LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario

CREBBP inactivation sensitizes B cell acute lymphoblastic leukemia to ferroptotic cell death upon BCL2 inhibition

B-cell acute lymphoblastic leukemia (B-ALL) is a leading cause of death in childhood and outcomes in adults remain dismal. There is therefore an urgent clinical need for therapies that target the highest risk cases. Mutations in the histone acetyltransferase CREBBP confer high-risk and increased chemoresistance in ALL. Performing a targeted drug-screen in isogenic human cell lines, we identify a number of small molecules that specifically target CREBBP-mutated B-ALL, the most potent being the BCL2-inhibitor Venetoclax. Of note, this acts through a non-canonical mechanism resulting in ferroptotic rather than apoptotic cell death. CREBBP-mutated cell lines show differences in cell-cycle, metabolism, lipid composition and response to oxidative stress, predisposing them to ferroptosis, which are further dysregulated upon acquisition of Venetoclax resistance. Lastly, small-molecule inhibition of CREBBP pharmacocopies CREBBP-mutation, sensitizing B-ALL cells, regardless of genotype, to Venetoclax-induced ferroptosis in-vitro and in-vivo, providing a promising drug combination for broader clinical translation in B-ALL.

LipidSigR: a R-based solution for integrated lipidomics data analysis and visualization

Motivation

Lipidomics is a rapidly expanding field focused on studying lipid species and classes within biological systems. As the field evolves, there is an increasing demand for user-friendly, open-source software tools capable of handling large and complex datasets while keeping pace with technological advancements. LipidSig, a widely used web-based platform, has been instrumental in data analysis and visualization of lipidomics. However, its limitations become evident when users want to build customized workflows. To address the limitation, we developed a companion R package, LipidSigR, based on the R code of the LipidSig web platform.

Results

LipidSigR offers greater flexibility, allowing researchers with basic R programming skills to modify and adapt workflows according to their needs. It has been rigorously tested following CRAN guidelines to ensure compatibility and reproducibility. In demonstrating its functionality, we analyze the case with commonly used experimental design, case versus control, in lipidomics studies. Researchers can follow the use case to explore the key capabilities and build customized lipidomics data analysis workflows using LipidSigR.

Availability and implementation

LipidSigR is freely available from https://lipidsig.bioinfomics.org/lipidsigr/index.html and https://github.com/BioinfOMICS/LipidSigR.

Understanding the physiological mechanisms and therapeutic targets of diseases: Lipidomics strategies

As a pivotal branch of metabolomics, lipidomics studies global changes in lipid metabolism under different physiological and pathological conditions or drug interventions, discovers key lipid markers, and elaborates the associated lipid metabolism network. There are a considerable number of lipids in the host, which act on various functional networks such as metabolism and immune regulation. As an indispensable research method, lipidomics plays a key character in the analysis of lipid composition in organisms, the elaboration of the physiological mechanism of lipids, and the decoding of their character in the occurrence and development of diseases by exploring the character of lipids in the host environmental network. As an essential means of driving lipidomics research, High-throughput and High-resolution mass spectrometry is helpful in exploring disease phenotypic characteristics, diagnosing disease biomarkers, regulating related metabolic pathways, and discovering related active components. In this paper, we discuss the specific role of lipidomics in the analysis of disease diagnosis, prognosis and treatment, which is conducive to the realization of accurate and personalized medicine.

Parallel single-cell metabolic analysis and extracellular vesicle profiling reveal vulnerabilities with prognostic significance in acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive disease with a high relapse rate. In this study, we map the metabolic profile of CD34+(CD38low/-) AML cells and the extracellular vesicle signatures in circulation from AML patients at diagnosis. CD34+ AML cells display high antioxidant glutathione levels and enhanced mitochondrial functionality, both associated with poor clinical outcomes. Although CD34+ AML cells are highly dependent on glucose oxidation and glycolysis for energy, those from intermediate- and adverse-risk patients reveal increased mitochondrial dependence. Extracellular vesicles from AML are mainly enriched in stem cell markers and express antioxidant GPX3, with their profiles showing potential prognostic value. Extracellular vesicles enhance mitochondrial functionality and dependence on CD34+ AML cells via the glutathione/GPX4 axis. Notably, extracellular vesicles from adverse-risk patients enhance leukemia cell engraftment in vivo. Here, we show a potential noninvasive approach based on liquid 'cell-extracellular vesicle' biopsy toward a redefined metabolic stratification in AML.

