Template-based combinatorial enumeration of virtual compound libraries for lipids

Lipidomics in gestational diabetes mellitus

PURPOSE OF REVIEW

Epidemiological and mechanistic studies have reported relationships between blood lipids, mostly measured by traditional method in clinical settings, and gestational diabetes mellitus (GDM). Recent advances of high-throughput lipidomics techniques have made available more comprehensive lipid profiling in biological samples. This review aims to summarize evidence from prospective studies in assessing relations between blood lipids and GDM, and discuss potential underlying mechanisms.

RECENT FINDINGS

Mass spectrometry and nuclear magnetic resonance spectroscopy-based analytical platforms are extensively used in lipidomics research. Epidemiological studies have identified multiple novel lipidomic biomarkers that are associated with risk of GDM, such as certain types of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, cholesterol, and lipoproteins. However, the findings are inconclusive mainly due to the heterogeneities in study populations, sample sizes, and analytical platforms. Mechanistic evidence indicates that abnormal lipid metabolism may be involved in the pathogenesis of GDM by impairing pancreatic β-cells and inducing insulin resistance through several etiologic pathways, such as inflammation and oxidative stress.

SUMMARY

Lipidomics is a powerful tool to study pathogenesis and biomarkers for GDM. Lipidomic biomarkers and pathways could help to identify women at high risk for GDM and could be potential targets for early prevention and intervention of GDM.

Individuals with Metabolic Syndrome Show Altered Fecal Lipidomic Profiles with No Signs of Intestinal Inflammation or Increased Intestinal Permeability

BACKGROUND

Metabolic Syndrome (MetS) is a clinical diagnosis where patients exhibit three out of the five risk factors: hypertriglyceridemia, low high-density lipoprotein (HDL) cholesterol, hyperglycemia, elevated blood pressure, or increased abdominal obesity. MetS arises due to dysregulated metabolic pathways that culminate with insulin resistance and put individuals at risk to develop various comorbidities with far-reaching medical consequences such as non-alcoholic fatty liver disease (NAFLD) and cardiovascular disease. As it stands, the exact pathogenesis of MetS as well as the involvement of the gastrointestinal tract in MetS is not fully understood. Our study aimed to evaluate intestinal health in human subjects with MetS.

METHODS

We examined MetS risk factors in individuals through body measurements and clinical and biochemical blood analysis. To evaluate intestinal health, gut inflammation was measured by fecal calprotectin, intestinal permeability through the lactulose-mannitol test, and utilized fecal metabolomics to examine alterations in the host-microbiota gut metabolism.

RESULTS

No signs of intestinal inflammation or increased intestinal permeability were observed in the MetS group compared to our control group. However, we found a significant increase in 417 lipid features of the gut lipidome in our MetS cohort. An identified fecal lipid, diacyl-glycerophosphocholine, showed a strong correlation with several MetS risk factors. Although our MetS cohort showed no signs of intestinal inflammation, they presented with increased levels of serum TNFα that also correlated with increasing triglyceride and fecal diacyl-glycerophosphocholine levels and decreasing HDL cholesterol levels.

CONCLUSION

Taken together, our main results show that MetS subjects showed major alterations in fecal lipid profiles suggesting alterations in the intestinal host-microbiota metabolism that may arise before concrete signs of gut inflammation or intestinal permeability become apparent. Lastly, we posit that fecal metabolomics could serve as a non-invasive, accurate screening method for both MetS and NAFLD.

