Biomarker discovery in mass spectrometry-based urinary proteomics

Efficient isolation of encoded microparticles in a degassed micromold for highly sensitive and multiplex immunoassay with signal amplification

Multiplex detection of low-abundance protein biomarkers in biofluids can contribute to diverse biomedical fields such as early diagnosis and precision medicine. However, conventional techniques such as digital ELISA, microarray, and hydrogel-based assay still face limitations in terms of efficient protein detection due to issues with multiplexing capability, sensitivity, or complicated assay procedures. In this study, we present the degassed micromold-based particle isolation technique for highly sensitive and multiplex immunoassay with enzymatic signal amplification. Using degassing treatment of nanoporous polydimethylsiloxane (PDMS) micromold, the encoded particles are isolated in the mold within 5 min absorbing trapped air bubbles into the mold by air suction capability. Through 10 min of signal amplification in the isolated spaces by fluorogenic substrate and horseradish peroxidase labeled in the particle, the assay signal is amplified with one order of magnitude compared to that of the standard hydrogel-based assay. Using the signal amplification assay, vascular endothelial growth factor (VEGF) and chorionic gonadotropin beta (CG beta), the preeclampsia-related protein biomarkers, are quantitatively detected with a limit of detection (LoD) of 249 fg/mL and 476 fg/mL in phosphate buffer saline. The multiplex immunoassay is conducted to validate negligible non-specific detection signals and robust recovery rates in the multiplex assay. Finally, the VEGF and CG beta in real urine samples are simultaneously and quantitatively detected by the developed assay. Given the high sensitivity, multiplexing capability, and process simplicity, the presented particle isolation-based signal amplification assay holds significant potential in biomedical and proteomic fields.

Comparative Analysis of Protein Quantification by the SomaScan Assay versus Orthogonal Methods in Urine from People with Diabetic Kidney Disease

To our knowledge, calibration curves or other validations for thousands of SomaScan aptamers are not publicly available. Moreover, the abundance of urine proteins obtained from these assays is not routinely validated with orthogonal methods (OMs). We report an in-depth comparison of SomaScan readout for 23 proteins in urine samples from patients with diabetic kidney disease (n = 118) vs OMs, including liquid chromatography-targeted mass spectrometry (LC-MS), ELISA, and nephelometry. Pearson correlation between urine abundance of the 23 proteins from SomaScan 3.2 vs OMs ranged from -0.58 to 0.86, with a median (interquartile ratio, [IQR]) of 0.49 (0.18, 0.53). In multivariable linear regression, the SomaScan readout for 6 of the 23 examined proteins (26%) was most strongly associated with the OM-derived abundance of the same (target) protein. For 3 of 23 (13%), the SomaScan and OM-derived abundance of each protein were significantly associated, but the SomaScan readout was more strongly associated with OM-derived abundance of one or more "off-target" proteins. For the remaining 14 proteins (61%), the SomaScan readouts were not significantly associated with the OM-derived abundance of the targeted proteins. In 6 of the latest group, the SomaScan readout was not associated with urine abundance of any of the 23 quantified proteins. To sum, over half of the SomaScan results could not be confirmed by independent orthogonal methods.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Identification of Endothelial Cell Protein C Receptor by Urinary Proteomics as Novel Prognostic Marker in Non-Recovery Kidney Injury

Acute kidney injury is a common and complex complication that has high morality and the risk for chronic kidney disease among survivors. The accuracy of current AKI biomarkers can be affected by water retention and diuretics. Therefore, we aimed to identify a urinary non-recovery marker of acute kidney injury in patients with acute decompensated heart failure. We used the isobaric tag for relative and absolute quantification technology to find a relevant marker protein that could divide patients into control, acute kidney injury with recovery, and acute kidney injury without recovery groups. An enzyme-linked immunosorbent assay of the endothelial cell protein C receptor (EPCR) was used to verify the results. We found that the EPCR was a usable marker for non-recovery renal failure in our setting with the area under the receiver operating characteristics 0.776 ± 0.065; 95%CI: 0.648-0.905, (p < 0.001). Further validation is needed to explore this possibility in different situations.

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

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

DNAzyme-based faithful probing and pulldown to identify candidate biomarkers of low abundance

Biomarker discovery is essential for the understanding, diagnosis, targeted therapy and prognosis assessment of malignant diseases. However, it remains a huge challenge due to the lack of sensitive methods to identify disease-specific rare molecules. Here we present MORAC, molecular recognition based on affinity and catalysis, which enables the effective identification of candidate biomarkers with low abundance. MORAC relies on a class of DNAzymes, each cleaving a sole RNA linkage embedded in their DNA chain upon specifically sensing a complex system with no prior knowledge of the system's molecular content. We show that signal amplification from catalysis ensures the DNAzymes high sensitivity (for target probing); meanwhile, a simple RNA-to-DNA mutation can shut down their RNA cleavage ability and turn them into a pure affinity tool (for target pulldown). Using MORAC, we identify previously unknown, low-abundance candidate biomarkers with clear clinical value, including apolipoprotein L6 in breast cancer and seryl-tRNA synthetase 1 in polyps preceding colon cancer.

Predicting autosomal dominant polycystic kidney disease progression: review of promising Serum and urine biomarkers

