Standardisation and optimisation techniques in gut microbiome community analysis

With the emergence of high throughput next-generation sequencing the importance of the human gut microbiota as regulators, modulators and maintainers of human health and disease became more and more imminent. Advances in sequencing in the last two decades enabled the analysis of the composition and dynamics of the gut microbiome in unprecedented resolution and complexity. Investigations of this complex community by marker gene studies allowed assertions on presence, absence and ecological dynamics of gut bacteria. Several studies discovered strong relationships between the gut microbiota and human health. Some of these bacteria are shown to be essential for daily life processes like digestion, nutrition uptake, pathogen resistance and immune maturation. Likewise, disturbances of this close relationship, called dysbiosis, have been found to be associated with diseases like diabetes, obesity, colon cancer and inflammatory bowel disease. All this renders the gut microbiome as a highly relevant target of research in medical diagnostics and microbiome community analysis a valid hypothesis building tool. Nevertheless, the vast amount of different methodologies and lack of broadly accepted standards to create and handle gut microbiome abundance data complicates reproducible or replicable findings across studies. Especially in settings, where samples diverge significantly in their total biomass or microbial load, the analysis of the microbiome is hampered. Several efforts to allow accurate inter sample comparisons have been undertaken, including the use of relative abundances or random feature sub-sampling (rarefaction). While these methodologies are the most frequently used, they are not fully capable to correct for these sample-wide differences. To increase comparability between samples the use of exogenous spike-in bacteria is proposed to correct for sample specific differences in microbial load. The methodology is tested on a dilution experiment with known differences between samples and successfully applied on a clinical microbiome data set. These experiments suggest that current analysis methods lack a pivotal angle on the data, that is comparability between samples differing in microbial load. Meanwhile, the proposed spike-in based calibration to microbial load (SCML) allows for accurate estimation of ratios of absolute endogenous bacteria abundances. Furthermore, microbiome community analysis is heavily dependent on the resolution of the underlying read count data. While resolutions such as operational taxonomic units (OTUs) generally overestimate diversity and create highly redundant and sparse datasets, agglomerations to common taxonomy can obfuscate distinct read count patterns of possible sub-populations inside the given taxonomy. Even though the ladder agglomeration strategy might be valid for taxonomy with low phenotypical divergence, plenty taxonomic lineages in fact contain highly diverse sub-species. Thus, a more appropriate taxonomic unit would adapt its resolution for those densely populated branches, allowing for different count resolutions inside the same community. Here the concept of adaptive taxonomic units (ATUs) is introduced and applied on a perturbation experiment including mice receiving antibiotics. For this data set the different classical count resolutions (i.e. collapsed to order, family or genus etc.) produce highly contradictory results. Meanwhile, adaptive taxonomic units (ATUs) derived by hierarchical affinity merging (HAM) adapt the granularity of taxonomy to the underlying sequencing data. Branches of bacterial phylogeny that are highly covered in the data set receive a higher resolution