Characterization of Extracellular Vesicles Secreted in Lentiviral Producing HEK293SF Cell Cultures

Lentiviral vectors (LVs) are a powerful tool for gene and cell therapy and human embryonic kidney cells (HEK293) have been extensively used as a platform for production of these vectors. Like most cells and cellular tissues, HEK293 cells release extracellular vesicles (EVs). EVs released by cells share similar size, biophysical characteristics and even a biogenesis pathway with cell-produced enveloped viruses, making it a challenge to efficiently separate EVs from LVs. Thus, EVs co-purified with LVs during downstream processing, are considered “impurities” in the context of gene and cell therapy. A greater understanding of EVs co-purifying with LVs is needed to enable improved downstream processing. To that end, EVs from an inducible lentivirus producing cell line were studied under two conditions: non-induced and induced. EVs were identified in both conditions, with their presence confirmed by transmission electron microscopy and Western blot. EV cargos from each condition were then further characterized by a multi-omic approach. Nineteen proteins were identified by mass spectrometry as potential EV markers to differentiate EVs in LV preparations. Lipid composition of EV preparations before and after LV induction showed similar enrichment in phosphatidylserine. RNA cargos in EVs showed enrichment in transcripts involved in viral processes and binding functions. These findings provide insights on the product profile of lentiviral preparations and could support the development of improved separation strategies aimed at removing co-produced EVs.

LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics

SUMMARY

Ion mobility-mass spectrometry (IM-MS) has showed great application potential for lipidomics. However, IM-MS based lipidomics is significantly restricted by the available software for lipid structural identification. Here, we developed a software tool, namely, LipidIMMS Analyzer, to support the accurate identification of lipids in IM-MS. For the first time, the software incorporates a large-scale database covering over 260 000 lipids and four-dimensional structural information for each lipid [i.e. m/z, retention time (RT), collision cross-section (CCS) and MS/MS spectra]. Therefore, multi-dimensional information can be readily integrated to support lipid identifications, and significantly improve the coverage and confidence of identification. Currently, the software supports different IM-MS instruments and data acquisition approaches.

AVAILABILITY AND IMPLEMENTATION

The software is freely available at: http://imms.zhulab.cn/LipidIMMS/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

A randomized 3-way crossover study indicates that high-protein feeding induces de novo lipogenesis in healthy humans

BACKGROUND

Dietary changes have led to the growing prevalence of type 2 diabetes and nonalcoholic fatty liver disease. A hallmark of both disorders is hepatic lipid accumulation, derived in part from increased de novo lipogenesis. Despite the popularity of high-protein diets for weight loss, the effect of dietary protein on de novo lipogenesis is poorly studied. We aimed to characterize the effect of dietary protein on de novo lipid synthesis.

METHODS

We use a 3-way crossover interventional study in healthy males to determine the effect of high-protein feeding on de novo lipogenesis, combined with in vitro models to determine the lipogenic effects of specific amino acids. The primary outcome was a change in de novo lipogenesis–associated triglycerides in response to protein feeding.

RESULTS

We demonstrate that high-protein feeding, rich in glutamate, increases de novo lipogenesis–associated triglycerides in plasma (1.5-fold compared with control; P < 0.0001) and liver-derived very low-density lipoprotein particles (1.8-fold; P < 0.0001) in samples from human subjects (n = 9 per group). In hepatocytes, we show that glutamate-derived carbon is incorporated into triglycerides via palmitate. In addition, supplementation with glutamate, glutamine, and leucine, but not lysine, increased triglyceride synthesis and decreased glucose uptake. Glutamate, glutamine, and leucine increased activation of protein kinase B, suggesting that induction of de novo lipogenesis occurs via the insulin signaling cascade.

CONCLUSION

These findings provide mechanistic insight into how select amino acids induce de novo lipogenesis and insulin resistance, suggesting that high-protein feeding to tackle diabetes and obesity requires greater consideration.

FUNDING

The research was supported by UK Medical Research Council grants MR/P011705/1, MC_UP_A090_1006 and MR/P01836X/1. JLG is supported by the Imperial Biomedical Research Centre, National Institute for Health Research (NIHR).

