Benefits and limits of decellularization on mass-spectrometry-based extracellular matrix proteome analysis of mouse kidney.

The extracellular matrix (ECM) is composed of collagens, ECM glycoproteins, and proteoglycans (also named core matrisome proteins) that are critical for tissue structure and function, and matrisome-associated proteins that balance the production and degradation of the ECM proteins. The identification and quantification of core matrisome proteins using mass spectrometry is often hindered by their low abundance and their propensity to form macromolecular insoluble structures. In this study, we aimed to investigate the added value of decellularization in identifying and quantifying core matrisome proteins in mouse kidney. The decellularization strategy combined freeze-thaw cycles and sodium dodecyl sulphate treatment. We found that decellularization preserved 95% of the core matrisome proteins detected in non-decellularized kidney and revealed few additional ones. Decellularization also led to an average of 59 times enrichment of 96% of the core matrisome proteins as the result of the successful removal of cellular and matrisome-associated proteins. However, the enrichment varied greatly among core matrisome proteins, resulting in a misrepresentation of the native ECM composition in decellularized kidney. This should be brought to the attention of the matrisome research community, as it highlights the need for caution when interpreting proteomic data obtained from a decellularized organ.

Tissue proteomics repositories for data reanalysis.

We are approaching the third decade since the establishment of the very first proteomics repositories back in the mid-'00s. New experimental approaches and technologies continuously enrich the field while producing vast amounts of mass spectrometry data. Together with initiatives to establish standard terminology and file formats, proteomics is rapidly transforming into a mature component of systems biology. Here we describe the ProteomeXchange consortium repositories. We specifically search, collect and evaluate public human tissue datasets (categorized as "complete" by the repository) submitted in 2015-2022, to both map the existing information and assess the data set reusability. Human tissue data are variably represented in the repositories reviewed, ranging between 10% and 25% of the total data submitted, with cancers being the most represented, followed by neuronal and cardiovascular diseases. About half of the retrieved data sets were found to lack annotations or metadata necessary to directly replicate the analysis. This poses a rough challenge to data reusability and highlights the need to increase awareness of the mage-tab file format for metadata in the community. Overall, proteomics repositories have evolved greatly over the past 7 years, as they have grown in size and become equipped with various powerful applications and tools that enable data searching and analytical tasks. However, to make the most of this potential, priority must be given to finding ways to secure detailed metadata for each submission, which is likely the next major milestone for proteomics repositories.

Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease.

BACKGROUND AND HYPOTHESIS: Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies. METHODS: The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models. RESULTS: In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state. CONCLUSION: Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.

Complement Cascade Proteins Correlate with Fibrosis and Inflammation in Early-Stage Type 1 Diabetic Kidney Disease in the Ins2Akita Mouse Model.

Diabetic kidney disease (DKD) is characterized by histological changes including fibrosis and inflammation. Evidence supports that DKD is mediated by the innate immune system and more specifically by the complement system. Using Ins2Akita T1D diabetic mice, we studied the connection between the complement cascade, inflammation, and fibrosis in early DKD. Data were extracted from a previously published quantitative-mass-spectrometry-based proteomics analysis of kidney glomeruli of 2 (early DKD) and 4 months (moderately advanced DKD)-old Ins2Akita mice and their controls A Spearman rho correlation analysis of complement- versus inflammation- and fibrosis-related protein expression was performed. A cross-omics validation of the correlation analyses' results was performed using public-domain transcriptomics datasets (Nephroseq). Tissue sections from 43 patients with DKD were analyzed using immunofluorescence. Among the differentially expressed proteins, the complement cascade proteins C3, C4B, and IGHM were significantly increased in both early and later stages of DKD. Inflammation-related proteins were mainly upregulated in early DKD, and fibrotic proteins were induced in moderately advanced stages of DKD. The abundance of complement proteins with fibrosis- and inflammation-related proteins was mostly positively correlated in early stages of DKD. This was confirmed in seven additional human and mouse transcriptomics DKD datasets. Moreover, C3 and IGHM mRNA levels were found to be negatively correlated with the estimated glomerular filtration rate (range for C3 rs = -0.58 to -0.842 and range for IGHM rs = -0.6 to -0.74) in these datasets. Immunohistology of human kidney biopsies revealed that C3, C1q, and IGM proteins were induced in patients with DKD and were correlated with fibrosis and inflammation. Our study shows for the first time the potential activation of the complement cascade associated with inflammation-mediated kidney fibrosis in the Ins2Akita T1D mouse model. Our findings could provide new perspectives for the treatment of early DKD as well as support the use of Ins2Akita T1D in pre-clinical studies.

Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios.

BACKGROUND: There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. METHODS: Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. RESULTS: In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17-1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47-1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39-1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). CONCLUSION: The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

RGS14414-Mediated Activation of the 14-3-3ζ in Rodent Perirhinal Cortex Induces Dendritic Arborization, an Increase in Spine Number, Long-Lasting Memory Enhancement, and the Prevention of Memory Deficits.

The remedy of memory deficits has been inadequate, as all potential candidates studied thus far have shown limited to no effects and a search for an effective strategy is ongoing. Here, we show that an expression of RGS14414 in rat perirhinal cortex (PRh) produced long-lasting object recognition memory (ORM) enhancement and that this effect was mediated through the upregulation of 14-3-3ζ, which caused a boost in BDNF protein levels and increase in pyramidal neuron dendritic arborization and dendritic spine number. A knockdown of the 14-3-3ζ gene in rat or the deletion of the BDNF gene in mice caused complete loss in ORM enhancement and increase in BDNF protein levels and neuronal plasticity, indicating that 14-3-3ζ-BDNF pathway-mediated structural plasticity is an essential step in RGS14414-induced memory enhancement. We further observed that RGS14414 treatment was able to prevent deficits in recognition, spatial, and temporal memory, which are types of memory that are particularly affected in patients with memory dysfunctions, in rodent models of aging and Alzheimer's disease. These results suggest that 14-3-3ζ-BDNF pathway might play an important role in the maintenance of the synaptic structures in PRh that support memory functions and that RGS14414-mediated activation of this pathway could serve as a remedy to treat memory deficits.

Reviewing the Regulators of COL1A1.

The collagen family contains 28 proteins, predominantly expressed in the extracellular matrix (ECM) and characterized by a triple-helix structure. Collagens undergo several maturation steps, including post-translational modifications (PTMs) and cross-linking. These proteins are associated with multiple diseases, the most pronounced of which are fibrosis and bone diseases. This review focuses on the most abundant ECM protein highly implicated in disease, type I collagen (collagen I), in particular on its predominant chain collagen type I alpha 1 (COLα1 (I)). An overview of the regulators of COLα1 (I) and COLα1 (I) interactors is presented. Manuscripts were retrieved searching PubMed, using specific keywords related to COLα1 (I). COL1A1 regulators at the epigenetic, transcriptional, post-transcriptional and post-translational levels include DNA Methyl Transferases (DNMTs), Tumour Growth Factor ß (TGFß), Terminal Nucleotidyltransferase 5A (TENT5A) and Bone Morphogenic Protein 1 (BMP1), respectively. COLα1 (I) interacts with a variety of cell receptors including integrinß, Endo180 and Discoidin Domain Receptors (DDRs). Collectively, even though multiple factors have been identified in association to COLα1 (I) function, the implicated pathways frequently remain unclear, underscoring the need for a more spherical analysis considering all molecular levels simultaneously.

Glycosylation Analysis of Urinary Peptidome Highlights IGF2 Glycopeptides in Association with CKD.

Chronic kidney disease (CKD) is prevalent in 10% of world's adult population. The role of protein glycosylation in causal mechanisms of CKD progression is largely unknown. The aim of this study was to identify urinary O-linked glycopeptides in association to CKD for better characterization of CKD molecular manifestations. Urine samples from eight CKD and two healthy subjects were analyzed by CE-MS/MS and glycopeptides were identified by a specific software followed by manual inspection of the spectra. Distribution of the identified glycopeptides and their correlation with Age, eGFR and Albuminuria were evaluated in 3810 existing datasets. In total, 17 O-linked glycopeptides from 7 different proteins were identified, derived primarily from Insulin-like growth factor-II (IGF2). Glycosylation occurred at the surface exposed IGF2 Threonine 96 position. Three glycopeptides (DVStPPTVLPDNFPRYPVGKF, DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) exhibited positive correlation with Age. The IGF2 glycopeptide (tPPTVLPDNFPRYP) showed a strong negative association with eGFR. These results suggest that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place, which may reflect changes in mature IGF2 protein. Further experiments corroborated this hypothesis as IGF2 increased plasma levels were observed in CKD patients. Protease predictions, considering also available transcriptomics data, suggest activation of cathepsin S with CKD, meriting further investigation.

