Biology subjects

Nogales, J.

Publications and source records attributed to Nogales, J..

14 recordsLinked to original sources

bioRxiv2026

Engineering phenotypic heterogeneity for functional organization in microbial populations

Engineering microbial populations to perform complex functions requires programming not only cellular behaviour but also the functional organization through which biological activities are distributed across populations. While phenotypic heterogeneity is often regarded as variability to suppress, it can also serve as a foundation for organizing specialized functions within genetically homogeneous microbial systems. Here, we introduce PROMETEO, a modular genetic-circuit framework that programs population composition through architecture-encoded regulatory and translational asymmetries. Using a library of 27 asymmetric bistable circuits, we demonstrate that circuit architecture reproducibly specifies phenotypic distributions spanning a broad range of population compositions without continuous external induction. Programmed population structures remained stable over serial propagation and were qualitatively conserved across Escherichia coli and Pseudomonas putida. Stochastic and deterministic modelling accurately predicted architecture-dependent population compositions and hysteresis regimes, providing a quantitative framework for rational design. We further show that programmable population composition supports multiple modes of functional organization, including stable parallel specialization, inducible temporal redistribution of cellular states, and spatial ecological compartmentalization through biofilm-associated and planktonic subpopulations. As a demonstration of these capabilities, architecture-programmed organization enabled distributed Congo Red biotransformation through coordinated reductive and oxidative activities, achieving up to 98% dye removal in spatially compartmentalized populations. Together, these results establish population composition as a programmable property of genetic circuit design and provide a general strategy for engineering distributed functions within genetically homogeneous microbial populations.

synthetic biology↗

bioRxiv2025

GALLANT: A Scalable Computational Platform for Microbial Consortium Interaction and Population Heterogeneity Analysis

Metabolic modeling enables the prediction of functional capabilities in organisms and microbial communities from genomic data. However, current workflows for genome-scale metabolic model (GEM) reconstruction and contextualization remain time-consuming and technically demanding, particularly when integrating multi-omics data or deriving community-level models from taxonomic profiles. Although recent advances have improved automation and omics integration, challenges persist in incorporating heterogeneous datasets such as single-cell RNA sequencing and in interpreting microbiomes from 16S rRNA data. We present a computational tool for rapid, automated GEM generation with integrated support for contextualization using transcriptomic and single-cell omics data. The platform also enables the construction of core consortium metabolic models from 16S rRNA profiles, facilitating systems-level interpretation of both single-organism and community-scale datasets. This streamlined pipeline offers a scalable solution for microbiome research, including population heterogeneity analysis and metabolic engineering. Its utility has been demonstrated by exploring phenotypic heterogeneity within Bacillus subtilis populations and identifying metabolic interactions among members of a cyanobacteria-enriched microbial consortium. SummaryMetabolic modeling has become a cornerstone in systems biology, enabling researchers to simulate and analyze the metabolic capabilities of individual organisms and complex microbial communities. Genome-scale metabolic models (GEMs) have been widely used to explore metabolic phenotypes, guide metabolic engineering, and interpret omics data. Despite the increasing availability of genomic and metagenomic data, the generation and contextualization of metabolic models remains a time-consuming and technically demanding process, often requiring manual curation and domain-specific expertise. At the community level, modeling microbial consortia is gaining momentum, particularly with the rise of microbiome research. Tools like MICOM (Diener et al., 2020) and CarveMe (Machado et al., 2018) have advanced the ability to create metabolic models for entire communities, yet there remains a significant gap when it comes to systematically deriving community-level models from marker-gene data such as 16S rRNA sequencing. While 16S data provides a cost-effective snapshot of microbial composition, translating this into functional, mechanistic models of community metabolism is still an emerging area, typically involving multiple steps of taxonomic inference, genome mapping, and model aggregation. Recent advances in automation and algorithmic approaches have facilitated the reconstruction of draft models (Tarzi et al., 2024), yet the field continues to face challenges in the accurate contextualization of these models with experimental data. Multi-omics integration, including transcriptomics, proteomics, metabolomics, and especially single-cell RNA sequencing, has the potential to significantly improve the predictive power of metabolic models by providing condition-specific and cell-type-specific constraints. However, integrating these heterogeneous data sources remains a complex task, often limited by compatibility issues, lack of standardized pipelines, and computational overhead.

bioinformatics↗

bioRxiv2025

Model-driven exploration of underground metabolism reveals drivers of metabolic innovation in Pseudomonas putida

