Biology subjects

Mazza, C.

Publications and source records attributed to Mazza, C..

9 recordsLinked to original sources

bioRxiv2026

Biphasic bacterial community assembly predicted from generalized first principles of monoculture growth and inferred species interactions

Microbial communities occur in all habitats, yet how individual growth on available nutrients scales to community assembly remains poorly understood. This gap stems largely from the unknown effects of species interactions. These interactions arise because individual populations both consume and transform primary substrates into metabolites exploitable by others, and because parasitic and predatory mechanisms can release cellular building blocks that enable nutrient reuse. Here, we present a mathematical framework that predicts community growth and compositional succession from monoculture growth kinetics, resource availability, and species interaction parameters. To parametrize species interactions, we use a simulated-annealing optimization algorithm to search parameter space for sets that minimize the difference between modeled community growth and experimental time series from soil microcosms inoculated with defined communities of 20 or 21 soil isolates, with or without an opportunistic bacteriovorous member. The optimized interaction parameter sets were then used to predict growth dynamics in an independent 21-member community and in species drop-out communities. We find that community development is biphasic: an initial phase dominated by competition for primary resources driven by inherent strain growth kinetics, followed by a phase governed by cross-feeding and biomass formation on released byproducts. Paired metatranscriptomic analysis corroborated predicted shifts in individual growth states and revealed metabolic repurposing associated with the sudden renewed availability of metabolites and cellular building blocks. Model simulations that excluded species interactions reproduced only one-fifth of the observed community biomass, highlighting the importance of cross-feeding for soil community growth. Overall, models that integrate monoculture growth kinetics with inferred species interactions can predict the dynamics of medium-complexity communities from starting inocula even when environmental nutrient composition is largely unknown.

microbiology↗

bioRxiv2026

Spatially mediated interactions shape founder-cell fitness and community assembly in multi-species soil bacteria

Microbial communities spontaneously colonize pristine environments, yet how species growth kinetics and individual cell variation shape community assembly remains poorly understood. Here, we use time-lapse microscopy imaging to track the division of individual founder cells in communities composed of up to seven soil bacterial isolates grown on nutrient surfaces. With cell lineage tracking, we quantify species-specific absolute biomass formation and growth kinetics from early growth through stationary phase. The reproductive success of individual founder cells depended on the timing of their first cell division, which determined their access to the primary substrate and their maximum growth rates. In mixed-species communities, founder cell success also depended on species-specific, substrate-dependent growth rates and yields. In addition, spatial factors - such as cells positioning, distances to non-kin neighbours, and identities of co-occurring species - further influenced outcomes. In spatially structured communities, interspecific interactions were globally governed by competition for primary substrates. We also observed cross-feeding of leaked metabolites, reflected in fluctuating paired interaction strengths and interaction signs. Species-pair interactions differed locally, with cells within distances of less than 15 {micro}m exhibiting opposite interaction behaviours. Global pairwise interactions predicted from monoculture growth kinetics were observed in approximately half of the measured pairs, whereas measured paired interactions generally weakened in combinations of three or more species. Using a spatially explicit agent-based Monod growth model that includes interspecific interactions, we accurately predicted the compositions of seven-member communities. Overall, our results indicate that emergent, spatially mediated interspecific interactions between cells of different bacterial species primarily drive local and temporal changes in individual cell growth rates, which in turn determine final biomass formation. Because most natural microbial habitats are spatially structured, stochastic founder-cell positioning and fitness differences are key determinants of locally formed interaction patterns and species coexistence.

microbiology↗

bioRxiv2024

Inferring Bacterial Interspecific Interactions from Microcolony Growth Expansion

Interactions between species are thought to be crucial for modulating their growth and behaviour within communities, and determinant for the emergence of community functions. Several different interaction concepts exist, but there is no consensus on how interactions should be quantified and integrated in community growth theory. Here we expand on existing concepts of real-time measurements of pure culture microcolony growth to develop and benchmark coculture microcolony experiments, and show how these can both parametrize growth kinetic and interspecific interaction effects. We follow surface growth by time-lapse microscopy of fluorescently tagged Pseudomonas putida and Pseudomonas veronii under substrate competition with succinate, or under substrate indifference with D-mannitol and putrescine. Monoculture-grown microcolonies showed substrate concentration dependent expansion rates as expected from Monod relations, whereas individual microcolony yields were strongly dependent on densities and spatial positioning of founder cells. Maximum specific growth rates in cocultures under substrate competition were diminished by ca. 15%, which was seeding-density independent. The collective P. putida population dominated growth over that of P. veronii, but with 27% yield loss under competition compared to monoculture growth; and 90% for that of P. veronii. Incidental local reversal of competition was observed where P. veronii microcolonies profited at the detriment of P. putida, and between 9 and 43% of P. veronii microcolonies grew bigger than expected from bulk competition, depending on seeding density. Simulations with a cell-agent Monod surface growth model suggested that colony expansion rate decrease in competitive coculture is caused by metabolite cross-feeding, which was supported by exometabolite analysis during and after growth of the strains on their individual or swapped supernatant. Coculture microcolony growth experiments thus provide a flexible platform for analysis of kinetic and interspecific interactions, expanding from individual microcolony phenotypic effects to averaged behaviour across all microcolony pairs. The system in theory is scalable to follow real-time growth of multiple species simultaneously into communities.

