. 2023 May 2;35(5):887-905.e11.

doi: 10.1016/j.cmet.2023.03.018. Epub 2023 Apr 18.

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Nicolas Wieder¹, Juliana Coraor Fried², Choah Kim², Eriene-Heidi Sidhom², Matthew R Brown³, Jamie L Marshall³, Carlos Arevalo³, Moran Dvela-Levitt⁴, Maria Kost-Alimova³, Jonas Sieber⁵, Katlyn R Gabriel³, Julian Pacheco³, Clary Clish³, Hamdah Shafqat Abbasi³, Shantanu Singh³, Justine C Rutter⁶, Martine Therrien³, Haejin Yoon⁷, Zon Weng Lai⁸, Aaron Baublis⁸, Renuka Subramanian⁹, Ranjan Devkota¹⁰, Jonnell Small¹¹, Vedagopuram Sreekanth¹², Myeonghoon Han³, Donghyun Lim³, Anne E Carpenter³, Jason Flannick¹³, Hilary Finucane¹⁴, Marcia C Haigis¹⁵, Melina Claussnitzer¹⁶, Eric Sheu⁹, Beth Stevens¹⁷, Bridget K Wagner¹⁰, Amit Choudhary¹⁸, Jillian L Shaw³, Juan Lorenzo Pablo³, Anna Greka¹⁹

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA; Department of Neurology with Experimental Neurology and Berlin Institute of Health, Charité, 10117 Berlin, Germany.
² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
³ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁴ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
⁵ Department of Endocrinology, Metabolism and Cardiovascular Systems, University of Fribourg, Fribourg, Switzerland.
⁶ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA.
⁷ Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center for Cancer Research at Harvard, Boston, MA 02115, USA.
⁸ Harvard Chan Advanced Multiomics Platform, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁹ Laboratory for Surgical and Metabolic Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Chemical Biology and Therapeutics Science, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹¹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Chemical Biology and Therapeutics Science, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Divisions of Renal Medicine and Engineering, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹³ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA.
¹⁴ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Mass General Hospital, Boston, MA 02114, USA.
¹⁵ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center for Cancer Research at Harvard, Boston, MA 02115, USA.
¹⁶ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹⁷ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Boston Children's Hospital, F.M. Kirby Neurobiology Center, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston, MA 02115, USA.
¹⁸ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Chemical Biology and Therapeutics Science, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Divisions of Renal Medicine and Engineering, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA. Electronic address: agreka@bwh.harvard.edu.

PMID: 37075753
PMCID: PMC10257950
DOI: 10.1016/j.cmet.2023.03.018

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Nicolas Wieder et al. Cell Metab. 2023.

. 2023 May 2;35(5):887-905.e11.

doi: 10.1016/j.cmet.2023.03.018. Epub 2023 Apr 18.

Authors

Affiliations

¹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA; Department of Neurology with Experimental Neurology and Berlin Institute of Health, Charité, 10117 Berlin, Germany.
² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
³ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁴ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA; The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
⁵ Department of Endocrinology, Metabolism and Cardiovascular Systems, University of Fribourg, Fribourg, Switzerland.
⁶ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA.
⁷ Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center for Cancer Research at Harvard, Boston, MA 02115, USA.
⁸ Harvard Chan Advanced Multiomics Platform, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁹ Laboratory for Surgical and Metabolic Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Chemical Biology and Therapeutics Science, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹¹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Chemical Biology and Therapeutics Science, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Divisions of Renal Medicine and Engineering, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹³ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA.
¹⁴ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Mass General Hospital, Boston, MA 02114, USA.
¹⁵ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center for Cancer Research at Harvard, Boston, MA 02115, USA.
¹⁶ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹⁷ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Boston Children's Hospital, F.M. Kirby Neurobiology Center, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston, MA 02115, USA.
¹⁸ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Boston, MA 02115, USA; Chemical Biology and Therapeutics Science, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Divisions of Renal Medicine and Engineering, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁹ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA. Electronic address: agreka@bwh.harvard.edu.

