Biology subjects

Soyer, O.

Publications and source records attributed to Soyer, O..

3 recordsLinked to original sources

bioRxiv2018

A low-cost DIY device for high resolution, continuous measurement of microbial growth dynamics

High-resolution data on microbial growth dynamics allow characterisation of microbial physiology, as well as optimisation of genetic alterations thereof. Such data are routinely collected using bench-top spectrophotometers or so-called plate readers. These equipments present several drawbacks: (i) measurements from different devices cannot be compared directly, (ii) proprietary nature of devices makes it difficult for standardisation methods to be developed across devices, and (iii) high costs limit access to devices, which can become a bottleneck for researchers, especially for those working with anaerobic organisms or at higher containment level laboratories. These limitations could be lifted, and data reproducibility improved, if the scientific community could adopt standardised, low-cost and open-source devices that can be built in-house. Here, we present such a device, MicrobeMeter, which is a do-it-yourself (DIY), simple, yet robust photometer with continuous data-logging capability. It is built using 3D-printing and open-source Arduino platform, combined with purpose-built electronic circuits. We show that MicrobeMeter displays linear relation between culture density and turbidity measurement for microbes from different phylogenetic domains. In addition, culture density estimated from MicrobeMeter measurements produced less variance compared against three commercial bench-top spectrophotometers, indicating that its measurements are less affected by the differences in cell types. We show the utility of MicrobeMeter, as a programmable wireless continuous measurement device, by collecting long-term growth dynamics up to 458 hours from aerobic and anaerobic cultures. We provide a full open-source description of MicrobeMeter and its implementation for faster adaptation and future development by the scientific community. The blueprints of the device, as well as ready-to-assemble kit versions are also made available through www.humanetechnologies.co.uk.

microbiology↗

bioRxiv2017

Impact of spatial organization on a novel auxotrophic interaction among soil microbes

A key prerequisite to achieve a deeper understanding of microbial communities and to engineer synthetic ones is to identify the individual metabolic interactions among key species and how these interactions are affected by different environmental factors. Deciphering the physiological basis of species-species and species-environment interactions in spatially organized environment requires reductionist approaches using ecologically and functionally relevant species. To this end, we focus here on a specific defined system to study the metabolic interactions in a spatial context among a plant-beneficial endophytic fungus Serendipita indica, and the soil-dwelling model bacterium Bacillus subtilis. Focusing on the growth dynamics of S. indica under defined conditions, we identified an auxotrophy in this organism for thiamine, which is a key co-factor for essential reactions in the central carbon metabolism. We found that S. indica growth is restored in thiamine-free media, when co-cultured with B. subtilis. The success of this auxotrophic interaction, however, was dependent on the spatial and temporal organization of the system; the beneficial impact of B. subtilis was only visible when its inoculation was separated from that of S. indica either in time or space. These findings describe a key auxotrophic interaction in the soil among organisms that are shown to be important for plant ecosystem functioning, and point to the potential importance of spatial and temporal organization for the success of auxotrophic interactions. These points can be particularly important for engineering of minimal functional synthetic communities as plant-seed treatments and for vertical farming under defined conditions.

systems biology↗

bioRxiv2017

Integrated Human-Virus Metabolic Modelling Predicts Host-Based Antiviral Targets Against Chikungunya, Dengue and Zika Viruses

Current and reoccurring viral epidemic outbreaks such as those caused by Zika virus illustrate the need for rapid development of antivirals. Such development would be immensely facilitated by computational approaches that can provide experimentally testable predictions for possible antiviral strategies. A key factor that has not been considered fully to date in the study of antiviral targets is the high dependence of viruses to their host metabolism for reproduction. Here, we focus on this dependence and develop a stoichiometric, genome-scale metabolic model that integrates human macrophage cell metabolism with the biochemical demands arising from virus production. Focusing this approach to currently epidemic viruses Chikungunya, Dengue and Zika, we find that each virus causes specific alterations in the host metabolic flux towards fulfilling their individual biochemical demands as predicted by their genome and capsid structure. Subsequent analysis of this integrated model allows us to predict a set of host reactions, which when constrained can inhibit virus production. We show that this prediction recovers most of the known targets of existing antiviral drugs, while highlighting a set of hitherto unexplored reactions with either broad or virus specific antiviral potential. Thus, this computational approach allows rapid generation of experimentally testable hypotheses for novel antiviral targets within a host.\n\nSIGNIFICANCE STATEMENTA key challenge in combatting any new and emerging virus outbreaks is rapid drug development. In particular, generation of experimentally testable hypotheses through computational approaches is mostly lacking. Here, we address this gap by developing host-virus metabolic models for three viruses that cause current (or previously) epidemic viral outbreaks. We develop viral biomass functions using information from their genomes and physical structure, and incorporate these within a genome-scale metabolic model of human macrophage cells. The resulting integrated model allows us to predict host reactions, which when blocked, stop the system from attaining optimal viral production. These predictions recover currently known antiviral targets within human cells, and highlight a set of new reactions that are hitherto not explored for antiviral capacity.

systems biology↗