Beyond the heterologous expression of genes to generate microbes with novel properties, evolutionary engineering offers a complementary approach by exploiting adaptive processes to refine and expand cellular functionalities in biotechnology. A promising source for the generation of novel metabolic functions is the so-called underground metabolism, i.e., the subnetwork conformed by catalytically inefficient promiscuous enzymatic activities without an apparent physiological role. In this work, the potential of this underground metabolism as a source of novel phenotypes has been assessed in the soil bacterium Pseudomonas putida KT2440. To accomplish this, the high-quality genome-scale metabolic model iJN1462 was updated and expanded by the known set of promiscuous activities in this organism. The new metabolic space was explored to detect latent metabolic traits that could emerge through adaptive laboratory evolution (ALE) in order to broaden the range of available nutrients for P. putida. Using ALE, strains capable to degrade N-acetyl-L-alanine, an overproduced metabolite in HIV patients, were obtained. A multidisciplinary characterization of these evolved strains revealed that adaptation arose through synergistic and additive effects involving modifications in enzymes and transcription factors. This work demonstrates how underground metabolism can be exploited to expand the metabolic versatility of P. putida.

systems biology↗

bioRxiv2025

A novel expression system enabling scalable production of glycosylated flavonoids in Escherichia coli W using a plant-derived toxic gene

Glycosylated flavonoids are plant-derived compounds of significant interest due to their enhanced solubility, stability, and bioavailability, offering therapeutic potential across pharmaceutical, nutraceutical, and cosmetic sectors. However, their complex biosynthesis in plants hinders scalable production. In this study, we present an innovative microbial platform based on a phosphate-responsive Pliar53 promoter system in non-model chassis Escherichia coli W to enable efficient, regulated heterologous expression of the SbaiC7OGT gene from Scutellaria baicalensis. This platform circumvents common limitations associated with conventional inducible systems that rely on costly or toxic chemical inducers. The engineered E. coli SBG2413 strain demonstrated high titers of naringenin-7-O-glucoside (prunin) and exhibited broad substrate compatibility for the biosynthesis of other flavonoid glycosides. Our findings establish a cost-effective, scalable solution for industrial production of glycosylated flavonoids, with potential applicability to co-culture systems and microbial consortia. Highlights- A phosphate-response gene expression system (Pliar53) enables stable expression of a toxic flavonoid glycosyltransferase. - Translational optimization using the bicistronic BCD2 RBS and high-copy plasmids boosts prunin production from naringenin. - E. coli W SBG2413strain achieves > 9 g/L prunin in fed-batch bioreactor with 70% conversion. - The platform developed allows efficient glycosylation of structurally diverse flavonoids including flavones, flavanones, and isoflavones.

molecular biology↗

bioRxiv2025

Dyr726, a brain-penetrant inhibitor of PI3Kα, Type III receptor tyrosine kinases, and WNT signalling

The vast majority of clinical small molecule multi-kinase inhibitors (mKI) report abject failures in targeting cancers with high stem cell contents like high-grade glioma and colorectal cancers. The FDA-approved mKIs to date ablate receptor tyrosine kinase signaling but do not target the paradoxical WNT signaling which is a key survival driver for the self-renewing cancer stem cells. The WNT pathway enhances cancer plasticity and triggers relapse of highly heterogenous tumours. Using de novo synthesis and structure-activity-relationship (SAR) studies with blood-brain-barrier (BBB) penetrant mKI scaffolds, we designed a highly potent and selective small molecule inhibitor of PI3K, PDGFR/KIT, and the WNT pathway denoted Dyr726. Dyr726 is superior to clinical mKIs and inhibits PI3K-AKT-mTOR and WNT-pathway signaling at multiple nodes thereby impeding proliferation, invasion, and tumour growth. Phospho-proteomic, structural, and target engagement analyses, combined with in vitro, in vivo efficacy, and pharmacokinetic studies reveal that Dyr726 is a brain-penetrant small molecule which effectively reduces tumour volume and extends survival of murine orthotopic models. Our current work establishes a first-in-class brain penetrant small molecule mKI which simultaneously antagonize the PI3K-AKT-mTOR and WNT pathways in preclinical cancer stem cell cultures, adult and pediatric primary organoids, and orthotopic murine models with positive efficacy in combination with clinical standard of care.

cancer biology↗

bioRxiv2023

Bdellovibrio's Prey-Independent Growth is Fuelled by Amino Acids as a Carbon Source

