Acinetobacter type VI secretion system comprises a non-canonical membrane complex

A. baumannii can rapidly acquire new resistance mechanisms and persist on abiotic surface, enabling the colonization of asymptomatic human host. In Acinetobacter the type VI secretion system (T6SS) is involved in twitching, surface motility and is used for interbacterial competition allowing the bacteria to uptake DNA. A. baumannii possesses a T6SS that has been well studied for its regulation and specific activity, but little is known concerning its assembly and architecture. The T6SS nanomachine is built from three architectural sub-complexes. Unlike the baseplate (BP) and the tail-tube complex (TTC), which are inherited from bacteriophages, the membrane complex (MC) originates from bacteria. The MC is the most external part of the T6SS and, as such, is subjected to evolution and adaptation. One unanswered question on the MC is how such a gigantesque molecular edifice is inserted and crosses the bacterial cell envelope. The A. baumannii MC lacks an essential component, the TssJ lipoprotein, which anchors the MC to the outer membrane. In this work, we studied how A. baumannii compensates the absence of a TssJ. We have characterized for the first time the A. baumannii’s specific T6SS MC, its unique characteristic, its membrane localization, and assembly dynamics. We also defined its composition, demonstrating that its biogenesis employs three Acinetobacter-specific envelope-associated proteins that define an intricate network leading to the assembly of a five-proteins membrane super-complex. Our data suggest that A. baumannii has divided the function of TssJ by (1) co-opting a new protein TsmK that stabilizes the MC and by (2) evolving a new domain in TssM for homo-oligomerization, a prerequisite to build the T6SS channel. We believe that the atypical species-specific features we report in this study will have profound implication in our understanding of the assembly and evolutionary diversity of different T6SSs, that warrants future investigation.This work was funded by the Centre National de la Recherche Scientifique, the Aix-Marseille Université, and grants from the Agence Nationale de la Recherche (ANR-18-CE11-0023-01) and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) to ED. ED is supported by the Institut National de la Santé et de la Recherche Médicale (INSERM). YC is funded by a Doctoral school PhD fellowship from the FRM (ECO20160736014 & FDT201904008052). OK is funded by a Doctoral school PhD fellowship from DGA and Aix-Marseille University and by the FRM (01D19024292-A AID & FDT202204014851). PS post-doctoral fellowship was supported by the European Respiratory Society under the ERS Long-Term Fellowship grant agreement LTRF - 202101-00862. IFM is funded by ANR-17-CE11-0039. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

