs VAMP4 and VAMP7. Secretomics reveals VGF secretory defects in VAMP4 and VAMP7 knockout (KO) neuronal cells. In contrast, VAMP2 KO "regulated secretion-null" and ATG5 KO "autophagy-null" cells release more VGF. VGF is partially associated with extracellular vesicles and LAMP1+ endolysosomes. LRRK2 expression increases VGF perinuclear localization and impairs its secretion. Retention using selective hooks (RUSH) assays show that a pool of VGF traffics through VAMP4+ and VAMP7+ compartments, and LRRK2 expression delays its transport to the cell periphery. Overexpression of LRRK2 or VAMP7-longin domain impairs VGF peripheral localization in primary cultured neurons. Altogether, our results suggest that LRRK2 might regulate VGF secretion via interaction with VAMP4 and VAMP7.

Protein glycosylation in urine as a biomarker of diseases

Human body fluids have become an indispensable resource for clinical research, diagnosis and prognosis. Urine is widely used to discover disease-specific glycoprotein biomarkers because of its recurrently non-invasive collection and disease-indicating properties. While urine is an unstable fluid in that its composition changes with ingested nutrients and further as it is excreted through micturition, urinary proteins are more stable and their abnormal glycosylation is associated with diseases. It is known that aberrant glycosylation can define tumor malignancy and indicate disease initiation and progression. However, a thorough and translational survey of urinary glycosylation in diseases has not been performed. In this article, we evaluate the clinical applications of urine, introduce methods for urine glycosylation analysis, and discuss urine glycoprotein biomarkers. We emphasize the importance of mining urinary glycoproteins and searching for disease-specific glycosylation in various diseases (including cancer, neurodegenerative diseases, diabetes, and viral infections). With advances in mass spectrometry-based glycomics/glycoproteomics/glycopeptidomics, characterization of disease-specific glycosylation will optimistically lead to the discovery of disease-related urinary biomarkers with better sensitivity and specificity in the near future.

LRRK2 and GBA1 variant carriers have higher urinary bis(monacylglycerol) phosphate concentrations in PPMI cohorts

We quantified concentrations of three isoforms of the endolysosomal lipid, bis(monoacylglycerol) phosphate (BMP) in the urine of deeply phenotyped cohorts in the Parkinson's Progression Markers Initiative: LRRK2 G2019S PD (N = 134) and non-manifesting carriers (NMC) (G2019S+ NMC; N = 182), LRRK2 R1441G PD (N = 15) and R1441G+ NMC (N = 15), GBA1 N409S PD (N = 76) and N409S+ NMC (N = 178), sporadic PD (sPD, N = 379) and healthy controls (HC) (N = 190). The effects of each mutation and disease status were analyzed using nonparametric methods. Longitudinal changes in BMP levels were analyzed using linear mixed models. At baseline, all LRRK2 carriers had 3-7× higher BMP levels compared to HC, irrespective of the disease status. GBA1 N409S carriers also showed significant, albeit smaller, elevation (~30-40%) in BMP levels compared to HC. In LRRK2 G2019S PD, urinary BMP levels remained stable over two years. Furthermore, baseline BMP levels did not predict disease progression as measured by striatal DaT imaging, MDS-UPDRS III Off, or MoCA in any of the cohorts. These data support the utility of BMP as a target modulation biomarker in therapeutic trials of genetic and sPD but not as a prognostic or disease progression biomarker.

Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson's Disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success.

Alterations in the LRRK2-Rab pathway in urinary extracellular vesicles as Parkinson's disease and pharmacodynamic biomarkers