than those that were infrequently observed. The algorithm hereby merges operational taxonomic units (OTUs) guided not only by sequence dissimilarity, but also by count distribution and OTU size. Due to the agglomeration the number of features is reduced significantly, lowering the complexity of the data, while preserving distributional patterns only observable at OTU level. Consequently, the sparsity of the count data is reduced significantly such that every ATU accumulates reasonable count number and can thus be reliably analysed. The algorithm is provided in the form of the R-Package dOTUClust

Adjusting microbiome profiles for differences in microbial load by spike-in bacteria

Background: Next-generation 16S ribosomal RNA gene sequencing is widely used to determine the relative composition of the mammalian gut microbiomes. However, in the absence of a reference, this does not reveal alterations in absolute abundance of specific operational taxonomic units if microbial loads vary across specimens. Results: Here we suggest the spiking of exogenous bacteria into crude specimens to quantify ratios of absolute bacterial abundances. We use the 16S rDNA read counts of the spike-in bacteria to adjust the read counts of endogenous bacteria for changes in total microbial loads. Using a series of dilutions of pooled faecal samples from mice containing defined amounts of the spike-in bacteria Salinibacter ruber, Rhizobium radiobacter and Alicyclobacillus acidiphilus, we demonstrate that spike-in-based calibration to microbial loads allows accurate estimation of ratios of absolute endogenous bacteria abundances. Applied to stool specimens of patients undergoing allogeneic stem cell transplantation, we were able to determine changes in both relative and absolute abundances of various phyla, especially the genus Enterococcus, in response to antibiotic treatment and radio-chemotherapeutic conditioning. Conclusion: Exogenous spike-in bacteria in gut microbiome studies enable estimation of ratios of absolute OTU abundances, providing novel insights into the structure and the dynamics of intestinal microbiomes

Reference point insensitive molecular data analysis

Motivation: In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed. Results: Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets

Unveiling microbial structures during raw microalgae digestion and co-digestion with primary sludge to produce biogas using semi-continuous AnMBR systems

[EN] Methane production from microalgae can be enhanced through anaerobic co-digestion with carbon-rich substrates and thus mitigate the inhibition risk associated with its low C:N ratio. Acclimated microbial communities for microalgae disruption can be used as a source of natural enzymes in bioenergy production. However, co-substrates with a certain microbial diversity such as primary sludge might shift the microbial structure. Substrates were generated in a Water Resource Recovery Facility (WRRF) and combined as follows: Scenedesmus or Chlorella digestion and microalgae co-digestion with primary sludge. The study was performed using two lab-scale Anaerobic Membrane Bioreactors (AnMBR). During three years, different feedstocks scenarios for methane production were evaluated with a special focus on the microbial diversity of the AnMBR. 