Adhesion as a weapon in microbial competition

Microbes attach to surfaces and form dense communities known as biofilms, which are central to how microbes live and influence humans. The key defining feature of biofilms is adhesion, whereby cells attach to one another and to surfaces, via attachment factors and extracellular polymers. While adhesion is known to be important for the initial stages of biofilm formation, its function within biofilm communities has not been studied. Here we utilise an individual-based model of microbial groups to study the evolution of adhesion. While adhering to a surface can enable cells to remain in a biofilm, consideration of within-biofilm competition reveals a potential cost to adhesion: immobility. Highly adhesive cells that are resistant to movement face being buried and starved at the base of the biofilm. However, we find that when growth occurs at the base of a biofilm, adhesion allows cells to capture substratum territory and force less adhesive, competing cells out of the system. This process may be particularly important when cells grow on a host epithelial surface. We test the predictions of our model using the enteric pathogen Vibrio cholerae, which produces an extracellular matrix important for biofilm formation. Flow cell experiments indicate that matrix-secreting cells are highly adhesive and form expanding clusters that remove non-secreting cells from the population, as predicted by our simulations. Our study shows how simple physical properties, such as adhesion, can be critical to understanding evolution and competition within microbial communities.     

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs

Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.     

Measurement of the Copy Number of the Master Quorum-Sensing Regulator of a Bacterial Cell

Quorum-sensing is the mechanism by which bacteria communicate and synchronize group behaviors. Quantitative information on parameters such as the copy number of particular quorum-sensing proteins should contribute strongly to understanding how the quorum-sensing network functions. Here, we show that the copy number of the master regulator protein LuxR in Vibrio harveyi can be determined in vivo by exploiting small-number fluctuations of the protein distribution when cells undergo division. When a cell divides, both its volume and LuxR protein copy number, N, are partitioned with slight asymmetries. We measured the distribution functions describing the partitioning of the protein fluorescence and the cell volume. The fluorescence distribution is found to narrow systematically as the LuxR population increases, whereas the volume partitioning is unchanged. Analyzing these changes statistically, we determined that N = 80-135 dimers at low cell density and 575 dimers at high cell density. In addition, we measured the static distribution of LuxR over a large (3000) clonal population. Combining the static and time-lapse experiments, we determine the magnitude of the Fano factor of the distribution. This technique has broad applicability as a general in vivo technique for measuring protein copy number and burst size.     

Ligand-induced asymmetry in histidine sensor kinase complex regulates quorum sensing

Bacteria sense their environment using receptors of the histidine sensor kinase family, but how kinase activity is regulated by ligand binding is not well understood. Autoinducer-2 (AI-2), a secreted signaling molecule originally identified in studies of the marine bacterium Vibrio harveyi, regulates quorum-sensing responses and allows communication between different bacterial species. AI-2 signal transduction in V. harveyi requires the integral membrane receptor LuxPQ, comprised of periplasmic binding protein (LuxP) and histidine sensor kinase (LuxQ) subunits. Combined X-ray crystallographic and functional studies show that AI-2 binding causes a major conformational change within LuxP, which in turn stabilizes a quaternary arrangement in which two LuxPQ monomers are asymmetrically associated. We propose that formation of this asymmetric quaternary structure is responsible for repressing the kinase activity of both LuxQ subunits and triggering the transition of V. harveyi into quorum-sensing mode.     

AI-1 influences the kinase activity but not the phosphatase activity of LuxN of Vibrio harveyi

The Gram-negative bacterium Vibrio harveyi produces and responds to three autoinducers, AI-1, AI-2, and CAI-1 to regulate cell density dependent gene expression by a process referred to as quorum sensing. The concentration of the autoinducers is sensed by three cognate hybrid sensor kinases, and information is channeled via the HPt protein LuxU to the response regulator LuxO. Here, a detailed biochemical study on the enzymatic activities of the membrane-integrated hybrid sensor kinase LuxN, the sensor for N-(d-3-hydroxybutanoyl)homoserine lactone (AI-1), is provided. LuxN was heterologously overproduced as the full-length protein in Escherichia coli. LuxN activities were characterized in vitro and are an autophosphorylation activity with an unusually high ATP turnover rate, stable LuxU phosphorylation, and a slow phosphatase activity with LuxU approximately P as substrate. The presence of AI-1 affected the kinase but not the phosphatase activity of LuxN. The influence of AI-1 on the LuxN--> LuxU signaling step was monitored, and in the presence of AI-1, the kinase activity of LuxN, and hence the amount of LuxU approximately P produced, were significantly reduced. Half-maximal inhibition of kinase activity by AI-1 occurred at 20 mum. Together, these results indicate that AI-1 directly interacts with LuxN to down-regulate its autokinase activity and suggest that the key regulatory step of the AI-1 quorum sensing system of Vibrio harveyi is AI-1-mediated repression of the LuxN kinase activity.     

Chemotaxis to chitin oligosaccharides by Vibrio furnissii, a chitinivorous marine bacterium

We have reported that Vibrio furnissii, a chitinivorous marine bacterium, expresses a complex apparatus for adhesion/deadhesion to chitin analogues (1). In the present studies, we show that this organism exhibits a chemotactic response (swarming) to chitin oligosaccharides at concentrations as low as 10 microM. In contrast, V. furnissii exhibits slight to no chemotaxis to other utilizable compounds (glycerol, lactate, amino acids), with the exception of L-glutamic acid. V. furnissii may lack the tar (aspartate) receptor of Escherichia coli.