Carnosine Dipeptidase Research Articles

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

Abstract Background Obesity, a key risk factor for cardiometabolic diseases, is poorly evaluated by body mass index (BMI), which doesn't account for visceral fat and fat distribution. Given that obesity-related cardiac changes can manifest in surface ECG changes, we hypothesised that an AI-ECG model could predict BMI, and the discrepancy between AI-predicted and actual BMI (delta-BMI) could indicate cardiometabolic health. We also sought to understand the biological mechanisms contributing to the AI-ECG-derived delta-BMI. Methods The AI-ECG model was developed using a data split of 60/10/30% in a USA secondary care dataset of 512,950 ECGs and externally validated in the UK Biobank (N = 42,386), employing a residual neural network architecture. Model performance was evaluated using Mean Absolute Error (MAE), Pearson correlation coefficient (r), and coefficient of determination (R2). The study aimed at identifying incident cardiometabolic diseases, using Cox regression analyses adjusted for BMI, age, and sex. To assess the underlying biological associations of delta-BMI, phenome-wide, genome-wide, metabolome-wide, and proteome-wide association studies were performed. Results In the test set, the model achieved an MAE of 3.95 (3.93–3.97), r of 0.65 (0.65-0.66), and R2 of 0.43 (0.42-0.43). In the UKB, the model achieved an MAE of 2.94 (2.91–2.96), r of 0.62 (0.62–0.63) and R2 of 0.39 (0.38-0.40). The top tertile of delta-BMI showed an increased risk of future cardiometabolic disease (test set: HR 1.15, p<0.001; UKB: HR 1.58, p<0.001) and diabetes mellitus (test set: HR 1.25, p<0.001; UKB: HR 2.28, p<0.001) after adjusting for covariates including measured BMI. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits. These results highlight delta-BMI's potential as a non-invasive, ECG-based biomarker for cardiometabolic risk stratification. Conclusion Our study demonstrates the effectiveness of an AI-enhanced ECG model in accurately predicting BMI, validated across diverse populations. The introduction of delta-BMI as a predictor of cardiometabolic disease offers valuable additional prognostic insights beyond traditional BMI assessments. Our exploration of biological pathways, spanning phenotypic, genotypic, metabolomic, and proteomic associations, contributes to a comprehensive understanding of the association between delta-BMI and cardiometabolic disease. The clinical implications could extend to enhanced screening strategies, personalised risk assessment, and motivation for lifestyle interventions.

CNDP2: An Enzyme Linking Metabolism and Cardiovascular Diseases?

The heart requires a substantial amount of energy to function, utilising various substrates including lipids, glucose and lactate as energy sources. In times of increased stress, lactate becomes the primary energy source of the heart, but persistently elevated lactate levels are linked to poor patient outcomes and increased mortality. Recently, carnosine dipeptidase II (CNDP2) was discovered to catalyse the formation of Lac-Phe, an exercise-induced metabolite derived from lactate, which has been shown to suppress appetite in mice and reduce adipose tissue in humans. This review discusses CNDP2, including its role in lactate clearance, carnosine hydrolysis, oxidative stress regulation, and involvement in metabolite regulation. The association between CNDP2 and cardiometabolic and renal diseases is also explored, and knowledge gaps are highlighted. CNDP2 appears to be a complex participant in human physiological processes and disease, necessitating additional research to unveil its functions and potential therapeutic applications.

Proteomic Profiling of HDL in Newly Diagnosed Breast Cancer Based on Tumor Molecular Classification and Clinical Stage of Disease.

