Identification of Lysine Succinylation Substrates and the Succinylation Regulatory Enzyme CobB in Escherichia coli

Lysine succinylation is a newly identified protein post-translational modification pathway present in both prokaryotic and eukaryotic cells. However, succinylation substrates and regulatory enzyme(s) remain largely unknown, hindering the biological study of this modification. Here we report the identification of 2,580 bacterial lysine succinylation sites in 670 proteins and 2,803 lysine acetylation (Kac) sites in 782 proteins, representing the first lysine succinylation dataset and the largest Kac dataset in wild-type E. coli. We quantified dynamic changes of the lysine succinylation and Kac substrates in response to high glucose. Our data showed that high-glucose conditions led to more lysine-succinylated proteins and enhanced the abundance of succinyllysine peptides more significantly than Kac peptides, suggesting that glucose has a more profound effect on succinylation than on acetylation. We further identified CobB, a known Sir2-like bacterial lysine deacetylase, as the first prokaryotic desuccinylation enzyme. The identification of bacterial CobB as a bifunctional enzyme with lysine desuccinylation and deacetylation activities suggests that the eukaryotic Kac-regulatory enzymes may have enzymatic activities on various lysine acylations with very different structures. In addition, it is highly likely that lysine succinylation could have unique and more profound regulatory roles in cellular metabolism relative to lysine acetylation under some physiological conditions.Lysine acetylation (Kac)¹ is a dynamic and evolutionarily conserved post-translational modification (PTM) that is known to be involved in the regulation of diverse cellular processes (1–9). The status of this modification is controlled by two groups of enzymes with opposing enzymatic activities, lysine acetyltransferases that add an acetyl group to the lysine (Lys or K) residue, and histone lysine deacetylases (HDACs) that remove the acetyl group (10–16). HDACs are grouped into several categories (17): class I (HDAC1, -2, -3, and -8), class IIA (HDAC4, -5, -7, and -9), class IIB (HDAC6 and -10), class III (Sirt1–7), and class IV (HDAC11). The weak deacetylation activities of some HDACs (e.g. Sirt4–7 and HDAC4, -5, and -7–11), as well as the demonstration of Sirt5 as a desuccinylation and demalonylation enzyme, suggest that some HDAC enzymes have activities that are independent of acetylation (18, 19).For a long period of time, lysine acetylation was considered as a protein modification that was restricted to nuclei (20). The identification of cytosolic Kac substrates and the localization of some HDACs outside nuclei suggest a non-nuclear function of lysine acetylation (13, 21, 22). The first proteomic screening identified hundreds of substrate proteins in cytosolic and mitochondrial fractions and demonstrated high abundance of Kac in mitochondrial proteins and metabolic enzymes (23). This result implies that Kac has diverse non-nuclear roles and can regulate functions of metabolism and mitochondria (23). Since then, we and others have extensively characterized the cellular acetylome (5, 9, 24–26).The lysine succinylation (Ksucc) and lysine malonylation pathways are two PTM pathways that were recently identified and comprehensively validated in both bacterial and mammalian cells, with multiple substrate proteins identified, using HPLC-MS/MS, co-elution of synthetic peptides, isotopic labeling, Western blotting analysis using pan-anti-Ksucc antibodies, and proteomics analysis (18, 27). We also showed that Ksucc is present in core histones (29). In yeast histones, some Ksucc sites are located in regions where histones make close contact with DNA, suggesting that Ksucc sites may be involved in gene regulation by changing the chromatin structure (29). We then found that Sirt5, a member of the class III family of HDACs, can function as a desuccinylation enzyme in vitro and in vivo (18, 19). In a recent study, we revealed that Sirt5 is a key regulatory enzyme of Ksucc and that Ksucc proteins are abundant among a group of mitochondrial enzymes that are predominantly involved in fatty acid metabolism, amino acid degradation, and the tricarboxylic acid cycle (28). Importantly, Ksucc is very dynamic not only in mammalian cells, but also in bacteria (27, 29). These lines of evidence strongly suggest that lysine succinylation is likely an important PTM in the regulation of cellular functions.Although key elements of the Ksucc pathway are being identified in mammalian cells, their counterparts in bacteria remain largely unknown. We and others have used a proteomics approach to identify Kac substrates in bacteria (26, 30, 31, 52). The Sir2-like enzyme CobB is the best-studied protein deacetylase in bacteria (8). CobB was initially identified as an enzyme required for the activation of acetyl-CoA synthetase (8). Recently, CobB was shown to play roles in bacterial energy metabolism (31) and stress response (32). Those studies indicated that Kac is an evolutionarily conserved PTM with a role in energy metabolism in prokaryotes. Nevertheless, dynamic changes of lysine acetylation in bacteria have not been studied. In addition, substrates of lysine succinylation and their regulatory enzymes are not known.In this paper, we report a quantitative proteomic approach based on stable isotope labeling by amino acids in cell culture (SILAC) to identify and quantify changes in bacterial lysine succinylation, as well as lysine acetylation, in response to glucose, a major energy source. Our screening detected 2,580 lysine-succinylated sites in 670 proteins and 2,803 Kac sites in 782 proteins in Escherichia coli. Our quantitative proteomics data show that glucose had a more profound effect on Ksucc than on Kac. In addition, we found that CobB, a known prokaryotic deacetylase, had dual enzymatic activities to catalyze the removal of two structurally different lysine acyl groups, acetyl and succinyl, from the modified lysine residues.     