Exploratory Study Analyzing the Urinary Peptidome of T2DM Patients Suggests Changes in ECM but Also Inflammatory and Metabolic Pathways Following GLP-1R Agonist Treatment.

Type II diabetes mellitus (T2DM) accounts for approximately 90% of all diabetes mellitus cases in the world. Glucagon-like peptide-1 receptor (GLP-1R) agonists have established an increased capability to target directly or indirectly six core defects associated with T2DM, while the underlying molecular mechanisms of these pharmacological effects are not fully known. This exploratory study was conducted to analyze the effect of treatment with GLP-1R agonists on the urinary peptidome of T2DM patients. Urine samples of thirty-two T2DM patients from the PROVALID study ("A Prospective Cohort Study in Patients with T2DM for Validation of Biomarkers") collected pre- and post-treatment with GLP-1R agonist drugs were analyzed by CE-MS. In total, 70 urinary peptides were significantly affected by GLP-1R agonist treatment, generated from 26 different proteins. The downregulation of MMP proteases, based on the concordant downregulation of urinary collagen peptides, was highlighted. Treatment also resulted in the downregulation of peptides from SERPINA1, APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF, PIGR, and TTR, many of which were previously found to be associated with increased insulin resistance and inflammation. The findings indicate potential molecular mechanisms of GLP-1R agonists in the context of the management of T2DM and the prevention or delaying of the progression of its associated diseases.

Data-Independent Acquisition Mass Spectrometry Analysis of FFPE Rectal Cancer Samples Offers In-Depth Proteomics Characterization of the Response to Neoadjuvant Chemoradiotherapy.

Locally advanced rectal cancer (LARC) presents a challenge in identifying molecular markers linked to the response to neoadjuvant chemoradiotherapy (nCRT). This study aimed to utilize a sensitive proteomic method, data-independent mass spectrometry (DIA-MS), to extensively analyze the LARC proteome, seeking individuals with favorable initial responses suitable for a watch-and-wait approach. This research addresses the unmet need to understand the response to treatment, potentially guiding personalized strategies for LARC patients. Post-treatment assessment included MRI scans and proctoscopy. This research involved 97 LARC patients treated with intense chemoradiotherapy, comprising radiation and chemotherapy. Out of 97 LARC included in this study, we selected 20 samples with the most different responses to nCRT for proteome profiling (responders vs. non-responders). This proteomic approach shows extensive proteome coverage in LARC samples. The analysis identified a significant number of proteins compared to a prior study. A total of 915 proteins exhibited differential expression between the two groups, with certain signaling pathways associated with response mechanisms, while top candidates had good predictive potential. Proteins encoded by genes SMPDL3A, PCTP, LGMN, SYNJ2, NHLRC3, GLB1, and RAB43 showed high predictive potential of unfavorable treatment outcome, while RPA2, SARNP, PCBP2, SF3B2, HNRNPF, RBBP4, MAGOHB, DUT, ERG28, and BUB3 were good predictive biomarkers of favorable treatment outcome. The identified proteins and related biological processes provide promising insights that could enhance the management and care of LARC patients.

Multicentric validation of diagnostic tests based on BC-116 and BC-106 urine peptide biomarkers for bladder cancer in two prospective cohorts of patients.

BACKGROUND: Non-invasive urine-based biomarkers can potentially improve current diagnostic and monitoring protocols for bladder cancer (BC). Here we assess the performance of earlier published biomarker panels for BC detection (BC-116) and monitoring of recurrence (BC-106) in combination with cytology, in two prospectively collected patient cohorts. METHODS: Of the 602 patients screened for BC, 551 were found eligible. For the primary setting, 73 patients diagnosed with primary BC (n = 27) and benign urological disorders, including patients with macroscopic haematuria, cystitis and/or nephrolithiasis (n = 46) were included. In total, 478 patients under surveillance were additionally considered (83 BC recurrences; 395 negative for recurrence). Urine samples were analysed with capillary electrophoresis-mass spectrometry. The biomarker score was estimated via support vector machine-based software. RESULTS: Validation of BC-116 biomarker panel resulted in 89% sensitivity and 67% specificity (AUCBC-116 = 0.82). A diagnostic score based on cytology and BC-116 resulted in good (AUCNom116 = 0.85) but not significantly better performance (P = 0.5672). A diagnostic score including BC-106 and cytology was evaluated (AUCNom106 = 0.82), significantly outperforming both cytology (AUCcyt = 0.72; P = 0.0022) and BC-106 (AUCBC-106 = 0.67; P = 0.0012). CONCLUSIONS: BC-116 biomarker panel is a useful test for detecting primary BC. BC-106 classifier integrated with cytology showing >95% negative predictive value, might be useful for decreasing the number of cystoscopies during surveillance.