The association between high-density lipoprotein (HDL) cholesterol and breast cancer (BC) remains controversial due to the high complexity of the HDL particle and its functionality. The HDL proteome was determined in newly diagnosed BC classified according to the molecular type [luminal A or B (LA or LB), HER2, and triple-negative (TN)] and clinical stage of the disease. Women (n = 141) aged between 18 and 80 years with BC, treatment-naïve, and healthy women [n = 103; control group (CT)], matched by age and body mass index, were included. Data-independent acquisition (DIA) proteomics was performed in isolated HDL (D = 1.063-1.21 g/mL). Results: Paraoxonase1, carnosine dipeptidase1, immunoglobulin mMu heavy chain constant region (IGHM), apoA-4, and transthyretin were reduced, and serum amyloid A2 and tetranectin were higher in BC compared to CT. In TNBC, apoA-1, apoA-2, apoC-2, and apoC-4 were reduced compared to LA, LB, and HER2, and apoA-4 compared to LA and HER2. ComplementC3, lambda immunoglobulin2/3, serpin3, IGHM, complement9, alpha2 lysine rich-glycoprotein1, and complement4B were higher in TNBC in comparison to all other types; complement factor B and vitamin D-binding protein were in contrast to LA and HER2, and plasminogen compared to LA and LB. In grouped stages III + IV, tetranectin and alpha2-macroglobulin were reduced, and haptoglobin-related protein; lecithin cholesterol acyltransferase, serum amyloid A1, and IGHM were increased compared to stages I + II. Conclusions: A differential proteomic profile of HDL in BC based on tumor molecular classification and the clinical stage of the disease may contribute to a better understanding of the association of HDL with BC pathophysiology, treatment, and outcomes.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Identification of brain-enriched proteins in CSF as biomarkers of relapsing remitting multiple sclerosis

BackgroundMultiple sclerosis (MS) is a clinically and biologically heterogenous disease with currently unpredictable progression and relapse. After the development and success of neurofilament as a cerebrospinal fluid (CSF) biomarker, there is reinvigorated interest in identifying other markers of or contributors to disease. The objective of this study is to probe the predictive potential of a panel of brain-enriched proteins on MS disease progression and subtype.MethodsThis study includes 40 individuals with MS and 14 headache controls. The MS cohort consists of 20 relapsing remitting (RR) and 20 primary progressive (PP) patients. The CSF of all individuals was analyzed for 63 brain enriched proteins using a method of liquid-chromatography tandem mass spectrometry. Wilcoxon rank sum test, Kruskal-Wallis one-way ANOVA, logistic regression, and Pearson correlation were used to refine the list of candidates by comparing relative protein concentrations as well as relation to known imaging and molecular biomarkers.ResultsWe report 30 proteins with some relevance to disease, clinical subtype, or severity. Strikingly, we observed widespread protein depletion in the disease CSF as compared to control. We identified numerous markers of relapsing disease, including KLK6 (kallikrein 6, OR = 0.367, p < 0.05), which may be driven by active disease as defined by MRI enhancing lesions. Other oligodendrocyte-enriched proteins also appeared at reduced levels in relapsing disease, namely CNDP1 (carnosine dipeptidase 1), LINGO1 (leucine rich repeat and Immunoglobin-like domain-containing protein 1), MAG (myelin associated glycoprotein), and MOG (myelin oligodendrocyte glycoprotein). Finally, we identified three proteins—CNDP1, APLP1 (amyloid beta precursor like protein 1), and OLFM1 (olfactomedin 1)—that were statistically different in relapsing vs. progressive disease raising the potential for use as an early biomarker to discriminate clinical subtype.ConclusionsWe illustrate the utility of targeted mass spectrometry in generating potential targets for future biomarker studies and highlight reductions in brain-enriched proteins as markers of the relapsing remitting disease stage.

Investigating the Role of Carnosine Dipeptidase II (CNDP2) in Heart Disease

Investigating the Role of Carnosine Dipeptidase II (CNDP2) in Heart Disease

Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma

Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167–7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922–41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374–12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780–10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771–0.894) and 0.821 (95 % CI 0.720–0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.

A proteomic classifier panel for early screening of colorectal cancer: a case control study

BackgroundDiagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease.MethodsWe enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts.ResultsThe seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%.ConclusionsThis seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma.

Carnosine dipeptidase 1 (CNDP1), an enzyme integral to the hydrolysis of dipeptides containing histidine, plays an indispensable role in myriad physiological processes, including hydrolysis of proteins, maturation of specific biochemical functionalities within proteins, tissue regeneration, and regulation of cell cycle. However, the implications of CNDP1 in oncogenesis and its prognostic value are not yet fully elucidated. Initially, we procured the GSE40367 dataset from the Gene Expression Omnibus and established a protein-protein interaction network. Thereafter, we conducted functional and pathway enrichment analyses utilizing GO, KEGG, and GSEA. Moreover, we undertook an association analysis concerning the expression of CNDP1 with immune infiltration, along with survival analysis across various cancers and specifically in hepatocellular carcinoma (HCC). Our study uncovered a total of 2,248 differentially expressed genes, with a down-regulation of CNDP1 in HCC and other cancers. Our explorations into the relationship between CNDP1 and immune infiltration disclosed a negative correlation between CNDP1 expression and the presence of immune cells in HCC. Survival analyses revealed that diminished expression of CNDP1 correlates with an adverse prognosis in HCC and several other types of cancer. These observations intimate that CNDP1 holds promise as a novel prognostic biomarker for both pan-cancer and HCC.

