Method for evaluating the chronological evolution of antibiotic resistance genes in lake sediments

Abstract

The present application relates to the field of environmental microorganism and gene detection technology, and particularly relates to a method for evaluating the chronological evolution of antibiotic resistance genes in lake sediments. 137 Cs, 210 Pb dating method constructs high-precision sedimentary chronology sequence, high-throughput fluorescent quantitative PCR technology is used to detect the abundance of antibiotic resistance genes in the layered samples, and then based on the chronology sequence and gene abundance data, the historical evolution rule of the abundance of antibiotic resistance genes in the time dimension is analyzed. The present application first combines the sediment chronology and high-throughput gene detection technology, realizes the historical reconstruction of the antibiotic resistance genes in the lake, and provides reliable data support for analyzing the evolution driving mechanism and environmental health risk assessment.

Description

Technical Field

[0001] This invention relates to the field of environmental microbiology and gene detection technology, specifically to a method for assessing the chronological evolution of antibiotic resistance genes in lake sediments. Background Technology

[0002] Antibiotic resistance genes (ARGs), as emerging environmental pollutants, have been widely detected in water bodies and sediments worldwide. Current research largely focuses on their horizontal spatial distribution or indirect risk assessments based on hydrological and water quality parameters. However, these methods lack research on the evolutionary patterns of ARGs over time, especially in sensitive ecosystems such as high-altitude lakes.

[0003] High-altitude lake sediments record long-term environmental change information, but current technologies have not systematically combined sediment chronology with high-throughput detection of ARGs, making it difficult to reveal the historical input patterns, evolutionary trends and driving mechanisms of ARGs. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing the chronological evolution of antibiotic resistance genes in lake sediments.

[0005] The present invention adopts the following technical solution: This invention provides a method for assessing the chronological evolution of antibiotic resistance genes in lake sediments, the method comprising the following steps: S1. Collect lake sediment core samples and layer the sediment core samples at 1-3 cm intervals to obtain layered sediment samples; S2. Determine the age of stratified sediment samples and construct a sediment age sequence; S3: Determine the abundance of antibiotic resistance genes in stratified sediment samples; S4: Combining the sedimentary chronology sequence with the abundance of antibiotic resistance genes in the stratified sediment samples, draw a historical evolution diagram of the abundance of antibiotic resistance genes with the chronology sequence, analyze the chronological evolution of antibiotic resistance genes over time, and complete the assessment of the chronological evolution of antibiotic resistance genes in lake sediments.

[0006] This invention combines lake sediment chronology with high-throughput genetic testing technology. It involves collecting and stratifying columnar sediments, and then utilizing... 210 Pb, 137 Cs dating was used to construct a high-precision sedimentary chronology sequence. Simultaneously, high-throughput quantitative PCR was employed to detect the abundance of various aerogenetic genes (ARGs) in samples from different strata. Correlation analysis between the chronological data and gene abundance data revealed the historical evolution patterns, spatiotemporal differentiation characteristics, and driving mechanisms of ARGs on a centennial timescale. This method represents the first successful historical reconstruction of lake ARGs, providing a new perspective for environmental health risk assessment.

[0007] Furthermore, the sampling depth is 0.5~1.5m.

[0008] Furthermore, the dating of the stratified sediment samples described in S2 is based on... 210 Pb, 137 Cs dating was performed using the constant specific activity CIC model. 210 Pb dating was used to calculate the depositional age of each sedimentary layer sample, in order to... 137 The position of the Cs accumulation peak was used as a time marker to calibrate the calculation results.

[0009] Furthermore, the abundance of the antibiotic resistance gene described in S3 was detected by high-throughput quantitative PCR.

[0010] Furthermore, the abundance data of the antibiotic resistance genes are obtained through the following steps: using DNA extracted from the stratified sediment sample as a template, amplification and detection are performed using a high-throughput quantitative PCR system, and the absolute abundance and / or relative abundance of each antibiotic resistance gene are calculated based on the cycle threshold and amplification efficiency.

[0011] Furthermore, the detection limit of the cycle threshold is 30~32, and the screening range of the amplification efficiency is between 90% and 110%.

