Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Next-generation sequencing and microfluidic analysis can identify and characterize the complex native microbial community in a sample, with little, if any, selection of artifacts.Optical measurement methods are currently the most promising fully scalable techniques for online monitoring and control of mixed-culture processes.The potential of infrared techniques, Raman spectroscopy, and mass spectrometry currently appear to be underestimated and underused for all aspects of the identification, characterization, monitoring, and control of mixed and co-culture processes. Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses. Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses. An old tradition is revolutionizing modern biotechnology: microbial collaboration. Mixed-culture applications, such as wastewater treatment, composting, and a broad spectrum of fermentative food preparations, are the historical foundation of current biotechnology. Now, a targeted assembly of microorganisms to perform concerted bioproductions is forming a new cutting edge in biotechnology. Over the past decade, the research field of defined mixed cultures has gained increased attention due to their potential for process intensification and the chance to produce unknown secondary metabolites [1.Bertrand S. et al.Metabolite induction via microorganism co-culture: a potential way to enhance chemical diversity for drug discovery.Biotechnol. Adv. 2014; 32: 1180-1204Crossref PubMed Scopus (216) Google Scholar]. In the old-school mixed-culture approach, the driving force to develop mixed culture-specific (online) measurement techniques was very limited. Reasons for this might be the rather low dynamic of these processes and the strong closeness to natural processes and equilibria with no need for control. In other words, there was simply no need for a deeper understanding of these robust systems. However, this is now changing. 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Interface. 2014; 11Crossref PubMed Scopus (242) Google Scholar]. Compared with classic monoculture processes, the analysis of mixed and co-cultures can be structured into four fields with different requirements for the applied measurement technology (Figure 1, Key Figure): identification of species (mainly for mixed cultures); characterization of interactions; characterization of a process; and the control of that process. With respect to the necessary measurement technologies, there is a gradient in complexity from identification with only offline methods to process control with the necessity for online measured parameters (Box 1).Box 1Classification of Monitoring TechniquesMonitoring techniques for bioprocess analysis and control can be classified in three categories (Figure I): offline, atline, and online. These categories are defined according to the location of the analytical system in relation to the bioreactor. Offline measurement systems (Figure IA) comprise manual or automatic sampling. Samples are afterwards analyzed in an external laboratory workflow and data are generally obtained with a temporal delay. Therefore, the information is not available for any bioreactor control strategies. In atline measurements (Figure IB), samples are analyzed by the side of the bioreactor. Data are typically available with an analysis-specific delay but generally more quickly compared with offline analyses. The third category is online analysis. For online measurements, in situ and bypass configurations are possible (Figure IC). In situ measurements are the preferred technology, since a sensor is directly located inside the bioreactor. All signals are directly available and, therefore, predestined for any kind of control. Alternatively, a sensor can also be located in a bypass, which increases the flexibility regarding size and geometry of the applied measurement technology. However, transfer of biomass in a bypass may cause changes in the physicochemical conditions of the sample, due to oxygen limitations and or heterogeneities caused by insufficient mixing.Figure ISchematic Illustration of Offline, Atline and Online Measurement Technologies for Bioprocess Control.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Monitoring techniques for bioprocess analysis and control can be classified in three categories (Figure I): offline, atline, and online. These categories are defined according to the location of the analytical system in relation to the bioreactor. Offline measurement systems (Figure IA) comprise manual or automatic sampling. Samples are afterwards analyzed in an external laboratory workflow and data are generally obtained with a temporal delay. Therefore, the information is not available for any bioreactor control strategies. In atline measurements (Figure IB), samples are analyzed by the side of the bioreactor. Data are typically available with an analysis-specific delay but generally more quickly compared with offline analyses. The