57% of the population was shared between the different feedstock scenarios, revealing the importance of Anaerolineaceae members besides Smithella and Methanosaeta genera. The addition of primary sludge enhanced the microbial diversity of the system during both Chlorella and Scenedesmus co-digestion and promoted different microbial structures. Aceticlastic methanogen Methanosaeta was dominant in all the feedstock scenarios. A more remarkable role of syntrophic fatty acid degraders (Smithella, Syntrophobacteraceae) was observed during co-digestion when only microalgae were digested. However, no significant changes were observed in the microbial composition during anaerobic microalgae digestion when feeding only Chlorella or Scenedesmus. This is the first work revealing the composition of complex communities for semi-continuous bioenergy production from WRRF streams. The stability and maintenance of a microbial core over-time in semi-continuous AnMBRs is here shown supporting their future application in full-scale systems for raw microalgae digestion or codigestion.The Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) are gratefully acknowledged for their support to this research work through CTM2011-28595-C02-02 and CTM2014-54980-C2-1-R projects. The authors are thankful to Ph.D. Silvia Greses and Ph.D. candidate Rebecca Serna-Garcia (Universitat de Valencia, Spain) for allowing the collection of digestate samples from their bioreactors and providing a brief data characterization of their performance. As well, authors thank the support of Maria Paches (IIAMA, Valencia, Spain) during phytoplankton monitoring in the photobioreactor plant. Finally, the sequencing service from FISABIO (Valencia, Spain) is also gratefully acknowledged for their technical support during the design stage of this work.Zamorano-López, N.; Borrás, L.; Seco, A.; Aguado García, D. (2020). Unveiling microbial structures during raw microalgae digestion and co-digestion with primary sludge to produce biogas using semi-continuous AnMBR systems. The Science of The Total Environment. 699:1-12. https://doi.org/10.1016/j.scitotenv.2019.134365S112699APHA, APHA/AWWA/WEF, 2012. In: Standard Methods for the Examination of Water and Wastewater. Stand. Methods, pp. 541 doi.org/ISBN 9780875532356.Astals, S., Musenze, R. S., Bai, X., Tannock, S., Tait, S., Pratt, S., & Jensen, P. D. (2015). Anaerobic co-digestion of pig manure and algae: Impact of intracellular algal products recovery on co-digestion performance. Bioresource Technology, 181, 97-104. doi:10.1016/j.biortech.2015.01.039Baudelet, P.-H., Ricochon, G., Linder, M., & Muniglia, L. (2017). A new insight into cell walls of Chlorophyta. Algal Research, 25, 333-371. doi:10.1016/j.algal.2017.04.008Bovio, P., Cabezas, A., & Etchebehere, C. (2018). Preliminary analysis ofChloroflexipopulations in full-scale UASB methanogenic reactors. Journal of Applied Microbiology, 126(2), 667-683. doi:10.1111/jam.14115Calusinska, M., Goux, X., Fossépré, M., Muller, E. E. L., Wilmes, P., & Delfosse, P. (2018). A year of monitoring 20 mesophilic full-scale bioreactors reveals the existence of stable but different core microbiomes in bio-waste and wastewater anaerobic digestion systems. Biotechnology for Biofuels, 11(1). doi:10.1186/s13068-018-1195-8Carrillo-Reyes, J., Barragán-Trinidad, M., & Buitrón, G. (2016). Biological pretreatments of microalgal biomass for gaseous biofuel production and the potential use of rumen microorganisms: A review. Algal Research, 18, 341-351. doi:10.1016/j.algal.2016.07.004Chen, C., Ming, J., Yoza, B. A., Liang, J., Li, Q. X., Guo, H., … Wang, Q. (2019). Characterization of aerobic granular sludge used for the treatment of petroleum wastewater. Bioresource Technology, 271, 353-359. doi:10.1016/j.biortech.2018.09.132Cheng, W., Chen, H., Yan, S., & Su, J. (2014). Illumina sequencing-based analyses of bacterial communities during short-chain fatty-acid production from food waste and sewage sludge fermentation at different pH values. World Journal of Microbiology and Biotechnology, 30(9), 2387-2395. doi:10.1007/s11274-014-1664-6Colzi Lopes, A., Valente, A., Iribarren, D., & González-Fernández, C. (2018). Energy