A preliminary discussion on carnosine dipeptidase 1 as a potential novel biomarker for the diagnostic and prognostic evaluation of hepatocellular carcinoma

Objective: To explore carnosine dipeptidase 1 (CNDP1) potential value as a diagnostic and prognostic evaluator of hepatocellular carcinoma (HCC). Methods: A gene chip and GO analysis were used to screen the candidate marker molecule CNDP1 for HCC diagnosis. 125 cases of HCC cancer tissues, 85 cases of paracancerous tissues, 125 cases of liver cirrhosis tissues, 32 cases of relatively normal liver tissue at the extreme end of hepatic hemangioma, 66 cases from serum samples of HCC, and 82 cases of non-HCC were collected. Real-time fluorescent quantitative PCR, immunohistochemistry, western blot, and enzyme-linked immunosorbent assay were used to detect the differences in mRNA and protein expression levels of CNDP1 in HCC tissue and serum. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival were used to analyze and evaluate the value of CNDP1 in the diagnosis and prognosis of HCC patients. Results: The expression level of CNDP1 was significantly reduced in HCC cancer tissues. The levels of CNDP1 were significantly lower in the cancer tissues and serum of HCC patients than those in liver cirrhosis patients and normal controls. ROC curve analysis showed that the area under the curve of serum CNDP1 in the diagnosis of HCC patients was 0.753 2 (95% CI 0.676-0.830 5), and the sensitivity and specificity were 78.79% and 62.5%, respectively. The combined detection of serum CNDP1 and serum alpha-fetoprotein (AFP) significantly improved the diagnostic accuracy (AUC = 0.820 6, 95% CI 0.753 5-0.887 8). The diagnostic sensitivity and specificity of serum CNDP1 for AFP-negative HCC patients were 73.68% and 68.75% (AUC = 0.793 1, 95% CI 0.708 8-0.877 4), respectively. In addition, the level of serum CNDP1 distinguished small liver cancer (tumor diameter < 3 cm) (AUC = 0.757 1, 95% CI 0.637 4-0.876 8). Kaplan-Meier survival analysis showed that CNDP1 was associated with a poor prognosis in HCC patients. Conclusion: CNDP1 may be a potential biomarker for the diagnostic and prognostic evaluation of HCC, and it has certain complementarity with serum AFP.

Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections

The detailed mechanisms of COVID-19 infection pathology remain poorly understood. To improve our understanding of SARS-CoV-2 pathology, we performed a multi-omics and correlative analysis of an immunologically naïve SARS-CoV-2 clinical cohort from blood plasma of uninfected controls, mild, and severe infections. Consistent with previous observations, severe patient populations showed an elevation of pulmonary surfactant levels. Intriguingly, mild patients showed a statistically significant elevation in the carnosine dipeptidase modifying enzyme (CNDP1). Mild and severe patient populations showed a strong elevation in the metabolite L-cystine (oxidized form of the amino acid cysteine) and enzymes with roles in glutathione metabolism. Neutrophil extracellular traps (NETs) were observed in both mild and severe populations, and NET formation was higher in severe vs. mild samples. Our correlative analysis suggests a potential protective role for CNDP1 in suppressing PSPB release from the pulmonary space whereas NET formation correlates with increased PSPB levels and disease severity. In our discussion we put forward a possible model where NET formation drives pulmonary occlusions and CNDP1 promotes antioxidation, pleiotropic immune responses, and vasodilation by accelerating histamine synthesis.

Fibrinogen and Complement Factor H Are Promising CSF Protein Biomarkers for Parkinson's Disease with Cognitive Impairment─A Proteomics-ELISA-Based Study.