PMID: 37075753
PMCID: PMC10257950
DOI: 10.1016/j.cmet.2023.03.018

Abstract

Cellular exposure to free fatty acids (FFAs) is implicated in the pathogenesis of obesity-associated diseases. However, there are no scalable approaches to comprehensively assess the diverse FFAs circulating in human plasma. Furthermore, assessing how FFA-mediated processes interact with genetic risk for disease remains elusive. Here, we report the design and implementation of fatty acid library for comprehensive ontologies (FALCON), an unbiased, scalable, and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids associated with decreased membrane fluidity. Furthermore, we prioritized genes that reflect the combined effects of harmful FFA exposure and genetic risk for type 2 diabetes (T2D). We found that c-MAF-inducing protein (CMIP) protects cells from FFA exposure by modulating Akt signaling. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.

Keywords: CMIP; GWAS; erucic acid; kidney; lipidomics; microglia; obesity; pancreatic β cell; transcriptomics; type 2 diabetes.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests N.W., J.C.F., and A.G. are co-inventors of a patent on the composition, method, and use for FFA screening, application no: 52199-550P01US. A.G. serves as a founding advisor to a new company launched by Atlas Ventures, an agreement reviewed and managed by Brigham and Women’s Hospital, Mass General Brigham, and the Broad Institute of MIT and Harvard in accordance with their conflict of interest policies.

Figures

**Figure 1.. FALCON, a multiplexed platform for the systematic interrogation of structurally diverse FFAs, defines 5 FFA clusters**
(A) Analysis workflow for FALCON. (B) Schematic of triacylglyceride (TAG) synthesis from FFAs. Shown here are the monoacylglycerol (top) and the glycerol-3-phosphate pathway (bottom). For simplicity, the acylation of dihydroxyacetone phosphate is not shown. (C) Qualitative correlation of structural features (number of C atoms, number of double bonds) of externally applied FFAs from the library (x axis) versus structural features of endogenous TAGs (y axis) measured by lipidomics. Distinct TAG profiles were detected in cells treated with SFAs, MUFAs, or PUFAs. (D) Five FFA clusters (c1–c5) were identified after hierarchical clustering of transcriptomic profiles derived from exposure to each of 61 FFAs (STAR Methods). (E) Cell Painting analysis of immunofluorescence images from cells exposed to each of 61 FFAs (STAR Methods) independently clustered together the FFAs transcriptomically assigned to c2 and separately the FFAs assigned to c5.

**Figure 2.. Transcriptomic analysis identifies key biological responses to FFAs, and functional assays validate novel FFA clustering**
(A) Hierarchical clustering of a gene set enrichment matrix (based on normalized enrichment scores of gene sets, NES) revealed gene set modules of interest. Representative leading edge genes from each module are listed on the right. (B and C) Scatterplots of two independent replicates of cell viability (B) and ER Ca²⁺ level measurements (C) (n = 5–7 replicates/FFA/screen). Closed dots represent FFAs that showed significant difference (p < 0.05, Bonferroni) from controls in both replicates; open dots represent non-significant FFAs in at least one replicate. Colors indicate corresponding FFA cluster membership (Figure 1D). (D) Summary of functional assays. Top bar represents FFA clusters derived from transcriptomic analysis. Second color bar represents clusters derived from Cell Painting (cellular morphology) analysis (STAR Methods). Bar in grayscale indicates classical grouping of FFAs based on saturation level. The next two bars are heatmaps displaying log₂ fold changes of ER Ca²⁺ levels and cell viability, respectively. x axis labels show FFA structure (in simplified molecular input line entry system [SMILES]). The box highlights the 20 FFAs in cluster 2 (c2) identified as the lipotoxicity cluster. Highlighted FFAs were chosen as cluster representatives for further downstream studies.