balance and life cycle assessment of a microalgae-based wastewater treatment plant: A focus on alternative biogas uses. Bioresource Technology, 270, 138-146. doi:10.1016/j.biortech.2018.09.005Córdova, O., Chamy, R., Guerrero, L., & Sánchez-Rodríguez, A. (2018). Assessing the Effect of Pretreatments on the Structure and Functionality of Microbial Communities for the Bioconversion of Microalgae to Biogas. Frontiers in Microbiology, 9. doi:10.3389/fmicb.2018.01388Correa, D. F., Beyer, H. L., Fargione, J. E., Hill, J. D., Possingham, H. P., Thomas-Hall, S. R., & Schenk, P. M. (2019). Towards the implementation of sustainable biofuel production systems. Renewable and Sustainable Energy Reviews, 107, 250-263. doi:10.1016/j.rser.2019.03.005Crutchik, D., Frison, N., Eusebi, A. L., & Fatone, F. (2018). Biorefinery of cellulosic primary sludge towards targeted Short Chain Fatty Acids, phosphorus and methane recovery. Water Research, 136, 112-119. doi:10.1016/j.watres.2018.02.047De Vrieze, J., Christiaens, M. E. R., & Verstraete, W. (2017). The microbiome as engineering tool: Manufacturing and trading between microorganisms. New Biotechnology, 39, 206-214. doi:10.1016/j.nbt.2017.07.001De Vrieze, J., Pinto, A. J., Sloan, W. T., & Ijaz, U. Z. (2018). The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool. Microbiome, 6(1). doi:10.1186/s40168-018-0449-9Dodsworth, J. A., Blainey, P. C., Murugapiran, S. K., Swingley, W. D., Ross, C. A., Tringe, S. G., … Hedlund, B. P. (2013). Single-cell and metagenomic analyses indicate a fermentative and saccharolytic lifestyle for members of the OP9 lineage. Nature Communications, 4(1). doi:10.1038/ncomms2884Dojka, M. A., Harris, J. K., & Pace, N. R. (2000). Expanding the Known Diversity and Environmental Distribution of an Uncultured Phylogenetic Division of Bacteria. Applied and Environmental Microbiology, 66(4), 1617-1621. doi:10.1128/aem.66.4.1617-1621.2000Farag, I. F., Davis, J. P., Youssef, N. H., & Elshahed, M. S. (2014). Global Patterns of Abundance, Diversity and Community Structure of the Aminicenantes (Candidate Phylum OP8). PLoS ONE, 9(3), e92139. doi:10.1371/journal.pone.0092139Fontana, A., Kougias, P. G., Treu, L., Kovalovszki, A., Valle, G., Cappa, F., … Campanaro, S. (2018). Microbial activity response to hydrogen injection in thermophilic anaerobic digesters revealed by genome-centric metatranscriptomics. Microbiome, 6(1). doi:10.1186/s40168-018-0583-4Garrido-Cardenas, J. A., Manzano-Agugliaro, F., Acien-Fernandez, F. G., & Molina-Grima, E. (2018). Microalgae research worldwide. Algal Research, 35, 50-60. doi:10.1016/j.algal.2018.08.005González-Camejo, J., Jiménez-Benítez, A., Ruano, M. V., Robles, A., Barat, R., & Ferrer, J. (2019). Optimising an outdoor membrane photobioreactor for tertiary sewage treatment. Journal of Environmental Management, 245, 76-85. doi:10.1016/j.jenvman.2019.05.010Gonzalez-Fernandez, C., Sialve, B., & Molinuevo-Salces, B. (2015). Anaerobic digestion of microalgal biomass: Challenges, opportunities and research needs. Bioresource Technology, 198, 896-906. doi:10.1016/j.biortech.2015.09.095Gonzalez-Fernandez, C., Barreiro-Vescovo, S., de Godos, I., Fernandez, M., Zouhayr, A., & Ballesteros, M. (2018). Biochemical methane potential of microalgae biomass using different microbial inocula. Biotechnology for Biofuels, 11(1). doi:10.1186/s13068-018-1188-7González-González, L. M., Correa, D. F., Ryan, S., Jensen, P. D., Pratt, S., & Schenk, P. M. (2018). Integrated biodiesel and biogas production from microalgae: Towards a sustainable closed loop through nutrient recycling. Renewable and Sustainable Energy Reviews, 82, 1137-1148. doi:10.1016/j.rser.2017.09.091Greses, S., Gaby, J. C., Aguado, D., Ferrer, J., Seco, A., & Horn, S. J. (2017). Microbial