LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy.

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

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Integrating proteomic, lipidomic and metabolomic data to construct a global metabolic network of lethal ventricular tachyarrhythmias (LVTA) induced by aconitine

BackgroundThe recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, −γ, and –δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry.ResultsIn order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content.ConclusionsWe found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1292-2) contains supplementary material, which is available to authorized users.

Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today. We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.

BackgroundAlzheimer’s Disease (AD) is a complex disorder and much of its etiopathology is still unknown. Here, we applied dimensionality reduction methods to disentangle cyptic patterns in CSF proteomic and lipidomic data.MethodWe studied 1121 CSF samples using targeted lipidomics based on liquid chromatography (LC)‐MS/MS (mass spectrometry), generated by Lipometrix (Lueven, Belgium), and proteomic data generated by Somalogic (Boulder, Colorado) using the SOMAscan 7k Assay. We independently computed the principal components for the proteomic and lipidomic datasets using good quality lipids (N=388) and proteins (N=2469). CSF samples were obtained by lumbar punctures at ACE Alzheimer Center (Barcelona, Spain), including patients at different points of the dementia continuum (SCD, MCI and dementia). Principal components were calculated using the princomp() function in R.ResultPC1 explained a substantial fraction of the variance in lipidomic (∼40%) and proteomic (∼60%) datasets and was highly correlated between both omics (R2=0.43; p=2.3·10‐138, Figure 1). We explored the association profile of the PCs to AD risk factors (age, sex, APOE, PRS, diabetes, hypertension, dyslipidemia, BMI), endophenotypes (abeta, tau), disease progression and total protein in the CSF. PC1, as well as the individual lipid species, were strongly associated with abeta, tau and total CSF protein levels (Figure 2). Finally, we conducted a GWAS of the first 20 lipidomic and proteomic PCs. PC1 displayed a GWS signal at chr3q28 in both omics. This region has been previously linked with CSF tau levels and brain morphology. Subsequent lipidomic PCs were associated with variants in the FADS1/FADS2 locus, while other proteomic PCs were associated with variants in the APOE locus (Figure 3).ConclusionOur findings revealed a shared major contributor to the variance in CSF between lipidomic and proteomic data, which is related to the tau, abeta and total protein signature, and identified a QTL associated to both the lipidomic and proteomic PC1. Accounting for this major variance contributor may enhance future studies involving CSF biomarkers. Subsequent principal components had specific association profiles to AD risk factors and endophenotypes.

Bronchoalveolar Lavage Lipidomic Profiles Can Predict Short-Term Changes in Lung Function in Lung Transplant Recipients

The conservation status of amphibians is often assessed using public databases because of a lack of up-to-date field data. However, it is crucial that this kind of data is used carefully, evaluating the reliability and the consistency of the information. In Italy, the conservation status of the painted frog, Discoglossus pictus , was recently assessed using public databases, and the outcome highlighted a worrying situation for the species. We analyzed the reliability of these conclusions and reassessed the status of the painted frog, taking into account its insular distribution and new data of species occurrence. Our results contrast with the previous analysis and show how an incautious use of public databases can lead to inaccurate assessment of the amphibian conservation status.

SNPs, Haplotypes, and Cancer: Applications in Molecular Epidemiology

Extension of least squares spectral resolution algorithm to high-resolution lipidomics data

In spite of the increasing importance of information technology, there are several example of failures. Systems are built with unrealistic assumptions about individual and organizational behavior. Public databases are important mediators of information. We use a media metaphor for databases to derive a model where we can identify a series of conditions for the well-functioning of public databases. These conditions are not easily met in practical work with such databases. One should therefore be careful with ambitions regarding the design, use and impact of information technology.

