Intracellular pathways for lignin catabolism in white-rot fungi.

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.

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts.

Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R. toruloides, we leveraged previously reported high-throughput fitness data to identify potential regulators of pentose catabolism. Two genes were selected for further investigation, a putative transcription factor (RTO4_12978, Pnt1) and a homolog of a glucose transceptor involved in carbon catabolite repression (RTO4_11990). Overexpression of Pnt1 increased the specific growth rate approximately twofold early in cultures on xylose and increased the maximum specific growth by 18% while decreasing accumulation of arabitol and xylitol in fast-growing cultures. Improved growth dynamics on xylose translated to a 120% increase in the overall rate of xylose conversion to fatty alcohols in batch culture. Proteomic analysis confirmed that Pnt1 is a major regulator of pentose catabolism in R. toruloides. Deletion of RTO4_11990 increased the growth rate on xylose, but did not relieve carbon catabolite repression in the presence of glucose. Carbon catabolite repression signaling networks remain poorly characterized in R. toruloides and likely comprise a different set of proteins than those mainly characterized in ascomycete fungi.

Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Listeria monocytogenes virulence factors, including listeriolysin O, are secreted in biologically active extracellular vesicles.

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.

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

Regulation of bacterial stringent response by an evolutionarily conserved ribosomal protein L11 methylation.

Lysine and arginine methylation is an important regulator of enzyme activity and transcription in eukaryotes. However, little is known about this covalent modification in bacteria. In this work, we investigated the role of methylation in bacteria. By reanalyzing a large phyloproteomics data set from 48 bacterial strains representing six phyla, we found that almost a quarter of the bacterial proteome is methylated. Many of these methylated proteins are conserved across diverse bacterial lineages, including those involved in central carbon metabolism and translation. Among the proteins with the most conserved methylation sites is ribosomal protein L11 (bL11). bL11 methylation has been a mystery for five decades, as the deletion of its methyltransferase PrmA causes no cell growth defects. Comparative proteomics analysis combined with inorganic polyphosphate and guanosine tetra/pentaphosphate assays of the ΔprmA mutant in Escherichia coli revealed that bL11 methylation is important for stringent response signaling. In the stationary phase, we found that the ΔprmA mutant has impaired guanosine tetra/pentaphosphate production. This leads to a reduction in inorganic polyphosphate levels, accumulation of RNA and ribosomal proteins, and an abnormal polysome profile. Overall, our investigation demonstrates that the evolutionarily conserved bL11 methylation is important for stringent response signaling and ribosomal activity regulation and turnover. IMPORTANCE: Protein methylation in bacteria was first identified over 60 years ago. Since then, its functional role has been identified for only a few proteins. To better understand the functional role of methylation in bacteria, we analyzed a large phyloproteomics data set encompassing 48 diverse bacteria. Our analysis revealed that ribosomal proteins are often methylated at conserved residues, suggesting that methylation of these sites may have a functional role in translation. Further analysis revealed that methylation of ribosomal protein L11 is important for stringent response signaling and ribosomal homeostasis.

Graphene oxide exposure alters gut microbial community composition and metabolism in an in vitro human model.

Graphene oxide (GO) nanomaterials have unique physicochemical properties that make them highly promising for biomedical, environmental, and agricultural applications. There is growing interest in the use of GO and extensive in vitro and in vivo studies have been conducted to assess its nanotoxicity. Although it is known that GO can alter the composition of the gut microbiota in mice and zebrafish, studies on the potential impacts of GO on the human gut microbiome are largely lacking. This study addresses an important knowledge gap by investigating the impact of GO exposure- at low (25 mg/L) and high (250 mg/L) doses under both fed (nutrient rich) and fasted (nutrient deplete) conditions- on the gut microbial communitys' structure and function, using an in vitro model. This model includes simulated oral, gastric, small intestinal phase digestion of GO followed by incubation in a colon bioreactor. 16S rRNA amplicon sequencing revealed that GO exposure resulted in a restructuring of community composition. 25 mg/L GO induced a marked decrease in the Bacteroidota phylum and increased the ratio of Firmicutes to Bacteroidota (F/B). Untargeted metabolomics on the supernatants indicated that 25 mg/L GO impaired microbial utilization and metabolism of substrates (amino acids, carbohydrate metabolites) and reduced production of beneficial microbial metabolites such as 5-hydroxyindole-3-acetic acid and GABA. Exposure to 250 mg/L GO resulted in community composition and metabolome profiles that were very similar to the controls that lacked both GO and digestive enzymes. Differential abundance analyses revealed that 3 genera from the phylum Bacteroidota (Bacteroides, Dysgonomonas, and Parabacteroides) were more abundant after 250 mg/L GO exposure, irrespective of feed state. Integrative correlation network analysis indicated that the phylum Bacteroidota showed strong positive correlations to multiple microbial metabolites including GABA and 3-indoleacetic acid, are much larger number of correlations compared to other phyla. These results show that GO exposure has a significant impact on gut microbial community composition and metabolism at both low and high GO concentrations.

