Modulation of Myelopoiesis Progenitors Is an Integral Component of Trained Immunity.

Trained innate immunity fosters a sustained favorable response of myeloid cells to a secondary challenge, despite their short lifespan in circulation. We thus hypothesized that trained immunity acts via modulation of hematopoietic stem and progenitor cells (HSPCs). Administration of ß-glucan (prototypical trained-immunity-inducing agonist) to mice induced expansion of progenitors of the myeloid lineage, which was associated with elevated signaling by innate immune mediators, such as IL-1ß and granulocyte-macrophage colony-stimulating factor (GM-CSF), and with adaptations in glucose metabolism and cholesterol biosynthesis. The trained-immunity-related increase in myelopoiesis resulted in a beneficial response to secondary LPS challenge and protection from chemotherapy-induced myelosuppression in mice. Therefore, modulation of myeloid progenitors in the bone marrow is an integral component of trained immunity, which to date, was considered to involve functional changes of mature myeloid cells in the periphery.

Constitutive formation of caveolae in a bacterium.

Caveolin plays an essential role in the formation of characteristic surface pits, caveolae, which cover the surface of many animal cells. The fundamental principles of caveola formation are only slowly emerging. Here we show that caveolin expression in a prokaryotic host lacking any intracellular membrane system drives the formation of cytoplasmic vesicles containing polymeric caveolin. Vesicle formation is induced by expression of wild-type caveolins, but not caveolin mutants defective in caveola formation in mammalian systems. In addition, cryoelectron tomography shows that the induced membrane domains are equivalent in size and caveolin density to native caveolae and reveals a possible polyhedral arrangement of caveolin oligomers. The caveolin-induced vesicles or heterologous caveolae (h-caveolae) form by budding in from the cytoplasmic membrane, generating a membrane domain with distinct lipid composition. Periplasmic solutes are encapsulated in the budding h-caveola, and purified h-caveolae can be tailored to be targeted to specific cells of interest.

Lysosomal fusion and SNARE function are impaired by cholesterol accumulation in lysosomal storage disorders.

Partial Retraction of: The EMBO Journal (2010) 29: 3607-3620. DOI: 10.1038/emboj.2010.237 | Published online 24 September 2010 Journal statement The journal contacted the authors in February 2022 about potential image insertions and duplications in Fig 4A and 4E. In the absence of source data, the authors are retracting Fig 4A, the lower panel of Fig 4E (LAMP1 immunoblot), and the following statements in the text that rely on these data: "Quantitative analysis showed that the percentage of Flotillin-1 associated with DRMs was increased in LSD endolysosomal membranes (Figure 4A), indicating an increased amount of cholesterol-enriched regions in these membrane samples." "LAMP1 also displayed a similar distribution profile in WT and LSD cells (Figure 4E)". Author statement The authors could not verify the aberrations in panel A of Fig 4 and the lower immunoblot (LAMP1) of 4E because the original source data are no longer available (12 years after publication, which is beyond the institute's 10-year data retention policy). The authors wish to clarify that the main conclusions of the paper are not affected by the retraction of Figure panels 4A and 4E for the following reasons: Figure panel 4A supports the observation that there are increased cholesterol-enhanced regions in LSD samples. This finding is also supported by data provided in figs 4B, 4C and 4D. Figure panel 4E: The LAMP1 blot in Fig 4E shows that the distribution of protein normally excluded from DRMs is not altered between Wt and LSD samples. This result is also supported by the upper blot in this panel (Transferrin receptor). The authors apologize for these errors and agree with this corrigendum; no response could be obtained from AL.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Cholesteryl hemiazelate identified in CVD patients causes in vitro and in vivo inflammation.

