Bacterial protein networks: properties and functions

Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems

The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.

A dynamic duo: Understanding the roles of FtsZ and FtsA for Escherichia coli cell division through in vitro approaches

Bacteria divide by binary fission. The protein machine responsible for this process is the divisome, a transient assembly of more than 30 proteins in and on the surface of the cytoplasmic membrane. Together, they constrict the cell envelope and remodel the peptidoglycan layer to eventually split the cell into two. For Escherichia coli, most molecular players involved in this process have probably been identified, but obtaining the quantitative information needed for a mechanistic understanding can often not be achieved from experiments in vivo alone. Since the discovery of the Z-ring more than 30 years ago, in vitro reconstitution experiments have been crucial to shed light on molecular processes normally hidden in the complex environment of the living cell. In this review, we summarize how rebuilding the divisome from purified components - or at least parts of it - have been instrumental to obtain the detailed mechanistic understanding of the bacterial cell division machinery that we have today.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Interaction networks of Escherichia coli replication proteins under different bacterial growth conditions

In this work we analyzed protein-protein interactions (PPIs) formed by E. coli replication proteins under three disparate bacterial growth conditions. The chosen conditions corresponded to fast exponential growth, slow exponential growth and growth cessation at the stationary phase. We performed affinity purification coupled with mass spectrometry (AP-MS) of chromosomally expressed proteins (DnaA, DnaB, Hda, SeqA, DiaA, DnaG, HolD, NrdB), tagged with sequential peptide affinity (SPA) tag. Composition of protein complexes was characterized using MaxQuant software. To filter out unspecific interactions, we employed double negative control system and we proposed qualitative and quantitative data analysis strategies that can facilitate hits identification in other AP-MS datasets. Our motivation to undertake this task was still insufficient understanding of molecular mechanisms coupling DNA replication to cellular growth. Previous works suggested that such control mechanisms could involve physical interactions of replication factors with metabolic or cell envelope proteins. However, the dynamic replication protein interaction network (PIN) obtained in this study can be used to characterize links between DNA replication and various cellular processes in other contexts.

Systematic analysis of drug combinations against Gram-positive bacteria

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

Tocopherol-assisted magnetic Ag-Fe3O4-TiO2 nanocomposite for photocatalytic bacterial-inactivation with elucidation of mechanism and its hazardous level assessment with zebrafish model

In recent years, many endeavours have been prompted with photocatalytic nanomaterials by the need to eradicate pathogenic microorganisms from water bodies. Herein, a tocopherol-assisted Ag-Fe₃O₄-TiO₂ nanocomposite (TAFTN) was synthesized for photocatalytic bacterial inactivation. The prepared TAFTN became active under sunlight due to its narrowed bandgap, inactivating the bacterial contaminants via photo-induced ROS stress. The ROS radicals destroy bacteria by creating oxidative stress, which damages the cell membrane and cellular components such as nucleic acids and proteins. For the first time, the nano-LC-MS/MS-based quantitative proteomics reveals that the disrupted proteins are involved in a variety of cellular functions; the most of these are involved in the metabolic pathway, eventually leading to bacterial death during TAFTN-photocatalysis under sunlight. Furthermore, the toxicity analysis confirmed that the inactivated bacteria seemed to have no detrimental impact on zebrafish model, showing that the disinfected water via TAFTN-photocatalysis is enormously safe. Furthermore, the TAFTN-photocatalysis successfully killed the bacterial cells in natural seawater, indicating the consistent photocatalytic efficacy when recycled repeatedly. The results of this work demonstrate that the produced nanocomposite might be a powerful recyclable and sunlight-active photocatalyst for environmental water treatment.

Co-fitness analysis identifies a diversity of signal proteins involved in the utilization of specific c-type cytochromes

Purpose

c-Type cytochromes are essential for extracellular electron transfer (EET) in electroactive microorganisms. The expression of appropriate c-type cytochromes is an important feature of these microorganisms in response to different extracellular electron acceptors. However, how these diverse c-type cytochromes are tightly regulated is still poorly understood.

Methods

In this study, we identified the high co-fitness genes that potentially work with different c-type cytochromes by using genome-wide co-fitness analysis. We also constructed and studied the co-fitness networks that composed of c-type cytochromes and the top 20 high co-fitness genes of them.

Results

We found that high co-fitness genes of c-type cytochromes were enriched in signal transduction processes in Shewanella oneidensis MR-1 cells. We then checked the top 20 co-fitness proteins for each of the 41 c-type cytochromes and identified the corresponding signal proteins for different c-type cytochromes. In particular, through the analysis of the high co-fitness signal protein for CymA, we further confirmed the cooperation between signal proteins and c-type cytochromes and identified a novel signal protein that is putatively involved in the regulation of CymA. In addition, we showed that these signal proteins form two signal transduction modules.

Conclusion

Taken together, these findings provide novel insights into the coordinated utilization of different c-type cytochromes under diverse conditions.

Global Landscape of Native Protein Complexes in Synechocystis sp. PCC 6803

Synechocystis sp. PCC 6803 (hereafter: Synechocystis) is a model organism for studying photosynthesis, energy metabolism, and environmental stress. Although known as the first fully sequenced phototrophic organism, Synechocystis still has almost half of its proteome without functional annotations. In this study, by using co-fractionation coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS), we define 291 multi-protein complexes, encompassing 24,092 protein-protein interactions (PPIs) among 2062 distinct gene products. This information not only reveals the roles of photosynthesis in metabolism, cell motility, DNA repair, cell division, and other physiological processes, but also shows how protein functions vary from bacteria to higher plants due to changes in interaction partners. It also allows us to uncover the functions of hypothetical proteins, such as Sll0445, Sll0446, and Sll0447 involved in photosynthesis and cell motility, and Sll1334 involved in regulation of fatty acid biogenesis. Here we present the most extensive PPI data for Synechocystis so far, which provide critical insights into fundamental molecular mechanisms in cyanobacteria.

Deconstructing the Phage-Bacterial Biofilm Interaction as a Basis to Establish New Antibiofilm Strategies

The bacterial biofilm constitutes a complex environment that endows the bacterial community within with an ability to cope with biotic and abiotic stresses. Considering the interaction with bacterial viruses, these biofilms contain intrinsic defense mechanisms that protect against phage predation; these mechanisms are driven by physical, structural, and metabolic properties or governed by environment-induced mutations and bacterial diversity. In this regard, horizontal gene transfer can also be a driver of biofilm diversity and some (pro)phages can function as temporary allies in biofilm development. Conversely, as bacterial predators, phages have developed counter mechanisms to overcome the biofilm barrier. We highlight how these natural systems have previously inspired new antibiofilm design strategies, e.g., by utilizing exopolysaccharide degrading enzymes and peptidoglycan hydrolases. Next, we propose new potential approaches including phage-encoded DNases to target extracellular DNA, as well as phage-mediated inhibitors of cellular communication; these examples illustrate the relevance and importance of research aiming to elucidate novel antibiofilm mechanisms contained within the vast set of unknown ORFs from phages.

