Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Abstract

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

The effect of acetate on population heterogeneity in different cellular characteristics of Escherichia coli in aerobic batch cultures

“…This inhibition becomes very significant in a defined medium when acetate is sole carbon source . It is known that bacterial populations become more asynchronous under stress and contain cells at all stages of the division cycle among others due to an increase in noise in gene expression leading to a higher level of heterogeneity . Hence, our hypothesis was that the enhanced stress during growth on acetate would lead to increased heterogeneity levels of the population, compared to growth on glucose.…”

“…The CV can be correlated to noise in gene expression, which describes how expression of two identical copies of a gene leads to deviations in different cells of an isogenic population (intrinsic noise), respectively, is influenced by interaction of the cell with its environment (extrinsic noise) . Noise in gene expression was found to be one major cause of population heterogeneity influencing bioprocess efficiency . Upon occurrence, it leads to a broadening of distributions or multimodality .…”

“…Additionally, an inverse correlation between noise and gene expression could be established; highly expressed genes exhibit low levels of noise whereas the opposite was found for weakly expressed genes . Therefore, it was suspected that noise in gene expression is used as a regulatory strategy to increase, respectively, decrease the level of population heterogeneity depending on what is beneficial for the cell population …”

Enhancing cell and gene therapy manufacture through the application of advanced fluorescent optical sensors (Review)

“…In order to facilitate these changes sensory systems that accurately detect and allow subsequent control of population heterogeneity and quality must ultimately be selected from the scientific advances and translated to commercial scale. 89 For fluorescent optical sensors, in particular, fluorescent nanosensors, to acquire broad utility as a medical diagnostic tools it is important that their signals can be accurately obtained from thick and turbid samples, such as tissue and organs. Currently, it is challenging to obtain fluorescent signals at depths using high powered objective lenses, due to their short working distance, while turbid samples are known to scatter light such that accurate signal reconstruction is challenging.…”

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

“…Phenotypic heterogeneity has been shown to have an impact on medical care, concerning antibiotic resistance, treatment persistence, and biofilm formation (Turner et al, 2000;Sumner and Avery, 2002;Balaban et al, 2004;Grote et al, 2015;Dhar et al, 2016;Sadiq et al, 2017;Van Den Bergh et al, 2017) but also in biomedical research for drug discovery, cancer therapy, and diagnostics due to the variable effectiveness of care treatments on different phenotypes (Almendro et al, 2013;Gough et al, 2017). Understanding cell-to-cell heterogeneity in bioprocessing is currently a big challenge due to its implications in processes' performance and therefore product yield in largescale production (Delvigne et al, 2014(Delvigne et al, , 2017. Hence, the quantitation of heterogeneity becomes relevant for an efficient process optimization in every field of biotechnology.…”