Probing the Global Cellular Responses to Lipotoxicity Caused by Saturated Fatty Acids.

Excessive levels of saturated fatty acids are toxic to cells, although the basis for this lipotoxicity remains incompletely understood. Here, we analyzed the transcriptome, lipidome, and genetic interactions of human leukemia cells exposed to palmitate. Palmitate treatment increased saturated glycerolipids, accompanied by a transcriptional stress response, including upregulation of the endoplasmic reticulum (ER) stress response. A comprehensive genome-wide short hairpin RNA (shRNA) screen identified >350 genes modulating lipotoxicity. Among previously unknown genetic modifiers of lipotoxicity, depletion of RNF213, a putative ubiquitin ligase mutated in Moyamoya vascular disease, protected cells from lipotoxicity. On a broader level, integration of our comprehensive datasets revealed that changes in di-saturated glycerolipids, but not other lipid classes, are central to lipotoxicity in this model. Consistent with this, inhibition of ER-localized glycerol-3-phosphate acyltransferase activity protected from all aspects of lipotoxicity. Identification of genes modulating the response to saturated fatty acids may reveal novel therapeutic strategies for treating metabolic diseases linked to lipotoxicity.

Interrogating Kinase-Substrate Relationships with Proximity Labeling and Phosphorylation Enrichment.

Kinases govern many cellular responses through the reversible transfer of a phosphate moiety to their substrates. However, pairing a substrate with a kinase is challenging. In proximity labeling experiments, proteins proximal to a target protein are marked by biotinylation, and mass spectrometry can be used for their identification. Here, we combine ascorbate peroxidase (APEX) proximity labeling and a phosphorylation enrichment-based workflow, Phospho-APEX (pAPEX), to rapidly identify phosphorylated and biotinylated neighbor proteins which can be considered for candidate substrates. The pAPEX strategy enriches and quantifies differences in proximity for proteins and phosphorylation sites proximal to an APEX2-tagged kinase under the kinase "ON" and kinase "OFF" conditions. As a proof of concept, we identified candidate substrates of MAPK1 in HEK293T and HCT116 cells and candidate substrates of PKA in HEK293T cells. In addition to many known substrates, C15orf39 was identified and confirmed as a novel MAPK1 substrate. In all, we adapted the proximity labeling-based platform to accommodate phosphorylation analysis for kinase substrate identification.

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression.

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.