A subset of amino acids may induce de novo lipogenesis in humans, suggesting that use of high-protein diets to tackle diabetes requires greater consideration.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time-Ion Mobility Mass Spectrometry

Unknown metabolites represent a bottleneck in untargeted metabolomics research. Ion mobility-mass spectrometry (IM-MS) facilitates lipid identification because it yields collision cross section (CCS) information that is independent from mass or lipophilicity. To date, only a few CCS values are publicly available for complex lipids such as phosphatidylcholines, sphingomyelins, or triacylglycerides. This scarcity of data limits the use of CCS values as an identification parameter that is orthogonal to mass, MS/MS, or retention time. A combination of lipid descriptors was used to train five different machine learning algorithms for automatic lipid annotations, combining accurate mass ( m/ z), retention time (RT), CCS values, carbon number, and unsaturation level. Using a training data set of 429 true positive lipid annotations from four lipid classes, 92.7% correct annotations overall were achieved using internal cross-validation. The trained prediction model was applied to an unknown milk lipidomics data set and allowed for class 3 level annotations of most features detected in this application set according to Metabolomics Standards Initiative (MSI) reporting guidelines.

Opinion article on lipidomics: Inherent challenges of lipidomic analysis of sphingolipids

A challenge for sphingolipidomic analysis is the vast number of subspecies, including a large number of isomers-a complication that was even appreciated by the original discoverer of sphingolipids J. L. W. Thudichum (The Chemistry of the Brain, p. x, 1884): "In the course of my researches many unforeseen complications arose, prominent amongst which were those caused by the occurrence of chemical principles having the same atomic or elementary composition, but differing in other chemical, or in physical properties, varieties producing the phenomenon which in chemistry is termed isomerism." Therefore, it is essential to choose the appropriate method(s) for the goal of the analysis, to know the assumptions and limitations of method(s) used, and to temper interpretation of the data accordingly. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.

Making a membrane on the other side of the wall

The outer membrane (OM) of Gram-negative bacteria is positioned at the frontline of the cell's interaction with its environment and provides a barrier against influx of external toxins while still allowing import of nutrients and excretion of wastes. It is a remarkable asymmetric bilayer with a glycolipid surface-exposed leaflet and a glycerophospholipid inner leaflet. Lipid asymmetry is key to OM barrier function and several different systems actively maintain this lipid asymmetry. All OM components are synthesized in the cytosol before being secreted and assembled into a contiguous membrane on the other side of the cell wall. Work in recent years has uncovered the pathways that transport and assemble most of the OM components. However, our understanding of how phospholipids are delivered to the OM remains notably limited. Here we will review seminal works in phospholipid transfer performed some 40years ago and place more recent insights in their context. This article is part of a Special Issue entitled: Bacterial Lipids edited by Russell E. Bishop.

The LUX Score: A Metric for Lipidome Homology

A lipidome is the set of lipids in a given organism, cell or cell compartment and this set reflects the organism’s synthetic pathways and interactions with its environment. Recently, lipidomes of biological model organisms and cell lines were published and the number of functional studies of lipids is increasing. In this study we propose a homology metric that can quantify systematic differences in the composition of a lipidome. Algorithms were developed to 1. consistently convert lipids structure into SMILES, 2. determine structural similarity between molecular species and 3. describe a lipidome in a chemical space model. We tested lipid structure conversion and structure similarity metrics, in detail, using sets of isomeric ceramide molecules and chemically related phosphatidylinositols. Template-based SMILES showed the best properties for representing lipid-specific structural diversity. We also show that sequence analysis algorithms are best suited to calculate distances between such template-based SMILES and we adjudged the Levenshtein distance as best choice for quantifying structural changes. When all lipid molecules of the LIPIDMAPS structure database were mapped in chemical space, they automatically formed clusters corresponding to conventional chemical families. Accordingly, we mapped a pair of lipidomes into the same chemical space and determined the degree of overlap by calculating the Hausdorff distance. We named this metric the ‘Lipidome jUXtaposition (LUX) score’. First, we tested this approach for estimating the lipidome similarity on four yeast strains with known genetic alteration in fatty acid synthesis. We show that the LUX score reflects the genetic relationship and growth temperature better than conventional methods although the score is based solely on lipid structures. Next, we applied this metric to high-throughput data of larval tissue lipidomes of Drosophila. This showed that the LUX score is sufficient to cluster tissues and determine the impact of nutritional changes in an unbiased manner, despite the limited information on the underlying structural diversity of each lipidome. This study is the first effort to define a lipidome homology metric based on structures that will enrich functional association of lipids in a similar manner to measures used in genetics. Finally, we discuss the significance of the LUX score to perform comparative lipidome studies across species borders.

