https://orcid.org/0000-0002-4188-6966
Figures
Abstract
Antibiotic-resistant (AR) bacterial infections are disproportionately experienced by Black communities in the U.S. Exposure to AR bacteria could occur when untreated sewage enters homes through sanitary sewer overflows (SSOs) or basement sewage backups (“backups”). Through a community-engaged pilot study in Baltimore, Maryland, which has a large Black population where many live below the poverty line, we evaluated the presence of methicillin-resistant Staphylococcus aureus (MRSA), methicillin-susceptible S. aureus (MSSA), coagulase-negative staphylococci (CoNS), methicillin-resistant CoNS (MR-CoNS), and E. coli in homes experiencing sewage events. We collected surface (n = 41) and standing water (n = 6) samples from 40 homes in neighborhoods where SSOs or backups frequently occur. Samples were processed using modified standard membrane filtration. A subset of isolates underwent whole genome sequencing (WGS). Among participants, 95% (38/40) identified as Black and 95% (38/40) self-reported having an SSO or backup. Over 72% (29/40) of participants had a sewage event within six months of sampling, and 42.5% (17/40) had an event less than one month prior. Nearly 17% (1/6) of water samples were positive for MRSA and MR-CoNS and 66.7% (4/6) positive for CoNS and E. coli. No MRSA was found on sampled surfaces, but multidrug-resistant (MDR) MSSA, MR-CoNS, CoNS and E. coli were detected on 2.4% (1/41), 12.2% (5/41), 80.5% (33/41), and 26.8% (11/41) of surfaces, respectively. Detection of target bacteria differed significantly between homes with events that occurred less than one month compared to those where events occurred more than one month prior to sample collection (p = 0.046). Five of six isolates analyzed by WGS for ARGs were MDR and 85 distinct virulence genes were identified among isolates analyzed for VFGs (n = 3). Our results suggest that SSOs or backups could be a source of AR bacteria exposure in underserved communities, and therefore a critical research area for public health and urban planning.
Citation: Woerner EMH, Anderson-Coughlin BL, Alves PBR, Levell-Young T, Barlow CM, Smith-Hams T, et al. (2025) Antibiotic-resistant bacteria detected in homes impacted by sewage. PLOS Water 4(6): e0000375. https://doi.org/10.1371/journal.pwat.0000375
Editor: Silvia Monteiro,, Universidade de Lisboa Instituto Superior Tecnico, PORTUGAL
Received: December 19, 2024; Accepted: April 21, 2025; Published: June 5, 2025
Copyright: © 2025 Woerner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All laboratory analysis data are within the manuscript as well as summarized human subjects data. No individual level participant data can be shared as stipulated by the University of Maryland Institutional Review Board (IRB) approval for this project. Contact information for the University of Maryland IRB can be found here: https://research.umd.edu/irb
Funding: This work was supported by the JPB Environmental Health Fellowship Program at the T.H. Chan School of Public Health of Harvard University; Award ID: 030555-00001 (to MDH). EMH was supported by NRT-INFEWS: UMD Global STEWARDS (STEM Training at the Nexus of Energy, WAter Reuse and FooD Systems) that was awarded to the University of Maryland School of Public Health by the National Science Foundation National Research Traineeship Program, Grant number 1828910. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
In the United States, there are nearly 3 million antibiotic-resistant (AR) bacterial infections each year that contribute to approximately 35,000–48,000 deaths [1]. AR bacterial infections are projected to be the leading cause of death by 2050 [1]. Methicillin-resistant Staphylococcus aureus (MRSA) has been identified by the Centers for Disease Control and Prevention (CDC) as a serious threat, having caused approximately 323,700 infections and 10,600 deaths in 2017 out of the total number of AR bacterial infections and deaths [2]. The antibiotic susceptible form of S. aureus is also a public health concern (Table 1). While methicillin-susceptible S. aureus (MSSA) is a commensal organism commonly found in the nose of approximately 20–30% of the population [8–10], it is also an opportunistic pathogen. MSSA caused over 119,000 bloodstream infections in 2017 in the United States [3]. The wide range of virulence factor genes (VFGs) that can be present in S. aureus allows for the pathogen to impact multiple organs and cause infections, including meningitis, pneumonia, osteomyelitis, and sepsis [11]. Coagulase-negative staphylococci (CoNS) are part of the normal skin and mucosal microbiome [12,13] (8,9). Once thought to be simple commensal organisms, CoNS have emerged as effective opportunistic pathogens causing nosocomial infections [12–14]. Infections from CoNS can be problematic due to the prevalence of multidrug resistant strains [13,15]. Table 1 outlines the public health significance of the various Staphylococcus species described above.
Traditionally believed to have healthcare/hospital-related origins, several studies over the past decade have identified environmental sources of AR bacteria, MSSA, and CoNS including wastewater [16–20]. For example, MRSA can enter the wastewater stream when colonized humans shed the bacteria from the nose, skin, and feces [16,17,21,22]. MSSA found in freshwater has been associated with proximity to wastewater treatment plant discharge [9]. A 2017 study identified reclaimed wastewater as a potential exposure pathway for CoNS and methicillin-resistant coagulase-negative staphylococci (MR-CoNS) (Table 1) [14]. MR-CoNs colonization was higher in individuals conducting spray irrigation with reclaimed wastewater compared to the control group of office workers [14]. AR CoNS could serve as a source of resistance genes for S. aureus [15,23].