Évolution, structure et inhibition des systèmes de sécrétion bactériens

L'évolution a façonné une variété de mécanismes utilisés par les pathogènes humains pour coloniser l'hôte. Chez les bactéries, cette colonisation est assistée par des systèmes de sécrétion. La modulation de ces systèmes bactériens est essentielle pour le développement de thérapies antivirulences ciblant les pathogènes antibiorésistants. Les méthodes computationnelles fournissent des stratégies rationnelles pour élucider les mécanismes qui gouvernent ces systèmes et guider la conception d'inhibiteurs.Cette thèse explore plusieurs aspects de deux systèmes bactériens, les systèmes de sécrétion de type 6 et le système de sécrétion de type 2 (SST6 et SST2). La structure, l'inhibition et l'évolution de ces deux systèmes ont été étudiées en combinant des méthodes d'analyse de séquences et de modélisation moléculaire. Tout d'abord, j'ai conçu un inhibiteur de l'assemblage du SST6, qui a été validé expérimentalement. Deuxièmement, j'ai examiné un SST6 non canonique via la cooccurrence de gènes, et la modélisation de protéines. Troisièmement, j'ai modélisé un filament SST2 complet pour étudier son mécanisme de sécrétion. Quatrièmement, j'ai analysé le réseau d'interaction p-p obtenu à partir de cellules bactériennes entières. Enfin, pour étudier l'évolution des systèmes de sécrétion, j'ai introduit SOMseq, une nouvelle méthode qui permet de visualiser l'évolution des gènes dans un graphique en trois dimensions et estimer la coévolution des gènes. En conclusion, ces résultats montrent comment la synergie entre les efforts informatiques et expérimentaux est utile pour comprendre la complexité des systèmes bactériens et pour concevoir des thérapies de façon efficace.Evolution has shaped a variety of mechanisms employed by pathogens to colonize the host. In bacteria, this colonization is assisted by secretion systems, which are composite machines that translocate virulence factors to the extracellular space or directly into target cells. Regulating these bacterial systems is essential for developing antivirulence therapeutics to respond against antibiotic-resistant pathogens. Computational methods provide rational strategies to decipher the detailed mechanisms governing these systems and guide the design of inhibitors.This thesis explores several aspects of two bacterial systems, the type 6 and the type 2 secretion systems (T6SS and T2SS). The structure, inhibition, and evolution of these two systems were studied by combining sequence analysis and molecular modeling methods. First, based on the accumulated structural information on T6SS, I designed an inhibitor for the complex assembly, which was experimentally validated. Second, I examined a non-canonical T6SS via genes co-occurrence, sequence motif analysis, and protein modeling. Third, I modeled a complete T2SS filament to study its secretion mechanism. Fourth, I analyzed the protein-protein interaction network obtained from entire bacterial cells. Finally, to study the evolution of the secretion systems, I introduced SOMseq, a novel method used to visualize gene evolution in a compact three-dimensional (3D) graph and estimate gene coevolution. In conclusion, these results show how continuous feedback between computational and experimental efforts is essential for understanding the complexity of bacterial systems and efficiently designing therapeutics

Quantitative Structural Interpretation of Protein Crosslinks

International audienceChemical crosslinking, combined with mass spectrometry analysis, is a key source of information for characterizing the structure of large protein assemblies, in the context of molecular modeling. In most approaches, the interpretation is limited to simple spatial restraints, neglecting physico-chemical interactions between the crosslinker and the protein and their flexibility. Here we present a method, named NRGXL (new realistic grid for crosslinks), which models the flexibility of the crosslinker and the linked side-chains, by explicitly sampling many conformations. Also, the method can efficiently deal with overall protein dynamics. This method creates a physical model of the crosslinker and associated energy. A classifier based on it outperforms others, based on Euclidean distance or solvent-accessible distance and its efficiency makes it usable for validating 3D models from crosslinking data. NRGXL is freely available as a web server at: https://nrgxl.pasteur.fr

Coevolution-guided mapping of the Type VI secretion membrane complexbaseplate interface

The type VI secretion system (T6SS) is a multiprotein weapon evolved by Gram-negative bacteria to deliver effectors into eukaryotic cells or bacterial rivals. The T6SS uses a contractile mechanism to propel an effector-loaded needle into its target. The contractile tail is built on an assembly platform, the baseplate, which is anchored to a membrane complex. Baseplate-membrane complex interactions are mainly mediated by contacts between the C-terminal domain of the TssK baseplate component and the cytoplasmic domain of the TssL inner membrane protein. Currently, the structural details of this interaction are unknown due to the marginal stability of the TssK-TssL complex. Here we conducted a mutagenesis study based on putative TssK-TssL contact pairs identified by co-evolution analyses. We then evaluated the impact of these mutations on T6SS activity, TssK-TssL interaction and sheath assembly and dynamics in enteroaggregative Escherichia coli. Finally, we probed the TssK-TssL interface by disulfide cross-linking, allowing to propose a model for the baseplate-membrane complex interface

Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks

Traditional drug discovery programs are being transformed by the advent of machine learning methods. Among these, Generative AI methods (GM) have gained attention due to their ability to design new molecules and enhance specific properties of existing ones. However, current GM methods have limitations, such as low affinity towards the target, unknown ADME/PK properties, or the lack of synthetic tractability. To improve the applicability domain of GM methods, we have developed a workflow based on a variational autoencoder coupled with active learning steps. The designed GM workflow iteratively learns from molecular metrics, including drug likeliness, synthesizability, similarity, and docking scores. In addition, we also included a hierarchical set of criteria based on advanced molecular modeling simulations during a final selection step. We tested our GM workflow on two model systems, CDK2 and KRAS. In both cases, our model generated chemically viable molecules with a high predicted affinity toward the targets. Particularly, the proportion of high-affinity molecules inferred by our GM workflow was significantly greater than that in the training data. Notably, we also uncovered novel scaffolds significantly dissimilar to those known for each target. These results highlight the potential of our GM workflow to explore novel chemical space for specific targets, thereby opening up new possibilities for drug discovery endeavors

