Biology subjects

Bajic, D.

Publications and source records attributed to Bajic, D..

15 recordsLinked to original sources

bioRxiv2026

Conserved emergent traits enable biobank-scale prediction of community function

Predicting the function of a microbial community from the identity of its members is a key goal of ecology and biotechnology. Here, we show that the systematic variation in how strains contribute to community function is phylogenetically conserved. This allows us to predict the collective function of communities solely from their member's taxonomic identity. Using soymilk fermentation as a model, we measured three industrially relevant functions - acidification, texture, sensory grade - across 307 synthetic communities combining 33 phylogenetically diverse lactic acid bacteria strains. The functional effect of a strain scaled linearly with the function of the receiving community, allowing us to summarize each strain's contribution across communities with two parameters, intercept and slope. These parameters were phylogenetically conserved, allowing us to impute their values for 17 previously unassayed strains, and use them to predict the three functions in 14 newly assembled communities. By redefining community function in terms of conserved emergent species traits, our results enable predictive engineering of microbial consortia at scale, and open a path towards genomic and evolutionary dissection of complex community phenotypes.

microbiology↗

bioRxiv2025

Linking structure to function in high performing electrosynthetic biofilm communities

Biofilm-based microbial electrosynthesis (MES) is a promising technology that converts CO2 into industrially relevant organic compounds using renewable energy sources. The highest performing MES systems reported to date consist of biofilm-driven microbial communities. However, for a successful deployment of this technology, we still need to overcome key challenges, including the long colonization time of the biocathode and the difficulty of controlling the product profile. To address these challenges, it is crucial to better understand the key microbial components responsible for the desired metabolic products, and how they assemble and function as a community. In this study, we conducted an in-depth characterisation of three high-performing mixed MES communities using metagenomics, metaproteomics and advanced analysis of metagenome-derived metabolic networks. Our findings identified Eubacterium limosum, Sporomusa sphaeroides and Clostridium aromativorans as key contributors to the production of acetate, butyrate and caproate via the Wood-Ljungdahl and the reverse {beta}-oxidation pathways. A higher production of butyrate and caproate was observed in reactors with higher abundance of C. aromativorans, an organism only recently discovered and never reported in gas-fermenting systems before. The recovered genome of C. aromativorans, reconstructed in a single circular fragment, provides a more comprehensive genomic representation than the current reference. In addition, we found genes related to lactate and ethanol production from acetyl-CoA in the metagenomes, including proteomic evidence for lactate production. This study provides key insights into the microbial players, metabolic processes and microbial community features driving product formation in biofilm-based MES, bringing this technology closer to industrial application.

systems biology↗

bioRxiv2025

The delivery of nano-formulated drugs to solid tumours is selectively increased by co-application of the vascular disrupting agent CA4P

Improving the efficacy of existing cytotoxic chemotherapeutics requires increasing drug delivery to tumours while minimising systemic toxicity. Formulating these drugs as nanoparticles can reduce their exposure to healthy tissues, but broadly applicable strategies to enhance tumoral accumulation are lacking. Here, we show that co-administering small molecule vascular disrupting agents together with nanoparticle formulations (e.g. diagnostic reporters, or clinical drugs irinotecan and doxorubicin) increases their tumoral uptake by up to threefold, without raising systemic exposure. In a syngeneic mouse model of triple-negative breast cancer, this enhancement diminished when co-treatments were repeated, limiting its therapeutic benefit. However, since most solid tumour types are susceptible to vascular disrupting agents, this approach may be a broadly applicable strategy to improve the selectivity of drug delivery: with particular relevance for single dose use in diagnostic or research settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/669501v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@a5b07corg.highwire.dtl.DTLVardef@1e5dcd2org.highwire.dtl.DTLVardef@49134org.highwire.dtl.DTLVardef@1d90aae_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Metabolic similarity and the predictability of microbial community assembly

AbstractWhen microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

ecology↗

bioRxiv2023

The architecture of metabolic networks constrains the evolution of microbial resource hierarchies

Microbial strategies for resource use are an essential determinant of their fitness in complex habitats. When facing environments with multiple nutrients, microbes often use them sequentially according to a preference hierarchy, resulting in well-known patterns of diauxic growth. In theory, the evolutionary diversification of metabolic hierarchies could represent a mechanism supporting coexistence and biodiversity by enabling temporal segregation of niches. Despite this ecologically critical role, the extent to which substrate preference hierarchies can evolve and diversify remains largely unexplored. Here we used genome-scale metabolic modeling to systematically explore the evolution of metabolic hierarchies across a vast space of metabolic network genotypes. We find that only a limited number of metabolic hierarchies can readily evolve, corresponding to the most commonly observed hierarchies in genome-derived models. We further show how the evolution of novel hierarchies is constrained by the architecture of central metabolism, which determines both the propensity to change ranks between pairs of substrates and the effect of specific reactions on hierarchy evolution. Our analysis sheds light on the genetic and mechanistic determinants of microbial metabolic hierarchies, opening new research avenues to understand their evolution, evolvability and ecology.

