Lysine acetylation is a common protein post-translational modification in bacteria and eukaryotes. Unlike phosphorylation, whose functional role in signaling has been established, it is unclear what regulatory mechanism acetylation plays and whether it is conserved across evolution. By performing a proteomic analysis of 48 phylogenetically distant bacteria, we discovered conserved acetylation sites on catalytically essential lysine residues that are invariant throughout evolution. Lysine acetylation removes the residue’s charge and changes the shape of the pocket required for substrate or cofactor binding. Two-thirds of glycolytic and tricarboxylic acid (TCA) cycle enzymes are acetylated at these critical sites. Our data suggest that acetylation may play a direct role in metabolic regulation by switching off enzyme activity. We propose that protein acetylation is an ancient and widespread mechanism of protein activity regulation.

Outer membrane vesicles produced by Gram-negative bacteria have been studied for half a century but the possibility that Gram-positive bacteria secrete extracellular vesicles (EVs) was not pursued until recently due to the assumption that the thick peptidoglycan cell wall would prevent their release to the environment. However, following their discovery in fungi, which also have cell walls, EVs have now been described for a variety of Gram-positive bacteria. EVs purified from Gram-positive bacteria are implicated in virulence, toxin release, and transference to host cells, eliciting immune responses, and spread of antibiotic resistance. Listeria monocytogenes is a Gram-positive bacterium that causes listeriosis. Here we report that L. monocytogenes produces EVs with diameters ranging from 20 to 200 nm, containing the pore-forming toxin listeriolysin O (LLO) and phosphatidylinositol-specific phospholipase C (PI-PLC). Cell-free EV preparations were toxic to mammalian cells, the murine macrophage cell line J774.16, in a LLO-dependent manner, evidencing EV biological activity. The deletion of plcA increased EV toxicity, suggesting PI-PLC reduced LLO activity. Using simultaneous metabolite, protein, and lipid extraction (MPLEx) multiomics we characterized protein, lipid, and metabolite composition of bacterial cells and secreted EVs and found that EVs carry the majority of listerial virulence proteins. Using immunogold EM we detected LLO at several organelles within infected human epithelial cells and with high-resolution fluorescence imaging we show that dynamic lipid structures are released from L. monocytogenes during infection. Our findings demonstrate that L. monocytogenes uses EVs for toxin release and implicate these structures in mammalian cytotoxicity. The pathogenic Gram-positive bacterium Listeria monocytogenes is the etiological agent of listeriosis, a disease with serious consequences for pregnant women, newborns, and immunocompromised persons. Healthy individuals who have ingested large amounts of L. monocytogenes can suffer from gastroenteritis when the bacterium passes through the gastrointestinal barrier (1-4). L. monocytogenes can cause spontaneous abortions in pregnant women and meningoencephalitis by crossing the placental and blood-brain barriers, respectively (5). To invade cells, cross these barriers, and evade the immune system, L. monocytogenes has a sophisticated intracellular life cycle and pathogenic strategy (6, 7). Initially, L. monocytogenes invades various cell types, including nonphagocytic cells, by utilizing two internalins, internalin A (InlA) and internalin B (InlB), with a minor contribution by the pore-forming toxin listeriolysin O (LLO), 7 to induce uptake of the bacterium (1, 5, 8-11). Once internalized in the host vacuole, L. monocytogenes employs LLO, phosphatidylcholinespecific phospholipase (PC-PLC), and phosphatidylinositolspecific phospholipase C (PI-PLC) to disrupt the single vacuolar membrane, releasing the bacterium into the cytoplasm The authors declare that they have...

Lignin is a biopolymer found in plant cell walls that accounts for 30% of the organic carbon in the biosphere. White-rot fungi (WRF) are considered the most efficient organisms at degrading lignin in nature. While lignin depolymerization by WRF has been extensively studied, the possibility that WRF are able to utilize lignin as a carbon source is still a matter of controversy. Here, we employ 13C-isotope labeling, systems biology approaches, and in vitro enzyme assays to demonstrate that two WRF, Trametes versicolor and Gelatoporia subvermispora, funnel carbon from lignin-derived aromatic compounds into central carbon metabolism via intracellular catabolic pathways. These results provide insights into global carbon cycling in soil ecosystems and furthermore establish a foundation for employing WRF in simultaneous lignin depolymerization and bioconversion to bioproducts—a key step toward enabling a sustainable bioeconomy.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.Given its ease of use and low operational cost, GC-MS has applications with broad societal effect, such as detection of metabolic disease in newborns, toxicology, doping, forensics, food science and clinical testing. The predominant ionization technique in GC-MS is electron ionization (EI), in which all compounds are ionized by high-energy (70-eV) electrons. Because fragmentation occurs with ionization, EI GC-MS data are subjected to spectral deconvolution, a process that separates fragmentation ion patterns for each eluting molecule into a composite mass spectrum.The 70 eV for ionizing electrons in GC-MS has been the standard, making it possible to use decades-old EI reference spectra for annotation 1 . There are ~1.2 million reference spectra that have been accumulated and curated over a period of more than 50 years 2 . Many tools and repositories for GC-MS data have been introduced [3][4][5][6][7][8][9][10][11][12][13][14][15] ; however, much of GC-MS data processing is restricted to vendor-specific formats and software 8 . Currently, deconvolution requires setting multiple parameters manually [3][4][5] or posessing computational skills to run the software 7 . Also, the lack of data sharing in a uniform format precludes data comparison between laboratories and prevents taking advantage of repository-scale information and community knowledge, resulting in infrequent reuse of GC-MS data 8,[11][12][13][14][15] .Although batch modes exist, deconvolution quality is currently not enhanced by using information from all other files. To leverage across-file information, improve scalability of spectral deconvolution and eliminate the need for manually setting the deconvolution parameters (m/z error correction of the ions and peak shapeslopes of raising and trailing edges, peak RT shifts and noise/intensity thresholds), we developed an algorithmic learning strategy for auto-deconvolution (Fig. 1a-f). We deployed this functionality within GNPS/MassIVE (https://gnps.ucsd.edu) 16 (Fig. 1f-i). To promote analysis reproducibility, all GNPS jobs performed are retained in the 'My User' space and can be shared as hyperlinks.This user-independent 'automatic' parameter optimization is accomplished via fast Fourier transform (FFT), multiplication and inverse Fourier transform for each ion across an entire data set, followed by an unsupervised non-negative matrix factorization (NMF) (one-layer neural network). Then, the compositional consistency of spectral patterns for each spec...