1H NMR metabolomics of microbial metabolites in the four MW agricultural biogas plant reactors: A case study of inhibition mirroring the acute rumen acidosis symptoms.

In this study, nuclear magnetic resonance (1H NMR) spectroscopic profiling was used to provide a more comprehensive view of microbial metabolites associated with poor reactor performance in a full-scale 4 MW mesophilic agricultural biogas plant under fully operational and also under inhibited conditions. Multivariate analyses were used to assess the significance of differences between reactors whereas artificial neural networks (ANN) were used to identify the key metabolites responsible for inhibition and their network of interaction. Based on the results of nm-MDS ordination the subsamples of each reactor were similar, but not identical, despite homogenization of the full-scale reactors before sampling. Hence, a certain extent of variability due to the size of the system under analysis was transferred into metabolome analysis. Multivariate analysis showed that fully active reactors were clustered separately from those containing inhibited reactor metabolites and were significantly different. Furthermore, the three distinct inhibited states were significantly different from each other. The inhibited metabolomes were enriched in acetate, caprylate, trimethylamine, thymine, pyruvate, alanine, xanthine and succinate. The differences in the metabolic fingerprint between inactive and fully active reactors observed in this study resembled closely the metabolites differentiating the (sub) acute rumen acidosis inflicted and healthy rumen metabolomes, creating thus favorable conditions for the growth and activity of pathogenic bacteria. The consistency of our data with those reported before for rumen ecosystems shows that 1H NMR based metabolomics is a reliable approach for the evaluation of metabolic events at full-scale biogas reactors.

[1]  Jože Panjan,et al.  Addressing case specific biogas plant tasks: industry oriented methane yields derived from 5L Automatic Methane Potential Test Systems in batch or semi-continuous tests using realistic inocula, substrate particle sizes and organic loading. , 2014, Bioresource technology.

[2]  A. Kondo,et al.  Comparison of metabolomic profiles of microbial communities between stable and deteriorated methanogenic processes. , 2014, Bioresource technology.

[3]  David I. Ellis,et al.  A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. , 2015, Analytica chimica acta.

[4]  J. Nocek Bovine acidosis: implications on laminitis. , 1997, Journal of dairy science.

[5]  Sonia Heaven,et al.  Quantifying the percentage of methane formation via acetoclastic and syntrophic acetate oxidation pathways in anaerobic digesters. , 2018, Waste management.

[6]  J. L. Mangan,et al.  The formation and distribution of methylamine in the ruminant digestive tract. , 1964, The Biochemical journal.

[7]  B. Stres,et al.  Full-scale agricultural biogas plant metal content and process parameters in relation to bacterial and archaeal microbial communities over 2.5 year span. , 2018, Journal of environmental management.

[8]  Suiying Huang,et al.  How Stable Is Stable? Function versus Community Composition , 1999, Applied and Environmental Microbiology.

[9]  O Hammer-Muntz,et al.  PAST: paleontological statistics software package for education and data analysis version 2.09 , 2001 .

[10]  J. Loor,et al.  Induction of Subacute Ruminal Acidosis Affects the Ruminal Microbiome and Epithelium , 2016, Front. Microbiol..

[11]  Ø. Hammer,et al.  PAST: PALEONTOLOGICAL STATISTICAL SOFTWARE PACKAGE FOR EDUCATION AND DATA ANALYSIS , 2001 .

[12]  L. T. Angenent,et al.  High n-caprylate productivities and specificities from dilute ethanol and acetate: chain elongation with microbiomes to upgrade products from syngas fermentation , 2016 .

[13]  Maria Westerholm,et al.  Biogas production through syntrophic acetate oxidation and deliberate operating strategies for improved digester performance , 2016 .

[14]  David S. Wishart,et al.  HMDB 3.0—The Human Metabolome Database in 2013 , 2012, Nucleic Acids Res..

[15]  C. Stead,et al.  Diversity of endotoxin and its impact on pathogenesis. , 2006 .

[16]  A. R. Neill,et al.  Conversion of choline methyl groups through trimethylamine into methane in the rumen. , 1978, The Biochemical journal.

[17]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[18]  Largus T Angenent,et al.  Methanogenic population dynamics during startup of a full-scale anaerobic sequencing batch reactor treating swine waste. , 2002, Water research.

[19]  David S. Wishart,et al.  Accurate, Fully-Automated NMR Spectral Profiling for Metabolomics , 2014, PloS one.

