微生物学报

Genus	Strain	Sample source	Nitrogen source	Carbon source	T/℃	pH	t/h	Nitrogen removal efficiency(%)	Phosphorusremoval efficiency(%)	C/N	Initial nitrogencontent (mg/L)	Initial phosphoruscontent (mg/L)	References
Raoultella	YX-4	Wastewater treatment pond at the pig farm	NH₄⁺, NO₃^-, NO₂^-	Disodium succinate	25	7.0	96	99.0	85.0	15	200.0	100.0	[5]
Pseudomonas	ADP-19	Polluted sediments and kimchi processing wastewater	NH₄⁺	Sodium acetate	30	7.0-8.0	48	96.5	73.3	4	100.0	20.0	[12]
YG-24	Eutrophic sediments from lake Taihu	NH₄⁺, NO₃^-, NO₂^-	Sodium carbonate	25	7.2	24	88.1	51.2	8	33.9	2.3±0.1	[13]
ZK-2	A city wastewater treatment facility in Guangzhou	NH₄⁺	Sodium acetate	-	7.0	-	82.6	87.2	-	35.4±1.9	5.2±0.9	[14]
K14	Sediment accumulation in freshwater farms in Tianjin	NH₄⁺	Sodium pyruvate	27	7.5	24	99.8	98.0	10	110.1	26.2	[15]
Pseudoxanthomonas	YP1	Sequencing batch reactor (SBR) for aerobic treatment of particulate matter	NO₃^-	Sodium citrate	30	8.0	20	75.3	-	-	30.0	8.0	[16]
Delftia	ZK-1	Exit of the aeration tank at a wastewater treatment facility in Guangzhou	NH₄⁺, peptone	Sodium acetate	26±1	7.8	8	98.7	90.7	-	14.0	10.0	[17]
Bacillus	GHSP10	Wastewater generated by aquaculture operations	NH₄⁺, NO₃^-, NO₂^-	Glucose, sodium acetate	28	7.5	48	99.5	-	20	50.0	-	[18]
Thauera	SND5	Anoxic treatment tank at a sewage treatment plant in Singapore	NH₄⁺, NO₃^-, NO₂^-	Lactic acid	30	7.5	26	100.0	-	10	83.4	-	[19]
Escherichia	J16	Aerobic biological tanks at the Taiyuan city wastewater treatment facility	NO₃^-	Glucose, peptone, sodium acetate	30	7.2-8.0	24	94.5	96.0	-	69.3	8.9	[20]
Agrobacterium	LAD9	Liquid waste from the landfill	NO₃^-, NO₂^-	Disodium succinate	30	7.0	24	100.0	76.6	8	100.0	10.0	[21]
Enterobacter	HW-15	Phosphorus-laden river water in southwestern Hubei Province, China.	NH₄⁺, NO₃^-, NO₂^-	Sodium acetate	30	7.2	24	93.2	62.0	8	100.0	35.6	[22]
Acinetobacter	C-13	SBR for laboratory use	NH₄⁺, NO₃^-, NO₂^-	Sodium acetate	30	7.5	24	100.0	69.7	-	100.0	4.4	[23]

Genus	Strain	Sample source	Nitrogen source	Carbon source	T/℃	pH	t/h	Nitrogen removal efficiency(%)	Phosphorusremoval efficiency(%)	C/N	Initial nitrogencontent (mg/L)	Initial phosphoruscontent (mg/L)	References
Raoultella	YX-4	Wastewater treatment pond at the pig farm	NH₄⁺, NO₃^-, NO₂^-	Disodium succinate	25	7.0	96	99.0	85.0	15	200.0	100.0	[5]
Pseudomonas	ADP-19	Polluted sediments and kimchi processing wastewater	NH₄⁺	Sodium acetate	30	7.0-8.0	48	96.5	73.3	4	100.0	20.0	[12]
YG-24	Eutrophic sediments from lake Taihu	NH₄⁺, NO₃^-, NO₂^-	Sodium carbonate	25	7.2	24	88.1	51.2	8	33.9	2.3±0.1	[13]
ZK-2	A city wastewater treatment facility in Guangzhou	NH₄⁺	Sodium acetate	-	7.0	-	82.6	87.2	-	35.4±1.9	5.2±0.9	[14]
K14	Sediment accumulation in freshwater farms in Tianjin	NH₄⁺	Sodium pyruvate	27	7.5	24	99.8	98.0	10	110.1	26.2	[15]
Pseudoxanthomonas	YP1	Sequencing batch reactor (SBR) for aerobic treatment of particulate matter	NO₃^-	Sodium citrate	30	8.0	20	75.3	-	-	30.0	8.0	[16]
Delftia	ZK-1	Exit of the aeration tank at a wastewater treatment facility in Guangzhou	NH₄⁺, peptone	Sodium acetate	26±1	7.8	8	98.7	90.7	-	14.0	10.0	[17]
Bacillus	GHSP10	Wastewater generated by aquaculture operations	NH₄⁺, NO₃^-, NO₂^-	Glucose, sodium acetate	28	7.5	48	99.5	-	20	50.0	-	[18]
Thauera	SND5	Anoxic treatment tank at a sewage treatment plant in Singapore	NH₄⁺, NO₃^-, NO₂^-	Lactic acid	30	7.5	26	100.0	-	10	83.4	-	[19]
Escherichia	J16	Aerobic biological tanks at the Taiyuan city wastewater treatment facility	NO₃^-	Glucose, peptone, sodium acetate	30	7.2-8.0	24	94.5	96.0	-	69.3	8.9	[20]
Agrobacterium	LAD9	Liquid waste from the landfill	NO₃^-, NO₂^-	Disodium succinate	30	7.0	24	100.0	76.6	8	100.0	10.0	[21]
Enterobacter	HW-15	Phosphorus-laden river water in southwestern Hubei Province, China.	NH₄⁺, NO₃^-, NO₂^-	Sodium acetate	30	7.2	24	93.2	62.0	8	100.0	35.6	[22]
Acinetobacter	C-13	SBR for laboratory use	NH₄⁺, NO₃^-, NO₂^-	Sodium acetate	30	7.5	24	100.0	69.7	-	100.0	4.4	[23]

Technology	Signal type	Sorting speed (events/s)^a	Advantages	Disadvantages	References
FACS	Intrinsic fluorescence and external labeling	>10 000	Rapid cell sorting technology that can process large sample volumes	Relatively few cells exhibit natural incidental fluorescence, and external fluorescent labeling may influence cellular metabolic activity	[60-61]
MACS	Magnetic response	2-10, >2 000	Facilitates the enrichment of target cells	Insufficient capacity for accurate single-cell sorting, only highly enriched, external magnetic labelling may impair cell activity, small range of applications	[60,62]
RACS	Cellular components/isotopic labelling	2-5	Directly sorts individual cells from environmental samples without labeling or disruption, making it ideal for precise single-cell sorting	Raman signal is susceptible to fluorescence interference, low throughput, high instrument cost	[58,66]
FlowRACS	Cellular components/isotopic labelling	>600	Utilizing RACS in conjunction with microfluidics enables high-throughput sorting of environmental flora	Raman signal is susceptible to fluorescence interference, high instrument cost	[65-66]