[0012] Furthermore, the antibiotic resistance genes include one or more of the following: aminoglycosides, β-lactams, MLSBs, tetracyclines, sulfonamides, vancomycins, and multidrug resistance genes.

[0013] Furthermore, the method is used for lake environmental risk assessment.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, through a combination of vertical sediment column sampling, chronology, and high-throughput gene detection technology, has for the first time achieved historical reconstruction of antibiotic resistance genes (ARGs) in plateau lakes. It provides time-series evolutionary data of ARGs, compensating for the shortcomings of existing spatial detection methods; isotope dating ensures accuracy of the time scale; and simultaneous acquisition of sediment age, nutrient content, antibiotic residues, microbial communities, and ARG data provides multidimensional data support for elucidating the driving mechanisms of ARG evolution. High-throughput qPCR technology enables simultaneous quantification of multiple ARG types, with high detection efficiency and sensitivity; providing a new perspective for environmental evolution and health risk assessment in plateau lakes. Attached Figure Description

[0015] Figure 1 This is a map showing the sampling points.

[0016] Figure 2This is a sample image taken at the scene.

[0017] Figure 3 For point A 137 Cs and 210 Pb activity distribution map, where A is the column sample at point A. 137 Activity distribution diagram of Cs, B is 210 The activity variation of Pb excess at different depths, where C is the logarithm of the ln value. 210 Fitting plots of Pb and activity values ​​at different depths, where D is a schematic diagram of the age corresponding to different depths of the point.

[0018] Figure 4 For point B 137 Cs and 210 Pb activity distribution map, where A is the column sample at point B. 137 Activity distribution diagram of Cs, B is 210 The activity variation of Pb excess at different depths, where C is the logarithm of the ln value. 210 Fitting plots of Pb and activity values ​​at different depths, where D is a schematic diagram of the age corresponding to different depths of the point.

[0019] Figure 5 For point C 137 Cs and 210 Pb activity distribution map, where A is the column sample at point C. 137 Activity distribution diagram of Cs, B is 210 The activity variation of Pb excess at different depths, where C is the logarithm of the ln value. 210 Fitting plots of Pb and activity values ​​at different depths, where D is a schematic diagram of the age corresponding to different depths of the point.

[0020] Figure 6 For point D 137 Cs and 210 Pb activity distribution map, where A is the column sample at point D. 137 Activity distribution diagram of Cs, B is 210 The activity variation of Pb excess at different depths, where C is the logarithm of the ln value. 210 Fitting plots of Pb and activity values ​​at different depths, where D is a schematic diagram of the age corresponding to different depths of the point.

[0021] Figure 7 Spatiotemporal comparison diagram of ARGs and MGES diversity.

[0022] Figure 8 A graph showing the abundance and species diversity of ARGs over time.

[0023] Figure 9 This is a graph showing the abundance and species diversity of MGEs over time.

[0024] Figure 10 The relative abundance plots of ARGs and MGEs are shown.

[0025] Figure 11 The absolute abundance plots for ARGs and MGEs are shown. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments, but this should not be construed as limiting the invention. Unless otherwise specified, the technical means used in the following embodiments are conventional means well known to those skilled in the art, and the materials, reagents, etc. used in the following embodiments are commercially available unless otherwise specified.

[0027] Implementation Column 1 I. Collection and dating of columnar sediments in Erhai Lake.

[0028] 1. Sampling point setting Based on the characteristics of the Erhai Lake system and the distribution of human activities, four sampling points were set up in the north (point A), the northern lake center (point B), the southern lake center (point C), and the south (point D). The specific locations of the sampling points are as follows: Figure 1 As shown in Table 1.

[0029] Table 1: Basic Information of Sampling Points 2. Collection of columnar sediments Core samples were collected to a depth of 1 m using a UWITEC gravity drilling system. Sediment samples were stored at -20°C after collection. Secondary sampling of the sediment cores was performed at 2 cm intervals using a core cutter. The central portion of the core was used for subsequent DNA extraction and qPCR analysis, while the remaining portions were used for geochronological analysis. 137 Cs- 210 Pb and accelerator mass spectrometer 14 C-ray analysis was used to establish an age model for the sedimentary core. Field sampling images are shown below. Figure 2 As shown.

