Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases

Urine proteome profile of firefighters with exposure to emergency fire-induced smoke: A pilot study to identify potential carcinogenic effects

Firefighters are frequently exposed to a variety of chemicals formed from smoke, which pose a risk for numerous diseases, including cancer. Comparative urine proteome profiling could significantly improve our understanding of the early detection of potential cancer biomarkers. In this study, for the first time, we conducted a comparative protein profile analysis of 20 urine samples collected from ten real-life firefighters prior to and following emergency fire-induced smoke. Using a label-free quantitative proteomics platform, we identified and quantified 1325 unique protein groups, of which 45 proteins showed differential expressions in abundance in response to fire-smoke exposure (post) compared to the control (pre). Pathway analysis showed proteins associated with epithelium development (e.g., RHCG, HEG1, ADAMTSL2) and Alzheimer's disease (SORL1) were significantly increased in response to smoke exposure samples. A protein-protein-network study showed a possible link between these differentially abundant proteins and the known cancer gene (TP53). Moreover, a cross-comparison analysis revealed that seven proteins-ALDH1A1, APCS, POMC, COL2A1, RDX, DDAH2, and SDC4 overlapped with the previously published urine cancer proteome datasets, suggesting a potential cancer risk. Our findings demonstrated that the discovery proteomic platform is a promising analytical technique for identifying potential non-invasive biomarkers associated with fire-smoke exposure in firefighters that may be related to cancer.

Brain Plasticity Modulator p75 Neurotrophin Receptor in Human Urine after Different Acute Brain Injuries-A Prospective Cohort Study

Acute brain injuries (ABIs) pose a substantial global burden, demanding effective prognostic indicators for outcomes. This study explores the potential of urinary p75 neurotrophin receptor (p75NTR) concentration as a prognostic biomarker, particularly in relation to unfavorable outcomes. The study involved 46 ABI patients, comprising sub-cohorts of aneurysmal subarachnoid hemorrhage, ischemic stroke, and traumatic brain injury. Furthermore, we had four healthy controls. Samples were systematically collected from patients treated at the University Hospital of Turku between 2017 and 2019, at early (1.50 ± 0.70 days) and late (9.17 ± 3.40 days) post-admission time points. Urinary p75NTR levels, measured by ELISA and normalized to creatinine, were compared against patients' outcomes using the modified Rankin Scale (mRS). Early urine samples showed no significant p75NTR concentration difference between favorable and unfavorable mRS groups. In contrast, late samples exhibited a statistically significant increase in p75NTR concentrations in the unfavorable group (p = 0.033), demonstrating good prognostic accuracy (AUC = 70.9%, 95% CI = 53-89%, p = 0.03). Assessment of p75NTR concentration changes over time revealed no significant variation in the favorable group (p = 0.992) but a significant increase in the unfavorable group (p = 0.009). Moreover, p75NTR concentration was significantly higher in ABI patients (mean ± SD 40.49 ± 28.83-65.85 ± 35.04 ng/mg) compared to healthy controls (mean ± SD 0.54 ± 0.44 ng/mg), irrespective of sampling time or outcome (p < 0.0001). In conclusion, late urinary p75NTR concentrations emerged as a potential prognostic biomarker for ABIs, showing increased levels associated with unfavorable outcomes regardless of the specific type of brain injury. While early samples exhibited no significant differences, the observed late increases emphasize the time-dependent nature of this potential biomarker. Further validation in larger patient cohorts is crucial, highlighting the need for additional research to establish p75NTR as a reliable prognostic biomarker across various ABIs. Additionally, its potential role as a diagnostic biomarker warrants exploration.

Noninvasive urinary protein signatures combined clinical information associated with microvascular invasion risk in HCC patients

BACKGROUND

Microvascular invasion (MVI) is the main factor affecting the prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to identify accurate diagnostic biomarkers from urinary protein signatures for preoperative prediction.

METHODS

We conducted label-free quantitative proteomic studies on urine samples of 91 HCC patients and 22 healthy controls. We identified candidate biomarkers capable of predicting MVI status and combined them with patient clinical information to perform a preoperative nomogram for predicting MVI status in the training cohort. Then, the nomogram was validated in the testing cohort (n = 23). Expression levels of biomarkers were further confirmed by enzyme-linked immunosorbent assay (ELISA) in an independent validation HCC cohort (n = 57).