third category is online analysis. For online measurements, in situ and bypass configurations are possible (Figure IC). In situ measurements are the preferred technology, since a sensor is directly located inside the bioreactor. All signals are directly available and, therefore, predestined for any kind of control. Alternatively, a sensor can also be located in a bypass, which increases the flexibility regarding size and geometry of the applied measurement technology. However, transfer of biomass in a bypass may cause changes in the physicochemical conditions of the sample, due to oxygen limitations and or heterogeneities caused by insufficient mixing. While all information about substrates, metabolic products, and cell morphologies is accessible as a summary parameter of all microbial activities via established offline methods, only a few techniques are available providing specific information about the mixed-culture composition and the individual-specific performance. Consequently, the in-depth characterization of an ongoing cultivation and directed optimization of process parameters via available online analysis technology are currently difficult to realize. Even more, with this lack of insight regarding the community and functional role, interactions, and so on, of specific subpopulation members, control of a specific mixed-culture composition is unachievable. In our opinion, the lack of analytical tools to study processes, especially at larger scales, is a major reason for the discrepancy between the high scientific interest in mixed cultures but the limited number of successfully commercialized defined mixed and co-culture processes in biotechnology [18.Sabra W. Zeng A.P. Mixed microbial cultures for industrial biotechnology: success, chance, and challenges.in: Grunwald P. Industrial Biocatalysis. Pan Stanford Publishing, 2014: 201-233Google Scholar]. Therefore, the focus of this review is the discussion of advantages and disadvantages of existing measurement techniques to resolve and control population dynamics of mixed-culture processes. Researchers who are new in the field of mixed cultures could be guided by this article to find a suitable measurement technique to analyze their specific microbial community. There are subtle structural, biochemical, and genetic differences between different species and strains of naturally occurring microorganisms (Figure 2). Whereas genetic differences are used for offline strain identification, differences in cell morphology and structural composition, such as compartments, can be used for online monitoring (Figure 2A). Differences within the metabolism lead to different biochemical characteristics, such as protein/metabolite composition and quantity. The infrared spectrum of these compounds gives a unique ‘fingerprint’ for different cells. Differences within fatty acid-related compounds, carbonyl residuals of proteins, the carboxylic groups of peptide, free amino acids and polysaccharides, as well as phospholipids, can be measured by different spectral regions [19.Maity J.P. et al.Identification and discrimination of bacteria using Fourier transform infrared spectroscopy.Spectrochim. Acta A Mol. Biomol. 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The image in Figure 2B gives an example: it shows a high-resolution optical insight into a co-culture comprising Aspergillus terreus GFP1 (green) and Trichoderma reesei RFP1 (red) grown on cellulose (blue). Alternatively, fluorescence proteins can be used as reporters that visualize distinct metabolic traits and, thus, are only temporally expressed [28.Costantini L.M. et al.A palette of fluorescent proteins optimized for diverse cellular environments.Nat. Commun. 2015; 6: 7670Crossref PubMed Scopus (101) Google Scholar,29.Tebo A.G. Gautier A. A split fluorescent reporter with rapid and reversible complementation.Nat. Commun. 2019; 10: 2822Crossref PubMed Scopus (7) Google Scholar]. Compared with naturally occurring cellular differences, synthetic engineering of features is restricted to microorganisms that are genetically accessible [30.Farkas J.A. et al.Genetic techniques for the Archaea.Annu. Rev. Genet. 2013; 47: 539-561Crossref PubMed Scopus (36) Google Scholar]. 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However, when, for example, only the dynamics of certain subgenera or dominant species of a population are of interest, the population dynamics can be also estimated in a semiquantitative way using population fingerprinting methods, such as terminal restriction fragment length polymorphism (t-RFLP), lipid profiling, and quinone profiling [39.Spiegelman D. et al.A survey of the methods for the characterization of microbial consortia and communities.Can. J. Microbiol. 2005; 51: 355-386Crossref PubMed Scopus (120) Google Scholar, 40.Sabra W. et al.Biosystems analysis and engineering of microbial consortia for industrial biotechnology.Eng. Life Sci. 2010; 10: 407-421Crossref Scopus (101) Google Scholar, 41.Schmidt J.K. et al.A novel concept combining experimental and mathematical analysis for the identification of unknown interspecies effects in a mixed culture.Biotechnol. 