Method for high-resolution quantification of influence of antibiotics on microbial activity of sediment

Abstract

The invention relates to the technical field of microbial ecology, and discloses a high-resolution quantitative method for influence of antibiotics on microbial activity of sediment, which comprises the following steps: S1, sediment preparation and time sequence SIP culture experiment, S2, DNA extraction and DNA-SIP process, S3, quantitative polymerase chain reaction, S4, DNA sequencing of DNA-SIP density gradient, S5, sequence processing, S5, sequencing, S6, sequencing, S7, sequencing, S7, sequencing, S8, sequencing, S8, sequencing, S8, sequencing and S8, sequencing. And S6, calculating the percentage of the total community DNA of the prokaryotes marked by 13C. According to the method, time sequence culture of coast sediments is combined with DNA-SIP and high-throughput sequencing analysis, the acute influence of high-concentration sulfadimidine (SMT) in wastewater on metabolic activity of different groups in sediment prokaryotic biocenosis is successfully evaluated, and quantitative SIP (qSIP) is used for quantifying the acute influence on the high-resolution level of phylogenetic development. Results show that high-concentration SMT in the aquaculture wastewater generates stimulation or inhibition of different degrees on metabolic activity of different bacteria.

Description

Technical Field

[0001] The invention relates to the technical field of microbial ecology, and in particular to a method for high-resolution quantitative analysis of the effects of antibiotics on the activity of sediment microbial groups based on DNA-stable isotope probes. Background Art

[0002] The discharge of wastewater from human activities leads to antibiotic contamination of coastal sediments. The long-term threat of low-concentration antibiotics to the environment has been widely studied, such as the accumulation and spread of antibiotic resistance genes. However, high concentrations of antibiotics in wastewater have a dramatic impact on the structure and metabolic activity of coastal sediment microbial communities, and we still know little about this acute impact. In the natural environment, the diversity of microbial species and metabolic functions is extremely high, and only about 1% of microbial groups are recognized by humans. Therefore, identifying environmental microbial groups and their metabolic functions has always been one of the difficult problems in microbial ecology. DNA-SIP is a molecular biology technique that is not based on pure culture and can establish a direct connection between the identification of microorganisms in the environment and their metabolic functions. In the present invention, we conducted a time series incubation (2, 3, 5 and 7 days) of coastal sediments under the influence of wastewater under SMT exposure and non-SMT exposure conditions. Through DNA-SIP technology and subsequent high-throughput sequencing, we identified active prokaryotic groups that metabolize organic matter in wastewater under the influence of high-concentration SMT, and the metabolic activity of different groups has significant changes and differences in the time gradient. We also found that SMT accelerated the consumption of wastewater organic carbon by prokaryotic communities in coastal sediments.

[0003] However, this stimulation led to a reduction in microbial diversity and suppressed key low-abundance groups involved in sulfur respiration, nitrification, and degradation of complex organic matter, which may threaten the health of coastal ecosystems. The recovery of metabolic activity of these functional groups was observed at the end of the culture (day 7), indicating that intermittent wastewater discharge may help restore the ecological functions of microbial communities to alleviate the threat of high-concentration antibiotics in wastewater to the coastal sediment ecological environment. In order to better evaluate the acute effects and potential hazards of high-concentration antibiotics on the coastal sediment ecological environment, the present invention further quantified the effects of high-concentration SMT on the metabolic activity of different prokaryotic groups in a time gradient with high resolution (at the OTUs level) through an improved quantitative SIP (qSIP) algorithm. Summary of the invention

[0004] The present invention is a method for high-resolution quantitative analysis of the effects of antibiotics on the activity of sediment microbial communities based on DNA-stable isotope probes. The purpose of the present invention is to evaluate the acute response of sediment prokaryotic communities to high-concentration SMT in aquaculture wastewater through time series culture of coastal sediments combined with qSIP, and to quantify with high resolution the effects of high-concentration SMT on the metabolic activities of different groups in the prokaryotic community over a time gradient.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for high-resolution quantitative analysis of the effect of antibiotics on sediment microbial activity, the method comprising the following steps:

[0006] S1. Sediment preparation and time series SIP culture experiment

[0007] Sediment samples were collected from the coastal area of ​​Xiamen City. Visible plant and animal matter and stones were removed before homogenizing the sediments. Then, 10 mL of artificial aquaculture wastewater (SMT) containing the antibiotic sulfamethoxazole (SMT) was added to the sediments. 13 C or 12 C-labeled C6H 12 O6: 600mg L –1 、NH4Cl:60mg L –1 、KNO3:2mg L –1 、NaH2PO4:20mg L –1 , Urea: 60mgL –1 and sodium chloride: 10 psu) were added to 20 g of sediment to form 40 μg g –1 The final concentration of SMT was 1.547 μg / mL. A control group without antibiotics was also established. The culture flasks were incubated at room temperature in the dark and shaken every 24 hours.

[0008] S2. DNA extraction and DNA-SIP process

[0009] use The SPIN soil kit (MP biological preparation kit) was used to extract DNA from sediment microorganisms from approximately 0.5 g (wet weight) of sediment, and the extracted DNA was evaluated for purity and quality using NanoDrop2000c (Thermo Scientific, America). 13 C-labeled and unlabeled DNA: About 3 μg of DNA was added to the cesium chloride solution to obtain a final density of 1.725 g mL –1 The centrifuge solution was injected into a 5.1 mL quick-seal centrifuge tube and centrifuged in a VTi 65.2 rotor at 140,000 g, 20 °C and vacuum for 48 h. Immediately after centrifugation, the density gradient solution was added at 250 μL min–1 The density gradient solution was evenly divided into 20 fractions at a speed of 1.5 μm and the DNA in each density gradient fraction was recovered;

[0010] S3. Quantitative polymerase chain reaction

[0011] The primers Bac-338f / 518r (Park and Crowley, 2005) were used in CFX 96 TM qPCR quantification was performed using a real-time system (BIO-RAD), and the reaction mixture (20 μL) contained 10 μL of Premix ExTaq TM II, 0.5 μM of each forward and reverse primer, 1 μL template DNA, 5 ng bovine serum albumin (BSA) solution and enzyme-free water, each reaction mixture was run three times with this program: initial enzyme activation at 95 °C for 3 min, followed by 40 cycles of 95 °C: 30 s, 58 °C: 30 s, and 72 °C: 30 s;

[0012] S4. DNA sequencing of DNA-SIP density gradient fractions

[0013] According to the qPCR results of the cesium chloride components of each sample, the distribution curves of the abundance of prokaryotic 16S rRNA genes along the cesium chloride density gradient were analyzed. During the time series incubation, there were significant differences in the distribution curves between antibiotic-exposed and antibiotic-free samples, indicating that antibiotics significantly affected the microbial community. 13 Assimilation and absorption of C-glucose. Therefore, we performed high-throughput sequencing of the prokaryotic 16S rRNA gene to identify at high resolution the effects of antibiotics on the metabolic activity of different groups in the prokaryotic community throughout the time series of cultivation. For each sample, all or continuous density gradient fractions of DNA with DNA distribution were used for high-throughput sequencing;

[0014] S5. Sequence processing

[0015] The V3-V4 hypervariable region of the prokaryotic 16S rRNA gene was amplified using barcode sequences and prokaryotic universal primers 515F and 806R (Pichler et al., 2018), and the amplicons were sequenced on the Illumina MiSeq PE300 platform. After sequencing, the sequences were analyzed according to the standard operating procedures of the Mothur software (www.mothur.org / wiki / MiSeq_SOP), including the classification of multiple sequences into taxonomic operational units (OTUs) and the phylogenetic classification annotation of OTUs. OTUs were divided according to 97% sequence similarity;

[0016] S6, the total prokaryotic community DNA was 13 The percentage of C markers

[0017] Prokaryotic use 13 C-glucose will 13 C assimilates into their DNA and causes the density of the DNA to increase. If the metabolic activity of the prokaryotic organism is higher, the 12 C is 13 The higher the proportion of C substitution, the higher its density. Based on this theory, we used the quantitative SIP (qSIP) method to calculate the DNA 13 The percentage of C labeling (PL- 13 C). First, the total abundance of the prokaryotic community (f) and the average density of prokaryotic DNA were calculated using formula 1 and formula 2, respectively.

[0018]

[0019] f k is the abundance of 16S rRNA gene in the density gradient fraction k of each sample, ρ k is the density of density gradient component k.