Cardioprotection by selective SGLT-2 inhibitors in a non-diabetic mouse model of myocardial ischemia/reperfusion injury: a class or a drug effect?

Major clinical trials with sodium glucose co-transporter-2 inhibitors (SGLT-2i) exhibit protective effects against heart failure events, whereas inconsistencies regarding the cardiovascular death outcomes are observed. Therefore, we aimed to compare the selective SGLT-2i empagliflozin (EMPA), dapagliflozin (DAPA) and ertugliflozin (ERTU) in terms of infarct size (IS) reduction and to reveal the cardioprotective mechanism in healthy non-diabetic mice. C57BL/6 mice randomly received vehicle, EMPA (10 mg/kg/day) and DAPA or ERTU orally at the stoichiometrically equivalent dose (SED) for 7 days. 24 h-glucose urinary excretion was determined to verify SGLT-2 inhibition. IS of the region at risk was measured after 30 min ischemia (I), and 120 min reperfusion (R). In a second series, the ischemic myocardium was collected (10th min of R) for shotgun proteomics and evaluation of the cardioprotective signaling. In a third series, we evaluated the oxidative phosphorylation capacity (OXPHOS) and the mitochondrial fatty acid oxidation capacity by measuring the respiratory rates. Finally, Stattic, the STAT-3 inhibitor and wortmannin were administered in both EMPA and DAPA groups to establish causal relationships in the mechanism of protection. EMPA, DAPA and ERTU at the SED led to similar SGLT-2 inhibition as inferred by the significant increase in glucose excretion. EMPA and DAPA but not ERTU reduced IS. EMPA preserved mitochondrial functionality in complex I&II linked oxidative phosphorylation. EMPA and DAPA treatment led to NF-kB, RISK, STAT-3 activation and the downstream apoptosis reduction coinciding with IS reduction. Stattic and wortmannin attenuated the cardioprotection afforded by EMPA and DAPA. Among several upstream mediators, fibroblast growth factor-2 (FGF-2) and caveolin-3 were increased by EMPA and DAPA treatment. ERTU reduced IS only when given at the double dose of the SED (20 mg/kg/day). Short-term EMPA and DAPA, but not ERTU administration at the SED reduce IS in healthy non-diabetic mice. Cardioprotection is not correlated to SGLT-2 inhibition, is STAT-3 and PI3K dependent and associated with increased FGF-2 and Cav-3 expression.

Validation of diagnostic nomograms based on CE-MS urinary biomarkers to detect clinically significant prostate cancer.

PURPOSE: Prostate cancer (PCa) is one of the most common cancers and one of the leading causes of death worldwide. Thus, one major issue in PCa research is to accurately distinguish between indolent and clinically significant (csPCa) to reduce overdiagnosis and overtreatment. In this study, we aim to validate the usefulness of diagnostic nomograms (DN) to detect csPCa, based on previously published urinary biomarkers. METHODS: Capillary electrophoresis/mass spectrometry was employed to validate a previously published biomarker model based on 19 urinary peptides specific for csPCa. Added value of the 19-biomarker (BM) model was assessed in diagnostic nomograms including prostate-specific antigen (PSA), PSA density and the risk calculator from the European Randomized Study of Screening. For this purpose, urine samples from 147 PCa patients were collected prior to prostate biopsy and before performing digital rectal examination (DRE). The 19-BM score was estimated via a support vector machine-based software based on the pre-defined cutoff criterion of - 0.07. DNs were subsequently developed to assess added value of integrative diagnostics. RESULTS: Independent validation of the 19-BM resulted in an 87% sensitivity and 65% specificity, with an AUC of 0.81, outperforming PSA (AUC PSA: 0.64), PSA density (AUC PSAD: 0.64) and ERSPC-3/4 risk calculator (0.67). Integration of 19-BM with the rest clinical variables into distinct DN, resulted in improved (AUC range: 0.82-0.88) but not significantly better performances over 19-BM alone. CONCLUSION: 19-BM alone or upon integration with clinical variables into DN, might be useful for detecting csPCa by decreasing the number of biopsies.