LipidOne 2.0: A Web Tool for Discovering Biological Meanings Hidden in Lipidomic Data

LipidOne 2.0 (https://lipidone.eu) is a new web bioinformatic tool for the analysis of lipidomic data. It facilitates the exploration of the three structural levels of lipids: classes, molecular species, and lipid building blocks (acyl, alkyl, or alkenes chains). The tool's flexibility empowers users to seamlessly include or exclude experimental groups and lipid classes at any stage of the analysis. LipidOne 2.0 offers a range of mono- and multivariate statistical analyses, specifically tailored to each structural level. This includes a novel lipid biomarker identification function, integrating four diverse statistical parameters. LipidOne 2.0 incorporates Lipid Pathway analysis across all three structural levels of lipids. Users can identify lipid-involved reactions through case-control comparisons, generating lists of genes/enzymes and their activation states based on Z scores. Accessible without the need for registration, LipidOne 2.0 provides a user-friendly and efficient platform for exploring and analyzing lipidomic data. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Dataset preparation for LipidOne 2.0 Support Protocol: Lipid nomenclature from spectrometric experiments Basic Protocol 2: Uploading a dataset into LipidOne 2.0 Basic Protocol 3: Data mining of lipidomic dataset by LipidOne 2.0.

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

iLipidome: enhancing statistical power and interpretability using hidden biosynthetic interdependencies in the lipidome

Numerous biological processes and diseases are influenced by lipid composition. Advances in lipidomics are elucidating their roles, but analyzing and interpreting lipidomics data at the systems level remain challenging. To address this, we present iLipidome, a method for analyzing lipidomics data in the context of the lipid biosynthetic network, thus accounting for the interdependence of measured lipids. iLipidome enhances statistical power, enables reliable clustering and lipid enrichment analysis, and links lipidomic changes to their genetic origins. We applied iLipidome to investigate mechanisms driving changes in cellular lipidomes following supplementation of docosahexaenoic acid (DHA) and successfully identified the genetic causes of alterations. We further demonstrated how iLipidome can disclose enzyme-substrate specificity and pinpoint prospective glioblastoma therapeutic targets. Finally, iLipidome enabled us to explore underlying mechanisms of cardiovascular disease and could guide the discovery of early lipid biomarkers. Thus, iLipidome can assist researchers studying the essence of lipidomic data and advance the field of lipid biology.

Metabolic Profiling as an Approach to Differentiate T-Cell Acute Lymphoblastic Leukemia Cell Lines Belonging to the Same Genetic Subgroup

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive tumor mainly affecting children and adolescents. It is driven by multiple genetic mutations that together define the leukemic phenotype. Interestingly, based on genetic alterations and/or deregulated expression, at least six genetic subgroups have been recognized. The TAL/LMO subgroup is one of the most represented genetic subgroups, characterizing 30-45% of pediatric T-ALL cases. The study of lipid and metabolic profiles is increasingly recognized as a valuable tool for comprehending the development and progression of tumors. In this study, metabolic and lipidomic analysis via LC/MS have been carried out on four T-ALL cell lines belonging to the TAL/LMO subgroup (Jurkat, Molt-4, Molt-16, and CCRF-CEM) to identify new potential metabolic biomarkers and to provide a subclassification of T-ALL cell lines belonging to the same subgroup. A total of 343 metabolites were annotated, including 126 polar metabolites and 217 lipid molecules. The statistical analysis, for both metabolic and lipid profiles, shows significant differences and similarities among the four cell lines. The Molt-4 cell line is the most distant cell line and CCRF-CEM shows a high activity in specific pathways when compared to the other cell lines, while Molt-16 and Jurkat show a similar metabolic profile. Additionally, this study highlighted the pathways that differ in each cell line and the possible enzymes involved using bioinformatic tools, capable of predicting the pathways involved by studying the differences in the metabolic profiles. This experiment offers an approach to differentiate T-ALL cell lines and could open the way to verify and confirm the obtained results directly in patients.