Enteric bacteria, such as E. coli, enter the wastewater stream from human and animal feces or contaminated water from bathing or laundry [24]. E. coli are Gram-negative bacteria that are used as an indicator of fecal contamination in water quality studies and, depending on the species, can be opportunistic pathogens. In Lake Michigan, E. coli levels were elevated after heavy rainfall and linked to combined sewer overflows (CSOs) and sanitary sewer overflows (SSOs) [25]. SSOs occur when untreated sewage is released from municipal sewer systems [26]. SSOs can occur when extreme precipitation events overwhelm the capacity of infrastructure, particularly aging infrastructure, which can cause sewage laden water to overflow [26,27]. SSOs occur across the globe and throughout the U.S. The U.S. Environmental Protection Agency (EPA) estimates that at least 23,000–75,000 SSOs occur each year in the U.S., although this estimate does not include sewage backups into buildings (e.g., homes and built structures), sometimes referred to as basement backups (“backups”) [26]. SSOs and backups are connected in the context of the illicit discharge of untreated sewage, but diverge in the exact location of that discharge. Although underestimating the true extent of sewage discharges, the EPA SSO estimates are the best national U.S. data source currently available for understanding how pervasive backups might be. However, some local and state government agencies define SSOs to include sewage backups that occur inside private homes, including Baltimore City, Maryland, USA. The Baltimore City Health Department defines SSOs as occurring “when there is a blockage or break in the sanitary sewer line causing wastewater to flow out of the collection system” further stating that “[t]hey can occur either inside or outside of a home or building, in the middle of the street, or even near a stream” [28]. The Baltimore sewer system has frequent SSOs due to an old and failing system and more frequent intense rainfall events associated with climate change [27,29,30]. According to the Chesapeake Bay Foundation, there were more than 3,900 SSOs in Baltimore, Maryland from 2011-2017 spilling more than 400 million liters of sewage into the city’s streets, homes, streams, and rivers and eventually the Chesapeake Bay. Whereas the Baltimore City Department of Public Works estimated that over 1.6 million gallons of sewage overflowed in Baltimore from May to August 2022 [31]. Differences in SSOs estimates could be the result of numerous factors including changes over time as well as the operational definition being used, data sources, and formal reporting. In Baltimore, estimates of SSOs and backups come through residents’ reports to a city-operated non-emergency phone line (311) or online system. The development of storm and sewer infrastructure to manage and convey runoff and waste have effectively lagged behind these changing environmental conditions that have become more frequent and intense [32]. Furthermore, it is suspected that although Black communities are not inherently more susceptible to risks or pathogens, due to historic discrimination in planning and investment, they likely occupy built environments that are in disrepair [33,34]. Previous community-engaged investigations have found that SSOs occur more often in neighborhoods with a high percentage of Black residents [35]. As a result, these communities are persistently exposed to raw sewage, likely containing waterborne pathogens and possibly AR bacteria. Residents can be exposed in their homes to pathogens contained in untreated wastewater as a result of SSOs and backups, either during these events or through contact with the wastewater or surfaces impacted by the wastewater following these events.
Our study aims to address critical knowledge gaps regarding the presence of MRSA, MSSA, MR-CoNS, or E. coli on surfaces (i.e., walls and floors) in homes impacted by SSOs or backups, or standing water resulting from these events. Further, while we know that wastewater can be a source of AR bacteria, there is a lack of research on the presence of virulence factors of AR bacteria in standing water from sewage backup and overflow events and home surfaces impacted by these events [36]. Using whole genome sequencing can provide additional information about antibiotic resistance genes, virulence factors, and relatedness to isolates from known sources. We used a collaborative approach that involved community members, researchers, and stakeholder organizations working together throughout project conception, field sampling, and analysis. If AR bacteria and other pathogens are present in homes that have been impacted by sewage, home surfaces contaminated by these events could represent an important transmission route for AR bacteria. This is of particular concern when considering environmental justice, defined by EPA as the just treatment and equitable inclusion of all people in decisions that impact human and environmental health [37], because Black communities often have less well-maintained infrastructure due to structural disinvestment and also face disproportionately higher AR infection rates, including MRSA [33,38].To better understand the risk of AR exposure in homes due to sewage-related events, we conducted a pilot study in Baltimore, Maryland, to evaluate the presence of MRSA, MSSA, MR-CoNS, and E. coli on surfaces and in standing water in impacted homes.
Materials and methods
Ethics statement
The survey, environmental sampling, and consent approach were approved by the University of Maryland Institutional Review Board project number 1770058. Participants provided informed verbal consent prior to completing the survey. The participants were asked to read the consent statement and provide verbal consent as witnessed by the research team members and community partners. Participants could receive a copy of the consent form to keep upon request. All participants were 18 years of age or older. The recruitment period began on May 23, 2022 and ended on August 7, 2022.
Study sites
Samples were collected from May-August 2022 from 40 homes in two Baltimore neighborhoods identified by our community partners as having high occurrences of SSOs and/or backups. Study participants were recruited through snowball convenience sampling starting with impacted residents identified by our community partners and door-to-door recruitment in target neighborhoods. Additional detailed information about our community-engaged approaches is described in [27]. Baltimore is a city with 63% Black residents and 19% of the population living in poverty. To maintain confidentiality, the two neighborhoods are referred to as Neighborhood A and B in this manuscript. Neighborhood A is a lower-income majority Black community in south Baltimore. Neighborhood A consists of over 3,000 housing units with nearly 8,000 residents. Neighborhood B is a mixed-income majority Black community in northeast Baltimore. Neighborhood B has almost double the residents as Neighborhood A, with approximately 15,000 residents living in around 6,500 housing units. Approximately 90% of the residents living in Neighborhood A and B are Black [39]. Community participation was led by our community partners including the non-profit organization Blue Water Baltimore (BWB) as well as community leaders and residents from Neighborhoods A and B.
