Urine sample preparation in 96-well filter plates to characterize inflammatory and infectious diseases of the urinary tract

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.

Mass spectrometry-based proteomic exploration of the small urinary extracellular vesicles in ANCA-associated vasculitis in comparison with total urine

ANCA-associated vasculitis (AAV) is a rare, but potentially severe autoimmune disease, even nowadays displaying increased mortality and morbidity. Finding early biomarkers of activity and prognosis is thus very important. Small extracellular vesicles (EVs) isolated from urine can be considered as a non-invasive source of biomarkers. We evaluated several protocols for urinary EV isolation. To eliminate contaminating non-vesicular proteins due to AAV associated proteinuria we used proteinase K treatment. We investigated the differences in proteomes of small EVs of patients with AAV compared to healthy controls by label-free LC-MS/MS. In parallel, we performed an analogous proteomic analysis of urine samples from identical patients. The study results showed significant differences and similarities in both EV and urine proteome, the latter one being highly affected by proteinuria. Using bioinformatics tools we explored differentially changed proteins and their related pathways with a focus on the pathophysiology of AAV. Our findings indicate significant regulation of Golgi enzymes, such as MAN1A1, which can be involved in T cell activation by N-glycans glycosylation and may thus play a key role in pathogenesis and diagnosis of AAV. SIGNIFICANCE: The present study explores for the first time the changes in proteomes of small extracellular vesicles and urine of patients with renal ANCA-associated vasculitis compared to healthy controls by label-free LC-MS/MS. Isolation of vesicles from proteinuric urine samples has been modified to minimize contamination by plasma proteins and to reduce co-isolation of extraluminal proteins. Differentially changed proteins and their related pathways with a role in the pathophysiology of AAV were described and discussed. The results could be helpful for the research of potential biomarkers in renal vasculitis associated with ANCA.

Current state of the art for enhancing urine biomarker discovery

INTRODUCTION

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state.

AREAS COVERED

High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

Comprehensive Metaproteomic Analyses of Urine in the Presence and Absence of Neutrophil-Associated Inflammation in the Urinary Tract

Inflammation in the urinary tract results in a urinary proteome characterized by a high dynamic range of protein concentrations and high variability in protein content. This proteome encompasses plasma proteins not resorbed by renal tubular uptake, renal secretion products, proteins of immune cells and erythrocytes derived from trans-urothelial migration and vascular leakage, respectively, and exfoliating urothelial and squamous epithelial cells. We examined how such proteins partition into soluble urine (SU) and urinary pellet (UP) fractions by analyzing 33 urine specimens 12 of which were associated with a urinary tract infection (UTI). Using mass spectrometry-based metaproteomic approaches, we identified 5,327 non-redundant human proteins, 2,638 and 4,379 of which were associated with SU and UP fractions, respectively, and 1,206 non-redundant protein orthology groups derived from pathogenic and commensal organisms of the urogenital tract. Differences between the SU and UP proteomes were influenced by local inflammation, supported by respective comparisons with 12 healthy control urine proteomes. Clustering analyses showed that SU and UP fractions had proteomic signatures discerning UTIs, vascular injury, and epithelial cell exfoliation from the control group to varying degrees. Cases of UTI revealed clusters of proteins produced by activated neutrophils. Network analysis supported the central role of neutrophil effector proteins in the defense against invading pathogens associated with subsequent coagulation and wound repair processes. Our study expands the existing knowledge of the urinary proteome under perturbed conditions, and should be useful as reference dataset in the search of biomarkers.

Proteomics progresses in microbial physiology and clinical antimicrobial therapy

Clinical microbial identification plays an important role in optimizing the management of infectious diseases and provides diagnostic and therapeutic support for clinical management. Microbial proteomic research is aimed at identifying proteins associated with microbial activity, which has facilitated the discovery of microbial physiology changes and host–pathogen interactions during bacterial infection and antimicrobial therapy. Here, we summarize proteomic-driven progresses of host–microbial pathogen interactions at multiple levels, mass spectrometry-based microbial proteome identification for clinical diagnosis, and antimicrobial therapy. Proteomic technique progresses pave new ways towards effective prevention and drug discovery for microbial-induced infectious diseases.