Autosomal dominant polycystic kidney disease (ADPKD) is one of the leading causes of end-stage renal disease. In spite of the recent tremendous progress in the understanding of ADPKD pathogenesis, the molecular mechanisms of the disease remain incompletely understood. Considering emerging new targeted therapies for ADPKD, it has become crucial to disclose easily measurable and widely available biomarkers for identifying patients with future rapid disease progression. This review encompasses all the research with a shared goal of identifying promising serum or urine biomarkers for predicting ADPKD progression or response to therapy. The rate of the ADPKD progress varies significantly between patients. The phenotypic variability is only partly explained by the underlying genetic lesion diversity. Considering significant decline in kidney function in ADPKD is not usually evident until at least 50% of the parenchyma has been destroyed, conventional kidney function measures, such as glomerular filtration rate (GFR), are not suitable for monitoring disease progression in ADPKD, particularly in its early stages. Since polycystic kidney enlargement usually precedes the decline in GFR, height-adjusted total kidney volume (ht-TKV) has been accepted as an early biomarker for assessing disease severity in ADPKD patients. However, since measuring ht-TKV is time-consuming and observer-dependent, the identification of a sensitive and quickly measurable biomarker is of a great interest for everyday clinical practice. Throughout the last decade, due to development of proteomic and metabolomic techniques and the enlightenment of multiple molecular pathways involved in the ADPKD pathogenesis, a number of urine and serum protein biomarkers have been investigated in ADPKD patients, some of which seem worth of further exploring. These include copeptin, angiotensinogen, monocyte chemoattractant protein 1, kidney injury molecule-1 and urine-to-plasma urea ratio among many others. The aim of the current review is to provide an overview of all of the published evidence on potentially clinically valuable serum and urine biomarkers that could be used for predicting disease progression or response to therapy in patients with ADPKD. Hopefully, this review will encourage future longitudinal prospective clinical studies evaluating proposed biomarkers as prognostic tools to improve management and outcome of ADPKD patients in everyday clinical practice.

A Brief Review of Diagnostic Techniques and Clinical Management in Chronic Kidney Disease

Given its increasing incidence and detrimental effects on life expectancy and quality of life, chronic kidney disease (CKD) is a significant worldwide health concern. This review article provides a complete summary of current information on the diagnosis and management of CKD, focusing on recent advances and innovative approaches. The article discusses the most current findings on CKD risk assessment, emphasizing the need for early diagnosis utilizing better biomarkers and predictive models. A rigorous examination of diagnostic tools such as albumin-to-creatinine ratio (ACR) in urine and glomerular filtration rate (GFR) highlights their importance in determining CKD phases and etiologies. In terms of therapy, the study explores evidence-based techniques to reduce the development of CKD, such as enhanced blood pressure control, glycemic management in diabetic patients, dietary changes, and renin-angiotensin-aldosterone system (RAAS) blocking. Novel therapeutic approaches, including antifibrotic and precision medicine, are evaluated regarding their potential to revolutionize CKD treatment. The study also underlines the need for multidisciplinary therapy and patient education to achieve the best possible CKD patient outcomes. It also highlights the financial and social effects of CKD, highlighting the importance of early treatment to lower medical expenses and enhance the patient's standard of living. Finally, this review article provides a comprehensive update on CKD diagnosis and treatment, highlighting present successes alongside future potential. It is a valuable resource for healthcare professionals, academics, and policymakers who want to improve CKD treatment methods and patient outcomes.

Gelsolin, an Actin-Binding Protein: Bioinformatic Analysis and Functional Significance in Urothelial Bladder Carcinoma

The involvement of the actin-regulatory protein, gelsolin (GSN), in neoplastic transformation has been reported in different cancers including bladder cancer. However, the exact mechanism by which GSN influences bladder cancer development is not well understood. Here, we sought to reveal the functional significance of GSN in bladder cancer by undertaking a comprehensive bioinformatic analysis of TCGA datasets and through the assessment of multiple biological functions. GSN expression was knocked down in bladder cancer cell lines with two siRNA isoforms targeting GSN. Proliferation, migration, cell cycle and apoptosis assays were carried out. GSN expression, enrichment analysis, protein-protein interaction and immune infiltration analysis were verified through online TCGA tools. The data indicated that GSN expression is associated with bladder cancer proliferation, migration and enhanced cell apoptosis through regulation of NF-κB expression. GSN expression correlated with various inflammatory cells and may influence the immunity of the tumor microenvironment. Computational analysis identified several interacting partners which are associated with cancer progression and patient outcome. The present results demonstrate that GSN plays an important role in bladder cancer pathogenesis and may serve as a potential biomarker and therapeutic target for cancer therapy.

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.

Blood- and Urine-Based Liquid Biopsy for Early-Stage Cancer Investigation: Taken Clear Renal Cell Carcinoma as a Model

Liquid biopsy is a noninvasive technique that can provide valuable information for disease characterization by using biofluids as a source of biomarkers. Proteins found in biofluids can offer a wealth of information for understanding pathological processes. In this study, we used early-stage clear cell renal cell carcinoma (ccRCC) as a model to explore the proteomic relationships among tissue, plasma, and urine. We analyzed samples of tumor tissue, plasma, and urine from a cohort of 27 ccRCC patients with T1-2 stage and 27 matched healthy controls, using liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis. We integrated the differential proteins found in the three types of samples to explore ccRCC-associated molecular changes. Our results showed that both plasma and urine proteomes could reflect functional changes in tumor tissue. In plasma, cytoskeletal proteins and metabolic enzymes were differentially expressed, while in urine, adhesion molecules and defense proteins showed differential levels. The differential proteins found in plasma and urine both reflect the binding and catalytic activity of tumor tissue. Additionally, proteins only changed in biofluids could reflect body immune response changes, with plasma proteins involved in actin cytoskeleton and oxidative stress, and urine proteins involved in granulocyte adhesion and leukocyte extravasation signaling. Plasma and urine proteins could effectively distinguish RCC from control, with good performances (plasma/urine: 92.6%/92.6% specificity, 96.3%/92.6% sensitivity, and an area under the curve of 0.981/0.97). In conclusion, biofluids could not only reflect functional changes in tumor tissue but also reflect changes in the body's immune response. These findings will benefit the understanding of body biomarkers in tumors and the discovery of potential disease biomarkers.