Technology	Signal type	Sorting speed (events/s)^a	Advantages	Disadvantages	References
FACS	Intrinsic fluorescence and external labeling	>10 000	Rapid cell sorting technology that can process large sample volumes	Relatively few cells exhibit natural incidental fluorescence, and external fluorescent labeling may influence cellular metabolic activity	[60-61]
MACS	Magnetic response	2-10, >2 000	Facilitates the enrichment of target cells	Insufficient capacity for accurate single-cell sorting, only highly enriched, external magnetic labelling may impair cell activity, small range of applications	[60,62]
RACS	Cellular components/isotopic labelling	2-5	Directly sorts individual cells from environmental samples without labeling or disruption, making it ideal for precise single-cell sorting	Raman signal is susceptible to fluorescence interference, low throughput, high instrument cost	[58,66]
FlowRACS	Cellular components/isotopic labelling	>600	Utilizing RACS in conjunction with microfluidics enables high-throughput sorting of environmental flora	Raman signal is susceptible to fluorescence interference, high instrument cost	[65-66]

单细胞视角下污水反硝化聚磷菌资源挖掘与代谢机制的研究进展

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孙鹏城 ¹^,² , 潘慧慧 ² , 荆玉姝 ⁴ , 夏文香 ¹ , 任义尚 ² , 荆晓艳 ²^,³^,^*

微生物学报 | 微生物资源新技术新方法 2025,65(4): 1650-1666

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微生物学报 | 微生物资源新技术新方法 2025, 65(4): 1650-1666

单细胞视角下污水反硝化聚磷菌资源挖掘与代谢机制的研究进展

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孙鹏城¹^,², 潘慧慧², 荆玉姝⁴, 夏文香¹, 任义尚², 荆晓艳²^,³^,^*

作者信息

^1.青岛理工大学环境与市政工程学院，山东青岛

^2.中国科学院青岛生物能源与过程研究所，单细胞中心，山东青岛

^3.青岛科技大学生物工程学院，山东青岛

^4.青岛水务集团环境能源有限公司，山东青岛

Advances in resource exploration and metabolic mechanism of denitrifying phosphorus-accumulating organisms in wastewater from a single-cell perspective

Pengcheng SUN¹^,², Huihui PAN², Yushu JING⁴, Wenxiang XIA¹, Yishang REN², Xiaoyan JING²^,³^,^*

Affiliations

^1.School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao, Shandong, China

^2.Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China

^3.College of Biological Engineering, Qingdao University of Science and Technology, Qingdao, Shandong, China

^4.Qingdao Water Group Environmental Energy Co. , Ltd. , Qingdao, Shandong, China

出版时间: 2025-04-04 doi: 10.13343/j.cnki.wsxb.20240650

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摘要

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随着水体富营养化和水资源短缺问题的加剧，高效污水处理技术的开发变得尤为迫切。传统脱氮除磷工艺因微生物间的污泥龄差异和碳源竞争难以实现氮和磷的高效同步去除。反硝化聚磷菌(denitrifying phosphorus-accumulating organisms, DPAOs)具备同时去除氮和磷的能力，展现出显著的污水处理潜力。然而，基于群体水平的研究往往忽视了细胞间的异质性，导致对DPAOs脱氮除磷机制的理解不够深入。采用传统的“先养后筛”法获得的高效DPAOs菌株数量有限，且在实际污水处理环境中其稳定性和适应性面临诸多挑战。单细胞分析技术为深入理解微生物的生态位和代谢机制提供了全新视角。非破坏性单细胞表型识别技术，如单细胞拉曼光谱技术(single cell Raman spectroscopy, SCRS)与培养技术耦合，为DPAOs菌株的挖掘开辟了新途径。本文综述了DPAOs菌株资源挖掘及其代谢机制的研究现状与进展，重点探讨了单细胞技术在揭示DPAOs脱氮除磷机制及资源挖掘中的潜力，为DPAOs的研究和实际应用提供了新的理论基础和技术支持，从而推动高效污水处理技术的发展。

关键词

单细胞技术 / 反硝化聚磷菌 / 拉曼光谱 / 微生物资源挖掘 / 代谢机制

Abstract

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Amidst the escalating issues of water eutrophication and water resource scarcity, the development of high-efficiency wastewater treatment technologies has become increasingly imperative. Traditional nitrogen and phosphorus removal processes face challenges in achieving efficient simultaneous elimination of nitrogen and phosphorus due to the disparities in sludge age and the competition for carbon sources among microorganisms. Denitrifying phosphorus-accumulating organisms (DPAOs) possess the capability to remove both nitrogen and phosphorus, demonstrating significant potential in wastewater treatment. However, population-level studies often overlook cellular heterogeneity, leading to an inadequate understanding of the nitrogen and phosphorus removal mechanisms of DPAOs. Moreover, the “traditional culture-first, screen-second” method yields a limited number of efficient DPAO strains, the stability and adaptability of which face challenges in actual wastewater treatment environments. Single-cell analysis technologies provide new perspectives for a deeper understanding of microbial ecological niches and metabolic mechanisms. Coupling non-destructive single-cell phenotypic identification technologies, such as single-cell Raman spectroscopy (SCRS), with the culture method paves new avenues for the exploration of DPAO strains. This review summarizes the research status and progress in the exploration of DPAO strain resources and their metabolic mechanisms, focusing on the potential of single-cell technologies in revealing the mechanisms of nitrogen and phosphorus removal by DPAOs and in the exploration of DPAO resources. The aim is to provide a new theoretical foundation and technical support for the research and application of DPAOs, thereby promoting the development of efficient wastewater treatment technologies.

Key words

single-cell technology / denitrifying phosphorus-accumulating organisms / Raman spectrum / microbial resource mining / metabolic mechanism

引用本文

孙鹏城, 潘慧慧, 荆玉姝, 夏文香, 任义尚, 荆晓艳. 单细胞视角下污水反硝化聚磷菌资源挖掘与代谢机制的研究进展. 微生物学报, 2025 , 65 (4) : 1650 -1666 . DOI: 10.13343/j.cnki.wsxb.20240650

Pengcheng SUN, Huihui PAN, Yushu JING, Wenxiang XIA, Yishang REN, Xiaoyan JING. Advances in resource exploration and metabolic mechanism of denitrifying phosphorus-accumulating organisms in wastewater from a single-cell perspective[J]. Acta Microbiologica Sinica, 2025 , 65 (4) : 1650 -1666 . DOI: 10.13343/j.cnki.wsxb.20240650

正文

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水体富营养化是指水中氮、磷等营养元素超标所引发的环境污染现象。藻类的过度繁殖不仅导致水生生物死亡，还会释放藻毒素，对人类健康构成严重威胁。城市化进程的加速使得用水量急剧增加，大量未经处理或处理不达标的污水被排入自然水体，进一步加剧了水体富营养化问题。因此，对污水进行脱氮和除磷处理后再排放，成为缓解水体富营养化的必要措施^[1]。生物法利用微生物的代谢活动降解污染物，具有经济、高效、无二次污染等优点，被广泛应用于城市污水的脱氮和除磷处理。然而，传统的脱氮和除磷过程是由反硝化菌(denitrifying bacteria, DB)和聚磷菌(phosphorus accumulating organisms, PAOs) 2种不同类群的微生物分别完成的，这2种微生物适宜于不同的环境条件，在同一污水处理系统中，由于污泥龄、溶解氧条件的差异以及对碳源的竞争，导致脱氮和除磷效率难以兼顾，已无法满足实际的污水处理需求^[2]。

