Anaerobic digestion of sludge by different pretreatments: Changes of amino acids and microbial community

doi:10.1007/s11783-021-1458-7

Front. Environ. Sci. Eng.

2022, Vol. 16

Issue (2) : 23 https://doi.org/10.1007/s11783-021-1458-7

RESEARCH ARTICLE

Anaerobic digestion of sludge by different pretreatments: Changes of amino acids and microbial community

Keke Xiao^1,², Zecong Yu^1,², Kangyue Pei^1,², Mei Sun^1,², Yuwei Zhu^1,², Sha Liang^1,², Huijie Hou^1,², Bingchuan Liu^1,², Jingping Hu^1,², Jiakuan Yang^1,^2,³(

)

¹. School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
². Hubei Provincial Engineering Laboratory of Solid Waste Treatment, Disposal and Recycle Technology, Wuhan 430074, China
³. State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan 430074, China

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Abstract

•Tryptophan protein, and aromatic protein I/II were the key identified proteins.

•Cysteine was more correlated with methane production than other amino acids.

•The presence of cysteine can promote methane production and degradation of VFAs.

•The presence of cysteine can lower ORP and increase biomass activity.

•Predominant Tissierella and Proteiniphilum were noted in pretreated sludge samples.

Many studies have investigated the effects of different pretreatments on the performance of anaerobic digestion of sludge. However, the detailed changes of dissolved organic nitrogen, particularly the release behavior of proteins and the byproducts of protein hydrolysis-amino acids, are rarely known during anaerobic digestion of sludge by different pretreatments. Here we quantified the changes of three types of proteins and 17 types of amino acids in sludge samples solubilized by ultrasonic, thermal, and acid/alkaline pretreatments and their transformation during anaerobic digestion of sludge. Tryptophan protein, aromatic protein I, aromatic protein II, and cysteine were identified as the key dissolved organic nitrogen responsible for methane production during anaerobic digestion of sludge, regardless of the different pretreatment methods. Different from the depletion of other amino acids, cysteine was resistant to degradation after an incubation period of 30 days in all sludge samples. Meanwhile, the “cysteine and methionine metabolism (K00270)” was absent in all sludge samples by identifying 6755 Kyoto Encyclopedia of Genes and Genomes assignments of genes hits. Cysteine contributed to the generation of methane and the degradation of acetic, propionic, and n-butyric acids through decreasing oxidation-reduction potential and enhancing biomass activity. This study provided an alternative strategy to enhance anaerobic digestion of sludge through in situ production of cysteine.

Keywords Sludge pretreatments Dissolved organic nitrogen Proteins Amino acids Structural equation model Metagenomic sequencing analysis.

Corresponding Author(s): Jiakuan Yang

Issue Date: 01 June 2021

Cite this article:

Keke Xiao,Zecong Yu,Kangyue Pei, et al. Anaerobic digestion of sludge by different pretreatments: Changes of amino acids and microbial community[J]. Front. Environ. Sci. Eng., 2022, 16(2): 23.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1458-7
https://academic.hep.com.cn/fese/EN/Y2022/V16/I2/23

Fig.1 Changes of protein (PN) contents in S-EPS and B-EPS in sludge before and after different pretreatments: (a) ultrasonic intensities, (b) temperatures, and (c) pH values; changes of polysaccharide (PS) contents in S-EPS and B-EPS in sludge after different pretreatments: (d) ultrasonic intensities, (e) temperatures, and (f) pH values; changes of total organic carbon (TOC) contents in S-EPS and B-EPS in sludge after different pretreatments: (g) ultrasonic intensities, (h) temperatures, and (i) pH values. B-EPS denotes bound extracellular polymeric substances; S-EPS denotes soluble extracellular polymeric substances.

Fig.2 Changes of amino acid contents in the supernatant of sludge before and after different pretreatments: (a) ultrasonic intensities, (b) temperatures, and (c) pH values; changes of SCOD in the supernatant of sludge after different pretreatments: (d) ultrasonic intensities, (e) temperatures, and (f) pH values. Ala denotes alanine; Arg denotes arginine; Asp denotes aspartic acid; Cys denotes cysteine; Glu denotes glutamic acid; Gly denotes glycine; His denotes histidine; Ile denotes isoleucine; Lys denotes lysine; Leu denotes leucine; Met denotes methionine; Pro denotes proline; Phe denotes phenylalanine; SCOD denotes soluble chemical oxygen demand; Ser denotes serine; Tyr denotes tyrosine; Thr denotes threonine; Val denotes valine.

Fig.3 The changes of amino acids during anaerobic digestion of sludge at different sampling time at the 0th, 5th, 10th, 20th, and 30th day: (a) Control (mixed feed sludge and seed sludge); sludge pretreated with different ultrasonic intensities of (b) 0.2 W/L, (c) 0.4 W/L, (d) 1 W/L, (e) 1.5 W/L; sludge pretreated with different temperatures of (f) 30°C, (g) 60°C, (h) 90°C, (i) 120°C, and sludge pretreated at different pH values of (j) pH 2, (k) pH 4, (l) pH 7, and (m) pH 10. The Y-axis means the concentration of amino acids, with unit of mg N/L. Arg denotes arginine; Asp denotes aspartic acid; Cys denotes cysteine; His denotes histidine; Lys denotes lysine; Met denotes methionine; Phe denotes phenylalanine; Pro denotes proline; Ser denotes serine; Thr denotes threonine; Val denotes valine.

Fig.4 Methane production in sludge samples after different pretreatments: (a) ultrasonic intensities, (b) temperatures, and (c) pH values after a digestion period of 30 days.

Fig.5 (a) The correlation between methane production, and different types of proteins and amino acids; (b) the structural equation model (SEM) of methane production, and different types of proteins and amino acids. Significance levels are indicated: ∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.

Fig.6 (a) The taxonomic analysis of bacterial community in feed sludge, seed sludge, the mixture of feed sludge and seed sludge (Control), Control30, UL30, TH30, and AL30 (% denotes the percentage of each bacterium to the total bacteria); (b) the taxonomic analysis of archaeal community in feed sludge, seed sludge, Control, Control30, UL30, TH30, and AL30 (% denotes the percentage of each methanogen to the total methanogens); (c) the PCA plot of microbial composition on genus level in feed sludge, seed sludge, the mixture of feed sludge and seed sludge (Control), Control30, UL30, TH30, and AL30; (d) relative abundance of Tissierella genera based on the 16S rRNA gene sequencing results (% denotes the abundance of Tissierella genera to the total bacteria genera); (e) the genes assigned to enzymes directly related to utilization or production of NH₃ in the samples of Control30, UL30, TH30, and AL30 (% denotes the percentage of each enzyme to the total enzymes); (f) key genes involved in amino acid metabolism in the samples of Control30, UL30, TH30, and AL30. AL30 denotes sludge pretreated at pH of 10 exposed to a digestion period of 30 days (% denotes the percentage of each gene to the total gene hits); PCA denotes principal component analysis; Control30 denotes the mixture of feed sludge and seed sludge exposed to a digestion period of 30 days; TH30 denotes sludge pretreated at 120°C exposed to a digestion period of 30 days; UL30 denotes sludge pretreated with ultrasonic intensity of 1.5 W/mL exposed to a digestion period of 30 days.

Enzyme (EC number)	After an incubation of 30 days				Description	Reaction	Microbial community at the order level
Enzyme (EC number)	Control30	UL30	TH30	AL30	Description	Reaction	Microbial community at the order level
6.3.1.2	0.04713	0.04736	0.04911	0.04789	Glutamate–ammonia ligase	ATP+ L-glutamate+ NH₃ = ADP+ phosphate+ L-glutamine	Unclassified
1.7.99.1	0.02316	0.02322	0.02468	0.02301	Hydroxylamine reductase	NH₃ + H₂O+ acceptor= hydroxylamine+ reduced acceptor	Unclassified
1.7.2.2	0.00564	0.00557	0.00655	0.00631	Nitrite reductase (cytochrome; ammonia forming)	NH₃ + 2H₂O+ 6 ferricytochrome c= nitrite+ 6 ferrocytochrome c+ 7H⁺	Chthoniobacterales
6.3.1.1	0.00362	0.00362	0.00414	0.00374	Aspartate–ammonia ligas	ATP+ L-aspartate+ NH₃ = AMP+ diphosphate+ L-asparagine	Unclassified
6.3.4.2	0.03271	0.03375	0.03437	0.03398	CTP synthase (glutamine hydrolysing)	L-glutamine+ H₂O= L-glutamate+ NH₃	Unclassified
4.3.1.3	0.02936	0.02999	0.02973	0.02991	Histidine ammonia-lyase	L-histidine= urocanate+ NH₃	Unclassified
2.1.2.10	0.02402	0.02454	0.02406	0.02371	Aminomethyltransferase	[protein]-S8-aminomethyldihydrolipoyllysine+ tetrahydrofolate= [protein]-dihydrolipoyllysine+ 5,10methylenetetrahydrofolate+ NH₃	Rhodocyclales
4.3.1.17	0.0137	0.01301	0.01282	0.01269	L-serine ammonia-lyase	L-serine= pyruvate+ NH₃ (overall reaction)	Rhodobacterales
4.3.1.12	0.00414	0.00431	0.00379	0.00395	Ornithine cyclodeaminase	L-ornithine= L-proline+ NH₃	Rhodobacterales
4.3.1.18	0.000336	0.000386	0.000342	0.00035	D-serine ammonia-lyase	D-serine= pyruvate+ NH₃	Burkholderiales
4.3.1.19	0.01737	0.01906	0.01817	0.01893	Threonine ammonia-lyase	L-threonine= 2-oxobutanoate+ NH₃	Unclassified
4.3.1.4	0.01754	0.01757	0.0173	0.01795	Formimidoyltetrahydrofolate cyclodeaminase	5-formimidoyltetrahydrofolate= 5,10methenyltetrahydrofolate+ NH₃	Unclassified
4.3.1.15	0.0009778	0.00111	0.00101	0.00109	Diaminopropionate ammonia-lyase	2,3-diaminopropanoate+ H₂O= pyruvate+ 2 NH₃	Unclassified
2.5.1.61	0.01089	0.01062	0.01122	0.01135	Hydroxymethylbilane synthase	4 porphobilinogen+ H₂O= hydroxymethylbilane+ 4 NH₃	Hydrogenophilales
4.3.1.2	0.00516	0.00515	0.00519	0.00527	Methylaspartate ammonia-lyase	L-threo-3-methylaspartate= mesaconate+ NH₃	Burkholderiales
4.3.1.7	0.00554	0.00531	0.00616	0.00559	Ethanolamine ammonia-lyase	ethanolamine= acetaldehyde+ NH₃	Burkholderiales

Tab.1 The relative abundance (%) and description of genes assigned to enzymes and the related microbes related to utilization or production of NH₃ in the different sludge samples (% denotes the percentage of each enzyme to the total enzymes).

Fig.7 The influences of cysteine concentrations on: (a) biogas production, (b) ORP changes, and (c) biomass activity after a digestion period of 5 days; the changes of (d) acetic acid concentration, (e) propionic acid concentration, and (f) n-butyric acid concentration after the 0 h and 12 h of incubation. ATP denotes adenosine triphosphate; Cys denotes cysteine; ORP denotes oxidation-reduction potential; RLU denotes relative luminescence.

Fig.8 Proposed metabolic pathway for methane production from the degradation of DON with the in situ generation of cysteine during anaerobic digestion of sludge by the different pretreatments.

1	L Appels, J Baeyens, J Degreve, R Dewil (2008). Principles and potential of the anaerobic digestion of waste-activated sludge. Progress in Energy and Combustion Science, 34(6): 755–781 https://doi.org/10.1016/j.pecs.2008.06.002
2	S Astals, M Peces, D J Batstone, P D Jensen, S Tait (2018). Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems. Water Research, 143(1): 127–135 https://doi.org/10.1016/j.watres.2018.06.021
3	J C A Bardwell, K Mcgovern, J Beckwith (1991). Identification of a protein required for disulfide bond formation in vivo. Cell, 67(3): 581–589 https://doi.org/10.1016/0092-8674(91)90532-4
4	A W Carpenter, S N Laughton, M R Wiesner (2015). Enhanced biogas production from nanoscale zero valent iron-amended anaerobic bioreactors. Environmental Engineering Science, 32(8): 647–655 https://doi.org/10.1089/ees.2014.0560
5	H Carrère, C Dumas, A Battimelli, D J Batstone, J P Delgenès, J P Steyer, I Ferrer (2010). Pretreatment methods to improve sludge anaerobic degradability: A review. Journal of Hazardous Materials, 183(1-3): 1–15 https://doi.org/10.1016/j.jhazmat.2010.06.129
6	S S Chen, B Dong, X H Dai, H Y Wang, N Li, D H Yang (2019). Effects of thermal hydrolysis on the metabolism of amino acids in sewage sludge in anaerobic digestion. Waste Management (New York, N.Y.), 88(2): 309–318 https://doi.org/10.1016/j.wasman.2019.03.060
7	S S Chen, D H Yang, B Dong, N Li, X H Dai (2020). Sludge age impacted the distribution, occurrence state and structure of organic compounds in activated sludge and affected the anaerobic degradability. Chemical Engineering Journal, 384(1): 123261 https://doi.org/10.1016/j.cej.2019.123261
8	S Y Chen, X Z Dong (2005). Proteiniphilum acetatigenes gen. nov., sp nov., from a UASB reactor treating brewery wastewater. International Journal of Systematic and Evolutionary Microbiology, 55(6): 2257–2261 https://doi.org/10.1099/ijs.0.63807-0
9	S S Chishti, S Nazrul Hasnain, M Altaf Khan (1992). Studies on the recovery of sludge protein. Water Research, 26(2): 241–248 https://doi.org/10.1016/0043-1354(92)90224-R
10	S Christensen, R M Mcmahon, J L Martin, W M Huston (2019). Life inside and out: making and breaking protein disulfide bonds in Chlamydia. Critical Reviews in Microbiology, 45(1): 33–50 https://doi.org/10.1080/1040841X.2018.1538933
11	L A De Graaf (2000). Denaturation of proteins from a non-food perspective. Journal of Biotechnology, 79(3): 299–306 https://doi.org/10.1016/S0168-1656(00)00245-5
12	L J Deleu, M A Lambrecht, J Van De Vondel, J A Delcour (2019). The impact of alkaline conditions on storage proteins of cereals and pseudo-cereals. Current Opinion in Food Science, 25(1): 98–103 https://doi.org/10.1016/j.cofs.2019.02.017
13	A Gonzalez, A T W M Hendriks, J B Van Lier, M De Kreuk (2018). Pre-treatments to enhance the biodegradability of waste activated sludge: Elucidating the rate limiting step. Biotechnology Advances, 36(5): 1434–1469 https://doi.org/10.1016/j.biotechadv.2018.06.001
14	J B Grace, K A Bollen (2005). Interpreting the results from multiple regression and structural equation models. Bulletin of the Ecological Society of America, 86(4): 283–295 https://doi.org/10.1890/0012-9623(2005)86[283:ITRFMR]2.0.CO;2
15	L P Hao, F Lu, P J He, L Li, L M Shao (2011). Predominant contribution of syntrophic acetate oxidation to thermophilic methane formation at high acetate concentrations. Environmental Science & Technology, 45(2): 508–513 https://doi.org/10.1021/es102228v
16	J Hwang, L Zhang, S Seo, Y W Lee, D Jahng (2008). Protein recovery from excess sludge for its use as animal feed. Bioresource Technology, 99(18): 8949–8954 https://doi.org/10.1016/j.biortech.2008.05.001
17	M Karimi, M T Ignasiak, B Chan, A K Croft, L Radom, C H Schiesser, D I Pattison, M J Davies (2016). Reactivity of disulfide bonds is markedly affected by structure and environment: Implications for protein modification and stability. Scientific Reports, 6(1): 1–12 https://doi.org/10.1038/srep38572
18	F Labib, J F Ferguson, M M Benjamin, M Merigh, L N Ricker (1992). Anaerobic butyrate degradation in a fluidized-bed reactor: Effects of increased concentrations of hydrogen and acetate. Environmental Science & Technology, 26(2): 369–376 https://doi.org/10.1021/es00026a019
19	H Li, Y Y Jin, R Mahar, Z Y Wang, Y F Nie (2008). Effects and model of alkaline waste activated sludge treatment. Bioresource Technology, 99(11): 5140–5144 https://doi.org/10.1016/j.biortech.2007.09.019
20	X Li, S Chen, B Dong, X Dai (2020). New insight into the effect of thermal hydrolysis on high solid sludge anaerobic digestion: Conversion pathway of volatile sulphur compounds. Chemosphere, 244(1): 125466 https://doi.org/10.1016/j.chemosphere.2019.125466
21	K W Liao, H D Hu, S J Ma, H Q Ren (2019). Effect of microbial activity and microbial community structure on the formation of dissolved organic nitrogen (DON) and bioavailable DON driven by low temperatures. Water Research, 159(2): 397–405 https://doi.org/10.1016/j.watres.2019.04.049
22	H Liu, Y Chen (2018). Enhanced methane production from food waste using cysteine to increase biotransformation of i-monosaccharide, volatile fatty acids, and biohydrogen. Environmental Science & Technology, 52(6): 3777–3785 https://doi.org/10.1021/acs.est.7b05355
23	R Z Liu, X Yu, P F Yu, X S Guo, B Zhang, B Y Xiao (2019). New insights into the effect of thermal treatment on sludge dewaterability. Science of the Total Environment, 656(1): 1082–1090 https://doi.org/10.1016/j.scitotenv.2018.11.436
24	D Lu, K K Xiao, Y Chen, Y N A Soh, Y Zhou (2018). Transformation of dissolved organic matters produced from alkaline-ultrasonic sludge pretreatment in anaerobic digestion: From macro to micro. Water Research, 142(2): 138–146 https://doi.org/10.1016/j.watres.2018.05.044
25	Y Maspolim, Y Zhou, C H Guo, K K Xiao, W J Ng (2015). The effect of pH on solubilization of organic matter and microbial community structures in sludge fermentation. Bioresource Technology, 190(1): 289–298 https://doi.org/10.1016/j.biortech.2015.04.087
26	A J Mawson, R L Earle, F V Larsen (1991). Degradation of acetic and propionic acids in the methane fermentation. Water Research, 25(12): 1549–1554 https://doi.org/10.1016/0043-1354(91)90187-U
27	D Renard, L Lavenant-Gourgeon, A Lapp, M Nigen, C Sanchez (2014). Enzymatic hydrolysis studies of arabinogalactan-protein structure from Acacia gum: The self-similarity hypothesis of assembly from a common building block. Carbohydrate Polymers, 112(4): 648–661 https://doi.org/10.1016/j.carbpol.2014.06.041
28	D S Shen, J Yin, X Q Yu, M Z Wang, Y Y Long, J Shentu, T Chen (2017). Acidogenic fermentation characteristics of different types of protein-rich substrates in food waste to produce volatile fatty acids. Bioresource Technology, 227(5): 125–132 https://doi.org/10.1016/j.biortech.2016.12.048
29	A Sheykhfard, F Haghighi (2020). Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran. Journal of Safety Research, 72(1): 1–8 https://doi.org/10.1016/j.jsr.2019.11.002
30	X B Tian, C Wang, A P Trzcinski, L Lin, W J Ng (2015). Interpreting the synergistic effect in combined ultrasonication-ozonation sewage sludge pre-treatment. Chemosphere, 140(1): 63–71 https://doi.org/10.1016/j.chemosphere.2014.09.007
31	X Wang, Y B Li, Y Zhang, Y R Pan, L Li, J X Liu, D Butler (2019). Stepwise pH control to promote synergy of chemical and biological processes for augmenting short-chain fatty acid production from anaerobic sludge fermentation. Water Research, 155(2): 193–203 https://doi.org/10.1016/j.watres.2019.02.032
32	H Q Wen, D F Xing, G J Xie, T M Yin, N Q Ren, B F Liu (2019). Enhanced photo-fermentative hydrogen production by synergistic effects of formed biofilm and added L-cysteine. Renewable Energy, 139(3): 643–650 https://doi.org/10.1016/j.renene.2019.02.127
33	B R Wu, B J Ni, K Horvat, L Y Song, X L Chai, X H Dai, D Mahajan (2017). Occurrence state and molecular structure analysis of extracellular proteins with implications on the dewaterability of waste activated sludge. Environmental Science & Technology, 51(16): 9235–9243 https://doi.org/10.1021/acs.est.7b02861
34	K Xiao, C Guo, Y Zhou, Y Maspolim, W J Ng (2016a). Acetic acid effects on methanogens in the second stage of a two-stage anaerobic system. Chemosphere, 144(3): 1498–1504 https://doi.org/10.1016/j.chemosphere.2015.10.035
35	K K Xiao, Y Chen, X Jiang, W Y Seow, C He, Y Yin, Y Zhou (2017). Comparison of different treatment methods for protein solubilisation from waste activated sludge. Water Research, 122(4): 492–502 https://doi.org/10.1016/j.watres.2017.06.024
36	K K Xiao, Y Chen, X Jiang, V K Tyagi, Y Zhou (2016b). Characterization of key organic compounds affecting sludge dewaterability during ultrasonication and acidification treatments. Water Research, 105(3): 470–478 https://doi.org/10.1016/j.watres.2016.09.030
37	K K Xiao, C H Guo, Y Zhou, Y Maspolim, W J Ng (2016c). Acetic acid effects on methanogens in the second stage of a two-stage anaerobic system. Chemosphere, 144(1): 1498–1504 https://doi.org/10.1016/j.chemosphere.2015.10.035
38	K K Xiao, C H Guo, Y Zhou, Y Maspolim, J Y Wang, W J Ng (2013). Acetic acid inhibition on methanogens in a two-phase anaerobic process. Biochemical Engineering Journal, 75(2): 1–7 https://doi.org/10.1016/j.bej.2013.03.011
39	G Yang, P Y Zhang, G M Zhang, Y Y Wang, A Q Yang (2015). Degradation properties of protein and carbohydrate during sludge anaerobic digestion. Bioresource Technology, 192(3): 126–130 https://doi.org/10.1016/j.biortech.2015.05.076
40	W Zeng, B X Li, X D Wang, X L Bai, Y Z Peng (2016). Influence of nitrite accumulation on “Candidatus Accumulibacter” population structure and enhanced biological phosphorus removal from municipal wastewater. Chemosphere, 144(2): 1018–1025 https://doi.org/10.1016/j.chemosphere.2015.08.064
41	B B Zhang, Q M Xian, J P Zhu, A M Li, T T Gong (2015). Characterization, DBPs formation, and mutagenicity of soluble microbial products (SMPs) in wastewater under simulated stressful conditions. Chemical Engineering Journal, 279(1): 258–263 https://doi.org/10.1016/j.cej.2015.05.039

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