Acinetobacter type VI secretion system comprises a non-canonical membrane complex

A. baumannii can rapidly acquire new resistance mechanisms and persist on abiotic surface, enabling the colonization of asymptomatic human host. In Acinetobacter the type VI secretion system (T6SS) is involved in twitching, surface motility and is used for interbacterial competition allowing the bacteria to uptake DNA. A. baumannii possesses a T6SS that has been well studied for its regulation and specific activity, but little is known concerning its assembly and architecture. The T6SS nanomachine is built from three architectural sub-complexes. Unlike the baseplate (BP) and the tail-tube complex (TTC), which are inherited from bacteriophages, the membrane complex (MC) originates from bacteria. The MC is the most external part of the T6SS and, as such, is subjected to evolution and adaptation. One unanswered question on the MC is how such a gigantesque molecular edifice is inserted and crosses the bacterial cell envelope. The A. baumannii MC lacks an essential component, the TssJ lipoprotein, which anchors the MC to the outer membrane. In this work, we studied how A. baumannii compensates the absence of a TssJ. We have characterized for the first time the A. baumannii's specific T6SS MC, its unique characteristic, its membrane localization, and assembly dynamics. We also defined its composition, demonstrating that its biogenesis employs three Acinetobacter-specific envelope-associated proteins that define an intricate network leading to the assembly of a five-proteins membrane super-complex. Our data suggest that A. baumannii has divided the function of TssJ by (1) co-opting a new protein TsmK that stabilizes the MC and by (2) evolving a new domain in TssM for homo-oligomerization, a prerequisite to build the T6SS channel. We believe that the atypical species-specific features we report in this study will have profound implication in our understanding of the assembly and evolutionary diversity of different T6SSs, that warrants future investigation.ISSN:1553-7374ISSN:1553-736

Structural prediction study of TsmK an <i>Acinetobacter</i>-specific protein related to Ketoacyl synthases.

Analysis of the TsmK predicted structure. (A) AlphaFold2 confident score (predicted LDDT per position) mapped on the model. (B) Topology of the secondary elements of the predicted model for TsmK. The diagram depicts the secondary structure organization of the A. baumannii TsmK model generated with AlphaFold2. The major and minor β-sheets, as well as the long loop, are highlighted with colored squares. Inconsistent regions are represented with pale colors. Five antiparallel β-strands, β1, β7, β6, β4, and β5 assemble into a β-sheet (referred to as the major β-sheet). The α-helices are present at the loops formed between each pair of consecutive β-strand and decorate both sides of the β-sheet. AlphaFold2 predicted a second β-sheet (referred to as minor β-sheet) not present in TrRosetta and RaptorX predictions. This β-sheet is formed with the two β-strands of the converging hairpin (β9 and β10) and the β11, β12, and β8 (C) Structural superimposition of the three structural models of A. baumannii TsmK. The major and minor β-sheets as well as the long loop are highlighted with colored squares. Structural precision ranges between 0 and 15 Å (blue to red) (D) Topology of the secondary elements of the Ketoacyl synthase domain from Acyltransferase type I polyketide synthase (PKS) (PDB 4TKT). The homologous regions between TsmK and the KS domain of the polyketide synthase are highlighted in dashed colors.</p

Monitoring T6SS MC function of accessory proteins.