Expression or phosphorylation levels of leucine-rich repeat kinase 2 (LRRK2) and its Rab substrates have strong potential as disease or pharmacodynamic biomarkers. The main objective of this study is therefore to assess the LRRK2-Rab pathway for use as biomarkers in human, non-human primate (NHP) and rat urine. With urine collected from human subjects and animals, we applied an ultracentrifugation based fractionation protocol to isolate small urinary extracellular vesicles (uEVs). We used western blot with antibodies directed against total and phosphorylated LRRK2, Rab8, and Rab10 to measure these LRRK2 and Rab epitopes in uEVs. We confirm the presence of LRRK2 and Rab8/10 in human and NHP uEVs, including total LRRK2 as well as phospho-LRRK2, phospho-Rab8 and phospho-Rab10. We also confirm LRRK2 and Rab expression in rodent uEVs. We quantified LRRK2 and Rab epitopes in human cohorts and found in a first cohort that pS1292-LRRK2 levels were elevated in individuals carrying the LRRK2 G2019S mutation, without significant differences between healthy and PD groups, whether for LRRK2 G2019S carriers or not. In a second cohort, we found that PD was associated to increased Rab8 levels and decreased pS910-LRRK2 and pS935-LRRK2. In animals, acute treatment with LRRK2 kinase inhibitors led to decreased pT73-Rab10. The identification of changes in Rab8 and LRRK2 phosphorylation at S910 and S935 heterologous phosphosites in uEVs of PD patients and pT73-Rab10 in inhibitor-dosed animals further reinforces the potential of the LRRK2-Rab pathway as a source of PD and pharmacodynamic biomarkers in uEVs.

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling

Summary

We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates.

Availability and implementation

promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

From Lysosomal Storage Disorders to Parkinson's Disease - Challenges and Opportunities

Lysosomes are specialized organelles with an acidic pH that act as recycling hubs for intracellular and extracellular components. They harbour numerous different hydrolytic enzymes to degrade substrates like proteins, peptides, and glycolipids. Reduced catalytic activity of lysosomal enzymes can cause the accumulation of these substrates and loss of lysosomal integrity, resulting in lysosomal dysfunction and lysosomal storage disorders (LSDs). Post-mitotic cells, such as neurons, seem to be highly sensitive to damages induced by lysosomal dysfunction, thus LSDs often manifest with neurological symptoms. Interestingly, some LSDs and Parkinson's disease (PD) share common cellular pathomechanisms, suggesting convergence of aetiology of the two disease types. This is further underlined by genetic associations of several lysosomal genes involved in LSDs with PD. The increasing number of lysosome-associated genetic risk factors for PD makes it necessary to understand functions and interactions of lysosomal proteins/enzymes both in health and disease, thereby holding the potential to identify new therapeutic targets. In this review, we highlight genetic and mechanistic interactions between the complex lysosomal network, LSDs and PD, and elaborate on methodical challenges in lysosomal research.

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.

Longitudinal clinical and biomarker characteristics of non-manifesting LRRK2 G2019S carriers in the PPMI cohort

We examined 2-year longitudinal change in clinical features and biomarkers in LRRK2 non-manifesting carriers (NMCs) versus healthy controls (HCs) enrolled in the Parkinson's Progression Markers Initiative (PPMI). We analyzed 2-year longitudinal data from 176 LRRK2 G2019S NMCs and 185 HCs. All participants were assessed annually with comprehensive motor and non-motor scales, dopamine transporter (DAT) imaging, and biofluid biomarkers. The latter included cerebrospinal fluid (CSF) Abeta, total tau and phospho-tau; serum urate and neurofilament light chain (NfL); and urine bis(monoacylglycerol) phosphate (BMP). At baseline, LRRK2 G2019S NMCs had a mean (SD) age of 62 (7.7) years and were 56% female. 13% had DAT deficit (defined as <65% of age/sex-expected lowest putamen SBR) and 11% had hyposmia (defined as ≤15th percentile for age and sex). Only 5 of 176 LRRK2 NMCs developed PD during follow-up. Although NMCs scored significantly worse on numerous clinical scales at baseline than HCs, there was no longitudinal change in any clinical measures over 2 years or in DAT binding. There were no longitudinal differences in CSF and serum biomarkers between NMCs and HCs. Urinary BMP was significantly elevated in NMCs at all time points but did not change longitudinally. Neither baseline biofluid biomarkers nor the presence of DAT deficit correlated with 2-year change in clinical outcomes. We observed no significant 2-year longitudinal change in clinical or biomarker measures in LRRK2 G2019S NMCs in this large, well-characterized cohort even in the participants with baseline DAT deficit. These findings highlight the essential need for further enrichment biomarker discovery in addition to DAT deficit and longer follow-up to enable the selection of NMCs at the highest risk for conversion to enable future prevention clinical trials.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes

Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

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130 CpGs have been used by two or more aging clocks applied to human buccal tissue

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Common CpG genes are linked to the adaptive immune system and telomere maintenance

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Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock

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Several compounds associated with common CpG genes regulate lifespan in animals

Biocompatible exosome-modified fibrin gel accelerates the recovery of spinal cord injury by VGF-mediated oligodendrogenesis

Exosomes show potential for treating patients with spinal cord injury (SCI) in clinical practice, but the underlying repair mechanisms remain poorly understood, and biological scaffolds available for clinical transplantation of exosomes have yet to be explored. In the present study, we demonstrated the novel function of Gel-Exo (exosomes encapsulated in fibrin gel) in promoting behavioural and electrophysiological performance in mice with SCI, and the upregulated neural marker expression in the lesion site suggested enhanced neurogenesis by Gel-Exo. According to the RNA-seq results, Vgf (nerve growth factor inducible) was the key regulator through which Gel-Exo accelerated recovery from SCI. VGF is related to myelination and oligodendrocyte development according to previous reports. Furthermore, we found that VGF was abundant in exosomes, and Gel-Exo-treated mice with high VGF expression indeed showed increased oligodendrogenesis. VGF was also shown to promote oligodendrogenesis both in vitro and in vivo, and lentivirus-mediated VGF overexpression in the lesion site showed reparative effects equal to those of Gel-Exo treatment in vivo. These results suggest that Gel-Exo can thus be used as a biocompatible material for SCI repair, in which VGF-mediated oligodendrogenesis is the vital mechanism for functional recovery.

VGF: A prospective biomarker and therapeutic target for neuroendocrine and nervous system disorders

Neuroendocrine regulatory polypeptide VGF (nerve growth factor inducible) was firstly found in the rapid induction of nerve growth factor on PC12 cells. It was selectively distributed in neurons and many neuroendocrine tissues. This paper reviewed the latest literatures on the gene structure, transcriptional regulation, protein processing, distribution and potential receptors of VGF. The neuroendocrine roles of VGF and its derived polypeptides in regulating energy, water electrolyte balance, circadian rhythm and reproductive activities were also summarized. Furthermore, based on the experimental evidence in vivo and in vitro, dysregulation of VGF in different neuroendocrine diseases and the possible mechanism mediated by VGF polypeptides were discussed. We next discussed the potential as the clinical diagnosis and therapy for VGF related diseases in the future.

Many kinds of oxidized proteins are present more in the urine of the elderly

Background

Many studies have shown an association between aging and oxidation. To our knowledge, there have been no studies exploring aging-related urine proteome modifications. The purpose of this study was to explore differences in global chemical modifications of urinary protein at different ages.

Methods

Discovery (n=38) cohort MS data including children, young and old groups were downloaded from three published studies, and this data was analyzed using open-pFind for identifying modifications. Verification cohort human samples (n=28) including young, middle-aged, and old groups, rat samples (n=7) at three-time points after birth, adulthood, and old age were collected and processed in the laboratory simultaneously based on label-free quantification combined with pFind.

Results

Discovery cohort: there were 28 kinds of differential oxidations in the old group that were higher than those in the young or children group in. Verification cohort: there were 17 kinds of differential oxidations of 49 oxidized proteins in the middle and old groups, which were significantly higher than those in the young group. Both oxidations and oxidized proteins distinguished different age groups well. There were also 15 kinds of differential oxidations in old age higher than others in the rat cohort. The results showed that the validation experiment was basically consistent with the results of the discovery experiment, showing that the level of oxidized proteins in urine increased significantly with age.

Conclusions

Our study is the first to show that oxidative proteins occur in urine and that oxidations are higher in older than younger ages. Perhaps improving the degree of excretion of oxidative protein in vivo through the kidney is helpful for maintaining the homeostasis of the body’s internal environment, delaying aging and the occurrence of senile diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09360-2.

