Interpreting the lipidome: bioinformatic approaches to embrace the complexity

Transformative potentials, challenges and innovative solutions of lipidomics in multiple clinical applications

Lipidomics, a rapidly evolving field within metabolomics, provides comprehensive insights into lipid profiles and their roles in health and disease. Advances in lipidomics have enabled the discovery of novel biomarkers with significant clinical applications, revolutionizing the diagnosis, prognosis, and therapeutic monitoring of various diseases. Emerging methodologies, including high-resolution mass spectrometry (HRMS), Ion mobility spectrometry (IMS), and Supercritical Fluid Chromatography (SFC) have enhanced lipid identification and quantification with remarkable analytical whip hands. These advancements are complemented by innovative sample preparation techniques ensuring the recovery of diverse lipid species with minimal degradation. Biomarker discovery with lipidomics has illuminated critical pathways in numerous diseases, including cardiovascular disorders, neurodegenerative conditions, metabolic syndromes, and cancers. Specific lipid classes, such as sphingolipids (SLs) and phospholipids (PLs) have been linked to Alzheimer's disease and diabetes, respectively, while oxylipins and eicosanoids are emerging as inflammatory biomarkers. Furthermore, lipidomic profiles have shown promise in personalized medicine, enabling the stratification of patient sub-populations and tailoring treatment strategies. This review emphasizes the latest innovative developments in analytical technologies, advanced sample preparation techniques and challenges for lipidomics research including bioinformatic tools on multiple clinical conditions. By exploring these cutting-edge developments, this review highlights the transformative potential of lipidomics in biomarker discovery across diverse clinical applications.

Lipidome visualisation, comparison, and analysis in a vector space

A shallow neural network was used to embed lipid structures in a 2- or 3-dimensional space with the goal that structurally similar species have similar vectors. Tests on complete lipid databanks show that the method automatically produces distributions which follow conventional lipid classifications. The embedding is accompanied by the web-based software, Lipidome Projector. This displays user lipidomes as 2D or 3D scatterplots for quick exploratory analysis, quantitative comparison and interpretation at a structural level. Examples of published data sets were used for a qualitative comparison with literature interpretation.

Lipidomics-based natural products for chronic kidney disease treatment

Chronic kidney disease (CKD) is by far the most prevalent disease in the world and is now a major global public health problem because of the increase in diabetes, hypertension and obesity. Traditional biomarkers of kidney function lack sensitivity and specificity for early detection and monitoring of CKD progression, necessitating more sensitive biomarkers for early diagnostic intervention. Dyslipidemia is a hallmark of CKD. Advancements in mass spectrometry (MS)-based lipidomics platforms have facilitated comprehensive analysis of lipids in biological samples and have revealed changes in the lipidome that are associated with metabolic disorders, which can be used as new biomarkers for kidney diseases. It is also critical for the discovery of new therapeutic targets and drugs. In this article, we focus on lipids in CKD, lipidomics methodologies and their applications in CKD. Additionally, we introduce novel biomarkers identified through lipidomics approaches and natural products derived from lipidomics for the treatment of CKD. We believe that our study makes a significant contribution to literature by demonstrating that natural products can improve CKD from a lipidomic perspective.

Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex

The field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein complexes and serving as a powerful tool in protein-protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n-squared complexity. We here introduce an algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. We compare our algorithm to other state-of-the-art crosslinking search engines commonly used in the field and conclude that MS Annika unifies high sensitivity, accurate FDR estimation and computational performance, outperforming competing tools. Application of this algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible.

Genetic causal association between lipidomic profiles, inflammatory proteomics, and aortic stenosis: a Mendelian randomization investigation

BACKGROUND

Aortic stenosis (AS) is a prevalent and serious valvular heart disease with a complex etiology involving genetic predispositions, lipid dysregulation, and inflammation. The specific roles of lipid and protein biomarkers in AS development are not fully elucidated. This study aimed to elucidate the causal relationships between lipidome, inflammatory proteins, and AS using Mendelian randomization (MR), identifying potential therapeutic targets.

METHODS

Utilizing data from large-scale genome-wide association studies (GWAS) and genome-wide protein quantitative trait loci (pQTL) studies, we conducted MR analyses on 179 plasma lipidome and 91 inflammatory proteins to assess their causal associations with AS. Our approach included Inverse Variance Weighting (IVW), Wald ratio, and robust adjusted profile score (RAPS) analyses to refine these associations. MR-Egger regression was used to address directional horizontal pleiotropy.

RESULTS

Our MR analysis showed that genetically predicted 50 lipids were associated with AS, including 38 as risk factors [(9 Sterol ester, 18 Phosphatidylcholine, 4 Phosphatidylethanolamine, 1 Phosphatidylinositol and 6 Triacylglycerol)] and 12 as protective. Sterol ester (27:1/17:1) emerged as the most significant risk factor with an odds ratio (OR) of 3.11. Additionally, two inflammatory proteins, fibroblast growth factor 19 (FGF19) (OR = 0.830, P = 0.015), and interleukin 6 (IL-6) (OR = 0.729, P = 1.79E-04) were significantly associated with reduced AS risk. However, a two-step MR analysis showed no significant mediated correlations between these proteins and the lipid-AS pathway.

