Autophagy inhibition prevents lymphatic malformation progression to lymphangiosarcoma by decreasing osteopontin and Stat3 signaling.

Lymphatic malformation (LM) is a vascular anomaly originating from lymphatic endothelial cells (ECs). While it mostly remains a benign disease, a fraction of LM patients progresses to malignant lymphangiosarcoma (LAS). However, very little is known about underlying mechanisms regulating LM malignant transformation to LAS. Here, we investigate the role of autophagy in LAS development by generating EC-specific conditional knockout of an essential autophagy gene Rb1cc1/FIP200 in Tsc1iΔEC mouse model for human LAS. We find that Fip200 deletion blocked LM progression to LAS without affecting LM development. We further show that inhibiting autophagy by genetical ablation of FIP200, Atg5 or Atg7, significantly inhibited LAS tumor cell proliferation in vitro and tumorigenicity in vivo. Transcriptional profiling of autophagy-deficient tumor cells and additional mechanistic analysis determine that autophagy plays a role in regulating Osteopontin expression and its down-stream Jak/Stat3 signaling in tumor cell proliferation and tumorigenicity. Lastly, we show that specifically disrupting FIP200 canonical autophagy function by knocking-in FIP200-4A mutant allele in Tsc1iΔEC mice blocked LM progression to LAS. These results demonstrate a role for autophagy in LAS development, suggesting new strategies for preventing and treating LAS.

Urinary epidermal growth factor is a novel biomarker for early diagnosis of antibody mediated kidney allograft rejection: A urinary proteomics analysis.

Although antibody mediated rejection (AMR) accounts for 20-30% of all acute renal allograft rejections, introducing biomarkers for a timely detection of allograft rejection has been remained challenging. This study investigated novel diagnostic biomarkers of AMR by examining of urine proteome in renal transplant patients. Thirty-six patients with kidney transplantation including 22 AMR patients and 14 patients with stable renal function (control group) were enrolled in this study. Urinary samples were collected and Label free quantification (LFQ) proteomics technique was applied on urine samples and data was subjected to Random Forest (RF) algorithm to predict main candidate proteins contributing in AMR. Finally, applicability of candidate diagnostic biomarkers was evaluated in new sets of AMR subjects, stable patients and healthy volunteers. A total of 1020 proteins were detected in urine proteome. RF algorithm predicted 20 differentially expressed proteins with the highest sensitivity and specificity and combination of EGF, COL6A, and NID-1 was identified as possible panel for early diagnosis of AMR. Applicability of EGF as diagnostic biomarker was validated in urine samples of independent set of AMR subjects. This is the first urinary proteomics study in AMR patients demonstrating that urinary EGF might be used as early diagnostic biomarker for AMR. SIGNIFICANCE: Renal antibody mediated rejection (AMR) accounts for 20-30% of all acute rejections of allografted kidneys. Although several possible biomarkers have been proposed to predict AMR, ineffectiveness of current urinary biomarkers in early diagnosing of AMR patients and in distinguishing AMR subjects from patients with stable kidney function casts doubts on their applicability in clinic. Here for the first time and based on the analysis of urinary proteome we showed that uEGF and uEGF/Cr might be candidate biomarkers to predict AMR with high diagnostic power.

Predictive Biomarker Panel in Proliferative Lupus Nephritis- Two-Dimensional Shotgun Proteomics.

INTRODUCTION: Lupus nephritis (LN) is one of the most serious complications of systemic lupus erythematous (SLE). With no specific clinical or laboratory manifestation to predict response to treatment, this study was aimed to provide a panel of predictive biomarkers of response before initiation of treatment. METHODS: Liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis was performed on plasma and urine samples of 11 patients with biopsy proven proliferative LN at the time of biopsy. Unsupervised principal component analysis (PCA), orthogonal projection to latent structures discriminant analysis (OPLS-DA), gene ontology annotation and protein mapping were performed on 326 proteins in plasma and 1381 proteins in urine samples. RESULTS: Samples of eight patients achieved complete remission and three reached partial remission were analyzed. The mean 24-hour protein excretion was 3259 mg/day and the mean eGFR was 87.73 cc/min. OPLS-DA analysis of plasma samples showed a clear discrimination for complete and partial remission patients. Twenty plasma proteins and ten urine proteins with the highest fold changes and AUCs were selected as candidate biomarkers (IGHV1-18, PI16, IGHD, C3, FCER2, EPS8L2, CTTN, BLVRB). This plasma and urine biomarker panel is involved in oxidative stress, acute inflammation, reduction in regulatory T cells, complement pathway consumption, and proximal tubule bicarbonate reclamation. CONCLUSION: Our suggested panel of plasma and urine biomarkers can precisely discriminate patients with possibility of complete response to treatment. It seems that the higher indices of inflammation will associate with better chance of achieving complete remission.

Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study.

In this paper, joint modeling of longitudinal ordinal measurements and time to some events of interest as competing risks is discussed. For this purpose, a latent variable sub-model under linear mixed-effects assumption is considered for modeling ordinal longitudinal measurements. Also, a Weibull cause-specific sub-model is used to model competing risks data. These two sub-models are simultaneously considered in a unique model by a shared parameter model framework. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing 15 years of lipid and glucose follow-up study in Tehran.

A Noninvasive Urine Metabolome Panel as Potential Biomarkers for Diagnosis of T Cell-Mediated Renal Transplant Rejection.

Acute T cell-mediated rejection (TCMR) is a major complication after renal transplantation. TCMR diagnosis is very challenging and currently depends on invasive renal biopsy and nonspecific markers such as serum creatinine. A noninvasive metabolomics panel could allow early diagnosis and improved accuracy and specificity. We report, in this study, on urine metabolome changes in renal transplant recipients diagnosed with TCMR, with a view to future metabolomics-based diagnostics in transplant medicine. We performed urine metabolomic analyses in three study groups: (1) 7 kidney transplant recipients with acute TCMR, (2) 15 kidney transplant recipients without rejection but with impaired kidney function, and (3) 6 kidney transplant recipients with stable renal function, using 1H-nuclear magnetic resonance. Multivariate modeling of metabolites suggested a diagnostic panel where the diagnostic accuracy of each metabolite was calculated by receiver operating characteristic curve analysis. The impaired metabolic pathways associated with TCMR were identified by pathway analysis. In all, a panel of nine differential metabolites encompassing nicotinamide adenine dinucleotide, 1-methylnicotinamide, cholesterol sulfate, gamma-aminobutyric acid (GABA), nicotinic acid, nicotinamide adenine dinucleotide phosphate, proline, spermidine, and alpha-hydroxyhippuric acid were identified as novel potential metabolite biomarkers of TCMR. Proline, spermidine, and GABA had the highest area under the curve (>0.7) and were overrepresented in the TCMR group. Nicotinate and nicotinamide metabolism was the most important pathway in TCMR. These findings call for clinical validation in larger study samples and suggest that urinary metabolomics warrants future consideration as a noninvasive research tool for TCMR diagnostic innovation.

Urine and serum NMR-based metabolomics in pre-procedural prediction of contrast-induced nephropathy.

Contrast induced nephropathy (CIN) has been reported to be the third foremost cause of acute renal failure. Metabolomics is a robust technique that has been used to identify potential biomarkers for the prediction of renal damage. We aim to analyze the serum and urine metabolites changes, before and after using contrast for coronary angiography, to determine if metabolomics can predict early development of CIN. 66 patients undergoing elective coronary angiography were eligible for enrollment. Urine and serum samples were collected prior to administration of CM and 72 h post procedure and analyzed by nuclear magnetic resonance. The significant differential metabolites between patients who develop CIN and patients who have stable renal function after angiography were identified using U test and receiver operating characteristic analysis was performed for each metabolite candidate. Potential susceptible pathways to cytotoxic effect of CM were investigated by pathway analysis. A predictive panel composed of six urinary metabolites had the best area under the curve. Glutamic acid, uridine diphosphate, glutamine and tyrosine were the most important serum predictive biomarkers. Several pathways related to amino acid and nicotinamide metabolism were suggested as impaired pathways in CIN prone patients. Changes exist in urine and serum metabolomics patterns in patients who do and do not develop CIN after coronary angiography hence metabolites may be potential predictive identifiers of CIN.

Downregulation of Protein Inhibitor of Activated STAT (PIAS) 1 Is Possibly Involved in the Process of Allograft Rejection.