[0020] PL- 13 C(P) is calculated using Equation 3:

[0021]

[0022] and In the same group of controls 13 C-glucose added to samples and 12 The average density of prokaryotic DNA in samples spiked with C-glucose, △ρ represents 13 The density difference between DNA with 100% C labeling rate and unlabeled DNA (=0.036 g mL -1 ).

[0023] S7. PL- 13 C

[0024] According to the calculation of total community PL- 13 C method, further by sequencing the density gradient fraction DNA to obtain the relative abundance p of prokaryotic group i in density gradient fraction k ik , combined with the total prokaryotic abundance f in each density gradient fraction i , calculate the abundance y of each prokaryotic group in each density gradient ij (Formula 4):

[0025] y i.k =p i.k ×f k (4)

[0026] The total abundance of prokaryotic group i in each sample (y i ) is calculated using Formula 5:

[0027]

[0028] The DNA density of prokaryotic group i (W i ) is calculated using Formula 6:

[0029]

[0030] PL- of prokaryotic group i 13 C(T i ) is calculated using Formula 7:

[0031]

[0032] W i.13C and W i.12C In the same group of controls 13 C-glucose added to samples and 12 The DNA density of prokaryotic group i in the sample spiked with C-glucose, △ρ represents 13 The density difference between DNA with 100% C labeling rate and unlabeled DNA (=0.036 g mL -1 ).

[0033] Examples of data used in the formula are shown in Table 1.

[0034] Table 1. Antibiotic exposure on day 2 of culture (SMT+ 13 C_2d), no antibiotics ( 13 C_2d) Experimental samples and controls ( 12 C_2d) Density, 16S rRNA gene abundance and relative abundance of representative OUTs in each density gradient fraction of the sample.

[0035]

[0036]

[0037] Preferably, in step S1, on the 2nd, 3rd, 5th and 7th day of incubation (the sampling time interval and frequency are adjusted according to the metabolic activity of the microorganisms), 2 g of slurry is collected, inactivated with 10% ZnCl, and the collected sediment is stored at -80°C until DNA extraction.

[0038] Preferably, in step S2, the density of each density gradient fraction of DNA-SIP is measured at 20°C using a digital refractometer, two volumes of polyethylene glycol solution (30% polyethylene glycol 6000, w / v, 1.6 M sodium chloride and 20 μg glycogen) are added to precipitate the DNA in each fraction, resuspended in 35 μL sterile Milli Q water, and then stored at -80°C.

[0039] Preferably, the amplification efficiency of the qPCR in step S3 is 95-100%, wherein R2>0.99. Its specificity is verified by melting curve and agarose gel electrophoresis, and any unclear products are sequenced to confirm their authenticity.

[0040] Preferably, the method described in steps S4-S7 allows us to obtain the distribution of the relative abundance of each OTUs in the continuous density gradient, thereby achieving high-resolution quantification of the effects of antibiotics on the metabolic activity of different prokaryotic groups.

[0041] The beneficial effects of the present invention are:

[0042] 1. Through DNA-SIP, high-throughput sequencing analysis and qSIP algorithm, we quantified the effects of high-concentration antibiotics in wastewater on the metabolic activities of different groups of sediment prokaryotic communities at high resolution (OTUs level). The present invention accurately and high-resolution quantifies the effects of antibiotics on the metabolic activities of highly diverse, unculturable prokaryotic groups in coastal sediments, which helps to better predict the damage caused by antibiotics in wastewater to coastal sediment ecosystems;

[0043] 2. Through time series culture combined with qSIP, the present invention found that the metabolic activity of the same active group has significant changes in the time gradient, and the metabolic activity of different active groups at the same time also has significant differences, indicating that different groups have differences in balancing antibiotic resistance and growth metabolism when facing antibiotic pressure. The time series culture combined with the qSIP method used in the present invention helps to more accurately evaluate the impact of antibiotics on the metabolic activity of prokaryotes. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a standardized distribution diagram of the abundance of the prokaryotic 16S rRNA gene in a CsCl density gradient of the present invention;

[0045] Figure 2 The prokaryotic 16S rRNA gene of the present invention 13 C labeling rate (a) and abundance (b) change with culture time;