Untargeted Lipidomic Approach for Studying Different Nervous System Tissues of the Murine Model of Krabbe Disease

Krabbe disease is a rare neurodegenerative disease with an autosomal recessive character caused by a mutation in the GALC gene. The mutation leads to an accumulation of psychosine and a subsequent degeneration of oligodendrocytes and Schwann cells. Psychosine is the main biomarker of the disease. The Twitcher mouse is the most commonly used animal model to study Krabbe disease. Although there are many references to this model in the literature, the lipidomic study of nervous system tissues in the Twitcher model has received little attention. This study focuses on the comparison of the lipid profiles of four nervous system tissues (brain, cerebellum, spinal cord, and sciatic nerve) in the Twitcher mouse compared to the wild-type mouse. Altogether, approximately 230 molecular species belonging to 19 lipid classes were annotated and quantified. A comparison at the levels of class, molecular species, and lipid building blocks showed significant differences between the two groups, particularly in the sciatic nerve. The in-depth study of the lipid phenotype made it possible to hypothesize the genes and enzymes involved in the changes. The integration of metabolic data with genetic data may be useful from a systems biology perspective to gain a better understanding of the molecular basis of the disease.

Comparison between Sickle Cell Disease Patients and Healthy Donors: Untargeted Lipidomic Study of Erythrocytes

Sickle cell disease (SCD) is one of the most common severe monogenic disorders in the world caused by a mutation on HBB gene and characterized by hemoglobin polymerization, erythrocyte rigidity, vaso-occlusion, chronic anemia, hemolysis, and vasculopathy. Recently, the scientific community has focused on the multiple genetic and clinical profiles of SCD. However, the lipid composition of sickle cells has received little attention in the literature. According to recent studies, changes in the lipid profile are strongly linked to several disorders. Therefore, the aim of this study is to dig deeper into lipidomic analysis of erythrocytes in order to highlight any variations between healthy and patient subjects. 241 lipid molecular species divided into 17 classes have been annotated and quantified. Lipidomic profiling of SCD patients showed that over 24% of total lipids were altered most of which are phospholipids. In-depth study of significant changes in lipid metabolism can give an indication of the enzymes and genes involved. In a systems biology scenario, these variations can be useful to improve the understanding of the biochemical basis of SCD and to try to make a score system that could be predictive for the severity of clinical manifestations.

Accurate Analysis of Lipid Building Blocks Using the Tool LipidOne

LC/MS-based analysis techniques combined with specialized lipid platforms allow the qualitative and quantitative determination of thousands of lipid molecules. Each individual molecule can be considered as an assembly of smaller parts, often called building blocks that are the result of a myriad of biochemical synthesis and transformation processes. LipidOne is a new lipidomic tool that automatically highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and between experimental groups. Thanks to LipidOne, the discovered differences among lipid building blocks can be easily linked to the activity of specific enzymes.

Circulating extracellular particles from severe COVID-19 patients show altered profiling and innate lymphoid cell-modulating ability

Introduction

Extracellular vesicles (EVs) and particles (EPs) represent reliable biomarkers for disease detection. Their role in the inflammatory microenvironment of severe COVID-19 patients is not well determined. Here, we characterized the immunophenotype, the lipidomic cargo and the functional activity of circulating EPs from severe COVID-19 patients (Co-19-EPs) and healthy controls (HC-EPs) correlating the data with the clinical parameters including the partial pressure of oxygen to fraction of inspired oxygen ratio (PaO2/FiO2) and the sequential organ failure assessment (SOFA) score.

Methods

Peripheral blood (PB) was collected from COVID-19 patients (n=10) and HC (n=10). EPs were purified from platelet-poor plasma by size exclusion chromatography (SEC) and ultrafiltration. Plasma cytokines and EPs were characterized by multiplex bead-based assay. Quantitative lipidomic profiling of EPs was performed by liquid chromatography/mass spectrometry combined with quadrupole time-of-flight (LC/MS Q-TOF). Innate lymphoid cells (ILC) were characterized by flow cytometry after co-cultures with HC-EPs or Co-19-EPs.

Results

We observed that EPs from severe COVID-19 patients: 1) display an altered surface signature as assessed by multiplex protein analysis; 2) are characterized by distinct lipidomic profiling; 3) show correlations between lipidomic profiling and disease aggressiveness scores; 4) fail to dampen type 2 innate lymphoid cells (ILC2) cytokine secretion. As a consequence, ILC2 from severe COVID-19 patients show a more activated phenotype due to the presence of Co-19-EPs.

Discussion

In summary, these data highlight that abnormal circulating EPs promote ILC2-driven inflammatory signals in severe COVID-19 patients and support further exploration to unravel the role of EPs (and EVs) in COVID-19 pathogenesis.