Boon, Nico

Ganigué, Ramon

Gildemyn, Sylvia

Kerckhof, Frederiek-Maarten

Sabbe, Kevin

English

Ghent University Academic Bibliography

Flow cytometry as tool to monitor chain 1 elongation performance 2 Kevin Sabbe*,a,b, Sylvia Gildemynb, Frederiek-Maarten 3 Kerckhofa, Nico Boona, Ramon Ganigué a 4 * presenter, Kevin.Sabbe@UGent.be 5 a Ghent University, Belgium; b OWS nv, Belgium 6  7 HIGHLIGHTS:  8 • Flow cytometry is a rapid and reliable method to monitor the 9 microbial community in a chain elongating reactor 10 • Phenotypic fingerprints of different reactor states can be 11 differentiated via flow cytometry. 12 • A reactor microbiome for lactic acid chain elongation, disturbed due 13 to caproic acid toxicity, has the potential to return to its stable state 14 during recovery. 15  16 BACKGROUND: During the past years there has been a growing awareness 17 that we need to strive to a circular bio economy, which has stimulated the 18 development of novel biological processes for the production of added-value 19 platform chemicals from organic waste streams. These new developments 20 go hand in hand with an increasing need for process stability and 21 performance insurance. Therefore, a good monitoring strategy is essential. 22 The state-of-the-art mainly relies on monitoring physicochemical input and 23 output parameters. Here we report the development of a novel monitoring 24 strategy in which microbial community (MC)-parameters play a key role. 25 The MC-parameters are obtained via flow cytometry (FCM), which is a MC 26 analysis technique that allows a fast assessment of its phenotypical 27 diversity [1,2]. FCM as a process monitoring and control tool has mostly 28 been applied to the MC in environments containing little nutrients and lower 29 microbial abundance, such as drinking water [3]. More recently, the use of 30 FCM is emerging for the analysis of the MC and the detection of disturbances 31 through flow cytometric fingerprinting [3,4]. 32 In this study, FCM is studied as a key tool for the fast detection of 33 community changes and applied to caproic acid (CA) production via lactic 34 acid (LA) chain elongation, with the potential of applying this on other 35 fermentation processes in the future. We operated mixed-culture CSTR-36 bioreactors that were fed with a synthetic medium that contains LA (20.8 37 g/L) as main carbon source. During reactor operation, several 38 physicochemical parameters (pH, electroconductivity, biogas yield and 39 composition, carboxylate yields and spectrum, etc.) were monitored along 40 with the microbiology. From the FCM data, the cell counts and diversity 41 parameters were derived. The community structure was determined as a 42 phenotypic fingerprint (PFP) based on the identification of phenotypes with 43 a model-based approach based on Gaussian Mixture Models (GMM) [5]. 44 RESULTS & DISCUSSION: During reactor operation, periods of decreased 45 performance or process failure were observed. The results of one period of 46 process failure are presented. Physiochemically, process failure was initially 47 observed as a sudden, complete stop of the gas production, while prior to 48 this event, around 1.2 Lbiogas.Lreactor-1.d-1 containing 23.25% H2 and 73.75% 49 CO2 was produced. H2 and CO2 are by-products of LA chain elongation [6-50 9]. Analysis of the carboxylate spectrum showed the highest CA 51 concentration (8.0 g/L) observed in the reactors at the moment of process 52 failure. LA accumulated up to 8.4 g/L and the CA concentration decreased. 53 Activity resumed once the CA-concentration had decreased to 3.2 g/L. It is 54 therefore hypothesised that CA-toxicity was a possible cause of process 55 failure in the process under study. 56 A GMM-model was generated to determine the PFP of the reactor samples. 57 Clear shifts in the community structure based on the PFP could be observed 58 during this period (figure 1, a). Ordination of the samples based on the 59 Bray-Curtis dissimilarities via Canonical Correspondence Analysis (CCA) 60 shows the evolution of the reactor samples during normal operation, 61 process failure and recovery (figure 1, b). Samples taken prior to process 62 failure cluster together in the CCA plot, whereas the samples taken during 63 process failure and recovery are ordinated in a counterclockwise pattern. 64 The further in the recovery phase, the closer the samples are ordinated to 65 the plotted to the cluster of samples from before process failure. This 66 indicates that the microbiome performing LA chain elongation can return to 67 its stable state prior to the crash. 68 69 Figure 1. a) absolute phenotypic distribution over time, based on a GMM-70 model with seven identified phenotypes. b) CCA analysis of the samples 71 based on the Bray-Curtis dissimilarities, constrained by the LA, acetic acid 72 (AA), butyric acid (BA) and valeric acid (VA) concentrations, and the HCl 73 requirements for pH control. 74 CONCLUSION: FCM is a suitable technique for fast determination of the 75 PFP of a mixed-culture microbiome. Changes in reactor performance can be 76 monitored as well on microbial level. FCM is therefore a promising tool to 77 be included into the monitoring strategy of LA chain elongation, with the 78 potential of extending its application on other bioproduction processes.  79 a) b) REFERENCES: 80 1.  Park, H.-S, Schumacher, R., Kilbane II, J.J., New method to 81 characterize microbial diversity using flow cytometry, Journal of Industrial 82 Microbiology & Biotechnology, 32, 94–102 83 2.  Props, R., Monsieurs, P., Mysara, M., Clement, L., Boon, N., 84 Measuring the biodiversity of microbial communities by flow cytometry., 85 Methods in Ecology and Evolution, 7, 1376-1358 86 3.  Props, R., Rubbens, P., Besmer, M., Buysschaert, B., Sigrist, J., 87 Weilenmann, H., Waegemann, W., Boon, N., Hammes, F., Detection of 88 microbial disturbances in a drinking water microbial community through 89 continuous acquisition and advanced analysis of flow cytometry data., 90 Water Research, 145, 73-82 91 4.  Buysschaert, B., Kerckhof, F.-M., Vandamme, P., De Baets, B., Boon, 92 N., Flow cytometric fingerprinting for microbial strain discrimination and 93 physiological characterization., Cytometry Part A, 93, 201-212 94 5.  Rubbens, P., Props, R., Kerckhof, F.-M., Boon, N., & Waegeman, W., 95 PhenoGMM: Gaussian mixture modelling of microbial cytometry data 96 enables efficient predictions of biodiversity., BioRxiv, 641464 97 6.  de Araújo Cavalvante, W., Carrhá Leitão, R, Gehring, T.A., Angenent, 98 L.T., Tédde Santealla, S., Anaerobic Fermentation for N-Caproic Acid 99 Production: A Review., Process Biochemistry, 54, 106-119 100 7.  De Groot, V., Coma, M., Arnot, T., Leak, D.J., Lanham, A.B., Medium 101 Chain Carboxylic Acids from Complex Organic Feedstocks by Mixed Culture 102 Fermentation., Molecules, 24, 398 103 8.  Kucek, L.A., Xu, J., Nguyen, M., Angenent, L.T., Conversion of l-104 lactate into n-caproate by a continuously fed reactor microbiome., Water 105 Research, 93, 163-171 106 9.  Zhu, X., Zhou, Y., Wang, Y., Wu, T., Li, X., Li, D., Tao, Y., Production 107 of High-Concentration n-Caproic Acid from Lactate through 108 

Flow cytometry as tool to monitor chain elongation performance

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Flow cytometry as tool to monitor chain elongation performance

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