Capillary Electrophoresis Coupled To Mass Spectrometry Research Articles

Differentiating primary and secondary FSGS using non-invasive urine biomarkers.

Focal segmental glomerulosclerosis (FSGS) is divided into genetic, primary (p), uncertain cause, and secondary (s) forms. The subclasses differ in management and prognosis with differentiation often being challenging. We aimed to identify specific urine proteins/peptides discriminating between clinical and biopsy-proven pFSGS and sFSGS. Sixty-three urine samples were collected in two different centers (19 pFSGS and 44 sFSGS) prior to biopsy. Samples were analysed using capillary electrophoresis-coupled mass spectrometry. For biomarker definition, datasets of age-/sex-matched normal controls (NC, n=98) and patients with other chronic kidney diseases (CKDs, n=100) were extracted from the urinary proteome database. Independent specificity assessment was performed in additional data of NC (n=110) and CKD (n=170). Proteomics data from patients with pFSGS were first compared to NC (n=98). This resulted in 1179 biomarker (P<0.05) candidates. Then, the pFSGS group was compared to sFSGS, and in a third step, pFSGS data were compared to data from different CKD etiologies (n=100). Finally, 93 biomarkers were identified and combined in a classifier, pFSGS93. Total cross-validation of this classifier resulted in an area under the receiving operating curve of 0.95. The specificity investigated in an independent set of NC and CKD of other etiologies was 99.1% for NC and 94.7% for CKD, respectively. The defined biomarkers are largely fragments of different collagens (49%). A urine peptide-based classifier that selectively detects pFSGS could be developed. Specificity of 95%-99% could be assessed in independent samples. Sensitivity must be confirmed in independent cohorts before routine clinical application.

Urinary peptide analysis to predict the response to blood pressure medication.

The risk of diabetic kidney disease (DKD) progression is significant despite treatment with renin-angiotensin system (RAS) blocking agents. Current clinical tools cannot predict whether or not patients will respond to treatment with RAS inhibitors (RASi). We aimed to investigate whether proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor and angiotensin-receptor blockers treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR equations, for DKD progression. We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 years) based on the eGFR values of each GFR equation. Urine samples were analysed using capillary electrophoresis-coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi. The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts. The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients.

Multiple urinary peptides display distinct sex-specific distribution.

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry (CE-MS) datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic, or predictive classifiers for each individual sex.

MO386: Visualising and Differentiating Kidney Disorders by Urinary Peptidomics Using a Machine Learning Approach

Abstract BACKGROUND AND AIMS Currently &amp;gt;20 000 native peptides in urine are known that are highly dynamic and able to display the status of different organs, especially the kidney. The characterization of urinary peptide profiles (UPP) enables the depiction of kidney disease severity, progression, fibrosis, and informs about the disease etiology. Advanced machine learning algorithms enable combining the changes in the very complex UPP associated with specific disease etiologies and reducing the dataspace to only few dimensions. Here, we show the application of a supervised machine learning pipeline for the visualization of different CKD etiologies based on high-dimensional peptidomics data, toward non-invasive disease classification. METHOD The Uniform Manifold Approximation and Projection (UMAP) algorithm was used as a novel nonlinear dimensionality-reduction technique to visualize and differentiate the UPP of patients with CKD of different etiologies. UPP of individual CKD patients (with diabetic kidney disease DKD, (n = 386), IgA nephropathy (n = 743) and vasculitis (n = 150)) and 369 healthy controls were extracted from the Human Urinary Proteome Database which contains &amp;gt;85 000 proteomics datasets analyzed using capillary electrophoresis coupled mass spectrometry. About 80% of the extracted datasets were used as a training and 20% as validation set. RESULTS When applying supervised-UMAP to the DKD patient and control datasets, excellent separation with an F1 score of 99.5% ± 0.9% in the training set, and 93.1% ± 3.3% in the independent test set could be observed. Subsequently, this approach was applied to differentiate controls and three kidney diseases (DKD, IgA nephropathy and vasculitis) simultaneously. In the training set an accuracy of up to 98% in DN and controls, and an overall F1 score of 93.7% ± 2.3% (Figure) was achieved. In the independent test set, accuracy decreased as expected to around 90% for controls, 83.8% for IgA nephropathy, 79.2% for DKD and 66.7% for vasculitis. The overall F1 score in the test set is 81.9% ± 2.2%. Of note, controls (n = 369) were consistently classified with the highest accuracy across all groups, the disease with smallest sample size (vasculitis, n = 150) always showed the lowest accuracy. A substantial proportion of vasculitis was classified as IgA nephropathy, which has the largest sample size of n = 743. For the validation of the pipeline the permutation test was used. Permutation test was repeated 100 times using all the samples of CKD -free controls and three kidney diseases. The resulted scores were normally distributed, with a mean of 32.5% and standard deviation of 1.2%. Compared with the true F1 score, which was calculated as 81.9% from above, the probability of obtaining such a high score by chance is very low (P &amp;lt; 0.01). CONCLUSION We show that UMAP combined with supervised machine learning applied to high dimensional peptidomics data, enables distinguishing multiple kidney diseases with good accuracy and with very small standard deviation between multiple train-test splits. To our knowledge, our study is the first of its kind to reduce the complexity of the urinary peptidome to a single point in space, and categorize disease etiology based on the spatial information. The approach presented has the potential to enable non-invasive differential diagnosis of kidney disease etiologies. To improve accuracy of this non-invasive method, inclusion of additional clinical parameters will be tested.