RESULTS

Urinary proteomic features of healthy controls are mainly characterized by active metabolic processes. Cell adhesion and cell proliferation-related pathways were highly defined in the HCC group, such as extracellular matrix organization, cell-cell adhesion, and cell-cell junction organization, which confirms the malignant phenotype of HCC patients. Based on the expression levels of four proteins: CETP, HGFL, L1CAM, and LAIR2, combined with tumor diameter, serum AFP, and GGT concentrations to establish a preoperative MVI status prediction model for HCC patients. The nomogram achieved good concordance indexes of 0.809 and 0.783 in predicting MVI in the training and testing cohorts.

CONCLUSIONS

The four-protein-related nomogram in urine samples is a promising preoperative prediction model for the MVI status of HCC patients. Using the model, the risk for an individual patient to harbor MVI can be determined.

Tutorial review for peptide assays: An ounce of pre-analytics is worth a pound of cure

The recent increase in peptidomimetic-based medications and the growing interest in peptide hormones has brought new attention to the quantification of peptides for diagnostic purposes. Indeed, the circulating concentrations of peptide hormones in the blood provide a snapshot of the state of the body and could eventually lead to detecting a particular health condition. Although extremely useful, the quantification of such molecules, preferably by liquid chromatography coupled to mass spectrometry, might be quite tricky. First, peptides are subjected to hydrolysis, oxidation, and other post-translational modifications, and, most importantly, they are substrates of specific and nonspecific proteases in biological matrixes. All these events might continue after sampling, changing the peptide hormone concentrations. Second, because they include positively and negatively charged groups and hydrophilic and hydrophobic residues, they interact with their environment; these interactions might lead to a local change in the measured concentrations. A phenomenon such as nonspecific adsorption to lab glassware or materials has often a tremendous effect on the concentration and needs to be controlled with particular care. Finally, the circulating levels of peptides might be low (pico- or femtomolar range), increasing the impact of the aforementioned effects and inducing the need for highly sensitive instruments and well-optimized methods. Thus, despite the extreme diversity of these peptides and their matrixes, there is a common challenge for all the assays: the need to keep concentrations unchanged from sampling to analysis. While significant efforts are often placed on optimizing the analysis, few studies consider in depth the impact of pre-analytical steps on the results. By working through practical examples, this solution-oriented tutorial review addresses typical pre-analytical challenges encountered during the development of a peptide assay from the standpoint of a clinical laboratory. We provide tips and tricks to avoid pitfalls as well as strategies to guide all new developments. Our ultimate goal is to increase pre-analytical awareness to ensure that newly developed peptide assays produce robust and accurate results.

Peptidomics Analysis Reveals the Buccal Gland of Jawless Vertebrate Lamprey as a Source of Multiple Bioactive Peptides

Various proteins with antibacterial, anticoagulant, and anti-inflammatory properties have been identified in the buccal glands of jawless blood-sucking vertebrate lampreys. However, studies on endogenous peptides in the buccal gland of lampreys are limited. In this study, 4528 endogenous peptides were identified from 1224 precursor proteins using peptidomics and screened for bioactivity in the buccal glands of the lamprey, Lethenteron camtschaticum. We synthesized four candidate bioactive peptides (VSLNLPYSVVRGEQFVVQA, DIPVPEVPILE, VVQLPPVVLGTFG, and VPPPPLVLPPASVK), calculated their secondary structures, and validated their bioactivity. The results showed that the peptide VSLNLPYSVVRGEQFVVQA possessed anti-inflammatory activity, which significantly increased the expression of anti-inflammatory factors and decreased the expression of inflammatory factors in THP-1 cells. The peptide VVQLPPVVLGTFG showed antibacterial activity against some gram-positive bacteria. The peptide VSLNLPYSVVRGEQFVQA possessed good ACE inhibitory activity at low concentrations, but no dose-related correlation was observed. Our study revealed that the buccal glands of the jawless vertebrate lamprey are a source of multiple bioactive peptides, which will provide new insights into the blood-sucking mechanism of lamprey.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

Colorimetric Aptasensor for the Visual and Microplate Determination of Clusterin in Human Urine Based on Aggregation Characteristics of Gold Nanoparticles

Clusterin has the potential to become the biomarker of multiple diseases, but its clinical quantitative detection methods are limited, which restricts its research progress as a biomarker. A rapid and visible colorimetric sensor for clusterin detection based on sodium chloride-induced aggregation characteristic of gold nanoparticles (AuNPs) was successfully constructed. Unlike the existing methods based on antigen-antibody recognition reactions, the aptamer of clusterin was used as the sensing recognition element. The aptamer could protect AuNPs from aggregation caused by sodium chloride, but clusterin bound with aptamer detached it from AuNPs, thereby inducing aggregation again. Simultaneously, the color change from red in the dispersed state to purple gray in the aggregated state made it possible to preliminarily judge the concentration of clusterin by observation. This biosensor showed a linear range of 0.02-2 ng/mL and good sensitivity with a detection limit of 5.37 pg/mL. The test results of clusterin in spiked human urine confirmed that the recovery rate was satisfactory. The proposed strategy is helpful for the development of label-free point-of-care testing equipment for clinical testing of clusterin, which is cost-effective and feasible.

A peptide-centric approach to analyse quantitative proteomics data- an application to prostate cancer biomarker discovery

Bottom-up proteomics is a popular approach in molecular biomarker research. However, protein analysts have realized the limitations of protein-based approaches for identifying and quantifying proteins in complex samples, such as the identification of peptides sequences shared by multiple proteins and the difficulty in identifying modified peptides. Thus, there are many exciting opportunities to improve analysis methods. Here, an alternative method focused on peptide analysis is proposed as a complement to the conventional proteomics data analysis. To investigate this hypothesis, a peptide-centric approach was applied to reanalyse a urine proteome dataset of samples from prostate cancer patients and controls. The results were compared with the conventional protein-centric approach. The relevant proteins/peptides to discriminate the groups were detected based on two approaches, p-value and VIP values obtained by a PLS-DA model. A comparison of the two strategies revealed high inconsistency between protein and peptide information and greater involvement of peptides in key PCa processes. This peptide analysis unveiled discriminative features that are lost when proteins are analyzed as homogeneous entities. This type of analysis is innovative in PCa and integrated with the widely used protein-centric approach might provide a more comprehensive view of this disease and revolutionize biomarker discovery. SIGNIFICANCE: In this study, the application of a protein and peptide-centric approaches to reanalyse a urine proteome dataset from prostate cancer (PCa) patients and controls showed that many relevant proteins/peptides are missed by the conservative nature of p-value in statistical tests, therefore, the inclusion of variable selection methods in the analysis of the dataset reported in this work is fruitful. Comparison of protein- and peptide-based approaches revealed a high inconsistency between protein and peptide information and a greater involvement of peptides in key PCa processes. These results provide a new perspective to analyse proteomics data and detect relevant targets based on the integration of peptide and protein information. This data integration allows to unravel discriminative features that normally go unnoticed, to have a more comprehensive view of the disease pathophysiology and to open new avenues for the discovery of biomarkers.

Proteomic Identification of Saliva Proteins as Noninvasive Diagnostic Biomarkers

Many biomedically relevant biomarkers are proteins with characteristic biochemical properties and a relatively restricted subcellular distribution. The comparative and mass spectrometry-based proteomic analysis of body fluids can be particularly instrumental for the targeted identification of novel protein biomarkers with pathological relevance. In this respect, new research efforts in biomarker discovery focus on the systematic mapping of the human saliva proteome, as well as the pathobiochemical identification of disease-related modifications or concentration changes in specific saliva proteins. As a product of exocrine secretion, saliva can be considered an ideal source for the biochemical identification of new disease indicators. Importantly, saliva represents a body fluid that is continuously available for diagnostic and prognostic assessments. This chapter gives an overview of saliva proteomics, including a discussion of the usefulness of both liquid chromatography and two-dimensional gel electrophoresis for efficient protein separation in saliva proteomics.