APEX2-Mediated Proximity Labeling Resolves the DDIT4-Interacting Proteome.

DNA damage-inducible transcript 4 (DDIT4) is a ubiquitous protein whose expression is transiently increased in response to various stressors. Chronic expression has been linked to various pathologies, including neurodegeneration, inflammation, and cancer. DDIT4 is best recognized for repressing mTORC1, an essential protein complex activated by nutrients and hormones. Accordingly, DDIT4 regulates metabolism, oxidative stress, hypoxic survival, and apoptosis. Despite these well-defined biological functions, little is known about its interacting partners and their unique molecular functions. Here, fusing an enhanced ascorbate peroxidase 2 (APEX2) biotin-labeling enzyme to DDIT4 combined with mass spectrometry, the proteins in the immediate vicinity of DDIT4 in either unstressed or acute stress conditions were identified in situ. The context-dependent interacting proteomes were quantitatively but not functionally distinct. DDIT4 had twice the number of interaction partners during acute stress compared to unstressed conditions, and while the two protein lists had minimal overlap in terms of identity, the proteins' molecular function and classification were essentially identical. Moonlighting keratins and ribosomal proteins dominated the proteomes in both unstressed and stressed conditions, with many of their members having established non-canonical and indispensable roles during stress. Multiple keratins regulate mTORC1 signaling via the recruitment of 14-3-3 proteins, whereas ribosomal proteins control translation, cell cycle progression, DNA repair, and death by sequestering critical proteins. In summary, two potentially distinct mechanisms of DDIT4 molecular function have been identified, paving the way for additional research to confirm and consolidate these findings.

A systematic re-examination of processing of MHCI-bound antigenic peptide precursors by endoplasmic reticulum aminopeptidase 1.

Endoplasmic reticulum aminopeptidase 1 (ERAP1) trims antigenic peptide precursors to generate mature antigenic peptides for presentation by major histocompatibility complex class I (MHCI) molecules and regulates adaptive immune responses. ERAP1 has been proposed to trim peptide precursors both in solution and in preformed MHCI-peptide complexes, but which mode is more relevant to its biological function remains controversial. Here, we compared ERAP1-mediated trimming of antigenic peptide precursors in solution or when bound to three MHCI alleles, HLA-B*58, HLA-B*08, and HLA-A*02. For all MHCI-peptide combinations, peptide binding onto MHCI protected against ERAP1-mediated trimming. In only a single MHCI-peptide combination, trimming of an HLA-B*08-bound 12-mer progressed at a considerable rate, albeit still slower than in solution. Results from thermodynamic, kinetic, and computational analyses suggested that this 12-mer is highly labile and that apparent on-MHC trimming rates are always slower than that of MHCI-peptide dissociation. Both ERAP2 and leucine aminopeptidase, an enzyme unrelated to antigen processing, could trim this labile peptide from preformed MHCI complexes as efficiently as ERAP1. A pseudopeptide analogue with high affinity for both HLA-B*08 and the ERAP1 active site could not promote the formation of a ternary ERAP1/MHCI/peptide complex. Similarly, no interactions between ERAP1 and purified peptide-loading complex were detected in the absence or presence of a pseudopeptide trap. We conclude that MHCI binding protects peptides from ERAP1 degradation and that trimming in solution along with the dynamic nature of peptide binding to MHCI are sufficient to explain ERAP1 processing of antigenic peptide precursors.

The urinary proteomics classifier chronic kidney disease 273 predicts cardiovascular outcome in patients with chronic kidney disease.

BACKGROUND: The urinary proteomic classifier chronic kidney disease 273 (CKD273) is predictive for the development and progression of chronic kidney disease (CKD) and/or albuminuria in type 2 diabetes. This study evaluates its role in the prediction of cardiovascular (CV) events in patients with CKD Stages G1-G5. METHODS: We applied the CKD273 classifier in a cohort of 451 patients with CKD Stages G1-G5 followed prospectively for a median of 5.5 years. Primary endpoints were all-cause mortality, CV mortality and the composite of non-fatal and fatal CV events (CVEs). RESULTS: In multivariate Cox regression models adjusting for age, sex, prevalent diabetes and CV history, the CKD273 classifier at baseline was significantly associated with total mortality and time to fatal or non-fatal CVE, but not CV mortality. Because of a significant interaction between CKD273 and CV history (P = 0.018) and CKD stages (P = 0.002), a stratified analysis was performed. In the fully adjusted models, CKD273 classifier was a strong and independent predictor of fatal or non-fatal CVE only in the subgroup of patients with CKD Stages G1-G3b and without a history of CV disease. In those patients, the highest tertile of CKD273 was associated with a >10-fold increased risk as compared with the lowest tertile. CONCLUSIONS: The urinary CKD273 classifier provides additional independent information regarding the CV risk in patients with early CKD stage and a blank CV history. Determination of CKD273 scores on a random urine sample may improve the efficacy of intensified surveillance and preventive strategies by selecting patients who potentially will benefit most from early risk management.