microbiology↗

bioRxiv2023

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general interaction models have been formulated, but what is currently lacking are models that link mono-culture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate indifference), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for mono-cultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback loops in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model over constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from mono-culture growth experiments. 1 Author summaryCorrectly predicting growth of communities of diverse bacterial taxa remains a challenge, because of the very different growth properties of individual members and their myriads of interactions that can influence growth. Here we tried to improve and empirically validate mathematical models that combine theory of bacterial growth kinetics (i.e., Monod models) with mathematical definition of interaction parameters. We focused in particular on common cases of shared primary substrates (i.e., competition) and independent substrates (i.e., indifference) in an experimental system consisting of one fast-growing and one slower growing Pseudomonas species. Growth kinetic parameters derived from mono-culture experiments included in a Monod-type consumer-resource model explained some 75% of biomass formation of either species in co-culture, but underestimated the observed growth improvement when either of the species started as a minority compared to the other. This suggested an in important role of cross-feeding, whereby released metabolites from one of the partners is utilized by the other. Inclusion of cross-feeding feedback in the two-species Monod growth model largely explained empirical data at all species-starting ratios, in particular when cross-feeding is activated in almost binary manner as a function of metabolite concentration. Our results also indicate the importance of cross-feeding for minority taxa, which can explain their survival despite being poorly competitive.

microbiology↗

bioRxiv2023

Complexity of spatiotemporal plantar pressure patterns during everyday behaviours

The human foot sole is the primary interface with the external world during balance and walking, and also provides important tactile information on the state of contact. However, prior studies on plantar pressure have focused mostly on summary metrics such as overall force or centre of pressure under limited conditions. Here, we recorded spatiotemporal plantar pressure patterns with high spatial resolution while participants completed a wide range of daily activities, including balancing, locomotion, and jumping tasks. Contact area differed across task categories, but was only moderately correlated with the overall force experienced by the foot sole. The centre of pressure was often located outside the contact area or in locations experiencing relatively low pressure, and therefore a result of disparate contact regions spread widely across the foot. Non-negative matrix factorisation revealed low-dimensional spatial complexity that increased during interaction with unstable surfaces. Additionally, pressure patterns at the heel and metatarsals decomposed into separately located and robustly identifiable components, jointly capturing most variance in the signal. These results suggest optimal sensor placements to capture task-relevant spatial information and provide insight into how pressure varies spatially on the foot sole during a wide variety of natural behaviours.

neuroscience↗

bioRxiv2022

Deformable image registration for automatic muscle segmentation and the generation of augmented imaging datasets

Muscle segmentation is a process relied upon to gather medical image-based muscle characterisation, useful in directly assessing muscle volume and geometry, that can be used as inputs to musculoskeletal modelling pipelines. Manual or semi-automatic techniques are typically employed to segment the muscles and quantify their properties, but they require significant manual labour and incur operator repeatability issues. In this study an automatic process is presented, aiming to segment all lower limb muscles from Magnetic Resonance (MR) imaging data simultaneously using three-dimensional (3D) deformable image registration. Twenty-three of the major lower limb skeletal muscles were segmented from five subjects, with an average Dice similarity coefficient of 0.72, and average absolute relative volume error of 12.7% (average relative volume error of -2.2%) considering the optimal subject combinations. Segmented MR imaging datasets of the lower limb are not widely available in the literature, limiting the potential of new, probabilistic methods such as deep learning to be used in the context of muscle segmentation. In this work, Non-linear deformable image registration is used to generate 69 manually checked, segmented, 3D, artificial datasets, allowing access for future studies to use these new methods, with a large amount of reliable reference data.

bioengineering↗

bioRxiv2020

An analog to digital converter controls bistable transfer competence of a widespread integrative and conjugative element