Protein-protein interaction network of E. coli K-12 has significant high-dimensional cavities: New insights from algebraic topological studies

As a model system, Escherichia coli (E. coli) has been used to study various life processes. A dramatic paradigm shift has occurred in recent years, with the study of single proteins moving towards the study of dynamically-interacting proteins, especially protein-protein interaction (PPI) networks. However, despite the importance of PPI networks, little is known about the intrinsic nature of the network structure, especially high-dimensional topological properties . By introducing general hypergeometric distribution, here we reconstruct a statistically-reliable combined PPI network of E. coli (E.coli-PPI-Network) from several datasets. Unlike traditional graph analysis, algebraic topology was introduced to analyze the topological structures of the E.coli-PPI-Network, including high-dimensional cavities and cycles. Random networks with the same node and edge number (RandomNet), or scale-free networks with the same degree distribution (RandomNet-SameDD) were produced as controls. We discovered that the E.coli-PPI-Network had special algebraic typological structures, exhibiting more high-dimensional cavities and cycles, compared to RandomNets or, importantly, RandomNet-SameDD. Based on these results, we defined degree of involved q dimensional cycles of proteins (q-DC_protein ) in the network, a novel concept which relies on the integral structure of the network and is different from traditional node degree or hubs. Finally, top proteins ranked by their 1-DC_protein were identified. In conclusion, by introducing mathematical and computer technologies, we discovered novel algebraic topological properties of the E.coli-PPI-Network, which has special high-dimensional cavities and cycles, and thereby revealed certain intrinsic rules of information flow underlining bacteria biology.

A study of the antibacterial mechanism of pinocembrin against multidrug-resistant Aeromonas hydrophila

Aeromonas hydrophila is a common pathogen in fish that has caused severe economic losses in aquaculture worldwide. With the emergence of bacterial resistance, it is necessary to develop new drugs to combat bacterial infection, particularly for multidrug-resistant bacteria. In this study, the antibacterial activity of pinocembrin was investigated by observing bacterial growth and microscopic structure, and its mechanism of action was identified by investigating its effect on protein and DNA. The antibacterial susceptibility test indicated that pinocembrin inhibits A. hydrophila growth. The minimal inhibitory concentration and minimum bactericidal concentration were 256 μg/mL and 512 μg/mL, respectively. Ultrastructurally, the bacteria treated with pinocembrin showed surface roughness and plasmolysis. When bacteria were treated with 512 μg/mL pinocembrin, lactate dehydrogenase activity and soluble protein content decreased significantly, and electrical conductivity and DNA exosmosis levels increased by 4.21 ± 0.64% and 15.98 ± 1.93 mg/L, respectively. Staining with 4', 6-Diamidino-2-phenylindole showed that the nucleic acid fluorescence intensity and density decreased after the treatment with pinocembrin. Pinocembrin may inhibit the growth of A. hydrophila by increasing cell membrane permeability and affecting protein and DNA metabolism. Thus, pinocembrin is a candidate drug for the treatment of A. hydrophila infection in aquaculture.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Antibacterial Activity of Chinese Red Propolis against Staphylococcus aureus and MRSA

The antibacterial activity of propolis has long been of great interest, and the chemical composition of propolis is directly dependent on its source. We recently obtained a type of propolis from China with a red color. Firstly, the antibacterial properties of this unusual propolis were determined against Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA). Studies on its composition identified and quantified 14 main polyphenols of Chinese red propolis extracts (RPE); quantification was carried out using liquid chromatography triple quadrupole tandem mass spectrometry (LC-QQQ-MS/MS) and RPE was found to be rich in pinobanksin, pinobanksin-3-acetate, and chrysin. In vitro investigations of its antibacterial activity revealed that its activity against S. aureus and MRSA is due to disruption of the cell wall and cell membrane, which then inhibits bacterial growth. Despite its similar antibacterial activities against S. aureus and MRSA, metabolomic analysis further revealed the effects of RPE on bacteria metabolism were different. The untargeted metabolomic results showed that a total of 7 metabolites in 12 metabolic pathways had significant changes (Fold change > 2, p < 0.05 *) after RPE treatment in S. aureus, while 11 metabolites in 9 metabolic pathways had significant changes (Fold change > 2, p < 0.05 *) after RPE treated on MRSA. Furthermore, RPE downregulated several specific genes related to bacterial biofilm formation, autolysis, cell wall synthesis, and bacterial virulence in MRSA. In conclusion, the data obtained indicate that RPE may be a promising therapeutic agent against S. aureus and MRSA.

TRScore: a three-dimensional RepVGG-based scoring method for ranking protein docking models

MOTIVATION

Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications.

RESULTS

We present a new deep learning-based scoring method for ranking protein-protein docking models based on a three-dimensional (3D) RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set, and overall obtained a significant improvement over existing methods in cross validation and independent evaluations.

AVAILABILITY

Codes available at: https://github.com/BioinformaticsCSU/TRScore.

Bayesian-based decipherment of in-depth information in bacterial chemical sensing beyond pleasant/unpleasant responses

Chemical sensing is vital to the survival of all organisms. Bacterial chemotaxis is conducted by multiple receptors that sense chemicals to regulate a single signalling system controlling the transition between the direction (clockwise vs. counterclockwise) of flagellar rotation. Such an integrated system seems better suited to judge chemicals as either favourable or unfavourable, but not for identification purposes though differences in their affinities to the receptors may cause difference in response strength. Here, an experimental setup was developed to monitor behaviours of multiple cells stimulated simultaneously as well as a statistical framework based on Bayesian inferences. Although responses of individual cells varied substantially, ensemble averaging of the time courses seemed characteristic to attractant species, indicating we can extract information of input chemical species from responses of the bacterium. Furthermore, two similar, but distinct, beverages elicited attractant responses of cells with profiles distinguishable with the Bayesian procedure. These results provide a basis for novel bio-inspired sensors that could be used with other cell types to sense wider ranges of chemicals.