Parkinson's disease (PD) with cognitive impairment (PDCI) is essentially diagnosed through clinical and neuropsychological examinations. There is a need to identify biomarkers to foresee cognitive decline in them. We performed label-free unbiased nontargeted proteomics (Q-TOF LC/MS-MS) on the CSF of non-neurological control; PDCI; PD; and normal pressure hydrocephalus (NPH) patients, followed by targeted ELISA for validation. Of the 281 proteins identified, 42 were differentially altered in PD, PDCI, and NPH. With a certain overlap, 28 proteins were altered in PDCI and 25 proteins were altered in NPH. Five significantly upregulated proteins in PDCI were fibrinogen, gelsolin, complement factor-H, and apolipoproteins A-I and A-IV, whereas carnosine dipeptidase-1, carboxypeptidase-E, dickkopf-3, and secretogranin-3 precursor proteins were downregulated. Those uniquely altered in NPH were the insulin-like growth factor-binding protein, ceruloplasmin, α-1 antitrypsin, VGF nerve growth factor, and neural cell adhesion molecule L1-like protein. The ELISA-derived protein concentrations correlated with neuropsychological scores of certain cognitive domains. In PDCI, the Wisconsin card sorting percentile correlated negatively with fibrinogen. Intraperitoneal injection of native fibrinogen caused motor deficits in C57BL/6J mice as assessed by the pole test. Thus, a battery of proteins such as fibrinogen-α-chain, CFAH, and APOA-I/APOA-IV alongside neuropsychological assessment could be reliable biomarkers to distinguish PDCI and NPH.

PSX-A-16 Late-Breaking: Maternal nutrient restriction and re-alimentation influences liver protein expression in fetal sheep

Abstract To determine the effects of maternal nutrient restriction and re-alimentation on the fetal liver proteome, 48 pregnant ewes with singletons were fed a control diet [100% National Research Council (NRC) requirements (CON)] starting at the beginning of gestation. On day 50 of gestation, a subset of ewes were fed either CON or 60% NRC requirements (RES). A subset of ewes were euthanized at day 90 of gestation (n = 7/treatment), and fetal liver samples collected. Remaining ewes were maintained on the current diet (CON-CON, n=6; RES-RES, n = 7) or switched to alternative diet (CON-RES, RES-CON; n=7/treatment). On day 130 of gestation, remaining ewes were euthanized, and fetal samples collected. Fetal liver proteins were extracted, digested by trypsin and subjected to multiplexed, label-based quantitative mass spectrometry analysis integrating Tandem Mass Tags. Proteins were identified and quantified using Proteome Discoverer (v2.5, Thermo Scientific) and differential abundance analysis was performed using ANOVA and post hoc Tukey’s HSD test. Hierarchal clustering analysis showed clustering of treatments by day of gestation. However, differences were also observed between treatments. At day 90 of gestation, 23 proteins were differentially expressed in RES compared with CON among which glycyl tRNA synthetase and pyruvate carboxylase were increased 12% and 10%, respectively (P &amp;lt; 0.03). At day 130 of gestation, 24, 5, and 71 proteins were differentially expressed in CON-RES, RES-CON, and RES-RES, respectively, compared with CON-CON. Carnosine dipeptidase 2 was decreased 7% in CON-RES and rho associated protein kinase and glycogen synthase 2 were increased 20% and 26% in RES-CON and RES-RES, respectively, compared with CON-CON (P &amp;lt; 0.04). These results indicate that nutrient restriction during mid- and late-gestation impacts expression of proteins involved in gluconeogenesis, glycogenesis, and the formation of carnosine, an integral molecule in beta-oxidation, and that re-alimentation alters proteins involved in cell migration pathways. Supported by USDA-AFRI grants 2016-67016-24884 and 2017-67016-26568.

