Analysis of HubP-dependent cell pole protein targeting in Vibrio cholerae uncovers novel motility regulators

In rod-shaped bacteria, the emergence and maintenance of long-axis cell polarity is involved in key cellular processes such as cell cycle, division, environmental sensing and flagellar motility among others. Many bacteria achieve cell pole differentiation through the use of polar landmark proteins acting as scaffolds for the recruitment of functional macromolecular assemblies. In Vibrio cholerae a large membrane-tethered protein, HubP, specifically interacts with proteins involved in chromosome segregation, chemotaxis and flagellar biosynthesis. Here we used comparative proteomics, genetic and imaging approaches to identify additional HubP partners and demonstrate that at least six more proteins are subject to HubP-dependent polar localization. These include a cell-wall remodeling enzyme (DacB), a likely chemotaxis sensory protein (HlyB), two presumably cytosolic proteins of unknown function (VC1210 and VC1380) and two membrane-bound proteins, named here MotV and MotW, that exhibit distinct effects on chemotactic motility. We show that while both ΔmotW and ΔmotV mutants retain monotrichous flagellation, they present significant to severe motility defects when grown in soft agar. Video-tracking experiments further reveal that ΔmotV cells can swim in liquid environments but are unable to tumble or penetrate a semisolid matrix, whereas a motW deletion affects both tumbling frequency and swimming speed. Motility suppressors and gene co-occurrence analyses reveal co-evolutionary linkages between MotV, a subset of non-canonical CheV proteins and flagellar C-ring components FliG and FliM, whereas MotW regulatory inputs appear to intersect with specific c-di-GMP signaling pathways. Together, these results reveal an ever more versatile role for the landmark cell pole organizer HubP and identify novel mechanisms of motility regulation.

Precise measurement of nanoscopic septin ring structures in deep learning-assisted quantitative superresolution microscopy

The combination of image analysis and fluorescence superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning-based object recognition enables the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, deep learning methods are prone to different forms of bias. A biased recognition of structures may prohibit the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. What is required to overcome this problem is a detailed investigation of potential sources of bias and the rigorous testing of trained models using real or simulated data covering a wide dynamic range of possible results. Here we combine single molecule localization-based superresolution microscopy of septin ring structures with the training of several different deep learning models for a quantitative investigation of bias resulting from different training approaches and finally quantitative changes in septin ring structures. We find that trade-off exists between measurement accuracy and the dynamic range of recognized phenotypes. Using our trained models, we furthermore find that septin ring size can be explained by the number of subunits they are assembled from alone. Our work provides a new experimental system for the investigation of septin polymerization.

Chemical-Biology-derived in vivo Sensors: Past, Present, and Future

To understand the complex biochemistry and biophysics of biological systems, one needs to be able to monitor local concentrations of molecules, physical properties of macromolecular assemblies and activation status of signaling pathways, in real time, within single cells, and at high spatio-temporal resolution. Here we look at the tools that have been / are being / need to be provided by chemical biology to address these challenges. In particular, we highlight the utility of molecular probes that help to better measure mechanical forces and flux through key signalling pathways. Chemical biology can be used to both build biosensors to visualize, but also actuators to perturb biological processes. An emergent theme is the possibility to multiplex measurements of multiple cellular processes. Advances in microscopy automation now allow us to acquire datasets for 1000's of cells. This produces high dimensional datasets that require computer vision approaches that automate image analysis. The high dimensionality of these datasets are often not immediately accessible to human intuition, and, similarly to 'omics technologies, require statistical approaches for their exploitation. The field of biosensor imaging is therefore experiencing a multidisciplinary transition that will enable it to realize its full potential as a tool to provide a deeper appreciation of cell physiology.