Identifying the nutritional requirements and growth conditions of microorganisms is crucial for determining their applicability in industry and understanding their role in clinical ecology. Predatory bacteria such as Bdellovibrio bacteriovorus have emerged as promising tools for combating infections by human bacterial pathogens due to their natural killing features. Bdellovibrios lifecycle occurs inside prey cells, using the cytoplasm as a source of nutrients and energy. However, this lifecycle supposes a challenge when determining the specific uptake of metabolites from the prey to complete the growth inside cells, a process that has not been completely elucidated. Here, following a model-based approach we illuminate the ability of Bdellovibrio bacteriovorus to replicate DNA, increase biomass, and generate adenosine triphosphate (ATP) in an amino acid-based rich media in the absence of prey, keeping intact its predatory capacity. In this culture, we determined the main carbon sources used and their preference, being glutamate, serine, aspartate, isoleucine, and threonine. This study offers new insights into the role of predatory bacteria in natural environments and establishes the basis for developing new Bdellovibrio applications using appropriate metabolic and physiological methodologies.

microbiology↗

bioRxiv2023

Development and Optimisation of in vitro Sonodynamic Therapy for Glioblastoma

BackgroundSonodynamic therapy (SDT) is currently on critical path for glioblastoma therapeutics. However, the molecular mechanisms underlying the therapeutic functions of SDT remain enigmatic due to the lack of intricately optimised instrumentation capable of modulating SDT delivery in vitro. We established and validated an automated in vitro SDT system and assessed its SDT efficacy. MethodsAn automated in vitro SDT system was established and validated to allow the application and mapping of focused ultrasound fields under varied acoustic exposure conditions and setup configurations. Ultrasound field simulations were performed to assess the acoustic energy exposure. Systematic in vitro investigations were performed to optimise ultrasound frequency, intensity, plate base material, thermal effect, and the integration of live cells. ResultsIn the presence of 5-ALA, focused ultrasound induces apoptotic cell death in primary patient-derived glioma cells with concurrent upregulation of intracellular reactive oxygen species. Intriguingly, primary glioma stem neurospheres also exhibit remarkably reduced 3D growth upon SDT exposure. Further, ultrasound field simulations performed established acoustic field distribution and the impact of acoustic standing waves within the well-plates. ConclusionsThe optimised in vitro SDT system and associated ultrasound field simulations establish the basis for further understanding of SDT and its therapeutic potentials in cancer.

bioengineering↗

bioRxiv2023

New strategy for bioplastic and exopolysaccharides production: Enrichment of field microbiomes with cyanobacteria

Seven photosynthethic microbiomes were collected from field environmental samples to test their potential in polyhydroxybutirate (PHB) and exopolysaccharides (EPS) production, two alternatives to chemical-based polymers. Microscope observations together with microbial sequence analysis revealed the microbiome enrichment in cyanobacteria after culture growth under phosphorus limitation. PHB and EPS production were studied under three culture factors (phototrophy, mixotrophy and heterotrophy) by evaluating and optimizing the effect of three parameters (organic and inorganic carbon and days under light:dark cycles) by Box-Behnken design. Results showed that optimal conditions for both biopolymers synthesis were microbiome-dependent; however, the addition of organic carbon boosted PHB production in all the tested microbiomes, producing up to 14%dcw PHB with the addition of 1.2 g acetate{middle dot}L-1 and seven days under light:dark photoperiods. The highest EPS production was 59 mg{middle dot}L-1 with the addition of 1.2 g acetate{middle dot}L-1 and four days under light:dark photoperiods. The methodology used in this article is suitable for enriching microbiomes in cyanobacteria, and for testing the best conditions for bioproducts synthesis for further scale up.

bioengineering↗

bioRxiv2023

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

bioRxiv2023

Technical upgrade of an open-source liquid handler to support bacterial colony screening

The optimization of genetically engineered biological constructs is a key step to deliver high-impact biotechnological applications. The use of high-throughput DNA assembly methods allows the construction of enough genotypic variants to successfully cover the target design space. This, however, entails extra workload for researchers during the screening stage of candidate variants. Despite the existence of commercial colony pickers, their high price excludes small research laboratories and budget-adjusted institutions from accessing such extensive screening capability. In this work we present COPICK, a technical solution to automatize colony picking in an open-source liquid handler Opentrons OT-2. COPICK relies on a mounted camera to capture images of regular Petri dishes and detect microbial colonies for automated screening. COPICKs software can then automatically select the best colonies according to different criteria (size, color and fluorescence) and execute a protocol to pick them for further analysis. Benchmark tests performed for E. coli and P. putida colonies delivers a raw picking performance over pickable colonies of 82% with an accuracy of 73.4% at an estimated rate of 240 colonies/h. These results validate the utility of COPICK, and highlight the importance of ongoing technical improvements in open-source laboratory equipment to support smaller research teams.

bioengineering↗

bioRxiv2022

Golden Standard: A complete standard, portable, and interoperative MoClo tool for model and non-model bacterial hosts.