反硝化聚磷菌(denitrifying phosphorus-accumulating organisms, DPAOs)的发现克服了传统生物脱氮除磷中DB与PAOs在碳源竞争和污泥龄矛盾方面的问题。DPAOs能以硝酸盐(NO₃^-)或亚硝酸盐(NO₂^-)作为电子受体，同时实现氮、磷的高效去除。由DPAOs理论发展而来的反硝化除磷(denitrifying phosphorus removal, DPR)技术，具有高脱氮除磷效率和低能耗的特点，与传统生物脱氮除磷工艺相比，可节约30%的曝气能耗、50%的污泥产出以及50%的碳源投加^[3]，尤其在当前城市低碳源进水的条件下，具有巨大的应用潜力。

DPAOs的发现和应用使得污水处理更为经济和高效。然而，在实际应用中，废水成分和流量波动等环境因素常常导致DPR效果不稳定，甚至引发工艺崩溃^[4]。一些DPAOs菌株在受控的实验室条件下表现出良好的脱氮除磷性能，但在实际污水环境中的适应性和效能尚未得到充分验证^[5]。此外，已培养的菌株在污泥系统中的比例较小，可能无法充分代表DPAOs的核心种群，如具有特定功能的菌属如脱氯单胞菌属(Dechloromonas)和Candidatus Accumulibacter phosphatis在可培养的菌群中所占比例极低^[6]。基于菌群分析的功能变化，可能无法精确反映单个微生物的代谢动态^[7]，这种不确定性增加了揭示DPAOs代谢机制的复杂性。因此，采用单细胞分辨率对污水中原位DPAOs进行研究显得尤为重要。

单细胞分析技术的发展为DPAOs的原位研究开辟了新途径，并为菌株资源的挖掘提供了创新视角。该技术能够在不依赖微生物培养的情况下，直接对单细胞进行分析，从而揭示原位环境中细胞群体的内部异质性；非破坏性的单细胞分析技术，如SCRS能够在不破坏细胞的情况下识别微生物的形态与代谢物等特异性表型，这为与下游培养或单细胞基因测序等技术的整合提供了可能性^[8]。结合表型与基因型分析，研究人员可以更加全面地理解细胞的代谢状态及其在原位环境中的生态功能。SCRS通过特异性识别与非侵入性特点可实现“先筛后养”的策略。在无污染的液相分选环境中，细胞活性得以最大限度保持。拉曼图谱为分选后的单细胞提供了明确的身份识别，结合微流控芯片和光镊操作系统，能够精准控制和分选目标细胞。分选后的细胞采用单管单细胞培养，可有效避免非目标细胞对营养物质的竞争，从而更容易挖掘被忽视的原位菌种资源。

1 DPAOs的资源挖掘现状

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1.1 DPAOs菌株的分离与鉴定

传统获得DPAOs菌株的方法通常采用“先养后筛”的策略。首先，在平板上无差别地分离和纯化菌株，然后通过溴麝香草酚蓝(bromothymol blue, BTB)染色、硝酸盐还原产气进行反硝化功能的初筛^[9]。随后，在厌氧和好氧条件下分别对聚β-羟基丁酸盐(poly-β-hydroxybutyrate, PHB)和多聚磷酸盐(polyphosphate, Poly-P)进行染色，以验证聚磷功能^[10]。最后，通过在模拟人工废水中培养并检测氮、磷含量^[11]，评估菌株的脱氮和除磷性能。截至目前，研究人员已在多种生态环境中筛选和分离出DPAOs菌株，主要包括假单胞菌属(Pseudomonas)^[12-15]、假黄单胞菌属(Pseudoxanthomonas)^[16]、柔武氏菌属(Raoultella)^[5]、代尔夫特菌属(Delftia)^[17]、芽孢杆菌属(Bacillus)^[18]、索氏菌属(Thauera)^[19]、埃希氏菌属(Escherichia)^[20]、农杆菌属(Agrobacterium)^[21]、肠杆菌属(Enterobacter)^[22]、不动杆菌属(Acinetobacter)^[23]等。表1展示了一些DPAOs菌株的脱氮和除磷特性，它们在实验室条件下表现出高效的脱氮和除磷性能。例如，从养猪场废水中分离的菌株解鸟氨酸柔武氏菌(Raoultella ornithinolytica) YX-4在最佳培养条件下，NO₃^-和NO₂^-的去除率分别达到97%和93%^[5]。这些数据表明，这种菌株在废水处理中具有巨大的潜力和广阔的应用前景。

分子生物学的进步推动了对未培养DPAOs菌的鉴定。根据ppk基因，Ca. Accumulibacter可分为I型和II型2种类型，每种类型都有多个具有不同代谢特性的分支；在这些分支中，I型的特定分支IC表现出强大的反硝化能力，能够利用O₂、NO₂^-和NO₃^-作为电子受体进行磷酸盐(PO₄^3-)摄取^[24]。尽管Ca. Accumulibacter属在污水处理厂中普遍存在，并且在实验室规模的反应器中进行了富集研究，但目前仍未实现其培养。同样地，Dechloromonas属已被证实为活跃的DPAOs，在污水处理中积极参与DPR^[25]。一项针对丹麦及全球范围内24个污水处理厂的研究揭示了Dechloromonas属的丰度和分布情况，发现12种不同的Dechloromonas，但大多数是新发现且未培养的物种^[26]。这些结果表明，污水处理厂中仍存在大量未被挖掘的微生物资源。

1.2 DPAOs资源挖掘的挑战

尽管分子生物学的发展有助于研究人员识别未培养的DPAOs菌属，但基于标记基因推断的功能和性状与实际表现并不完全一致^[27]。这表明，仍需深入挖掘和综合研究功能菌株。传统的“先养后筛”方法虽为DPAOs菌株的培养提供了方法学依据，但也存在一些限制。首先，平板上的无差别培养和纯化菌株耗时耗力，大量稀释涂布可能导致非目标细胞的生长^[11]。其次，在筛选方法上，BTB染色虽可验证反硝化产碱特性，但pH升高也可能是其他代谢过程的结果，特异性仍需提高^[9]。此外，4′,6-二脒基- 2-苯基吲哚(4′,6-diamidino-2-phenylindole, DAPI)染色法虽能对Poly-P进行特异性染色，但同时对细胞有毒害作用，可能影响细胞培养的活性^[28]。最后，由于人工培养基难以模拟实际污水组分，实验室的功能评估可能失真，DPAOs菌株在实际污水环境中的适应性和效能未充分验证^[5]。因此，迫切需要开发特异性、无损、非标记的方法，以挖掘原位DPAOs菌种资源，并准确评估它们在自然环境中的活性和功能。

