Project description:BackgroundAtopic dermatitis (AD) is one of the most common chronic inflammatory skin diseases. Dupilumab, a monoclonal antibody that targets the interleukin (IL)-4 and IL-13 receptors, has been widely used in AD because of its efficacy. However, metabolic changes occurring in patients with AD in response to dupilumab remains unknown. In this study, we integrated metabolomics and lipidomics analyses with clinical data to explore potential metabolic alterations associated with dupilumab therapeutic efficacy. In addition, we investigated whether the development of treatment side effects was linked to the dysregulation of metabolic pathways.MethodsA total of 33 patients with AD were included in the current study, with serum samples collected before and after treatment with dupilumab. Comprehensive metabolomic and lipidomic analyses have previously been developed to identify serum metabolites (including lipids) that vary among treatment groups. An orthogonal partial least squares discriminant analysis model was established to screen for differential metabolites and metabolites with variable importance in projection > 1 and p < 0.05 were considered potential metabolic biomarkers. MetaboAnalyst 5.0 was used to identify related metabolic pathways. Patients were further classified into two groups, well responders (n = 19) and poor responders (n = 14), to identify differential metabolites between the two groups.ResultsThe results revealed significant changes in serum metabolites before and after 16 weeks of dupilumab treatment. Variations in the metabolic profile were more significant in the well-responder group than in the poor-responder group. Pathway enrichment analysis revealed that differential metabolites derived from the well-responder group were mainly involved in glycerophospholipid metabolism, valine, leucine and isoleucine biosynthesis, the citrate cycle, arachidonic acid metabolism, pyrimidine metabolism, and sphingolipid metabolism.ConclusionSerum metabolic profiles of patients with AD varied significantly after treatment with dupilumab. Differential metabolites and their related metabolic pathways may provide clues for understanding the effects of dupilumab on patient metabolism.
Project description:Diabetic retinopathy (DR) is a major cause of blindness worldwide and may be non-proliferative (NPDR) or proliferative (PDR). To Investig.gate the metabolomic and lipidomic characteristics of plasma in DR patients, plasma samples were collected from patients with type 2 diabetes mellitus (DR group) with PDR (n = 27), NPDR (n = 18), or no retinopathy (controls, n = 21). Levels of 54 and 41 metabolites were significantly altered in the plasma of DR patients under positive and negative ion modes, respectively. By subgroup analysis, 74 and 29 significantly changed plasma metabolites were detected in PDR patients compared with NPDR patients under positive and negative ion modes, respectively. KEGG analysis indicated that pathways such as biosynthesis of amino acids and neuroactive ligand-receptor interaction were among the most enriched pathways in altered metabolites in the DR group and PDR subgroup. Moreover, a total of 26 and 41 lipids were significantly changed in the DR group and the PDR subgroup, respectively. The panel using the 29-item index could discriminate effectively between diabetic patients with and without retinopathy, and the panel of 22 items showed effective discrimination between PDR and NPDR. These results provide a basis for further research into the therapeutic targets associated with these metabolite and lipid alterations.
Project description:BackgroundEarly diagnosis of hepatocellular carcinoma (HCC) is essential towards the improvement of prognosis and patient survival. Circulating markers such as α-fetoprotein (AFP) and micro-RNAs represent useful tools but still have limitations. Identifying new markers can be fundamental to improve both diagnosis and prognosis. In this approach, we harness the potential of metabolomics and lipidomics to uncover potential signatures of HCC.MethodsA combined untargeted metabolomics and lipidomics plasma profiling of 102 HCV-positive patients was performed by HILIC and RP-UHPLC coupled to Mass Spectrometry. Biochemical parameters of liver function (AST, ALT, GGT) and liver cancer biomarkers (AFP, CA19.9 e CEA) were evaluated by standard assays.ResultsHCC was characterized by an elevation of short and long-chain acylcarnitines, asymmetric dimethylarginine, methylguanine, isoleucylproline and a global reduction of lysophosphatidylcholines. A supervised PLS-DA model showed that the predictive accuracy for HCC class of metabolomics and lipidomics was superior to AFP for the test set (100.00% and 94.40% vs 55.00%). Additionally, the model was applied to HCC patients with AFP values < 20 ng/mL, and, by using only the top 20 variables selected by VIP scores achieved an Area Under Curve (AUC) performance of 0.94.ConclusionThese exploratory findings highlight how metabo-lipidomics enables the distinction of HCC from chronic HCV conditions. The identified biomarkers have high diagnostic potential and could represent a viable tool to support and assist in HCC diagnosis, including AFP-negative patients.
Project description:We investigated a panel of 21 genes by parallel sequencing on the Ion Torrent Personal Genome Machine platform. We sequenced 65 CRCs that were treated with cetuximab or panitumumab ( 37 samples were responsive and 28 were resistant).