**Figure 3.. FALCON is applicable at scale to different cell types**
(A) Heatmap showing comparison of viability changes across 3 different cell types: MIN6 pancreatic β cells, human iPSC-derived microglia, and human kidney tubular epithelial cells. c2 FFAs are toxic for all three cell types studied. (B) Representative images from all 3 cell types highlighting the toxicity of c2 FFAs including erucic acid (EA). EA induces cell death in β cells (green, Hoescht), in microglia (green, GFP), and in kidney tubular epithelial cells (red, propidium iodide). Scale bars, 100 μm. (C–E) Bar plots indicate change in cell viability relative to BSA induced by OA, PA, or EA in each cell type after exposure to FFAs at 500 μM for 72 h (MIN6) (C), 500 μM for 15 h (epithelial cells) (D), or 250 μM for 24 h (microglia) (E). PA and EA consistently induce cell death across all three cell types as assessed by one-way ANOVA followed by Dunnett’s test (*p < 0.05, ****p < 0.0001). Data are mean ± SD.

**Figure 4.. Cell biological hallmarks of lipotoxicity characterize the c2 FFA cluster**
(A) Cell viability after treatment with representative FFAs for 48 h. Percentage of apoptotic cells (positive y axis) and reduction in cell viability (negative y axis) are presented. Data are mean ± SD. Student’s t test (two-sided), ****p < 0.0001, corrected for multiple testing (Bonferroni), n = 5 wells. (B) Dose-response curve of viable cell numbers after 65 h of EA or OA treatment compared with BSA. EA is toxic in a concentration-dependent manner; OA is not toxic. (C) Western blots show ATF4 and CHOP induction by PA and EA (lipotoxic c2 FFAs). CPT1A induction is a control for intracellular FFA delivery. BSA, negative control. (D) Quantification of peak amplitude as readout for ER Ca²⁺ levels, relative to negative control (BSA). Data are mean ± SD. Student’s t test (two-sided), *p < 0.05, ****p < 0.0001, corrected for multiple testing (Benjamini-Hochberg, entire FFA library). (E) Quantification of RELA translocation as percentage of total number of cells (t = 18 h, n = 5 replicates). Data are mean ± SD. Student’s t test (two-sided), ****p < 0.0001, corrected for multiple testing (Bonferroni). (F) Glucose-stimulated insulin secretion (GSIS) after FFA exposure (FFA = 500 μM, t = 24 h, n = 6 wells) was measured by ELISA. (G) Autophagosome formation was assessed by imaging LC3B puncta normalized to the number of total cells (t = 48 h, n = 4 wells). Data are mean ± SD. One-way ANOVA followed by Dunnett’s test. For (A) and (D)–(G), bar color represents cluster identity (green, c3; red, c2; blue, c5; Figure 1D). (H) Representative images of LC3B immunofluorescence (gray) in MIN6 cells treated with FFAs, BSA as negative control, or autophagy-modulating drugs. Nuclei were detected by Hoechst (blue). c2 FFAs induce autophagosomes. Scale bars, 25 μm. (I) Number of iPSC-derived β cells after exposure to EA or OA for 24, 48, or 72 h at 250, 500, or 750 μM. Only EA decreased cell count in a dose- and time-dependent manner. All EA conditions are significant per two-way ANOVA (p < 0.0001); all OA conditions are not significant. Data are mean ± SD, n = 4–6 wells/time point/FFA. (J) Representative images of iPSC-derived β cells after treatment with BSA, EA, or OA for 48 h at 500 μM. Nuclei are marked by Hoechst and β cell identity is marked by C-peptide staining (orange). Scale bars, 100 μm. (K) Quantification of C-peptide positive cells human β cells dissociated from cadaveric primary islets normalized to the BSA control after exposure to each of 10 FFAs at 3 different concentrations (t = 5 days, n = 6 wells). c2 cluster MUFAs decreased human β cell viability in a dose-dependent manner. Data are mean ± SD. Multiple t test with Bonferroni correction (gold, p < 0.05; blue, p < 0.01; green, p < 0.001; red, p < 0.0001). (L) Representative images of human islets after treatment with BSA, EA, or OA. Nuclei are marked by Hoechst, and β cell identity is marked by C-peptide staining (orange). Scale bars, 100 μm.