evolutionary biology↗

bioRxiv2023

A Structured Evaluation of Genome-Scale Constraint-Based Modeling Tools for Microbial Consortia

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools. Author summaryConstraint-based modeling employing genome-scale reconstructions of microbial species has become one of the most successful approaches for studying, analyzing, and engineering microbial consortia. Over the past decade, many constraint-based modeling tools have been published to examine an immense variety of microbial consortia spanning from the application areas of bioremediation to food and health biotechnology. However, new potential users lack an overview of the quality and performance of existing metabolic modeling tools that would guide their choice. To tackle this issue, we examined 24 tools for genome-scale metabolic modeling of microbial consortia. After an initial qualitative screening, we quantitatively evaluated 14 adequate tools against published experimental data that included different organisms and conditions. We conducted simulations and evaluated model features such as predictive accuracy, computational time, and tractability in capturing critical physiological properties. We found that, generally, more up-to-date, accessible, and documented tools were superior in many important aspects of model quality and performance. Although, in some cases, we observed tradeoffs in older, less elaborate tools that can be more accurate or flexible. This work has broad implications to help researchers navigate the most suitable tools, and suggests to developers opportunities for improvement of the currently existing capabilities for metabolic modeling of multi-species microbial consortia.

systems 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

Emergent coexistence in multispecies microbial communities

Microbial communities are highly diverse, and understanding the factors that promote and modulate this diversity is a major area of research in microbial ecology. Recent work has proposed a reductionist perspective to microbial coexistence, where pairwise coexistence between strains in isolation is required for their coexistence in a more complex community. In this view, species exclusion in pairwise co-culture would preclude their coexistence in a more complex community too. An alternative view is that coexistence is a more complex property of the entire community, requiring the presence of additional community members. If this view were correct, competitive exclusion in pairwise co-culture would not necessarily preclude species coexistence in more complex community contexts. Empirically testing these alternative hypotheses is complicated by the intractably high microbial diversity of most natural communities, and the challenges of reconstituting every pair of coexisting species under the exact same habitat where their community of origin was assembled. To address this challenge, we have experimentally reconstituted all possible pairwise co-cultures between stably coexisting species from 13 different, low-diversity microbial enrichment communities, which had previously been assembled in a well-controlled synthetic habitat. We find that, when isolated from the rest of their community members, most species pairs fail to coexist. This result highlights the importance of community context for microbial coexistence and indicates that pairwise exclusion may not reflect the ability of species to coexist in more complex, multispecies ecosystems.

ecology↗

bioRxiv2020

Artificially selecting microbial communities using propagule strategies

Artificial selection is a promising approach to manipulate the function of microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both experiments used "propagule" strategies, in which a set of the best-performing communities are used as the inocula to form a new generation of communities. In both cases, the selected communities are compared to a control treatment where communities are randomly selected. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used a diverse set of natural communities as the inoculum, and the function under selection was the cross-feeding potential of the resulting communities towards a reference bacterial strain. In both experiments, the selected communities reached a higher mean and a higher maximum function than the control. In the first experiment this is caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, the strong response of the mean is caused by the large initial variance in function across communities, and is the immediate consequence of the spread of the top-performing community in the starting group, whose function does not increase. Our results are in agreement with basic expectations of artificial selection theory, pointing out some of the limitations of community-level selection experiments which can inform the design of future studies.

evolutionary biology↗

bioRxiv2020

Metabolic rules of microbial community assembly

To develop a quantitative theory that can predict how microbiomes assemble, and how they respond to perturbations, we must identify which descriptive features of microbial communities are reproducible and predictable, which are unpredictable, and why. The emergent metagenomic structure of communities is often quantitatively convergent in similar habitats, with highly similar fractions of the metagenome being devoted to the same metabolic pathways. By contrast, the species-level taxonomic composition is often highly variable even in replicate environments. The mechanisms behind these patterns are not yet understood. By studying the self-assembly of hundreds of communities in replicate, synthetic habitats, we show that the reproducibility of microbial community assembly reflects an emergent metabolic structure, which is quantitatively predictable from first-principles, genome-scale metabolic models. Taxonomic variability within functional groups arises through multistability in population dynamics, and the species-level community composition is predictably governed by the mutual competitive exclusion of two sub-dominant strains. Our findings provide a mechanistic bridge between microbial community structure at different levels of organization, and show that the evolutionary conservation of metabolic traits, both in terms of growth responses and niches constructed, can be leveraged to quantitatively predict the taxonomic and metabolic structure of microbial communities.

ecology↗

bioRxiv2019

Tolerance to NADH/NAD+ imbalance anticipates aging and anti-aging interventions

Redox couples coordinate cellular function, but the consequences of their imbalances are unclear. This is somewhat associated with the limitations of their experimental quantification. Here we circumvent these difficulties by presenting a new approach that characterizes fitness-based tolerance profiles to redox couple imbalances using an in silico representation of metabolism. Focusing on the NADH/NAD+ redox couple in yeast, we demonstrate that reductive disequilibria generate metabolic syndromes comparable to those observed in cancer cells. The tolerance of yeast mutants to redox disequilibrium can also explain 30% of the variability in their experimentally measured chronological lifespan. Moreover, by predicting the significance of some metabolites to help stand imbalances, we correctly identify nutrients underlying mechanisms of pathology, lifespan-protecting molecules or caloric restriction mimetics. Tolerance to redox imbalances becomes thus a valid framework to recognize fundamental properties of the aging phenotype while providing a firm biological rationale to assess anti-aging interventions.