Proteomics strategies for urothelial bladder cancer diagnosis, prognosis and treatment: Trends for tumor biomarker sources

Urothelial bladder cancer (UBC) is a heterogeneous multifactorial malignancy with a high recurrence rate. Current procedures for UBC diagnosis suffering from the lack of clinical sensitivity and specificity screening tests. Therefore, biomarkers have promising values to predict pathological conditions and can be considered as effective targets for early diagnosis, prognosis and antitumor immunotherapy. Recently, researchers have been interested for tumor proteins as biomarkers for different diseases. At present, proteomics methods have rapidly progressive that has potential identified biomarkers of UBC. Specifically, there has been several studies on the potential application of proteomics for the identification, quantification, and profiling of proteins for UBC in different sources. Based on these studies, using the panel of biomarkers as proteomic patterns may achieve higher sensitivity and specificity than single proteins in the diagnosis of UBC. In the present review, we evaluate recent literature related to the UBC proteome focusing especially on new proteomics techniques. Moreover, we classify UBC tumor biomarkers as diagnostic, prognostic, and therapeutic targets based on their sources (urine, serum/plasm, cell line, and tumor tissue) and we also discuss the advantages and limitations of each source. In this manner, this review article provides a critical assessment presentation of the advances in proteomics for all aspects of UBC diagnosis, prognosis, and treatment based on sources.

Proteomic Approaches and Potential Applications in Autosomal Dominant Polycystic Kidney Disease and Fabry Disease

Although rare, hereditary diseases, such as autosomal dominant polycystic kidney disease (ADPKD) and Fabry disease (FD) may significantly progress towards severe nephropathy. It is crucial to characterize it accurately, predict the course of the illness and estimate treatment effectiveness. A huge effort has been undertaken to find reliable biomarkers that might be useful for an early prevention of the disease progression and/or any invasive diagnostic procedures. The study of proteomics, or the small peptide composition of a sample, is a field of study under continuous development. Over the past years, several strategies have been created to study and define the proteome of samples from widely varying origins. However, urinary proteomics has become essential for discovering novel biomarkers in kidney disease. Here, the extracellular vesicles in human urine that contain cell-specific marker proteins from every segment of the nephron, offer a source of potentially valuable urinary biomarkers, and may play an essential role in kidney development and kidney disease. This review summarizes the relevant literature investigating the proteomic approaches and potential applications in the regular studies of ADPKD and FD.

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

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

Bilirubin level is decreased in patients with allergic rhinitis

BACKGROUND

There are limitations in detecting methods for early diagnosis and screening of allergic rhinitis. Considering the anti-inflammatory and anti-oxidative effects of bilirubin, this study aims to explore the relationship between bilirubin and allergic rhinitis and to identify bilirubin-related candidate urinary protein biomarkers associated with allergic rhinitis.

METHODS

63 allergic rhinitis patients (AR group) and 86 healthy controls (NC group) were enrolled. Venous blood was obtained to measure serum total IgE levels and bilirubin parameters. Patients in the AR group were then classified into the AR1 group (IgE > 125 IU/mL) and the AR2 group (IgE ≤ 125 IU/mL). After randomly selecting ten urine samples from the AR1 group, ten samples were chosen from the AR2 and the NC groups, respectively, according to age and gender matching. We employed a Tandem Mass Tag-Based liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomics approach and targeted parallel-reaction monitoring(PRM) to identify and validate urinary biomarkers for allergic rhinitis.

RESULTS

Compared with the NC group, the bilirubin levels of the AR group, AR1 group, and AR2 group were significantly lower. Although the bilirubin level of the AR1 group was lower than that of the AR2 group, the difference was not significant. Further urinary proteomics analysis found that the expression levels of proteins related to bilirubin metabolism and transportation in the AR1 and AR2 groups, including ABCC1, GSTA1, GSTO1, GSTM3, GSTM5, and BLVRB, were significantly higher than those in the NC group. By PRM-based quantification, GSTA1 and GSTO1 showed significant differences in different degrees of Allergic Rhinitis groups and healthy controls. The AUC of the combined diagnosis of GSTA1 and GSTO1 was 0.79 (95% CI 0.583-0.997, P = 0.007), and the sensitivity and specificity were 100% and 60.0%, respectively.

CONCLUSIONS

Bilirubin levels are associated with allergic rhinitis. Our study revealed that urine proteomics has a specific value for exploring the pathophysiological mechanism of bilirubin changes in AR patients and screening possible biomarkers.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

iTRAQ-based proteomics revealed baicalein enhanced oxidative stress of Candida albicans by up-regulating CPD2 expression

Baicalein could inhibit the growth and biofilm formation of Candida albicans, the most common clinical fungal pathogen. However, the antifungal mechanism of baicalein has not been elucidated. In this study, isobaric tags for relative and absolute quantification (iTRAQ) was used to verify the mechanism of antifungal fluconazole and baicalein. A total of 58 common proteins were detected in cells treated with fluconazole. These proteins encompassed fluconazole-targeted sterol synthesis pathway, including Erg11p, Erg6p, Erg3p, Erg25p, Erg5p, Erg10p and Ncp1p. Next, iTRAQ was applied to the comparison of baicalein-treated C. albicans proteins, which detected 16 common proteins. The putative NADH dehydrogenase Cpd2p and the ATP-binding cassette transporter Snq2p were the most up-regulated proteins with the treatment of baicalein. Our results showed that CPD2 disruption elevated C. albicans resistance to baicalein significantly both in vitro and in vivo. Further in-depth studies revealed that CPD2 disruption reduced the activation of C. albicans metacaspase and partially restored the mitochondrial membrane potential reduction caused by the treatment of baicalein, which indicated that CPD2 was involved in the apoptosis induced by baicalein. Consistently, under the treatment of baicalein, CPD2Δ/Δ mutant produced lower reactive oxygen species (ROS) that was critical in causing oxidative damage and apoptosis in C. albicans. These results indicated that baicalein could increase intracellular oxidative damage by up-regulating the expression of Cpd2p so as to inhibit the growth of C. albicans, which provides new insights for investigating the antifungal target of baicalein.