[0030] 3. Sediment dating Take an appropriate amount of layered sediment samples. Sediments from points A, B, C, and D are layered at 2cm intervals. Ten samples are taken from top to bottom for geochronological analysis. After freeze-drying and grinding through a 100-mesh sieve, a certain amount is weighed and placed into a sample box, which is then sealed and equilibrated for 3 weeks. Measurements are performed using a high-purity germanium gamma spectrometer (EG&G Ortec, USA). 210 Pb and 137 Specific activity of Cs. 226 Ra activity is measured by its daughter plants. 214 Pb (351.9 keV) and 214The gamma-ray intensity of Bi (609.3 keV) was obtained, with excess 210 Pb ( 210 Pb ex ) by the general 210 Pb minus 226 The calculations are partially supported by Ra.

[0031] Select 210 Pb, 137 Cs dating method, utilizing the Cs dating of columnar sediment samples 137 The position of the Cs accumulation peak in the vertical profile was used as a time marker, and the constant specific activity CIC model was selected. 210 Pb is calculated annually.

[0032] The CIC model was used to calculate sedimentation rates and ages. The constant specific activity model assumes a relatively stable system where, with increasing sediment accumulation rate, 210 The Pb flux also increases accordingly, and the F at the water-sediment interface... t / r t It is a constant value, which is the value in the surface sediment. 210 The specific activity of Pb, A0, represents the constant specific activity model. Assuming the radioactivity specific activity A0 in the sediment surface layer is constant, the radioactivity specific activity A at a certain depth Z is: A = A0e -λt This is the age-depth relationship in the CIC model. Where λ is... 210 The decay constant of Pb; t is the age at a certain depth Z; e is Euler's natural constant.

[0033] The linear relationship between radioactivity and depth is obtained as: lnA = lnA0 - λ / s × Z, with depth as the abscissa, ln( 210 Pb ex A linear fit is performed with α as the ordinate and s as the sedimentation rate. This yields the sedimentation rate and age of sediments within the last century. The sedimentary age of each layer is calculated based on the fitted equation, and then... 137 The Cs timescale (1963 peak) was calibrated.

[0034] Experimental results: sampling points 137 Cs and 210 The activity distribution diagram of Pb is shown below. Figures 3-6 The calculated sedimentation rates are shown in Table 2, and the corresponding ages are shown in Table 3.

[0035] Table 2: Deposition rate results at each location Table 3: Sedimentary Age Results at Each Location based on 210Pb-CIC mode and 137 Cs time-stamped joint calibration constructed a high-precision chronological sequence of historical sediments from four sampling points in Erhai Lake. The calculated average sedimentation rate at each point showed significant spatial differences: at point A in the north (0.26 cm·a) -1 > South Lake Center C point (0.21cm・a) -1 )> North Lake Center B point (0.197cm・a -1 > South Lake Center D point (0.15cm・a) -1 The spatial differences in sedimentation rates are closely related to the water dynamics of Erhai Lake, the intensity of human activities, and the characteristics of watershed erosion: The area surrounding point A in the north is the main livestock production area of ​​the Erhai Basin. Soil erosion caused by livestock farming and sediment carried into the lake by surface runoff drive the sedimentation rate higher than that in the lake center area. Frequent water exchange and large sediment input into the watershed exacerbate watershed erosion, resulting in the fastest sedimentation rate. Point D in the south is close to the natural outlet of the lake and is less affected by the rivers flowing into the lake. Furthermore, tourism development and agricultural activities are relatively weak, resulting in minimal disturbance to the sediments and the slowest sedimentation rate. The water dynamics in the lake center area (points B and C) are weak, the sediment deposition environment is stable, and the sediment input is low. Moreover, point B in the northern lake center is less affected by human activities than point C in the southern lake center, resulting in the lowest sedimentation rate.

[0036] II. High-throughput quantitative PCR (HT-qPCR) detection and historical evolution analysis.