[0046] Figure 3Phylogenetic tree diagram constructed for OTUs with relative abundance greater than 1% in the in situ (IS), active (H) and inactive (L) communities of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The present invention provides a method for high-resolution quantitative analysis of the effect of antibiotics on sediment microbial activity, characterized in that the method comprises the following steps:

[0049] S1. Sediment preparation and time series SIP culture experiment

[0050] Sediment samples were collected from the coastal area of ​​Xiamen City. Visible plant and animal matter and stones were removed before homogenizing the sediments. Then, 10 mL of artificial aquaculture wastewater (SMT) containing the antibiotic sulfamethoxazole (SMT) was added to the sediments. 13 C or 12 C-labeled C6H 12 O6: 600mg L –1 、NH4Cl:60mg L –1 、KNO3:2mg L –1 、NaH2PO4:20mg L –1 , Urea: 60mgL –1 and sodium chloride: 10 psu) were added to 20 g of sediment to form 40 μg g –1 The final concentration of SMT was 1.547 μg / mL. A control group without antibiotics was also established. The culture flasks were incubated at room temperature in the dark and shaken every 24 hours.

[0051] S2. DNA extraction and DNA-SIP process

[0052] use The SPIN soil kit (MP biological preparation kit) was used to extract DNA from sediment microorganisms from approximately 0.5 g (wet weight) of sediment, and the extracted DNA was evaluated for purity and quality using NanoDrop2000c (Thermo Scientific, America). 13 C-labeled and unlabeled DNA: About 3 μg of DNA was added to the cesium chloride solution to obtain a final density of 1.725 g mL –1The centrifuge solution was injected into a 5.1 mL quick-seal centrifuge tube and centrifuged in a VTi 65.2 rotor at 140,000 g, 20 °C and vacuum for 48 h. Immediately after centrifugation, the density gradient solution was added at 250 μL min –1 The density gradient solution was evenly divided into 20 fractions at a speed of 1.5 μm and the DNA in each density gradient fraction was recovered;

[0053] S3. Quantitative polymerase chain reaction

[0054] The primers Bac-338f / 518r (Park and Crowley, 2005) were used in CFX 96 TM qPCR quantification was performed using a real-time system (BIO-RAD), and the reaction mixture (20 μL) contained 10 μL of Premix ExTaq TM II, 0.5 μM of each forward and reverse primer, 1 μL template DNA, 5 ng bovine serum albumin (BSA) solution and enzyme-free water, each reaction mixture was run three times with the following program: initial enzyme activation at 95°C for 3 min, followed by 40 cycles of 95°C: 30 s, 58°C: 30 s, and 72°C: 30 s, Bac-338f / 518r (Park and Crowley, 2005) Reference: Park, J.-W., Crowley, DE, 2005. Normalization of soil DNA extraction for accurate quantification of target genes by real-time PCR and DGGE. Biotechniques 38, 579-586;

[0055] S4. DNA sequencing of DNA-SIP density gradient fractions

[0056] According to the qPCR results of the cesium chloride components of each sample, the distribution curves of the abundance of prokaryotic 16S rRNA genes along the cesium chloride density gradient were analyzed. During the time series incubation, there were significant differences in the distribution curves between antibiotic-exposed and antibiotic-free samples, indicating that antibiotics significantly affected the microbial community. 13 Assimilation and absorption of C-glucose. Therefore, we performed high-throughput sequencing of the prokaryotic 16S rRNA gene to identify at high resolution the effects of antibiotics on the metabolic activity of different groups in the prokaryotic community throughout the time series of cultivation. For each sample, all or continuous density gradient fractions of DNA with DNA distribution were used for high-throughput sequencing;

[0057] S5. Sequence processing

[0058] The V3-V4 hypervariable region of the prokaryotic 16S rRNA gene was amplified using barcode sequences and prokaryotic universal primers 515F and 806R (Pichler et al., 2018), and the amplicons were sequenced on the Illumina MiSeq PE300 platform. After sequencing, the sequences were analyzed according to the standard operating procedures of the Mothur software (www.mothur.org / wiki / MiSeq_SOP), including the classification of multiple sequences into taxonomic operational units (OTUs) and the phylogenetic classification annotation of OTUs. OTUs were divided according to 97% sequence similarity;

[0059] S6, the total prokaryotic community DNA was 13 The percentage of C markers

[0060] Prokaryotic use 13 C-glucose will 13 C assimilates into their DNA and causes the density of the DNA to increase. If the metabolic activity of the prokaryotic organism is higher, the 12 C is 13 The higher the proportion of C substitution, the higher its density. Based on this theory, we used the quantitative SIP (qSIP) method to calculate the DNA 13 The percentage of C labeling (PL- 13 C). First, the total abundance of the prokaryotic community (f) and the average density of prokaryotic DNA were calculated using formula 1 and formula 2, respectively.