Identification of the target protein of the metastatic colorectal cancer-specific aptamer W3 as a biomarker by aptamer-based target cells sorting and functional characterization

Metastasis is a leading cause of cancer-related deaths. Hence, the discovery of more reliable metastasis-related biomarkers is crucial to improve the survival rate of cancer patients. W3 is an aptamer previously produced by the subtractive cell-SELEX using metastatic colorectal cancer cells as target cells and non-metastatic cells as negative cells. In this study, we aimed to evaluate whether the target molecule of W3 can potentially act as a metastatic biomarker. First, we obtained two cell subpopulations with different expression levels of the target molecule by W3-based cell sorting. Subsequently, we demonstrated that W3^high cells have a higher metastatic potential than W3^low cells both in vitro and in vivo. Further, immunohistochemical analysis revealed that W3 target expression is positively associated with metastasis and poor prognosis of CRC patients. Using mass spectrometry (MS) combined with pull-down, we identified that Ephrin type-A receptor 2 (EphA2) is the target of W3. EphA2's potential as a metastatic predictor was demonstrated by capturing W3-positive circulating tumor cells from CRC patients using a W3 probe. Based on these results, we put forward a stratagem for cell-SELEX-based biomarker discovery: selecting an aptamer through subtractive cell-SELEX towards the phenotype of interest; evaluating the functional phenotype of the target molecule by aptamer-based target cell sorting and analysis of clinical samples; and identifying the aptamer's target molecule using MS and aptamer-based target enrichment. This stratagem not only shortens the time for the clinical application of aptamers but also enables a more targeted and efficient discovery of biomarkers.

Proteomics for Early Detection of Non-Muscle-Invasive Bladder Cancer: Clinically Useful Urine Protein Biomarkers

Bladder cancer is the fourth most common cancer in men, and most cases are non-muscle-invasive. A high recurrence rate is a critical problem in non-muscle-invasive bladder cancer. The availability of few urine tests hinders the effective detection of superficial and small bladder tumors. Cystoscopy is the gold standard for diagnosis; however, it is associated with urinary tract infections, hematuria, and pain. Early detection is imperative, as intervention influences recurrence. Therefore, urinary biomarkers need to be developed to detect these bladder cancers. Recently, several protein candidates in the urine have been identified as biomarkers. In the present narrative review, the current status of the development of urinary protein biomarkers, including FDA-approved biomarkers, is summarized. Additionally, contemporary proteomic technologies, such as antibody-based methods, mass-spectrometry-based methods, and machine-learning-based diagnosis, are reported. Furthermore, new strategies for the rapid and correct profiling of potential biomarkers of bladder cancer in urine are introduced, along with their limitations. The advantages of urinary protein biomarkers and the development of several related technologies are highlighted in this review. Moreover, an in-depth understanding of the scientific background and available protocols in research and clinical applications of the surveillance of non-muscle bladder cancer is provided.

Application of Mass Spectrometry Imaging in Uro-Oncology: Discovering Potential Biomarkers

A growing need is emerging worldwide for new molecular markers which could enhance the accuracy of diagnostic and therapeutic methods for detecting urogenital cancers. Mass spectrometry imaging (MSI) is a very promising tool in this regard. In this review, we attempt to provide a subjective summary of the latest publications on potential biomarkers of renal, bladder, prostate, and testicular malignancies detected with MSI through the eyes of a clinical urologist.

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The ‘omics’ techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

MALDI mass Spectrometry based proteomics for drug discovery & development

Matrix-assisted laser desorption/ ionization (MALDI) is a soft ionization technique for introducing wide range of analytes into a mass spectrometer (MS). MALDI MS is a powerful tool in drug discovery research and development, providing a high-throughput molecular analysis technique in both preclinical and clinical systems. In particular, MALDI MS is invaluable in the study of peptides and proteins that drive all biological functions. This technology is label-free, provides high specificity in molecular identification, and is high-throughput. MALDI MS has been used in biomarker discovery and quantitation in virtually all tissues, serum, plasma, CSF, and urine for diagnostics, patient stratification, and monitoring drug efficacy. Other applications include characterization of biological drugs, spatial mapping of biomarkers and drugs in tissues, drug screening, and toxicological assessment.

Psoriasis to Psoriatic Arthritis: The Application of Proteomics Technologies

Psoriatic disease (PsD) is a spectrum of diseases that affect both skin [cutaneous psoriasis (PsC)] and musculoskeletal features [psoriatic arthritis (PsA)]. A considerable number of patients with PsC have asymptomatic synovio-entheseal inflammations, and approximately one-third of those eventually progress to PsA with an enigmatic mechanism. Published studies have shown that early interventions to the very early-stage PsA would effectively prevent substantial bone destructions or deformities, suggesting an unmet goal for exploring early PsA biomarkers. The emergence of proteomics technologies brings a complete view of all involved proteins in PsA transitions, offers a unique chance to map all potential peptides, and allows a direct head-to-head comparison of interaction pathways in PsC and PsA. This review summarized the latest development of proteomics technologies, highlighted its application in PsA biomarker discovery, and discussed the possible clinical detectable PsA risk factors in patients with PsC.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

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

Urinary proteomics investigations into contrast-induced acute kidney injury

Some patients have a decline in renal function after contrast medium injection, and this phenomenon is called contrast-induced acute kidney injury (CI-AKI); a small number of people even suffer severe renal failure. To date, the mechanism of CI-AKI remains unclear. We aimed to identify novel potential biomarkers in the urine of patients with CI-AKI through LC-MS/MS and bioinformatics analysis. We enrolled patients who underwent coronary angiography (contrast agent: iohexol). The CI-AKI group included 4 cases, and the non-CI-AKI group included 20 cases. We mixed the 4 CI-AKI samples and 20 non-CI-AKI samples. Then, a 0.6 ml urine sample was used for proteome analysis with LC-MS/MS approach. Metascape, ExPASy, and the Human Protein Atlas were utilized for bioinformatics analysis. We obtained 724 and 830 urine proteins from the CI-AKI and non-CI-AKI groups, respectively. The distribution of the pI values and molecular weights (MWs) of postoperative urine proteins showed no significant difference between the CI-AKI group and the non-CI-AKI group. A total of 99differentially expressed proteins (DEPs) were detected, among which 18 proteins were detected only in tubule cells, and 19 proteins were detected in both tubule cells and glomeruli. With GO analysis, the GEPs were mainly associated with immune response and inflammation. Although biomarkers cannot be asserted from this single pilot study, our results may help advance the understanding of the mechanisms of CI-AKI and identify potential novel biomarkers for further investigation.