BACKGROUND: Protein inhibitors of activated STAT (PIAS) proteins are regarded as negative regulators of cytokine-signaling and potent immunosuppressive proteins. However, their role in the process of organ transplant rejection has not been elucidated. METHODS: In the current study, we compared transcript levels of PIAS1 to 4 in the peripheral blood of renal transplant recipients who experienced transplant rejection with those having normal transplant functions. Expression of PIAS1 was significantly higher in nonrejected group compared with the rejected group among male recipients; however, differences were insignificant among female recipients. Expressions of other PIAS genes were not different between study groups. Significant pairwise correlations were found between expression levels of PIAS genes in all study subgroups. The current investigation highlights the role of PIAS1 downregulation in the evolution of graft rejection and potentiates this gene as a predictive marker for transplant fate.

Expression Levels of lncRNAs in the Patients with the Renal Transplant Rejection.

PURPOSE: Long non-coding RNAs (lncRNAs) include a vast portion of human transcripts. They exert regulatory roles in immune responses and participate in diverse biological functions. Recent studies indicated dysregulation of lncRNAs in the process of transplant rejection. In the current study, we aimed at identification of the expression of five lncRNAs (OIP5-AS1, FAS-AS1, TUG1, NEAT1 and PANDAR) in association with the process of transplant rejection. MATERIAL AND METHODS: We assessed expression of these lncRNAs in the peripheral blood of 61 kidney transplant receivers including 29 transplant rejected patients and 32 transplant non-rejected patients using real time PCR technique. RESULTS: Expression of FAS-AS1 was significantly higher in rejected group compared to non-rejected group in males, however, differences between case and control groups were insignificant among females. For other lncRNAs no significant differences were detected between two study groups. Quantile regression model showed that patients' gender was an important parameter in determination of FAS-AS1 expression (Beta=-9.46, t=-2.82, P=0.007) but not for other lncRNAs expressions. Significant pairwise correlations were detected between expression levels of lncRNAs in a disease related manner. CONCLUSION: Based on the higher expression of FAS-AS1 in patients with transplant rejection, this lncRNA might be associated with the pathogenesis of renal transplant rejection.

Metabolomic Analysis of Membranous Glomerulonephritis: Identification of a Diagnostic Panel and Pathogenic Pathways.

BACKGROUND: Primary membranous glomerulonephritis (MGN) is a major cause of nephrotic syndrome in adults. Its diagnosis is based on invasive biopsy, and the current traditional serum or urinary biomarkers, such as the anti-phospholipase A2 receptor, are not adequately sensitive or specific. AIM OF THE STUDY: Our purpose is to identify a sensitive and specific noninvasive panel of biomarkers for the diagnosis of MGN by using metabolomic techniques and to explore the pathogenic pathways that are involved in disease development. METHODS: The urine metabolome of 66 MGN patients, 31 healthy controls, and 72 disease controls, were analyzed using nuclear magnetic resonance (NMR) and gas chromatography-tandem mass spectrometry (GC-MS/MS). Advanced multivariate statistical analyses were performed for the construction of diagnostic models and biomarker discovery. Receiver operating characteristic (ROC) curve analysis was used to suggest the most sensitive and specific diagnostic panel. RESULTS: The NMR-based diagnostic model showed allantoic acid and deoxyuridine as the most overrepresented and underrepresented biomarkers, respectively differentiating MGN from both control groups. The GC-MS/MS-based diagnostic model showed oxalic acid as the most overrepresented biomarker and 2-hydroxyglutaric acid lactone as the only underrepresented specific biomarker. A panel of a combination of the most accurate predictors of NMR and GC-MS/MS was composed of α-hydroxybutyric acid, 3,4-Dihydroxymandelic acid, 5α-cholestanone, 2-hydroxyglutaric acid lactone, nicotinamide, epicoprostanol, and palmitic acid. Nine impaired pathways were identified in MGN, such as pyrimidine metabolism and NAD salvage. CONCLUSIONS: This comprehensive metabolomic study of MGN indicates a panel of promising biomarkers, which is complementary to current traditional biomarkers, and needs to be validated in a larger cohort.

Suppressor of cytokine signaling genes in renal transplant receivers: Association with transplant fate.

