Biology subjects

Fedorec, A. J. H.

Publications and source records attributed to Fedorec, A. J. H..

11 recordsLinked to original sources

bioRxiv2024

Acute and early stress axis modulation in joint disease permanently reduces pain and emotional comorbidities

Chronic pain affects 20-30% of the population and imposes a significant socio-economic burden as it is often accompanied by substantial emotional comorbidities such as anxiety and depression. Yet, the mechanisms underlying the interactions between the sensory and emotional aspects of chronic pain remain poorly understood. Here, we investigated the role of FKBP51, a regulator of the stress response, in mediating both sensory and emotional symptoms of chronic pain. Inhibition of FKBP51, via genetic deletion or pharmacological blockade, in persistent joint pain reduced fast-onset sensory, functional and activity-related symptoms, as well as late anxio-depressive comorbidities. FKBP51 inhibition after the establishment of the hypersensitive state provided only temporary symptoms relief, while acute inhibition at disease onset protected from the full development of sensory and anxio-depressive symptoms for up to 6 months. Our results also indicated that early pain symptoms could predict the late sensory and emotional outcomes of chronic pain. RNA sequencing of spinal cord tissue revealed that late FKBP51 inhibition transiently altered nociceptive genes associated with mechanical hypersensitivity. In contrast, early inhibition persistently downregulated the Naaa gene, a key regulator of the transition to chronic pain, and reorganized spinal cilia. Our results indicate that early FKBP51 inhibition after injury can persistently reduce chronic pain and prevent the onset of associated emotional comorbidities by modulating critical spinal neurobiological pathways that play pivotal roles in the transition to chronic pain. Significance statementOur study reveals that early inhibition of FKBP51, a modulator in the stress axis, at the onset of joint damage provides sustained pain relief and significantly delays or prevents emotional comorbidities in a sex-dependent manner. In contrast, FKBP51 inhibition initiated after chronic pain is established results in only temporary symptoms improvement. These findings highlight a critical therapeutic window during which timely intervention can prevent the transition from acute to chronic pain. By establishing a predictive link between early therapeutic response and long-term outcomes, this work has important clinical implications for proactive and personalized chronic pain management.

neuroscience↗

bioRxiv2024

Bacterial microcompartment utilisation in the human commensal Escherichia coli Nissle 1917

Bacterial microcompartments (BMCs) are self-assembled protein structures often utilised by bacteria as a modular metabolic unit, enabling the catalysis and utilisation of less common carbon and nitrogen sources within a self-contained compartment. The ethanolamine (EA) utilisation (eut) BMC has been widely demonstrated in enteropathogens, such as Salmonella enterica, and current research is exploring its activity in the commensal species that populate the human gut. Escherichia coli Nissle 1917 (EcN) is a strong coloniser and probiotic in gut microbial communities, and has been used extensively for microbiome engineering. In this study, the utilisation of ethanolamine as a sole carbon source and the formation of the eut BMC in EcN were demonstrated through growth assays and visualisation with transmission electron microscopy. Subsequently, flux balance analysis was used to further investigate the metabolic activity of this pathway. It was found that not only is the utilisation of the eut BMC for the degradation of EA as a carbon source in EcN comparable to that of Salmonella enterica, but also that ammonium is released into solution as a byproduct in EcN but not in S. enterica. Control of EA-dependent growth was demonstrated using different concentrations of the operon inducer, vitamin-B12. We show that vitamin B12-dependent EA utilisation as the sole carbon source enables growth in EcN, and demonstrate the concurrent formation of the BMC shell and inducible control of the eut operon.

microbiology↗

bioRxiv2023

Predicting hypersensitivity and comorbid depressive-like behavior in late stages of joint disease using early weight bearing deficit

Chronic pain is a hallmark of joint diseases and is often accompanied by negative affective symptoms such as low mood, anxiety and memory dysfunction. Whether these may be the results of the more obvious sensory and functional symptoms of joint pain is poorly understood and this likely contributes to the difficulty in adequately managing this condition. Here, we have used two mouse models to address this lack of knowledge. Using a model of ankle inflammation and a model of knee osteoarthritis, we found that these models of joint pain induced weight bearing deficits of different magnitude but relatively similar mechanical allodynia that lasted at least 3 months. However, the models were accompanied by very different affective outcomes, as only the model of knee osteoarthritis, that led to significant early changes in activity and sleep patterns, was accompanied by an increase in negative affective behaviors, including cognitive impairments and depressive-like behavior. The models also had different molecular profiles at both spinal and hippocampal levels. Importantly, the functional outcomes measured in the early stages of the disease stage strongly correlated with sensory and emotional profiles at 3 months, suggesting that early functional measures may be used as predictors of the long-term symptoms associated with persistent joint pain. In conclusion, the predictive value of early measures of functional impact of joint disease could prove useful in the clinics for adapted therapeutic approaches for the prevention of emotional comorbidities and better pain management for patients with joint pain.