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

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

Pancreatic cancer cells spectral library by DIA-MS and the phenotype analysis of gemcitabine sensitivity

Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteome strategies are increasingly used for detecting and validating protein biomarkers and therapeutic targets. Here, based on an in-depth proteome analysis of seven pancreatic cancer cell lines, we built a pancreas-specific mass spectrum library containing 10633 protein groups and 184551 peptides. The proteome difference among the seven pancreatic cancer cells was significant, especially for the divergent expression of proteins related to epithelial-mesenchymal transition (EMT). The spectra library was applied to explore the proteome difference of PANC-1 and BxPC-3 cells upon gemcitabine (GEM) treatment, and potential GEM targets were identified. The cytotoxicity test and GEM target analysis found that HPAC, CFPAC-1, and BxPC-3 were sensitive to GEM treatment, whereas PANC-1 and AsPC-1 were resistant. Finally, we found EMT was significant for CFPAC-1, AsPC-1, and PANC-1 cells, whereas BxPC-3 and HPAC cells showed more typical epithelial features. This library provides a valuable resource for in-depth proteomic analysis on pancreatic cancer cell lines, meeting the urgent demands for cell line-dependent protein differences and targeted drug analysis.

Measurement(s)	protein expression profiling
Technology Type(s)	Mass Spectrometry

Urine Proteome in Distinguishing Hepatic Steatosis in Patients with Metabolic-Associated Fatty Liver Disease

Background: In patients with metabolic-associated fatty liver disease (MAFLD), hepatic steatosis is the first step of diagnosis, and it is a risk predictor that independently predicts insulin resistance, cardiovascular risk, and mortality. Urine biomarkers have the advantage of being less complex, with a lower dynamic range and fewer technical challenges, in comparison to blood biomarkers. Methods: Hepatic steatosis was measured by magnetic resonance imaging (MRI), which measured the proton density fat fraction (MRI-PDFF). Mild hepatic steatosis was defined as MRI-PDFF 5−10% and severe hepatic steatosis was defined as MRI-PDFF > 10%. Results: MAFLD patients with any kidney diseases were excluded. There were 53 proteins identified by mass spectrometry with significantly different expressions among the healthy control, mild steatosis, and severe steatosis patients. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of these significantly changed urinary molecular features correlated with the liver, resulting in the dysregulation of carbohydrate derivative/catabolic/glycosaminoglycan/metabolic processes, insulin-like growth factor receptor levels, inflammatory responses, the PI3K−Akt signaling pathway, and cholesterol metabolism. Urine alpha-1-acid glycoprotein 1 (ORM1) and ceruloplasmin showed the most significant correlation with the clinical parameters of MAFLD status, including liver fat content, fibrosis, ALT, triglycerides, glucose, HOMA-IR, and C-reactive protein. According to ELISA and western blot (30 urine samples, normalized to urine creatinine), ceruloplasmin (ROC 0.78, p = 0.034) and ORM1 (ROC 0.87, p = 0.005) showed moderate diagnostic accuracy in distinguishing mild steatosis from healthy controls. Ceruloplasmin (ROC 0.79, p = 0.028) and ORM1 (ROC 0.81, p = 0.019) also showed moderate diagnostic accuracy in distinguishing severe steatosis from mild steatosis. Conclusions: Ceruloplasmin and ORM1 are potential biomarkers in distinguishing mild and severe steatosis in MAFLD patients.

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.

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Differential Phospho-Signatures in Blood Cells Identify LRRK2 G2019S Carriers in Parkinson's Disease

Background

The clinicopathological phenotype of G2019S LRRK2‐associated Parkinson's disease (L2PD) is similar to idiopathic Parkinson's disease (iPD), and G2019S LRRK2 nonmanifesting carriers (L2NMCs) are at increased risk for development of PD. With various therapeutic strategies in the clinical and preclinical pipeline, there is an urgent need to identify biomarkers that can aid early diagnosis and patient enrichment for ongoing and future LRRK2‐targeted trials.

Objective

The objective of this work was to investigate differential protein and phospho‐protein changes related to G2019S mutant LRRK2 in peripheral blood mononuclear cells from G2019S L2PD patients and G2019S L2NMCs, identify specific phospho‐protein changes associated with the G2019S mutation and with disease status, and compare findings with patients with iPD.