MqsR, a Crucial Regulator for Quorum Sensing and Biofilm Formation, Is a GCU-specific mRNA Interferase in Escherichia coli

The mqsR gene has been shown to be positively regulated by the quorum-sensing autoinducer AI-2, which in turn activates a two-component system, the qseB-qseC operon. This operon plays an important role in biofilm formation in Escherichia coli. However, its cellular function has remained unknown. Here, we found that 1 base downstream of mqsR there is a gene, ygiT, that is co-transcribed with mqsR. Induction of mqsR caused cell growth arrest, whereas ygiT co-induction recovered cell growth. We demonstrate that MqsR (98 amino acid residues), which has no homology to the well characterized mRNA interferase MazF, is a potent inhibitor of protein synthesis that functions by degrading cellular mRNAs. In vivo and in vitro primer extension experiments showed that MqsR is an mRNA interferase specifically cleaving mRNAs at GCU. The mRNA interferase activity of purified MqsR was inhibited by purified YgiT (131 residues). MqsR forms a stable 2:1 complex with YgiT, and the complex likely functions as a repressor for the mqsR-ygiT operon by specifically binding to two different palindromic sequences present in the 5′-untranslated region of this operon.It has been reported that quorum sensing is involved in biofilm formation (1–4). mqsR expression was found to be induced by 8-fold in biofilms (5) and also by the quorum-sensing signal autoinducer AI-2, which is a species-nonspecific signaling molecule produced by both Gram-negative and Gram-positive bacteria, including Escherichia coli (6). It has been reported that induction of mqsR activates a two-component system, the qseB-qseC operon, which is known to play an important role in biofilm formation (6). Thus, it has been proposed that MqsR (98 amino acid residues) is a regulator of biofilm formation because it activates qseB, which controls the flhDC expression required for motility and biofilm formation in E. coli (6). However, the cellular function of MqsR has remained unknown.Interestingly, all free-living bacteria examined to date contain a number of suicide or toxin genes in their genomes (7, 8). Many of these toxins are co-transcribed with their cognate antitoxins in an operon (termed toxin-antitoxin (TA)² operon) and form a stable complex in the cell, so their toxicity is subdued under normal growth conditions (9–11). However, the stability of antitoxins is substantially lower than that of their cognate toxins, so any stress causing cellular damage or growth inhibition that induces proteases alters the balance between toxin and antitoxin, leading to toxin release in the cell.To date, 16 (12) TA systems have been reported on the E. coli genome, including relB-relE (13, 14), chpBI-chpBK (15), mazEF (16–18), yefM-yoeB (19, 20), dinJ-yafQ (21, 22), hipBA and hicAB (23, 24), prlF-yhaV (25), and ybaJ-hha (26). Interestingly, all of these TA operons appear to use similar modes of regulation: the formation of complexes between antitoxins and their cognate toxins to neutralize toxin activity and the ability of TA complexes to autoregulate their expression. The cellular targets of some toxins have been identified. CcdB directly interacts with gyrase A and blocks DNA replication (27, 28). RelE, which by itself has no endoribonuclease activity, appears to act as a ribosome-associating factor that promotes mRNA cleavage at the ribosome A-site (13, 29, 30). PemK (31), ChpBK (15), and MazF (32) are unique among toxins because they target cellular mRNAs for degradation by functioning as sequence-specific endoribonucleases to effectively inhibit protein synthesis and thereby cell growth.MazF, ChpBK, and PemK have been characterized as sequence-specific endoribonucleases that cleave mRNA at the ACA, ACY (Y is U, A, or G), and UAH (H is C, A, or U) sequences, respectively. They are completely different from other known endoribonucleases such as RNases E, A, and T1, as these toxins function as protein synthesis inhibitors by interfering with the function of cellular mRNAs. It is well known that small RNAs, such as mRNA-interfering cRNA (33), microRNA (34), and small interfering RNA (35), interfere with the function of specific RNAs. These small RNAs bind to specific mRNAs to inhibit their expression. Ribozymes also act on their target RNAs specifically and interfere with their function (36). Therefore, MazF, ChpBK, and PemK homologs form a novel endoribonuclease family that exhibits a new mRNA-interfering mechanism by cleaving mRNAs at specific sequences. Thus, they have been termed “mRNA interferases” (2).During our search for TA systems on the E. coli genome, we found that the mqsR gene is co-transcribed with a downstream gene, ygiT. These two genes appear to function as a TA system, as their size is small (98 residues for MqsR and 131 residues for YgiT) and their respective open reading frames are separated by 1 bp. In this study, we demonstrate that MqsR-YgiT is a new E. coli TA system consisting of a toxin, MqsR, and an antitoxin, YgiT. Moreover, we identify MqsR as a novel mRNA interferase that does not exhibit homology to MazF. This toxin cleaves RNA at GCU sequences in vivo and in vitro. The implication of this finding as to how this mRNA interferase is involved in cell physiology and biofilm formation will be discussed.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.