This readme.txt file was generated on 2022-09-29 by Huan He Recommended citation for the data: He, Huan; Bueno, Irene; Kim, Taegyu; Wammer, Kristine H.; LaPara, Timothy M.; Singer, Randall S.; Beaudoin, Amanda; Arnold, William A.. (2022). Determination of the antibiotic and antibiotic resistance footprint in surface water environment of a metropolitan area: Effects of anthropogenic activities. Retrieved from the Data Repository for the University of Minnesota. https://doi.org/10.13020/8XP6-RQ86. ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset: Determination of the antibiotic and antibiotic resistance footprint in surface water environment of a metropolitan area: Effects of anthropogenic activities 2. Author Information Author Contact: Huan He (heh@umn.edu); William A. Arnold (arnol032@umn.edu) Name: Huan He Institution: Department of Civil, Environmental, and Geo- Engineering, University of Minnesota Twin Cities, Minneapolis, MN, United States Email: heh@umn.edu ORCID: 0000-0002-0550-0974 Name: Irene Bueno Institution: Department of Veterinary and Biomedical Sciences, University of Minnesota Twin Cities, St. Paul, MN, United States Email: bueno004@umn.edu ORCID: N.A. Name: Taegyu Kim Institution: Department of Civil, Environmental, and Geo- Engineering, University of Minnesota Twin Cities, Minneapolis, MN, United States Email: kim00235@umn.edu ORCID: 0000-0002-6310-5323 Name: Kristine H. Wammer Institution: Department of Chemistry, College of Arts & Sciences, University of St Thomas, St Paul, MN, United States Email: kmwammer@stthomas.edu ORCID: 0000-0003-2067-892X Name: Timothy M. LaPara Institution: Department of Civil, Environmental, and Geo- Engineering, University of Minnesota Twin Cities, Minneapolis, MN, United States Email: lapar001@umn.edu ORCID: 0000-0002-5653-5309 Name: Randall S. Singer Institution: Department of Veterinary and Biomedical Sciences, University of Minnesota Twin Cities, St. Paul, MN, United States Email: rsinger@umn.edu ORCID: 0000-0002-5461-9330 Name: Amanda Beaudoin Institution: Minnesota Department of Health, P.O. Box 64975, St. Paul, United States Email: amanda.beaudoin@state.mn.us ORCID: N.A. Name: William A. Arnold Institution: Department of Civil, Environmental, and Geo- Engineering, University of Minnesota Twin Cities, Minneapolis, MN, United States Email: arnol032@umn.edu ORCID: 0000-0003-0814-5469 3. Date published or finalized for release: 2022-09-26 4. Date of data collection (single date, range, approximate date): 2020-06-30 to 2022-09-12 5. Geographic location of data collection (where was data collected?): Minnesota State (primarily in the Twin Cities metropolitan area), United States 6. Information about funding sources that supported the collection of the data: Minnesota Environmental and Natural Resources Trust Fund as recommended by the Legislative-Commission on Minnesota Resources 7. Overview of the data (abstract): This study investigated geospatial distributions of antibiotics and antibiotic resistance genes (ARGs) in surface waters and their associations with anthropogenic activities. During July to October 2020, the concentrations of antibiotics (water and sediment) and ARGs (sediment) were measured at 39 sites in the Twin Cities metropolitan area (Minnesota) that experience a gradient of impacts related to human activities. For water samples, the number of antibiotics detected and the concentrations of certain antibiotics (e.g., sulfonamides) positively correlated with urbanization indicators (e.g., urban percentage, population density, number of wastewater discharge points; Spearman's rho = 0.32 to 0.46, p =0.003-0.04) and negatively correlated with undeveloped land indicators (e.g., forest; Spearman's rho = -0.34 to -0.62, p =<0.00001-0.04). Antibiotics in sediments exhibited geospatial distribution different from that in corresponding water samples and exhibited no associations with anthropogenic factors. Relative abundances of ARGs were not associated with anthropogenic factors, but several ARGs (e.g., blaoxa, mexB, and sul2) were inversely related to the organic content of sediments (Spearman's rho = -0.38 to -0.44, p =0.01-0.04). Strong correlations were found among relative abundances of various ARGs and intI1 (rho >= 0.67, p < 0.05), highlighting their co-occurrence in (sub)urban surface waters. These results identified promising anthropogenic/environmental factors for predicting antibiotic geospatial distributions and useful gene markers to monitor ARGs in surface waters. -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC0 1.0 Universal (http://creativecommons.org/publicdomain/zero/1.0/) 2. Links to publications that cite or use the data: N.A. (the manuscript is currently under review) 3. Was data derived from another source? No If yes, list source(s): N.A. 4. Terms of Use: Data Repository for the U of Minnesota (DRUM) By using these files, users agree to