Proteomic Identification of Novel Secreted Antibacterial Toxins of the Serratia marcescens Type VI Secretion System

It has recently become apparent that the Type VI secretion system (T6SS) is a complex macromolecular machine used by many bacterial species to inject effector proteins into eukaryotic or bacterial cells, with significant implications for virulence and interbacterial competition. “Antibacterial” T6SSs, such as the one elaborated by the opportunistic human pathogen, Serratia marcescens, confer on the secreting bacterium the ability to rapidly and efficiently kill rival bacteria. Identification of secreted substrates of the T6SS is critical to understanding its role and ability to kill other cells, but only a limited number of effectors have been reported so far. Here we report the successful use of label-free quantitative mass spectrometry to identify at least eleven substrates of the S. marcescens T6SS, including four novel effector proteins which are distinct from other T6SS-secreted proteins reported to date. These new effectors were confirmed as antibacterial toxins and self-protecting immunity proteins able to neutralize their cognate toxins were identified. The global secretomic study also unexpectedly revealed that protein phosphorylation-based post-translational regulation of the S. marcescens T6SS differs from that of the paradigm, H1-T6SS of Pseudomonas aeruginosa. Combined phosphoproteomic and genetic analyses demonstrated that conserved PpkA-dependent threonine phosphorylation of the T6SS structural component Fha is required for T6SS activation in S. marcescens and that the phosphatase PppA can reverse this modification. However, the signal and mechanism of PpkA activation is distinct from that observed previously and does not appear to require cell–cell contact. Hence this study has not only demonstrated that new and species-specific portfolios of antibacterial effectors are secreted by the T6SS, but also shown for the first time that PpkA-dependent post-translational regulation of the T6SS is tailored to fit the needs of different bacterial species.Gram-negative bacteria have evolved several specialized protein secretion systems to secrete a wide variety of substrate proteins into the extracellular milieu or to inject them into other, often eukaryotic, cells (1). Secreted proteins and their associated secretion systems are very important in bacterial virulence and interactions with other organisms (2). One of the most recent discoveries in this field is the Type VI secretion system (T6SS),¹ which occurs widely across bacterial species (3, 4) and can target proteins to both bacterial and eukaryotic cells (5). The significance of the T6SS is becoming increasingly apparent. It has been implicated in virulence, commensalism, and symbiosis with eukaryotes (5, 6). Additionally, in many bacteria, the T6SS is now implicated in antibacterial activity. T6SS-mediated antibacterial killing appears to be important for competition between bacterial species, for example within the resident microflora of a eukaryotic host (5, 7).Secretion by the T6SS relies on 13 conserved core components which are predicted to form a large machinery associated with the cell envelope, including membrane-bound and bacteriophage tail-like subassemblies (8, 9). The membrane bound subassembly consists of inner membrane proteins (TssLM) and an outer membrane lipoprotein (TssJ) and is anchored to the cell wall. The phage tail-like assembly consists of several proteins that show structural homology with T4 phage tail proteins or are organized in similar structures (10). Hcp (TssD) proteins form hexameric rings and are thought to stack into tube-like structures (11, 12). This Hcp tube is believed to be capped by a trimer of VgrG (TssI) proteins, which share structural homology with the needle of the T4 phage tail (10, 13). In addition, VipA (TssB) and VipB (TssC) form a large tubular structure highly reminiscent of the T4 phage tail sheath (14, 15). Such similarities have led to the idea that the T6SS resembles an inverted contractile bacteriophage infection machinery and injects substrates via an Hcp/VgrG needle into other cells. Recent models propose that the VipA/B sheath surrounds the Hcp/VgrG needle and contraction of the VipA/B tube pushes the Hcp/VgrG needle out of the cell (16–18). It has been postulated that this mechanism can be triggered by close contact with other neighboring cells (19–21).Assembly, localization, and remodelling of VipA/B tubules in vivo depend on the AAA+ ATPase ClpV (TssH), another essential core component of the T6SS (14, 16, 17). ClpV also interacts with the accessory component Fha (TagH) (22, 23), which is found in a subset of T6SSs (4). The Fha protein has an N-terminal