Project description:Heart failure (HF), leading as one of the main causes of mortality, has become a serious public health issue with high prevalence around the world. Single cardiomyocyte (CM) metabolomics promises to revolutionize the understanding of HF pathogenesis since the metabolic remodeling in the human hearts plays a vital role in the disease progression. Unfortunately, current metabolic analysis is often limited by the dynamic features of metabolites and the critical needs for high-quality isolated CMs. Here, high-quality CMs were directly isolated from transgenic HF mice biopsies and further employed in the cellular metabolic analysis. The lipids landscape in individual CMs was profiled with a delayed extraction mode in time-of-flight secondary ion mass spectrometry. Specific metabolic signatures were identified to distinguish HF CMs from the control subjects, presenting as possible single-cell biomarkers. The spatial distributions of these signatures were imaged in single cells, and those were further found to be strongly associated with lipoprotein metabolism, transmembrane transport, and signal transduction. Taken together, we systematically studied the lipid metabolism of single CMs with a mass spectrometry imaging method, which directly benefited the identification of HF-associated signatures and a deeper understanding of HF-related metabolic pathways.
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:Ossification of the posterior longitudinal ligament (OPLL) is formed by heterogeneous ossification of posterior longitudinal ligament. The patho-mechanism of OPLL is still largely unknown. MicroRNAs are small nucleatides that function as regulators of gene expression in almost any biological process. However, few microRNAs are reported to have a role in the pathological process of OPLL. Therefore, we performed high-throughput microRNA sequencing and transcriptome sequencing of primary OPLL and PLL cells in order to decipher the interacting network of microRNAs in OPLL. MRNA and microRNA profiles were done using primary culture cells of human ossification of the posterior longitudinal ligament (OPLL) tissue and normal posterior longitudinal ligament (PLL) tissue.
Project description:The spectrum of nonalcoholic fatty liver disease (NAFLD) includes steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis. Recognition and timely diagnosis of these different stages, particularly NASH, is important for both potential reversibility and limitation of complications. Liver biopsy remains the clinical standard for definitive diagnosis. Diagnostic tools minimizing the need for invasive procedures or that add information to histologic data are important in novel management strategies for the growing epidemic of NAFLD. We describe an "omics" approach to detecting a reproducible signature of lipid metabolites, aqueous intracellular metabolites, SNPs, and mRNA transcripts in a double-blinded study of patients with different stages of NAFLD that involves profiling liver biopsies, plasma, and urine samples. Using linear discriminant analysis, a panel of 20 plasma metabolites that includes glycerophospholipids, sphingolipids, sterols, and various aqueous small molecular weight components involved in cellular metabolic pathways, can be used to differentiate between NASH and steatosis. This identification of differential biomolecular signatures has the potential to improve clinical diagnosis and facilitate therapeutic intervention of NAFLD.
Project description:Ossification of the posterior longitudinal ligament (OPLL) is formed by heterogeneous ossification of posterior longitudinal ligament. The patho-mechanism of OPLL is still largely unknown. Recently, disorders of metabolism are thought to be the center of many diseases such as OPLL. Advanced glycation end product (AGE) are accumulated in many extracellular matrixes such as ligament fibers, and it can functions as cellular signal through its receptor (RAGE), contributing to various events such as atherosclerosis or oxidative stress. However, its role in OPLL formation is not yet known. Therefore, we performed high-through-put RNA sequencing on primary posterior longitudinal ligament cells treated with different doses of AGEs (1µM, 5µM and negative control), with or without BMP2 (1µM). mRNA profiles of Primary human posterior longitudinal ligament cells stimulated with various stimuli (Control, 1µM AGE-BSA, 5µM AGE-BSA, 1µM AGE-BSA with BMP2, 5µM AGE-BSA with BMP2) were generated by deep sequencing on Ion Proton
Project description:Amyotrophic lateral sclerosis (ALS), the commonest adult-onset motor neuron disorder, is characterized by a survival span of only 2-5 years after onset. Relevant biomarkers or specific metabolic signatures would provide powerful tools for the management of ALS. The main objective of this study was to investigate the cerebrospinal fluid (CSF) lipidomic signature of ALS patients by mass spectrometry to evaluate the diagnostic and predictive values of the profile. We showed that ALS patients (n = 40) displayed a highly significant specific CSF lipidomic signature compared to controls (n = 45). Phosphatidylcholine PC(36:4), higher in ALS patients (p = 0.0003) was the most discriminant molecule, and ceramides and glucosylceramides were also highly relevant. Analysis of targeted lipids in the brain cortex of ALS model mice confirmed the role of some discriminant lipids such as PC. We also obtained good models for predicting the variation of the ALSFRS-r score from the lipidome baseline, with an accuracy of 71% in an independent set of patients. Significant predictions of clinical evolution were found to be correlated to sphingomyelins and triglycerides with long-chain fatty acids. Our study, which shows extensive lipid remodelling in the CSF of ALS patients, provides a new metabolic signature of the disease and its evolution with good predictive performance.
Project description:Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data.
Project description:Nonalcoholic fatty liver disease (NAFLD) is often accompanied by systemic metabolic disorders such as hyperglycemia, insulin resistance, and obesity. The relationship between NAFLD and systemic metabolic disorders has been well reviewed before, however, the metabolic changes that occur in hepatocyte itself have not been discussed. In NAFLD, many metabolic pathways have undergone significant changes in hepatocyte, such as enhanced glycolysis, gluconeogenesis, lactate production, tricarboxylic acid (TCA) cycle, and decreased ketone body production, mitochondrial respiration, and adenosine triphosphate (ATP) synthesis, which play a role in compensating or exacerbating disease progression, and there is close and complex interaction existed between these metabolic pathways. Among them, some metabolic pathways can be the potential therapeutic targets for NAFLD. A detailed summary of the metabolic characteristics of hepatocytes in the context of NAFLD helps us better understand the pathogenesis and outcomes of the disease.
Project description:Repetitive sequences are hotspots of evolution at multiple levels. However, due to technical difficulties involved in their assembly and analysis, the role of repeats in tumor evolution is poorly understood. We developed a rigorous motif-based methodology to quantify variations in the repeat content of proteomes and genomes, directly from proteomic and genomic raw sequence data, and applied it to analyze a wide range of tumors and normal tissues. We identify high similarity between the repeat-instability in tumors and their patient-matched normal tissues, but also tumor-specific signatures, both in protein expression and in the genome, that strongly correlate with cancer progression and robustly predict the tumorigenic state. In a patient, the hierarchy of genomic repeat instability signatures accurately reconstructs tumor evolution, with primary tumors differentiated from metastases. We find an inverse relationship between repeat-instability and point mutation load, within and across patients, and independently of other somatic aberrations. Thus, repeat-instability is a distinct, transient and compensatory adaptive mechanism in tumor evolution.