(A)Bacterial competition experiments. Table associated with the Fig 1C. survival of E. coli rifampicin-resistant after incubation with ATCC 17978 WT and several mutants. (B) Phenotypic complementation of tsmK mutation measuring the Hcp secretion. Dot blot probing for Hcp secretion in supernatants, by A. baumannii ATCC 17978 WT, ΔtssM and the mutant ΔtsmK, transformed with the plasmid control empty (pVRL1) or the plasmid overexpressing TsmK. The TsmK protein production was induced by 1 mM IPTG. (TIF)</p

Subcellular position of the TssM-CTD.

(A) Fluorescent clusters of the TssM protein observed in TIRF. Image associated with the Fig 5A. Projection of the average in a part of a full field of microscope acquisition. The corresponding cells have been circled in blue. Scale: 2 μm. (B-C) Schematic representation of the different stages leading to the identification of the subcellular position of the TssM foci. For each channel (sfGFP and mCherry) a line is drawn from which is created a Gaussian representing the intensity of fluorescence, depending on the position (right panel). The positions of the green foci (TssM) and the outer membrane (Omp28) are thus determined by the x axis of the maximum value of their respective Gaussian. The difference between the position of the green foci (x) and the red membrane (y) is calculated in order to determine the distance between the two marked regions (in absolute value) as well as the subcellular location of the C-terminal of TssM (negative results: the foci is “outside” the cell; positive results: the foci is “inside” the cell) (left panel). On the right panel, structured illumination microscopy (SIM) images of the C-terminal TssM-sfGFP regarding the Omp28-mCherry label. Scale = 0.1 μm. On the left panel, an example of an intensity profile (blue) is shown. Example of Gaussian representing the intensity sfGFP and mCherry fluorescence related to one green foci. (TIF)</p

<i>A</i>. <i>baumannii</i> T6SS essential for the MC biogenesis.

(A) Fluorescent cluster of the TssMsfGFP protein in A. baumannii tssM-sfGFP strain. On the left panel, the images represent the average over a time lapse acquisition of 61 frames (10s / frame). scale = 2 μm. The right panel represents the histograms of cluster distribution of the main axis of the cell with a preferential accumulation at the poles of the cell. (n > 1000 cells, two biological replicates). Comparison of the cellular distribution of TssM-sfGFP foci between the WT and tsmK mutant (right panels). (B) Statistical analysis of the amount of fluorescence foci in the different mutants. On the left, a graphical representation of the percentage of cells with zero (n = 0), one (n = 1), two (n = 2), or more than three (n = 3) foci in the different A. baumannii mutants. On the right, a graphical representation of the average number of foci per cell in the different mutants. The experiment was performed in triplicate on three different groups of cells. (C-E) Dynamic study of the fluorescence foci of the TssM-sfGFP in WT and tsmK mutant. Fluorescence microscopy in TIRF mode captured an average of 61 images every 10 seconds, revealing multiple fluorescent foci within the cells. Cell masks were generated using the Cellpose cyto2 model, and FIJI’s MicrobeJ plugin, set in "rod-shape" mode. The lower panel shows the average of images processed with a bandpass FFT filter and background subtraction that enhances the foci contrast. (D) Kymographs derived from intensity profiles measured along the cell contour, representing time (61 images every 10 seconds) on the vertical axis and cell contour length in microns on the horizontal axis. Traces of foci movement along the cell contour are evident, with fixed and stable foci yielding vertical traces, some corresponding to the whole acquisition duration (the height of the kymograph = 610 seconds). (E). Violin plots for the quantitative analysis of foci traces between the WT and tsmK mutant (3 biological replications). The difference in means was tested by t.test (R software) and yields a p value F) Biogenesis of the A. baumannii-T6SS MC and the original degradation of the tail after contraction. Time-lapse fluorescence microscopy recordings showing localization and dynamics of the mCherryClpV and TssMsfGFP fusions proteins. Individual images were taken every 40 sec. The positions of the foci are indicated by an asterisk. The scale bars are 1 μm. The lines (from top to bottom) represent the phase contrast, the mCherry channel, the sfGFP channel and the superposition of the two channels. Below, a schematic representation of the sequential biogenesis of the T6SS membrane complex.</p