The effects of C/N (10–25) on the relationship of substrates, metabolites, and microorganisms in “inhibited steady-state” of anaerobic digestion
Graphical abstract
Introduction
Anaerobic digestion (AD) is widely used in many countries around the world as an effective way to reduce organic waste and produce clean energy (Lin et al., 2018). AD can successfully treat wet waste containing less than 40% dry matter, and offers significant advantages over other forms of livestock manure treatment (Ward et al., 2008). However, a bottleneck of livestock manure AD lies in this material's high nitrogen content (derived mainly from urea, uric acid, and protein) and low carbon to nitrogen (C/N) ratios (Peng et al., 2018). Ammonia is the final product of the breakdown of nitrogen-rich substrates, and it tends to accumulate when there is a continuous inflow of degradable nitrogen-rich feedstock and insufficient utilization by microorganisms (Mahdy et al., 2020). Excess ammonia is a well-known inhibitor of AD, leading to instability and inefficiency in the AD process (Capson-Tojo et al., 2020). Ammonia inhibition often causes the accumulation of volatile fatty acids (VFAs) (Sun et al., 2016). However, the high concentration of ammonia also enhances the buffering capacity of the system, thus resulting in a neutral pH even as VFAs accumulate (Peng et al., 2018). For this reason, the AD system reaches an “inhibited steady-state,” where the reactor performance is low but stable. Therefore, the “inhibited steady-state” is the result of a slight degree of ammonia inhibition. In this state, both VFAs and ammonia accumulate, partially disturbing microbial metabolism (Li et al., 2015). Traditionally, measurements of total ammonia nitrogen (TAN) or free ammonia nitrogen (FAN) have been used to predict ammonia inhibition (Dong et al., 2019). However, the toxicity of ammonia may vary widely, making it difficult to rely on the absolute level of ammonia nitrogen as the sole determinant of ammonia inhibition. For example, Yang et al. (2018) reported that reactor performance decreases when the TAN reaches 2 g/L. In contrast, Sun et al. (2016) showed that ammonia inhibition does not occur when the TAN concentration is less than 7 g/L. A more precise evaluation of ammonia inhibition may be obtained by collecting microbiological data. For example, methanogenic archaea are very sensitive to ammonia (Chen et al., 2018). Therefore, it should be useful to investigate methanogenic archaea to aid in the evaluation of ammonia inhibition.
Some methods can change the C/N in the actual AD projects. For example, the co-digestion of livestock manure with a carbon-rich substrate such as agricultural residue has been used as a primary method to adjust C/N (Ning et al., 2019). Recirculation is commonly practiced in China to reduce the volume of liquid discharge from biogas projects. This practice also provides a means to change the influent C/N because recirculation returns soluble carbon and nitrogen metabolites along with the liquid. (Pezzolla et al., 2017). Generally, maximum outputs of biogas and methane can be achieved when the C/N is around 25 (Piatek et al., 2016; Wang et al., 2012). The C/N of livestock manure ranges from 9 to 16 (as shown in Fig. S1 in Supplementary materials), and the “inhibited steady-state” occurs in the range of 10–25. Several articles have reported about the effects of C/N on AD systems; however, these studies have often focused on the impacts of C/N on biogas production and microbial community structure (Dai et al., 2016; Piątek et al., 2016; Ning et al., 2019). Little research has focused on the “inhibited steady-state,” which occupies a relatively narrow range in the C/N scale. Especially lacking is information on how the various environmental factors (mainly ammonia, VFAs, pH, and alkalinity) and the microorganisms (mainly bacteria and archaea) in the AD system reach an equilibrium state. Compared to complete ammonia inhibition, the “inhibited steady-state” occurs more frequently in actual biogas projects. Thus, clarifying the characteristics of this state will provide useful information for biogas plant operators. Such information will also lead to a better understanding of the mechanism of ammonia inhibition, leading to the development of strategies to alleviate this problem.
Second-generation gene sequencing technology has allowed researchers to better describe microbial community structure in complex systems (Huo et al., 2017; Cai et al., 2017). Moreover, new analytical tools and various sequence databases have facilitated the mining of large microbiome datasets for knowledge with specific biological significance. For example, linear discriminant analysis (LDA) can identify microorganisms with significantly different levels of abundance (Zhou et al., 2019). LDA effect size (LEfSe) analysis can identify potential biomarkers that can differentiate between different groups. Microbial network analysis uses co-occurrence or correlation to connect between microorganisms within a network to show the relationships between species and identify keystone taxa that drive community composition and function (Banerjee et al., 2018; Gu et al., 2019). The Tax4Fun software package can predict the functional pathways and critical enzymes of a system using functional annotation based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases. This tool analyzes the metabolic diversity of a system without looking at the microbial community (Huo et al., 2017; Song et al., 2019). In this study, the aforementioned bioinformatic tools were used to analyze the mechanism behind the inhibited steady-state.
To reproduce the “inhibited steady-state,” the influent C/N were adjusted from 10 to 50 by varying the co-digestion and recirculation conditions in ten continuous stirred tank reactors (CSTRs). Reactor performance, metabolites, and microbial characteristics under different C/Ns were investigated to determine the microbial basis of the “inhibited steady state.” The aims of this study are: (1) to explore the relationships between C/N, intermediate metabolites, and reactor performance during the “inhibited steady state”; and (2) to analyze the structure of microbial communities during the “inhibited steady state” and relate this to reactor performance using bioinformatic tools.
Section snippets
Substrate and inoculum
Pig manure (PM) was collected from a farm at the China Agricultural University (CAU) located in Zhuozhou, China. PM was stored in a sealed 30 L barrel at room temperature (20–25°C). The PM was completely stirred before it was fed to the CSTRs every day. Corn stover (CS) was collected from a CAU Experimental Station located in Shangzhuang, Beijing. The CS was ground using a high-speed pulverizer (FW100, Taisite, Tianjin, China) and then stored at room temperature (20–25°C). The inoculum for the
Results and discussion
This section is organized as follows: Firstly, the macro-level data from the reactors (fermentation parameters and metabolites) are analyzed to investigate the characteristics that lead to the formation of the inhibited steady-state. Secondly, the microbial-level data are summarized and discussed to investigate the microbial community structure in the inhibited steady-state. Thirdly, the correlation network and LEfSe analysis are combined to reveal the relationship between microbes and
Conclusion
The “inhibited steady-state” was successfully reproduced by changing the substrate composition and recirculation regime to adjust the influent C/N within the range of 10–14. There was a clear transition between Methanosaeta and Methanosarcina as the influent C/N changed, and this succession may be used as a biological indicator of ammonia inhibition. The results suggest that as the influent C/N gradually decreases, the aceticlastic pathway is gradually replaced by the hydrogenotrophic pathway.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This study was supported by the National Key Research and Development Program of China (Grant number: 2019YFC0408700).
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