CRISPRi screens with ProteoStat recovered major cellular proteostasis components

A) MG132-treated cells (at submaximal dose) were stained, sorted into top and bottom quartiles (ProteoStat-high and ProteoStat-low, respectively), and imaged. Red: ProteoStat; Blue: Hoechst 33342. B) FACS schematic. Stained cells are sorted using gates that correct for correlation between staining intensity and cell size. C) Volcano plot visualizing CRISPRi screen results (output from MAGeCK analysis with biological duplicate experiments). Genes in select GO Terms identified with GSEA (in D) are highlighted. D) GO Terms identified with GSEA. Positive Enrichment scores indicate an increased likelihood of genes in the GO Term to increase ProteoStat phenotype when knocked-down.

Lipid uptake and metabolism modulate proteostasis

A) Volcano plot (same as Fig 1B) highlighting select genes involved in lipid uptake and metabolism. B & E) Comparing effects on ProteoStat phenotypes of single-gene vs double-gene KDs (n ≥ 2) C) Effects of candidate gene KDs on ProteoStat phenotypes in cells maintained in media with regular or delipidated FBS for 4 days. (n = 3) D) Effects of supplementing free cholesterol or human LDL in rescuing the ProteoStat-lowering phenotype of cells grown in media with delipidated FBS (DL media) for 5 days (n = 3). Phenotypes in B-E were normalized first to an internal staining control, and then to cells infected with non-targeting guides. (*: p<0.05; **: p<0.01; t-test)

Sphingolipid and cholesterol ester mediate the effect of lipid perturbation on proteostasis

A) ProteoStat phenotype vs abundance of indicated lipid species in cells with CRISPRi KD of only a core set of lipid-related target genes, or including other ProteoStat perturbing genes (insets). Both ProteoStat phenotype and lipid abundance were normalized to cells carrying non-targeting guides. Least squares linear regression models (dotted red lines) were generated for the ProteoStat phenotypes and corresponding lipid abundances, and the Coefficient of determination, R2, values for each model is shown. B) Histograms of R2 values obtained for all lipid species within each indicated lipid class as in A. p-values were calculated using K-S test comparing R2 values within each lipid class versus that of all lipids. C) Schematic of the series of constructs expressing different variations of PSAP (full-length, truncated, carrying mutations deleterious to individual Sap peptides, or individual functional Sap peptides alone). Colored boxes depict individual Sap peptides. Deleterious mutations are depicted by X’s on the corresponding Sap peptide. C) ProteoStat phenotypes in cells overexpressing various PSAP variants in E, in conjunction with either PSAP KD or non-targeting control (n=3, #: p>0.05; t-test).

Effect of lipid perturbation on proteostasis is not mediated through the health and function of lysosomes and proteasomes

A) Representative fluorescence microscopy images of K562 cells expressing TagBFP-GAL3, either untreated or treated with LLOMe for 1.5 hours. B) Gal3 puncta per unit cell area quantified in fluorescence microscopy images taken with CRISPRi cells having various target gene KDs and either untreated or treated with LLOMe (n=2). C-F) Flow cytometry quantification of phenotypes in K562 cells with various target gene KDs. C: TR-X fluorescence (after protease-induced unquenching) as an indicator of lysosomal protease function; D: Ratio of lysosome-targeted sfGFP fluorescence (pH sensitive) to mCherry fluorescence (pH insensitive) as an indicator of lysosomal pH; E: LiperFluo staining as an indicator of lipid peroxidation level; F: LysoTracker-Red fluorescence as an indicator of lysosomal mass and pH. G-H) Efficacy of the 5 antioxidant cocktail (5AO) in rescuing the effects of target gene KDs on G) LiperFluo or H) ProteoStat phenotypes. (*: p<0.05; **: p<0.01; t-test) I) Flow cytometry-based assay with the Proteasome Activity Probe was validated with proteasome inhibitors. J) Effect of target gene KDs on proteasome activity.