[0037] 1. High-throughput qPCR detection of antibiotic resistance genes HT-qPCR was performed on the collected sediments using the WaferGen SmartChip real-time quantitative PCR system (WaferGen Inc., USA). A total of 297 genes were included: 16S, AAC(3)-Ia, AAC(3)-Ib, AAC(3)-Id, AAC(3)-Iic, AAC(3)-IId_IIa_IIe, AAC(3)-IV, AAC(3)-Via, AAC(3)-Xa, AAC(6')-Ib, AAC(6')-Ig, AAC(6')-IIa, AAC(6')-IIc, AAC(6')-Im ... )-Ip, AAC(6')-Ir, AAC(6')-Is, AAC(6')-Iv, AAC(6')-Iw, AAC(6')-Iz, aacA_aphD, aacA43, aadA, aa dA10, aadA16, aadA17, aadA2, aadA21, aadA5, aadA6, aadA7, aadA9, ACC-1, acrA, acrB, AcrF, acrR, ACT beta-lac, adeA, ANT(2'')-Ia, ANT(6), ANT(6)-Ia, ANT(6)-Ib, APH(3')-Ia, APH(3'')-Ia, APH(3')-Ib, APH(3')-VII Ia, APH(4)-Ib, APH(6)-Ia, APH(6)-Ic, APH(6)-Id, APH(9)-Ib, APH3-III, APHA3, apmA, arr-2, arr-3, arsA, bacA, BEL beta-lac, blaSFO, cadC, CARB beta-lac, CARB-2, cat, catB2, catB3, catB8, catB9, catIII, catP, catQ, CcrA, CcrA beta-lac, cefa_qacelta, ceoA, CfxA beta-lac, class C beta-lac, cmlA1, cmlA5, cmlv, cmx, CMY beta-lac, CMY-MOXbeta-lac, copA, CphA beta-lac, cphA2, cro, CTX-M beta-lac, CTX-M-1_3_15, czcA, dfrA1, dfrA10, dfrA12, dfrA14, dfrA15, dfrA17, dfrA21, dfrA22, dfrA25, dfrA27, dfrB4, dfrB-multi, dfrC, dfrK, DHAbeta-lac、EAE_05855、emrD、EreA、EreB、Erm(35)、Erm(36)、Erm(K)、e rm(O)、ErmA、ErmB、ErmD、ErmE、ErmF、ErmH、ErmQ、ErmS、fabK、floR、FOX beta-lac、GESbeta-lac、GOB beta-lac、HERA beta-lac、IMI beta-lac、IMIR beta-lac、IMP beta-lac、IncI1_repI1、IncN_rep、IncP_oriT、IncQ_oriT、IncW_trwAB、 intI1, intl2, intl3, IS1111, IS1133, IS1247, IS200-1, IS200-2, IS21-IS As29、IS256、IS26、IS3、IS6 / 257、IS6100、IS613、IS630、IS91、ISaba3-Ac ineto、ISCR1、ISEcp1、ISEfm1-Entero、ISPps1-pseud、ISSm2-Xanthob、L1 beta-lac、LEN beta-lac, lmrA, lnuA, lnuB, lnuC, lnuF, lsaC, marR, MCR-1.1, mdtA, mdtE, mdtG, mdtH, mef(B) and m el_1、mepA、merA-marko、MexA、MexB、MexE、MIRbeta-lac、mobA、mphA、mphB、msrC、msrE、mtrD、NDM beta-lac、nimE、nisB、OCH beta-lac, oleC, OprD, optrA, oqxA, orf37-IS26, orf39-IS26, OXA-10 、OXY-1-1、OXY-2-1、pAKD1-IncP-1β、pbrT、pBS228-IncP-1α、pcoA、PDC beta-lac、penA、pikR2、pmrA、qacA_B、qacF_H、qacH_351、QepA_1_2、QnrB4、QnrB46_47_48、QnrB-bo b_resign、QnrD、QnrS1_S3_S5、QnrS2、QnrVC1_VC3_VC6、QnrVC4_VC5_VC7、ROB-1、SAT-4、SHV-11、SME beta-lac、spec_aph、strA、sugE、sul1、sul2、sulA_folP、tcrB、TEMbeta-lac, terW, tet(39), tet(40), tet(44), tet32, tetA, tetA(P), tetB, tetB(P), tetC, tetD, tetE, tetG, tetH, tetJ, tetL, tetM, tetO, tetQ, tetR, tetS, tetT, tetW, tetX, TLA beta-lac, Tn3, TN5, TN5403, tnpA-1, tnpA-2, tnpA-3, tnpA-4, tnpA-5, tnpA-6, tnpA-7, tolC, Tp614, tra-A, trb-C, trf a, ttgA, ttgB, vanA, vanB, vanC, vanC2_vanC3, vanHB, vanHD, vanRB, vanTC, vanTE, vanWB, vanXB, vanYD, vatA, vatB, VEB beta-lac, vgaB, VIM beta-lac, traN, ecfX-P.aeruginosa, mecA-Staphylococci, gltA-K. pneumoniae and ompA-A. baumannii comprises 228 ARGs (covering aminoglycosides, β-lactams, macrolide-lincosamide-streptomycin B (MLSB) classes, tetracyclines, sulfonamides, vancomycins, multidrugs, etc.), 57 mobile genetic elements (MGEs): transposases, integrases, insertion sequences, plasmid-related genes, 10 MRGs (metal resistance genes), and 1 16S rRNA gene.