Viral genome-capsid core senses host environments to prime RNA release

Viruses are metastable macromolecular assemblies containing a nucleic acid core packaged by capsid proteins that are primed to disassemble in host-specific environments leading to genome release and replication. The mechanism of how viruses sense environmental changes associated with host entry to prime them for disassembly is unknown. We have applied a combination of mass spectrometry, cryo-EM, and simulation-assisted structure refinement to Turnip crinkle virus (TCV), which serves as a model non-enveloped icosahedral virus (Triangulation number = 3, 180 copies/icosahedron). Our results reveal genomic RNA tightly binds a subset of viral coat proteins to form a stable RNA-capsid core which undergoes conformational switching in response to host-specific environmental changes. These changes include: i) Depletion of Ca 2+ which triggers viral particle expansion ii) Increase in osmolytes further disrupt interactions of outer coat proteins from the RNA-capsid core to promote complete viral disassembly. A cryo-EM structure of the expanded particle shows that RNA is asymmetrically extruded from a single 5-fold axis during disassembly. The genomic RNA:capsid protein interactions confer metastability to the TCV capsid and drive release of RNA from the disassembling virion within the plant host cell.

A Framework for Stochastic Optimization of Parameters for Integrative Modeling of Macromolecular Assemblies

Integrative modeling of macromolecular assemblies requires stochastic sampling, for example, via MCMC (Markov Chain Monte Carlo), since exhaustively enumerating all structural degrees of freedom is infeasible. MCMC-based methods usually require tuning several parameters, such as the move sizes for coarse-grained beads and rigid bodies, for sampling to be efficient and accurate. Currently, these parameters are tuned manually. To automate this process, we developed a general heuristic for derivative-free, global, stochastic, parallel, multiobjective optimization, termed StOP (Stochastic Optimization of Parameters) and applied it to optimize sampling-related parameters for the Integrative Modeling Platform (IMP). Given an integrative modeling setup, list of parameters to optimize, their domains, metrics that they influence, and the target ranges of these metrics, StOP produces the optimal values of these parameters. StOP is adaptable to the available computing capacity and converges quickly, allowing for the simultaneous optimization of a large number of parameters. However, it is not efficient at high dimensions and not guaranteed to find optima in complex landscapes. We demonstrate its performance on several examples of random functions, as well as on two integrative modeling examples, showing that StOP enhances the efficiency of sampling the posterior distribution, resulting in more good-scoring models and better sampling precision.

Multifrequency STD NMR Unveils the Interactions of Antibiotics With Burkholderia multivorans Biofilm Exopolysaccharide

Biofilms confine bacterial cells within self-produced matrices, offering advantages such as protection from antibiotics and entrapment of nutrients. Polysaccharides are major components in these macromolecular assemblies, and their interactions with other chemicals are of high relevance for the benefits provided by the biofilm 3D molecular matrix. NMR is a powerful technique for the study and characterization of the interactions between molecules of biological relevance. In this study, we have applied multifrequency saturation transfer difference (STD) NMR and DOSY NMR approaches to elucidate the interactions between the exopolysaccharide produced by Burkholderia multivorans C1576 (EpolC1576) and the antibiotics kanamycin and ceftadizime. The NMR strategies presented here allowed for an extensive characterization at an atomic level of the mechanisms behind the implication of the EpolC1576 in the recalcitrance phenomena, which is the ability of bacteria in biofilms to survive in the presence of antibiotics. Our results suggest an active role for EpolC1576 in the recalcitrance mechanisms toward kanamycin and ceftadizime, though through two different mechanisms.

Molecular architecture of SARS-CoV-2 envelope by integrative modeling

Despite tremendous efforts by research community during the COVID-19 pandemic, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M (membrane) protein. The molecular dynamics simulations allowed us to test the envelop stability under different configurations and revealed that M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns formed by M’s transmembrane and intra-virion (endo) domains. These results were in agreement with the experimental data, demonstrating a generic and versatile integrative approach to model the structure of a virus de novo, providing insights into critical roles of structural proteins in the viral assembly and integration, and proposing new targets for the antiviral therapies.