Because of their role in health and disease, lipids are often the focus of biochemical studies. Advances in analytical biochemistry have made it possible to detect all the lipids from a cell, tissue or organism (termed lipidome). Much of this research is based on model organisms, but it is difficult to transfer results from a fruit fly or yeast to human biochemistry. A central problem is that there is no agreed-upon method for comparing lipidomes. We have developed the LUX score, which enables us to determine the homology between lipidomes. All constituent lipids are first embedded in a chemical space according to their similarity to each other. When we treat all lipids as points in such a space, one can overlay different lipidomes and estimate their differences. We expect that this kind of metric will be useful for translating findings from model organisms to human diseases and in understanding fundamental biological processes.

Egg phospholipids and cardiovascular health

Eggs are a major source of phospholipids (PL) in the Western diet. Dietary PL have emerged as a potential source of bioactive lipids that may have widespread effects on pathways related to inflammation, cholesterol metabolism, and high-density lipoprotein (HDL) function. Based on pre-clinical studies, egg phosphatidylcholine (PC) and sphingomyelin appear to regulate cholesterol absorption and inflammation. In clinical studies, egg PL intake is associated with beneficial changes in biomarkers related to HDL reverse cholesterol transport. Recently, egg PC was shown to be a substrate for the generation of trimethylamine N-oxide (TMAO), a gut microbe-dependent metabolite associated with increased cardiovascular disease (CVD) risk. More research is warranted to examine potential serum TMAO responses with chronic egg ingestion and in different populations, such as diabetics. In this review, the recent basic science, clinical, and epidemiological findings examining egg PL intake and risk of CVD are summarized.

Lipidomics technologies at the end of the first decade and the beginning of the next

The lipidome is composed of all of the biomolecules defined as lipids, which encompass compounds of amazing structural diversity and complexity. It has been ∼1 decade since the study of "lipidomics" was begun in earnest, and the technologies and tools for data analysis have advanced considerably over this period. This workshop summarized the scope of the lipidome and technologies for its analysis, lipidomics databases and other online tools, and examples of the application of lipidomics to nutritional research. The slides from the workshop, online lipidomics tools, and databases are available at http://www.lipidmaps.org.

LipidBlast in silico tandem mass spectrometry database for lipid identification

Current tandem mass spectral libraries for lipid annotations in metabolomics are limited in size and diversity. We provide a freely available computer generated in-silico tandem mass spectral library of 212,516 MS/MS spectra covering 119,200 compounds from 26 lipid compound classes, including phospholipids, glycerolipids, bacterial lipoglycans and plant glycolipids. Platform independence is shown by using tandem mass spectra from 40 different mass spectrometer types including low-resolution and high-resolution instruments.

LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics

Protein sequence databases are the pillar upon which modern proteomics is supported, representing a stable reference space of predicted and validated proteins. One example of such resources is UniProt, enriched with both expertly curated and automatic annotations. Taken largely for granted, similar mature resources such as UniProt are not available yet in some other "omics" fields, lipidomics being one of them. While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts. This work aims to reduce the gap by developing an equivalent resource to UniProt called 'LipidHome', providing theoretically generated lipid molecules and useful metadata. Using the 'FASTLipid' Java library, a database was populated with theoretical lipids, generated from a set of community agreed upon chemical bounds. In parallel, a web application was developed to present the information and provide computational access via a web service. Designed specifically to accommodate high throughput mass spectrometry based approaches, lipids are organised into a hierarchy that reflects the variety in the structural resolution of lipid identifications. Additionally, cross-references to other lipid related resources and papers that cite specific lipids were used to annotate lipid records. The web application encompasses a browser for viewing lipid records and a 'tools' section where an MS1 search engine is currently implemented. LipidHome can be accessed at http://www.ebi.ac.uk/apweiler-srv/lipidhome.