The urinary proteomic profile of arterial stiffness in the general population

Abstract Background Although arterial stiffness is an independent predictor of cardiovascular outcomes, its physiopathology remains unclear. Purpose This study aimed to investigate the urinary proteomic profile of aortic stiffness and provide insights into pathogenetic processes of arterial stiffness by pathway analysis. Methods In 669 participants (mean age, 50.5 years; 48.9% men) randomly recruited from the Flemish population, we measured carotid-femoral pulse wave velocity (PWV) by applanation tonometry. The proteomics of urine samples was quantified by using capillary electrophoresis coupled mass spectrometry. The proteomic data were analysed by the orthogonal projections to latent structures, a supervised dimensional reduction statistic method and summarised as a urinary proteomic (UP) score. Results The mean values were 7.56±2.02 m/s for PWV and 7.59±1.95 unit for the UP score. PWV was significantly associated with the UP score before and after adjustment for the potential covariates (β coefficient: 0.81 and 0.75, respectively; p&amp;lt;0.001). The significant proteins in the urinary proteomic profile consisted of 43 kinds of proteins, including collagen I, II and III, fibrinogen, matrix Gla-protein, apolipoprotein A-I and A-VI. The pathways annotated by the significant proteins mainly involved in fibrosis, signal conduction, platelet activation and aggregation. Conclusions In conclusion, the urinary proteomic profile could be a new biomarker of aortic stiffness and the altered proteins may link to the underlying mechanisms and holds the potential to discover novel therapeutic targets for arterial stiffness. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): The Internal Funds KU Leuven (STG-18-00379) Distribution and CorrelationThe Enrichment Pathways

Association of the chronic kidney disease urinary proteomic predictor CKD273 with clinical risk factors of graft failure in kidney allograft recipients.

Kidney transplantation is the best treatment option for end-stage kidney disease but is still associated with long-term graft failure. In this study, we evaluated the application of urinary proteomics to identify grafts with high failure risk before initial decline of estimated glomerular filtration rate (eGFR) with irreversible graft changes. Fifty-two living donor kidney transplant recipients (KTR) with 8-year follow-up were enrolled. All patients underwent clinical examination and had a routine laboratory screening at 3, 6, 12, 24, 36, 48 and 96 months post-transplantation, including creatinine, urea, albumin and 24-hproteinuria. Graft function was estimated according to Nankivell. Urine samples at Month 24 were analysed by capillary electrophoresis coupled mass spectrometry followed by classification with the chronic kidney disease classifier CKD273. CKD273 showed significant correlation with serum creatinine at every time point and moderate inverse correlation for the slope in glomerular filtration rates by Nankivell (r = -0.29, P=0.05). Receiver operating characteristics analysis for graft loss and death within the next 6 years after proteomic analysis resulted in an area under curve value of 0.89 for CKD273 being superior to 0.67 for Nankivell eGFR. Stratification into CKD273-positive and -negative patient groups revealed a hazard ratio of 16.5 for prevalence of graft loss in case of CKD273 positivity. Using a representative KTR cohort with 8-year follow-up, we could demonstrate significant value of CKD273 for risk stratification of graft loss. This study provides the conceptual basis for further evaluation of CKD273 as a prognostic tool for long-term graft function risk stratification by large prospective clinical trials.

