Biology subjects

Meacock, O. J.

Publications and source records attributed to Meacock, O. J..

4 recordsLinked to original sources

bioRxiv2023

Environment-organism feedbacks drive changes in ecological interactions

Ecological interactions, the impact of one organism on the growth and death of another, underpin our understanding of the long-term composition and the functional properties of communities. In recent years, the context-dependency of interactions - their tendency to change values in different environments, locations and at different times - has become an increasingly important theme in ecological research. However, an overarching theoretical assumption has been that external environmental factors are responsible for driving these changes. Here, we derive a theoretical interaction framework which teases apart the separate roles played by these extrinsic environmental inputs and the intrinsic environmental changes driven by organisms within the environment itself. At the heart of our theory is the instantaneous interaction, a quantity that captures the feedback between environmental composition and the growth of organisms within it. In the limit that intrinsic, organismdriven environmental change dominates over external drivers, we find that this feedback can give rise to temporal and spatial context-dependencies as organisms modify the environment over time and/or space. We use small synthetic microbial communities as model ecosystems to demonstrate the power of this framework, using it to predict time-dependent intra-specific interactions in a toxin degradation system and to relate time and spatial dependencies in crossfeeding communities. Our framework helps to explain the ubiquity of interaction context-dependencies in systems where population changes are driven by environmental changes - such as microbial communities - by placing the environment on an equal theoretical footing as the organisms that inhabit it.

ecology↗

bioRxiv2023

Cell motility greatly empowers bacterial contact weapons

Many bacteria kill competitors using short-range weapons, such as the Type VI Secretion System (T6SS) and Contact Dependent Inhibition (CDI). While these can deliver powerful toxins, they rely on direct contact between attacker and target cells. We hypothesised that movement enables attackers to contact more targets and thus greatly empower their weapons. To explore this, we developed individual-based and continuum models to show that motility greatly improves contact-dependent toxin delivery through two underlying processes. First, genotypic mixing increases the inter-strain contact probability of attacker and sensitive cells. Second, target switching ensures attackers constantly attack new cells, instead of repeatedly hitting the same cell. We test our predictions with the pathogen Pseudomonas aeruginosa, using genetically engineered strains to study the interaction between CDI and twitching motility. As predicted, we find that motility massively improves the effectiveness of CDI, in some cases up to 10,000-fold. Moreover, we demonstrate that both mixing processes occur using timelapse single-cell microscopy and quantify their relative importance by combining experimental data with our models. Our work shows how bacteria combine cell movement with contact-based weapons to launch powerful attacks on their competitors.

microbiology↗

bioRxiv2021

Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/Tracker

Most bacteria live attached to surfaces in densely-packed communities1,2. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms3-8, these approaches typically rely on a large number of user-defined parameters that must be carefully tuned to effectively track cells. Testing a given parameter combination can take hours to days depending on the size of the dataset, making iterative optimisation impractical. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while maintaining ease of use. Our approach, rooted in information theory, largely eliminates the need for users to iteratively adjust parameters manually and make qualitative assessments of the resulting cell trajectories. Instead, FAST measures multiple distinguishing "features" for each cell and then autonomously quantifies the amount of unique information each feature provides. We then use these measurements to determine how data from different features should be combined to minimize tracking errors. Comparing our algorithm with a naive approach that uses cell position alone revealed that FAST produced 4 to 10 times fewer tracking errors. The modular design of FAST combines our novel tracking method with tools for segmentation, extensive data visualisation, lineage assignment, and manual track correction. It is also highly extensible, allowing users to extract custom information from images and seamlessly integrate it into downstream analyses. FAST therefore enables high-throughput, data-rich analyses with minimal user input. It has been released for use either in Matlab or as a compiled stand-alone application, and is available at https://bit.ly/3vovDHn, along with extensive tutorials and detailed documentation.

microbiology↗

bioRxiv2020

Droplet printing reveals the importance of micron-scale structure for bacterial ecology

Bacteria often live in diverse communities where the spatial arrangement of strains and species is considered critical for their ecology, including whether strains can coexist, which are ecologically dominant, and how productive they are as a community1,2. However, a test of the importance of spatial structure requires manipulation at the fine scales at which this structure naturally occurs3-8. Here we develop a droplet-based printing method to arrange different bacterial genotypes across a sub-millimetre array. We use this to test the importance of fine-scale spatial structure by printing strains of the gut bacterium Escherichia coli that naturally compete with one another using protein toxins9,10. This reveals that the spatial arrangement of bacterial genotypes is important for ecological outcomes. Toxin-producing strains largely eliminate susceptible non-producers when genotypes are well-mixed. However, printing strains side-by-side creates an ecological refuge such that susceptible strains can coexist with toxin producers, even to the extent that a susceptible strain outnumbers the toxin producer. Head-to-head competitions between toxin producers also reveals strong effects, where spatial structure can make the difference between one strain winning and mutual destruction. Finally, we print different potential barriers between two competing strains to understand why space is so important. This reveals the importance of processes that limit the free diffusion of molecules. Specifically, we show that cells closest to a toxin producer bind to and capture toxin molecules, which creates a refuge for their clonemates. Our work provides a new method to generate customised bacterial communities with defined spatial distributions, and reveals that micron-scale changes in these distributions can drive major shifts in their ecology.

microbiology↗