Suppressor of cytokine signaling (SOCS) proteins have acknowledged roles in regulation of immune responses. Moreover, their role in the evolution of allograft rejection is being elucidated. In the current investigation, we measured transcript levels of SOCS1-4 in the peripheral blood of a group of renal transplant recipients including both rejected and non-rejected allografts. Expression analyses showed that relative expression of SOCS2 was significantly higher in transplant-rejected male patients compared to non-rejected group. However, such significant difference was not detected between female subjects. Expression of SOCS2 was significantly higher in T-cell-mediated rejection group compared with non-rejected individuals with creatinine rise (Relative expression difference [95% CrI] =6.74 [0.94, 12.65], P = 0.043). Conversely, SOCS4 expression was significantly lower in T-cell-mediated rejection group compared with non-rejected individuals with creatinine rise (Relative expression difference [95% CrI] = -0.35 [-0.63, -0.1], P = 0.008). Patterns of correlations between expression levels of SOCS genes were different in non-rejected group. The obtained results indicate the role SOCS genes in development of allograft rejection.

Chondroitin sulfate degradation and eicosanoid metabolism pathways are impaired in focal segmental glomerulosclerosis: Experimental confirmation of an in silico prediction.

Introduction: Focal segmental glomerulosclerosis (FSGS), the most common primary glomerular disease, is a diverse clinical entity that occurs after podocyte injury. Although numerous studies have suggested molecular pathways responsible for the development of FSGS, many still remain unknown about its pathogenic mechanisms. Two important pathways were predicted as candidates for the pathogenesis of FSGS in our previous in silico analysis, whom we aim to confirm experimentally in the present study. Methods: The expression levels of 4 enzyme genes that are representative of "chondroitin sulfate degradation" and "eicosanoid metabolism" pathways were investigated in the urinary sediments of biopsy-proven FSGS patients and healthy subjects using real-time polymerase chain reaction (RT-PCR). These target genes were arylsulfatase, hexosaminidase, cyclooxygenase-2 (COX-2), and prostaglandin I2 synthase. The patients were sub-divided into 2 groups based on the range of proteinuria and glomerular filtration rate and were compared for variation in the expression of target genes. Correlation of target genes with clinical and pathological characteristics of the disease was calculated and receiver operating characteristic (ROC) analysis was performed. Results: A combined panel of arylsulfatase, hexosaminidase, and COX-2 improved the diagnosis of FSGS by 76%. Hexosaminidase was correlated with the level of proteinuria, while COX-2 was correlated with interstitial inflammation and serum creatinine level in the disease group. Conclusion: Our data supported the implication of these target genes and pathways in the pathogenesis of FSGS. In addition, these genes can be considered as non-invasive biomarkers for FSGS.

THE METABOLOMICS SIGNATURE ASSOCIATED WITH RESPONSIVENESS TO STEROID THERAPY IN FOCAL SEGMENTAL GLOMERULOSCLEROSIS: A PILOT STUDY.

BACKGROUND: Focal segmental glomerulosclerosis (FSGS) is considered one of the most severe glomerular diseases and around 80% of cases are resistant to steroid treatment. Since a large proportion of steroid-resistant (SR) FSGS patients progress to end-stage renal disease, other therapeutic strategies may benefit this population. However, identification of non-invasive biomarkers to predict this high-risk population is needed. OBJECTIVE: We aimed to identify the biomarker candidates to distinguish SR from steroid-sensitive (SS) patients using metabolomics approach and to identify the possible molecular mechanism of resistance. METHODS: Urine was collected from biopsy-proven FSGS patients eligible for monotherapy with prednisolone. Patients were followed for 6-8 weeks and categorized as SS or SR. Metabolite profile of urine samples was analyzed by one-dimensional 1H-nuclear magnetic resonance (1H-NMR). Predictive biomarker candidates and their diagnostic importance impaired molecular pathways in SR patients, and the common target molecules between biomarker candidates and drug were predicted. RESULTS: Homovanillic acid, 4-methylcatechol, and tyrosine were suggested as the significant predictive biomarker candidates, while L-3,4-dihydroxyphenylalanine, norepinephrine, and gentisic acid had high accuracy as well. Tyrosine metabolism was the most important pathway that is perturbed in SR patients. Common targets of the action of biomarker candidates and prednisolone were molecules that contributed in apoptosis. CONCLUSION: Urine metabolites including homovanillic acid, 4-methylcatechol, and tyrosine may serve as potential non-invasive predictive biomarkers for evaluating the responsiveness of FSGS patients.