Sample collection
A total of 41 surface swabs (one swab per home, except for one home where two swabs were collected at the request of the participant) and six standing water samples were collected from Baltimore Neighborhoods A (n = 22 surface swabs, n = 5 water samples) and B (n = 19 surface swabs, n = 1 water sample) from May-August 2022. Surface swab samples were collected from surfaces in the home (e.g., basement floors and walls) impacted by wastewater through SSOs. Standing water samples were also collected when available, but this was dependent on when the SSO or backup events had occurred. The source and age of the standing water, as well as cleaning methods used and demographic information, was provided by the resident participants through an in-person survey described in detail in Goode et al. [40]. Homes where events happened weeks or months prior to sampling, or where cleanup had already been completed, did not have water available for sampling. The timing of the sampling events and number of homes sampled in each neighborhood was based on the availability of our community partners and the resident participants.
Residents identified areas in the home that were exposed to sewage. Surface samples were collected from impacted basement floors or walls using methods modified from those used to recover E. coli from homes with high fecal contamination in Peru [41]. Impacted surfaces were sampled for E. coli by passing a sterilized Swiffer dry sweeping cloth (Swiffer, El Paso, TX) over a 225 cm2 area. The cloth was added to a sterile Whirl-Pak bag (Cole-Palmer, Vernon Hills, IL) containing 5 mL of sterile deionized water. Surfaces were sampled for MSSA and MRSA using methods modified from those developed for MRSA and MSSA surface sampling in healthcare settings [42,43]. To sample surfaces for MRSA and MSSA, a 25 cm2 area was swabbed with a Copan Diagnostics ESwab (Thermo Fisher Scientific, Waltham, MA) pre-moistened with Amies media. eSwabs were stored in Amies media until processing was performed. When standing water was present in the homes from an SSO or backup as identified by the participant, water was collected in 1-L sterile Nalgene Wide-Mouth HDPE Packaging Bottles (Thermo Fisher Scientific, Waltham, MA). All samples were transported to the laboratory at 4°C and processed within 24 hr as described in the “Isolation” selection below. Isolates were identified as target bacteria through biochemical testing and polymerase chain reaction (PCR), and a subset underwent whole genome sequencing as described in detail below.
Isolation
MRSA, MSSA, and MR-CoNS were isolated from surface and water samples using previously described methods [20]. To recover MRSA, MSSA, and MR-CoNS from water samples, membrane filtration was performed by vacuum filtering 300 mL of water through 0.45-μm, 47-mm mixed cellulose ester filters (Cole-Palmer, Vernon Hills, IL). Filters were enriched in 40 mL of m Staphylococcus broth (BD DIFCO, Midland Scientific Inc., La Vista, Nebraska) and incubated for 24 hr. To recover MRSA, MSSA, and MR-CoNS from surface samples, eSwabs were added to 5 mL of m Staphylococcus broth, vortexed, and incubated for 18 hr at 37°C. After incubation, m Staphylococcus broth from both the water and surface samples was plated in duplicate on MRSASelect (Bio-Rad Laboratories, Hercules, CA) or CHROMagar MRSA II (BD, Franklin Lakes, NJ) for MRSA isolation and Baird Parker agar (HiMedia Laboratories, Lincoln University, PA) for MSSA, MR-CoNS, and CoNS isolation.
E. coli were recovered from Swiffer clothes by rinsing cloths with 100 mL of 1X Phosphate Buffered Saline (PBS) (Electron Microscopy Sciences, Hatfield, PA). The 1X PBS rinse was vacuum filtered with three dilutions (1 mL, 10 mL, 100 mL) of each water sample onto 0.45-μm, 47-mm mixed cellulose ester filters. Filters were placed on MI agar (BD DIFCO, Midland Scientific Inc., La Vista, NE) and incubated for 24 hr at 35°C. E. coli colonies (blue on MI agar) were isolated from the total coliforms grown on the MI agar using a two-step purification process. First, E. coli isolates were grown on MacConkey agar (Research Products International, Prospect, IL) overnight at 35°C. Then, isolates presumptively identified as E. coli (hot pink on MacConkey agar) were grown on TSA agar (BD, Franklin Lakes, NJ) overnight at 35°C. Pure cultures were used for downstream identification.