[0038] Reaction system (100 nL): 1×LightCycler 480 SYBR Green I Master (Roche, USA), 1 ng / μL bovine serum albumin, primer concentration 500 nM, 3 ng / μL DNA template, and nuclease-free water added to 100 nL. Three technical replicates were set up for each sample, and a negative control was included.

[0039] Thermal cycling conditions: 95℃ pre-denaturation for 10 min; 95℃ denaturation for 30 s, 60℃ annealing for 30 s, 40 cycles. Melting curves were automatically generated to verify amplification specificity. The cycle threshold (Ct) was automatically determined by SmartChip software, and the lower limit of detection was set to 31. Only data with amplification efficiency between 90% and 110% were used for analysis.

[0040] 2. Historical evolution data analysis By combining the gene abundance data of ARGs in each layer with the sediment age sequence, a historical evolution abundance map of ARGs was drawn to analyze the historical evolution patterns of ARG diversity and abundance.

[0041] Spatiotemporal distribution characteristics of different types of ARGs, MGEs, and MRGs in sediments from four areas of Erhai Lake ( Figure 7 This study revealed the spatial differentiation patterns of different types of genes. From the perspective of ARG types, the diversity of the four major classes of ARGs—aminoglycosides, β-lactamases, MLSBs, and tetracyclines—was significantly higher in the northern region (A) than in other regions. However, the diversity of vancomycin and trimethoprim ARGs showed no significant difference among the four regions, exhibiting a wide distribution and spatial uniformity. This pattern reflects the type-specific and region-specific nature of antibiotic selection pressure: aminoglycosides, β-lactamases, MLSBs, and tetracyclines are the four most widely used antibiotics in livestock and poultry farming and agricultural production in the Erhai Lake basin, mainly concentrated in the northern region. Therefore, their corresponding ARGs are under strong selective pressure in the northern region, resulting in significantly increased diversity. In contrast, vancomycin and trimethoprim antibiotics are used less frequently in the Erhai Lake basin and have a relatively uniform spatial distribution; therefore, their corresponding ARG diversity shows no significant regional differences.

[0042] From the evolutionary characteristics of ARGs ( Figure 8 and Figure 9 From 1880 to 1948 (the pre-antibiotic era), the abundance and diversity of ARGs were extremely low, with a very gradual trend. Abundance remained below 8.00E+06 copies / g, and the number of species was less than 10, indicating that under natural conditions without artificial antibiotic input, only a small number of natural resistance genes existed in Erhai Lake sediments, and their evolution rate was slow and regulated by the natural environment. After 1948, with the invention and widespread use of antibiotics, the abundance and diversity of ARGs began to rise slowly; after 1960, the abundance and diversity of ARGs increased rapidly, and the rate of increase accelerated further after 2000. In 2020, the abundance of ARGs approached 4.00E+07 copies / g, and the number of species reached 60, representing increases of 5 times and 6 times respectively compared to before the invention of antibiotics. MGEs, as the carriers of ARGs, have always evolved around the proliferation of ARGs and are "response factors" to the characteristics of ARGs. The synchronous evolution of MGEs and ARGs is a direct manifestation of their close relationship. The selective pressure of antibiotics promotes the proliferation of ARGs, and the proliferation of ARGs in turn promotes the activation and evolution of MGEs. This is because MGEs can enhance the antibiotic resistance of microorganisms by mediating the horizontal gene transfer of ARGs, thereby gaining a survival advantage under selective pressure.