CONCLUSION

This study reveals complex lipid and protein interactions in AS, identifying potential molecular targets for therapy. These results go beyond traditional lipid profiling and significantly advance our genetic and molecular understanding of AS, highlighting potential pathways for intervention and prevention.

Metabolomic and lipidomic fingerprints in inflammatory skin diseases - Systemic illumination of atopic dermatitis, hidradenitis suppurativa and plaque psoriasis

Auto-inflammatory skin diseases place considerable symptomatic and emotional burden on the affected and put pressure on healthcare expenditures. Although most apparent symptoms manifest on the skin, the systemic inflammation merits a deeper analysis beyond the surface. We set out to identify systemic commonalities, as well as differences in the metabolome and lipidome when comparing between diseases and healthy controls. Lipidomic and metabolomic LC-MS profiling was applied, using plasma samples collected from patients suffering from atopic dermatitis, plaque-type psoriasis or hidradenitis suppurativa or healthy controls. Plasma profiles revealed a notable shift in the non-enzymatic anti-oxidant defense in all three inflammatory disorders, placing cysteine metabolism at the center of potential dysregulation. Lipid network enrichment additionally indicated the disease-specific provision of lipid mediators associated with key roles in inflammation signaling. These findings will help to disentangle the systemic components of autoimmune dermatological diseases, paving the way to individualized therapy and improved prognosis.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations

BACKGROUND

Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.

METHODS

Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.

RESULTS

The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.

CONCLUSION

The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

ADViSELipidomics: a workflow for analyzing lipidomics data

SUMMARY

ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPS classification) and, together with information available on the samples, performs several exploratory and statistical analyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomics has a user-friendly graphical user interface and supports an extensive series of interactive graphics.

AVAILABILITY AND IMPLEMENTATION

ADViSELipidomics is freely available at https://github.com/ShinyFabio/ADViSELipidomics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Analysis of Muscle Lipidome in Juvenile Rainbow Trout Fed Rapeseed Oil and Cochayuyo Meal

This study aimed to analyze the effects on the lipidome of juvenile Oncorhynchus mykiss muscle fed 90% Brassica napus “rapeseed” oil and different amounts of Durvillaea antarctica “Cochayuyo” meal (1.5, 3 and 6%) as a replacement for cellulose. The analysis allowed for the identification of 329 lipids, mainly represented by phospholipids and fatty esters. The inclusion of Brassica napus oil significantly increased the levels of C18:2 species and fatty esters of hydroxylated fatty acids, which could play a bioactive role in human health. One of the most abundant lipids in all fillets was Phosphatidylcholine 33:6, which, according to the literature, could be considered a biomarker for the identification of Oncorhynchus mykiss. In all experimental diets, the species Phosphatidylethanolamine 15:1-18:24 showed four-fold higher levels than the control; increments of n-3- and n-6-rich phospholipids were also observed. Diets containing Durvillaea antarctica meal did not generate more significant variation in fish muscle phospholipids relative to the muscle of the rapeseed-oil-only group. These lipid species consist of medium- and long-chain fatty acids with different degrees of unsaturation. Still, it appears that the rapeseed oil masks the lipid contribution of the meal, possibly due to the low levels of total lipids in the macroalgae.

The Importance of Lipidomic Approach for Mapping and Exploring the Molecular Networks Underlying Physical Exercise: A Systematic Review

Maintaining appropriate levels of physical exercise is an optimal way for keeping a good state of health. At the same time, optimal exercise performance necessitates an integrated organ system response. In this respect, physical exercise has numerous repercussions on metabolism and function of different organs and tissues by enhancing whole-body metabolic homeostasis in response to different exercise-related adaptations. Specifically, both prolonged and intensive physical exercise produce vast changes in multiple and different lipid-related metabolites. Lipidomic technologies allow these changes and adaptations to be clarified, by using a biological system approach they provide scientific understanding of the effect of physical exercise on lipid trajectories. Therefore, this systematic review aims to indicate and clarify the identifying biology of the individual response to different exercise workloads, as well as provide direction for future studies focused on the body’s metabolome exercise-related adaptations. It was performed using five databases (Medline (PubMed), Google Scholar, Embase, Web of Science, and Cochrane Library). Two author teams reviewed 105 abstracts for inclusion and at the end of the screening process 50 full texts were analyzed. Lastly, 14 research articles specifically focusing on metabolic responses to exercise in healthy subjects were included. The Oxford quality scoring system scale was used as a quality measure of the reviews. Information was extracted using the participants, intervention, comparison, outcomes (PICOS) format. Despite that fact that it is well-known that lipids are involved in different sport-related changes, it is unclear what types of lipids are involved. Therefore, we analyzed the characteristic lipid species in blood and skeletal muscle, as well as their alterations in response to chronic and acute exercise. Lipidomics analyses of the studies examined revealed medium- and long-chain fatty acids, fatty acid oxidation products, and phospholipids qualitative changes. The main cumulative evidence indicates that both chronic and acute bouts of exercise determine significant changes in lipidomic profiles, but they manifested in very different ways depending on the type of tissue examined. Therefore, this systematic review may offer the possibility to fully understand the individual lipidomics exercise-related response and could be especially important to improve athletic performance and human health.