Reversible Click Chemistry Tag for Universal Proteome Sample Preparation for Top-Down and Bottom-Up Analysis

Successful proteome analysis requires reliable sample preparation beginning with protein solubilization and ending with a sample free of contaminants, ready for downstream analysis. Most proteome sample preparation technologies utilize precipitation or filter-based separation, both of which have significant disadvantages. None of the current technologies are able to prepare both intact proteins or digested peptides. Here, we introduce a reversible protein tag, ProMTag, that enables whole proteome capture, cleanup, and release of intact proteins for top-down analysis. Alternatively, the addition of a novel Trypsin derivative to the workflow generates peptides for bottom-up analysis. We show that the ProMTag workflow yields >90% for intact proteins and >85% for proteome digests. For top-down analysis, ProMTag cleanup improves resolution on 2D gels; for bottom-up exploration, this methodology produced reproducible mass spectrometry results, demonstrating that the ProMTag method is a truly universal approach that produces high-quality proteome samples compatible with multiple downstream analytical techniques. Data are available via ProteomeXchange with identifier PXD027799.

Early detection of kidney cancer using urinary proteins: a truly non-invasive strategy

OBJECTIVES

To review urinary protein biomarkers as potential non-invasive, easily obtainable, early diagnostic tools in renal cell carcinoma (RCC).

METHODS

A PubMed database search was performed up to the year 2020 to identify primary studies reporting potential urinary protein biomarkers for RCC. Separate searches were conducted to identify studies describing appropriate methods of developing cancer screening programmes and detection of cancer biomarkers.

RESULTS

Several urinary protein biomarkers are under validation for RCC diagnostics, e.g. aquaporin-1, perilipin-2, carbonic anhydrase-9, Raf-kinase inhibitory protein, nuclear matrix protein-22, 14-3-3 Protein β/α and neutrophil gelatinase-associated lipocalin. However, none has yet been validated or approved for clinical use due to low sensitivity or specificity, inconsistencies in appropriate study design, or lack of external validation.

CONCLUSIONS

Evaluation of biomarkers' feasibility, sample preparation and storage, biomarker validation, and the application of novel technologies may provide a solution that maximises the potential for a truly non-invasive biomarker in early RCC diagnostics.

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors

Background

Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.

Methods

In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis.

Results

In total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively.

Conclusion

The novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.

Large-Scale Proteomic Assessment of Urinary Extracellular Vesicles Highlights Their Reliability in Reflecting Protein Changes in the Kidney

BACKGROUND

Urinary extracellular vesicles (uEVs) are secreted into urine by cells from the kidneys and urinary tract. Although changes in uEV proteins are used for quantitative assessment of protein levels in the kidney or biomarker discovery, whether they faithfully reflect (patho)physiologic changes in the kidney is a matter of debate.

METHODS

Mass spectrometry was used to compare in an unbiased manner the correlations between protein levels in uEVs and kidney tissue from the same animal. Studies were performed on rats fed a normal or high K⁺ diet.

RESULTS

Absolute quantification determined a positive correlation (Pearson R=0.46 or 0.45, control or high K⁺ respectively, P<0.0001) between the approximately 1000 proteins identified in uEVs and corresponding kidney tissue. Transmembrane proteins had greater positive correlations relative to cytoplasmic proteins. Proteins with high correlations (R>0.9), included exosome markers Tsg101 and Alix. Relative quantification highlighted a monotonic relationship between altered transporter/channel abundances in uEVs and the kidney after dietary K⁺ manipulation. Analysis of genetic mouse models also revealed correlations between uEVs and kidney.

CONCLUSION

This large-scale unbiased analysis identifies uEV proteins that track the abundance of the parent proteins in the kidney. The data form a novel resource for the kidney community and support the reliability of using uEV protein changes to monitor specific physiologic responses and disease mechanisms.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry

The identification of metabolites in biological samples is challenging due to their chemical and structural diversity. Ion mobility spectrometry (IMS) separates ionized molecules based on their mobility in a carrier buffer gas giving information about the ionic shape by measuring the rotationally averaged collision cross-section (CCS) value. This orthogonal descriptor, in combination with the m/z, isotopic pattern distribution, and MS/MS spectrum, has the potential to improve the identification of molecular molecules in complex mixtures. Urine metabolomics can reveal metabolic differences, which arise as a result of a specific disease or in response to therapeutic intervention. It is, however, complicated by the presence of metabolic breakdown products derived from a wide range of lifestyle and diet-related byproducts, many of which are poorly characterized. In this study, we explore the use of trapped ion mobility spectrometry (TIMS) via LC parallel accumulation with serial fragmentation (PASEF) for urine metabolomics. A total of 362 urine metabolites were characterized from 80 urine samples collected from healthy volunteers using untargeted metabolomics employing HILIC and RP chromatography. Additionally, three analytes (Trp, Phe, and Tyr) were selected for targeted quantification. Both the untargeted and targeted data was highly reproducible and reported CCS measurements for identified metabolites were robust in the presence of the urine matrix. A comparison of CCS values among different laboratories was also conducted, showing less than 1.3% ΔCCS values across different platforms. This is the first report of a human urine metabolite database compiled with CCS values experimentally acquired using an LC-PASEF TIMS-qTOF platform.