[0043] The evolution rate of ARGs and MGEs after the invention of antibiotics was much higher than before, directly confirming that artificial antibiotic input is the core factor driving the historical evolution of ARGs in Erhai Lake sediments. The rapid increase after 1960 was closely related to the rapid development of the global antibiotic industry and the intensification of human activities in the Erhai Lake basin (such as the large-scale livestock and poultry farming and the intensification of agricultural planting). The further acceleration after 2000 reflects that the rapid socio-economic development of the Erhai Lake basin brought stronger pollutant input, which in turn promoted the rapid enrichment of ARGs and MGEs.

[0044] Figure 10 The study presents the relative abundance characteristics of different types of ARGs and MGEs in sediments from four regions of Erhai Lake. The core patterns are characterized by significant spatial differentiation and clear type characteristics.

[0045] Almost all types of ARGs and MGEs exhibited a relative abundance distribution pattern of northern region (A) > northern lake center (B) > southern lake center (C) > southern region (D). The peak relative abundance of total ARGs in the northern region was close to 1E-2, while in the southern region it was only below 1E-6, a difference of several orders of magnitude. This pattern ruled out the influence of microbial biomass and directly confirmed the stronger antibiotic selective pressure in the northern watershed. The large amount of antibiotic input in the northern region caused microorganisms to carry a large number of ARGs in their bodies to adapt to environmental pressure, resulting in a significant increase in the relative abundance of ARGs per unit of microorganism. In contrast, the antibiotic selective pressure in the southern region was weak, and microorganisms did not need to carry a large number of ARGs to survive, thus the relative abundance was significantly lower.

[0046] The relative abundance of macrolide antibiotics was the highest among MLSB, aminoglycoside, and tetracycline ARGs. Among them, the relative abundance of MLSB ARGs in the northern region was close to 1E-2, which was the highest among all ARG types, reflecting that macrolide antibiotics had the most significant selection pressure in the Erhai Lake basin. In contrast, the relative abundance of vancomycin and trimethoprim ARGs was the lowest, and the spatial differentiation was not significant, reflecting that the selection pressure of these antibiotics was relatively weak.

[0047] Furthermore, sulfonamide ARGs exhibited specific enrichment characteristics in the northern region, with their relative abundance several times higher than in other areas. This is closely related to the development of animal husbandry in the Erhai Lake basin. Sulfonamide antibiotics are among the most widely used broad-spectrum antibiotics in livestock and poultry farming. The large-scale pig and dairy farming in the northern Erhai Lake basin has led to a large amount of sulfonamide antibiotics entering the lake with the wastewater, creating strong selective pressure and thus promoting the specific enrichment of sulfonamide ARGs.

[0048] Absolute abundance directly reflects the actual content of ARGs and MGEs in sediments and is a core indicator for assessing the environmental risk of antibiotic resistance pollution. Figure 11 This study presents the absolute abundance characteristics of different types of ARGs and MGEs in sediments from four regions of Erhai Lake. In the northern region (A), the peak absolute abundance of total ARGs is close to 1E+7 copies / g, and the absolute abundance of total MGEs is approximately 5E+6 copies / g, both the highest among the four regions. In the southern region (D), the absolute abundance of total ARGs is only 1E+2 copies / g, and the absolute abundance of total MGEs is less than 1E+2 copies / g, a difference of nearly several orders of magnitude. This significant spatial difference is the result of the combined effect of microbial biomass and the carrying capacity per unit of microorganism, thus exhibiting a significant enrichment characteristic in absolute abundance. Conversely, the southern region has low microbial biomass and a low carrying capacity per unit of microorganism, resulting in extremely low absolute abundance.

