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

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

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Computational Lipidomics and Lipid Bioinformatics: Filling In the Blanks

Lipids are highly diverse metabolites of pronounced importance in health and disease. While metabolomics is a broad field under the omics umbrella that may also relate to lipids, lipidomics is an emerging field which specializes in the identification, quantification and functional interpretation of complex lipidomes. Today, it is possible to identify and distinguish lipids in a high-resolution, high-throughput manner and simultaneously with a lot of structural detail. However, doing so may produce thousands of mass spectra in a single experiment which has created a high demand for specialized computational support to analyze these spectral libraries. The computational biology and bioinformatics community has so far established methodology in genomics, transcriptomics and proteomics but there are many (combinatorial) challenges when it comes to structural diversity of lipids and their identification, quantification and interpretation. This review gives an overview and outlook on lipidomics research and illustrates ongoing computational and bioinformatics efforts. These efforts are important and necessary steps to advance the lipidomics field alongside analytic, biochemistry, biomedical and biology communities and to close the gap in available computational methodology between lipidomics and other omics sub-branches

Proposal for a common nomenclature for fragment ions in mass spectra of lipids.

Advances in mass spectrometry-based lipidomics have in recent years prompted efforts to standardize the annotation of the vast number of lipid molecules that can be detected in biological systems. These efforts have focused on cataloguing, naming and drawing chemical structures of intact lipid molecules, but have provided no guidelines for annotation of lipid fragment ions detected using tandem and multi-stage mass spectrometry, albeit these fragment ions are mandatory for structural elucidation and high confidence lipid identification, especially in high throughput lipidomics workflows. Here we propose a nomenclature for the annotation of lipid fragment ions, describe its implementation and present a freely available web application, termed ALEX123 lipid calculator, that can be used to query a comprehensive database featuring curated lipid fragmentation information for more than 430,000 potential lipid molecules from 47 lipid classes covering five lipid categories. We note that the nomenclature is generic, extendable to stable isotope-labeled lipid molecules and applicable to automated annotation of fragment ions detected by most contemporary lipidomics platforms, including LC-MS/MS-based routines

Automated, parallel mass spectrometry imaging and structural identification of lipids

We report a method that enables automated data-dependent acquisition of lipid tandem mass spectrometry data in parallel with a high-resolution mass spectrometry imaging experiment. The method does not increase the total image acquisition time and is combined with automatic structural assignments. This lipidome-per-pixel approach automatically identified and validated 104 unique molecular lipids and their spatial locations from rat cerebellar tissue.</p

Analysis of the airway microbiota of healthy individuals and patients with chronic obstructive pulmonary disease by T-RFLP and clone sequencing.

Chronic obstructive pulmonary disease (COPD) is a progressive, inflammatory lung disease that affects a large number of patients and has significant impact. One hallmark of the disease is the presence of bacteria in the lower airways.The aim of this study was to analyze the detailed structure of microbial communities found in the lungs of healthy individuals and patients with COPD. Nine COPD patients as compared and 9 healthy individuals underwent flexible bronchoscopy and BAL was performed. Bacterial nucleic acids were subjected to terminal restriction fragment (TRF) length polymorphism and clone library analysis. Overall, we identified 326 T-RFLP band, 159 in patients and 167 in healthy controls. The results of the TRF analysis correlated partly with the data obtained from clone sequencing. Although the results of the sequencing showed high diversity, the genera Prevotella, Sphingomonas, Pseudomonas, Acinetobacter, Fusobacterium, Megasphaera, Veillonella, Staphylococcus, and Streptococcus constituted the major part of the core microbiome found in both groups. A TRF band possibly representing Pseudomonas sp. monoinfection was associated with a reduction of the microbial diversity. Non-cultural methods reveal the complexity of the pulmonary microbiome in healthy individuals and in patients with COPD. Alterations of the microbiome in pulmonary diseases are correlated with disease

CID of lipid molecules produces several types of fragments that can be used for annotating intact lipid molecules at three different levels.

<p>The shorthand notation of the fragment ions is described in the sections: Results and discussion, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s001" target="_blank">S1 Text</a>. LCFs, lipid class-selective fragments; MLFs, molecular lipid species-specific fragments; DBFs, double bond location-specific fragments.</p

Annotated fragment ion spectra of representative lipid molecules from five different lipid categories.

<p>Fragment ion <i>m/z</i> values are denoted according to the three-step procedure outlined in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.g002" target="_blank">Fig 2</a></b>. The shorthand notation includes nomenclature based on both charged and neutral fragments (separated by “|”) (step 2). Annotation shown in boldface is prioritized based on the guidelines outlined in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.g002" target="_blank">Fig 2</a></b> (step 3). Non-prioritized shorthand notation is occasionally omitted to avoid overly congested mass spectra. The representation of fragment ion <i>m/z</i> values by mass-balanced chemical reactions and fragment structures are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188394#pone.0188394.s005" target="_blank">S3 Fig</a></b> (step 1). A) Negative FTMS² spectrum of deprotonated ACoA 19:0. B) Positive FTMS² spectrum of ammoniated TAG 18:0–18:1–18:2. C) Positive FTMS² spectrum of protonated PE O-18:1p/20:4. D) Negative FTMS² spectrum of deprotonated and doubly charged CL 14:1–14:1–14:-15:1. E) Negative FTMS³ spectrum of FA 18:1 carboxylate anion <i>m/z</i> 281.3 derived from PC 16:0–18:1(9). F) Positive FTMS² spectrum of protonated SM 18:1;2/17:0. G) Negative FTMS² spectrum of deprotonated Cer 18:1;2/17:0;1. H) Positive FTMS² spectrum of ammoniated SE 27:1/19:0 (cholesteryl ester 19:0).</p

Identification of PS 20:4–22:6 in mouse hippocampus.

<p>A) Negative FTMS spectrum of mouse hippocampus. The precursor ion matching deprotonated PS 42:10 is highlighted in boldface. B) Negative FTMS² spectrum of <i>m/z</i> 854.6 with detection of MLFs and LCFs matching PS 20:4–22:6, annotated in boldface.</p