Tailoring Escherichia coli Chemotactic Sensing towards Cadmium by Computational Redesign of Ribose-Binding Protein

Periplasmic binding proteins such as ribose-binding proteins (RBPs) are involved in the bacterial chemotaxis two-component system. RBP selectively identifies and interacts with ribose to induce a conformational change that leads to chemotaxis. Here, we report the development of an engineered Escherichia coli (E. coli) strain expressing a redesigned RBP that can effectively sense cadmium ions and regulate chemotactic movement of cells toward a cadmium ion gradient. RBP was computationally redesigned to bind cadmium ions and produce the conformational change required for chemoreceptor binding. The successful design, CdRBP1, binds to cadmium ions with a dissociation constant of 268 nM. When CdRBP1 was expressed in the periplasmic space of E. coli, the bacteria became live cadmium ion hunters with high selectivity over other divalent metal ions. This work presents an example of making cadmium ions, which are toxic for most organisms, as an attractant to regulate cells movement. Our approach also demonstrates that RBP can be precisely designed to develop metal-detecting living systems for potential applications in synthetic biology and environmental studies.

IMPORTANCE Cadmium pollution is one of the major environmental problems due to excessive release and accumulation. New technologies that can auto-detect cadmium ions with good biocompatibility are in urgent need. In this study, we engineered the bacterial chemotaxis system to positively sense cadmium ions by redesigning ribose-binding protein (RBP) to tightly bind cadmium ion and produce the right conformational change for receptor binding and signaling. Our engineered E. coli cells can auto-detect and chase cadmium ions with divalent metal ion selectivity. Many attempts have been carried out to redesign RBP at the ribose binding site with little success. Instead of the ribose binding site, we introduced the cadmium binding site in the opening of the ribose binding pocket by a specially developed computational algorithm. Our design strategy can be applied to engineer live bacteria with autonomous detection and remediation abilities for metal ions or other chemicals in the future.

Combined Analysis of the Time-Resolved Transcriptome and Proteome of Plant Pathogen Xanthomonas oryzae pv. oryzae

Xanthomonas oryzae pv. oryzae (Xoo) is a plant pathogen responsible for causing bacterial blight in rice. The immediate alterations in Xoo upon initial contact with rice are essential for pathogenesis. We studied time-resolved genome-wide gene expression in pathogenicity-activated Xoo cells at the transcriptome and proteome levels. The early response genes of Xoo include genes related to cell motility, inorganic ion transport, and effectors. The alteration of gene expression is initiated as early as few minutes after the initial interaction and changes with time. The time-resolved comparison of the transcriptome and proteome shows the differences between transcriptional and translational expression peaks in many genes, although the overall expression pattern of mRNAs and proteins is conserved. The discrepancy suggests an important role of translational regulation in Xoo at the early stages of pathogenesis. The gene expression analysis using time-resolved transcriptome and proteome provides unprecedented valuable information regarding Xoo pathogenesis.

Uncertainty in protein-ligand binding constants: asymmetric confidence intervals versus standard errors

Equilibrium binding constants (K_b) between chemical compounds and target proteins or between interacting proteins provide a quantitative understanding of biological interaction mechanisms. Reported uncertainties of measured experimental parameters are critical for decision-making in many scientific areas, e.g., in lead compound discovery processes and in comparing computational predictions with experimental results. Uncertainties in measured K_b values are commonly represented by a symmetric normal distribution, often quoted in terms of the experimental value plus-minus the standard deviation. However, in general, the distributions of measured K_b (and equivalent K_d) values and the corresponding free energy change ΔG_b are all asymmetric to varying degree. Here, using a simulation approach, we illustrate the effect of asymmetric K_b distributions within the realm of isothermal titration calorimetry (ITC) experiments. Further we illustrate the known, but perhaps not widely appreciated, fact that when distributions of any of K_b, K_d and ΔG_b are transformed into each other, their degree of asymmetry is changed. Consequently, we recommend that a more accurate way of expressing the uncertainties of K_b, K_d, and ΔG_b values is to consistently report 95% confidence intervals, in line with other authors' suggestions. The ways to obtain such error ranges are discussed in detail and exemplified for a binding reaction obtained by ITC.

Plasmids shape the diverse accessory resistomes of Escherichia coli ST131

The human gut microbiome includes beneficial, commensal and pathogenic bacteria that possess antimicrobial resistance (AMR) genes and exchange these predominantly through conjugative plasmids. Escherichia coli is a significant component of the gastrointestinal microbiome and is typically non-pathogenic in this niche. In contrast, extra-intestinal pathogenic E. coli (ExPEC) including ST131 may occupy other environments like the urinary tract or bloodstream where they express genes enabling AMR and host cell adhesion like type 1 fimbriae. The extent to which commensal E. coli and uropathogenic ExPEC ST131 share AMR genes remains understudied at a genomic level, and we examined this here using a preterm infant resistome. We found that individual ST131 had small differences in AMR gene content relative to a larger shared resistome. Comparisons with a range of plasmids common in ST131 showed that AMR gene composition was driven by conjugation, recombination and mobile genetic elements. Plasmid pEK499 had extended regions in most ST131 Clade C isolates, and it had evidence of a co-evolutionary signal based on protein-level interactions with chromosomal gene products, as did pEK204 that had a type IV fimbrial pil operon. ST131 possessed extensive diversity of selective type 1, type IV, P and F17-like fimbriae genes that was highest in subclade C2. The structure and composition of AMR genes, plasmids and fimbriae vary widely in ST131 Clade C and this may mediate pathogenicity and infection outcomes.

Exoproteomics for Better Understanding Pseudomonas aeruginosa Virulence

Pseudomonas aeruginosa is the most common human opportunistic pathogen associated with nosocomial diseases. In 2017, the World Health Organization has classified P. aeruginosa as a critical agent threatening human health, and for which the development of new treatments is urgently necessary. One interesting avenue is to target virulence factors to understand P. aeruginosa pathogenicity. Thus, characterising exoproteins of P. aeruginosa is a hot research topic and proteomics is a powerful approach that provides important information to gain insights on bacterial virulence. The aim of this review is to focus on the contribution of proteomics to the studies of P. aeruginosa exoproteins, highlighting its relevance in the discovery of virulence factors, post-translational modifications on exoproteins and host-pathogen relationships.