Project description:Lithocholic acid (LCA), alpha-naphthyl isothiocyanate (ANIT), 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC), and ethinyl estradiol (EE) are four commonly used chemicals for the construction of acute intrahepatic cholestasis. In order to better understand the mechanisms of acute cholestasis caused by these chemicals, the metabolic characteristics of each model were summarized using lipidomics and metabolomics techniques. The results showed that the bile acid profile was altered in all models. The lipid metabolism phenotype of the LCA group was most similar to that of primary biliary cirrhosis (PBC) patients. The ANIT group and the DDC group had similar metabolic disorder characteristics, which were speculated to be related to hepatocyte necrosis and inflammatory pathway activation. The metabolic profile of the EE group was different from other models, suggesting that estrogen-induced cholestasis had its special mechanism. Ceramide and acylcarnitine accumulation was observed in all model groups, indicating that acute cholestasis was closely related to mitochondrial dysfunction. With a deeper understanding of the mechanism of acute intrahepatic cholestasis, this study also provided a reference for the selection of appropriate chemicals for cholestatic liver disease models.
Project description:BackgroundLung cancer remains the leading cause of mortality from malignant tumors, non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, and individualized diagnosis and treatment is an effective trend. The individual characteristics of different traditional Chinese medicine (TCM) syndromes of NSCLC patients may be revealed by highly specific molecular profiles.MethodsIn this study, 10 NSCLC patients with Qi deficiency and Yin deficiency (QDYD) syndrome and 10 patients with Qi deficiency of lung-spleen (QDLS) syndrome in TNM stage III-IV as well as 10 healthy volunteers were enrolled. Aiming at the varied syndromes of NSCLC patients with "Yin deficiency" as the main difference, a proteomics research based on data-independent acquisition (DIA) was developed. Of the dysregulated proteins in NSCLC patients, lipid metabolism was significantly enriched. Thereafter, nontargeted lipidomics research based on UPLC-Q-TOF/MS was performed in 16 patients, with 8 individuals randomly selected from each syndrome group. Furthermore, the considerably different characteristics between the syndromes and pathological mechanisms of NSCLC were screened by statistical and biological integrations of proteomics and lipidomics and the differential metabolic pathways of the two similar syndromes were further explored. Besides, lipids biomarkers were verified by a clinically used anticancer Chinese medicine, and the level of key differential proteins in the two syndromes was also validated using ELISA.ResultsThe results showed that glycerophospholipid metabolism, sphingolipid metabolism, glycolipid metabolism, and primary bile acid biosynthesis were altered in NSCLC patients and that glycerophospholipid metabolism was significantly changed between the two syndromes in lipidomics analysis. Among the proteins and lipids, ALDOC and lysophosphatidylcholine (LPCs) were revealed to have a strong relationship by statistical and biological integration analysis, and could effectively distinguish QDLS and QDYD syndromes. Notably, the patients with different syndromes had the most typical metabolic patterns in glycerophospholipid metabolism and glycolysis, reflecting the differences in the syndromes dominated by "Yin deficiency".ConclusionsALDOC and LPCs could be employed for the differentiation of NSCLC patients with QDLS and QDYD syndromes, and "Yin deficiency" might be associated with glycerophospholipid metabolism and glycolysis pathway. The results provided a theoretical basis for "Syndrome differentiation" in TCM diagnosis. Moreover, the developed integrated strategy could also provide a reference for individualized diagnosis and treatment of other diseases.
Project description:Changes in cellular metabolism contribute to the development and progression of tumors, and can render tumors vulnerable to interventions. However, studies of human cancer metabolism remain limited due to technical challenges of detecting and quantifying small molecules, the highly interconnected nature of metabolic pathways, and the lack of designated tools to analyze and integrate metabolomics with other âomics data. Our study generates the largest comprehensive metabolomics dataset on a single cancer type, and provides a significant advance in integration of metabolomics with sequencing data. Our results highlight the massive re-organization of cellular metabolism as tumors progress and acquire more aggressive features. The results of our work are made available through an interactive public data portal for cancer research community. 10 RNA samples from human ccRCC tumors analyzed from the high glutathione cluster
Project description:Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30-40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra-performance liquid chromatography-mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.
Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:SARS-CoV-2 infection has become a major public health burden and is known to affect many organs with the respiratory system being involved in a majority of cases. Here we undertook a mass spectrometry-based proteomic approach to test whether viral proteins could be detected in urine of patients with COVID-19. Urine samples from 39 patients positive for SARS-CoV-2 by RT-PCR were analyzed by mass spectrometry. We detected peptides from the nucleocapsid protein of the SARS-CoV-2 virus in 12 out of 35 urine samples. A set of five samples each from positive and negative groups were further used to study the host response. In conclusion, we demonstrated the identification of viral antigens in urine using mass spectrometry which further suggests that urine could be a potential source of infection with implications in disease transmission and also the changes in protein composition in urine may provide insights in understanding the disease pathogenesis.