domain with a forkhead associated motif, which is predicted to bind phospho-threonine peptides (24). In Pseudomonas aeruginosa, Fha1 is phosphorylated by the Thr/Ser kinase PpkA (TagE) and dephosphorylated by the phosphatase PppA (TagG), and the phosphorylation state of Fha1 regulates the activity of the T6SS (22, 23). Phosphorylation of Fha in P. aeruginosa is also controlled by additional components, which act upstream of PpkA and form a regulatory cascade for T6SS activation (22, 25). Although homologs of PpkA and PppA have been identified in the T6SS gene clusters of certain other bacteria (3), the regulation of the T6SS by post-translational protein phosphorylation has not yet been experimentally investigated outside of Pseudomonas.To understand how the T6SS affects eukaryotic and bacterial cells, it is critical to identify substrate proteins secreted by the T6SS. The VgrG and Hcp proteins were the first identified T6SS substrates and appear to be generally secreted to the external milieu by all T6SSs (26). However, as mentioned above, Hcp and VgrG are core components of the T6SS machinery and therefore represent extracellular components of the secretion apparatus rather than genuine secreted effector proteins. Nonetheless, a limited number of VgrG homologs with extra functional effector domains at the C terminus have been identified or predicted, which account for some of the T6SS dependent effects seen against bacteria and eukaryotes. For example, the C-terminal domain of VgrG-1 from Vibrio cholerae shows actin crosslinking activity in eukaryotic cells (13, 27) and the C-terminal domain of V. cholerae VgrG-3 has bacterial cell wall hydrolase activity (28, 29).Recently, following much effort in the field, a small number of proteins secreted by the T6SS, but not structural components, have been experimentally identified. These proteins are regarded as true secreted substrates of the T6SS, with effector functions in target cells (29–35). For example, antibacterial T6SS-secreted effector proteins with peptidoglycan amidase (cell wall hydrolysis) function, the Type VI amidase effector (Tae) proteins, have been identified in Burkholderia thailandensis (32), P. aeruginosa (31), and Serratia marcescens (30). These Tae proteins play a role in T6SS-mediated antibacterial killing activity and genes encoding four families of Tae protein have been widely identified in other bacteria with T6SSs (32). T6SS-secreted effector proteins which are not peptidoglycan hydrolases have also been reported, including Tse2 secreted by P. aeruginosa, which acts in the bacterial cytoplasm (31), and the VasX and TseL proteins secreted by the V. cholerae T6SS, which are suggested to target membrane lipids (29, 34, 35). In the case of antibacterial T6SSs, the secreting bacterial cells are protected from their own T6SS effector proteins by specific immunity proteins (29–32, 35). However, given the large number of T6SSs in different bacterial species and their apparent ability to secrete multiple substrates, experimentally identified T6-secreted effector proteins still remain surprisingly scarce.Here we report the identification of multiple T6SS-secreted effector proteins in S. marcescens. S. marcescens is an opportunistic pathogen, for example causing ocular infections, nosocomial septicemia and pneumonia (36). Previously, we have identified a T6SS in S. marcescens Db10, which targets and efficiently kills other bacterial cells and plays a role in antibacterial competition (37). We have recently demonstrated that this T6SS secretes two antibacterial effectors, the Tae4 homologs Ssp1 and Ssp2, with cognate immunity proteins Rap1a and Rap2a (30).In this work, we report the analysis of the T6SS-dependent secretome of S. marcescens by label-free quantitation (LFQ) mass spectrometry and describe the identification and characterization of four novel T6SS-secreted effector proteins. These were confirmed as antibacterial toxins and specific immunity proteins were identified. Additionally, this global secretomic analysis, in combination with genetic and phosphoproteomic analyses, demonstrated that a post-translational phosphorylation system influences the ability of the S. marcescens T6SS to secrete effector proteins. Although this system uses homologs of the P. aeruginosa PpkA, PppA and Fha components, the circumstances and impact of Fha phosphorylation were shown to vary between organisms.     

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]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

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]     

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]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[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]