Oxidation of PUFAs in LDLs trapped in the arterial intima plays a critical role in atherosclerosis. Though there have been many studies on the atherogenicity of oxidized derivatives of PUFA-esters of cholesterol, the effects of cholesteryl hemiesters (ChEs), the oxidation end products of these esters, have not been studied. Through lipidomics analyses, we identified and quantified two ChE types in the plasma of CVD patients and identified four ChE types in human endarterectomy specimens. Cholesteryl hemiazelate (ChA), the ChE of azelaic acid (n-nonane-1,9-dioic acid), was the most prevalent ChE identified in both cases. Importantly, human monocytes, monocyte-derived macrophages, and neutrophils exhibit inflammatory features when exposed to subtoxic concentrations of ChA in vitro. ChA increases the secretion of proinflammatory cytokines such as interleukin-1ß and interleukin-6 and modulates the surface-marker profile of monocytes and monocyte-derived macrophage. In vivo, when zebrafish larvae were fed with a ChA-enriched diet, they exhibited neutrophil and macrophage accumulation in the vasculature in a caspase 1- and cathepsin B-dependent manner. ChA also triggered lipid accumulation at the bifurcation sites of the vasculature of the zebrafish larvae and negatively impacted their life expectancy. We conclude that ChA behaves as an endogenous damage-associated molecular pattern with inflammatory and proatherogenic properties.

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort.

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach. Multiple machine intelligence models were trained to predict obesity estimates, i.e., body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and body fat percentage (BFP), and validated in 250 randomly chosen participants of the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Comparison of the different models revealed that the lipidome predicted BFP the best (R2 = 0.73), based on a Lasso model. In this model, the strongest positive and the strongest negative predictor were sphingomyelin molecules, which differ by only 1 double bond, implying the involvement of an unknown desaturase in obesity-related aberrations of lipid metabolism. Moreover, we used this regression to probe the clinically relevant information contained in the plasma lipidome and found that the plasma lipidome also contains information about body fat distribution, because WHR (R2 = 0.65) was predicted more accurately than BMI (R2 = 0.47). These modeling results required full resolution of the lipidome to lipid species level, and the predicting set of biomarkers had to be sufficiently large. The power of the lipidomics association was demonstrated by the finding that the addition of routine clinical laboratory variables, e.g., high-density lipoprotein (HDL)- or low-density lipoprotein (LDL)- cholesterol did not improve the model further. Correlation analyses of the individual lipid species, controlled for age and separated by sex, underscores the multiparametric and lipid species-specific nature of the correlation with the BFP. Lipidomic measurements in combination with machine intelligence modeling contain rich information about body fat amount and distribution beyond traditional clinical assays.

Lipidomics: coming to grips with lipid diversity.

Although lipids are biomolecules with seemingly simple chemical structures, the molecular composition of the cellular lipidome is complex and, currently, poorly understood. The exact mechanisms of how compositional complexity affects cell homeostasis and its regulation also remain unclear. This emerging field is developing sensitive mass spectrometry technologies for the quantitative characterization of the lipidome. Here, we argue that lipidomics will become an essential tool kit in cell and developmental biology, molecular medicine and nutrition.

Revitalizing membrane rafts: new tools and insights.

Ten years ago, we wrote a Review on lipid rafts and signalling in the launch issue of Nature Reviews Molecular Cell Biology. At the time, this field was suffering from ambiguous methodology and imprecise nomenclature. Now, new techniques are deepening our insight into the dynamics of membrane organization. Here, we discuss how the field has matured and present an evolving model in which membranes are occupied by fluctuating nanoscale assemblies of sphingolipids, cholesterol and proteins that can be stabilized into platforms that are important in signalling, viral infection and membrane trafficking.

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study.

AIMS/HYPOTHESIS: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. METHODS: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster. RESULTS: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. CONCLUSIONS/INTERPRETATION: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.

A lipid E-MAP identifies Ubx2 as a critical regulator of lipid saturation and lipid bilayer stress.

Biological membranes are complex, and the mechanisms underlying their homeostasis are incompletely understood. Here, we present a quantitative genetic interaction map (E-MAP) focused on various aspects of lipid biology, including lipid metabolism, sorting, and trafficking. This E-MAP contains ∼250,000 negative and positive genetic interaction scores and identifies a molecular crosstalk of protein quality control pathways with lipid bilayer homeostasis. Ubx2p, a component of the endoplasmic-reticulum-associated degradation pathway, surfaces as a key upstream regulator of the essential fatty acid (FA) desaturase Ole1p. Loss of Ubx2p affects the transcriptional control of OLE1, resulting in impaired FA desaturation and a severe shift toward more saturated membrane lipids. Both the induction of the unfolded protein response and aberrant nuclear membrane morphologies observed in cells lacking UBX2 are suppressed by the supplementation of unsaturated FAs. Our results point toward the existence of dedicated bilayer stress responses for membrane homeostasis.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes.

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

How Can Omic Science be Improved?

One of the promises of multiomic analysis was to transform the clinical diagnostics to deliver much more exact phenotyping of disease states. However, despite enormous investments, the transformation of clinical routine has not taken place. There are many reasons for this lack of success but one is the failure to deliver quantitative and reproducible data. This failure is not only impeding progress in clinical phenotyping but also in the application of omic science in systems biology. The focus in this Viewpoint will be on lipidomics but the lessons learned are generally applicable.

Hopanoids as functional analogues of cholesterol in bacterial membranes.

The functionality of cellular membranes relies on the molecular order imparted by lipids. In eukaryotes, sterols such as cholesterol modulate membrane order, yet they are not typically found in prokaryotes. The structurally similar bacterial hopanoids exhibit similar ordering properties as sterols in vitro, but their exact physiological role in living bacteria is relatively uncharted. We present evidence that hopanoids interact with glycolipids in bacterial outer membranes to form a highly ordered bilayer in a manner analogous to the interaction of sterols with sphingolipids in eukaryotic plasma membranes. Furthermore, multidrug transport is impaired in a hopanoid-deficient mutant of the gram-negative Methylobacterium extorquens, which introduces a link between membrane order and an energy-dependent, membrane-associated function in prokaryotes. Thus, we reveal a convergence in the architecture of bacterial and eukaryotic membranes and implicate the biosynthetic pathways of hopanoids and other order-modulating lipids as potential targets to fight pathogenic multidrug resistance.

N-Glycosylation as determinant of epidermal growth factor receptor conformation in membranes.

The epidermal growth factor receptor (EGFR) regulates several critical cellular processes and is an important target for cancer therapy. In lieu of a crystallographic structure of the complete receptor, atomistic molecular dynamics (MD) simulations have recently shown that they can excel in studies of the full-length receptor. Here we present atomistic MD simulations of the monomeric N-glycosylated human EGFR in biomimetic lipid bilayers that are, in parallel, also used for the reconstitution of full-length receptors. This combination enabled us to experimentally validate our simulations, using ligand binding assays and antibodies to monitor the conformational properties of the receptor reconstituted into membranes. We find that N-glycosylation is a critical determinant of EGFR conformation, and specifically the orientation of the EGFR ectodomain relative to the membrane. In the absence of a structure for full-length, posttranslationally modified membrane receptors, our approach offers new means to structurally define and experimentally validate functional properties of cell surface receptors in biomimetic membrane environments.

Cell membranes: A subjective perspective.

Cell membranes have developed a tremendous complexity of lipids and proteins geared to perform the functions cells require. The lipids have for long remained in the background and are now regaining their role as important building blocks of cells. Their main function is to form the matrix of our cell membranes where they support a variety of functions essential for life. This 2-dimensional fluid matrix has evolved unexpected material properties that involve both lipid-lipid and lipid-protein interactions. This perspective is a short summary of the challenges that this field faces and discusses potential ways and means for coming to grips with the properties of this incredible fluid. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Glycosylphosphatidylinositol-anchored proteins: Membrane organization and transport.

Glycosylphosphatidylinositol (GPI)-anchored proteins (GPI-APs) are a class of membrane proteins containing a soluble protein attached by a conserved glycolipid anchor to the external leaflet of the plasma membrane. In polarized epithelial cells, GPI-APs are predominantly sorted to the apical surface in the trans-Golgi network (TGN) by clustering in sphingolipid- and cholesterol-dependent microdomains (or rafts), which have been proposed to act as apical sorting platforms. Recent data indicate that the mechanisms of GPI-AP sorting, occurring in the Golgi, control both the membrane transport of GPI-APs and their specific activity at the apical surface of fully polarized epithelial cells. Here, we discuss the most recent findings and the factors regulating apical sorting of GPI-APs at the Golgi in polarized epithelial cells. We also underline the differences in the plasma membrane organization of GPI-APs between polarized and non-polarized cells supporting the existence of various mechanisms that control GPI-AP organization in different cell types.

Membrane raft association is a determinant of plasma membrane localization.

The lipid raft hypothesis proposes lateral domains driven by preferential interactions between sterols, sphingolipids, and specific proteins as a central mechanism for the regulation of membrane structure and function; however, experimental limitations in defining raft composition and properties have prevented unequivocal demonstration of their functional relevance. Here, we establish a quantitative, functional relationship between raft association and subcellular protein sorting. By systematic mutation of the transmembrane and juxtamembrane domains of a model transmembrane protein, linker for activation of T-cells (LAT), we generated a panel of variants possessing a range of raft affinities. These mutations revealed palmitoylation, transmembrane domain length, and transmembrane sequence to be critical determinants of membrane raft association. Moreover, plasma membrane (PM) localization was strictly dependent on raft partitioning across the entire panel of unrelated mutants, suggesting that raft association is necessary and sufficient for PM sorting of LAT. Abrogation of raft partitioning led to mistargeting to late endosomes/lysosomes because of a failure to recycle from early endosomes. These findings identify structural determinants of raft association and validate lipid-driven domain formation as a mechanism for endosomal protein sorting.

Orm family proteins mediate sphingolipid homeostasis.

Despite the essential roles of sphingolipids both as structural components of membranes and critical signalling molecules, we have a limited understanding of how cells sense and regulate their levels. Here we reveal the function in sphingolipid metabolism of the ORM genes (known as ORMDL genes in humans)-a conserved gene family that includes ORMDL3, which has recently been identified as a potential risk factor for childhood asthma. Starting from an unbiased functional genomic approach in Saccharomyces cerevisiae, we identify Orm proteins as negative regulators of sphingolipid synthesis that form a conserved complex with serine palmitoyltransferase, the first and rate-limiting enzyme in sphingolipid production. We also define a regulatory pathway in which phosphorylation of Orm proteins relieves their inhibitory activity when sphingolipid production is disrupted. Changes in ORM gene expression or mutations to their phosphorylation sites cause dysregulation of sphingolipid metabolism. Our work identifies the Orm proteins as critical mediators of sphingolipid homeostasis and raises the possibility that sphingolipid misregulation contributes to the development of childhood asthma.

Functional convergence of hopanoids and sterols in membrane ordering.

Liquid-ordered phases are one of two biochemically active membrane states, which until now were thought to be a unique consequence of the interactions between eukaryotic membrane lipids. The formation of a liquid-ordered phase depends crucially on the ordering properties of sterols. However, it is not known whether this capacity exists in organisms that lack sterols, such as bacteria. We show that diplopterol, the simplest bacterial hopanoid, has similar properties and that hopanoids are bacterial "sterol surrogates" with the ability to order saturated lipids and to form a liquid-ordered phase in model membranes. These observations suggest that the evolution of an ordered biochemically active liquid membrane could have evolved before the oxygenation of Earth's surface and the emergence of sterols.