Methods

We performed an unbiased phospho‐proteomic study by isobaric label–based mass spectrometry using peripheral blood mononuclear cell group pools from a LRRK2 cohort from Spain encompassing patients with G2019S L2PD (n = 20), G2019S L2NMCs (n = 20), healthy control subjects (n = 30), patients with iPD (n = 15), patients with R1441G L2PD (n = 5), and R1441G L2NMCs (n = 3) (total N = 93).

Results

Comparing G2019S carriers with healthy controls, we identified phospho‐protein changes associated with the G2019S mutation. Moreover, we uncovered a specific G2019S phospho‐signature that changes with disease status and can discriminate patients with G2019S L2PD, G2019S L2NMCs, and healthy controls. Although patients with iPD showed a differential phospho‐proteomic profile, biological enrichment analyses revealed similar changes in deregulated pathways across the three groups.

Conclusions

We found a differential phospho‐signature associated with LRRK2 G2019S for which, consistent with disease status, the phospho‐profile from PD at‐risk G2019S L2NMCs was more similar to healthy controls than patients with G2019S L2PD with the manifested disease. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Bioengineered models of Parkinson's disease using patient-derived dopaminergic neurons exhibit distinct biological profiles in a 3D microenvironment

Three-dimensional (3D) in vitro culture systems using human induced pluripotent stem cells (hiPSCs) are useful tools to model neurodegenerative disease biology in physiologically relevant microenvironments. Though many successful biomaterials-based 3D model systems have been established for other neurogenerative diseases, such as Alzheimer's disease, relatively few exist for Parkinson's disease (PD) research. We employed tissue engineering approaches to construct a 3D silk scaffold-based platform for the culture of hiPSC-dopaminergic (DA) neurons derived from healthy individuals and PD patients harboring LRRK2 G2019S or GBA N370S mutations. We then compared results from protein, gene expression, and metabolic analyses obtained from two-dimensional (2D) and 3D culture systems. The 3D platform enabled the formation of dense dopamine neuronal network architectures and developed biological profiles both similar and distinct from 2D culture systems in healthy and PD disease lines. PD cultures developed in 3D platforms showed elevated levels of α-synuclein and alterations in purine metabolite profiles. Furthermore, computational network analysis of transcriptomic networks nominated several novel molecular interactions occurring in neurons from patients with mutations in LRRK2 and GBA. We conclude that the brain-like 3D system presented here is a realistic platform to interrogate molecular mechanisms underlying PD biology.

Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

Dynamic urine proteome changes in a rat model of simvastatin-induced skeletal muscle injury

Statin-associated muscle symptoms (SAMS) are the main side effects of statins. Currently, there are no effective biomarkers for accurate clinical diagnosis. Urine is not subject to homeostatic control and therefore accumulates early changes, making it an ideal biomarker source. We therefore examined urine proteome changes associated with SAMS. Here, we established a SAMS rat model by intragastric intubation with simvastatin (80 mg/kg). Biochemical analyses and hematoxylin and eosin staining were used to evaluate the degree of muscle injury. The urine proteome on days 3, 6, 9 and 14 was profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Differential proteins on day 14 of SAMS were mainly associated with glycolysis/gluconeogenesis, pyruvate metabolism, metabolism of reactive oxygen species and apoptosis, which were associated with the pathological mechanism of SAMS. Among the 14 differential proteins on day 3, Fibrinogen gamma chain (FIBG), Osteopontin (OSTP) and C-reactive protein (CRP) were associated with muscle damage, while EH domain-containing protein 1(EHD1), Cubilin (CUBN) and Fibronectin (FINC) were associated with the pathogenic mechanisms of SAMS. Our preliminary results indicated that the urine proteome can reflect early changes in the SAMS rat model, providing the potential for monitoring drug side effects in future clinical research. SIGNIFICANCE: This study demonstrate that the early muscle damage caused by simvastatin can be reflected in urinary proteins. The urine proteome also has the potential to reflect the pharmacology and toxicology of drugs in future clinical research.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.