[0061]

[0062] f k is the abundance of 16S rRNA gene in the density gradient fraction k of each sample, ρ k is the density of density gradient component k.

[0063] PL- 13 C(P) is calculated using Equation 3:

[0064]

[0065] and In the same group of controls 13 C-glucose added to samples and 12 The average density of prokaryotic DNA in samples spiked with C-glucose, △ρ represents 13 The density difference between DNA with 100% C labeling rate and unlabeled DNA (=0.036 g mL -1 ).

[0066] S7. PL- 13 C

[0067] According to the calculation of total community PL- 13 C method, further by sequencing the density gradient fraction DNA to obtain the relative abundance p of prokaryotic group i in density gradient fraction k ik , combined with the total prokaryotic abundance f in each density gradient fraction i , calculate the abundance y of each prokaryotic group in each density gradient ij (Formula 4):

[0068] y i.k =p i.k ×f k (4)

[0069] The total abundance of prokaryotic group i in each sample (y i ) is calculated using Formula 5:

[0070]

[0071] The DNA density of prokaryotic group i (W i ) is calculated using Formula 6:

[0072]

[0073] PL- of prokaryotic group i 13 C(T i ) is calculated using Formula 7:

[0074]

[0075] W i.13C and W i.12C In the same group of controls 13 C-glucose added to samples and 12 The DNA density of prokaryotic group i in the sample spiked with C-glucose, △ρ represents 13 The density difference between DNA with 100% C labeling rate and unlabeled DNA (=0.036 g mL -1 ).

[0076] Examples of data used in the formula are shown in Table 1.

[0077] Table 1. Antibiotic exposure on day 2 of culture (SMT+ 13 C_2d), no antibiotics ( 13 C_2d) Experimental samples and controls ( 12 C_2d) Density, 16S rRNA gene abundance and relative abundance of representative OUTs in each density gradient fraction of the sample.

[0078]

[0079]

[0080] In this embodiment: Step S1: On the 2nd, 3rd, 5th and 7th days of incubation (the sampling time interval and frequency are adjusted according to the metabolic activity of the microorganisms), 2 g of slurry is collected and inactivated with 10% ZnCl, and the collected sediment is stored at -80°C until DNA extraction.

[0081] In this embodiment: Step S2: At 20°C, the density of each density gradient fraction of DNA-SIP was measured using a digital refractometer, and two volumes of polyethylene glycol solution (30% polyethylene glycol 6000, w / v, 1.6 M sodium chloride and 20 μg glycogen) were added to precipitate the DNA in each fraction, resuspended in 35 μL sterile Milli Q water, and then stored at -80°C.

[0082] In this embodiment: the amplification efficiency of step S3 qPCR is 95-100%, where R2>0.99. Its specificity is verified by melting curve and agarose gel electrophoresis, and any unclear products are sequenced to confirm their authenticity.

[0083] In this embodiment: Steps S4-S7 allow us to obtain the distribution of the relative abundance of each OTUs in the continuous density gradient, thereby achieving high-resolution quantification of the effects of antibiotics on the metabolic activity of different prokaryotic groups.