community characterization during anaerobic digestion of Scenedesmus spp. under mesophilic and thermophilic conditions. Algal Research, 27, 121-130. doi:10.1016/j.algal.2017.09.002Greses, S., Zamorano-López, N., Borrás, L., Ferrer, J., Seco, A., & Aguado, D. (2018). Effect of long residence time and high temperature over anaerobic biodegradation of Scenedesmus microalgae grown in wastewater. Journal of Environmental Management, 218, 425-434. doi:10.1016/j.jenvman.2018.04.086Herrmann, C., Kalita, N., Wall, D., Xia, A., & Murphy, J. D. (2016). Optimised biogas production from microalgae through co-digestion with carbon-rich co-substrates. Bioresource Technology, 214, 328-337. doi:10.1016/j.biortech.2016.04.119Ju, F., Lau, F., & Zhang, T. (2017). Linking Microbial Community, Environmental Variables, and Methanogenesis in Anaerobic Biogas Digesters of Chemically Enhanced Primary Treatment Sludge. Environmental Science & Technology, 51(7), 3982-3992. doi:10.1021/acs.est.6b06344Kadnikov, V. V., Mardanov, A. V., Beletsky, A. V., Karnachuk, O. V., & Ravin, N. V. (2019). Genome of the candidate phylum Aminicenantes bacterium from a deep subsurface thermal aquifer revealed its fermentative saccharolytic lifestyle. Extremophiles, 23(2), 189-200. doi:10.1007/s00792-018-01073-5Klassen, V., Blifernez-Klassen, O., Wobbe, L., Schlüter, A., Kruse, O., & Mussgnug, J. H. (2016). Efficiency and biotechnological aspects of biogas production from microalgal substrates. Journal of Biotechnology, 234, 7-26. doi:10.1016/j.jbiotec.2016.07.015Klassen, V., Blifernez-Klassen, O., Wibberg, D., Winkler, A., Kalinowski, J., Posten, C., & Kruse, O. (2017). Highly efficient methane generation from untreated microalgae biomass. Biotechnology for Biofuels, 10(1). doi:10.1186/s13068-017-0871-4Leng, L., Yang, P., Singh, S., Zhuang, H., Xu, L., Chen, W.-H., … Lee, P.-H. (2018). A review on the bioenergetics of anaerobic microbial metabolism close to the thermodynamic limits and its implications for digestion applications. Bioresource Technology, 247, 1095-1106. doi:10.1016/j.biortech.2017.09.103Li, R., Duan, N., Zhang, Y., Liu, Z., Li, B., Zhang, D., & Dong, T. (2017). Anaerobic co-digestion of chicken manure and microalgae Chlorella sp.: Methane potential, microbial diversity and synergistic impact evaluation. Waste Management, 68, 120-127. doi:10.1016/j.wasman.2017.06.028Li, R., Duan, N., Zhang, Y., Liu, Z., Li, B., Zhang, D., … Dong, T. (2017). Co-digestion of chicken manure and microalgae Chlorella 1067 grown in the recycled digestate: Nutrients reuse and biogas enhancement. Waste Management, 70, 247-254. doi:10.1016/j.wasman.2017.09.016Mahdy, A., Mendez, L., Ballesteros, M., & González-Fernández, C. (2015). Algaculture integration in conventional wastewater treatment plants: Anaerobic digestion comparison of primary and secondary sludge with microalgae biomass. Bioresource Technology, 184, 236-244. doi:10.1016/j.biortech.2014.09.145Mansfeldt, C., Achermann, S., Men, Y., Walser, J.-C., Villez, K., Joss, A., … Fenner, K. (2019). Microbial residence time is a controlling parameter of the taxonomic composition and functional profile of microbial communities. The ISME Journal, 13(6), 1589-1601. doi:10.1038/s41396-019-0371-6McIlroy, S. J., Kirkegaard, R. H., Dueholm, M. S., Fernando, E., Karst, S. M., Albertsen, M., & Nielsen, P. H. (2017). Culture-Independent Analyses Reveal Novel Anaerolineaceae as Abundant Primary Fermenters in Anaerobic Digesters Treating Waste Activated Sludge. Frontiers in Microbiology, 8. doi:10.3389/fmicb.2017.01134Nakamura, K., Iizuka, R., Nishi, S., Yoshida, T., Hatada, Y., Takaki, Y., … Funatsu, T. (2016). Culture-independent method for identification of microbial enzyme-encoding