Systematic Localization of Escherichia coli Membrane Proteins

The molecular architecture and function of the Gram-negative bacterial cell envelope are dictated by protein composition and localization. Proteins that localize to the inner membranes (IM) and outer membranes (OM) of Gram-negative bacteria play critical and distinct roles in cellular physiology; however, approaches to systematically interrogate their distribution across both membranes and the soluble cell fraction are lacking. Here, we employed multiplexed quantitative mass spectrometry using tandem mass tag (TMT) labeling to assess membrane protein localization in a proteome-wide fashion by separating IM and OM vesicles from exponentially growing Escherichia coli K-12 cells on a sucrose density gradient. The migration patterns for >1,600 proteins were classified in an unbiased manner, accurately recapitulating decades of knowledge in membrane protein localization in E. coli For 559 proteins that are currently annotated as peripherally associated with the IM (G. Orfanoudaki and A. Economou, Mol Cell Proteomics 13:3674-3687, 2014, https://doi.org/10.1074/mcp.O114.041137) and that display potential for dual localization to either the IM or cytoplasm, we could allocate 110 proteins to the IM and 206 proteins to the soluble cell fraction based on their fractionation patterns. In addition, we uncovered 63 cases, in which our data disagreed with current localization annotation in protein databases. For 42 of these cases, we were able to find supportive evidence for our localization findings in the literature. We anticipate that our systems-level analysis of the E. coli membrane proteome will serve as a useful reference data set to query membrane protein localization, as well as to provide a novel methodology to rapidly and systematically map membrane protein localization in more poorly characterized Gram-negative species.IMPORTANCE Current knowledge of protein localization, particularly outer membrane proteins, is highly dependent on bioinformatic predictions. To date, no systematic experimental studies have directly compared protein localization spanning the inner and outer membranes of E. coli By combining sucrose density gradient fractionation of inner membrane (IM) and outer membrane (OM) proteins with multiplex quantitative proteomics, we systematically quantified localization patterns for >1,600 proteins, providing high-confidence localization annotations for 1,368 proteins. Of these proteins, we resolve the predominant localization of 316 proteins that currently have dual annotation (cytoplasmic and IM) in protein databases and identify new annotations for 42 additional proteins. Overall, we present a novel quantitative methodology to systematically map membrane proteins in Gram-negative bacteria and use it to unravel the biological complexity of the membrane proteome architecture in E. coli.

Is Graphene Oxide a Chemoattractant?

The use of nanomaterials to regulate cell surface receptors is considered a novel strategy to manipulate cell behaviors. However, recognition is important to drive nanoparticle-cell complex formation. Here, we report a novel approach that uses graphene oxide (GO) as a chemoattractant to lure bacteria to nanosurface, facilitating complex formation. The amount of Escherichia coli (E. coli) cells attracted into capillaries containing 20 mg/L GO was more than 8.6-fold higher than that attracted into capillaries containing 20 mg/L glucose. The inherent mechanism involved interference with transmembrane chemoreceptors and activation of the chemotactic system via GO attachment and a subsequent increase in cell aggregation and migration via self-secreted quorum sensing molecules. The key feature of this strategy is the potential to improve the efficiency of the nanoparticle-cell recognition pattern and to expedite the development of surface-contact-related nanotechnology.

Outer membrane lipoprotein NlpI scaffolds peptidoglycan hydrolases within multi-enzyme complexes in Escherichia coli

The peptidoglycan (PG) sacculus provides bacteria with the mechanical strength to maintain cell shape and resist osmotic stress. Enlargement of the mesh‐like sacculus requires the combined activity of peptidoglycan synthases and hydrolases. In Escherichia coli, the activity of two PG synthases is driven by lipoproteins anchored in the outer membrane (OM). However, the regulation of PG hydrolases is less well understood, with only regulators for PG amidases having been described. Here, we identify the OM lipoprotein NlpI as a general adaptor protein for PG hydrolases. NlpI binds to different classes of hydrolases and can specifically form complexes with various PG endopeptidases. In addition, NlpI seems to contribute both to PG elongation and division biosynthetic complexes based on its localization and genetic interactions. Consistent with such a role, we reconstitute PG multi‐enzyme complexes containing NlpI, the PG synthesis regulator LpoA, its cognate bifunctional synthase, PBP1A, and different endopeptidases. Our results indicate that peptidoglycan regulators and adaptors are part of PG biosynthetic multi‐enzyme complexes, regulating and potentially coordinating the spatiotemporal action of PG synthases and hydrolases.

FtsA G50E mutant suppresses the essential requirement for FtsK during bacterial cell division in Escherichia coli

In Escherichia coli, the N-terminal domain of the essential protein FtsK (FtsK_N) is proposed to modulate septum formation through the formation of dynamic and essential protein interactions with both the Z-ring and late-stage division machinery. Using genomic mutagenesis, complementation analysis, and in vitro pull-down assays, we aimed to identify protein interaction partners of FtsK essential to its function during division. Here, we identified the cytoplasmic Z-ring membrane anchoring protein FtsA as a direct protein-protein interaction partner of FtsK. Random genomic mutagenesis of an ftsK temperature-sensitive strain of E. coli revealed an FtsA point mutation (G50E) that is able to fully restore normal cell growth and morphology, and further targeted site-directed mutagenesis of FtsA revealed several other point mutations capable of fully suppressing the essential requirement for functional FtsK. Together, this provides insight into a potential novel co-complex formed between these components during division and suggests FtsA may directly impact FtsK function.

Compositional model based on factorial evolution for realizing multi-task learning in bacterial virulent protein prediction

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of applications for Multitask Learning (MTL) methods. Identification of Bacterial virulent protein is one such application that helps in understanding the virulence mechanism for the design of drug and vaccine. However, the limiting factor in a reliable prediction model is the scarcity of the experimentally verified training data. To deal with, casting the problem in a Multitask Learning scenario, could be beneficial. Reusability of auxiliary data from related multiple domains in the prediction of target domain with limited labeled data is the primary objective of multitask learning model. Due to the amalgamation of multiple related data, it is possible that the probability distribution between the features tends to vary. Therefore, to deal with change amongst the feature distribution, this paper proposes a composite model for multitask learning framework which is based on two principles: discovering the shared parameters for identifying the relationships between tasks and common underlying representation of features amongst the related tasks. Through multi-kernel and factorial evolution, the proposed framework able to discover the shared kernel parameters and latent feature representation that is common amongst the tasks. To examine the benefits of the proposed model, an extensive experiment is performed on the freely available dataset at VirulentPred web server. Based on the results, we found that multitask learning model performs better than the conventional single task model. Additionally, our findings state that if the distribution between the tasks is high, then training the multiple models yield slightly better prediction. However, if the data distribution difference is low, multitask learning significantly outperforms the individual learning.