Conjugative transfer of the integrative and conjugative element ICEclc in Pseudomonas requires development of a transfer competence state in stationary phase, which arises only in 3-5% of individual cells. The mechanisms controlling this bistable switch between non-active and transfer competent cells have long remained enigmatic. Using a variety of genetic tools and epistasis experiments in P. putida, we uncovered an upstream cascade of three consecutive transcription factor-nodes, which controls transfer competence initiation. Initiation activates a feedback loop, which stochastic modeling and experimental data demonstrated acts as a scalable converter of unimodal input to bistable output. The feedback loop further enables prolonged production of a transcription factor that ensures downstream transfer competence formation in activated cells. Phylogenetic analyses showed that the ICEclc regulatory factors are widespread among Gamma- and Beta-proteobacteria, highlighting its evolutionary conservation and prime importance to control the behaviour of this wide family of conjugative elements.

genomics↗

bioRxiv2019

A four-step regulatory cascade controls bistable transfer competence development of the integrative and conjugative element ICEclc in Pseudomonas

Genetic bistability controls different phenotypic programs in defined subpopulations of genetically identical bacteria. Conjugative transfer of the integrative and conjugative element ICEclc in Pseudomonas requires development of a transfer competence state in stationary phase, but this state arises only in 3-5% of individual cells. The mechanisms controlling and underlying the bistable switch between non-active and transfer competence cells have long remained enigmatic. Using a variety of genetic tools combined with stochastic modeling, we characterize here the factors and overall network architecture controlling bistable ICEclc activation of transfer competence. Two new key regulators (BisR and BisDC) were uncovered, that link the hierarchical cascade of ICEclc transfer competence activation to in total four regulatory nodes. The final activator complex named BisDC drives a positive feedback on its own transcription, and directly controls the "late" ICE promoters for excision and transfer. Stochastic mathematical modeling conceptually explained the arisal and maintenance of bistability by the feedback loop, and demonstrated its importance to guarantee consistent prolonged downstream output in activated cells. A minimized gene set allowing controllable bistable output in a Pseudomonas putida in absence of the ICEclc largely confirmed model predictions. Phylogenetic analyses further showed that the two new ICEclc regulatory factors are widespread among putative ICEs found in Gamma- and Beta- proteobacteria, highlighting the conceptual importance of our findings for the behaviour of this wide family of conjugative elements. Author summaryIntegrative and conjugative elements (ICEs) are mobile genetic elements present in virtually every bacterial species, which can confer adaptive functions to their host, such as antibiotic resistance or xenometabolic pathways. Integrated ICEs maintain by replication along with the genome of their bacterial host, but in order to transfer, the ICE excises and conjugates into a new recipient cell. Single-cell studies on a unique but widely representative ICE model from Pseudomonas (ICEclc) showed that transfer only occurs from a small dedicated subpopulation of cells that arises during stationary phase conditions. This bistable subpopulation differentiation is highly significant for ICE behaviour and fitness, but how it is regulated has remained largely unknown. The present work unveiled the architecture of the ICEclc transfer competence regulation, and showed its widespread occurrence among ICEs of the same family. Stochastic mathematical modeling explained how bistability is generated and maintained, prolonging the capacity of stationary phase cells to complete all stages of ICE activation.

microbiology↗

bioRxiv2018

Mathematical modeling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis

Organisms use environmental cues for directed navigation. Depending on the sensory modality and complexity of the involved sensory organs, different types of information may be processed. Understanding the basic logic behind navigational decisions critically depends on the complexity of the nervous system. Due to the comparably simple organization of the nervous system of the fruit fly larva, it stands as a powerful model to study decision-making processes that underlie directed navigation. Here, we formulate a stochastic method based on biased Markov chains to model the behavioral basis of negative phototaxis. We have quantitatively measured phototaxis in response to defined sensory inputs. We find that larvae make navigational decisions by taking into account both light intensities and its spatial gradients, and our model allows us to quantify how larvae minimize their exposure to light intensity and at the same time maximize their distance to the source of light. The response to the light field is a non-linear response and saturates above an intensity threshold. Our mathematical model simulates and predicts larval behavioral dynamics only using light intensity and directionality as input parameters. Moreover, it allows us to evaluate the relative importance of these two factors governing visual navigation. The model has been validated with experimental biological data yielding insight into the strategy that larvae use to achieve their goal with respect to the navigational cue of light, paving the way for future work to study the role of the different neuronal components in this mechanism.\n\nAuthor SummaryNavigational decision-making is a complex process during which the nervous system is able to decipher external input through molecular and cellular mechanisms to produce a spatially-coordinated behavioral output. Drosophila larvae provide an excellent model to understand these decision-making mechanisms as we can measure the behavioral output (larval navigation) in response to quantifiable external input (different light conditions). We have performed experiments to quantify larval light avoidance in order to subsequently design a mathematical model that quantitatively reproduces larval behavior. Our results allow us to characterize the relative importance of light intensity and directionality and yield insight into the neural algorithms used in the decision-making mechanism of larval phototaxis.

neuroscience↗