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Microbiol. 1989; 55: 1044-1046Crossref PubMed Google Scholar]16S rDNA sequencing, RAPD analysisBacteriaStaphylococcus, Lactobacillus, Methanosarcinales, Methanomicrobiales, and Methanobacteriales[46.Raskin L. et al.Characterization of microbial communities in anaerobic bioreactors using molecular probes.Antonie Van Leeuwenhoek. 1995; 68: 297-308Crossref PubMed Scopus (102) Google Scholar,47.Rebecchi A. et al.Physiological and molecular techniques for the study of bacterial community development in sausage fermentation.J. Appl. Microbiol. 1998; 84: 1043-1049Crossref PubMed Scopus (0) Google Scholar]Quantitative real-time PCR and specific PCR analysesBacteriaClostridium straminisolvens CSK1, Clostridium sp. strain FG4, Pseudoxanthomonas sp. strain M1-3, Brevibacillus sp. strain M1-5, and Bordetella sp. strain M1-6[43.Kato S. et al.Stable coexistence of five bacterial strains as a cellulose-degrading community.Appl. Environ. Microbiol. 2005; 71: 7099-7106Crossref PubMed Scopus (143) Google Scholar]Full-length 16S rDNA sequencingMicrobiomeUndefined[48.Dethlefsen L. et al.The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing.PLoS Biol. 2008; 6e280Crossref PubMed Scopus (1557) Google Scholar]Optical techniquesReporter strainsMicrobiomeUndefined[61.Mahler L. et al.Detection of antibiotics synthetized in microfluidic picolitre-droplets by various actinobacteria.Sci. Rep. 2018; 813087Crossref PubMed Scopus (21) Google Scholar,62.Mahler L. et al.Publisher Correction: Detection of antibiotics synthetized in microfluidic picolitre-droplets by various actinobacteria.Sci. Rep. 2018; 8: 15859Crossref PubMed Scopus (1) Google Scholar]Flow cytometry and t-RFLPBacteriaB. cepacia, S. aureus, and P. aeruginosa[51.Rüger M. et al.Species-specific viability analysis of Pseudomonas aeruginosa, Burkholderia cepacia and Staphylococcus aureus in mixed culture by flow cytometry.BMC Microbiol. 2014; 14: 15Crossref PubMed Scopus (23) Google Scholar]Flow cytometry with fluorescence-tagged strainsYeastPichia pastoris (different strains expressing different pathways)[58.Liu Y. et al.Engineered monoculture and co-culture of methylotrophic yeast for de novo production of monacolin J and lovastatin from methanol.Metab. Eng. 2018; 45: 189-199Crossref PubMed Scopus (49) Google Scholar]BacteriaDifferent Escherichia coli strains[59.Dinh C.V. et al.Development of a quorum-sensing based circuit for control of coculture population composition in a naringenin production system.ACS Synth. Biol. 2020; 9: 590-597Crossref PubMed Scopus (17) Google Scholar]Fluorescence in situ hybridization (FISH), oligonucleotide probesBacteriaAlphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Rhodobacter, Roseobacter, Rhodospirillum, and Rhodopseudomonas[60.Fradinho J.C. et al.Photosynthetic mixed culture polyhydroxyalkanoate (PHA) production from individual and mixed volatile fatty acids (VFAs): substrate preferences and co-substrate uptake.J. Biotechnol. 2014; 185: 19-27Crossref PubMed Scopus (40) Google Scholar]Fluorescence proteinsBacteria (biofilms)Lactobacillus rhamnosus[56.Spacova I. et al.Expression of fluorescent proteins in Lactobacillus rhamnosus to study host-microbe and microbe-microbe interactions.Microb. Biotechnol. 2018; 11: 317-331Crossref PubMed Scopus (9) Google Scholar]AbsorbanceBacteria and yeastsE. coli KO11, and S. cerevisiae D5A; Methylomicrobium buryatense 5GB1 and Scheffersomyces stipitis CBS 5773[55.Stone K.A. et al.A novel soft sensor approach for estimating individual biomass in mixed cultures.Biotechnol. Prog. 2017; 33: 347-354Crossref PubMed Scopus (4) Google Scholar]AutofluorescenceFungiAspergillus flavus, Micosporum gypseum, Micosoprum canis, Trichophyton rubrum, and Trichophyton tonsurans[23.Lin S.-J. et al.Multiphoton autofluorescence spectral analysis for fungus imaging and identification.Appl. Phys. Lett. 2009; 95043703Crossref Scopus (7) Google Scholar]Microscopic image analysis with fluorescence-tagged strainsBacterium and bacteriumDifferent E. coli strains[27.Stephens K. et al.Bacterial co-culture with cell signaling translator and growth controller modules for autonomously regulated culture composition.Nat. Commun. 2019; 10: 4129Crossref PubMed Scopus (35) Google Scholar]Particle size distributionEukaryote and bacteriaTetrahymena pyriformis, E. coli, and Azotobacter vinelandii[63.Drake J.F. Tsuchiya H.M. Differential counting in mixed cultures with coulter counters.Appl. Microbiol. 1973; 26: 9-13Crossref PubMed Google Scholar]Bacterium and yeastL. lactis and K. marxianus[52.Geinitz B. et al.Noninvasive tool for optical online monitoring of individual biomass concentrations in a defined coculture.Biotech. Bioeng. 2020; 117: 999-1011Crossref PubMed Scopus (0) Google Scholar]OthersCellulose consumptionFungus and yeastTrichoderma reesei and Ust