Biology subjects

Mannan, A. A.

Publications and source records attributed to Mannan, A. A..

6 recordsLinked to original sources

bioRxiv2025

Activated fatty acid synthesis pathway in macrophages propagates pathogenic fibroblast expansion after myocardial infarction

Metabolic pathways, such as fatty acid oxidation and oxidative phosphorylation, can modulate inflammatory cells. However, little is known about the effects of the fatty acid synthesis pathway in macrophages on inflammation and cardiac remodeling after myocardial infarction (MI). Using spatial metabolomics, here we show that cardiac macrophages residing in the infarct synthesize de novo fatty acids and increase the production of fatty acid enzymes including ACLY and FASN. Mice deficient in myeloid Acly and Fasn have improved cardiac function after MI and reduced fibrosis. Combining Cleavage Under Targets and Release Using Nuclease (CUT&RUN), RNA sequencing analysis of Acly-/-macrophages, and macrophage-specific in vivo gene silencing, we demonstrate that ACLY acetylates the promoter region of the upstream regulator Krt17, which drives the production of pro-fibrotic cytokines, including IL-33. Single-cell RNA sequencing of cardiac fibroblasts shows that the expansion of a population of fibroblasts (Fibroblast 5) expressing high levels of extracellular matrix genes after MI is confined in the absence of macrophage Acly. Finally, the analysis of spatial multi-omics data of human hearts with MI uncovers myofibroblasts with the Fibroblast 5 gene signature. These myofibroblasts are located near cardiac macrophages expressing high levels of ACLY. In summary, we show that macrophage ACLY and FASN are deleterious in MI pathogenesis.

immunology↗

bioRxiv2024

Design principles for engineering bacteria to maximise chemical production from batch cultures

Bacteria can be engineered to manufacture chemicals, but it is unclear how to optimally engineer a single cell to maximise production performance from batch cultures. Moreover, the performance of engineered production pathways is affected by competition for the hosts native resources. Here, using a "host-aware" computational framework which captures competition for both metabolic and gene expression resources, we uncover design principles for engineering the expression of host and production enzymes in a cell to maximise volumetric productivity and yield from batch cultures. Our results suggest that selecting strains in the lab for maximum growth and product synthesis can achieve close to maximum culture productivity and yield, but the growth-synthesis trade-off fundamentally limits production performance. We show that engineering genetic circuits to switch cells to a high synthesis-low growth state after first growing to a large population can further improve performance. By analysing different circuit topologies, we show that optimal performance is achieved by circuits that inhibit host metabolism to redirect it to product synthesis. Our results should facilitate construction of microbial cell factories with high and efficient production capabilities.

synthetic biology↗

bioRxiv2021

gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs

MotivationA widely applicable strategy to create cell factories is to knock out (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesise at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow. ResultsTo address this multi-objective optimization problem, we developed a software tool named gcFront - using a genetic algorithm it explores KOs that maximise cell growth, product synthesis, and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run - granting users flexibility in selecting designs to take to the lab. Availability and ImplementationgcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront, the repository of which is archived at Zenodo, DOI: 10.5281/zenodo.6338595 (Legon et al., 2022). Supplementary InformationSupplementary notes and data files are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Flux vs burden: biosensor optimization for dynamic pathway engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors.

synthetic biology↗

bioRxiv2020

Designing an irreversible metabolic switch for scalable induction of microbial chemical production

Bacteria can be harnessed to synthesise high-value chemicals. A promising strategy for increasing productivity uses inducible control systems to switch metabolism from growth to chemical synthesis once a large population of cell factories are generated. However, use of expensive chemical inducers limits scalability of this approach for biotechnological applications. Switching using cheap nutrients is an appealing alternative, but their tightly regulated uptake and consumption again limits scalability. Here, using mathematical models of fatty acid uptake in E. coli as an exemplary case study, we unravel how the cells native regulation and program of induction can be engineered to minimise inducer usage. We show that integrating positive feedback loops into the circuitry creates an irreversible metabolic switch, which, requiring only temporary induction, drastically reduces inducer usage. Our proposed switch should be widely applicable, irrespective of the product of interest, and brings closer the realization of scalable and sustainable microbial chemical production.

synthetic biology↗

bioRxiv2019

Metabolite sequestration enables rapid recovery from nutrient depletion

Microbes adapt their metabolism to take advantage of nutrients in their environment. Such adaptations control specific metabolic pathways to match energetic demands with nutrient availability. Upon depletion of nutrients, rapid pathway recovery is key to release cellular resources required for survival in the new nutritional condition. Yet little is known about the regulatory strategies that microbes employ to accelerate pathway recovery in response to nutrient depletion. Using the fatty acid catabolic pathway in Escherichia coli, here we show that fast recovery can be achieved by rapid release of a transcriptional regulator from a metabolite-sequestered complex. With a combination of mathematical modelling and experiments, we show that recovery dynamics depend critically on the rate of metabolite consumption and the exposure time to nutrient. We constructed strains with re-wired transcriptional regulatory architectures that highlight the metabolic benefits of negative autoregulation over constitutive and positive autoregulation. Our results have wide-ranging implications for our understanding of metabolic adaptations, as well as guiding the design of gene circuitry for synthetic biology and metabolic engineering.

systems biology↗