An update of urine and blood metabolomics in chronic kidney disease.

Chronic kidney disease is considered as a serious obstacle in global health, with increasing incidence and prevalence. In spite of numerous attempts by using recent omics technologies, specially metabolomics, for understanding pathophysiology, molecular mechanism and identification reliable consensus biomarkers for diagnosis and prognosis of this complex disease, the current biomarkers are still insensitive and many questions about its pathomechanism are still to be unanswered. This review is focused on recent findings about urine and serum/plasma metabolite biomarkers and changes in the pathways that occurs in the disease conditions. The urine and blood metabolome content in the normal and disease state is investigated based on the current metabolomics studies and well known metabolite candidate biomarkers for chronic kidney disease are discussed.

The metabolomics signature associated with responsiveness to steroid therapy in focal segmental glomerulosclerosis: a pilot study

Abstract Background Focal segmental glomerulosclerosis (FSGS) is considered one of the most severe glomerular diseases and around 80% of cases are resistant to steroid treatment. Since a large proportion of steroid-resistant (SR) FSGS patients progress to end-stage renal disease, other therapeutic strategies may benefit this population. However, identification of non-invasive biomarkers to predict this high-risk population is needed. Objective We aimed to identify the biomarker candidates to distinguish SR from steroid-sensitive (SS) patients using metabolomics approach and to identify the possible molecular mechanism of resistance. Methods Urine was collected from biopsy-proven FSGS patients eligible for monotherapy with prednisolone. Patients were followed for 6-8 weeks and categorized as SS or SR. Metabolite profile of urine samples was analyzed by one-dimensional 1H-nuclear magnetic resonance (1H-NMR). Predictive biomarker candidates and their diagnostic importance impaired molecular pathways in SR patients, and the common target molecules between biomarker candidates and drug were predicted. Results Homovanillic acid, 4-methylcatechol, and tyrosine were suggested as the significant predictive biomarker candidates, while L-3,4-dihydroxyphenylalanine, norepinephrine, and gentisic acid had high accuracy as well. Tyrosine metabolism was the most important pathway that is perturbed in SR patients. Common targets of the action of biomarker candidates and prednisolone were molecules that contributed in apoptosis. Conclusion Urine metabolites including homovanillic acid, 4-methylcatechol, and tyrosine may serve as potential non-invasive predictive biomarkers for evaluating the responsiveness of FSGS patients.

Metabolomics approach reveals urine biomarkers and pathways associated with the pathogenesis of lupus nephritis.

OBJECTIVES: lupus nephritis (LN) is a severe form of systemic lupus erythematosus (SLE) with renal complications. Current diagnosis is based on invasive renal biopsy and serum antibodies and complement levels that are not specific enough. The current study aims to identify new biomarker candidates for non-invasive diagnosis of LN and explore the pathogenic mechanisms that contribute to renal injury. MATERIALS AND METHODS: A metabolomics approach using 1H-nuclear magnetic resonance (1H-NMR), was developed for comparison of urine metabolic profile of 14 LN patients, 10 SLE patients, and 11 healthy controls (HCs). Differential biomarker candidates were identified by using multivariate modeling, and their diagnostic accuracy was evaluated by receiver operating characteristic analysis (ROC). RESULTS: Three metabolites were common in differentiating all three groups including beta-alanine, 2,2-dimethylsucssinic acid, and 3,4-Dihydroxyphenylacetaldehyde and suggested as a diagnostic panel for LN with AUC of 0.89, sensitivity of 81 %, and specificity of 100 %. Complementary analyses on pathways indicated that nicotinate and nicotinamide metabolism is the most important perturbed pathway in LN. CONCLUSION: Metabolomics is a useful tool for identification of biomarkers with the ability to diagnose LN patients and predict perturbed pathways responsible for renal injury.

Network analysis of membranous glomerulonephritis based on metabolomics data.

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component­reaction­enzyme­gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography­mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold­change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein­protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold­changes: Dopamine, carnosine, fumarate, nicotinamide D­ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L­citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine­4­hydroxlylase and cystathionine γ­lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine­containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

[Prediction of Molecular Signature, Potential Biomarkers, and Molecular Pathways Associated with Membranous Nephropathy Based on Protein Protein Interactions].