Identification
Presumptive MRSA (hot pink colonies on MRSASelect plates or Chromagar MRSA) and MSSA (black colonies with halos on Baird Parker plates) were verified using coagulase tests (BD) and catalase tests. Isolates were confirmed as MRSA, MR-CoNS, MSSA, or CoNS using PCR. DNA was extracted from each isolate using a modified heat shock method [44]. Isolates were suspended in 100 μL of a 7.5% Chelex (Bio-Rad, Hercules, CA) solution, heated to 100°C for 10 min, and cooled before the supernatant was recovered and stored until further processing [44]. PCR protocols developed in Rosenberg Goldstein et al., 2012 were followed for staphylococci confirmation [20]. The S. aureus-specific nuc gene was amplified using the nuc-F and nuc-R primers (Table 2). For differentiation of MRSA, the mecA gene was amplified using the mecA-F and mecA-R primers (Table 2). An internal control, targeting 16S rDNA with 16S-F and 16S-R primers, was also included in the multiplex assay. PCR was performed using 3 μL of template DNA, 2X Taq PCR Master Mix (Qiagen, Hilden, Germany), 2 mM MgCl, and the three primer sets as described in Table 2, in a 30 μL reaction volume. PCR reactions were run with an initial denaturing step of 95°C for 3 min, 34 cycles at 94°C for 30 sec, 55°C for 30 sec, 72°C for 30 sec, and a final extension at 72°C for 5 min. Staphylococcus aureus subsp. aureus Rosenbach (ATCC43300; ATCC, Manassas, VA) was processed using the method described for samples above and included for all PCR runs. For all DNA extractions and PCR runs, molecular grade water was included as a negative control.
Presumptive E. coli (purified isolates on TSA plates) were also confirmed using PCR. DNA extraction was performed using a heat shock method [19,47]. E. coli isolates were suspended in 200 μL of molecular grade water, heated to 100°C for 5 min, and cooled on ice for 5 min, and the heating and cooling was repeated. The uidA gene was amplified using uidA-F and uidA-R primers (Table 2) to confirm isolates as E. coli based on a protocol published by [46]. PCR was performed using 3 μL of template DNA, 1X PCR buffer, 1.5 mM MgCl2, 0.2 mM dNTPs, primers, and 0.6 μM TAQ (New England Biolabs, Ipswich, MA) in a 30 μL reaction volume. PCR reactions were run with an initial denaturing step of 95°C for 30 sec, 29 cycles at 95°C for 30 sec, 55°C for 30 sec, 72°C for 30 sec, and a final extension at 72°C for 5 min. Escherichia coli (Migula) Castellani and Chalmers (ATCC8739; ATCC) was processed using the method described for samples above and included for all PCR runs. For all DNA extractions and PCR runs, molecular grade water was included as a negative control.
Whole genome sequencing
Six bacterial isolates were selected after PCR confirmation for whole genome sequencing to identify antibiotic resistance genes, virulence factors, and relatedness to isolates from known sources. The subset consisted of two MRSA, one MSSA, and three MR-CoNS PCR-confirmed isolates.
Isolates were grown in 5 mL of m Staphylococcus broth at 35°C for 18–24 hr. The broths were briefly vortexed and 1 mL was centrifuged at 1,700 x g for 3 min to form bacterial pellets. The supernatants were removed and discarded and the pellets were resuspended in 500 μL of DNA suspension buffer, pH 8.0 (Teknova, Hollister, CA). Isolates were stored at -80°C until shipment to CosmosID.
CosmosID performed DNA extraction and nucleic acid quantitation prior to library preparation and sequencing. DNA extraction was performed using Qiagen DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany). DNA quantification was performed using Qubit dsDNA HS Assay Kit and Qubit 4 fluorometer (Thermofisher Scientific, Waltham, MA, USA). DNA library preparation was performed using 1ng of DNA, Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA), and IDT Unique Dual Indexes (IDTDNA, Coralville, IA, USA). Amplified DNA was quantified using the Qubit assay as described above. Sequencing of libraries was performed using the Illumina NovaSeq platform, 3 million sequencing depth, 2x150bp.
The BBDuk tool from the Joint Genome Institute (JGI) was used to trim and process raw paired end reads with a trimming parameter of 22 [48]. The careful parameter in the open-source tool SPAdes was used to assemble the FASTQ files containing sequencing data and to reduce the number of mismatches. CheckM lineage workflow (lineage_wf) was used to evaluate the genome quality [49,50]. Contig phylogenetic placement was evaluated using the CosmosID core genome SNP typing pipeline. Genome alignment was performed using Parsnp with the default parameters and phylogenetic relationships evaluated using FastTree2. Taxonomic identification was then performed using Genome Taxonomy Database Tool kit (GTDB-Tk). Isolates identified as MSSA and MRSA were additionally evaluated for presence of virulence factor genes (VFG). To determine AMR and VFG presence, the assembled genomes were screened using the Resfinder AMR and VFDB VF database using Abricate [51,52]. Genome annotation was performed using Prokka Annotation Pipeline [53]. Additionally, four isolates went through phylogeny and three isolates were subjected to multilocus sequencing typing (MLST) by using blast from de novo assemblies through PubMLST [54].
Reporting back
Residents who participated in the study were informed of their results by phone and/or email. In addition, residents were provided with information from CDC and EPA on how to clean up specific bacteria identified in their homes. Resources to further assist with cleanup were also provided. Residents were invited to contact the research team if they had additional questions or needed further consultation or information about the study. The research team also attended a community workshop in Baltimore to provide summarized results to residents.
Statistical analysis
Percentage of positive samples was calculated for each sampling event. Fisher’s exact test was used to assess differences in the presence of bacteria by “time since sewage event” categories. “Time since sewage event” responses were collapsed into categories of <1 month, 1–6 months, and > 6 months since the last sewage event. For some analyses, results of bacterial presence (E. coli, CoNS, MR-CoNS, MSSA, and MRSA) were combined into a category named “detection of at least one target bacteria”. P-values < 0.05 were defined as statistically significant. JMP Pro 17 and R version 4.3.1 with the “car” package [55,56] were used for all statistical analyses. Figures were generated using JMP Pro 17 and Microsoft PowerPoint software.