Carnosine dipeptidase II (CNDP2) protects cells under cysteine insufficiency by hydrolyzing glutathione-related peptides

The knockout (KO) of the cystine transporter xCT causes ferroptosis, a type of iron-dependent necrotic cell death, in mouse embryonic fibroblasts, but this does not occur in macrophages. In this study, we explored the gene that supports cell survival under a xCT deficiency using a proteomics approach. Analysis of macrophage-derived peptides that were tagged with iTRAQ by liquid chromatography-mass spectrometry revealed a robust elevation in the levels of carnosine dipeptidase II (CNDP2) in xCT KO macrophages. The elevation in the CNDP2 protein levels was confirmed by immunoblot analyses and this elevation was accompanied by an increase in hydrolytic activity towards cysteinylglycine, the intermediate degradation product of glutathione after the removal of the γ-glutamyl group, in xCT KO macrophages. Supplementation of the cystine-free media of Hepa1-6 cells with glutathione or cysteinylglycine extended their survival, whereas the inclusion of bestatin, an inhibitor of CNDP2, counteracted the effects of these compounds. We established CNDP2 KO mice by means of the CRISPR/Cas9 system and found a decrease in dipeptidase activity in the liver, kidney, and brain. An acetaminophen overdose (350 mg/kg) showed not only aggravated hepatic damage but also renal injury in the CNDP2 KO mice, which was not evident in the wild-type mice that were receiving the same dose. The aggravated renal damage in the CNDP2 KO mice was consistent with the presence of abundant levels of CNDP2 in the kidney, the organ prone to developing ferroptosis. These collective data imply that cytosolic CNDP2, in conjugation with the removal of the γ-glutamyl group, recruits Cys from extracellular GSH and supports redox homeostasis of cells, particularly in epithelial cells of proximal tubules that are continuously exposed to oxidative insult from metabolic wastes that are produced in the body.

Comprehensive analysis of the long non-coding RNA expression profile and functional roles in a contrast-induced acute kidney injury rat model.

Long non-coding RNAs (lncRNAs) have been identified as a class of regulatory RNAs that participate in both physiological and pathological conditions, including acute kidney injury. However, the roles of lncRNA dysregulation in the pathogenesis of contrast-induced acute kidney injury (CI-AKI) are largely unknown. In the present study, the expression profiles of lncRNAs in kidney tissue were compared between rats with CI-AKI and controls using high-throughput RNA sequencing. In total, 910 differentially expressed (DE) lncRNAs (DElncRNAs), including 415 downregulated and 495 upregulated lncRNAs, were identified at 12 h after intra-arterial iodinated contrast medium injection (fold change ≥2; P<0.05). Eight DElncRNAs were further selected and validated using reverse transcription-quantitative polymerase chain reaction. A previous study defined microRNA (miRNA) and mRNA expression changes in the same CI-AKI model. In the present study, a lncRNA-mRNA co-expression network comprising 349 DElncRNAs and 202 DEmRNAs was constructed. The function of these DElncRNAs was mainly associated with oxidative stress and inflammation. Additionally, lncRNA-associated competing endogenous RNA (ceRNA) analysis revealed a network comprising 40 DElncRNA nodes, 5 DEmiRNA nodes and 59 DEmRNA nodes. Among which, the carnosine dipeptidase 1-specific and the transmembrane protein 184B-specific networks were likely to be associated with CI-AKI. The results of the present study revealed the expression profile and potential roles of lncRNAs in CI-AKI, and provide a framework for further mechanistic studies.

Proteomic Analysis of Serum Differentially Expressed Proteins Between Allergic Bronchopulmonary Aspergillosis and Asthma.

Allergic bronchopulmonary aspergillosis (ABPA) constantly develops in asthmatics, which has not been fully investigated. This study aimed to investigate serum differentially expressed proteins(DEPs) between ABPA and asthma using the new approach isobaric tags by relative and absolute quantitation (iTRAQ). Each 16 serum samples from ABPA or asthmatic subjects were pooled and screened using iTRAQ. After bioinformatic analysis, five candidate DEPs were validated in the enlarged serum samples from additional 21 ABPA, 31 asthmatic and 20 healthy subjects using ELISA. A receiver operating characteristic (ROC) curve was used to estimate the diagnostic power of carnosine dipeptidase 1 (CNDP1). A total of 29 DEPs were screened out between ABPA and asthmatic groups. Over half of them were enriched in proteolysis and regulation of protein metabolic process. Further verification showed serum levels of immunoglobulin heavy constant gamma 1, α-1-acid glycoprotein 1, corticosteroid-binding globulin and vitronectin were neither differentially altered between ABPA and asthma nor consistent with the proteomic analysis. Only serum CNDP1 was significantly decreased in ABPA patients, compared with asthmatics and healthy controls (P < 0.01 and P < 0.05). The ROC analysis determined 10.73ng/mL as the cutoff value of CNDP1, which could distinguish ABPA among asthmatics (AUC 0.770, 95%CI 0.632-0.875, P < 0.001). This study firstly identified serological DEPs between ABPA and asthma using the new technique iTRAQ. Serum CNDP1 might assist the differential diagnosis of ABPA from asthma and serve as a new pathogenetic factor in fungal colonization and sensitization.