systems biology↗

bioRxiv2019

An antibody-tumor necrosis factor fusion protein that synergizes with oxaliplatin for treatment of colorectal cancer

We have cloned and characterized a novel fusion protein (Sm3E-TNF), consisting of the monoclonal antibody Sm3E in single-chain Fv fragment format, fused to murine tumor necrosis factor. The protein, which was expressed in mammalian cells and purified as a non-covalent stable homotrimer, bound to the cognate carcinoembryonic antigen (CEA) and retained tumor necrosis factor activity. A quantitative biodistribution experiment, performed in immunocompetent mice with CT26 colon carcinomas transfected with human CEA, revealed that Sm3E-TNF was able to preferentially accumulate in the tumors with excellent selectivity (tumor:blood ratio = 56:1, twenty-four hours after intravenous administration). The fusion protein mediated a rapid hemorrhagic necrosis of a large portion of the tumor mass, but a rim survived and eventually regrew. Surprisingly, the combination of Sm3E-TNF with 5-fluorouracil led to a reduction of therapeutic activity, while a combination with oxaliplatin led to a prolonged stabilization, with complete tumor eradication in 40% of treated mice. These therapy results were confirmed in a second immunocompetent mouse model of colorectal cancer (CEA-transfected C51 tumors) and provide a rationale for the possible clinical use of oxaliplatin in combination with fully-human antibody-TNF fusions.

bioengineering↗

bioRxiv2018

High-order interactions dominate the functional landscape of microbial consortia

Understanding the link between community composition and function is a major challenge in microbial ecology, with implications for the management of natural microbiomes and the design of synthetic consortia. For this purpose, it is critical to understand the extent to which community functions and properties can be predicted from species traits and what role is played by complex interactions. Inspired by the study of complex genetic interactions and fitness landscapes, here we have examined how the amylolytic function of combinatorial assemblages of seven starch-degrading soil bacteria depends on the functional contributions from each species and their interactions. Filtering our experimental results through the theory of enzyme kinetics, we show that high-order functional interactions dominate the amylolytic rate of our consortia, even though this function is biochemically simple, redundantly distributed in the community, and additive in the absence of inter-species interactions. As the community grows in size, the contribution of high-order functional interactions grows too, making the community function increasingly unpredictable. We can explain the prevalence of high order effects and their sign from the redundancy of ecological interactions in the network, in particular from redundant facilitation towards a high-performing community member. Our results suggest that even simple functions can be dominated by complex interactions, posing challenges for the predictability and bottom-up engineering of ecosystem function in complex multi-species communities.

ecology↗

bioRxiv2018

On the deformability of an empirical fitness landscape by microbial evolution

A fitness landscape is a map between the genotype and its reproductive success in a given environment. The topography of fitness landscapes largely governs adaptive dynamics, constraining evolutionary trajectories and the predictability of evolution. Theory suggests that this topography can be \"deformed\" by mutations that produce substantial changes to the environment. In spite of its importance, the deformability of fitness landscapes has not been systematically studied beyond abstract models, and little is known about its reach and consequences in empirical systems. Here we have systematically characterized the deformability of the genome-wide metabolic fitness landscape of the bacterium E. coli. Deformability is quantified by the non-commutativity of epistatic interactions, which we experimentally demonstrate in mutant strains on the path to an evolutionary innovation. Our analysis shows that the deformation of fitness landscapes by metabolic mutations rarely affects evolutionary trajectories in the short-range. However, mutations with large environmental effects leave these as a \"legacy\", producing long-range landscape deformations in distant regions of the genotype space that affect the fitness of later descendants. Our methods and results provide the basis for an integration between adaptive and eco-evolutionary dynamics with complex genetics and genomics.

evolutionary biology↗

bioRxiv2017

Emergent Simplicity in Microbial Community Assembly

Microbes assemble into complex, dynamic, and species-rich communities that play critical roles in human health and in the environment. The complexity of natural environments and the large number of niches present in most habitats are often invoked to explain the maintenance of microbial diversity in the presence of competitive exclusion. Here we show that soil and plant-associated microbiota, cultivated ex situ in minimal synthetic environments with a single supplied source of carbon, universally re-assemble into large and dynamically stable communities with strikingly predictable coarse-grained taxonomic and functional compositions. We find that generic, non-specific metabolic cross-feeding leads to the assembly of dense facilitation networks that enable the coexistence of multiple competitors for the supplied carbon source. The inclusion of universal and non-specific cross-feeding in ecological consumer-resource models is sufficient to explain our observations, and predicts a simple determinism in community structure, a property reflected in our experiments.

ecology↗