The antibacterial mechanism of oridonin against methicillin-resistant Staphylococcus aureus (MRSA)

Context: Methicillin-resistant Staphylococcus aureus (MRSA) is a very harmful bacterium. Oridonin, a component in Rabdosia rubescens (Hemsl.) Hara (Lamiaceae), is widely used against bacterial infections in China. Objective: We evaluated oridonin effects on MRSA cell membrane and wall, protein metabolism, lactate dehydrogenase (LDH), DNA and microscopic structure. Materials and methods: Broth microdilution and flat colony counting methods were used to measure oridonin minimal inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) against USA300 strain. Electrical conductivity and DNA exosmosis were analysed to study oridonin effects (128 μg/mL) on cell membrane and wall for 0, 1, 2, 4 and 6 h. Sodium dodecyl sulphate polyacrylamide gel electrophoresis was used to detect effects on soluble protein synthesis after 6, 10 and 16 h. LDH activity was examined with an enzyme-linked immunosorbent assay. Effects of oridonin on USA300 DNA were investigated with DAPI staining. Morphological changes in MRSA following oridonin treatment were determined with scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results: Oridonin MIC and MBC values against USA300 were 64 and 512 μg/mL, respectively. The conductivity and DNA exosmosis level of oridonin-treated USA300 improved by 3.20±0.84% and increased by 58.63 ± 1.78 μg/mL, respectively. LDH and soluble protein levels decreased by 30.85±7.69% and 27.51 ± 1.39%, respectively. A decrease in fluorescence intensity was reported with time. Oridonin affected the morphology of USA300. Conclusions: Oridonin antibacterial mechanism was related to changes in cell membrane and cell wall permeability, disturbance in protein and DNA metabolism, and influence on bacterial morphology. Thus, oridonin may help in treating MRSA infection.

Clustering as a Means To Control Nitrate Respiration Efficiency and Toxicity in Escherichia coli

Most bacteria rely on the redox activity of respiratory complexes embedded in the cytoplasmic membrane to gain energy in the form of ATP and of an electrochemical gradient established across the membrane. Nevertheless, production of harmful and toxic nitric oxide by actively growing bacteria as either an intermediate or side-product of nitrate respiration challenges how homeostasis control is exerted. Here, we show that components of the nitrate electron transport chain are clustered, likely influencing the kinetics of the process. Nitric oxide production from this respiratory chain is controlled and handled through a multiprotein complex, including detoxifying systems. These findings point to an essential role of compartmentalization of respiratory components in bacterial cell growth.

Respiration is a fundamental process that has to optimally respond to metabolic demand and environmental changes. We previously showed that nitrate respiration, crucial for gut colonization by enterobacteria, is controlled by polar clustering of the nitrate reductase increasing the electron flux through the complex. Here, we show that the formate dehydrogenase electron-donating complex, FdnGHI, also clusters at the cell poles under nitrate-respiring conditions. Its proximity to the nitrate reductase complex was confirmed by its identification in the interactome of the latter, which appears to be specific to the nitrate-respiring condition. Interestingly, we have identified a multiprotein complex dedicated to handle nitric oxide resulting from the enhanced activity of the electron transport chain terminated by nitrate reductase. We demonstrated that the cytoplasmic NADH-dependent nitrite reductase NirBD and the hybrid cluster protein Hcp are key contributors to regulation of the nitric oxide level during nitrate respiration. Thus, gathering of actors involved in respiration and NO homeostasis seems to be critical to balancing maximization of electron flux and the resulting toxicity.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Interrogating the Essential Bacterial Cell Division Protein FtsQ with Fragments Using Target Immobilized NMR Screening (TINS)

The divisome is a large protein complex that regulates bacterial cell division and therefore represents an attractive target for novel antibacterial drugs. In this study, we report on the ligandability of FtsQ, which is considered a key component of the divisome. For this, the soluble periplasmic domain of Escherichia coli FtsQ was immobilized and used to screen a library of 1501 low molecular weight (< 300 Da), synthetic compounds for those that interact with the protein. A primary screen was performed using target immobilized NMR screening (TINS) and yielded 72 hits. Subsequently, these hits were validated in an orthogonal assay. At first, we aimed to do this using surface plasmon resonance (SPR), but the lack of positive control hampered optimization of the experiment. Alternatively, a two-dimensional heteronuclear single quantum coherence (HSQC) NMR spectrum of FtsQ was obtained and used to validate these hits by chemical shift perturbation (CSP) experiments. This resulted in the identification of three fragments with weak affinity for the periplasmic domain of FtsQ, arguing that the ligandability of FtsQ is low. While this indicates that developing high affinity ligands for FtsQ is far from straightforward, the identified hit fragments can help to further interrogate FtsQ interactions.

Evolutionary design principles in metabolism

Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.

Comparative proteomic analysis of four biotechnological strains Lactococcus lactis through label-free quantitative proteomics

Lactococcus lactis is a bacteria with high biotechnological potential, where is frequently used in the amino acid production and production of fermented dairy products, as well as drug delivery systems and mucosal vaccine vector. The knowledge of a functional core proteome is important extremely for both fundamental understanding of cell functions and for synthetic biology applications. In this study, we characterized the L. lacits proteome from proteomic analysis of four biotechnological strains L. lactis: L. lactis subsp. lactis NCDO2118, L. lactis subsp. lactis IL1403, L. lactis subsp. cremoris NZ9000 and L. lactis subsp. cremoris MG1363. Our label-free quantitative proteomic analysis of the whole bacterial lysates from each strains resulted in the characterization of the L. lactis core proteome that was composed by 586 proteins, which might contribute to resistance of this bacterium to different stress conditions as well as involved in the probiotic characteristic of L. lactis. Kegg enrichment analysis shows that ribosome, metabolic pathways, pyruvate metabolism and microbial metabolism in diverse environments were the most enriched. According to our quantitative proteomic analysis, proteins related to translation process were the more abundant in the core proteome, which represent an important step in the synthetic biology. In addition, we identified a subset of conserved proteins that are exclusive of the L. lactis subsp. cremoris or L. lactis subsp. lactis, which some are related to metabolic pathway exclusive. Regarding specific proteome of NCDO2118, we detected 'strain-specific proteins'. Finally, proteogenomics analysis allows the identification of proteins, which were not previously annotated in IL1403 and MG1363. The results obtained in this study allowed to increase our knowledge about the biology of L. lactis, which contributes to the implementation of strategies that make it possible to increase the biotechnological potential of this bacterium.