[20]  Eugene L. Madsen,et al.  Comparative Survey of Rumen Microbial Communities and Metabolites across One Caprine and Three Bovine Groups, Using Bar-Coded Pyrosequencing and 1H Nuclear Magnetic Resonance Spectroscopy , 2012, Applied and Environmental Microbiology.

[21]  M. Nikolausz,et al.  Changing Feeding Regimes To Demonstrate Flexible Biogas Production: Effects on Process Performance, Microbial Community Structure, and Methanogenesis Pathways , 2015, Applied and Environmental Microbiology.

[22]  Blaž Stres,et al.  Methane Yield Database: Online infrastructure and bioresource for methane yield data and related metadata. , 2015, Bioresource technology.

[23]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[24]  L. T. Angenent,et al.  Waste Conversion into n-Caprylate and n-Caproate: Resource Recovery from Wine Lees Using Anaerobic Reactor Microbiomes and In-line Extraction , 2016, Front. Microbiol..

[25]  O. Alzahal,et al.  An Investigation into Rumen Fungal and Protozoal Diversity in Three Rumen Fractions, during High-Fiber or Grain-Induced Sub-Acute Ruminal Acidosis Conditions, with or without Active Dry Yeast Supplementation , 2017, Front. Microbiol..

[26]  M. Trent,et al.  Invited review: Diversity of endotoxin and its impact on pathogenesis , 2006, Journal of endotoxin research.

[27]  Ying Zhang,et al.  HMDB: the Human Metabolome Database , 2007, Nucleic Acids Res..

[28]  S. Kolbl,et al.  Mixture of primary and secondary municipal wastewater sludge as a short‐term substrate in 2 MW agricultural biogas plant: site‐specific sustainability of enzymatic and ultrasound pretreatments , 2016 .

[29]  J. Benedito,et al.  Ruminal Acidosis in Feedlot: From Aetiology to Prevention , 2014, TheScientificWorldJournal.

[30]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[31]  D. Wishart,et al.  A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows. , 2012, Journal of dairy science.

[32]  Johannes Kabisch,et al.  Fueling the future with biomass: Processes and pathways for a sustainable supply of hydrocarbon fuels and biogas , 2017, Engineering in life sciences.

[33]  B. Knapp,et al.  Removal of Free Extracellular DNA from Environmental Samples by Ethidium Monoazide and Propidium Monoazide , 2008, Applied and Environmental Microbiology.

[34]  S. Kolbl,et al.  Potential for valorization of dehydrated paper pulp sludge for biogas production: Addition of selected hydrolytic enzymes in semi-continuous anaerobic digestion assays , 2017 .

[35]  E. Davis,et al.  Metabolic Origin of Urinary Methylamine in the Rat , 1961, Nature.

[36]  H. Insam,et al.  Rhizosphere bacteria and fungi associated with plant growth in soils of three replanted apple orchards , 2015, Plant and Soil.

[37]  B. Svensson,et al.  Alanine as an end product during fermentation of monosaccharides byClostridium strain P2 , 1995, Antonie van Leeuwenhoek.

[38]  Maria De Iorio,et al.  Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN , 2014, Nature Protocols.

[39]  R. H. Smith,et al.  Degradation of nucleic acids in the rumen , 1973, British Journal of Nutrition.

[40]  David S. Wishart,et al.  Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows , 2010, Metabolomics.

[41]  Robert Powers,et al.  The future of NMR-based metabolomics. , 2017, Current opinion in biotechnology.

[42]  Craig S. Criddle,et al.  Flexible Community Structure Correlates with Stable Community Function in Methanogenic Bioreactor Communities Perturbed by Glucose , 2000, Applied and Environmental Microbiology.

[43]  T. Ebbels,et al.  Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts , 2007, Nature Protocols.

[44]  M. Schloter,et al.  Intestinal Metagenomes and Metabolomes in Healthy Young Males: Inactivity and Hypoxia Generated Negative Physiological Symptoms Precede Microbial Dysbiosis , 2018, Front. Physiol..

[45]  Craig S. Criddle,et al.  Parallel Processing of Substrate Correlates with Greater Functional Stability in Methanogenic Bioreactor Communities Perturbed by Glucose , 2000, Applied and Environmental Microbiology.

[46]  M. Schloter,et al.  Hypoxia and Inactivity Related Physiological Changes (Constipation, Inflammation) Are Not Reflected at the Level of Gut Metabolites and Butyrate Producing Microbial Community: The PlanHab Study , 2017, Front. Physiol..

[47]  David S. Wishart,et al.  HMDB: a knowledgebase for the human metabolome , 2008, Nucleic Acids Res..