**Figure 5.. Long single-bond chain in MUFA (EA) induces a distinct lipidomic profile that is associated with increased membrane rigidity**
(A) Selected features from the decision tree analysis based on meta-features of highest importance (mean decrease accuracy; STAR Methods). The longest single-bond chain is the meta-feature that predicts the inclusion of 13(Z), 16(Z), 19(Z)-docosatrienoic acid as the only PUFA in the c2 cluster and distinguishes between toxic EA and non-toxic OA. (B) Accumulation of longer unsaturated acyl chains found by lipidomic analysis of MIN6 cells treated for 24 h with 500 μM EA (left). A network analysis of the biochemical relationship (lines) between significantly enriched lipid species in the EA-induced lipidome highlighted the accumulation of EA (22:1)-containing triglyceride (TG) species (right). (C) Membrane rigidity as measured by the GP index of Laurdan dye in INS1E β cells after 12 h treatment with 3–6 different concentrations of BSA, PA, EA, or OA. PA increases membrane rigidity proportionally to its concentration; EA decreases membrane rigidity at low concentration similar to OA; but at higher, toxic levels EA increases membrane rigidity similar to PA. Data are mean ± SEM; n = 6 wells.

**Figure 6.. Integration of lipotoxicity transcriptomic signature with T2D GWAS dataset identifies *CMIP* as mediator of genetic and environmental risk for disease**
(A) Gene set analysis (GSA) results based on the c2 lipotoxicity signature. T2D GWAS genes were ranked based on MAGMA score. A T1D and a schizophrenia GWAS dataset served as negative controls. Lipotoxicity gene sets are defined as top DE genes (1%, 5%, and 10%) in transcriptomic profiles from the c2 cluster ranked by p value and log₂ fold change (LFC). GSA showed significant enrichment (FDR < 0.05) for the top 5% (boxed) and 10% lipotoxicity gene sets. (B) Enrichment analysis for all FFA clusters (top 5% gene set) revealed that the c2 cluster gene set is uniquely enriched in the T2D GWAS dataset. (C) Scatterplot of genes based on T2D MAGMA rank (x axis) and lipotoxicity rank (y axis). Horizontal boundary defines top 5% lipotoxicity signature genes, vertical boundary defines top 600 MAGMA-ranked T2D genes. As demonstrated in the schematic (top), genes located in the left (blue) quadrants are associated with T2D genetic risk, genes located in the bottom (yellow) quadrants are associated with lipotoxicity environmental risk, and genes located in the bottom left (green) quadrant are associated with both genetic and environmental risk. Genes of interest (red) drove the enrichment of the lipotoxicity signature in the GWAS dataset. (D) Expression pattern for the top 25 overlapping T2D-lipotoxicity genes across all FFA clusters. Size of dots represents the percentage of FFAs/cluster that induce significant differential expression of corresponding gene (p < 0.05, Benjamini-Hochberg); colors represent strength and directionality of transcriptional changes (log₂ fold change). (E) Venn diagram comparing the results of the analysis using the PA-induced signature alone compared with using the c2 lipotoxicity signature derived from all 20 lipotoxic FFAs. 19/25 genes, including *GLP1R* and *CMIP*, would have been missed if the analysis was limited to the PA-induced signature alone.

