Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed "CACulator" serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.</p

MoSBi:Automated signature mining for molecular stratification and subtyping

SignificanceMolecular patient stratification and disease subtyping are ongoing and high-impact problems that rely on the identification of characteristic molecular signatures. Current computational methods show high sensitivity to custom parameterization, which leads to inconsistent performance on different molecular data. Our new method, MoSBi (molecular signature identification using biclustering), 1) enables so far unmatched high performance for stratification and subtyping across datasets of various different biomolecules, 2) provides a scalable solution for visualizing the results and their correspondence to clinical factors, and 3) has immediate practical relevance through its automatic workflow where individual selection, parameterization, screening, and visualization of biclustering algorithms is not required. MoSBi is a major step forward with a high impact for clinical and wet-lab researchers.AbstractThe improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.</p

Kupffer cells are protective in alcoholic steatosis

Massive accumulation of lipids is a characteristic of alcoholic liver disease. Excess of hepatic fat activates Kupffer cells (KCs), which affect disease progression. Yet, KCs contribute to the resolution and advancement of liver injury. Aim of the present study was to evaluate the effect of KC depletion on markers of liver injury and the hepatic lipidome in liver steatosis (Lieber-DeCarli diet, LDC, female mice, mixed C57BL/6J and DBA/2J background). LDC increased the number of dead hepatocytes without changing the mRNA levels of inflammatory cytokines in the liver. Animals fed LDC accumulated elevated levels of almost all lipid classes. KC ablation normalized phosphatidylcholine and phosphatidylinositol levels in LDC livers, but had no effect in the controls. A modest decline of trigylceride and diglyceride levels upon KC loss was observed in both groups. Serum aminotransferases and hepatic ceramide were elevated in all animals upon KC depletion, and in particular, cytotoxic very long-chain ceramides increased in the LDC livers. Meta-biclustering revealed that eight lipid species occurred in more than 40% of the biclusters, and four of them were very long-chain ceramides. KC loss was further associated with excess free cholesterol levels in LDC livers. Expression of inflammatory cytokines did, however, not increase in parallel. In summary, the current study described a function of KCs in hepatic ceramide and cholesterol metabolism in an animal model of LDC liver steatosis. High abundance of cytotoxic ceramides and free cholesterol predispose the liver to disease progression suggesting a protective role of KCs in alcoholic liver diseases

Structural characterization of suppressor lipids by high-resolution mass spectrometry

Rationale: Suppressor lipids were originally identified in 1993 and reported to encompass six lipid classes that enable Saccharomyces cerevisiae to live without sphingolipids. Structural characterization, using non-mass spectrometric approaches, revealed that these suppressor lipids are very long chain fatty acid (VLCFA)- containing glycerophospholipids with polar head groups that are typically incorporated into sphingolipids. Here we report, for the first time, the structural characterization of the yeast suppressor lipids using high-resolution mass spectrometry.Methods: Suppressor lipids were isolated by preparative chromatography and subjected to structural characterization using hybrid quadrupole time-of-flight and ion trap-orbitrap mass spectrometry.Results: Our investigation recapitulates the overall structural features of the suppressor lipids and provides an in-depth characterization of their fragmentation pathways. Tandem mass analysis identified the positionally defined molecular lipid species phosphatidylinositol (PI) 26:0/16:1, PI mannoside (PIM) 16:0/26:0 and PIM inositol-phosphate (PIMIP) 16:0/26:0 as abundant suppressor lipids. This finding differs from the original study that only inferred the positional isomer PI 16:0/26:0 and prompts new insight into the biosynthesis of suppressor lipids. Moreover, we also report the identification of a novel suppressor lipid featuring an amino sugar residue linked to a VLCFA-containing PI molecule.Conclusions: Fragmentation pathways of yeast suppressor lipids have been delineated. In addition, the fragmentation information has been added to our open source ALEX lipid database to support automated identification and quantitative monitoring of suppressor lipids in yeast and bacteria that produce similar lipid molecules

Liver Lipids of Patients with Hepatitis B and C and Associated Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) still remains a difficult to cure malignancy. In recent years, the focus has shifted to lipid metabolism for the treatment of HCC. Very little is known about hepatitis B virus (HBV) and C virus (HCV)-related hepatic lipid disturbances in non-malignant and cancer tissues. The present study showed that triacylglycerol and cholesterol concentrations were similar in tumor adjacent HBV and HCV liver, and were not induced in the HCC tissues. Higher levels of free cholesterol, polyunsaturated phospholipids and diacylglycerol species were noted in non-tumorous HBV compared to HCV liver. Moreover, polyunsaturated phospholipids and diacylglycerols, and ceramides declined in tumors of HBV infected patients. All of these lipids remained unchanged in HCV-related HCC. In HCV tumors, polyunsaturated phosphatidylinositol levels were even induced. There were no associations of these lipid classes in non-tumor tissues with hepatic inflammation and fibrosis scores. Moreover, these lipids did not correlate with tumor grade or T-stage in HCC tissues. Lipid reprogramming of the three analysed HBV/HCV related tumors mostly resembled HBV-HCC. Indeed, lipid composition of non-tumorous HCV tissue, HCV tumors, HBV tumors and HBV/HCV tumors was highly similar. The tumor suppressor protein p53 regulates lipid metabolism. The p53 and p53S392 protein levels were induced in the tumors of HBV, HCV and double infected patients, and this was significant in HBV infection. Negative correlation of tumor p53 protein with free cholesterol indicates a role of p53 in cholesterol metabolism. In summary, the current study suggests that therapeutic strategies to target lipid metabolism in chronic viral hepatitis and associated cancers have to consider disease etiology

The AIMe registry for artificial intelligence in biomedical research

peer reviewe

Lipid Bioinformatics:Computational strategies for analyses of shotgun lipidomic data

Lipidomics is a rapidly emerging eld under the omics umbrella with the dedicated goal to unravel cellular lipids, their structure, compositions, functions and regulation to link them to metabolism. Advances in lipid-centric analytical chemistry and mass spectrometry methods such as shotgun lipidomics, enabled high-throughput generation of high-resolution mass spectrometry data from complex lipid experiments. However, bioinformatics solutions for data analysis have not been well established and is trailing other omics elds. In addition, integration of heterogeneous data sets is a central paradigm of the omics approach and with the aid of bioinformatics data analysis is transformed from lipid-centric to integrative methodology. This thesis presents bioinformatics strategies on both. First, a publicly available analysis platform ALEX123 was developed that provides an automatic solution for the analysis of shotgun lipidomics data from raw mass spectra to a processed integrated dataset. It is capable of revealing molecular lipid species compositions on the basis of distinctive fragmentation patterns. Second, a highly integrative screening method for yet uncharacterized proteins is presented predicting lipid altering function with lipid metabolic pathway-specicity through the application of de novo network enrichment, a well established integrative computational method. Analysis of experimental validation data is fully based on ALEX123