Higher senescence associated secretory phenotype and lower defense mediator in urinary extracellular vesicles of elders with and without Parkinson disease

Youth fountain and aging culprits are usually sought and identified in blood but not urine. Extracellular vesicles (EVs) possess parental cell properties, circulate in blood, CSF and urine, and provide paracrine and remote cell–cell communication messengers. This study investigated whether senescence‐associated secretory phenotype (SASP) and immune defense factors in EVs of urine could serve as biomarkers in elderly individuals with and without a comorbidity. Urine samples from young adults and elderly individuals with and without Parkinson disease (PD) were collected and stored at − 80 °C until studies. Urine EVs were separated from a drop-through solution and confirmed by verifying CD9, CD63, CD81 and syntenin expression. The EVs and drop-through solution were subjected to measurement of SASP cytokines and defense factors by Milliplex array assays. Many SASP cytokines and defense factors could be detected in urinary EVs but not urinary solutions. Elderly individuals (age > 60) had significantly higher levels of the SASP-associated factors IL-8, IP-10, GRO, and MCP-1 in EVs (p < 0.05). In contrast, some defense factors, IL-4, MDC and IFNα2 in EVs had significantly lower levels in elderly adults than in young adults (age < 30). Patients with and without PD exhibited a similar SASP profile in EVs but significantly lower levels of IL-10 in the EVs from patients with PD. This study used a simple device to separate urinary EVs from solution for comparisons of SASP and defense mediators between young adults and elders with and without PD. Results from this study indicate that aging signature is present in EVs circulating to urine and the signatures include higher inflammatory mediators and lower defense factors in urinary EVs but not solutions, suggesting a simple method to separate urinary EVs from solutions for searching aging mechanistic biomarkers may make prediction of aging and monitoring of anti-senolytic interventions possible.

Urinary proteomics for kidney dysfunction: insights and trends

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

Early disease biomarkers can be found using animal models urine proteomics

INTRODUCTION

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

AREAS COVERED

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

EXPERT COMMENTARY

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

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

Urinary extracellular vesicles: A position paper by the Urine Task Force of the International Society for Extracellular Vesicles

Urine is commonly used for clinical diagnosis and biomedical research. The discovery of extracellular vesicles (EV) in urine opened a new fast-growing scientific field. In the last decade urinary extracellular vesicles (uEVs) were shown to mirror molecular processes as well as physiological and pathological conditions in kidney, urothelial and prostate tissue. Therefore, several methods to isolate and characterize uEVs have been developed. However, methodological aspects of EV separation and analysis, including normalization of results, need further optimization and standardization to foster scientific advances in uEV research and a subsequent successful translation into clinical practice. This position paper is written by the Urine Task Force of the Rigor and Standardization Subcommittee of ISEV consisting of nephrologists, urologists, cardiologists and biologists with active experience in uEV research. Our aim is to present the state of the art and identify challenges and gaps in current uEV-based analyses for clinical applications. Finally, recommendations for improved rigor, reproducibility and interoperability in uEV research are provided in order to facilitate advances in the field.

A Comprehensive Urine Proteome Database Generated From Patients With Various Renal Conditions and Prostate Cancer

Urine proteins can serve as viable biomarkers for diagnosing and monitoring various diseases. A comprehensive urine proteome database, generated from a variety of urine samples with different disease conditions, can serve as a reference resource for facilitating discovery of potential urine protein biomarkers. Herein, we present a urine proteome database generated from multiple datasets using 2D LC-MS/MS proteome profiling of urine samples from healthy individuals (HI), renal transplant patients with acute rejection (AR) and stable graft (STA), patients with non-specific proteinuria (NS), and patients with prostate cancer (PC). A total of ~28,000 unique peptides spanning ~2,200 unique proteins were identified with a false discovery rate of <0.5% at the protein level. Over one third of the annotated proteins were plasma membrane proteins and another one third were extracellular proteins according to gene ontology analysis. Ingenuity Pathway Analysis of these proteins revealed 349 potential biomarkers in the literature-curated database. Forty-three percentage of all known cluster of differentiation (CD) proteins were identified in the various human urine samples. Interestingly, following comparisons with five recently published urine proteome profiling studies, which applied similar approaches, there are still ~400 proteins which are unique to this current study. These may represent potential disease-associated proteins. Among them, several proteins such as serpin B3, renin receptor, and periostin have been reported as pathological markers for renal failure and prostate cancer, respectively. Taken together, our data should provide valuable information for future discovery and validation studies of urine protein biomarkers for various diseases.

Protocols for Preparation and Mass Spectrometry Analysis of Clinical Urine Samples to Identify Candidate Biomarkers of Schistosoma-Associated Bladder Cancer

Advances in mass spectrometry instrumentation have revolutionized analytical capability in clinical proteomics. In parallel, various sample preparation methods have been developed to try to address the inherent complexity and dynamic range of clinical samples, typically involving a combination of depletion of abundant proteins followed by extensive prefractionation. However, the depth of coverage routinely achieved in discovery proteomics experiments on peripheral fluids such as serum, still leaves something to be desired, especially if no depletion or prefractionation is done in order to increase the throughput of clinical samples. Remarkably, despite being an easily accessible, typically sterile and diagnostically rich clinical sample, urine is often overlooked and as such has received less development effort. As an ultrafiltrate of blood, urine contains proteins and protein fragments originating from all parts of the body which may have diagnostic or prognostic potential if accurately and reproducibly quantified. Here, we describe an efficient and simple method for the concentration of urine samples by methanol-chloroform precipitation and subsequent in-solution tryptic digestion prior to discovery or targeted mass spectrometry analysis. We exemplify this method by reference to the discovery of novel candidate urinary biomarkers of schistosomiasis. Importantly, the methods described here have been used to identify >1900 protein groups in human urine by label-free discovery proteomics, without requiring any prior depletion or prefractionation, making this approach amenable to high throughput clinical biomarker studies in many diseases.

Quantitative Proteomics Analysis for the Identification of Differential Protein Expression in Calf Muscles between Young and Old SD Rats Using Mass Spectrometry

Aging is associated with loss of muscle mass and strength that leads to a condition termed sarcopenia. Impaired conditions, morbidity, and malnutrition are the factors of devaluation of muscle fibers in aged animals. Satellite cells play an important role in maintaining muscle homeostasis during tissue regeneration and repair. Proteomic profiling on the skeletal muscle tissues of different age group rats helps to determine the differentially expressed (DE) proteins, which may eventually lead to the development of biomarkers in treating the conditions of sarcopenia. In this study, nanoscale liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) analysis was implemented in the calf tissues of young and old groups of rats. The mass spectrometry (MS) analysis revealed the presence of 335 differentially expressed proteins between the two different age conditions, among which those based on log-fold change 25 proteins were upregulated and 77 were downregulated. The protein-protein interaction network analysis revealed 18 upregulated proteins with three distinct interconnected networks and 57 downregulated proteins with two networks. Further, gene ontology (GO) enrichment analysis showed the biological process, cellular component, and molecular function of the differential proteins. Pathway enrichment analysis of the DE proteins identified nine significantly enriched pathways with a list of eight significant genes (Cryab, Hspb2, Acat1, Ak1, Adssl1, Anxa5, Gys1, Ogdh, Gc, and Adssl1). Quantification of significant genes by quantitative real-time polymerase chain reaction (qRT-PCR) confirmed the downregulation at the mRNA level. Western blot analysis of their protein expression showed concordant results on two candidate proteins (Ogdh and annexin 5) confirming their differential regulation between the two age groups of rats. Thus, these proteomic approaches on young and aged rats provide insights into the development of protein targets in the treatment of sarcopenia (muscle loss).