[0084] Results and discussion

[0085] 3.1 Prokaryotic community DNA 13 Determination of C-labeling degree

[0086] The negative control sample (12C-glucose) showed a single peak of prokaryotic 16S rRNA gene abundance across the CsCl density gradient, which was distributed in a narrow density range (1.7044–1.7076 g L-1) ( Figure 1). Therefore, this range represents the density distribution range of 12C-DNA. In samples added with 13C-glucose, one or two peaks of prokaryotic 16S rRNA gene abundance were observed, of which the light peak (1.7057–1.7093 g L-1) corresponded to the density of 12C-DNA, while the heavy peak located at 1.7268–1.7324 g L-1 indicated that the prokaryotic community effectively absorbed 13C-glucose. In samples exposed to SMT, a heavy peak was observed after two days of incubation, and the height of the heavy peak gradually decreased over time, while the light peak increased, indicating a shift in absorption from 13C to 12C. At the end of the 7-day incubation, only the light peak remained in the samples exposed to SMT ( Figure 1 d). In contrast, in samples not exposed to SMT, both light and heavy peaks appeared after two days of incubation. Over time, the height of the light peak gradually decreased, while the heavy peak gradually increased, but on the third day of incubation, the heavy peak showed a sudden decrease ( Figure 1 ). These phenomena qualitatively indicate that antibiotics significantly affect the metabolic activity of prokaryotic communities.

[0087] 3.2 Quantifying the effects of antibiotics on the metabolic activity of prokaryotic communities

[0088] Gene abundance distribution curves along the CsCl density gradient revealed significant differences in the 13C-glucose uptake activity of prokaryotic populations between SMT-exposed and unexposed samples throughout the incubation period. To quantify these differences, we calculated the DNA uptake of the prokaryotic 16S rRNA gene. 13 The percentage of C labeling (PL-13C) ( Figure 2 The results showed that in samples not exposed to SMT, the PL-13C value of the prokaryotic 16S rRNA gene decreased significantly from 40.8% on the second day to 13.7% on the third day, and then gradually increased to 42.8% on the seventh day ( Figure 2 ). In contrast, in the SMT-exposed samples, the PL-13C values ​​of the prokaryotic 16S rRNA gene decreased continuously from 45.2% on day 2 to 3.4% at the end of incubation on day 7.

[0089] 3.3 Phylogenetic analysis of active and inactive prokaryotic populations.

[0090] In the DNA-SIP analysis, the groups with labeled substrate absorption activity showed higher relative abundance in the heavy density gradient fraction than in the light density gradient fraction, or the relative abundance between the two was similar. In contrast, the relative abundance of groups with labeled substrate absorption activity tended to decrease significantly from the light density gradient fraction to the heavy density gradient fraction (Deng et al., 2018; Nelson and Carlson, 2012). The results showed that among γ-proteobacteria, Vibrio was the main active group that assimilated organic carbon in wastewater ( Figure 3 ). The degree of activity of different OUTs in this group varied during the incubation period due to the presence of SMTs. Specifically, OTU01 and OTU10 of Vibrio showed high activity in both SMT-exposed and non-exposed samples at the 2nd and 5th days of incubation. However, OTU01 showed higher activity in SMT-exposed samples, while OTU10 was more active in SMT-non-exposed samples. Notably, the activity of Vibrio OTU02 in SMT-exposed samples was significantly higher than that in SMT-non-exposed samples throughout the incubation period, and peaked on the 3rd day in SMT-exposed samples. This suggests that the competitive advantage between OTU01 / 10 and OTU02 changed during the incubation process.

[0091] Compared with Clade II, members of Clade III, including Halobacteriovorax (OTU07), Desulfuromonadales (OTU16, OTU38), and Bacteriovoracaceae (OTU19) in δ-Proteobacteria, and Rhodobacteraceae (OTU31) in α-Proteobacteria, showed opposite patterns in relative abundance distribution between SMT-exposed and non-exposed samples ( Figure 3 ). This contrasting pattern indicated that SMT significantly inhibited the activity of Clade III. Clade IV, including members of Actinomycetes and Chloroflexi KD4-96, showed high activity in both exposed and unexposed samples at the 3rd and 7th day of incubation, indicating a weaker effect of SMT on their activity. Clade V, such as Sulfurovum and Bacteroidia in Campylobacteria, showed no activity in both exposed and unexposed samples throughout the incubation period ( Figure 3 The above results show that the metabolic activity of different groups can be inhibited or stimulated by antibiotics, and some groups belonging to different species at the phylogenetic level also have significant differences in the changes in metabolic activity over time. 3.5 Phylogenetic analysis of active and inactive prokaryotic populations.