The genetic map of diabetic nephropathy: evidence from a systematic review and meta-analysis of genetic association studies.

Despite the extensive efforts of scientists, the genetic background of diabetic nephropathy (DN) has not yet been clarified. To elucidate the genetic variants that predispose to the development of DN, we conducted a systematic review and meta-analysis of all available genetic association studies (GAS) of DN. We searched in the Human Genome Epidemiology Navigator (HuGE Navigator) and PubMed for available GAS of DN. The threshold for meta-analysis was three studies per genetic variant. The association between genotype distribution and DN was examined using the generalized linear odds ratio (ORG). For variants with available allele frequencies, the examined model was the allele contrast. The pooled OR was estimated using the DerSimonian and Laird random effects model. The publication bias was assessed with Egger’s test. We performed pathway analysis of significant genes with DAVID 6.7. Genetic data of 606 variants located in 228 genes were retrieved from 360 GASs and were synthesized with meta-analytic methods. ACACB, angiotensin I-converting enzyme (ACE), ADIPOQ, AGT, AGTR1, AKR1B1, APOC1, APOE, ATP1B2, ATP2A3, CARS, CCR5, CGNL1, Carnosine dipeptidase 1 (CNDP1), CYGB-PRCD, EDN1, Engulfment and cell motility 1 (ELMO1), ENPP1, EPO, FLT4, FTO, GLO1, HMGA2, IGF2/INS/TH cluster, interleukin 1B (IL1B), IL8, IL10, KCNQ1, KNG, LOC101927627, Methylenetetrahydrofolate reductase, nitric oxide synthase 3 (NOS3), SET domain containing seven, histone lysine methyltransferase (SETD7), Sirtuin 1 (SIRT1), SLC2A1, SLC2A2, SLC12A3, SLC19A3, TCF7L2, TGFB1, TIMP1, TTC39C, UNC13B, VEGFA, WTAPP1, WWC1 as well as XYLT1 and three intergenic polymorphisms showed significant association with DN. Pathway analysis revealed the overrepresentation of six signalling pathways. The significant findings provide further evidence for genetic factors implication in DN offering new perspectives in discovery of new therapies.

Integrative Analysis of Human Plasma Proteome/Metabolome Reveals Central Role for Phospholipid, Retinol, and Histidine Metabolism after Gastric Bypass