genes by activity-based single-cell sequencing using a water-in-oil microdroplet platform. Scientific Reports, 6(1). doi:10.1038/srep22259Pachés, M., Romero, I., Hermosilla, Z., & Martinez-Guijarro, R. (2012). PHYMED: An ecological classification system for the Water Framework Directive based on phytoplankton community composition. Ecological Indicators, 19, 15-23. doi:10.1016/j.ecolind.2011.07.003Peces, M., Astals, S., Jensen, P. D., & Clarke, W. P. (2018). Deterministic mechanisms define the long-term anaerobic digestion microbiome and its functionality regardless of the initial microbial community. Water Research, 141, 366-376. doi:10.1016/j.watres.2018.05.028Qiao, J.-T., Qiu, Y.-L., Yuan, X.-Z., Shi, X.-S., Xu, X.-H., & Guo, R.-B. (2013). Molecular characterization of bacterial and archaeal communities in a full-scale anaerobic reactor treating corn straw. Bioresource Technology, 143, 512-518. doi:10.1016/j.biortech.2013.06.014Rinke, C. (2018). Single-Cell Genomics of Microbial Dark Matter. Microbiome Analysis, 99-111. doi:10.1007/978-1-4939-8728-3_7Rivière, D., Desvignes, V., Pelletier, E., Chaussonnerie, S., Guermazi, S., Weissenbach, J., … Sghir, A. (2009). Towards the definition of a core of microorganisms involved in anaerobic digestion of sludge. The ISME Journal, 3(6), 700-714. doi:10.1038/ismej.2009.2Robles, Á., Ruano, M. V., Charfi, A., Lesage, G., Heran, M., Harmand, J., … Ferrer, J. (2018). A review on anaerobic membrane bioreactors (AnMBRs) focused on modelling and control aspects. Bioresource Technology, 270, 612-626. doi:10.1016/j.biortech.2018.09.049Sanz, J. L., Rojas, P., Morato, A., Mendez, L., Ballesteros, M., & González-Fernández, C. (2017). Microbial communities of biomethanization digesters fed with raw and heat pre-treated microalgae biomasses. Chemosphere, 168, 1013-1021. doi:10.1016/j.chemosphere.2016.10.109Seco, A., Aparicio, S., González-Camejo, J., Jiménez-Benítez, A., Mateo, O., Mora, J. F., … Ferrer, J. (2018). Resource recovery from sulphate-rich sewage through an innovative anaerobic-based water resource recovery facility (WRRF). Water Science and Technology, 78(9), 1925-1936. doi:10.2166/wst.2018.492Sialve, B., Bernet, N., & Bernard, O. (2009). Anaerobic digestion of microalgae as a necessary step to make microalgal biodiesel sustainable. Biotechnology Advances, 27(4), 409-416. doi:10.1016/j.biotechadv.2009.03.001Skouteris, G., Hermosilla, D., López, P., Negro, C., & Blanco, Á. (2012). Anaerobic membrane bioreactors for wastewater treatment: A review. Chemical Engineering Journal, 198-199, 138-148. doi:10.1016/j.cej.2012.05.070Solden, L., Lloyd, K., & Wrighton, K. (2016). The bright side of microbial dark matter: lessons learned from the uncultivated majority. Current Opinion in Microbiology, 31, 217-226. doi:10.1016/j.mib.2016.04.020Solé-Bundó, M., Salvadó, H., Passos, F., Garfí, M., & Ferrer, I. (2018). Strategies to Optimize Microalgae Conversion to Biogas: Co-Digestion, Pretreatment and Hydraulic Retention Time. Molecules, 23(9), 2096. doi:10.3390/molecules23092096Solé-Bundó, M., Garfí, M., Matamoros, V., & Ferrer, I. (2019). Co-digestion of microalgae and primary sludge: Effect on biogas production and microcontaminants removal. Science of The Total Environment, 660, 974-981. doi:10.1016/j.scitotenv.2019.01.011Stämmler, F., Gläsner, J., Hiergeist, A., Holler, E., Weber, D., Oefner, P. J., … Spang, R. (2016). Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome, 4(1). doi:10.1186/s40168-016-0175-0Vanwonterghem, I., Jensen, P. D., Dennis, P. G., Hugenholtz, P., Rabaey, K., & Tyson, G. W. (2014). Deterministic processes guide long-term synchronised population dynamics in replicate anaerobic digesters. The ISME Journal, 8(10), 2015-2028. doi:10.1038/ismej.2014.50Wang, Y., Hammes, F., De Roy, K., Verstraete, W., & Boon, N. (2010). Past, present and future applications of flow cytometry in aquatic microbiology. Trends in Biotechnology, 28(8), 416-424. doi:10.1016/j.tibtech.2010.04.006Weinrich, S., Koch, S., Bonk, F., Popp, D., Benndorf, D., Klamt, S., & Centler, F. (2019). Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity. Frontiers in Microbiology, 10. doi:10.3389/fmicb.2019.01095Widder, S., Allen, R. J., Pfeiffer, T., Curtis, T. P., Wiuf, C., … Soyer, O. S. (2016). Challenges in microbial ecology: building predictive understanding of community function and dynamics. The ISME Journal, 10(11), 2557-2568. doi:10.1038/ismej.2016.45Xie, B., Gong, W., Tian, Y., Qu, F., Luo, Y., Du, X., … Liang, H. (2018). Biodiesel production with the simultaneous removal of nitrogen, phosphorus and COD in microalgal-bacterial communities for the treatment of anaerobic digestion effluent in photobioreactors. Chemical Engineering Journal, 350, 1092-1102. doi:10.1016/j.cej.2018.06.032Zamalloa, C., De Vrieze, J., Boon, N., & Verstraete, W. (2011). Anaerobic digestibility of marine microalgae Phaeodactylum tricornutum in a lab-scale anaerobic membrane bioreactor. Applied Microbiology and Biotechnology, 93(2), 859-869. doi:10.1007/s00253-011-3624-5Zamorano-López, N., Borrás, L., Giménez, J. B., Seco, A., & Aguado, D. (2019). Acclimatised rumen culture for raw microalgae conversion into biogas: Linking microbial community structure and operational parameters in anaerobic membrane bioreactors (AnMBR). Bioresource Technology, 290, 121787. doi:10.1016/j.biortech.2019.121787Zamorano-López, N., Greses, S., Aguado, D., Seco, A., & Borrás, L. (2019). Thermophilic anaerobic conversion of raw microalgae: Microbial community diversity in high solids retention systems. Algal Research, 41, 101533. doi:10.1016/j.algal.2019.101533Zou, Y., Xu, X., Li, L., Yang, F., & Zhang, S. (2018). Enhancing methane production from U. lactuca using combined anaerobically digested sludge (ADS) and rumen fluid pre-treatment and the effect on the solubilization of microbial community structures. Bioresource Technology, 254, 83-90. doi:10.1016/j.biortech.2017.12.054Lv, Z., Chen, Z., Chen, X., Liang, J., Jiang, J., & Loake, G. J. (2019). Effects of various feedstocks on isotope fractionation of biogas and microbial community structure during anaerobic digestion. Waste Management, 84, 211-219. doi:10.1016/j.wasman.2018.11.04

Auslegung und Inbetriebnahme der Sensorik, Aktorik und Avionik eines terrestrischen Landefahrzeugs

For the simulation of landings on extraterrestrial orbs like the moon models of the landing vehicles, so called landing demonstrators, are built. This allows to test the sensor and actuator system before their implementation in the outer space at a minimum of costs. For using the sensor and the actuator system in the outer space special requirements have to be considered. For example actuators for the attitude control based on aerodynamical principles can not be used because of the non-existent atmosphere. Instead actuators on the basis of the recoil principle are used to ensure a fast reaction. This thesis describes the planning and implementing of the avionic of such a landing demonstrator. In a first step, based on the planning and selection of the components, the cold gas system which is used for the attitude control is investigated. Potential optimizations for its electrical activation are introduced, implemented and conclusively evaluated. Furthermore the development of an especially to the requirements of the landing demonstrator adapted hardware is described, which is used for the connection of the sensors to the bord computer and for the activation of the actuator system