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Peptidoglycan (PG) is the main component of bacterial cell walls and the target for many antibiotics. PG biosynthesis is tightly coordinated with cell wall growth and turnover, and many of these control activities depend upon PASTA-domain containing eukaryotic-like serine/threonine protein kinases (PASTA-eSTK) that sense PG fragments. However, only a few PG biosynthetic enzymes are direct kinase substrates. Here, we identify the conserved ReoM protein as a novel PASTA-eSTK substrate in the Gram-positive pathogen Listeria monocytogenes. Our data show that the phosphorylation of ReoM is essential as it controls ClpCP-dependent proteolytic degradation of the essential enzyme MurA, which catalyses the first committed step in PG biosynthesis. We also identify ReoY as a second novel factor required for degradation of ClpCP substrates. Collectively, our data imply that the first committed step of PG biosynthesis is activated through control of ClpCP protease activity in response to signals of PG homeostasis imbalance.

PASTA domains are small modules expressed in bacteria and found in one or multiple copies at the C-terminal end of several penicillin binding proteins (PBPs) and Ser/Thr protein kinases (STPKs) and represent potential targets for a new class of antibiotics. PASTA domains are currently annotated as sensor domains, as they are thought to activate their cognate proteins in response to binding to opportune ligands. However, recent studies have shown that PASTA domains linked to proteins of different classes, STPKs or PBPs, do not share the same binding abilities. Despite this, there is currently no way to distinguish between PASTA domains from the two classes, since all of them share the same fold, independent of the class they belong to. To identify a predictive tool of class identification, we here analyse a pool of parameters, including amino acid compositions and total charges of PASTA domains either linked to PBPs or to STPKs. We screened sequences from Actinobacteria, Firmicutes and Bacteroidetes. The first two phyla include some of the most dangerous micro-organisms for human health such as Mycobacterium tuberculosis and Staphylococcus aureus . Based on this analysis, our study proposes a predictive method to assign PASTA domains with unknown origin to their corresponding enzyme class, based solely on sequence information.