Biology subjects

Diaz-Colunga, J.

Publications and source records attributed to Diaz-Colunga, J..

9 recordsLinked to original sources

bioRxiv2026

Statistical learning of bacterial growth in combinatorially constructed environments

Optimizing growth conditions and culture media is a major goal in microbiology. A challenge is that nutrients can have complex, non-additive effects on growth. The fact that resources interact with one another has long been known in ecology, but systematic maps of resource interactions at all orders have been lacking and it is not known whether interactions are primarily low order or fundamentally complex. To tackle this problem, we have followed a full factorial design approach and measured the growth of seven different bacterial species in all possible combinations of 8 carbon sources under carbon-limiting conditions. Our approach allows us to directly estimate interactions at all orders. Even though all C-sources stimulate growth on their own as well as in combination with other nutrients, most of them can also have negative effects on growth when they are added to at least some nutrient mixtures. We show that the switch from positive to negative fitness effects is governed by global epistasis among resources. An analysis of variance shows that additive effects and pairwise interactions explain most of the variation in fitness, allowing us to train simple regression models that accurately predict bacterial growth in novel environments. The generality of these findings across seven different bacterial strains belonging to two different families indicates that interactions between carbon sources under carbon-limiting conditions may be generally learnable from a relatively sparse set of constructed environments, enabling the rational optimization of growth conditions.

microbiology↗

bioRxiv2024

Full factorial construction of synthetic microbial communities

Constructing combinatorially complete species assemblages is often necessary to dissect the complexity of microbial interactions and to find optimal microbial consortia. At the moment, this is accomplished through either painstaking, labor intensive liquid handling procedures, or through the use of state-of-the-art microfluidic devices. Here we present a simple, rapid, low-cost, and highly accessible liquid handling methodology for assembling all possible combinations of a library of microbial strains, which can be implemented with basic laboratory equipment. To demonstrate the usefulness of this methodology, we construct a combinatorially complete set of consortia from a library of eight Pseudomonas aeruginosa strains, and empirically measure the community-function landscape of biomass productivity, identify the highest yield community, and dissect the interactions that lead to its optimal function. This easy to implement, inexpensive methodology will make the assembly of combinatorially complete microbial consortia easily accessible for all laboratories.

ecology↗

bioRxiv2023

Learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines emergent function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes - analogs to fitness landscapes - that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically-inferred landscapes quantitatively predict community functions from knowledge of strain presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species, and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes are typically not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show this observation is generic across many ecological models, suggesting community-function landscapes can be applied broadly across many contexts. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

ecology↗

bioRxiv2022

Predictability of the community-function landscape in wine yeast ecosystems

Predictively linking taxonomic composition and quantitative ecosystem functions is a major aspiration in microbial ecology, which must be resolved if we wish to engineer microbial consortia. Here, we have addressed this open question for an ecological function of major biotechnological relevance: alcoholic fermentation in wine yeast communities. By exhaustively phenotyping an extensive collection of naturally occurring wine yeast strains, we find that most enologically-relevant traits exhibit a strong phylogenetic signal, indicating that the most relevant functions in wine yeast communities can be predicted from taxonomy. Further, we demonstrate that the quantitative contributions of individual wine yeast strains to the community function followed simple quantitative rules. These regularities can be integrated to quantitatively predict the function of newly assembled consortia. Besides addressing a fundamental open question in functional ecology, our results and methodologies provide a blueprint for rationally managing microbial processes of biotechnological relevance.

microbiology↗

bioRxiv2022

Environmental modulation of global epistasis is governed by effective genetic interactions

Interactions between mutations (epistasis) can add substantial complexity to genotype-phenotype maps, hampering our ability to predict evolution. Yet, recent studies have shown that the fitness effect of a mutation can often be predicted from the fitness of its genetic background using simple, linear relationships. This phenomenon, termed global epistasis, has been leveraged to reconstruct fitness landscapes and infer adaptive trajectories in a wide variety of contexts. However, little attention has been paid to how patterns of global epistasis may be affected by environmental variation -- both from external sources or induced by the population itself through eco-evolutionary feedbacks -- despite this variation frequently being a major driver of evolution. By analyzing a four-mutation fitness landscape, here we show that patterns of global epistasis can be strongly modulated by the concentration of a drug in the environment. Using previous theoretical results, we demonstrate that this modulation can be explained by how specific gene-by-gene interactions are modified by drug dose. Importantly, our results highlight the need to incorporate potential environmental variation into the global epistasis framework in order to predict adaptation in dynamic environments.

evolutionary biology↗

bioRxiv2022

Emergent ecosystem functions follow simple quantitative rules

The emergence of community functions is the result of a complex web of interactions between organisms and their environment. This complexity poses a significant obstacle in quantitatively predicting ecological function from the species-level composition of a community. In this study, we demonstrate that the collective impact of interspecies interactions leads to the emergence of simple linear models that predict ecological function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise ecological interactions between species. Our results illuminate an unexplored path to quantitatively linking the composition and function of ecological communities, bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

ecology↗

bioRxiv2022

Diversity begets diversity under microbial niche construction

Microbial interactions are expected to modulate microbial diversity, but whether they inhibit or stimulate further diversity in complex microbial communities, and how, remains poorly understood. By assembling microbial communities in multiple identical habitats with a single limiting nutrient, here we provide direct evidence for the role of microbial niche construction and cross-feeding in driving a positive relationship between community diversity and focal lineage diversity in microbial communities. Combining these experiments with simulations, we show that this positive relationship is not inevitable but depends on the underlying metabolic structure of by-product secretions and uptake between different taxonomic levels.

ecology↗

bioRxiv2020

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question "Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?" or, shortly, "What genotype comes next?". Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting methods use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the methods results when key assumptions do not hold.

bioinformatics↗

bioRxiv2020

Top-down engineering of complex communities by directed evolution

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.

evolutionary biology↗