the Terms of Use. https://conservancy.umn.edu/pages/drum/policies/#terms-of-use --------------------- DATA & FILE OVERVIEW --------------------- File List Filename: Antibiotics & ARGs_Locations_Concentrations_Recoveries_092122.xlsx Short description: Antibiotics & ARGs_Locations_Concentrations_Recoveries -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: Antibiotics in Grab samples of water and sediments were collected during July-October 2020 from a total of 39 surface water sites (including lakes, rivers, and creeks) in Minnesota (MN), USA. Water and sediment samples were processed using solid phase extraction (SPE) and ultrasound-assisted extraction (UAE) methods, respectively, for extraction of antibiotics. Then the extracts of water/sediments were analyzed on a Thermo Scientific TSQ Vantage triple quadrupole tandem mass spectrometer (LC-MS/MS) equipped with a Waters Xselect CSH C18 (3.5 um, 130 Å, 50×2.1 mm) column, for analysis of a total of 24 target antibiotics. Sediment samples were also processed with DNA extraction, and the DNA extracts were analyzed with quantitative polymerase chain reaction (qPCR) assays for determination of 23 resistance-associated genes as well as the 16S rRNA gene contained in the sediment biomass. 2. Methods for processing the data: For determination of the target antibiotics and genes by LC-MS/MS and qPCR assays, respectively, calibration standards were included in each assay to generate calibration curves through linear regressions. Concentrations of antibiotics and genes in the samples were back-calculated based on the calibration curves. For antibiotics in water and sediment samples, concentrations were corrected with extraction recoveries, which were determined by spiking the target antibiotics with known concentrations to the samples. For resistance-associated genes in sediments, their relative abundances were calculated by dividing their concentrations over the 16S rRNA gene concentration in the corresponding sample. 3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel. Preservation format files also available in CSV. 4. Standards and calibration information, if appropriate: Information about standards and calibration of target antibiotics and genes are provided in the manuscript under review. In brief, standards of antibiotics were purchased from commercial suppliers and stock solutions at concentrations of 100 ppm (or mg/L) were prepared in methanol. Calibration standards of antibiotics were prepared in 20 mM ammonium acetate at concentrations of 0.5 - 500 ppb (or ug/L). Target gene standards were prepared by serially diluting synthetic double-stranded DNA segments (gBlocks® gene fragments; Integrated DNA Technologies) containing the sequence of the targeted gene, typically at concentrations of 7 to 8-order magnitude (e.g., 10^7 - 10^1 gene copies per reaction). 5. Environmental/experimental conditions: Samples were collected during the summer to early fall (July - October) of 2020. The basic environmental conditions of the samples (including pH, dissolved oxygen, and conductivity of water samples, and water, organic carbon, and the carbonate/inorganic content of sediment samples) are provided in the manuscript under review. 6. Describe any quality-assurance procedures performed on the data: For determination of antibiotics in water and sediment samples, extraction recoveries were determined by spiking the target antibiotics with known concentrations to the samples. The final antibiotic concentrations were all recovery corrected unless otherwise specified. Method blanks (i.e., ultrapure water and Ottawa sand for water and sediments, respectively) were included for every ~10 events of extractions to assess background contamination. For qPCR assays of the target genes, DNA extracts were 10 or 100-fold diluted prior to qPCR reactions to prevent inhibition of amplification; The amplification curves of all samples were visually compared to the amplification curves of standards to evaluate the possibility of the inhibition of amplification. No-template controls were included with all qPCR assays. 7. People involved with sample collection, processing, analysis and/or submission: All the authors listed above were involved in sample collection, processing, analysis and/or submission of this work. Besides, Dr. Matthew Berens and undergraduate students from the University of Minnesota (Wenjie Yuki Fan) and the University of Saint Thomas (Sarah J. Ziemann and Lauren Degn) provided assistance in water sampling and sample processing. Alex, Ben, and Lori Arnold and Greg Wammer provided assistance with sediment collection. Researchers at the University of Minnesota Cancer Center Mass Spectrometry Facility (Dr. Peter Villalta, Dr. Jingfang Huang, Dr. Yingchun Zhao, and Makenzie Pillsbury) and the Limnological Research Center LacCore Facility (Jessica Heck and Maya Grantier) provided technical support in LC-MS/MS analysis and sediment processing, respectively. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: Antibiotics & ARGs_Locations_Concentrations_Recoveries_092122.xlsx: 1.ABX_water_conc. sheet ----------------------------------------- Notes: For variables C-AF (concentrations of individual antibiotic compounds or total concentrations of each antibiotic class), "Number of non-detects" (in Row 48), "Number of detects" (in Row 49), and "Detection frequency (%)" (in Row 50) were calculated. "Number of non-detects" represents the number of samples with concentrations below method detection limits (MDLs); "Number of detects" represents the number of samples with concentrations no less than method detection limits (MDLs); and "Detection frequency" for each variable was calculated using "Number of detects" divided over the total number of samples (i.e., 39 for water samples in sheet 1). 1. Number of variables: 32 2. Number of cases/rows: 39 3. Missing data codes: None 4. Variable List A. Name: Description: B. Name: Description: C. Name: Description: D. Name: Description: E. Name: Description: F. Name: Description: G. Name: Description: H. Name: Description: I. Name: Description: J. Name: Description: K. Name: Description: L. Name: Description: M. Name: Description: N. Name: Description: O. Name: Description: P. Name: Description: Q. Name: Description: R. Name: Description: S. Name: Description: T. Name: Description: U. Name: Description: V. Name: Description: W. Name: Description: X. Name: Description: Y. Name: Description: Z. Name: Description: AA. Name: Description: Value labels if appropriate AB. Name: Description: AC. Name: Description: AD. Name: Description: AE. Name: Description: AF. Name: Description: AG. Name: Description: AH. Name: Description: ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: Antibiotics & ARGs_Locations_Concentrations_Recoveries_092122.xlsx: 2.ABX_sedimt_conc. sheet ----------------------------------------- Notes: For variables C-AF (concentrations of individual antibiotic compounds or total concentrations of each antibiotic class), "Number of non-detects" (in Row 39), "Number of detects" (in Row 40), and "Detection frequency (%)" (in Row 41) were calculated. "Number of non-detects" represents the number of samples with concentrations below method detection limits (MDLs); "Number of detects" represents the number of samples with concentrations no less than method detection limits (MDLs); and "Detection frequency" for each variable was calculated using "Number of detects" divided over the total number of samples (i.e., 30 for sediment samples in sheet 2). 1. Number of variables: 32 2. Number of cases/rows: 30 3. Missing data codes: None 4. Variable List A. Name: Description: B. Name: Description: C. Name: Description: D. Name: Description: E. Name: Description: F. Name: Description: G. Name: Description: H. Name: Description: I. Name: Description: J. Name: Description: K. Name: Description: L. Name: Description: M. Name: Description: N. Name: Description: O. Name: Description: P. Name: Description: Q. Name: Description: R. Name: Description: S. Name: Description: T. Name: Description: U. Name: Description: V. Name: Description: W. Name: Description: X. Name: Description: Y. Name: Description: Z. Name: Description: AA. Name: Description: AB. Name: Description: AC. Name: Description: AD. Name: Description: AE. Name: Description: AF. Name: Description: AG. Name: Description: AH. Name: Description: ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: Antibiotics & ARGs_Locations_Concentrations_Recoveries_092122.xlsx: 3.ABX_water_recovery sheet ----------------------------------------- 1. Number of variables: 37 2. Number of cases/rows: 117 3. Missing data codes: None 4. Variable List A. Name: Description: B. Name: Description: C. Name: Description: D. Name: Description: E. Name: Description: F. Name: Description: G. Name: Description: H. Name: Description: I. Name: Description: J. Name: Description: K. Name: Description: L. Name: Description: M. Name: Description: N. Name: Description: O. Name: Description: P. Name: Description: Q. Name: Description: R. Name: Description: S. Name: Description: T. Name: Description: U. Name: Description: V. Name: Description: W. Name: Description: X. Name: Description: Y. Name: Description: Z. Name: Description: AB. Name: Description: AC. Name: Description: AD. Name: <13C6-Sulfamethazine (surrogate for sulfonamides)> Description: AE. Name: <13C6-Sulfamethoxazole (internal standard for sulfonamides)> Description: AF. Name: <13C2-Erythromycin(-H2O) (internal standard for macrolides)> Description: AG. Name: Description: AH. Name: Description: AI. Name: Description: AJ. Name: Description: AK. Name: Description: AL. Name: Description: ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: Antibiotics & ARGs_Locations_Concentrations_Recoveries_092122.xlsx: 