2 DPAOs的代谢机制研究现状

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2.1 DPR机制

在厌氧条件下，DPAOs利用糖原提供的还原力将细胞内的Poly-P水解以产生能量，并主动从环境中吸收挥发性脂肪酸，将其转化为PHB储存在细胞内，同时释放PO₄^3-到环境中^[29]。其中，糖原的降解主要通过糖酵解途径进行，糖原分解产生的电子和腺苷三磷酸(adenosine triphosphate, ATP)主要用于将乙酰辅酶A转化为PHB，剩余的电子则在电子传递链(electron transport chain, ETC)中积累，并作为信号分子控制氧化还原电位，从而驱动Poly-P的水解^[30]。在缺氧条件下，DPAOs氧化细胞内的PHB以产生电子和ATP，促使细胞利用NO₃^-或NO₂^-作为电子受体，从而超量吸收环境中的PO₄^3-，并将其以Poly-P的形式存储在细胞内^[31]。PHB作为一种内源性聚合物，在DPR过程中发挥着双重作用，它不仅在PO₄^3-的积累过程中提供ATP，还通过在ETC中提供电子，形成质子驱动力，从而驱动NO₃^-或NO₂^-的反硝化还原反应^[32]。此外，PHB的氧化降解速度比外部碳源慢，这意味着它可以伴随整个除磷过程^[33]。DPAOs能够同时实现DPR，总能耗低于独立的PAOs和DB之和，因此可节约50%的碳源消耗和30%的曝气能耗。同时，内源性代谢微生物的生长速度较慢，污泥产生量也可以减少50%^[3]。

Diaz等^[34]研究表明，胞外聚合物(extracellular polymeric substances, EPS)在DPR过程中起着重要作用。EPS是一种细胞分泌的高分子聚合物，通常在物质转移、细胞结构保护和絮凝过程中起关键作用^[35]。Hou等^[36]发现，EPS中含有高达60%-80%的磷，表明EPS作为DPR活性污泥中的胞外磷储存库。Bahgat等^[37]研究发现，在缺氧阶段DPAOs对PO₄³⁻的去除效果与EPS中磷的积累密切相关。Wang等^[38]指出，EPS中的金属离子(如Cu²⁺、K⁺和Mg²⁺)能够诱导PO₄³⁻的络合，形成沉淀并被吸附，EPS对PO₄³⁻去除的贡献高达27%。此外，EPS还通过转化和运输磷，与细胞内Poly-P的积累代谢过程及PO₄³⁻的沉淀过程同步进行^[39]。

2.2 DPR的酶学过程

反硝化过程涉及一系列酶的协同催化，包括硝酸盐还原酶(nitrate reductase, Nar)、亚硝酸盐还原酶(nitrite reductase, Nir)、一氧化氮还原酶(nitric oxide reductase, Nor)和一氧化二氮还原酶(nitrous oxide reductase, Nos)，其反应路径为NO₃^-→NO₂^-→NO→N₂O→N₂^[40]。该过程可以分为4个主要步骤：第一步是在Nar的催化下，NO₃^-被还原为NO₂^-。根据酶的位置，Nar分为膜结合型和周质型(periplasmic nitrate reductase, Nap)。相较于Nap，Nar在细胞质中还原NO₃^-，并释放质子以提供能量，这种方式更加节能^[41]。第二步是在Nir催化下将NO₂^-还原为NO。虽然Nir通常用作反硝化的标记基因标识，但并不是唯一的，因为一些厌氧氨氧化细菌(anaerobic ammonium oxidation, Anammox)缺乏Nir基因，却仍能将NO₂^-还原为NO^[42]。第三步是在Nor的催化下将NO还原为N₂O。其中，N₂O是一种强效温室气体，温室效应是二氧化碳的298倍，因此，在反硝化过程中应尽量避免N₂O的积累^[29,42]。第四步是在Nos的催化下将N₂O转化为N₂。然而，Nos对环境变化敏感，例如，游离亚硝酸(free nitrous acid, FNA)可能抑制Nos的活性，进而导致N₂O的累积^[43-45]。Zhang等^[46]研究表明，适当保持NO₂⁻浓度可以增强DPAOs的FNA耐受性，并减少反硝化过程中N₂O的积累。此外，Gao等^[47]的宏基因组学研究表明，富含DPAOs的菌群中异常高水平的N₂O积累与非DPAOs微生物的存在相关。这些微生物的脱氮途径不完整，可能导致N₂O的积累，而不能完全转化为N₂。Ribera-Guardia等^[43]发现，不同反硝化酶之间的电子竞争是影响反硝化过程的重要因素，因为反硝化过程中的所有步骤都需要电子供给还原力，尤其在仅依赖外部碳源作为电子供体时，供体不足可能会加剧这种竞争^[43]。

在PO₄³^-的代谢过程中，有2种酶控制Poly-P的积累和水解^[48]。Poly-P的积累由多磷酸激酶(polyphosphate kinase, PPK)催化，负责将ATP末端的PO₄³^-残基转移到不断增长的Poly-P链上。PPK分为PPK1和PPK2，PPK1负责转移ATP上的PO₄³^-残基，而PPK2则负责转移三磷酸鸟苷或ATP上的PO₄³^-残基^[49]。Poly-P的水解由内切聚磷酸酶(endopolyphosphatase, PPN)和外切聚磷酸酶(exopolyphosphatase, PPX)催化。其中，PPN催化Poly-P链分解为较短的链，而PPX则进一步催化释放Poly-P链末端的PO₄³^-，同时释放能量^[50]。基于以上内容，DPAOs脱氮除磷的酶学过程如图1所示。

2.3 DPR代谢机制研究挑战

基因型构成了生物体潜在性状的遗传蓝图，而表型反映了这些性状在特定环境条件下的实际表现。分析微生物的基因型和表型是理解其代谢机制的必要手段^[51-52]。然而，在研究DPAOs的代谢机制时，传统方法仍面临诸多挑战。一方面，传统生理学方法依赖于细菌的可培养性来研究DPAOs的代谢机制，但大量DPAOs尚未被识别或成功培养^[34]。为了研究这些微生物，许多研究采用合成废水或实验室规模的序批式反应器(sequencing batch reactor, SBR)来富集DPAOs菌群。然而，与原位环境相比，实验室环境更为简单且易于调控，这种环境差异可能影响细胞特定性状的表达，从而导致代谢机制研究结果的失真^[6]。另一方面，尽管分子生物学技术在一定程度上促进了原位代谢机制的研究，但荧光原位杂交(fluorescence in situ hybridization, FISH)等技术仅适用于已鉴定的DPAOs^[53]。宏基因组学及其他群体水平的研究方法仍然无法明确特定微生物在DPR过程中的具体贡献^[54]。此外，这些分子技术通常具有破坏性，无法对DPAOs细胞进行后续研究。因此，对于DPR代谢机制的理解只能依赖对大量相关底物的测量进行间接推断。这种间接方法的局限性表明，传统方法在研究DPAOs代谢机制时仍面临诸多挑战。

