The nucleotide ppGpp is a highly conserved regulatory molecule in prokaryotes that helps tune growth rate to nutrient availability. Despite decades of study, how ppGpp regulates growth remains poorly understood. Here, we develop and validate a capture-compound mass spectrometry approach that identifies >50 putative ppGpp targets in Escherichia coli . These targets control many key cellular processes and include 13 enzymes required for nucleotide synthesis. We demonstrate that ppGpp inhibits the de novo synthesis of all purine nucleotides by directly targeting the enzyme PurF. By solving a structure of PurF bound to ppGpp, we design a mutation that ablates ppGpp-based regulation, leading to a dysregulation of purine nucleotide synthesis following ppGpp accumulation. Collectively, our results provide new insights into ppGpp-based growth control and a nearly comprehensive set of targets for future exploration. The capture compounds developed will also now enable the rapid identification of ppGpp targets in any species, including pathogens.

We have recently recapitulated metastasis of human PTEN/TP53-mutant PC in mouse using the RapidCaP system. Surprisingly, we found that this metastasis is driven by Myc-, and not Akt-activation. Here, we show that cell-cell communication by Il6 drives the Akt-Myc switch through activation of the Akt-suppressing phosphatase Phlpp2, when Pten and p53 are lost together, but not separately. Il6 then communicates a downstream program of Stat3-mediated Myc-activation, which drives cell proliferation. Similarly in tissues, peak proliferation in Pten/Trp53 mutant primary and metastatic PC does not correlate with activated Akt, but with Stat3/Myc activation instead. Mechanistically, Myc strongly activates the Akt phosphatase Phlpp2 in primary cells and PC metastasis. We show genetically that Phlpp2 is essential for dictating proliferation of Myc-mediated Akt-suppression. Collectively, our data reveal competition between two proto-oncogenes: Myc and Akt, which ensnarls the Phlpp2 gene to facilitate Myc-driven PC metastasis after loss of Pten and Trp53.

Intergenic long noncoding RNAs (lincRNAs) are the largest class of transcripts in the human genome. Although many have recently been linked to complex human traits, the underlying mechanisms for most of these transcripts remain undetermined. We investigated the regulatory roles of a high-confidence and reproducible set of 69 trait-relevant lincRNAs (TR-lincRNAs) in human lymphoblastoid cells whose biological relevance is supported by their evolutionary conservation during recent human history and genetic interactions with other trait-associated loci. Their enrichment in enhancer-like chromatin signatures, interactions with nearby trait-relevant protein-coding loci, and preferential location at topologically associated domain (TAD) boundaries provide evidence that TR-lincRNAs likely regulate proximal trait-relevant gene expression in cis by modulating local chromosomal architecture. This is consistent with the positive and significant correlation found between TR-lincRNA abundance and intra-TAD DNA-DNA contacts. Our results provide insights into the molecular mode of action by which TR-lincRNAs contribute to complex human traits.

Summary Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting, but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.