[0092] In summary, through DNA-SIP, high-throughput sequencing analysis and qSIP algorithm, we quantified the effects of high-concentration antibiotics in wastewater on the metabolic activities of different groups of sediment prokaryotic communities with high resolution (OTUs level). The present invention accurately and high-resolution quantifies the effects of antibiotics on the metabolic activities of highly diverse and uncultivated prokaryotic groups in coastal sediments, which helps to better predict the harm caused by antibiotics in wastewater to coastal sediment ecosystems. Then, through time series culture combined with qSIP, the present invention found that the metabolic activity of the same active group has significant changes in the time gradient, and the metabolic activities of different active groups at the same time are also significantly different, indicating that different groups have differences in weighing antibiotic resistance and growth metabolism when facing antibiotic pressure. The time series culture combined with the qSIP method used in the present invention helps to more accurately evaluate the effects of antibiotics on the metabolic activity of prokaryotes. It provides a valuable method for evaluating the effects of high-concentration antibiotics on the biochemical cycle of coastal sediments.

[0093] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0094] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-resolution quantitative method for the effect of antibiotics on sediment microbial activity, characterized in that: The method comprises the following steps: S1. Sediment preparation and time series SIP culture experiment Sediment samples were collected from the coastal area of ​​Xiamen City. Visible plant and animal matter and stones were removed before homogenizing the sediments. Then, 10 mL of artificial aquaculture wastewater (SMT) containing the antibiotic sulfamethoxazole (SMT) was added to the sediments. 13 C or 12 C-labeled C6H 12 O6: 600mg L –1 、NH4Cl:60mg L –1 、KNO3:2mg L –1 、NaH2PO4:20mg L –1 , Urea: 60mg L –1 and sodium chloride: 10 psu) were added to 20 g of sediment to form 40 μg g –1 The final concentration of SMT was 1.547 μg / mL. A control group without antibiotics was also established. The culture flasks were incubated at room temperature in the dark and shaken every 24 hours. S2. DNA extraction and DNA-SIP process use The SPIN soil kit (MP biological preparation kit) was used to extract DNA from sediment microorganisms from approximately 0.5 g (wet weight) of sediment, and the extracted DNA was evaluated for purity and quality using a NanoDrop 2000c (Thermo Scientific, America). 13 C-labeled and unlabeled DNA: About 3 μg of DNA was added to the cesium chloride solution to obtain a final density of 1.725 g mL –1 The centrifuge solution was injected into a 5.1 mL quick-seal centrifuge tube and centrifuged in a VTi 65.2 rotor at 140,000 g, 20 °C and vacuum for 48 h. Immediately after centrifugation, the density gradient solution was added at 250 μL min –1 The density gradient solution was evenly divided into 20 fractions at a speed of 1.5 μm and the DNA in each density gradient fraction was recovered; S3. Quantitative polymerase chain reaction The primers Bac-338f / 518r (Park and Crowley, 2005) were used in CFX 96 TM qPCR quantification was performed using a real-time system (BIO-RAD), and the reaction mixture (20 μL) contained 10 μL of Premix ExTaq TM II, 0.5 μM of each forward and reverse primer, 1 μL template DNA, 5 ng bovine serum albumin (BSA) solution and enzyme-free water, each reaction mixture was run three times with this program: initial enzyme activation at 95 °C for 3 min, followed by 40 cycles of 95 °C: 30 s, 58 °C: 30 s, and 72 °C: 30 s; S4. DNA sequencing of DNA-SIP density gradient fractions According to the qPCR results of the cesium chloride components of each sample, the distribution curves of the abundance of prokaryotic 16S rRNA genes along the cesium chloride density gradient were analyzed. During the time series incubation, there were significant differences in the distribution curves between antibiotic-exposed and antibiotic-free samples, indicating that antibiotics significantly affected the microbial community. 13 Assimilation and absorption of C-glucose. Therefore, we performed high-throughput sequencing of the prokaryotic 16S rRNA gene to identify at high resolution the effects of antibiotics on the metabolic activity of different groups in the prokaryotic community throughout the time series of cultivation. For each sample, all or continuous density gradient fractions of DNA with DNA distribution were used for high-throughput sequencing; S5. Sequence processing The V3-V4 hypervariable region of the prokaryotic 16S rRNA gene was amplified using barcode sequences and prokaryotic universal primers 515F and 806R (Pichler et al., 2018), and the amplicons were sequenced on the Illumina MiSeq PE300 platform. After sequencing, the sequences were analyzed according to the standard operating procedures of the Mothur software (www.mothur.org / wiki / MiSeq_SOP), including the classification of multiple sequences into taxonomic operational units (OTUs) and the phylogenetic classification annotation of OTUs. OTUs were divided according to 97% sequence similarity; S6, the total prokaryotic community DNA was 13 The percentage of C markers Prokaryotic use 13 C-glucose will 13 C assimilates into their DNA and causes the density of the DNA to increase. If the metabolic activity of the prokaryotic organism is higher, the 12 C is 13 The higher the proportion of C substitution, the higher its density. Based on this theory, we used the quantitative SIP (qSIP) method to calculate the DNA 13 The percentage of C labeling (PL- 13 C). First, the total abundance of the prokaryotic community (f) and the average density of prokaryotic DNA were calculated using formula 1 and formula 2, respectively. f k is the abundance of 16S rRNA gene in the density gradient fraction k of each sample, ρ k is the density of density gradient component k. PL- 13 C(P) is calculated using Equation 3: and In the same group of controls 13 C-glucose added to samples and 12 The average density of prokaryotic DNA in samples spiked with C-glucose, △ρ represents 13 The density difference between DNA with 100% C labeling and unlabeled DNA (=0.036 g mL -1 ). S7. PL- 13 C According to the calculation of total community PL- 13 C method, further by sequencing the density gradient fraction DNA to obtain the relative abundance p of prokaryotic group i in density gradient fraction k ik , combined with the total prokaryotic abundance f in each density gradient fraction i , calculate the abundance y of each prokaryotic group in each density gradient ij (Formula 4): and i.k =p i.k ×f k (4) The total abundance of prokaryotic group i in each sample (y i ) is calculated using Formula 5: The DNA density of prokaryotic group i (W i ) is calculated using Formula 6: PL- of prokaryotic group i 13 C(T i ) is calculated using Formula 7: W i.13C and W i.12C In the same group of controls 13 C-glucose added to samples and 12 The DNA density of prokaryotic group i in the sample spiked with C-glucose, △ρ represents 13 The density difference between DNA with 100% C labeling and unlabeled DNA (=0.036 g mL -1 ). Examples of data used in the formula are shown in Table 1. Table 1. Antibiotic exposure on day 2 of culture (SMT+ 13 C_2d), no antibiotics ( 13 C_2d) Experimental samples and controls ( 12 C_2d) Density, 16S rRNA gene abundance and relative abundance of representative OUTs in each density gradient fraction of the sample.