3 单细胞技术在DPAOs研究中的应用

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单细胞技术是一系列用于分析和研究单个细胞的方法，这些方法将研究的精度从群体细胞水平提升至单细胞水平，从而揭示细胞群体中的微小差异，有助于理解细胞的代谢功能和环境适应机制等过程^[55]。多种非破坏性的单细胞技术可以结合使用，例如先研究细胞表型，再耦合后续的培养与测序，这种方法被称为“新一代微生物生理学研究方法”^[8]。单细胞技术主要包括样品预处理、单细胞的识别分选、测序和培养等几个方面。在应用单细胞技术进行培养之前，关键是能够分离出完整的活性单细胞^[56-57]。因此，对于污水和土壤等环境样品的预处理，可以采用温和的碘海醇密度梯度离心法来分离和纯化微生物^[58]。该方法已被证实能够获得高活性的细胞富集物，同时结合超纯水充分洗样以去除环境背景干扰。此外，在实验过程中需严格遵循无菌操作，并设置空白对照，以排除外来微生物的污染。具体可参考中国科学院青岛生物能源与过程研究所单细胞中心自主研发的单细胞拉曼分选耦合测序(single-cell raman-activated sorting and sequencing, scRACS-Seq)技术，该技术能够快速、精准地将具有不同拉曼表型的细菌单细胞从菌群中分离出来，同时保证单细胞拉曼光谱的质量，分选准确性高达100%；使用来自靶标细胞周围水相的空液滴作为阴性对照，发现靶标细胞序列被菌群中其他细胞DNA污染的概率极低^[59]。这些工作充分证明了scRACS-Seq技术的灵敏度、特异性和可靠性。

3.1 单细胞技术在环境微生物资源挖掘中的应用

在单细胞培养过程中，需结合单细胞表型技术以识别目标细胞；随后，可利用光镊或微流控技术直接将目标细胞直接从细胞群体中出来，避免其他细胞的干扰；最后，在优化的培养基中进行培养，这一完整流程被称为“先筛后养”策略^[8]。根据细胞表型识别方法，可分为间接检测和直接检测2种类型。间接检测依赖于外部荧光标记或分子作为识别信号，常见的方法包括荧光激活细胞分选(fluorescence activated cell sorting, FACS)和磁性活化细胞分离(magnetic-activated cell sorting, MACS)^[60]。例如，Liang等^[61]通过监测荧光信号，并结合激光诱导前向转移技术(laser induction front transfer, LIFT)，成功地从复杂的土壤微生物群落中分离和培养表达绿色荧光蛋白的E. coli JM1099。Xuan等^[62]利用磁性纳米颗粒筛选和富集具有乙腈降解活性的功能细菌，并结合液滴微流控的单细胞分离方法，成功实现了乙腈降解功能细菌的原位分离；与传统方法相比，“先筛后养”技术能在物种层面上分离出更多的功能菌株。尽管基于标记的方法具有高度特异性的识别能力，但仍可能对细胞造成损伤。如果可以直接根据细胞内在特性进行分选，不仅能避免细胞损伤，还能减少设计新标记物所需的时间和成本。细胞形态作为细胞特性的外在表现，在细胞分选中的应用并不广泛，这主要是因为研究人员尚不确定如何将细胞形态与功能相结合；然而，已有研究开始关注形态学与反硝化功能之间的关系，为基于细胞形态筛选功能菌株提供了新的依据^[63]。近年来，SCRS的出现提供了一种非侵入性、无标记的方法，用于直接检测细胞内在信息。SCRS基于拉曼散射效应，能够探测生物细胞的分子振动，提供丰富的细胞化学特征。超过1 000个拉曼峰对应不同的细胞化合物，如蛋白质、核酸、脂质和碳水化合物，形成独特的拉曼指纹图谱^[64]。例如，Jing等^[58]利用拉曼激活细胞分选(Raman-activated cell sorting, RACS)技术耦合分选培养，成功实现了原位解磷菌的单细胞识别、分选和培养，展示了该技术在环境菌群资源挖掘中的巨大潜力。Wang等^[65]和刁志钿等^[66]将RACS与微流控技术相结合，研发了流式拉曼分选仪(flow-mode raman-activated cell sorter, FlowRACS)，进一步提高了单细胞分选的通量和效率，为生命暗物质的功能探测和资源挖掘提供了强有力的工具。不同细胞分选技术的比较见表2。

3.2 单细胞拉曼在DPAOs资源挖掘中的应用前景

SCRS在单细胞表型分析和培养中具有明显优势，有效克服了传统培养方法的不足，因而在DPAOs资源的挖掘中前景广阔。研究表明，DPR过程中的关键代谢产物包括聚合物Poly-P、PHB、糖原以及电子受体NO₃^-和NO₂^-[67-68]，这些代谢产物均具有特定的拉曼特征峰。

Poly-P是由数十至上百个磷酸基团聚合而成的生物大分子，能够在具备聚磷能力的细胞内大量积累，其储存量可达非PAOs细胞的4-10倍。Poly-P的拉曼特征峰包括P-O-P振动波段690-700 cm^-1和PO₂^-伸缩波段1 168-1 177 cm^-1[69]。因此，Poly-P的拉曼特征峰可作为评估聚磷能力的生物标志峰。

PHB是DPAOs在厌氧条件下积累的一种内源性碳源，既为摄取PO₄^3-提供能量，又作为反硝化过程的电子供体^[70]。研究表明，PHB的储备含量与反硝化性能密切相关，当细胞内PHB含量降低至5%以下时，反硝化活性会显著下降，甚至可能停止^[32]。Ding等^[71]研究表明，PHB可提高反硝化过程中关键酶Nar、Nir、Nor和Nos的表达，增强反硝化效率，同时受PHB调控的主要群落具有良好的反硝化能力。因此，利用PHB识别内源反硝化过程是一种有效策略。PHB的特征波段分别为434、839和1 723 cm^-1，Gelder等^[72]通过检测PHB在1 730 cm^-1的拉曼特征峰，实现了对细菌细胞中PHB含量的快速且无创监测。

NO₃^-和NO₂^-作为DPR过程中的电子受体，是区分DPAOs与PAOs的重要依据。它们的拉曼特征峰分别位于1 048 cm^-1和1 358 cm^-1，且强度与细胞中NO₃^-和NO₂^-的浓度成正比^[73]。然而，NO₃^-和NO₂^-经过异化还原后最终以N₂形式排出，可能使其在细胞内的积累量非常少^[47]。因此，即使存在拉曼特征峰，也必须考虑NO₃^-和NO₂^-在细胞内的浓度。

此外，研究表明DPAOs的细胞形态与其功能之间存在关联。Yun等^[74]的研究通过在厌氧/好氧和厌氧/缺氧的SBR中驯化富集PAOs与DPAOs，发现颗粒污泥中形成了不同形态的微生物，DPAOs主要由杆状微生物组成，而PAOs则以球形微生物为主；他们认为不同的电子受体(O₂、NO₃^-或NO₂^-)可能促使微生物形成特定的形态。Carvalho等^[75]的研究发现，在以乙酸为碳源的SBR中球形菌占主导，而以丙酸为碳源的SBR中杆状菌占主导；他们认为，不同电子供体影响微生物对电子受体的偏好，从而导致细胞形态的差异^[75]。这些形态学差异可能反映了细胞内部反硝化代谢活动和电子传递效率的差异^[76]。因此，通过形态识别反硝化功能是一种潜在的方法。