Genes within Genes in Bacterial Genomes

Genetic coding in bacteria largely operates via the "one gene-one protein" paradigm. However, the peculiarities of the mRNA structure, the versatility of the genetic code, and the dynamic nature of translation sometimes allow organisms to deviate from the standard rules of protein encoding. Bacteria can use several unorthodox modes of translation to express more than one protein from a single mRNA cistron. One such alternative path is the use of additional translation initiation sites within the gene. Proteins whose translation is initiated at different start sites within the same reading frame will differ in their N termini but will have identical C-terminal segments. On the other hand, alternative initiation of translation in a register different from the frame dictated by the primary start codon will yield a protein whose sequence is entirely different from the one encoded in the main frame. The use of internal mRNA codons as translation start sites is controlled by the nucleotide sequence and the mRNA folding. The proteins of the alternative proteome generated via the "genes-within-genes" strategy may carry important functions. In this review, we summarize the currently known examples of bacterial genes encoding more than one protein due to the utilization of additional translation start sites and discuss the known or proposed functions of the alternative polypeptides in relation to the main protein product of the gene. We also discuss recent proteome- and genome-wide approaches that will allow the discovery of novel translation initiation sites in a systematic fashion.

Symbiont evolution during the free-living phase can improve host colonization

For micro-organisms cycling between free-living and host-associated stages, where reproduction occurs in both of these lifestyles, an interesting inquiry is whether evolution during the free-living stage can be positively pleiotropic to microbial fitness in a host environment. To address this topic, the squid host Euprymna tasmanica and the marine bioluminescent bacterium Vibrio fischeri were utilized. Microbial ecological diversification in static liquid microcosms was used to simulate symbiont evolution during the free-living stage. Thirteen genetically distinct V. fischeri strains from a broad diversity of ecological sources (e.g. squid light organs, fish light organs and seawater) were examined to see if the results were reproducible in many different genetic settings. Genetic backgrounds that are closely related can be predisposed to considerable differences in how they respond to similar selection pressures. For all strains examined, new mutations with striking and facilitating effects on host colonization arose quickly during microbial evolution in the free-living stage, regardless of the ecological context under consideration for a strain’s genetic background. Microbial evolution outside a host environment promoted host range expansion, improved host colonization for a micro-organism, and diminished the negative correlation between biofilm formation and motility.

An amphiphilic metallaclip with enhanced fluorescence emission in water: synthesis and controllable self-assembly into multi-dimensional micro-structures

A new amphiphilic organoplatinum(ii) metallaclip with enhanced fluorescence emission in water and multi-dimensional well-defined micro-structures in CH₃OH–H₂O mixture was designed and fabricated successfully.

Interaction paths promote module integration and network-level robustness of spliceosome to cascading effects

The functionality of distinct types of protein networks depends on the patterns of protein-protein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits, which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

Self-Assembly of Metallacages into Multidimensional Suprastructures with Tunable Emissions

Cubic metallacages were arranged into multidimensional (one-, two-, and three-dimensional) suprastructures via multistep assembly. Four new shape-controllable, hybrid metallacages with modified substituents and tunable electronic properties were prepared using dicarboxylate ligands with various substituents (sodium sulfonate, nitro, methoxyl, and amine), tetra-(4-pyridylphenyl) ethylene, and cis-(PEt3)2Pt(OTf)2. The as-prepared metallacages were used as building blocks for further assembly. Diverse suprastructures with tunable emissions (λmax from 451 to 519 nm) and various substituents (-SO3Na, -NO2, -OCH3, and -NH2) were prepared depending on the substituents and solvents used.

Dissipative adaptation in driven self-assembly leading to self-dividing fibrils

Out-of-equilibrium self-assembly of proteins such as actin and tubulin is a key regulatory process controlling cell shape, motion and division. The design of functional nanosystems based on dissipative self-assembly has proven to be remarkably difficult due to a complete lack of control over the spatial and temporal characteristics of the assembly process. Here, we show the dissipative self-assembly of FtsZ protein (a bacterial homologue of tubulin) within coacervate droplets. More specifically, we show how such barrier-free compartments govern the local availability of the energy-rich building block guanosine triphosphate, yielding highly dynamic fibrils. The increased flux of FtsZ monomers at the tips of the fibrils results in localized FtsZ assembly, elongation of the coacervate compartments, followed by division of the fibrils into two. We rationalize the directional growth and division of the fibrils using dissipative reaction-diffusion kinetics and capillary action of the filaments as main inputs. The principle presented here, in which open compartments are used to modulate the rates of dissipative self-assembly by restricting the absorption of energy from the environment, may provide a general route to dissipatively adapting nanosystems exhibiting life-like behaviour.

Experimental evolution of diverse Escherichia coli metabolic mutants identifies genetic loci for convergent adaptation of growth rate

Cell growth is determined by substrate availability and the cell’s metabolic capacity to assimilate substrates into building blocks. Metabolic genes that determine growth rate may interact synergistically or antagonistically, and can accelerate or slow growth, depending on genetic background and environmental conditions. We evolved a diverse set of Escherichia coli single-gene deletion mutants with a spectrum of growth rates and identified mutations that generally increase growth rate. Despite the metabolic differences between parent strains, mutations that enhanced growth largely mapped to core transcription machinery, including the β and β’ subunits of RNA polymerase (RNAP) and the transcription elongation factor, NusA. The structural segments of RNAP that determine enhanced growth have been previously implicated in antibiotic resistance and in the control of transcription elongation and pausing. We further developed a computational framework to characterize how the transcriptional changes that occur upon acquisition of these mutations affect growth rate across strains. Our experimental and computational results provide evidence for cases in which RNAP mutations shift the competitive balance between active transcription and gene silencing. This study demonstrates that mutations in specific regions of RNAP are a convergent adaptive solution that can enhance the growth rate of cells from distinct metabolic states.

The loss of a metabolic function caused by gene deletion can be compensated, in certain cases, by the concurrent mutation of a second gene. Whether such gene pairs share a local chemical or regulatory relationship or interact via a non-local mechanism has implications for the co-evolution of genetic changes, development of alternatives to gene therapy, and the design of combination antimicrobial therapies that select against resistance. Yet, we lack a comprehensive knowledge of adaptive responses to metabolic mutations, and our understanding of the mechanisms underlying genetic rescue remains limited. We present results of a laboratory evolution approach that has the potential to address both challenges, showing that mutations in specific regions of RNA polymerase enhance growth rates of distinct mutant strains of Escherichia coli with a spectrum of growth defects. Several of these adaptive mutations are deleterious when engineered directly into the original wild-type strain under alternative cultivation conditions, and thus have epistatic rescue properties when paired with the corresponding primary metabolic gene deletions. Our combination of adaptive evolution, directed genetic engineering, and mathematical analysis of transcription and growth rate distinguishes between rescue interactions that are specific or non-specific to a particular deletion. Our study further supports a model for RNA polymerase as a locus of convergent adaptive evolution from different sub-optimal metabolic starting points.