Background: Membranous nephropathy (MN) is one of the causes of nephrotic syndrome in adults that lead to end-stage renal disease with an unknown molecular signature. The current diagnosis is based on renal biopsy, which is an invasive method and has several complications and challenges. Thus, identification of the novel biomarker candidates, as well as impaired pathways, will be helpful for non-invasive molecular-based diagnosis. Objectives: We aimed to study the molecular signature of MN and facilitate the systematic discovery of diagnostic candidate biomarkers, molecular pathway, and potential therapeutic targets using bioinformatics predictions. Methods: The protein-protein interaction (PPI) network of an integrated list of downloaded microarray data, differential proteins from a published proteomic study, and a list of retrieved scientific literature mining was constructed and analyzed in terms of functional modules, enriched biological pathways, hub genes, master regulator, and target genes. Results: These network analyses revealed several functional modules and hub genes including Vitamin D3 receptor, retinoic acid receptor RXR-alpha, interleukin 8, and SH3GL2. TEAD4 and FOXA1 were identified as the regulatory master molecules. LRP1 and ITGA3 were identified as the important target genes. Extracellular matrix organization, cell surface receptor signaling pathway, and defense and inflammatory response were found to be impaired in MN using functional analyses. A specific subnetwork for MN was suggested using PPI approach. Discussion: Omics data integration and systems biology analysis on the level of interaction networks provide a powerful approach for identification of pathway-specific biomarkers for MN.

Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints

Background: IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset were obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study. Methods: Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net (EN) regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis. Results: Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN. Conclusion: SLDA and EN had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination.

Metabolomics diagnostic approach to mustard airway diseases: a preliminary study.

OBJECTIVES: This study aims to evaluate combined proton nuclear magnetic resonance (1H NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) metabolic profiling approaches, for discriminating between mustard airway diseases (MADs) and healthy controls and for providing biochemical information on this disease. MATERIALS AND METHODS: In the present study, analysis of serum samples collected from 17 MAD subjects and 12 healthy controls was performed using NMR. Of these subjects, 14 (8 patients and 6 controls) were analyzed by GC-MS. Then, their spectral profiles were subjected to principal component analysis (PCA) and orthogonal partial least squares regression discriminant analysis (OPLS-DA). RESULTS: A panel of twenty eight metabolite biomarkers was generated for MADs, sixteen NMR-derived metabolites (3-methyl-2-oxovaleric acid, 3-hydroxyisobutyrate, lactic acid, lysine, glutamic acid, proline, hydroxyproline, dimethylamine, creatine, citrulline, choline, acetic acid, acetoacetate, cholesterol, alanine, and lipid (mainly VLDL)) and twelve GC-MS-derived metabolites (threonine, phenylalanine, citric acid, myristic acid, pentadecanoic acid, tyrosine, arachidonic acid, lactic acid, propionic acid, 3-hydroxybutyric acid, linoleic acid, and oleic acid). This composite biomarker panel could effectively discriminate MAD subjects from healthy controls, achieving an area under receiver operating characteristic curve (AUC) values of 1 and 0.79 for NMR and GC-MS, respectively. CONCLUSION: In the present study, a robust panel of twenty-eight biomarkers for detecting MADs was established. This panel is involved in three metabolic pathways including aminoacyl-tRNA biosynthesis, arginine, and proline metabolism, and synthesis and degradation of ketone bodies, and could differentiate MAD subjects from healthy controls with a higher accuracy.

A comprehensive narrative review of diagnostic biomarkers in human primary membranous nephropathy.

Membranous nephropathy (MN) is relatively major cause of nephrotic syndrome in adults which is recognized as an organ-specific autoimmune disease. The etiology of most cases is idiopathic, whereas the secondary MN is caused by systemic autoimmune diseases, infections, medications and malignancies. The idiopathic disease is developed by the formation of sub-epithelial immune complex deposits most likely due to binding the circulating auto-antibodies to intrinsic antigen on podocytes. The major auto antibody is the anti-phospholipase A2 receptor (anti-PLA2R), however, it is not enough sensitive. Several attempts for diagnostic biomarker identification by modern analytical technologies have been devoted recently. This article reviews the biomarker candidates for primary type of MN that are detected by different approaches on human subjects.