综上所述，聚磷功能可以通过Poly-P的拉曼特征峰进行检测，而反硝化功能则可以尝试利用NO₃^-、NO₂^-、PHB的拉曼特征峰或细胞形态进行识别。单细胞分选技术提供了一种“先筛后养”的新策略，不仅提高了DPAOs功能识别的准确性，还能减少对细胞染色的依赖，避免染色方法对细胞造成的毒性和活性损失。基于SCRS挖掘的原位功能菌，对原位环境具有更好的适应性，可以减少与土著菌的竞争抑制，从而更容易实现定殖应用。高通量流式拉曼分选仪FlowRACS能够进一步提高分选通量，满足复杂环境样本的大规模研究，有助于筛选出低丰度的稀有菌株。同时，结合单细胞基因组扩增技术，可以对难以培养的DPAOs菌株进行单细胞精度下的代谢途径和功能基因分析。这些优势将显著促进DPAOs资源的挖掘与功能研究，使研究人员能够更深入了解特定微生物的代谢特性和环境适应性，从而优化污水处理工艺。

3.3 单细胞技术在环境微生物代谢机制中的应用

单细胞技术将研究精度从群体细胞水平提升至单细胞水平，使研究者能够更加直观和深入地了解种群内部不同细胞的异质性，包括形态结构、代谢状态和功能等。非破坏性的单细胞分析技术，尤其是SCRS，能够无损地识别和分析细胞，已成为连接表型和基因型的关键工具，并在环境微生物代谢机制的研究中展现出巨大潜力。

无创光学显微镜和拉曼光谱能够表征细胞的形态及其内部代谢物。通过观察细胞在特定时刻所表达的表型特征，研究人员能够动态监测细胞的代谢变化及其对环境的响应机制。例如，Fu等^[77]通过光学显微镜动态观察微生物污泥形态变化及颗粒破碎的过程，揭示了甲烷传质过程对反硝化厌氧甲烷菌和Anammox形态的影响机制。SCRS检测可以形成细胞独特的“指纹图谱”，包含约1 000条特征峰，每条特征峰对应特定化学键的信息，通过与已知的拉曼特征峰图谱数据库对比，研究者可以快速识别菌株身份。Liu等^[78]利用SCRS成功实现了海洋微生物的快速鉴定，准确率达到95%。

SCRS与FISH技术联用，可将特定微生物身份与细胞内代谢分析关联，已用于原位功能细胞类群代谢机制研究。FISH技术通过设计特异性探针对目标细胞进行杂交，能够精确追踪和表征其分布。随后，通过检测目标细胞的拉曼光谱分析其生化成分。Wang等^[79]成功将SCRS-FISH技术应用于原位海水中未培养古细菌的研究，揭示了古细菌之间生成脂肪酸能力的差异。

SCRS与稳定同位素探测(stable isotope probing, SIP)技术联用，可以更细致地了解细胞的代谢与功能，常用的同位素包括¹⁵N、¹³C和²H^[80]。Cui等^[81]通过¹⁵N诱导的细胞色素c拉曼偏移，量化了复杂土壤菌群中细菌对N₂固定的贡献。Jing等^[82]利用¹³C诱导的类胡萝卜素拉曼偏移，检测并追踪了黄海海水中固定CO₂的微生物群体。Xu等^[83]通过SCRS结合²H，在单细胞水平上研究了碳底物的代谢途径，他们发现SCRS在2 070-2 300 cm^-1范围内存在清晰且强烈的C-D振动带，这为评估微生物的碳利用和代谢活性提供了新的依据。

SCRS的非破坏性单细胞表型分析能够保持细胞活性，使其可以直接对接测序，这对于难培养微生物代谢机制的研究尤为重要。房安然等^[84]利用SCRS成功获得了尚不可培养的Anammox细菌纯菌株，并在单细胞水平上解析了Anammox菌的基因组，重构了其代谢通路。Zhang等^[85]首次将高通量单细胞测序应用于活性污泥微生物研究，揭示了与宏基因组结果一致的群落组成变化；此外，在单细胞扩增的基因组中，27.5%的基因在物种水平上是新的，表明单细胞测序可以弥补宏基因组在揭示细胞基因组异质性方面的不足。然而，由于单细胞基因组的完整度和覆盖率有限，许多新见解可能被隐藏，因此，需要提高单细胞测序的通量。最近，一种高通量单细胞技术——epicPCR成功在单细胞水平上融合了功能基因与系统发育基因，并能够一次性对数百万个液滴进行测序，具备高特异性和高通量的优势；Wei等^[86]通过设计特异性引物和优化PCR步骤改进了该技术，使其能够更准确地扩增融合抗生素耐药基因(antibiotic resistance gene, ARG)和16S rRNA基因片段中的ARG序列，成功应用于ARG宿主细胞的特异性追踪及ARG在污水处理厂中的传播机制研究。

综上所述，光学显微镜和拉曼光谱以单细胞精度的高分辨率，允许研究人员细致观察微生物细胞形态及胞内成分的代谢变化。SCRS提供的丰富指纹图谱为细胞鉴定奠定了重要基础，同时FISH技术支持微生物细胞身份识别，SCRS-FISH联用技术已成功应用于原位环境中特定微生物类群的研究。SIP技术能详细追踪细胞内部物质的代谢变化，有助于深入解析其代谢过程。SCRS的非破坏性特质使分选后的细胞能够无缝对接下游测序技术，为表型与基因型之间的关联分析搭建了有效桥梁。此外，近年来高通量单细胞测序技术的发展有效弥补了宏基因组在揭示细胞基因组异质性方面的不足。这些多样化的单细胞技术不仅深化了对微生物代谢过程的理解，还为环境微生物的代谢机制分析提供了新的视角和思路。

3.4 单细胞技术在DPAOs代谢机制中的应用潜力

单细胞技术作为前沿的生物分析工具，正逐步揭开微生物代谢机制的面纱。尽管尚未直接应用于DPAOs的代谢机制研究，但从在PAOs和DB研究中的成功案例来看，单细胞技术在探索DPAOs的聚磷和反硝化功能方面展现出了出众的研究潜力。

SCRS能够直接检测细胞内关键聚合物的变化，从而解析其代谢动力学。传统的聚磷机制主要源于对Ca. Accumulibacter的研究。然而，由于尚未实现其纯培养，细胞内聚合物的动力学只能通过检测相关底物的变化来间接推测^[87]。相比之下，SCRS通过探测细胞内部的分子振动，为生成每个细胞独特的“指纹图谱”，通过分析图谱中特征峰的变化，可以有效地反映细胞内化合物的相互作用^[64]。例如，Majed等^[88]首次将SCRS应用于评估EBPR过程中的PAOs代谢动力学，揭示了厌氧条件下PAOs胞内Poly-P的消耗及好氧条件下的恢复过程。同时，基于Ca. Accumulibacter建立的聚磷代谢模型显然无法代表所有PAOs。Fernando等^[26]利用FISH-SCRS技术对污水处理厂中的Tetrasphaera属PAOs进行原位动力学研究，证明Tetrasphaera具有完全不同的聚磷代谢模式，且不会积累PHB，这打破了传统聚磷代谢机制的固有观念。因此，原位环境中的PAOs可能具有多样化的聚磷代谢方式，而SCRS为深入探索聚磷代谢机制提供了有力的工具。