Urine proteomics for prediction of disease progression in patients with IgA nephropathy.

Background Risk of kidney function decline in immunoglobulin A (IgA) nephropathy (IgAN) is significant and may not be predicted by available clinical and histological tools. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification.MethodsIn this multicentre study, urine samples were collected in 209 patients with biopsy-proven IgAN. Progression was defined by tertiles of the annual change of estimated glomerular filtration rate (eGFR) during follow-up. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models.ResultsOf the 209 patients, 64% were male. Mean age was 42 years, mean eGFR was 63 mL/min/1.73 m2 and median proteinuria was 1.2 g/day. We identified 237 urine peptides showing significant difference in abundance according to the tertile of eGFR change. These included fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, fibrinogen alpha and beta, titin, haemoglobin subunits, sodium/potassium-transporting ATPase subunit gamma, uromodulin, mucin-2, fractalkine, polymeric Ig receptor and insulin. An algorithm based on these protein fragments (IgAN237) showed a significant added value for the prediction of IgAN progression [AUC 0.89; 95% confidence interval (CI) 0.83–0.95], as compared with the clinical parameters (age, gender, proteinuria, eGFR and mean arterial pressure) alone (0.72; 95% CI 0.64–0.81).ConclusionsA urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease.

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response.

MO041URINE PROTEOMICS FOR PREDICTION OF DISEASE PROGRESSION IN PATIENTS WITH IGA NEPHROPATHY

Abstract Background and Aims Although IgA nephropathy (IgAN) is the most common primary glomerulonephritis in many parts of the world, risk of progressive kidney function decline is significant. Furthermore, the effect of immunosuppressive treatment in IgAN currently remains uncertain. There is no validated tool to predict disease progression or response to treatment. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts fast disease progression in patients with IgAN, thus enabling a personalized risk stratification and potentially improving patient management. Method In this multicenter study in 7 centers in Europe and in Canada urine samples were collected as part of clinical routine at time of biopsy in n= 300 patients (63% male, age 42±14 years) with biopsy proven IgAN. The follow-up data were collected for at least one year. Progressive disease was defined as an annual loss of kidney function (estimated glomerular filtration rate, eGFR) of more than -5ml/min/1.73m2 per year or when end-stage kidney disease (ESKD) was reached. The institutional review boards of each of the participating centers approved this study. Urine samples were analyzed using capillary electrophoresis coupled mass spectrometry (CE-MS). Whole proteome/peptidome profile was obtained for each sample. This study received funding from the European Union´s ERA PerMed program. Results Urine proteome/peptidome profiles were obtained from n=294 patients. On average, 2383 peptides were detected per sample. The data were subsequently divided into a discovery (n=154) and validation cohort (n=140). The comparison of the progressors (n=35) and non-progressors (n=119) in the discovery cohort resulted in the definition of more than 100 significant peptides. These included mainly fragments from collagen, mostly type 1 (decreased in progressors) and from different blood derived proteins like alpha-1-antitrypsin, alpha-2-HS-glycoprotein and apolipoproteins (increased in progressors). The distribution of the 100 most significant peptides in the progressor and non-progressor group is shown in the figure. The peptides were combined into a classifier using support vector machine. After optimizing the classifier employing a take-one-out procedure combined with n-1 cross-validation, the urine-peptide based algorithm enabled separation of progressors versus no progressors with an accuracy of 90% in take-one-out cross-validation. This classifier was subsequently applied to the validation cohort and resulted in highly significant separation of progressive from non-progressive IgAN patients. Furthermore, this classifier will be further applied blinded in an independent well characterized multicenter cohort of 267 IgAN patients. Conclusion We identified a urinary proteome profile which was associated with progressive loss of GFR in patients with IgAN. Further validation of this profile in an independent cohort is ongoing. The data indicate that CE-MS-based urinary proteomics enables identifying IgAN patients at high risk of disease progression. These patients may benefit from aggressive immunosuppressive treatment. Upon validation of the classifier in an independent cohort, its value in predicting response to immunosuppression will be assessed, aiming at establishing an innovative strategy that could improve patient management and personalize treatment of IgAN patients.