Rational selection of a biomarker panel targeting unmet clinical needs in kidney injury

The pipeline of biomarker translation from bench to bedside is challenging and limited biomarkers have been adopted to routine clinical care. Ideally, biomarker research and development should be driven by unmet clinical needs in health care. To guide researchers, clinical chemists and clinicians in their biomarker research, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has developed a structured questionnaire in which the clinical gaps in current clinical pathways are identified and desirable performance specifications are predefined. In kidney injury, the high prevalence of the syndrome acute kidney injury (AKI) in the hospital setting has a significant impact on morbidity, patient survival and health care costs, but the use of biomarkers indicating early kidney injury in daily patient care remains limited. Routinely, medical labs measure serum creatinine, which is a functional biomarker, insensitive for detecting early kidney damage and cannot distinguish between renal and prerenal AKI. The perceived unmet clinical needs in kidney injury were identified through the EFLM questionnaire. Nephrologists within our tertiary care hospital emphasized that biomarkers are needed for (1) early diagnosis of in-hospital AKI after a medical insult and in critically ill patients, (2) risk stratification for kidney injury prior to a scheduled (elective) intervention, (3) kidney injury monitoring in patients scheduled to receive nephrotoxic medication and after kidney transplantation and (4) differentiation between prerenal AKI and structural kidney damage. The biomarker search and selection strategy resulted in a rational selection of an eleven-protein urinary panel for kidney injury that target these clinical needs. To assess the clinical utility of the proposed biomarker panel in kidney injury, a multiplexed LC-MS test is now in development for the intended translational research.

Targeted urine proteomics in lupus nephritis - a meta-analysis

BACKGROUND

Proteomic approaches are central in biomarker discovery. While mass-spectrometry-based techniques are widely used, novel targeted proteomic platforms have enabled the high-throughput detection of low-abundance proteins in an affinity-based manner. Urine has gained growing attention as an ideal biofluid for monitoring renal disease including lupus nephritis (LN).

METHODS

Pubmed was screened for targeted proteomic studies of LN urine interrogating ≥1000 proteins. Data from the primary studies were combined and a meta-analysis was performed. Shared proteins elevated in active LN across studies were identified, and relevant pathways were elucidated using ingenuity pathway and gene ontology analysis. Urine proteomic data was cross-referenced against renal single-cell RNAseq data from LN kidneys.

RESULTS

Two high-throughput targeted proteomic platforms with capacity to interrogate ≥1000 proteins have been used to investigate LN urine. Twenty-three urine proteins were significantly elevated in both studies, including 10 chemokines, and proteins implicated in angiogenesis, and extracellular matrix turnover. Of these, Cathepsin S, CXCL10, FasL, ferritin, macrophage migration inhibitory factor (MIF), and resistin were also significantly elevated within LN kidneys.

CONCLUSION

Targeted urinary proteomics have uncovered multiple novel biomarkers for LN. Further validation in prospective cohorts and mechanistic studies are warranted to establish their clinical utility.

Current Status of Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry (MALDI-TOF MS) in Clinical Diagnostic Microbiology

Mass spectrometry (MS), a core technology for proteomics and metabolomics, is currently being developed for clinical applications. The identification of microorganisms in clinical samples using matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry (MALDI-TOF MS) is a representative MS-based proteomics application that is relevant to daily clinical practice. This technology has the advantages of convenience, speed, and accuracy when compared with conventional biochemical methods. MALDI-TOF MS can shorten the time used for microbial identification by about 1 day in routine workflows. Sample preparation from microbial colonies has been improved, increasing the accuracy and speed of identification. MALDI-TOF MS is also used for testing blood, cerebrospinal fluid, and urine, because it can directly identify the microorganisms in these liquid samples without prior culture or subculture. Thus, MALDI-TOF MS has the potential to improve patient prognosis and decrease the length of hospitalization and is therefore currently considered an essential tool in clinical microbiology. Furthermore, MALDI-TOF MS is currently being combined with other technologies, such as flow cytometry, to expand the scope of clinical applications.

The Urothelium: Life in a Liquid Environment

The urothelium, which lines the renal pelvis, ureters, urinary bladder, and proximal urethra, forms a high-resistance but adaptable barrier that surveils its mechanochemical environment and communicates changes to underlying tissues including afferent nerve fibers and the smooth muscle. The goal of this review is to summarize new insights into urothelial biology and function that have occurred in the past decade. After familiarizing the reader with key aspects of urothelial histology, we describe new insights into urothelial development and regeneration. This is followed by an extended discussion of urothelial barrier function, including information about the roles of the glycocalyx, ion and water transport, tight junctions, and the cellular and tissue shape changes and other adaptations that accompany expansion and contraction of the lower urinary tract. We also explore evidence that the urothelium can alter the water and solute composition of urine during normal physiology and in response to overdistension. We complete the review by providing an overview of our current knowledge about the urothelial environment, discussing the sensor and transducer functions of the urothelium, exploring the role of circadian rhythms in urothelial gene expression, and describing novel research tools that are likely to further advance our understanding of urothelial biology.