SCRS还可作为表征EBPR性能的潜在指标。目前，该技术已成功应用于复杂环境中细胞内聚合物PHB、糖原和Poly-P的识别与定量，有效评估了影响EBPR性能与稳定性的主要功能群体PAOs和GAOs的相对丰度^[89]。此外，先前观察到的PO₄³^-摄取与释放现象实际上源于不同Poly-P含量细胞丰度的变化^[90]。因此，通过SCRS检测具有Poly-P表型细胞丰度的变化，可以实时反映污水中的EBPR性能状态，从而指导污水处理工艺的调整与优化。

FISH-SCRS技术能够精确评估污水处理厂中不同PAOs对磷去除的贡献。由于污水处理厂的水质条件和工艺流程存在差异，主导除磷的PAOs也会有所不同。识别本土优势PAOs并深入研究其代谢机制，以相应调控工艺参数促进其富集，从而提升污水的EBPR性能。PAOs对总磷去除的贡献取决于细胞数量和细胞内最大饱和Poly-P含量^[88]。FISH-SCRS技术能够将细胞身份与胞内聚合物直接关联分析，从而准确评估不同PAOs的磷去除贡献。例如，Petriglieri等^[91]发现，在单细胞水平上，Ca. Accumulibacter的最大饱和Poly-P含量优于Dechloromonas和Tetrasphaera。然而，Fernando等^[26]对8个污水处理厂中的PAOs进行研究发现，Tetrasphaera在6个污水处理厂中的总磷去除贡献超过了Ca. Accumulibacter，这可能归因于Tetrasphaera通过发酵获取能量的特殊能力，从而在污水中形成了竞争优势。因此，FISH-SCRS技术有助于研究人员识别本土优势PAOs类群，有针对性地调控运行参数，从而提高污水EBPR的稳定性。

D₂O-SCRS技术可用于研究反硝化过程中的电子转移机制。反硝化是指异养微生物在相关酶的催化作用下，通过ETC将初始电子供体的电子转移至最终电子受体NO₃^-或NO₂^-以产生能量^[92]。电子传递效率直接影响反硝化性能，但由于电子转移速度极快且缺乏实时检测手段，电子转移机制至今仍不明确^[93]。近年来，D₂O-SCRS技术为单细胞层面上胞内物质转化提供了原位、非破坏性且实时的研究方法。Liu等利用该技术发现，纳米级零价铁是通过增加细胞色素c含量来促进电子向酶转移，从而实现与DB的协同效应；另外，纳米级零价铁的添加通过降低DB的代谢活性，提高了ATP在反硝化过程中的可用性，使反硝化效率高达99.8%^[94]。在特定条件下，代谢活性的降低可能并不妨碍反硝化，甚至可能带来积极影响。郭中瑞等^[95]进一步确认了DB的代谢活性与脱氮效率之间存在不同步性，展示了D₂O-SCRS技术在探索电子转移机制及代谢与反硝化效率关系中的应用潜力。

综上所述，单细胞技术的应用深化了对聚磷和反硝化代谢机制的理解。通过单细胞层面的细致观察和测量，揭示了代谢过程中的异质性与多样性，从而挑战了基于群体水平的推断，并提供了更准确、更深入的生物过程理解。尽管当前研究主要集中于聚磷和反硝化代谢机制的单独分析，但SCRS的成功应用表明，未来可以探索DPAOs在聚磷和反硝化代谢机制方面的综合表现。通过结合这2种功能，有望全面揭示DPAOs在原位环境中的代谢机制。

4 总结与展望

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DPAOs作为能够同时去除氮和磷的微生物资源，在缓解水体富营养化及应对水资源短缺方面展现出巨大潜力。挖掘DPAOs菌株并探究其代谢机制，以理解其在不同生态环境下的代谢途径，对于提升其在原位条件下的适应性和稳定性至关重要。基于前人文献，本文系统分析了DPAOs菌株资源挖掘及代谢机制的研究现状，并强调了单细胞技术在揭示DPAOs脱氮除磷机制及其资源挖掘中的潜力，总结如下：(1) 通过SCRS检测胞内聚合物Poly-P是识别DPAOs聚磷功能的直接有效方法，反硝化功能的潜在识别方法可利用与其关系密切的内源电子供体PHA，以及通常呈杆状的菌株形态；(2) FISH-SCRS技术能直接将细胞身份与胞内聚合物关联分析，但需获取目标细胞序列以设计探针；(3) D₂O-SCRS技术能够实时检测反硝化过程中的电子转移，有助于揭示反硝化异化还原过程与细胞代谢活性之间的代谢机制；(4) 单细胞测序可弥补宏基因组在揭示细胞基因组异质性方面的不足。

尽管单细胞技术展现出巨大的潜力，但在DPAOs研究中仍处于起步阶段，亟须进一步验证其有效性。此外，高成本和技术难度限制了单细胞技术的广泛应用。我们期待未来的创新能够降低分析成本，并与基因组学、代谢组学等领域结合，系统性探讨DPAOs的资源挖掘与代谢调控，为提升其在污水处理中的应用和工艺优化提供理论与技术支持。

基金

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国家自然科学基金(32270109)

参考文献

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文献

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参考文献引证文献

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2025年第65卷第4期

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doi: 10.13343/j.cnki.wsxb.20240650

接收时间：2024-10-21
首发时间：2026-02-06
出版时间：2025-04-04

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收稿日期：2024-10-21
录用日期：2024-12-09

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National Natural Science Foundation of China(32270109)

国家自然科学基金(32270109)

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^1.青岛理工大学环境与市政工程学院，山东青岛

^2.中国科学院青岛生物能源与过程研究所，单细胞中心，山东青岛

^3.青岛科技大学生物工程学院，山东青岛

^4.青岛水务集团环境能源有限公司，山东青岛

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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TxT