P0360URINARY PEPTIDOMIC ANALYSIS IN PROLIFERATIVE VERSUS NON-PROLIFERATIVE LUPUS NEPHRITIS : RESULTS OF THE PEPTIDU-LUP STUDY

Abstract Background and Aims Lupus nephritis (LN) is a frequent manifestation of Systemic Lupus Erythematosus (SLE). The therapeutic strategy relies on the result of kidney biopsy, which differentiates proliferative LN (PLN) from non-proliferative LN (NPLN). The analysis of the urinary peptidome has led to the identification of prognostic biomarkers in chronic kidney disease (CKD, namely the CKD273 classifier), or, as a liquid biopsy, for the diagnosis of glomerulonephritides. We verified whether urinary peptidomics could predict the severity of renal pathological injury and damage in LN. Method Urine samples, collected before kidney biopsy, were analyzed by capillary-electrophoresis coupled to mass-spectrometry (CE-MS). Urinary peptide profiles were compared between PLN and NPLN. Predictive values for chronic pathological lesions (glomerulosclerosis and interstitial fibrosis/tubular atrophy (IF/TA)), response to therapy and development of CKD were also assessed. Clinical characteristics, routine laboratory parameters and immunological SLE markers were collected at inclusion and prospectively for at least 24 months. Results We collected 100 urinary samples from patients with LN, forming a discovery (n=67) and an independent validation (n=33) cohort. Overall there were 69 patients with PLN (class III or IV +/-V with active lesions) and 31 patients with NPLN (class II or V or chronic lesions). In the discovery cohort, the abundance of 36 urinary peptides differed between PLN and NPLN (Mann-Whitney test). However, these peptides did not resist multiple testing correction. Among them, 17 could be sequenced (fragments of collagen-I, -II, -III, apolipoprotein A-I, Fibrinogen alpha chain and Histone H2B). Of the 17 sequenced peptides, 7 were also part of the CKD273 classifier. A mathematical model combining the 36 peptides classified PLN and NPLN patients from the validation cohort with an AUC of 0.75 and sensitivity and specificity of 0.81 and 0.53, respectively. Positivity of anti-dsDNA antibodies had the best sensitivity (0.96) and sterile pyuria the best specificity (0.68) to differentiate PLN from NPLN, and including the CKD273 score did not improve the accuracy of the predictive models. No urinary peptidomics profile was identified to predict early response to therapy in patients with LN. Glomerulosclerosis was observed in 63/100 biopsies. In the discovery cohort, the abundance of 38 urinary peptides (including 22 sequenced) differed between patients with vs without glomerulosclerosis (Mann-Whitney test). Again, these peptides did not resist multiple testing correction. Of the 22 sequenced peptides, 7 (different from the peptides associated with PLN) belonged to the CKD273 classifier. A mathematical model combining the 38 peptides classified patients from the validation cohort with an AUC of 0.75 and sensitivity and specificity of 0.82 and 0.54, respectively, for the presence of glomerulosclerosis. Although the quantification of IF/TA was correlated to the pre-existing CKD273 classifier, including the CKD273 score did not improve the prediction of IF/TA when tested in combination with simple clinical parameters. Only 3 patients developed CKD after a mean follow-up of 4 years, therefore contribution of urinary peptidomics for the prediction of CKD in LN could not be evaluated. Conclusion Different urinary peptidomics signatures were identified among patients with LN, according to the presence of active proliferative lesions or chronic lesions. However, these panels did not resist multiple testing correction, and did not improve the diagnostic accuracy when combined with clinical or immunological markers. The contribution of urinary peptidomics to predict CKD in patients with LN, or as an early predictor of renal flares, could not be evaluated. Kidney biopsy remains central for the care of patients with LN.

P0652THYMOSIN BETA 4 AND KIDNEY FUNCTION: AN INVERSE RELATIONSHIP?