2. The method for high-resolution quantitative analysis of the effect of antibiotics on sediment microbial activity according to claim 1, characterized in that: As described in step S1, on the 2nd, 3rd, 5th and 7th days of incubation (the sampling time interval and frequency were adjusted according to the metabolic activity of the microorganisms), 2 g of slurry was collected and inactivated with 10% ZnCl, and the collected sediment was stored at -80°C until DNA extraction.

3. The method for high-resolution quantitative determination of the effect of antibiotics on sediment microbial activity according to claim 1, characterized in that: As described in step S2, at 20°C, the density of each density gradient fraction of DNA-SIP was measured using a digital refractometer, and the DNA in each fraction was precipitated by adding two volumes of polyethylene glycol solution (30% polyethylene glycol 6000, w / v, 1.6 M sodium chloride and 20 μg glycogen), resuspended in 35 μL sterile Milli Q water, and then stored at -80°C.

4. The method for high-resolution quantitative determination of the effect of antibiotics on sediment microbial activity according to claim 1, characterized in that: The amplification efficiency of the qPCR described in step S3 was 95-100%, with R2>0.

99. Its specificity was verified by melting curve and agarose gel electrophoresis, and any ambiguous products were sequenced to confirm their authenticity.

5. The method for high-resolution quantitative analysis of the effect of antibiotics on sediment microbial activity according to claim 1, characterized in that: This method described in steps S4-S7 allows us to obtain the distribution of the relative abundance of each OTUs in the continuous density gradient fractions, thereby achieving high-resolution quantification of the effects of antibiotics on the metabolic activity of different prokaryotic groups.

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