Results
Participant demographics and home and sewage event characteristics
Participants overwhelmingly identified as Black or African American (38/40; 95%) (Table 3). Most participants were also female (23/40; 60%), 50–69 years old (22/40; 55%), and owned their homes (31/40; 77.5%). Ninety-five percent of participants (38/40) self-reported having an SSO or backup event, although all participants had some water event (ex. rainwater entering the home) impact their home. Participants reported a range of cleaning strategies in their homes after sewage events, including no cleaning (32.5%), cleaning on their own (45%), or using a cleaning service (22.5%). Over 72% of participants (29/40) reported having a sewage event within the past six months, with 42.5% (17/40) experiencing a sewage event less than one month prior to sampling (Table 3). For the homes where standing water was present and collected, the time since the sewage event occurred ranged from less than one week to 1–6 months prior to sample collection.
Presence of AR bacteria and E. coli in standing water samples
Water samples (n = 6) were collected from six homes with standing water at the time of sampling. Of the six water samples collected, one was positive for both MRSA and MR-CoNS (Table 4). MSSA was not isolated from any water sample, but CoNS and E. coli were present in 66.7% (4/6) of water samples. Fifty percent all water samples (3/6) were positive for more than one target bacteria.
Presence of AR bacteria and E. coli in surface samples
MRSA was not detected on any surfaces (Table 4). However, MSSA and MR-CoNS were detected on 2.4% (1/41) and 12.2% (5/41) of surfaces, respectively (Table 4). Additionally, 80.5% (33/41) surfaces were positive for CoNS, and 26.8% (11/41) surfaces were positive for E. coli. More than one target organism was isolated from 14 surface samples (34.1%).
Impact of neighborhood and cleaning on detection of target bacteria
No significant difference was observed in the detection of target bacteria (MRSA, MR-CoNS, MSSA, CoNS, or E. coli) in homes (surface and water samples) between neighborhoods A and B (p = 0.37). There were also no significant differences in detection of target bacteria across reported cleaning methods (i.e., no cleaning, self-cleaning, or cleaning services (p = 0.48). Additionally, there was no significant difference (p = 0.79) in detection of target bacteria in homes across the reported frequency of events, which included responses of “Almost never,” “Rarely,” “Occasionally,” “Often,” and “Frequently.”
Impact of time since last sewage event on detection of target bacteria
Thirty-five of the 40 study participants provided a response to the question regarding time since the last sewage event. At least one target bacteria was detected in 100% (17/17) of houses with events occurring less than one month prior to sampling, 75% (9/12) of homes with events 1–6 months prior, and 66.7% (4/6) of homes with events more than 6 months prior to sample collection (Table 5; Fig 1). Detection of at least one target bacteria differed significantly between homes with events that occurred less than 1 month prior to sample collection compared to those where events occurred more than 1 month prior to sample collection (p = 0.046). There was no significant difference in the detection of target bacteria in homes with sewage events 1–6 months and more than 6 months prior to sample collection. The one water sample positive for MRSA and MR-CoNS was collected from a home that did not provide information about time since the sewage event. MSSA was only detected in one surface sample from a house that reported having a sewage event less than 1 month prior to sample collection. MR-CoNS was detected in 23.5% (4/17) of homes with events less than 1 month, 8.3% (1/12) of homes with events 1–6 months, and none of the homes (0/6) with events occurring more than 6 months prior to sample collection. CoNS were detected in 100% (17/17) of homes with events less than 1 month, 66.7% (8/12) of homes with events 1–6 months, and 66.7% (4/6) of homes with events more than 6 months prior to sample collection. Similarly, E. coli was present in 58.8% (10/17) of homes with events less than 1 month, 16.7% (2/12) of homes with events 1–6 months, and 16.7% (1/6) of homes with events more than 6 months prior to sample collection.
Among homes where sewage events occurred less than 1 month prior to sample collection, one target bacteria was detected in 29.4% (5/17) of homes, two target bacteria detected in 52.9% (9/17) of homes, and three target bacteria detected in 17.6% (3/17) of homes (Fig 1). In homes with sewage events 1–6 months prior to sample collection, one target bacteria was detected in 58.3% (7/12) and two target bacteria were detected in 16.7% (2/12) of homes. In homes with sewage events more than 6 months prior to sample collection, one target bacteria was detected in 50% (3/6) and two target bacteria were detected in 16.7% (1/6) of homes. The number of target bacteria detected in homes differed significantly (p = 0.027) based on the time since the last sewage event categories.
Antibiotic resistance genes
Five of the six whole genome sequenced isolates were multidrug-resistant (MDR) (Fig 2). Fourteen ARGs spanning eight antibiotic classes were identified among the six isolates tested (Fig 2). All tested isolates contained at least one β-lactam resistance gene. Macrolide-lincosamide-streptogramin resistance genes were present in all tested isolates except for one MRSA isolate (n = 5, 83.3%). Two of the MR-CoNS isolates contained a large number of ARGs including those conferring resistance to antibiotics used to treat S. aureus and MRSA infections (ex. fusidic acid, chloramphenicol, fosfomycin, and tetracycline) (Fig 2).
Filled circles indicate presence of the gene in the isolate.