Modular cloning assembly has become a benchmark technology in synthetic biology. However, there is a mismatch between its impressive development and the standardization required to promote interoperability between the different systems available. The full development of the field is thus hampered by a surge of oftentimes incompatible organism-specific systems. To overcome these issues, we present Golden Standard (GS), a Type IIS assembly method underpinned by the Standard European Vector Architecture (SEVA). GS unlocks modular cloning applications with any type of microorganism and delivers consistent combinatorial multi-part assembly of standardized genetic elements to create genetic circuits of up to twenty transcription units. Reliance on the Golden Gate syntax renders GS fully compatible with many existing tools and it sets the path towards efficient reusability of available part libraries and assembled TUs. GS was fully validated in terms of DNA assembly performance, portability and phenotype engineering in model and non-model bacteria. In order to facilitate the widespread adoption and future community-driven development of GS, we provide a web-portal featuring: i) a repository of parts and vectors, ii) a SBOLHub for exchange and analysis of constructs and iii) Wizard and Setup tools to guide the design of constructs using stored and user-specific parts.

synthetic biology↗

bioRxiv2020

Providing new insights on the byphasic lifestyle of the predatory bacterium Bdellovibrio bacteriovorus through genome-scale metabolic modeling.

In this study we analyze the growth-phase dependent metabolic states of Bdellovibrio bacteriovorus by constructing a fully compartmented, mass and charge-balanced genome-scale metabolic model of this predatory bacterium (iCH457). Considering the differences between life cycle phases driving the growth of this predator, growth-phase condition-specific models have been generated allowing the systematic study of its metabolic capabilities. Using these unprecedented computational tools, we have been able to analyze, from a system level, the dynamic metabolism of the predatory bacteria as the life cycle progresses. We provide solid computational evidences supporting potential axenic growth of B. bacteriovoruss in a rich medium based its encoded metabolic capabilities. Our systems-level analysis confirms the presence of "energy-saving" mechanisms in this predator as well as an abrupt metabolic shift between the attack and intraperiplasmic growth phases. Our results strongly suggest that predatory bacterias metabolic networks have low robustness, likely hampering their ability to tackle drastic environmental fluctuations, thus being confined to stable and predictable habitats. Overall, we present here a valuable computational testbed based on predatory bacteria activity for rational design of novel and controlled biocatalysts in biotechnological/clinical applications. AUTHOR SUMMARYBacterial predation is an interspecific relationship widely extended in nature. Among other predators, Bdellovibrio and like organism (BALOs) have received recently a great attention due to the high potential applications such as biocontrol agent in medicine, agriculture, aquaculture and water treatment. Despite the increasing interest in of this predatory bacterium its complex lifestyle and growth conditions hamper the full exploitation of their biotechnological properties. In order to overcome these important shortcomings, we provide here the first genome-scale model of a predator bacterium constructed so far. By using the model as a computational testbed, we provide solid evidences of the metabolic autonomy of this interesting bacterium in term of growth, as well as its dynamic metabolism powering the biphasic life style of this predator. We found a low metabolic robustness thus suggesting that Bdellovibrio is more niche-specific than previously thought and that the environmental conditions governing predation may be relatively uniform. Overall, we provide here a valuable computational tool largely facilitating rational design of novel and controlled predator-based biocatalysts in biotechnological/clinical applications.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2017

Expanding The Computable Reactome In Pseudomonas putida Reveals Metabolic Cycles Providing Robustness

Genome-scale network reconstructions are organism-specific representations of metabolism and powerful tools for analyzing systemic metabolic properties. The use of reconstructions is limited by the lack of coverage of the metabolic reactome. We present an exhaustive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440, greatly expanding its computable metabolic states. The reconstruction, iJN1411, represents a significant expansion over other reconstructed bacterial metabolic networks. Computations based on the reconstruction exhibit high accuracy in predicting nutrient sources, growth rates, carbon flux distributions, and gene essentiality, thus providing a deep understanding of Pseudomonas metabolism. iJN1411 was used for: i) the assessment of the metabolic capabilities of P. putida as a species through multi-strain modeling, ii) deciphering the molecular mechanisms underlying metabolic robustness, and iii) identification of metabolic \"capacitors\" based on ATP-fueled metabolic cycles. This study represents the most complete and comprehensive bacterial metabolic reconstruction built to date, while providing computational and experimental evidence about how bacteria increase metabolic robustness, paving the way for engineering more robust biocatalysts and searching for drug targets in robust pathogens.

systems biology↗