LipidOz enables automated elucidation of lipid carbon-carbon double bond positions from ozone-induced dissociation mass spectrometry data.

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

Itaconic acid production is regulated by LaeA in Aspergillus pseudoterreus.

The global regulator LaeA controls secondary metabolism in diverse Aspergillus species. Here we explored its role in regulation of itaconic acid production in Aspergillus pseudoterreus. To understand its role in regulating metabolism, we deleted and overexpressed laeA, and assessed the transcriptome, proteome, and secreted metabolome prior to and during initiation of phosphate limitation induced itaconic acid production. We found that secondary metabolite clusters, including the itaconic acid biosynthetic gene cluster, are regulated by laeA and that laeA is required for high yield production of itaconic acid. Overexpression of LaeA improves itaconic acid yield at the expense of biomass by increasing the expression of key biosynthetic pathway enzymes and attenuating the expression of genes involved in phosphate acquisition and scavenging. Increased yield was observed in optimized conditions as well as conditions containing excess nutrients that may be present in inexpensive sugar containing feedstocks such as excess phosphate or complex nutrient sources. This suggests that global regulators of metabolism may be useful targets for engineering metabolic flux that is robust to environmental heterogeneity.

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium.

BACKGROUND: Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. RESULTS: We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function. CONCLUSION: This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling.

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.

Omics Approaches for Understanding Biogenesis, Composition and Functions of Fungal Extracellular Vesicles.

Extracellular vesicles (EVs) are lipid bilayer structures released by organisms from all kingdoms of life. The diverse biogenesis pathways of EVs result in a wide variety of physical properties and functions across different organisms. Fungal EVs were first described in 2007 and different omics approaches have been fundamental to understand their composition, biogenesis, and function. In this review, we discuss the role of omics in elucidating fungal EVs biology. Transcriptomics, proteomics, metabolomics, and lipidomics have each enabled the molecular characterization of fungal EVs, providing evidence that these structures serve a wide array of functions, ranging from key carriers of cell wall biosynthetic machinery to virulence factors. Omics in combination with genetic approaches have been instrumental in determining both biogenesis and cargo loading into EVs. We also discuss how omics technologies are being employed to elucidate the role of EVs in antifungal resistance, disease biomarkers, and their potential use as vaccines. Finally, we review recent advances in analytical technology and multi-omic integration tools, which will help to address key knowledge gaps in EVs biology and translate basic research information into urgently needed clinical applications such as diagnostics, and immuno- and chemotherapies to fungal infections.

A Histoplasma capsulatum Lipid Metabolic Map Identifies Antifungal Targets.

Lipids play a fundamental role in fungal cell biology, being essential cell membrane components and major targets of antifungal drugs. A deeper knowledge of lipid metabolism is key for developing new drugs and a better understanding of fungal pathogenesis. Here, we built a comprehensive map of the Histoplasma capsulatum lipid metabolic pathway by incorporating proteomic and lipidomic analyses. We performed genetic complementation and overexpression of H. capsulatum genes in Saccharomyces cerevisiae to validate reactions identified in the map and to determine enzymes responsible for catalyzing orphan reactions. The map led to the identification of both the fatty acid desaturation and the sphingolipid biosynthesis pathways as targets for drug development. We found that the sphingolipid biosynthesis inhibitor myriocin, the fatty acid desaturase inhibitor thiocarlide, and the fatty acid analog 10-thiastearic acid inhibit H. capsulatum growth in nanomolar to low-micromolar concentrations. These compounds also reduced the intracellular infection in an alveolar macrophage cell line. Overall, this lipid metabolic map revealed pathways that can be targeted for drug development. IMPORTANCE It is estimated that 150 people die per hour due to the insufficient therapeutic treatments to combat fungal infections. A major hurdle to developing antifungal therapies is the scarce knowledge on the fungal metabolic pathways and mechanisms of virulence. In this context, fungal lipid metabolism is an excellent candidate for developing drugs due to its essential roles in cellular scaffolds, energy storage, and signaling transductors. Here, we provide a detailed map of Histoplasma capsulatum lipid metabolism. The map revealed points of this fungus lipid metabolism that can be targeted for developing antifungal drugs.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

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.