Virulence factor genes
The presence of virulence factor genes (VFGs) was determined by whole genome sequencing of the three isolates confirmed as either MRSA (n = 2) or MSSA (n = 1)) (Fig 3). Among the three isolates tested, 85 VFGs were identified in the following virulence categories: iron acquisition (isdA-G) [57], adhesion (ace, fss1–2, prgB/asc10, clfA/B, ebp, icaA-D, icaR, map, sdrC-E, spa, srtB) [58–64], biofilm (bopD, ebpA-C, efaA, fsrA-C, srtC) (45,51), enzyme (adsA, aur, geh, lip, sspA-C, EF3023, gelE, sprE) [65–67], capsular (cap8A-G, cap8L-P, cpsA-K) [68,69], hemolysins (hlb, hld, hlgA-C, hly/hla) [68], and host immune evasion (chp, coa, esaA-C, essA-C, esxA-B, sbi, scn, sak) [62,70]. Twenty-six VFGs originating from Enterococcus faecalis were present in Isolate 1 (MRSA) including 4 adhesion, 9 biofilm, 10 capsular, and 3 enzyme genes. Additionally, Isolates 1 and 2 (both MRSA) contained the same fifty-six VFGs in categories including 7 iron acquisition, 14 adhesion, 7 enzyme, 12 capsular, 5 hemolysin, and 11 host immune evasion genes. Isolate 3 (MSSA) contained fifty-one VFGs including 7 iron acquisition, 10 adhesion, 6 enzyme, 12 capsular, 6 hemolysin, and 10 host immune evasion genes.
Filled circles indicate presence of the gene in the isolate.
Genomic characteristics
Isolates 3, 4, 5, and 6 were further evaluated using MLST and phylogenetic analyses. Isolate 3 was identified as S. aureus (MSSA) and was found to be most closely related with two known strains originating from humans in France (strain ST20130945, isolated from a 83-year-old man with a chronic prosthetic joint infection) [71] and China (strain 0316-H-5A, isolated from the nasal cavity of a healthy pig farm worker) [72]. Isolates 4 and 6 were identified as S. haemolyticus and S. epidermidis with MLST, respectively. While based on phylogenetic analyses, Isolate 5 was most closely related to S. ureilyticus. Isolate 4 (S. haemolyticus) was most closely related to strains isolated from human ear swabs (SH1275), which is highly similar to SCCmec type V, and human blood from a patient with sepsis [73]. Isolate 5 (S. ureilyticus) was most closely related to strains from human hosts in China while Isolate 6 (S. epidermidis) was most related to a strain isolated from humans in Switzerland and the Netherlands [74,75].
Discussion
Antibiotic-resistant bacteria in surface samples
We identified multidrug-resistant MSSA and MR-CoNS in surface samples in homes impacted by sewage. We isolated MR-CoNS from five surface samples (12.2%) and MDR MSSA from one surface sample (2.4%), but no MRSA. In our study, 26% of sampled homes experienced a sewage event < 1 week, 7.5% 1 to <2 weeks, 10% 2 weeks to < 1 month, and 45% > 1 month prior to sampling. Our detection rates are lower than one previous pilot study evaluating MRSA and MSSA in homes with young children and pets, but similar to MRSA and MSSA detection rates in previous studies evaluating a range of different surfaces in a variety of indoor environments, although surfaces were not impacted by sewage. Studies of MSSA and MRSA in homes have largely focused on transmission from humans, pets, and contaminated surfaces [76]. In a 2008 pilot study, surface samples were collected from 35 homes that included children in diapers and a dog or cat, and tested for MSSA and MRSA [77]. Surface samples were mainly collected from items commonly used by residents, (i.e., sponges, tub, garbage bins, etc.), as well as kitchen floors. Overall, MSSA was detected in 97% of homes and MRSA in 26% of homes. MRSA was only detected on high contact surfaces, such as sponges, sinks, and faucet handles. Of the 34 kitchen floor samples, only 18% were positive for MSSA and no floor samples were positive for MRSA [77]. A 2022 study evaluated surface samples collected from two meat processing facilities in Greece for the presence of MRSA and MSSA [78]. Researchers sampled 22 infrastructure surfaces and 16 meat-processing equipment surfaces across the two facilities. Only MSSA, not MRSA, was isolated from infrastructure (n = 1, 4.5%) and equipment (n = 1, 6.3%) surfaces [78]. The infrastructure surfaces, with no contact with meat products, had the least amount of recovered MSSA compared to the sampled meat, workers, meat processing equipment and incoming products. Only one isolate out of 160 isolates collected (0.6%) was phenotypically considered MRSA and was not isolated from a surface sample [78]. Studies on dental offices found that MRSA significantly declined (>90%) when first introduced to the surfaces, but was able to be detected after 4 months [79]. In a study by Roberts et al. that analyzed more than 1,000 surface samples from fire houses (surfaces included medic and fire trucks, fire gear, garages, kitchens, bathrooms, bedrooms, and gym areas), 4.1% of samples were positive for MRSA [80]. Using the same swabbing protocol, Roberts et al. collected 95 surface samples from dental school clinic offices, including “frequently touched” surfaces and office floors, and found that 8.4% of surfaces were positive for MRSA [81].