A Network Biology Approach to Decipher Stress Response in Bacteria Using Escherichia coli As a Model

The development of drug-resistant pathogenic bacteria poses challenges to global health for their treatment and control. In this context, stress response enables bacterial populations to survive extreme perturbations in the environment but remains poorly understood. Specific modules are activated for unique stressors with few recognized global regulators. The phenomenon of cross-stress protection strongly suggests the presence of central proteins that control the diverse stress responses. In this work, Escherichia coli was used to model the bacterial stress response. A Protein-Protein Interaction Network was generated by integrating differentially expressed genes in eight stress conditions of pH, temperature, and antibiotics with relevant gene ontology terms. Topological analysis identified 24 central proteins. The well-documented role of 16 central proteins in stress indicates central control of the response, while the remaining eight proteins may have a novel role in stress response. Cluster analysis of the generated network implicated RNA binding, flagellar assembly, ABC transporters, and DNA repair as important processes during response to stress. Pathway analysis showed crosstalk of Two Component Systems with metabolic processes, oxidative phosphorylation, and ABC transporters. The results were further validated by analysis of an independent cross-stress protection dataset. This study also reports on the ways in which bacterial stress response can progress to biofilm formation. In conclusion, we suggest that drug targets or pathways disrupting bacterial stress responses can potentially be exploited to combat antibiotic tolerance and multidrug resistance in the future.

Evolutionary Remodeling of Bacterial Motility Checkpoint Control

Regulatory networks play a central role in the relationship between genotype and phenotype in all organisms. However, the mechanisms that underpin the evolutionary plasticity of these networks remain poorly understood. Here, we used experimental selection for enhanced bacterial motility in a porous environment to explore the adaptability of one of the most complex networks known in bacteria. We found that the resulting phenotypic changes are mediated by adaptive mutations in several functionally different proteins, including multiple components of the flagellar motor. Nevertheless, this evolutionary adaptation could be explained by a single mechanism, namely remodeling of the checkpoint regulating flagellar gene expression. Supported by computer simulations, our findings suggest that the specific “bow-tie” topology of the checkpoint facilitates evolutionary tuning of the cost-benefit trade-off between motility and growth. We propose that bow-tie regulatory motifs, which are widespread in cellular networks, play a general role in evolutionary adaptation.

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Multiple mutations enhance swimming behavior under selection

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A universal trade-off relationship between motility and growth is observed

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Checkpoint remodeling provides a mechanism of evolutionary adaptation

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Bow-tie topology facilitates evolvability of the motility network

Global landscape of cell envelope protein complexes in Escherichia coli

Bacterial cell envelope protein (CEP) complexes mediate a range of processes, including membrane assembly, antibiotic resistance and metabolic coordination. However, only limited characterization of relevant macromolecules has been reported to date. Here we present a proteomic survey of 1,347 CEPs encompassing 90% inner- and outer-membrane and periplasmic proteins of Escherichia coli. After extraction with non-denaturing detergents, we affinity-purified 785 endogenously tagged CEPs and identified stably associated polypeptides by precision mass spectrometry. The resulting high-quality physical interaction network, comprising 77% of targeted CEPs, revealed many previously uncharacterized heteromeric complexes. We found that the secretion of autotransporters requires translocation and the assembly module TamB to nucleate proper folding from periplasm to cell surface through a cooperative mechanism involving the β-barrel assembly machinery. We also establish that an ABC transporter of unknown function, YadH, together with the Mla system preserves outer membrane lipid asymmetry. This E. coli CEP 'interactome' provides insights into the functional landscape governing CE systems essential to bacterial growth, metabolism and drug resistance.

The nucleoid as a scaffold for the assembly of bacterial signaling complexes

The FrzCD chemoreceptor from the gliding bacterium Myxococcus xanthus forms cytoplasmic clusters that occupy a large central region of the cell body also occupied by the nucleoid. In this work, we show that FrzCD directly binds to the nucleoid with its N-terminal positively charged tail and recruits active signaling complexes at this location. The FrzCD binding to the nucleoid occur in a DNA-sequence independent manner and leads to the formation of multiple distributed clusters that explore constrained areas. This organization might be required for cooperative interactions between clustered receptors as observed in membrane-bound chemosensory arrays.

More than Enzymes That Make or Break Cyclic Di-GMP-Local Signaling in the Interactome of GGDEF/EAL Domain Proteins of Escherichia coli

The bacterial second messenger bis-(3'-5')-cyclic diguanosine monophosphate (c-di-GMP) ubiquitously promotes bacterial biofilm formation. Intracellular pools of c-di-GMP seem to be dynamically negotiated by diguanylate cyclases (DGCs, with GGDEF domains) and specific phosphodiesterases (PDEs, with EAL or HD-GYP domains). Most bacterial species possess multiple DGCs and PDEs, often with surprisingly distinct and specific output functions. One explanation for such specificity is "local" c-di-GMP signaling, which is believed to involve direct interactions between specific DGC/PDE pairs and c-di-GMP-binding effector/target systems. Here we present a systematic analysis of direct protein interactions among all 29 GGDEF/EAL domain proteins of Escherichia coli Since the effects of interactions depend on coexpression and stoichiometries, cellular levels of all GGDEF/EAL domain proteins were also quantified and found to vary dynamically along the growth cycle. Instead of detecting specific pairs of interacting DGCs and PDEs, we discovered a tightly interconnected protein network of a specific subset or "supermodule" of DGCs and PDEs with a coregulated core of five hyperconnected hub proteins. These include the DGC/PDE proteins representing the c-di-GMP switch that turns on biofilm matrix production in E. coli Mutants lacking these core hub proteins show drastic biofilm-related phenotypes but no changes in cellular c-di-GMP levels. Overall, our results provide the basis for a novel model of local c-di-GMP signaling in which a single strongly expressed master PDE, PdeH, dynamically eradicates global effects of several DGCs by strongly draining the global c-di-GMP pool and thereby restricting these DGCs to serving as local c-di-GMP sources that activate specific colocalized effector/target systems.IMPORTANCE c-di-GMP signaling in bacteria is believed to occur via changes in cellular c-di-GMP levels controlled by antagonistic and potentially interacting pairs of diguanylate cyclases (DGCs) and c-di-GMP phosphodiesterases (PDEs). Our systematic analysis of protein-protein interaction patterns of all 29 GGDEF/EAL domain proteins of E. coli, together with our measurements of cellular c-di-GMP levels, challenges both aspects of this current concept. Knocking out distinct DGCs and PDEs has drastic effects on E. coli biofilm formation without changing the cellular c-di-GMP level. In addition, rather than generally coming in interacting DGC/PDE pairs, a subset of DGCs and PDEs operates as central interaction hubs in a larger "supermodule," with other DGCs and PDEs behaving as "lonely players" without contacts to other c-di-GMP-related enzymes. On the basis of these data, we propose a novel concept of "local" c-di-GMP signaling in bacteria with multiple enzymes that make or break the second messenger c-di-GMP.

