Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy.

Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in Mycobacterium smegmatis (Msm), a model for the human pathogen M. tuberculosis (Mtb). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential Msm proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future Mtb PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.

Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions.

Protein-protein interactions (PPIs) are ubiquitous in biology, yet a comprehensive structural characterization of the PPIs underlying biochemical processes is lacking. Although AlphaFold-Multimer (AF-M) has the potential to fill this knowledge gap, standard AF-M confidence metrics do not reliably separate relevant PPIs from an abundance of false positive predictions. To address this limitation, we used machine learning on well curated datasets to train a Structure Prediction and Omics informed Classifier called SPOC that shows excellent performance in separating true and false PPIs, including in proteome-wide screens. We applied SPOC to an all-by-all matrix of nearly 300 human genome maintenance proteins, generating ~40,000 predictions that can be viewed at predictomes.org, where users can also score their own predictions with SPOC. High confidence PPIs discovered using our approach suggest novel hypotheses in genome maintenance. Our results provide a framework for interpreting large scale AF-M screens and help lay the foundation for a proteome-wide structural interactome.

Adapting an Isobaric Tag-Labeled Yeast Peptide Standard to Develop Targeted Proteomics Assays.

Targeted proteomics strategies present a streamlined hypothesis-driven approach to analyze specific sets of pathways or disease related proteins. goDig is a quantitative, targeted tandem mass tag (TMT)-based assay that can measure the relative abundance differences for hundreds of proteins directly from unfractionated mixtures. Specific protein groups or entire pathways of up to 200 proteins can be selected for quantitative profiling, while leveraging sample multiplexing permits the simultaneous analysis of up to 18 samples. Despite these benefits, implementing goDig is not without challenges, as it requires access to an instrument application programming interface (iAPI), an elution order and spectral library, a web-based method builder, and dedicated companion software. In addition, the absence of an example test assay may dissuade researchers from testing or implementing goDig. Here, we repurpose the TKO11 standard─which is commercially available but may also be assembled in-lab─and establish it as a de facto test assay for goDig. We build a proteome-wide goDig yeast library, quantify protein expression across several gene ontology (GO) categories, and compare these results to a fully fractionated yeast gold-standard data set. Essentially, we provide a guide detailing the goDig-based quantification of TKO11, which can also be used as a template for user-defined assays in other species.

SARS-CoV-2 infection rewires host cell metabolism and is potentially susceptible to mTORC1 inhibition.

Viruses hijack host cell metabolism to acquire the building blocks required for replication. Understanding how SARS-CoV-2 alters host cell metabolism may lead to potential treatments for COVID-19. Here we profile metabolic changes conferred by SARS-CoV-2 infection in kidney epithelial cells and lung air-liquid interface (ALI) cultures, and show that SARS-CoV-2 infection increases glucose carbon entry into the TCA cycle via increased pyruvate carboxylase expression. SARS-CoV-2 also reduces oxidative glutamine metabolism while maintaining reductive carboxylation. Consistent with these changes, SARS-CoV-2 infection increases the activity of mTORC1 in cell lines and lung ALI cultures. Lastly, we show evidence of mTORC1 activation in COVID-19 patient lung tissue, and that mTORC1 inhibitors reduce viral replication in kidney epithelial cells and lung ALI cultures. Our results suggest that targeting mTORC1 may be a feasible treatment strategy for COVID-19 patients, although further studies are required to determine the mechanism of inhibition and potential efficacy in patients.

Asparagine couples mitochondrial respiration to ATF4 activity and tumor growth.

Mitochondrial respiration is critical for cell proliferation. In addition to producing ATP, respiration generates biosynthetic precursors, such as aspartate, an essential substrate for nucleotide synthesis. Here, we show that in addition to depleting intracellular aspartate, electron transport chain (ETC) inhibition depletes aspartate-derived asparagine, increases ATF4 levels, and impairs mTOR complex I (mTORC1) activity. Exogenous asparagine restores proliferation, ATF4 and mTORC1 activities, and mTORC1-dependent nucleotide synthesis in the context of ETC inhibition, suggesting that asparagine communicates active respiration to ATF4 and mTORC1. Finally, we show that combination of the ETC inhibitor metformin, which limits tumor asparagine synthesis, and either asparaginase or dietary asparagine restriction, which limit tumor asparagine consumption, effectively impairs tumor growth in multiple mouse models of cancer. Because environmental asparagine is sufficient to restore tumor growth in the context of respiration impairment, our findings suggest that asparagine synthesis is a fundamental purpose of tumor mitochondrial respiration, which can be harnessed for therapeutic benefit to cancer patients.

Differential Metabolic Reprogramming by Zika Virus Promotes Cell Death in Human versus Mosquito Cells.

Zika virus is a pathogen that poses serious consequences, including congenital microcephaly. Although many viruses reprogram host cell metabolism, whether Zika virus alters cellular metabolism and the functional consequences of Zika-induced metabolic changes remain unknown. Here, we show that Zika virus infection differentially reprograms glucose metabolism in human versus C6/36 mosquito cells by increasing glucose use in the tricarboxylic acid cycle in human cells versus increasing glucose use in the pentose phosphate pathway in mosquito cells. Infection of human cells selectively depletes nucleotide triphosphate levels, leading to elevated AMP/ATP ratios, AMP-activated protein kinase (AMPK) phosphorylation, and caspase-mediated cell death. AMPK is also phosphorylated in Zika virus-infected mouse brain. Inhibiting AMPK in human cells decreases Zika virus-mediated cell death, whereas activating AMPK in mosquito cells promotes Zika virus-mediated cell death. These findings suggest that the differential metabolic reprogramming during Zika virus infection of human versus mosquito cells determines whether cell death occurs.

Extracellular Matrix Remodeling Regulates Glucose Metabolism through TXNIP Destabilization.

The metabolic state of a cell is influenced by cell-extrinsic factors, including nutrient availability and growth factor signaling. Here, we present extracellular matrix (ECM) remodeling as another fundamental node of cell-extrinsic metabolic regulation. Unbiased analysis of glycolytic drivers identified the hyaluronan-mediated motility receptor as being among the most highly correlated with glycolysis in cancer. Confirming a mechanistic link between the ECM component hyaluronan and metabolism, treatment of cells and xenografts with hyaluronidase triggers a robust increase in glycolysis. This is largely achieved through rapid receptor tyrosine kinase-mediated induction of the mRNA decay factor ZFP36, which targets TXNIP transcripts for degradation. Because TXNIP promotes internalization of the glucose transporter GLUT1, its acute decline enriches GLUT1 at the plasma membrane. Functionally, induction of glycolysis by hyaluronidase is required for concomitant acceleration of cell migration. This interconnection between ECM remodeling and metabolism is exhibited in dynamic tissue states, including tumorigenesis and embryogenesis.