Although the CDC reports that MRSA can survive on surfaces for hours to weeks [2], limited recovery of MRSA from surfaces appears to be consistent among most studies [78,81](78,81). Our lack of detection of MRSA from surfaces and limited detection of MR-CoNS and MSSA from surface samples could be related to the long lag time between the sewage event and sampling for more than half of our participating homes, the sampling method, the small sample size, and/or cleaning that happened to impacted areas before sampling could have further decreased the recovery of Staphylococcus from our sampled surfaces. Because most residents in this study (67.5%) had cleaned their impacted surfaces after SSOs/backups and before samples were collected, this could have affected our ability to detect bacteria by killing viable bacteria. This is further supported by our finding of a significantly higher percentage of homes with target bacteria present if a sewage event had occurred within one month prior to sample collection compared to more than one month. In homes with target bacteria present that had a sewage event occur more than one month prior to sample collection, the target bacteria could have come from the sewage event, or the residents could be sources of these bacteria in their homes as S. aureus and E. coli are both part of the normal human flora. Staphylococci colonize the nares and intestines of 20–33% of the human population [82–84]. E. coli is the main microbial inhabitant of the intestines, with up to 90% of people colonized with E. coli, therefore these bacteria can be shed by colonized individuals [85]. However, because significantly more homes had the target bacteria present if they had experienced a sewage event in the month before sampling, our results suggest that sewage exposure could be the source of these bacteria in the month after a sewage event.
Antibiotic-resistant bacteria in water samples
We detected MRSA and MR-CoNS in one of six standing water samples collected from homes impacted by sewage. Previous studies have detected AR bacteria in raw sewage and wastewater at wastewater treatment plants, but to our knowledge never in standing water in homes impacted by SSOs or backups [86,87]. A previous study by the authors identified MRSA in 50% of wastewater samples at a wastewater treatment plant. Because wastewater treatment plants contain much larger volumes of water than the amount of water entering residential homes during SSOs and backups, the amount of MRSA at wastewater treatment plants, and the chances of isolating this bacteria, could be much higher than in homes impacted by sewage events [20]. In a study of CSOs, events similar to SSOs in that sewage enters unintended areas such as rivers and other water bodies, the presence of sewage was linked to greater rates of antimicrobial resistance in the water bodies [86]. Heterotrophs, enterobacteria, and enterococci resistant to amoxicillin, tetracycline, ciprofloxacin or sulfamethoxazole were all isolated from raw wastewater samples collected from a treatment plant in Portugal [88]. The association between AR bacteria and wastewater in previous studies, and the significant association between AR bacteria detection and time since a sewage event in the current study, suggests that SSOs could be a source of AR bacteria in homes.
Additional ecological studies have investigated the association between sanitation access such as sewage treatment, drinking water treatment, and ARGs [89–91]. Fuhrmeister et al. found that limited access to sanitation and water was associated with higher abundance of ARGs, especially in urban areas [90]. Further, there was a stronger association when looking at sanitation alone, although this finding was not statistically significant. Collignon et al. identified an association between infrastructure improvements and decreased prevalence of antibiotic-resistant organisms across 73 countries [89]. Our study continues to address research gaps about the impact of sanitation and sewage infrastructure on the presence of AR bacteria using individual household or parcel-level data collected through field sampling.
E. coli in water and surface samples
We detected E. coli on surfaces and in standing water in homes impacted by SSOs and backups. During SSOs, fecal coliforms typically range from 500,000–1 billion colonies per 100 mL water [92,93]. The presence of E. coli, a standard fecal indicator organism, suggests that fecal matter was present in the homes we sampled. The presence of E. coli also suggests that other pathogens present in fecal matter could be present in these homes.
Multidrug-resistance and virulence in Staphylococcus
The presence of MDR isolates in combination with the abundance of VFGs suggests a greater risk to the residents whose homes are impacted by SSOs and backups, beyond the detection of Staphylococcus spp. alone. We detected the methicillin-resistance gene (mecA) in 83.3% (5/6) of Staphylococcus isolates analyzed for ARGs via WGS, as expected from the PCR confirmation assays. Additionally, 100% of isolates tested (6/6) contained the blaZ gene conferring resistance to penicillin, ampicillin, amoxicillin, and piperacillin - even the one isolate susceptible to methicillin. A study evaluating 29,679 S. aureus genomes available through public databases and collected between 2001 and 2020 found 83% of isolates contained ARGs. ARGs were found within the chromosome conferring resistance to β-lactams, aminoglycosides, macrolide, and tetracycline in 95%, 54%, 30%, and 24% of the isolates, respectively [94]. Similarly, we found ARGs conferring resistance to β-lactams, aminoglycosides, macrolide, and tetracycline in 100% (6/6), 33.3% (2/6), 66.7% (4/6), and 33.3% (2/6), of isolates respectively. MDR S. aureus and MRSA have both been found to result in healthcare-associated infections with increased risks of mortality [95]. The large number of antibiotic classes that the tested MRSA, MR-CoNS, and MSSA isolates were resistant to raise concerns about limited treatment options for individuals infected with these bacteria, especially as it is well established that CoNS can serve as a reservoir of resistance genes that can be transferred to other CoNS species as well as S. aureus [7]. S. haemolyticus strain SH1275, which is closely related to Isolate 4 in this study, has been found to contain ARGs on chromosomes and plasmids that confer resistance to blaZ and mecA [73]. In a study by Smith and Andam evaluating the frequency of intra-CoNS gene transfer and recombination, S. haemolyticus (Isolate 4) and S. epidermidis (Isolate 6) were among the top five CoNS species for recombination, including for ARGs and VFGs [7].