Genus	Strain	Sample source	Nitrogen source	Carbon source	T/℃	pH	t/h	Nitrogen removal efficiency(%)	Phosphorusremoval efficiency(%)	C/N	Initial nitrogencontent (mg/L)	Initial phosphoruscontent (mg/L)	References
Raoultella	YX-4	Wastewater treatment pond at the pig farm	NH₄⁺, NO₃^-, NO₂^-	Disodium succinate	25	7.0	96	99.0	85.0	15	200.0	100.0	[5]
Pseudomonas	ADP-19	Polluted sediments and kimchi processing wastewater	NH₄⁺	Sodium acetate	30	7.0-8.0	48	96.5	73.3	4	100.0	20.0	[12]
YG-24	Eutrophic sediments from lake Taihu	NH₄⁺, NO₃^-, NO₂^-	Sodium carbonate	25	7.2	24	88.1	51.2	8	33.9	2.3±0.1	[13]
ZK-2	A city wastewater treatment facility in Guangzhou	NH₄⁺	Sodium acetate	-	7.0	-	82.6	87.2	-	35.4±1.9	5.2±0.9	[14]
K14	Sediment accumulation in freshwater farms in Tianjin	NH₄⁺	Sodium pyruvate	27	7.5	24	99.8	98.0	10	110.1	26.2	[15]
Pseudoxanthomonas	YP1	Sequencing batch reactor (SBR) for aerobic treatment of particulate matter	NO₃^-	Sodium citrate	30	8.0	20	75.3	-	-	30.0	8.0	[16]
Delftia	ZK-1	Exit of the aeration tank at a wastewater treatment facility in Guangzhou	NH₄⁺, peptone	Sodium acetate	26±1	7.8	8	98.7	90.7	-	14.0	10.0	[17]
Bacillus	GHSP10	Wastewater generated by aquaculture operations	NH₄⁺, NO₃^-, NO₂^-	Glucose, sodium acetate	28	7.5	48	99.5	-	20	50.0	-	[18]
Thauera	SND5	Anoxic treatment tank at a sewage treatment plant in Singapore	NH₄⁺, NO₃^-, NO₂^-	Lactic acid	30	7.5	26	100.0	-	10	83.4	-	[19]
Escherichia	J16	Aerobic biological tanks at the Taiyuan city wastewater treatment facility	NO₃^-	Glucose, peptone, sodium acetate	30	7.2-8.0	24	94.5	96.0	-	69.3	8.9	[20]
Agrobacterium	LAD9	Liquid waste from the landfill	NO₃^-, NO₂^-	Disodium succinate	30	7.0	24	100.0	76.6	8	100.0	10.0	[21]
Enterobacter	HW-15	Phosphorus-laden river water in southwestern Hubei Province, China.	NH₄⁺, NO₃^-, NO₂^-	Sodium acetate	30	7.2	24	93.2	62.0	8	100.0	35.6	[22]
Acinetobacter	C-13	SBR for laboratory use	NH₄⁺, NO₃^-, NO₂^-	Sodium acetate	30	7.5	24	100.0	69.7	-	100.0	4.4	[23]

Genus

Strain

Sample source

Nitrogen source

Carbon source

T/℃

t/h

Nitrogen removal efficiency(%)

Phosphorusremoval efficiency(%)

C/N

Initial nitrogencontent (mg/L)

Initial phosphoruscontent (mg/L)

References

Raoultella

YX-4

Wastewater treatment pond at the pig farm

NH₄⁺, NO₃^-, NO₂^-

Disodium succinate

7.0

99.0

85.0

200.0

100.0

[5]

Pseudomonas

ADP-19

Polluted sediments and kimchi processing wastewater

NH₄⁺

Sodium acetate

7.0-8.0

96.5

73.3

100.0

20.0

[12]

YG-24

Eutrophic sediments from lake Taihu

NH₄⁺, NO₃^-, NO₂^-

Sodium carbonate

7.2

88.1

51.2

33.9

2.3±0.1

[13]

ZK-2

A city wastewater treatment facility in Guangzhou

NH₄⁺

Sodium acetate

7.0

82.6

87.2

35.4±1.9

5.2±0.9

[14]

K14

Sediment accumulation in freshwater farms in Tianjin

NH₄⁺

Sodium pyruvate

7.5

99.8

98.0

110.1

26.2

[15]

Pseudoxanthomonas

YP1

Sequencing batch reactor (SBR) for aerobic treatment of particulate matter

NO₃^-

Sodium citrate

8.0

75.3

30.0

8.0

[16]

Delftia

ZK-1

Exit of the aeration tank at a wastewater treatment facility in Guangzhou

NH₄⁺, peptone

Sodium acetate

26±1

7.8

98.7

90.7

14.0

10.0

[17]

Bacillus

GHSP10

Wastewater generated by aquaculture operations

NH₄⁺, NO₃^-, NO₂^-

Glucose, sodium acetate

7.5

99.5

50.0

[18]

Thauera

SND5

Anoxic treatment tank at a sewage treatment plant in Singapore

NH₄⁺, NO₃^-, NO₂^-

Lactic acid

7.5

100.0

83.4

[19]

Escherichia

J16

Aerobic biological tanks at the Taiyuan city wastewater treatment facility

NO₃^-

Glucose, peptone, sodium acetate

7.2-8.0

94.5

96.0

69.3

8.9

[20]

Agrobacterium

LAD9

Liquid waste from the landfill

NO₃^-, NO₂^-

Disodium succinate

7.0

100.0

76.6

100.0

10.0

[21]

Enterobacter

HW-15

Phosphorus-laden river water in southwestern Hubei Province, China.

NH₄⁺, NO₃^-, NO₂^-

Sodium acetate

7.2

93.2

62.0

100.0

35.6

[22]

Acinetobacter

C-13

SBR for laboratory use

NH₄⁺, NO₃^-, NO₂^-

Sodium acetate

7.5

100.0

69.7

100.0

4.4

[23]

Technology	Signal type	Sorting speed (events/s)^a	Advantages	Disadvantages	References
FACS	Intrinsic fluorescence and external labeling	>10 000	Rapid cell sorting technology that can process large sample volumes	Relatively few cells exhibit natural incidental fluorescence, and external fluorescent labeling may influence cellular metabolic activity	[60-61]
MACS	Magnetic response	2-10, >2 000	Facilitates the enrichment of target cells	Insufficient capacity for accurate single-cell sorting, only highly enriched, external magnetic labelling may impair cell activity, small range of applications	[60,62]
RACS	Cellular components/isotopic labelling	2-5	Directly sorts individual cells from environmental samples without labeling or disruption, making it ideal for precise single-cell sorting	Raman signal is susceptible to fluorescence interference, low throughput, high instrument cost	[58,66]
FlowRACS	Cellular components/isotopic labelling	>600	Utilizing RACS in conjunction with microfluidics enables high-throughput sorting of environmental flora	Raman signal is susceptible to fluorescence interference, high instrument cost	[65-66]

Technology

Signal type

Sorting speed (events/s)^a

Advantages

Disadvantages

References

FACS

Intrinsic fluorescence and external labeling

>10 000

Rapid cell sorting technology that can process large sample volumes

Relatively few cells exhibit natural incidental fluorescence, and external fluorescent labeling may influence cellular metabolic activity

[60-61]

MACS

Magnetic response

2-10, >2 000

Facilitates the enrichment of target cells

Insufficient capacity for accurate single-cell sorting, only highly enriched, external magnetic labelling may impair cell activity, small range of applications

[60,62]

RACS

Cellular components/isotopic labelling

2-5

Directly sorts individual cells from environmental samples without labeling or disruption, making it ideal for precise single-cell sorting

Raman signal is susceptible to fluorescence interference, low throughput, high instrument cost

[58,66]

FlowRACS

Cellular components/isotopic labelling

>600

Utilizing RACS in conjunction with microfluidics enables high-throughput sorting of environmental flora

Raman signal is susceptible to fluorescence interference, high instrument cost

[65-66]