[0049] Aminoglycosides, MLSBs, and tetracyclines had the highest absolute abundance, accounting for over 70% of the total absolute abundance of ARGs. These are the core dominant resistance gene types in Erhai Lake sediments, and their environmental risk is relatively higher. Vancomycin and trimethoprim ARGs had the lowest absolute abundance, and their environmental risk is relatively lower. Among MGEs, transposases and insertion sequences had the highest absolute abundance, accounting for over 80% of the total absolute abundance of MGEs. These are the core carrier types mediating the horizontal spread of ARGs. Their high absolute abundance indicates a strong potential for ARG spread in the northern region, further increasing the risk of antibiotic resistance contamination in this area.

[0050] This invention uses 1m columnar sediments from four typical areas of Erhai Lake—northern, northern lake center, southern lake center, and southern region—as research objects. HT-qPCR technology was employed to systematically analyze the diversity, abundance, and spatiotemporal evolution characteristics of ARGs, MGEs, and MRGs. The invention systematically reveals the century-long historical occurrence patterns and core driving mechanisms of ARGs in Erhai Lake sediments, filling a gap in the study of ARG historical evolution in the Erhai Lake basin. It provides scientific data and theoretical support for risk assessment and precise control of antibiotic resistance pollution in plateau lakes. This study reveals the core spatiotemporal differentiation patterns of ARGs and MGEs in Erhai Lake sediments: Spatially, it exhibits a significant differentiation characteristic of high concentrations in the north and low concentrations in the south. The northern region is the core enrichment area of ​​antibiotic-resistant pollution, with significantly higher diversity and abundance of ARGs and MGEs compared to the southern region. This is closely related to the input of antibiotics and exogenous microorganisms brought about by the high intensity of livestock and poultry farming in the northern basin. Temporally, it shows a century-long evolutionary pattern of high concentrations at the surface and low concentrations at the depths. In the pre-antibiotic era (1880-1948), the abundance and variety of ARGs and MGEs were at low levels and their evolution was slow. After 1960, with the widespread use of antibiotics and the intensification of human activities in the basin, they increased exponentially and rapidly. This acceleration further accelerated after 2000, confirming that artificial antibiotic input is the core factor driving the historical evolution of ARGs, and that MGEs and ARGs exhibit highly synchronous evolutionary characteristics.

[0051] It should be noted that when numerical ranges are mentioned in the claims of this invention, it should be understood that the two endpoints of each numerical range and any value between the two endpoints can be selected. To avoid redundancy, the present invention describes preferred embodiments.

[0052] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0053] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for assessing the chronological evolution of antibiotic resistance genes in lake sediments, characterized in that, The method includes the following steps: S1. Collect lake sediment core samples and layer the sediment core samples at 1-3 cm intervals to obtain layered sediment samples; S2. Determine the age of stratified sediment samples and construct a sediment age sequence; S3. Determine the abundance of antibiotic resistance genes in stratified sediment samples; S4. Combining the sedimentary chronology sequence with the abundance of antibiotic resistance genes in the stratified sediment samples, draw a historical evolution diagram of the abundance of antibiotic resistance genes with the chronology sequence to complete the assessment of the chronological evolution of antibiotic resistance genes in lake sediments.

2. The method according to claim 1, characterized in that, The sampling depth described in S1 is 0.5~1.5m.

3. The method according to claim 1, characterized in that, The dating of the stratified sediment samples described in S2 is based on... 210 Pb, 137 Cs dating was performed using the constant specific activity CIC model. 210 Pb dating was used to calculate the depositional age of each sedimentary layer sample, in order to... 137 The position of the Cs accumulation peak was used as a time marker to calibrate the calculation results.

4. The method according to claim 1, characterized in that, The abundance of antibiotic resistance genes described in S3 was detected by high-throughput quantitative PCR.

5. The method according to claim 4, characterized in that, The abundance data of the antibiotic resistance genes are obtained through the following steps: using DNA extracted from the stratified sediment sample as a template, amplification and detection are performed using a high-throughput quantitative PCR system, and the absolute abundance and / or relative abundance of each antibiotic resistance gene are calculated based on the cycle threshold and amplification efficiency.

6. The method according to claim 5, characterized in that, The detection limit of the cycle threshold is 30~32, and the screening range of the amplification efficiency is between 90% and 110%.

7. The method according to claim 5, characterized in that, The antibiotic resistance genes include one or more of the following: aminoglycosides, β-lactams, MLSBs, tetracyclines, sulfonamides, vancomycins, and multidrug resistance genes.

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