Several of the antibiotics that the isolates contain ARGs for are important for treating S. aureus infections. Fusidic acid is used to topically treat S. aureus and MRSA skin infections. A recent review of fusidic acid resistance found that globally 2.6% of MRSA and 6.7% of MSSA were fusidic acid resistant [96]. Isolate 4 (S. haemolyticus) in our study contained the fusC gene conferring resistance to fusidic acid. In a study reviewing ARGs in 55 species of CoNS, resistance to fusidic acid was identified in 12 CoNS species including S. haemolyticus, which Isolate 4 was identified as through MLST [7]. The VFGs found in our study spanned iron acquisition, adhesion, biofilm, enzyme, capsular, hemolysin, and host immune evasion categories. Adhesion VFGs (e.g., clfA and spa) and host immune evasion VFGs (e.g., coa and sbi) could influence infection but also could be utilized in vaccine development [62].
Our MLST and phylogenetic analyses provided additional information about possible sources for Isolates 3 (MSSA), 4 (S. haemolyticus), 5 (S. ureilyticus), and 6 (S. epidermidis). That all of these isolates originated from human sources suggests that their presence in the homes sampled in this study could be traced to the human fecal matter introduced during SSOs and/or backups.
Limitations
Our study was limited by the small sample size (n = 40 homes). A larger sample size covering additional neighborhoods in Baltimore would allow for stronger comparisons between impacted communities and allow us to capture a more accurate representation of the scale of the problem. We sampled two neighborhoods in one city, but there are many other cities and communities across the U.S. that are impacted by SSOs and backups, including Washington, DC, Charleston, SC, Jackson, MS, and Houston, TX, among others [97]. These same communities are also on the frontline of the climate crisis and environmental injustices. Our ability to detect bacteria in these homes may also have been impacted by the lack of wet weather events. Our team conducted sampling during events arranged by our community partners that did not coincide with rainfall. This may have also impacted the ability to detect a significant association between weather and presence of bacteria in impacted homes. It is worth noting that residents have also reported problems with SSOs and backups during dry weather events. Future research should consider dry weather events that result in SSOs and backups. Our ability to detect our target bacteria may have also been affected by bacterial die-off and cleaning of impacted areas. It is also important to note that while our study focused on the mecA gene to determine methicillin resistance amongst Staphylococcus isolates, other genes, such as mecC, can also confer resistance to methicillin [98]. However, the mecA gene is the most common methicillin resistance gene amongst MRSA [99]. Among 1,069 S. aureus isolates collected in North American and European hospitals, the mecA gene was found in 95% of the isolates that were methicillin resistant and in every isolate that displayed multi-drug resistance [100].
Public health and the built environment impacts
Our ability to detect MRSA, MR-CoNS, and MDR MSSA even with a small sample size suggests that this research is worth repeating on a larger scale. The health of millions of people may be negatively impacted by SSOs and backups, particularly in disenfranchised neighborhoods. SSOs and backups, such as those impacting Baltimore City, disproportionately impact low-income and communities of color [101]. In Baltimore City, the median household income in 2018 was $48,840 and 18.9% of the city was living in poverty. Among 593,490 residents, 63% were Black. Black communities experience disproportionately higher rates of AR bacterial infections, including MRSA infections. From 2005-2014, Black people had higher community-associated MRSA infections rates (aRR, 2.78; 95% CI, 2.30–3.37) compared to White people [38]. Risks are particularly evident in marginalized urban neighborhoods with poorer stormwater and sanitation infrastructure and public works services [33]. Studies have shown that the concentration of S. aureus in homes significantly predicted S. aureus transmission [36]. Therefore, the home environment is important to infection spread and mitigation [36]. To properly evaluate and mitigate the risk of AR bacteria experienced by underserved communities living with SSOs and backups, more research is needed.
Future research directions
Based on our findings, future research should consider adopting a sampling strategy that prioritizes real-time response to SSOs and backups, including dry weather events. Sampling schemes should also include control homes that have not experienced SSOs. Comparing control and impacted homes could help elucidate the origin of AR bacteria and fecal indicators as a result of SSOs or individuals and pets within the homes.
Projects addressing the critical need for more research on health risks from sewage events are already underway including the Water Emergency Team (WET) project, described in detail in a perspective piece by Hendricks and Goldstein, 2024 [27]. WET is a community-driven rapid response project to address SSOs, sewage backups, and environmental contamination. WET is evaluating the impact of sewage events on homes, residents’ mental and physical health, and the risk of exposure to waterborne pathogens including AR bacteria. Through WET, surface and water samples, surveys on impacts, and indoor visual inspections are collected from both homes that have and have not experienced sewage events to better elucidate the impact of sewage events on the presence of AR bacteria in homes.
Conclusions
This is the first time that AR bacteria were analyzed and detected in homes impacted by SSOs and basement backups. Our approach utilized a combination of public health and urban planning approaches facilitated with community partnerships. We identified MRSA, MR-CoNS, MDR MSSA, and E. coli in a combination of water and surface samples from homes impacted by SSOs and backups. Our findings suggest that underserved Baltimore residents impacted by these events could be at risk for AR bacterial exposure. Poor planning and failing infrastructure are root causes driving this exposure. Additional research with larger sample sizes is needed to better assess the risk of AR bacterial exposure from SSOs and backups.
Acknowledgments
Thank you to our community partners and Baltimore residents for participation in our study. Thank you to the Water Quality, Outreach, and Wellness (WOW) Lab and SIRJ Lab team members for assistance with sample collection, survey administration, and lab analyses.
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