Mycobacterium avium isolates resistant to clarithromycin and azithromycin have been recovered from patients undergoing antibiotic therapy. Comparison of DNA fingerprints of sensitive and resistant isolates showed that resistance resulted from mutation of the original, sensitive isolate in five of seven patients. In the other two patients, the clarithromycin-resistant isolates were unrelated to the sensitive isolate, suggesting that the resistant isolate resulted from either superinfection or selection of a resistant strain from a polyclonal population. Investigation of the mechanisms of clarithromycin and azithromycin resistance in M. avium showed that high-level resistance resulted from a point mutation at position A-2058 in the 23S rRNA. Based on this finding, a rapid screen for clarithromycin-resistance in M. avium was developed based on PCR. Twenty-three clinical isolates were analyzed, seven of which were clarithromycin-resistant. The target product was amplified only in clarithromycin-resistant strains, all of which had mutations at position 2058. A polyuridylic acid (poly U)-dependent in vitro translation system from M. avium was developed to investigate the effect of antibiotics on protein synthesis. Clarithromycin was an effective inhibitor of protein synthesis in cell-free extracts of a susceptible M. avium strain, whereas a high-level resistant strain was less susceptible to clarithromycin in vitro. Mixtures of extracts from sensitive and resistant strains showed a pattern of clarithromycin inhibition similar to the resistant strain, suggesting that resistance may be dominant in partial diploids. Three M. avium strains exhibiting step-wise, intermediate resistance to azithromycin were characterized in comparison to the sensitive parent. All strains were similar in hydrophobicity, growth medium requirements, and growth response to temperature. The azithromycin-resistant strains were resistant to several unrelated agents, including ciprofloxacin, rifabutin, and ethidium bromide. Addition of carbonyl cyanide m-chlorophenylhydrazone (CCCP) did not lower minimal inhibitory concentrations (MICs) for ciprofloxacin or ethidium bromide. Cell-free extracts of the strains were as sensitive to azithromycin in vitro as the parent strain. The results rule out inactivation, efflux, and mutations in the target as resistance mechanisms, and suggest intermediate resistance may be due to altered permeability of the cell wall or membrane. / Ph. D.

The emergence of antibiotic resistance in riverine systems has become a growing issue worldwide. The use of watershed-scale models is popular with many other water quality issues but not in the case of antibiotic resistance. In this study, we introduce a watershed-scale bacteria fate and transport model to simulate antibiotic resistance in E. coli. This model was developed through amendments to an existing watershed-scale physically based hydrological model (SWAT), and the newly modified model was called SWAT-ARB. The SWAT-ARB model was employed in the receiving environment of a WWTP in the Adyar River basin, India. The SWAT-ARB model simulations of resistant fractions (resistant E. coli concentration/E. coli concentration) in stream water were analyzed by the flow levels with the application of a range of parameter values. It is concluded that the model can be used to test prevailing hypotheses and evaluate the current state of knowledge. For instance, model simulations suggest that the influx of ARB can be a primary driver of antibiotic resistance in rivers compared to ambient antibiotic concentrations. We used the SWAT-ARB model to quantify the impact of climate change on antibiotic resistance. Six climate models were used to obtain the future climates in two distinct scenarios. The model was applied to three watersheds as Adyar basin- India, Crab Creek basin- USA, and upper Viskan basin- Sweden. It was concluded that temperature increase may greatly affect the colder climates (Crab Creek and Viskan) with higher simulated resistant fractions. In case of Adyar basin, resistant fractions are alleviated in high flow conditions, while aggravated in low flow conditions. / Doctor of Philosophy / The antibiotic resistance occurs when bacteria no longer responds to antibiotics. Hence, the diseases that caused by resistant bacteria are harder to treat. These antibiotic resistant bacteria end up in our rivers because of our heavy use of antibiotics in human and animal treatments. Thus, the spread of antibiotic resistance has become a water quality issue in the rivers worldwide. Scientists generally use computer models to understand water quality issues in rivers. These computer models are important because of high cost of monitoring and their use in finding how environment works. Up to the date of this publication, there is no sophisticated enough model to simulate antibiotic resistance in rivers. Hence, we created a river basin scale model to simulate antibiotic resistance. We found that the influx of ARB can be a primary driver of antibiotic resistance in rivers compared to ambient antibiotic concentrations. The model was applied to three watersheds as Adyar basin- India, Crab Creek basin- USA, and upper Viskan basin- Sweden. It was concluded that temperature increase may greatly affect the colder climates (Crab Creek and Viskan) with higher antibiotic resistant bacteria compared to susceptible bacteria.