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

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

Proteomics Analysis of Formalin Fixed Paraffin Embedded Tissues in the Investigation of Prostate Cancer.

Prostate cancer (PCa) is one of the leading causes of death in men worldwide. The molecular features, associated with the onset and progression of the disease, are under vigorous investigation. Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources for large-scale studies; however, their application in proteomics is limited due to protein cross-linking. In this study, the adjustment of a protocol for the proteomic analysis of FFPE tissues was performed which was followed by a pilot application on FFPE PCa clinical samples to investigate whether the optimized protocol can provide biologically relevant data for the investigation of PCa. For the optimization, FFPE mouse tissues were processed using seven protein extraction protocols including combinations of homogenization methods (beads, sonication, boiling) and buffers (SDS based and urea-thiourea based). The proteome extraction efficacy was then evaluated based on protein identifications and reproducibility using SDS electrophoresis and high resolution LC-MS/MS analysis. Comparison between the FFPE and matched fresh frozen (FF) tissues, using an optimized protocol involving protein extraction with an SDS-based buffer following beads homogenization and boiling, showed a substantial overlap in protein identifications with a strong correlation in relative abundances (rs = 0.819, p < 0.001). Next, FFPE tissues (3 sections, 15 µm each per sample) from 10 patients with PCa corresponding to tumor (GS = 6 or GS ≥ 8) and adjacent benign regions were processed with the optimized protocol. Extracted proteins were analyzed by GeLC-MS/MS followed by statistical and bioinformatics analysis. Proteins significantly deregulated between PCa GS ≥ 8 and PCa GS = 6 represented extracellular matrix organization, gluconeogenesis, and phosphorylation pathways. Proteins deregulated between cancerous and adjacent benign tissues, reflected increased translation, peptide synthesis, and protein metabolism in the former, which is consistent with the literature. In conclusion, the results support the relevance of the proteomic findings in the context of PCa and the reliability of the optimized protocol for proteomics analysis of FFPE material.

Proteome-based classification of Nonmuscle Invasive Bladder Cancer.

DNA/RNA-based classification of bladder cancer (BC) supports the existence of multiple molecular subtypes, while investigations at the protein level are scarce. Here, we aimed to investigate if Nonmuscle Invasive Bladder Cancer (NMIBC) can be stratified to biologically meaningful groups based on the proteome. Tissue specimens from 117 patients at primary diagnosis (98 with NMIBC and 19 with MIBC), were processed for high-resolution proteomics analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The proteomics output was subjected to unsupervised consensus clustering, principal component analysis (PCA) and investigation of subtype-specific features, pathways, and gene sets. NMIBC patients were optimally stratified to three NMIBC proteomic subtypes (NPS), differing in size, clinicopathologic and molecular backgrounds: NPS1 (mostly high stage/grade/risk samples) was the smallest in size (17/98) and overexpressed proteins reflective of an immune/inflammatory phenotype, involved in cell proliferation, unfolded protein response and DNA damage response, whereas NPS2 (mixed stage/grade/risk composition) presented with an infiltrated/mesenchymal profile. NPS3 was rich in luminal/differentiation markers, in line with its pathological composition (mostly low stage/grade/risk samples). PCA revealed a close proximity of NPS1 and conversely, remoteness of NPS3 to the proteome of MIBC. Proteins distinguishing these two extreme subtypes were also found to consistently differ at the mRNA levels between high and low-risk subtypes of the UROMOL and LUND cohorts. Collectively, our study identifies three proteomic NMIBC subtypes and following a cross-omics validation in two independent cohorts, shortlists molecular features meriting further investigation for their biomarker or potentially therapeutic value.

Proteomics biomarkers for solid tumors: Current status and future prospects.

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.