Abstract Background and Aims Thymosin β4 (Tβ4) is an abundant peptide in urine and plasma, involved in multiple processes including cell morphology, wound healing, and inflammation. A major, yet not well defined role of Tβ4 was described in the context of kidney and cardiovascular disease. Knock out of Tβ4 resulted, among others, in increased albuminuria in a nephrotoxic nephritis model. In animal models it has been shown that Tβ4 can modify renal and cardiovascular disease, fibrosis and inflammation. However, most of the data are derived from animal models and the relevance of Tβ4 in human is less clear. As a first step towards investigating the potential relevance of Tβ4 in chronic kidney disease (CKD) in human, we investigated the abundance of Tβ4 in human urine. Method We investigated human urine in 2240 subjects with a wide spread of kidney function for the presence of Tβ4 using capillary-electrophoresis coupled mass spectrometry (CE-MS) and tandem mass spectrometry. In addition, we evaluated the presence of Tβ4 abundance in hemodialysis fluid of 10 subjects. Results The full length acetylated Tβ4 peptide could be identified in human urine in high abundance (molecular mass 4960.485 Da). In addition, an oxidized isoform could also be observed at high abundance. When investigating the distribution of both isoforms in control subjects and patients at different stages of CKD, a highly significant inverse association of the abundance of Tβ4 with eGFR could be observed for both, with rho -0.388 for the non-oxidized and -0.387 for the oxidized isoform, p&amp;lt;0.0001 in each case. In hemodialysate, Tβ4 is found as the most abundant peptide in 2 of the 10 analyzed subjects, on average the second most abundant peptide, with β2 microglobuline being the most abundant one. Conclusion Tβ4 is highly significantly inversely associated with kidney function, suggesting a role of Tβ4 in kidney disease and/or its complications. Based on the experimental data published from animal models, the increase in Tβ4 may reflect a potential physiological mechanism to counteract CKD and its complications. Investigation of association of Tβ4 levels with progression in CKD, cardiovascular complications and death may inform on a potentially beneficial effect of Tβ4, is currently ongoing in longitudinal datasets and will be presented.

P1457COMPARISON OF PEPTIDOMES BETWEEN URINE, PLASMA, AND HEMODIALYSIS FLUID INDICATES SELECTIVITY IN RENAL PEPTIDE PROCESSING

Abstract Background and Aims The physiological and related body fluids, urine, plasma and hemodialysis (HD) fluid spent dialysate (a mixture of technical HD fluid and patient´s ultrafiltrate) are rich biological sources of information especially in the context of kidney diseases. Of particular relevance are proteins and peptides, as they reflect essentially every biological process. Urine is produced as a result of glomerular plasma filtration and subsequent tubular reabsorption in the kidney. In analogy, HD spent dialysate is a result of ultrafiltration by an artificial kidney (dialysis membrane), but lacking tubular reabsorption and thereby accumulating in huge amounts of &amp;gt; 100 L per session comparable to primary urine. A recent study suggested that tubular reabsorption may be selective for specific proteins, and this process may represent a major difference between “natural” and artificial kidney. In this study, we aim at gaining insight into the composition of the peptidome/proteome in these body fluids of renal significance in a comprehensive analysis, in order to shed light on the relevant pathophysiological processes that take place in kidney, which may help developing better strategy in advanced-stage chronic kidney disease and detoxification in renal replacement therapies (RRT). Method We collected 15 plasma, 15 urine and 13 HD filtrate (spent dialysate during the first 30min of RRT) from age and sex matched subjects with stage 4 or above chronic kidney disease. Peptide identification and quantification were performed with capillary-electrophoresis coupled mass spectrometry and tandem mass spectrometry (CE-MS). The abundance, overlap and differences of the peptidome/proteome of the three body fluids were evaluated. Results We were able to detect 6281 urinary peptides, 1750 plasma peptides and 1728 peptides from spent dialysate. Of these we could identify 1764, 452 and 374 sequenced peptides, respectively Among them, there are 318 peptides detectable in both urine and spent dialysate, 307 detectable in both plasma and urine and only 191 detectable in both plasma and spent dialysate. Among the most abundant peptides in plasma were Thymosine-ß4 and fragments from hemoglobin, fibrinogen and collagen. In dialysate, the most abundant proteins detected in these patients were ß-2-microglobuline, Thymosine-ß4, and fragments from fibrinogen, while in urine fragments derived from collagen, albumin, and from alpha-1-antitrypsin were of the highest abundance. When investigating proteins/peptides found in at least 2 body fluids, a strong correlation in peptide abundance between urine and spent dialysate (P=4.2e-27, Rho=0.56), and a moderately strong correlation between HD fluid and plasma (P=3.7e-5, Rho=0.29) were detected. However, there is no significant correlation between peptide abundance in urine and plasma (P=0.22, Rho=0.07). Collagen peptides are the most abundant peptides in all three body fluids. Conclusion This dataset will serve as a valid basis to define the protein and peptide contents in blood, urine, and spent dialysate. The data show, as expected, substantial similarity between plasma and spent dialysate, but, surprisingly, very little similarity between urine and plasma. This further supports the hypothesis that tubular reabsorption may be selective for specific proteins and peptides, possibly excluding collagen-derived peptides. A more detailed investigation into the process and relevance of tubular reabsorption appears warranted in the light of the data. The fact that the samples investigated in this study are derived from advanced stage CKD patients must be taken into account, significant differences exist in comparison to urine produced by healthy kidney. The identification of molecular differences between urine and dialysate spent may present an opportunity to design future “biologically adapted” removal devices.