BackgroundThe pathogenicity of many Mendelian variants has been challenged by large-scale sequencing efforts. However, many rare and benign “disease mutations” are difficult to analyze due to their rarity. The Saudi Arabian variome is enriched for homozygosity due to inbreeding, a key advantage that can be exploited for the critical examination of previously published variants.ResultsWe collated all “disease-related mutations” listed in the Human Gene Mutation Database (HGMD) and ClinVar, including “variants of uncertain significance” (VOUS). We find that the use of public databases including 1000 Genomes, ExAC, and Kaviar can reclassify many of these variants as likely benign. Our Saudi Human Genome Program (SHGP) can reclassify many variants that are rare in public databases. Furthermore, SGPD allows us to observe many previously reported variants in the homozygous state and our extensive phenotyping of participants makes it possible to demonstrate the lack of phenotype for these variants, thus challenging their pathogenicity despite their rarity. We also find that 18 VOUS BRCA1 and BRCA2 variants that are listed in BRCA Exchange are present at least once in the homozygous state in patients who lack features of Fanconi anemia. Reassuringly, we could reciprocally demonstrate that none of those labeled as “pathogenic” were observed in the homozygous statue in individuals who lack Fanconi phenotype in our database.ConclusionOur study shows the importance of revisiting disease-related databases using public resources as well as of population-specific resources to improve the specificity of the morbid genome of Mendelian diseases in humans.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1102-1) contains supplementary material, which is available to authorized users.

To explore a noninvasive method for diagnosis of SEA-thalassemia and to investigate whether the regional factors affect the accuracy of this method. The method involved using a public database and bioinformatics software to construct parental haplotypes for proband and predicting fetal genotypes using relative haplotype dosage. We screened and downloaded sequencing data of couples who were both SEA-thalassemia carriers from the China National Genebank public data platform, and matched the sequencing data format with that of the reference panel using Ubuntu system tools. We then used Beagle software to construct parental haplotypes, predicted fetal haplotypes by relative haplotype dosage. Finally, we used Hidden Markov Model and Viterbi algorithm to determine fetal pathogenic haplotypes. All noninvasive fetal genotype diagnosis results were compared with gold standard gap-PCR electrophoresis results. Our method was successful in diagnosing 13 families with SEA-thalassemia carriers. The best diagnostic results were obtained when Southern Chinese Han was used as the reference panel, and 10 families showed full agreement between our noninvasive diagnostic results and the gap-PCR electrophoresis results. The accuracy of our method was higher when using a Chinese Han as the reference panel for haplotype construction in the Southern Chinese Han region as opposed to Beijing Chinese region. The combined use of public databases and relative haplotype dosage for diagnosing SEA-thalassemia is a feasible approach. Our method produces the best noninvasive diagnostic results when the test samples and population reference panel are closely matched in both ethnicity and geography. When constructing parental haplotypes with our method, it is important to consider the effect of region in addition to population background alone.

Errors in the interpretation of copy number variations due to the use of public databases as a reference

Even in Europe, where database rights(Sui Generis Database Rights) were first created, questions about the utility of these rights have been raised and discussions for revision is going on. We, who benchmarked these European cases at the time, also need to pay attention to the changes in EU. Therefore, in this study, the problems of the database rights were analyzed and future improvement tasks were derived. The results of the s tudy a re a s follows. First, the EU is changing the criteria for judging database rights infringement through the CJEU decision. In order to accommodate the innovation of data, the creation of added value through reuse of users and competitors and the possibility of innovation are accepted as the criteria for judging infringement. We also need to take this into account in our “significant investment” and “determination of infringement”. Second, it is necessary to think about the extension of the exception range of the database right. In Europe, through a separate legislative measure called the Data Act, a wide range of exceptions are being allowed by excluding the database right for databases acquired or created by the use of products or services. This is premised on the premise that the establishment and utilization of databases in various artificial intelligence services, including IoT, is essential anyway, so there is no need to induce investment by guaranteeing sui generis rights. As such, in certain cases, it is necessary to review the introduction of regulations that restrict database rights. Third, like public works, free use of public databases needs to be introduced. Public databases are already trying to create added value through private use through other individual laws. In addition, it is questionable whether the criteria for judging infringement, such as ‘significant investment’ and ‘risk of recovery of investment’, can be applied to the act of creating a database funded by the state finances. Therefore, it is necessary to review the introduction of database rights restrictions on public works.