Activity-Related Conformational Changes in d,d-Carboxypeptidases Revealed by In Vivo Periplasmic Förster Resonance Energy Transfer Assay in Escherichia coli

One of the mechanisms of β-lactam antibiotic resistance requires the activity of d,d-carboxypeptidases (d,d-CPases) involved in peptidoglycan (PG) synthesis, making them putative targets for new antibiotic development. The activity of PG-synthesizing enzymes is often correlated with their association with other proteins. The PG layer is maintained in the periplasm between the two membranes of the Gram-negative cell envelope. Because no methods existed to detect in vivo interactions in this compartment, we have developed and validated a Förster resonance energy transfer assay. Using the fluorescent-protein donor-acceptor pair mNeonGreen-mCherry, periplasmic protein interactions were detected in fixed and in living bacteria, in single samples or in plate reader 96-well format. We show that the d,d-CPases PBP5, PBP6a, and PBP6b of Escherichia coli change dimer conformation between resting and active states. Complementation studies and changes in localization suggest that these d,d-CPases are not redundant but that their balanced activity is required for robust PG synthesis.IMPORTANCE The periplasmic space between the outer and the inner membrane of Gram-negative bacteria contains many essential regulatory, transport, and cell wall-synthesizing and -hydrolyzing proteins. To date, no assay is available to determine protein interactions in this compartment. We have developed a periplasmic protein interaction assay for living and fixed bacteria in single samples or 96-well-plate format. Using this assay, we were able to demonstrate conformation changes related to the activity of proteins that could not have been detected by any other living-cell method available. The assay uniquely expands our toolbox for antibiotic screening and mode-of-action studies.

Plasticity first: molecular signatures of a complex morphological trait in filamentous cyanobacteria

Background

Filamentous cyanobacteria that differentiate multiple cell types are considered the peak of prokaryotic complexity and their evolution has been studied in the context of multicellularity origins. Species that form true-branching filaments exemplify the most complex cyanobacteria. However, the mechanisms underlying the true-branching morphology remain poorly understood despite of several investigations that focused on the identification of novel genes or pathways. An alternative route for the evolution of novel traits is based on existing phenotypic plasticity. According to that scenario – termed genetic assimilation – the fixation of a novel phenotype precedes the fixation of the genotype.

Results

Here we show that the evolution of transcriptional regulatory elements constitutes a major mechanism for the evolution of new traits. We found that supplementation with sucrose reconstitutes the ancestral branchless phenotype of two true-branching Fischerella species and compared the transcription start sites (TSSs) between the two phenotypic states. Our analysis uncovers several orthologous TSSs whose transcription level is correlated with the true-branching phenotype. These TSSs are found in genes that encode components of the septosome and elongasome (e.g., fraC and mreB).

Conclusions

The concept of genetic assimilation supplies a tenable explanation for the evolution of novel traits but testing its feasibility is hindered by the inability to recreate and study the evolution of present-day traits. We present a novel approach to examine transcription data for the plasticity first route and provide evidence for its occurrence during the evolution of complex colony morphology in true-branching cyanobacteria. Our results reveal a route for evolution of the true-branching phenotype in cyanobacteria via modification of the transcription level of pre-existing genes. Our study supplies evidence for the ‘plasticity-first’ hypothesis and highlights the importance of transcriptional regulation in the evolution of novel traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-017-1053-5) contains supplementary material, which is available to authorized users.

Emergent properties of bacterial chemotaxis pathway

The chemotaxis pathway of Escherichia coli is the most studied sensory system in prokaryotes. The highly conserved general architecture of this pathway consists of two modules which mediate signal transduction and adaptation. The signal transduction module detects and amplifies changes in environmental conditions and rapidly transmits these signals to control bacterial swimming behavior. The adaptation module gradually resets the activity and sensitivity of the first module after initial stimulation and thereby enables the temporal comparisons necessary for bacterial chemotaxis. Recent experimental and theoretical work has unraveled multiple quantitative features emerging from the interplay between these two modules. This has laid the groundwork for rationalization of these emerging properties in the context of the evolutionary optimization of the chemotactic behavior.

Understanding Robust Adaptation Dynamics of Gene Regulatory Network

Robust adaptation is a critical attribute for gene regulatory network (GRN), understanding the relationship between adaptation and the GRN topology, and corresponding parameters is a challenging issue. The work in this paper includes: first, seven constraint multiobjective optimization algorithms are used to find sufficient solutions to get more reliable statistic rules. Meanwhile, the algorithms are compared to facilitate the future algorithm selection; second, a fuzzy c-mean algorithm is used to analyze solutions and to classify the solutions into different groups; third, the histogram analysis for all satisfactory solutions shows the preferred parameter range, i.e., parameter motif. The contributions of this paper includes: 1) Two new adaptation indices i.e., peak time and settle down time, are proposed for the first time to give more accurate description of the robust adaptation. Our conclusion is that some solutions even with satisfactory sensitivity and precision are not practically of robust adaptation because of too long time needed. 2) The relationship between topology, parameter set, and robust adaptation of GRN is discovered in the sense of both preferred topology and parameter motif. Our conclusion is that the robust adaptation depends more on the GRN topology than the model parameter set in two feasible topologies.

A dynamic and adaptive network of cytosolic interactions governs protein export by the T3SS injectisome

Many bacteria use a type III secretion system (T3SS) to inject effector proteins into host cells. Selection and export of the effectors is controlled by a set of soluble proteins at the cytosolic interface of the membrane spanning type III secretion ‘injectisome’. Combining fluorescence microscopy, biochemical interaction studies and fluorescence correlation spectroscopy, we show that in live Yersinia enterocolitica bacteria these soluble proteins form complexes both at the injectisome and in the cytosol. Binding to the injectisome stabilizes these cytosolic complexes, whereas the free cytosolic complexes, which include the type III secretion ATPase, constitute a highly dynamic and adaptive network. The extracellular calcium concentration, which triggers activation of the T3SS, directly influences the cytosolic complexes, possibly through the essential component SctK/YscK, revealing a potential mechanism involved in the regulation of type III secretion.

Bacterial type III secretion systems (T3SS) play important roles in pathogenesis. Here, Diepold et al. show the dynamic nature of complexes formed of essential T3SS components in live bacteria, and that extracellular calcium concentrations influence these cytosolic complexes likely via SctK/YscK.