Urine proteome analysis as a discovery tool in patients with deep vein thrombosis and pulmonary embolism

Early and accurate detection of deep vein thrombosis (DVT) is an important clinical need. Based on the hypothesis that urinary peptides may hold information on DVT in conjunction with pulmonary embolism (PE), the study was aimed at identifying such peptide biomarkers using capillary electrophoresis coupled mass spectrometry. Patients with symptoms of unprovoked/idiopathic DVT and/or PE were examined by doppler-sonography or angio-computed tomography. Urinary proteome analysis allowed for identification of respective peptide biomarkers. To confirm their biological relevance, we induced PE in mice and assessed human ex vivo thrombi. We identified 62 urinary peptides as DVT-specific biomarkers, i.e. fragments of collagen type I and a fragment of fibrinogen β-chain. The presence of fibrinogen α/β in the acute thrombus, and collagen type I and osteopontin in the older, organized thrombus was demonstrated. The classifier DVT62 established through support vector machine (SVM) modeling based on the 62 identified peptides was validated in an independent cohort of 47 subjects (six cases and 41 controls) with a sensitivity of 100% and specificity of 83%. Urine proteome analysis enabled the detection of DVT-specific peptides, which were validated in human and mouse tissue. Furthermore, it allowed for the establishment of an urinary-proteome based classifier that is relatively specific for DVT. The data provide the basis for assessment of these biomarkers in a prospective clinical study.

Abstract B96: Non-viral gene therapy for pancreatic cancer, from preclinical models to phase II clinical trial

Abstract To date, pancreatic adenocarcinoma (PDAC) can’t be diagnosed early. Consequently, a majority of patient (80%) display an advanced disease that results in a low resection rate leading to a dismal overall median survival of less than 6 months. We extensively demonstrated that delivering SSTR2, DCK and UMK genes using non-viral vectors strongly inhibit tumor progression and dissemination in relevant experimental models of PDAC. Consequently, we designed a GMP-grade gene therapy product, namely CYL-02, encoding for the above mentioned therapeutic proteins, delivered by a non-viral vector for the management of 24 patients diagnosed with advanced PDAC. In this phase I clinical trial, gene therapy was administered in dose-escalation in the tumors using endoscopic ultrasound. Patients received gemcitabine during this protocol. We demonstrate that the intratumoral injection of the gene therapy product is feasible, well tolerated and safe, and results in the presence and the expression of CYL-02 in the tumors. Consequently, tumor progression is inhibited during the course of the protocol. In patients with locally advanced PDAC at the time of diagnosis, tumor size and serum levels of CA 19-9 significantly decreased following gene therapy regardless of the previous lines of treatment and median progression free survival and median overall survival reaches 6.4 and 11.4 months respectively. In addition, we carried out several advanced proteomic and circulating miRNAs analyses to identify diagnostic biomarkers predictive and/or indicative of treatment response and follow-up, to select clinical trial participants and/or to tailor therapy for individual patient. We already identified several biomarkers of clinical interest using either CE-MS (capillary electrophoresis coupled with mass spectrometry) or high throughput q(RT)PCR for peptide and miRNA identification, respectively, to create a clear prescription path for gene therapy in forthcoming clinical trials. Based on these preliminary yet very encouraging results, we propose that patients may clinically benefit from this approach. Accordingly, we are now entering phase II clinical trial using CYL-02 gene therapy product in patients diagnosed with locally advanced PDAC. Citation Format: Louis Buscail, Barbara Bournet, Fabienne Vernejoul, Gilles Cambois, Hubert Lulka, Naima Hanoun, Aline Meulle, Alix Vignolle-Vidoni, Odile Barbey, Fabian Gross, Rosine Guimbaud, Philippe Ottal, Gérard Tiraby, Pierre Cordelier. Non-viral gene therapy for pancreatic cancer, from preclinical models to phase II clinical trial. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2015;75(13 Suppl):Abstract nr B96.