neuroscience↗

bioRxiv2023

Emergent digital bio-computation through spatial diffusion and engineered bacteria

Building computationally capable biological systems has long been an aim of synthetic biology. The potential utility of biocomputing devices ranges from biosafety and environmental applications to diagnosis and personalised medicine. Here we present work for the design of bacterial computers which use spatial patterning to process information. Our computers are composed of bacterial colonies which, inspired by patterning in embryo development, receive information in the form of diffusible morphogen-like signals. A computation is encoded by the physical locations of the input sources of morphogen and the output receiver colonies. We demonstrate, mathematically and with engineered Escherichia coli, the simple digital logic capability of single bacterial colonies and show how additional colonies are required to build complex functions. Inspired by electronic design automation, an algorithm for designing optimal spatial circuits computing two-level digital functions is presented. This enhances the capability of our system to complex digital functions without increasing the biological complexity. We extend our experimental system to incorporate biosensing colonies as morphogen sources, demonstrating how a diagnostic device might be constructed. Our approach will open up new ways to perform biological computation, with applications in bioengineering, biomaterials and biosensing. Ultimately, these computational bacterial communities will help us explore information processing in natural biological systems.

synthetic biology↗

bioRxiv2021

Chaos in small microbial communities

Predictability is a fundamental requirement in biological engineering. As we move to building coordinated multicellular systems, the potential for such systems to display chaotic behaviour becomes a concern. Therefore understanding which systems show chaos is an important design consideration. We developed a methodology to explore the potential for chaotic dynamics in small microbial communities governed by resource competition, intercellular communication and competitive bacteriocin interactions. We show that we can expect to find chaotic states in relatively small synthetic microbial systems, understand the governing dynamics and provide insights into how to control such systems. This work is the first to query the existence of chaotic behaviour in synthetic microbial communities and has important ramifications for the fields of biotechnology, bioprocessing and synthetic biology.

microbiology↗

bioRxiv2020

Automated design of synthetic microbial communities

In naturally occurring microbial systems, species rarely exist in isolation. There is strong ecological evidence for a positive relationship between species diversity and the functional output of communities. The pervasiveness of these communities in nature highlights that there may be advantages for engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigates issues often found in engineering a monoculture, especially when functional complexity is increasing. Here, we demonstrate a methodology for designing robust synthetic communities that use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We explore model spaces for two and three strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and tuning the community composition.

synthetic biology↗

bioRxiv2020

FlopR: An open source software package for calibration and normalization of plate reader and flow cytometry data

The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the last two decades. However, the use of arbitrary units has limited the usefulness of this data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express GFP from a set of commonly used constitutive promoters. We then demonstrate its power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2019

Killing in response to competition stabilises synthetic microbial consortia

The scale of the biological systems we can engineer is limited by the burden that host cells can bear. Division-of-labour can spread that burden across a community of cells but competitive exclusion inevitably leads to the removal of less fit community members over time. Here, we leverage amensalism and competitive exclusion to stabilise multi-species communities by engineering a strain of Escherichia coli which secretes a toxin in response to competition. We show mathematically and experimentally that such a system can produce stable populations with a composition that is tunable by easily controllable parameters. This is the first system to use competitive exclusion to create a stable two-species consortia and the first to only require the engineering of a single strain.

synthetic biology↗

bioRxiv2018

Deep reinforcement learning for the control of bacterial populations in bioreactors

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity (in comparison with pure cultures) and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence - reinforcement learning - in the control of co-cultures within continuous bioreactors. We confirm that feedback via reinforcement learning can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment, by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

synthetic biology↗

bioRxiv2018

Two new plasmid post-segregational killing mechanisms for the implementation of synthetic gene networks in E. coli

Plasmids are the workhorse of both industrial biotechnology and synthetic biology, but ensuring they remain in bacterial cells is a challenge. Antibiotic selection, commonly used in the laboratory, cannot be used to stabilise plasmids in most real-world applications, and inserting dynamical gene networks into the genome is difficult. Plasmids have evolved several mechanisms for stability, one of which, post-segregational killing (PSK), ensures that plasmid-free cells do not grow or survive. Here we demonstrate the plasmid-stabilising capabilities of the axe/txe two component system and the microcin-V system in the probiotic bacteria Escherichia coli Nissle 1917 and show they can outperform the hok/sok system commonly used in biotechnological applications. Using plasmid stability assays, automated flow cytometry analysis, mathematical models and Bayesian statistics we quantified plasmid stability in vitro. Further, we used an in vivo mouse cancer model to demonstrate plasmid stability in a real-world therapeutic setting. These new PSK systems, plus the developed Bayesian methodology, will have wide applicability in clinical and industrial biotechnology.

synthetic biology↗

bioRxiv2016

A computational method for the investigation of multistable systems and its application to genetic switches

Genetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology. Here we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour. Our results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.

synthetic biology↗