**Figure 7.. *Cmip* deletion sensitizes β cells to FFA-mediated injury and cell death**
(A) *Cmip* deletion does not affect cell proliferation. Two-sided t test, n = 19 passages. (B) After exposure to lipotoxic PA or EA, cell death is increased in CMIP KO compared with WT cells. Non-toxic OA and PSA are rendered toxic in CMIP KO cells. Cell death was measured as percentage of viable cells compared with the non-treated control for WT and CMIP KO cells after 72 h exposure to FFAs (500 μM, n = 21 wells). Data are mean ± SD. Two-way ANOVA with multiple comparisons (Šídák correction,****p < 0.0001). (C) Representative images of the cell death assay showing increased susceptibility to EA in CMIP KO cells. Stains include nuclei (Hoechst, blue), apoptotic cells (caspase 3/7, green), and dead cells (propidium iodide, red). Scale bars, 100 μm. (D) Percentage of cells with RELA nuclear translocation after exposure to EA or BSA (500 μM) at 3 h intervals. Data are mean ± SD, n = 7 wells. Two-way ANOVA with multiple comparisons (Šídák correction, ****p < 0.0001). (E) Normalized insulin secretion at baseline after 24 h treatment with FFA (500 μM). Insulin secretion was reduced in CMIP KO cells upon exposure to lipotoxic FFAs. Data are mean ± SD. Two-way ANOVA with multiple comparisons (Holm-Šídák correction, *p < 0.05). (F) Reintroduction of CMIP in CMIP KO cells (CMIP rescue) attenuates the toxic effects of EA. Percentage of cell death after 24 h exposure to FFAs (250 μM, n = 9 wells). Data are mean ± SD. Two-way ANOVA with multiple comparisons (Tukey correction, ****p < 0.0001). (G) CMIP rescue partially restores insulin secretion in cells exposed to c2 FFAs. Normalized glucose-stimulated insulin secretion in WT and CMIP KO cells after 24 h treatment with FFA (500 μM). Data are mean ± SD. Two-way ANOVA with Šídák multiple comparison test (*p < 0.05, n = 3 wells). (H) PI3K p85ɑ immunoprecipitates with CMIP in β cells. Western blot displaying lysate input (left) or coimmunoprecipitation (coIP) with a CMIP or IgG control antibody (right) stained for CMIP (top) or PI3K (bottom) (n = 3 blots). (I) Phosphorylated Akt (pAkt) abundance is increased in CMIP KO cells compared with WT controls. EA exposure increases pAkt in WT cells, but not in CMIP KO cells indicating that CMIP deletion maximizes pAkt levels at baseline. Western blot for pAkt and total Akt after 24 h treatment with 500 μM FFAs (GAPDH, loading control; n = 3 blots). (J) In human iPSC-derived β cells, CMIP KO promotes cell death, in agreement with experiments in MIN6 β cells (B). Cell death in human iPSC-derived β cells after treatment with BSA, EA, or OA at 500 μM for 24 h (n = 24 wells). (****p < 0.0001, two-way ANOVA with Bonferroni multiple comparison test). (K) Representative images of cell death assay in human iPSC-derived β cells as measured by number of nuclei (Hoechst). Scale bars, 100 μm.

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Update of

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity.
Wieder N, Fried JC, Kim C, Sidhom EH, Brown MR, Marshall JL, Arevalo C, Dvela-Levitt M, Kost-Alimova M, Sieber J, Gabriel KR, Pacheco J, Clish C, Abbasi HS, Singh S, Rutter J, Therrien M, Yoon H, Lai ZW, Baublis A, Subramanian R, Devkota R, Small J, Sreekanth V, Han M, Lim D, Carpenter AE, Flannick J, Finucane H, Haigis MC, Claussnitzer M, Sheu E, Stevens B, Wagner BK, Choudhary A, Shaw JL, Pablo JL, Greka A. Wieder N, et al. bioRxiv [Preprint]. 2023 Feb 20:2023.02.19.529127. doi: 10.1101/2023.02.19.529127. bioRxiv. 2023. Update in: Cell Metab. 2023 May 2;35(5):887-905.e11. doi: 10.1016/j.cmet.2023.03.018. PMID: 36865221 Free PMC article. Updated. Preprint.

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FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Affiliations

FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Molecular Biology Databases