Urinary Proteomics for Early Diagnosis in Diabetic Nephropathy

Diabetic nephropathy (DN) is a progressive kidney disease, a well-known complication of long-standing diabetes. DN is the most frequent reason for dialysis in many Western countries. Early detection may enable development of specific drugs and early initiation of therapy, thereby postponing/preventing the need for renal replacement therapy. We evaluated urinary proteome analysis as a tool for prediction of DN. Capillary electrophoresis–coupled mass spectrometry was used to profile the low–molecular weight proteome in urine. We examined urine samples from a longitudinal cohort of type 1 and 2 diabetic patients (n = 35) using a previously generated chronic kidney disease (CKD) biomarker classifier to assess peptides of collected urines for signs of DN. The application of this classifier to samples of normoalbuminuric subjects up to 5 years prior to development of macroalbuminuria enabled early detection of subsequent progression to macroalbuminuria (area under the curve [AUC] 0.93) compared with urinary albumin routinely used to determine the diagnosis (AUC 0.67). Statistical analysis of each urinary CKD biomarker depicted its regulation with respect to diagnosis of DN over time. Collagen fragments were prominent biomarkers 3–5 years before onset of macroalbuminuria. Before albumin excretion starts to increase, there is a decrease in collagen fragments. Urinary proteomics enables noninvasive assessment of DN risk at an early stage via determination of specific collagen fragments.

Peptide Fingerprinting of Alzheimer's Disease in Cerebrospinal Fluid: Identification and Prospective Evaluation of New Synaptic Biomarkers

BackgroundToday, dementias are diagnosed late in the course of disease. Future treatments have to start earlier in the disease process to avoid disability requiring new diagnostic tools. The objective of this study is to develop a new method for the differential diagnosis and identification of new biomarkers of Alzheimer's disease (AD) using capillary-electrophoresis coupled to mass-spectrometry (CE-MS) and to assess the potential of early diagnosis of AD.Methods and FindingsCerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls was used to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of the new method compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic Alzheimer's disease, frontotemporal dementia, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD. This pattern was already detectable in patients with mild cognitive impairment (MCI). The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho181-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. Sequence analysis of the discriminating biomarkers identified fragments of synaptic proteins like proSAAS, apolipoprotein J, neurosecretory protein VGF, phospholemman, and chromogranin A.ConclusionsThe method may allow early differential diagnosis of various dementias using specific peptide fingerprints and identification of incipient AD in patients suffering from MCI. Identified biomarkers facilitate face validity for the use in AD diagnosis.

Markers of vascular disease in plasma from patients with chronic kidney disease identified by proteomic analysis

Chronic kidney disease (CKD) patients belong to the group of patients with a high prevalence of cardiovascular disease (CVD). Arterial calcification and aortic stiffness are currently used as surrogates for vascular alterations. However, still little is known about prediction and the patho-physiologic mechanisms leading to CVD. We applied capillary electrophoresis coupled mass spectrometry profiling to blood specimens collected from 34 CKD stage 5D patients suffering from vascular alterations to allow insights into the molecular pathology of the disease. Statistical comparison of plasma profiles from mild and severe CVD cases according to either arterial calcification or aortic stiffness unveiled 13 novel biomarkers for vascular disease. Tandem mass spectrometry identified four of these as fragments of collagen alpha-1 type I and III and one as fragment of apolipoprotein CIII. Integrated in a distinct pattern the candidates were validated using the moderate CVD cases among the 34 CKD patients (N=11) and an additional independent blinded cohort of CKD stage 4-5 patients (N=21), who all had not been considered during biomarker discovery. The panel distinguished mild and severe CVD with sensitivity of 89% and specificity of 67% in this independent cohort. This diagnostic phase I/II study supports the notion that vascular alterations are reflected by distinct changes in plasma profiles of CKD patients.

Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria

BackgroundPreviously the angiotensin II receptor blocker Irbesartan has been demonstrated to reduce the risk for progression from microalbuminuria to macroalbuminuria in type 2 diabetic patients. The purpose of this study was to evaluate the effect of treatment with Irbesartan in type 2 diabetic patients with microalbuminuria on the urinary proteome.MethodsHigh-resolution capillary-electrophoresis coupled to mass-spectrometry (CE-MS) was used to profile the low-molecular-weight proteome in urine of a subgroup of patients from a two year randomized irbesartan versus placebo therapy trial, which included hypertensive type 2 diabetic patients with microalbuminuria on ongoing antihypertensive medication (IRMA2-substudy).ResultsWe demonstrate that the therapy with 300 mg Irbesartan daily over a period of two years results in significant changes of the urinary proteome. Both, a classifier developed previously that consists of urinary peptides indicative of chronic kidney disease, as well as several individual peptides changed significantly after treatment. These changes were not observed in the placebo-treated individuals. Most prominent are changes of urinary collagen fragments associated with progression of diabetic nephropathy, indicating normalization in urinary peptides.ConclusionCE-MS analysis of urine enabled identification of peptides as potential surrogate markers for renoprotection in microalbuminuric type 2 diabetic patients, which show persistent improvement after longterm treatment with Irbesartan. The results suggest that a major benefit of treatment by Irbesartan may be improvement of collagen turnover, reduction of fibrosis. They further suggest that urinary proteome analysis could be utilized to assess potential benefit of therapeutic intervention, providing statistically significant results even on a small population.

Profiling of rat urinary proteomic patterns associated with drug‐induced nephrotoxicity using CE coupled with MS as a potential model for detection of drug‐induced adverse effects

We have investigated urine obtained from Sprague Dawley rats before and after administration of cis-Platin, aiming at the definition of biomarkers for drug-induced cytotoxicity. Rats were treated with 3 or 6 mg/kg cis-Platin (i.p., single injection) and urine samples were collected before and after drug or saline treatment. Analysis of the low molecular weight proteome (<20 kDa) using capillary-electrophoresis coupled mass spectrometry allowed us to tentatively identify 34 urinary peptides that show significant differences between control and treated animals, and hence may serve as a potential biomarker for cis-Platin-induced nephrotoxicity. These biomarkers were confirmed in a blinded assessment of additional samples. The blinded data also revealed time-dependency of induced changes. Some of the potential biomarkers could be sequenced. This information revealed great similarity between cis-Platin-induced changes and significant changes in the urinary proteome of patients suffering from tubular injury (Fanconi syndrome). Our study strongly suggests that (drug-induced) nephrotoxicity can be detected with high accuracy in laboratory rodents using urinary proteome analysis. The effects observed are very similar to those seen in corresponding human diseases and similar approaches may be very helpful in evaluating drug-induced organ damage in preclinical animal models. This study aiming at the definition of biomarkers for drug-induced cytotoxicity may serve as a proof-of-principle for the use of urinary proteomics in assessment of drug-induced nephrotoxicity.

Quantification of organic acids in particulate matter by coupling of thermally assisted hydrolysis and methylation with thermodesorption-gas chromatography–mass spectrometry

A quantitative method for the determination of organic acids in atmospheric particles is developed. The method couples a derivatisation step (thermally assisted hydrolysis and methylation) and a Curie point pyrolyser as a thermal desorption technique and gas chromatography–mass spectrometry (CPP-GC–MS). Among the reagents tested (tetramethylammonium hydroxide (TMAH), tetramethylammonium acetate (TMAAc) and phenyltrimethylammonium hydroxide (TMPAH)), the best performance was found using TMAAc as a derivatisation reagent for the reaction time of 4 s at 510 °C as heating temperature. Calibration was performed for a series of fatty acids (FA), dicarboxylic acids (DCA) and terpenoic acids (TA) under these conditions. Coefficients of determination ( R 2) were between 0.94 and 0.98. Limits of detection (LOD) were in the nanogram-range between 0.1 and 3.6 ng. The method is applied on atmospheric particle samples to obtain the quantification reproducibility and quantification limits. Reproducibility was determined in terms of relative standard deviations (RSD) for ambient aerosol samples collected by a high-volume-sampler (HVS, RSD = 6–45%, n = 10) and a Berner impactor (BI, RSD = 5–34%, n = 10). Based on 24 h sampling time the developed method enables quantification of all three classes of acids for both sampling techniques. Calibration data and presented volume concentrations are compared with literature data. A comparison with an off-line methylation-GC–MS using BF 3 as a derivatisation reagent and capillary electrophoresis coupled mass spectrometry (CE-MS) showed a good agreement. Minimal sample preparation is the main advantage of the developed method. Depending on the sensitivity requirements the present method can be a fast and simple alternative to GC–MS techniques with conventional sample preparation steps for semi-volatile organic acids.