Bariatric surgery has emerged as a potent approach to improve obesity-related metabolic comorbidities including type 2 diabetes (T2D). However, molecular mechanisms responsible for metabolic improvement after bariatric surgery remain incompletely understood. We profiled and integrated the fasting plasma proteome (aptamer-based Somalogic platform) and metabolome (MS, Metabolon) from patients with T2D at baseline and up to 3 years after randomization to either gastric bypass (GB) or nonsurgical diabetes/weight management (DMW) within the SLIMM-T2D longitudinal clinical trial (random subset, n=19 per group). To identify top-ranking pathways encompassing both proteomic and metabolomic data, we developed a systems approach employing two interdependent programs: ezlimma (for differential analysis) and Pathway Analysis via Network Smoothing (PANTS). PANTS smooths differential statistics over a large network of interacting proteins/metabolites, calculating significance by permutation. Top-ranking pathways were phospholipid (e.g., choline), retinol (e.g., retinol-binding protein 4, RBP4), and histidine metabolism (e.g., carnosine dipeptidase 1, CNDP1). Quantitative Western blot confirmed 26% reduction in RBP4 in GB vs. DWM; ELISA confirmed 66% reduction in CNDP1 in GB vs. DWM (both p&amp;lt;0.05, 3 months). CNDP1 change at 3 months was associated with improved metabolism at 1 year (BMI r=0.67, p&amp;lt;10-5; HbA1c r=0.61, p&amp;lt;10-4). Causal inference analysis in the entire cohort identified CNDP1 change at 3 months as the most likely protein to cause improved HbA1c at 1 year (p=0.01). Decreased CNDP1 occurred before significant weight loss, suggesting weight-independent impact on glycemia. Investigating modulation of CNDP1 activity, its enzymatic products Ala/His, and related metabolites will be important to determine if these pathways may serve as targets for T2D therapy. Disclosure J. Dreyfuss: None. Y. Yuchi: None. H. Pan: None. D.C. Simonson: Advisory Panel; Self; GI Windows, Inc.. Stock/Shareholder; Self; GI Windows, Inc.. Stock/Shareholder; Spouse/Partner; Phase V Technologies, Inc. A.H. Vernon: None. P. Aryal: None. B. Kahn: Advisory Panel; Self; Janssen Research &amp; Development. Research Support; Self; Janssen Research &amp; Development. Advisory Panel; Self; Alterna Biotech. K. Foster: None. S. Kasif: None. A. Goldfine: Employee; Self; Novartis AG. M.E. Patti: Research Support; Self; Janssen Pharmaceuticals, Inc.. Other Relationship; Self; Xeris Pharmaceuticals, Inc.. Research Support; Self; Ethicon US, LLC., Coviden, MedImmune. Other Relationship; Self; Novo Nordisk Inc., XOMA Corporation, AstraZeneca, Nestlé. Research Support; Self; Dexcom, Inc.. Consultant; Self; Eiger BioPharmaceuticals.

The zinc form of carnosine dipeptidase 2 (CN2) has dipeptidase activity but its substrate specificity is different from that of the manganese form

Carnosine dipeptidase II (CN2), a metallopeptidase present in the cytosol of various vertebrate tissues, catalyzes the hydrolysis of carnosine and several other dipeptides in the presence of Mn2+. Although the metal-binding center of mouse CN2 is also able to associate with Zn2+in vitro, it was not known whether the zinc form of CN2 has any enzymatic activity. In the present study, we show that Zn2+ has a higher affinity for binding to CN2 than Mn2+, as evidenced by native mass spectrometry. The issue of whether the zinc form of CN2 has enzymatic activity was also examined using various dipeptides as substrates. The findings indicate that the zinc form of CN2 catalyzes the hydrolysis of several different dipeptides including Leu-His, Met-His and Ala-His at a reaction rate comparable to that for its manganese form. On the other hand, the zinc form of CN2 did not catalyze the hydrolysis of carnosine and several other dipeptides that are hydrolyzed by the manganese form of CN2. Substrate specificity was also examined in HEK293T cells expressing CN2, and the findings indicate that Leu-His, Met-His, but not carnosine, were hydrolyzed in the cell culture. These results suggest that the zinc form of CN2 is an active enzyme, but with a different substrate specificity from that of the manganese form.

Serum Carnosine Dipeptidase 1 and Ubiquitin C - Terminal Hydrolase L1 as Markers of Brain Damage in Patients After Carotid Endarterectomy

Introduction: Carotid endarterectomy (CEA) is the recommended surgical procedure in the prevention of ischemic stroke. However, this procedure may lead to vascular neurological complications. The aim of the study was to measure serum carnosine dipeptidase 1 (CNDP1) and ubiquitin C - terminal hydrolase L1 (UCHL1) as markers of brain damage in patients that underwent CEA due to high-grade internal carotid artery stenosis. Material and Methods: This study included 25 patients. Blood samples were taken from the antecubital vein at three different intervals (within a 24 hour period prior to CEA, 12 hours following surgery, and 48 hours after surgery). Serum CNDP1 and UCHL1 levels were measured by a commercially available enzyme-linked immunosorbent assay (ELISA). Results: The study showed that serum CNDP1 and UCHL1 levels were significantly decreased 12 hours after CEA when compared to the level before the surgery. Furthermore, these enzymes levels were normalized 48 hours after CEA. Conclusion: Data from our study showed that CEA significantly affects serum CNDP1 and UCHL1 levels. Moreover, these enzyme levels seems to reflect a brain ischemia resulting from severe internal carotid artery stenosis in patients undergoing CEA.