4.ABX_sedimt_recovery ----------------------------------------- 1. Number of variables: 37 2. Number of cases/rows: 90 3. Missing data codes: None 4. Variable List A. Name: Description: B. Name: Description: C. Name: Description: D. Name: Description: E. Name: Description: F. Name: Description: G. Name: Description: H. Name: Description: I. Name: Description: J. Name: Description: K. Name: Description: L. Name: Description: M. Name: Description: N. Name: Description: O. Name: Description: P. Name: Description: Q. Name: Description: R. Name: Description: S. Name: Description: T. Name: Description: U. Name: Description: V. Name: Description: W. Name: Description: X. Name: Description: Y. Name: Description: Z. Name: Description: AB. Name: Description: AC. Name: Description: AD. Name: <13C6-Sulfamethazine (surrogate for sulfonamides)> Description: AE. Name: <13C6-Sulfamethoxazole (internal standard for sulfonamides)> Description: AF. Name: <13C2-Erythromycin(-H2O) (internal standard for macrolides)> Description: AG. Name: Description: AH. Name: Description: AI. Name: Description: AJ. Name: Description: AK. Name: Description: AL. Name: Description: ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: Antibiotics & ARGs_Locations_Concentrations_Recoveries_092122.xlsx: 5.ARG_sedimt sheet ----------------------------------------- Notes: For variables C-AC (qPCR raw data, i.e., gene concentration (copies/uL) measured in 100 uL DNA extracts after 10-fold dilution), method detection limits (MDLs) are provided for each gene in Rows 40-41. MDLs are provided in two forms: (a) in Row 40, MDLs in units of copies per reaction; and (b) in Row 41, MDLs in units of copies per uL, which was calculated using "MDLs in units of copies per reaction" divided over "2uL per reaction". For variables AE-BB (gene concentration in sediments (log10 copies/g dry sediment)), "Number of detects" (in Row 40), and "Detection frequency (%)" (in Row 41) were calculated. "Number of detects" represents the number of samples with gene concentrations no less than method detection limits (MDLs); and "Detection frequency" for each variable was calculated using "Number of detects" divided over the total number of samples (i.e., 30 for sediment samples in sheet 5). For variables C-AC, the blank cells represent the samples yielded "no amplification" in qPCR reaction, i.e., no signal of the target gene was detected. For variables AE-BY (gene concentration in sediments (log10 copies/g dry sediment) or Gene relative abundance in sediments (log10 copies/copy 16S rRNA gene)), "nd" represents the samples having gene concentrations below MDLs (including those yielding no amplification); "false negative" represents the samples yielding results above MDLs but having amplification curves different from the calibration standards (thus identified as false negatives), and in this study, two genes sul3 and ermB were identified as false negatives for all the samples and labeled with "*" in the corresponding variable names. 1. Number of variables: 77 2. Number of cases/rows: 30 3. Missing data codes: [wait for answer] 4. Variable List A. Name: Description: B. Name: Description: C. Name: Description: D. Name: Description: E. Name: Description: F. Name: <16S RNA gene> Description: G. Name: Description: H. Name: Description: I. Name: Description: J. Name: Description: K. Name: Description: L. Name: Description: M. Name: Description: N. Name: Description: O. Name: Description: P. Name: Description: Q. Name: Description: R. Name: Description: S. Name: Description: T. Name: Description: U. Name: Description: V. Name: Description: W. Name: Description: X. Name: Description: Y. Name: Description: Z. Name: Description: AA. Name: Description: AB. Name: Description: AC. Name: Description: AE. Name: <16S RNA gene> Description: AF. Name: Description: AG. Name: Description: AH. Name: Description: AI. Name: Description: AJ. Name: Description: AK. Name: Description: Value labels if appropriate AL. Name: Description: AM. Name: Description: AN. Name: Description: AO. Name: Description: AP. Name: Description: AQ. Name: Description: AR. Name: Description: AS. Name: Description: AT. Name: Description: AU. Name: Description: AV. Name: Description: AW. Name: Description: AX. Name: Description: AY. Name: Description: AZ. Name: Description: BA. Name: Description: BB. Name: Description: BC. Name: Description: BD. Name: Description: BE. Name: Description: BF. Name: Description: BG. Name: Description: BH. Name: Description: BI. Name: Description: BJ. Name: Description: BK. Name: Description: BL. Name: Description: BM. Name: Description: BN. Name: Description: BO. Name: Description: BP. Name: Description: BQ. Name: Description: BR. Name: Description: BS. Name: Description: BT. Name: Description: BU. Name: Description: BV. Name: Description: BW. Name: Description: BX. Name: Description: BY. Name: Description: BZ. Name: Description: