An unexpected link between fatty acid synthase and cholesterol synthesis in proinflammatory macrophage activation

Different immune activation states require distinct metabolic features and activities in immune cells. For instance, inhibition of fatty acid synthase (FASN), which catalyzes the synthesis of long-chain fatty acids, prevents the proinflammatory response in macrophages; however, the precise role of this enzyme in this response remains poorly defined. Consistent with previous studies, we found here that FASN is essential for lipopolysaccharide-induced, Toll-like receptor (TLR)-mediated macrophage activation. Interestingly, only agents that block FASN upstream of acetoacetyl-CoA synthesis, including the well-characterized FASN inhibitor C75, inhibited TLR4 signaling, while those acting downstream had no effect. We found that acetoacetyl-CoA could overcome C75's inhibitory effect, whereas other FASN metabolites, including palmitate, did not prevent C75-mediated inhibition. This suggested an unexpected role for acetoacetyl-CoA in inflammation that is independent of its role in palmitate synthesis. Our evidence further suggested that acetoacetyl-CoA arising from FASN activity promotes cholesterol production, indicating a surprising link between fatty acid synthesis and cholesterol synthesis. We further demonstrate that this process is required for TLR4 to enter lipid rafts and facilitate TLR4 signaling. In conclusion, we have uncovered an unexpected link between FASN and cholesterol synthesis that appears to be required for TLR signal transduction and proinflammatory macrophage activation

Metabolic memory underlying minimal residual disease in breast cancer.

Funder: European Molecular Biology LaboratoryFunder: European Molecular Biology Laboratory (EMBL)Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer-related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi-omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non-proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small-molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence

Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1.

The endogenous metabolite itaconate has recently emerged as a regulator of macrophage function, but its precise mechanism of action remains poorly understood. Here we show that itaconate is required for the activation of the anti-inflammatory transcription factor Nrf2 (also known as NFE2L2) by lipopolysaccharide in mouse and human macrophages. We find that itaconate directly modifies proteins via alkylation of cysteine residues. Itaconate alkylates cysteine residues 151, 257, 288, 273 and 297 on the protein KEAP1, enabling Nrf2 to increase the expression of downstream genes with anti-oxidant and anti-inflammatory capacities. The activation of Nrf2 is required for the anti-inflammatory action of itaconate. We describe the use of a new cell-permeable itaconate derivative, 4-octyl itaconate, which is protective against lipopolysaccharide-induced lethality in vivo and decreases cytokine production. We show that type I interferons boost the expression of Irg1 (also known as Acod1) and itaconate production. Furthermore, we find that itaconate production limits the type I interferon response, indicating a negative feedback loop that involves interferons and itaconate. Our findings demonstrate that itaconate is a crucial anti-inflammatory metabolite that acts via Nrf2 to limit inflammation and modulate type I interferons

Surface engineering: optimization of antigen presentation in self-assembled monolayers.

The formation of self-assembled monolayers (SAMs) on gold surfaces containing an antigenic peptide (NANP)6 and HS(CH2)11OH, and the specific binding of a monoclonal antibody to these layers were investigated by surface plasmon resonance (SPR). Peptides were synthesized by solid-state phase synthesis and were linked either to cysteine or to an alkyl-thiol to allow covalent attachment to gold. The content of the peptide in the SAMs was systematically varied, and the binding properties of the monoclonal antibody were compared with those measured by microcalorimetry in solution. At a critical peptide concentration in the SAM an optimal antibody binding and complete surface coverage was attained. At lower peptide concentrations, the amount of adsorbed antibody decreased; at higher peptide concentrations, the binding constant decreased. These effects can be explained if the accessibility of the antigenic epitopes depends on the peptide density. Addition of free antigen induced the desorption of bound antibodies and allowed accurate measurements of the dissociation rate constant. Binding constants obtained from steady-state measurements and from measurements of the kinetic rate constants were compared

Genomewide landscape of gene–metabolome associations in Escherichia coli

Abstract Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations

Genomewide landscape of gene–metabolome associations in Escherichia coli

Metabolism is one of the best‐understood cellular processes whose network topology of enzymatic reactions is determined by an organism's genome. The influence of genes on metabolite levels, however, remains largely unknown, particularly for the many genes encoding non‐enzymatic proteins. Serendipitously, genomewide association studies explore the relationship between genetic variants and metabolite levels, but a comprehensive interaction network has remained elusive even for the simplest single‐celled organisms. Here, we systematically mapped the association between > 3,800 single‐gene deletions in the bacterium Escherichia coli and relative concentrations of > 7,000 intracellular metabolite ions. Beyond expected metabolic changes in the proximity to abolished enzyme activities, the association map reveals a largely unknown landscape of gene–metabolite interactions that are not represented in metabolic models. Therefore, the map provides a unique resource for assessing the genetic basis of metabolic changes and conversely hypothesizing metabolic consequences of genetic alterations. We illustrate this by predicting metabolism‐related functions of 72 so far not annotated genes and by identifying key genes mediating the cellular response to environmental perturbations.ISSN:1744-429