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Schilling, B.

Publications and source records attributed to Schilling, B..

3 recordsLinked to original sources

Identification of novel protein lysine acetyltransferasesin Escherichia coli

Post-translational modifications, such as N{varepsilon}-lysine acetylation, regulate protein function. N{varepsilon}-lysine acetylation can occur either non-enzymatically or enzymatically. The non-enzymatic mechanism uses acetyl phosphate (AcP) or acetyl coenzyme A (AcCoA) as acetyl donors to modify an N{varepsilon}-lysine residue of a protein. The enzymatic mechanism uses N{varepsilon}-lysine acetyltransferases (KATs) to specifically transfer an acetyl group from AcCoA to N{varepsilon}-lysine residues on proteins. To date, only one KAT (YfiQ, also known as Pka and PatZ) has been identified in E. coli. Here, we demonstrate the existence of 4 additional E. coli KATs: RimI, YiaC, YjaB, and PhnO. In a genetic background devoid of all known acetylation mechanisms (most notably AcP and YfiQ) and one deacetylase (CobB), overexpression of these putative KATs elicited unique patterns of protein acetylation. We mutated key active site residues and found that most of them eliminated enzymatic acetylation activity. We used mass spectrometry to identify and quantify the specificity of YfiQ and the four novel KATs. Surprisingly, our analysis revealed a high degree of substrate specificity. The overlap between KAT-dependent and AcP-dependent acetylation was extremely limited, supporting the hypothesis that these two acetylation mechanisms play distinct roles in the post-translational modification of bacterial proteins. We further showed that these novel KATs are conserved across broad swaths of bacterial phylogeny. Finally, we determined that one of the novel KATs (YiaC) and the known KAT (YfiQ) can negatively regulate bacterial migration. Together, these results emphasize distinct and specific non-enzymatic and enzymatic protein acetylation mechanisms present in bacteria.\n\nImportanceN{varepsilon}-lysine acetylation is one of the most abundant and important post-translational modifications across all domains of life. One of the best-studied effects of acetylation occurs in eukaryotes, where acetylation of histone tails activates gene transcription. Although bacteria do not have true histones, N{varepsilon}-lysine acetylation is prevalent; however, the role of these modifications is mostly unknown. We constructed an E. coli strain that lacked both known acetylation mechanisms to identify four new N{varepsilon}-lysine acetyltransferases (RimI, YiaC, YjaB, and PhnO). We used mass spectrometry to determine the substrate specificity of these acetyltransferases. Structural analysis of selected substrate proteins revealed site-specific preferences for enzymatic acetylation that had little overlap with the preferences of the previously reported acetyl-phosphate non-enzymatic acetylation mechanism. Finally, YiaC and YfiQ appear to regulate flagellar-based motility, a phenotype critical for pathogenesis of many organisms. These acetyltransferases are highly conserved and reveal deeper and more complex roles for bacterial post-translational modification.

microbiology

Multi-Omic Profiling Reveals the Opposing Forces of Excess Dietary Sugar and Fat on Liver Mitochondria Protein Acetylation and Succinylation

Dietary macronutrient composition alters metabolism through several mechanisms, including post-translational modification (PTM) of proteins. To connect diet and molecular changes, here we performed short- and long-term feeding of mice with standard chow diet (SCD) and high-fat diet (HFD), with or without glucose or fructose supplementation, and quantified liver metabolites, 861 proteins, and 1,815 protein level-corrected mitochondrial acetylation and succinylation sites. Nearly half the acylation sites were altered by at least one diet; nutrient-specific changes in protein acylation sometimes encompass entire pathways. Although acetyl-CoA is an intermediate in both sugar and fat metabolism, acetyl-CoA had a dichotomous fate depending on its source; chronic feeding of dietary sugars induced protein hyperacetylation, whereas the same duration of HFD did not. Instead, HFD resulted in citrate accumulation, anaplerotic metabolism of amino acids, and protein hypo-succinylation. Together, our results demonstrate novel connections between dietary macronutrients, protein post-translational modifications, and regulation of fuel selection in liver.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/263426_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (53K):\norg.highwire.dtl.DTLVardef@1419c64org.highwire.dtl.DTLVardef@82a874org.highwire.dtl.DTLVardef@16023org.highwire.dtl.DTLVardef@4ed44e_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

systems biology

PIQED: Automated Identification And Quantification Of Protein Modifications From DIA-MS Data

Label-free quantification using data-independent acquisition (DIA) is a robust method for deep and accurate proteome quantification1,2. However, when lacking a pre-existing spectral library, as is often the case with studies of novel post-translational modifications (PTMs), samples are typically analyzed several times: one or more data dependent acquisitions (DDA) are used to generate a spectral library followed by DIA for quantification. This type of multi-injection analysis results in significant cost with regard to sample consumption and instrument time for each new PTM study, and may not be possible when sample amount is limiting and/or studies require a large number of biological replicates. Recently developed software (e.g. DIA-Umpire) has enabled combined peptide identification and quantification from a data-independent acquisition without any pre-existing spectral library3,4. Still, these tools are designed for protein level quantification. Here we demonstrate a software tool and workflow that extends DIA-Umpire to allow automated identification and quantification of PTM peptides from DIA. We accomplish this using a custom, open-source graphical user interface DIA-Pipe (https://github.com/jgmeyerucsd/PIQEDia/releases/tag/v0.1.2) (figure 1a).\n\nO_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC=\"FIGDIR/small/141382_fig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@41b8f3org.highwire.dtl.DTLVardef@d59733org.highwire.dtl.DTLVardef@b9a722org.highwire.dtl.DTLVardef@8be50c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Automated Qualitative and Quantitative Analysis of Post-Translational Modifications using DIA-Pipe. (a) Workflow of the all-DIA strategy for identification and quantification of PTMs. Modified peptides enriched from biological samples are analyzed by data-independent acquisition. All data analysis steps starting from instrument. wiff files, acquired on a TripleTOF 5600, can be completed using the DIA-Pipe GUI, including: (1) file conversion and pseudo-MS/MS spectra generation using DIA-Umpire, (2) database searching by MS-GF+, X! Tandem, and Comet followed by results refinement and combination using PeptideProphet/iProphet/PTMProphet, (3) automated spectral library generation and fragment area extraction using SkylineRunner, and finally, (4) Skyline report filtering and formatting for significance testing with mapDIA. (b) mProphet composite score distributions of target and second-best peaks picked by Skylineshowing essentially error-free peak picking by Skyline of peptides identified using pseudo-MS/MS spectra. (c) Distribution of coefficient of variations observed from three technical replicates for 1,182 acetylation sites. (d) Observed distributions of log2(fold change) computed by mapDIA using three technical replicates of 1X injection volume compared to three technical replicates of 0.5X injection volume; expected log2(fold change) value =1. (e) Number of identified peptides from each single replicate injection and from the combination of two or three replicates. An average of 1,655 acetylated peptides were identified per replicate. The combination of two or three replicates increased the number of identifications by 19% or 29%, respectively.\n\nC_FIG

systems biology