Remodeling of the Histoplasma Capsulatum Membrane Induced by Monoclonal Antibodies.

Antibodies play a central role in host immunity by directly inactivating or recognizing an invading pathogen to enhance different immune responses to combat the invader. However, the cellular responses of pathogens to the presence of antibodies are not well-characterized. Here, we used different mass spectrometry techniques to study the cellular responses of the pathogenic fungus Histoplasma capsulatum to monoclonal antibodies (mAb) against HSP60, the surface protein involved in infection. A proteomic analysis of H. capsulatum yeast cells revealed that mAb binding regulates a variety of metabolic and signaling pathways, including fatty acid metabolism, sterol metabolism, MAPK signaling and ubiquitin-mediated proteolysis. The regulation of the fatty acid metabolism was accompanied by increases in the level of polyunsaturated fatty acids, which further augmented the degree of unsaturated lipids in H. capsulatum's membranes and energy storage lipids, such as triacylglycerols, phosphatidylcholines, phosphatidylethanolamines and phosphatidylinositols. MAb treatment also regulated sterol metabolism by increasing the levels of cholesterol and ergosterol in the cells. We also showed that global changes in the lipid profiles resulted in an increased susceptibility of H. capsulatum to the ergosterol-targeting drug amphotericin B. Overall, our data showed that mAb induction of global changes in the composition of H. capsulatum membranes can potentially impact antifungal treatment during histoplasmosis.

Evaluation of chromosomal insertion loci in the Pseudomonas putida KT2440 genome for predictable biosystems design.

The development of Pseudomonas strains for industrial production of fuels and chemicals will require the integration of heterologous genes and pathways into the chromosome. Finding the most appropriate integration site to maximize strain performance is an essential part of the strain design process. We characterized seven chromosomal loci in Pseudomonas putida KT2440 for integration of a fluorescent protein expression construct. Insertion in five of the loci did not affect growth rate, but fluorescence varied by up to 27-fold. Three sites displaying a diversity of phenotypes with the fluorescent reporter were also chosen for the integration of a gene encoding a muconate importer. Depending on the integration locus, expression of the importer varied by approximately 3-fold and produced significant phenotypic differences. This work demonstrates the impact of the integration location on host viability, gene expression, and overall strain performance.

High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440.

Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM.

Concentration-dependent protein loading of extracellular vesicles released by Histoplasma capsulatum after antibody treatment and its modulatory action upon macrophages.

Diverse pathogenic fungi secrete extracellular vesicles (EV) that contain macromolecules, including virulence factors that can modulate the host immune response. We recently demonstrated that the binding of monoclonal antibodies (mAb) modulates how Histoplasma capsulatum load and releases its extracellular vesicles (EV). In the present paper, we addressed a concentration-dependent impact on the fungus' EV loading and release with different mAb, as well as the pathophysiological role of these EV during the host-pathogen interaction. We found that the mAbs differentially regulate EV content in concentration-dependent and independent manners. Enzymatic assays demonstrated that laccase activity in EV from H. capsulatum opsonized with 6B7 was reduced, but urease activity was not altered. The uptake of H. capsulatum by macrophages pre-treated with EV, presented an antibody concentration-dependent phenotype. The intracellular killing of yeast cells was potently inhibited in macrophages pre-treated with EV from 7B6 (non-protective) mAb-opsonized H. capsulatum and this inhibition was associated with a decrease in the reactive-oxygen species generated by these macrophages. In summary, our findings show that opsonization quantitatively and qualitatively modifies H. capsulatum EV load and secretion leading to distinct effects on the host's immune effector mechanisms, supporting the hypothesis that EV sorting and secretion are dynamic mechanisms for a fine-tuned response by fungal cells.

Ancient Regulatory Role of Lysine Acetylation in Central Metabolism.

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.IMPORTANCE Post-translational modifications can regulate the activity and localization of proteins inside the cell. Similar to phosphorylation, lysine acetylation is present in both eukaryotes and prokaryotes and modifies hundreds to thousands of proteins in cells. However, how lysine acetylation regulates protein function and whether such a mechanism is evolutionarily conserved is still poorly understood. Here, we investigated evolutionary and functional aspects of lysine acetylation by searching for acetylated lysines in a comprehensive proteomic data set from 48 phylogenetically distant bacteria. We found that lysine acetylation occurs in evolutionarily conserved lysine residues in catalytic sites of enzymes involved in central carbon metabolism. Moreover, this modification inhibits enzymatic activity. Our observations suggest that lysine acetylation is an evolutionarily conserved mechanism of controlling central metabolic activity by directly blocking enzyme active sites.