Aptamers in Drug Design: An Emerging Weapon to Fight a Losing Battle

Implementation of novel and biocompatible polymers in drug design is an emerging and rapidly growing area of research. Even though we have a large number of polymer materials for various applications, the biocompatibility of these materials remains as a herculean task for researchers. Aptamers provide a vital and efficient solution to this problem. They are usually small (ranging from 20 to 60 nucleotides, single-stranded DNA or RNA oligonucleotides which are capable of binding to molecules possessing high affinity and other properties like specificity. This review focuses on different aspects of Aptamers in drug discovery, starting from its preparation methods and covering the recent scenario reported in the literature regarding their use in drug discovery. We address the limitations of Aptamers and provide valuable insights into their future potential in the areas regarding drug discovery research. Finally, we explained the major role of Aptamers like medical imaging techniques, application as synthetic antibodies, and the most recent application, which is in combination with nanomedicines.

Fast protein analysis enabled by high-temperature hydrolysis

While the bottom-up protein analysis serves as a mainstream method for biological studies, its efficiency is limited by the time-consuming process for enzymatic digestion or hydrolysis as well as the post-digestion treatment prior to mass spectrometry analysis. In this work, we developed an enzyme-free microreaction system for fast and selective hydrolysis of proteins, and a direct analysis of the protein digests was achieved by nanoESI (electrospray ionization) mass spectrometry. Using the microreactor, proteins in aqueous solution could be selectively hydrolyzed at the aspartyl sites within 2 min at high temperatures (∼150 °C). Being free of salts, the protein digest solution could be directly analyzed using a mass spectrometer with nanoESI without further purification or post-digestion treatment. This method has been validated for the analysis of a variety of proteins with molecular weights ranging from 8.5 to 67 kDa. With introduction of a reducing agent into the protein solutions, fast cleavage of disulfide bonds was also achieved along with high-temperature hydrolysis, allowing for fast analysis of large proteins such as bovine serum albumin. The high-temperature microreaction system was also used with a miniature mass spectrometer for the determination of highly specific peptides from Mycobacterium tuberculosis antigens, showing its potential for point-of-care analysis of protein biomarkers.

Novel serum peptide model revealed by MALDI-TOF-MS and its diagnostic value in early bladder cancer

BACKGROUND

Bladder cancer is the ninth most common cancer worldwide and has high morbidity and mortality. We aimed to search for potential serum peptide biomarkers and establish a diagnostic model for early bladder cancer.

METHODS

A total of 67 bladder cancer patients and 64 healthy volunteers were randomly divided into a training set and testing set 1. There were 30 hematuria patients used as testing set 2. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry based on weak cation exchange magnetic beads was used to obtain and analyze the serum peptide profiles between bladder cancer patients and healthy volunteers in the training set. Serum peptide diagnostic model through a k-nearest neighbor algorithm, was established and validated, and significantly differentially expressed protein biomarkers were ultimately identified.

RESULTS

We constructed a diagnostic model containing five peptides (m/z 1954.9, m/z 2081.0, m/z 3938.3, m/z 3946.5, and m/z 4268.8). In the training set, the area under the curve (AUC) value of the five-peptide model was 0.923, and the sensitivity and specificity was 93.75% and 96.77%, respectively. In testing set 1, the sensitivity and specificity was 91.43% and 90.91%, respectively, and the specificity of testing set 2 was 73.33%. For early-stage bladder cancer, the sensitivity and specificity was 92.31% and 93.75%, respectively; the sensitivity of early low-grade bladder cancer was 90.00%; and the AUC value was 0.944.

CONCLUSION

The five-peptide diagnostic model established in this study had high sensitivity and specificity, especially in the diagnosis of early bladder cancer, and could differentiate between healthy volunteers and hematuria patients.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Marker Identification of the Grade of Dysplasia of Intraductal Papillary Mucinous Neoplasm in Pancreatic Cyst Fluid by Quantitative Proteomic Profiling

The incidence of patients with pancreatic cystic lesions, particularly intraductal papillary mucinous neoplasm (IPMN), is increasing. Current guidelines, which primarily consider radiological features and laboratory data, have had limited success in predicting malignant IPMN. The lack of a definitive diagnostic method has led to low-risk IPMN patients undergoing unnecessary surgeries. To address this issue, we discovered IPMN marker candidates by analyzing pancreatic cystic fluid by mass spectrometry. A total of 30 cyst fluid samples, comprising IPMN dysplasia and other cystic lesions, were evaluated. Mucus was removed by brief sonication, and the resulting supernatant was subjected to filter-aided sample preparation and high-pH peptide fractionation. Subsequently, the samples were analyzed by LC-MS/MS. Using several bioinformatics tools, such as gene ontology and ingenuity pathway analysis, we detailed IPMNs at the molecular level. Among the 5834 proteins identified in our dataset, 364 proteins were differentially expressed between IPMN dysplasia. The 19 final candidates consistently increased or decreased with greater IPMN malignancy. CD55 was validated in an independent cohort by ELISA, Western blot, and IHC, and the results were consistent with the MS data. In summary, we have determined the characteristics of pancreatic cyst fluid proteins and discovered potential biomarkers for IPMN dysplasia.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

iTRAQ-based comparative proteomics analysis reveals specific urinary biomarkers for various kidney diseases

Background: Proteome studies for multiple renal diseases is bare. Methodology & results: Using isobaric tags for relative and absolute quantitation labeling, many differentially expressed proteins (DEPs) were identified in acute kidney injury (AKI), AKI + chronic kidney disease (CKD), diabetic CKD and nondiabetic CKD with or without IgA nephropathy (IgAN). Comparative analysis indicated that 34, 35, 17, 91 and 14 unique DEPs were found in AKI, AKI + CKD, CKD, diabetic CKD and nondiabetic CKD. Compared with nondiabetic CKD with IgAN, 47 unique DEPs were found in that without IgAN. Serum amyloid A1 (SAA1) and hepatocyte growth factor activator were unregulated in AKI and nondiabetic CKD without IgAN, respectively. Regenerating islet-derived protein 3-α (Reg3A) upregulation is associated with AKI and AKI + CKD patients. Conclusion: This research contributes to urinary biomarker discovery from multiple renal diseases.