Widespread use of antibiotics in livestock production can result in the dissemination of bacteria carrying antibiotic resistance genes (ARGs) to the broader environment. Within agroecosystems, ARGs can pose a risk to livestock handlers, farmers, and ultimately consumers. The overall goals of this dissertation are to examine the presence of resistance (antibiotic, metal) in agricultural soils and evaluate the most critical potential points of best management control of antibiotic resistance spread along the agricultural production chain. The relative impacts of agricultural practices, manure management, native soil microbiota, and type of crop grown and harvested on the agricultural resistome are multi-dimensional and cannot be captured via a single analytical technique or by focusing on one specific point in the agricultural process. Culture-, molecular "indicator"-, and next-generation sequencing- based methods were employed to characterize antibiotic resistance via taxonomic and functional profiles on the broader manure, soil, and vegetable surface microbial communities through 16S rRNA amplicon sequencing and shotgun metagenomics. Although antibiotic concentrations dissipated in the soil after 28 days after amendment application, antibiotic resistance presence was recoverable throughout the entire 120d growing season in the compost and manure amendments, the amended soil, and on vegetable surfaces. The addition of organic fertilizers increased antibiotic resistance presence compared to background levels. Further, metals and metal resistance were also measured in the amended soils and were found to be at greater levels in the inorganically fertilized soils compared to the manures and compost amended soils. Analysis of the widespread agroecosystem microbial community composition and broader metagenome has characterized varying genera profiles in the soil and on the vegetable surfaces and specific ARG and mobile genetic element (plasmid) co-occurrences. These co-occurrences highlight which ARGs may be most critical for future antibiotic resistance dissemination research. It is imperative to employ multiple methods when measuring agricultural resistance, as one method alone may miss significant patterns and lead to different best management recommendations. Linking the livestock manure, soil, and vegetable surface-associated ARBs, ARGs, resistomes, and microbiomes will help identify critical control points for mitigation of agricultural dissemination of antibiotic resistance to the environment and food production. / Doctor of Philosophy / By 2050, it is estimated that antibiotic resistant infections will be the leading cause of death worldwide. It is important to consider human, animal, and environmental health when researching antibiotic resistance, which is known as a "One Health" approach. In this dissertation work, I focus on the environmental side of antibiotic resistance in our agricultural systems. Agriculture is a known source of antibiotic resistance due to its use of antibiotics in livestock as a treatment for illness, and in some instances, as a growth promoter. Over one growing season, I measured antibiotic resistance in an agricultural setting using many techniques. First, I analyzed the effects of inorganic (chemical) versus organic (manure and compost) fertilization on antibiotic resistance in the soil. I measured antibiotic resistance by growing antibiotic resistant bacteria, quantifying specific antibiotic resistant genes (ARGs) using DNA amplification, and quantifying all the ARGs in the soil using a next-generation sequencing (NGS) technique called shotgun metagenomics. I found that adding manure to the soil increases ARGs compared to background soil levels, and that composting in an effective management strategy in decreasing ARGs in the soil over time. Second, I analyzed the same effects of fertilization on metal resistance in the soil. I was able to use the same NGS dataset to measure metal resistance genes (MRGs). I found that adding inorganic chemical fertilizer increases MRGs in the agricultural soils compared to the organic (manure or compost) fertilizer. Additionally, I studied the microbes that live in the agricultural soils using another kind of NGS data specific for microbial identification. I found that although there were small differences between the microbial populations in the soil when fertilizers were added, they returned to similar composition over the growing season. Lastly, I measured antibiotic resistance and microbes throughout the entire agricultural system. I picked the point of fertilization (manure management), soil, and the lettuce surface to evaluate if antibiotic resistance spreads from the farm to the vegetable that ends up on a consumer's plate. I found that at each point antibiotic resistance is present, but at different levels. Composting reduces ARGs compared to raw manure. Agricultural soils may act as a natural buffer to antibiotic resistance. Lettuce plants grown in compost fertilized soils have less ARGs than lettuce plants grown in manure. There are many agricultural management practices that effectively reduce antibiotic resistance and using all of them plus many measurement methods will ultimately help farmers and consumers reduce antibiotic resistance in our agricultural systems.

Antibiotic resistance is currently a global threat spanning clinical, environmental, and geopolitical research domains. The environment is increasingly recognized as a key node in the spread of antibiotic resistance genes (ARGs), which confer antibiotic resistance to bacteria. Detecting ARGs in the environment is the first step in monitoring and controlling antibiotic resistance. In recent years, next-generation sequencing of environmental samples (metagenomic sequencing data) has become a prolific tool for the field of surveillance. Metagenomic data are nucleic acid sequences, or nucleotides, of environmental samples. Metagenomic sequencing data has been used over the years to detect and analyze ARGs. An intriguing instance of ARGs is the multi-functional ARG, where one ARG encodes two or more different antibiotic resistance functions. Multi-functional ARGs provide resistance to two or more antibiotics, thus should have evolutionary advantage over ARGs with resistance to single antibiotic. However, there is no tool readily available to detect these multi-functional ARGs in metagenomic data. In this study, we develop a computational pipeline to detect multi-functional ARGs in metagenomic data. The pipeline takes raw metagenomic data as the input and generates a list of potential multi-functional ARGs. A plot for each potential multi-functional ARG is also created, showing the location of the multi-functionalities in the sequence and the sequencing coverage level. We collected samples from three different sources: influent samples of a wastewater treatment plant, hospital wastewater samples, and reclaimed water samples, ran the pipeline, and identified 19, 57, and 8 potentially bi-functional ARGs in each source, respectively. Manual inspection of the results identified three most likely bi-functional ARGs. Interestingly, one bi-functional ARG, encoding both aminoglycoside and tetracycline resistance, appeared in all three data sets, indicating its prevalence in different environments. As the amount of antibiotics keeps increasing in the environment, multi-functional ARGs might become more and more common. The pipeline will be a useful computational tool for initial screening and identification of multi-functional ARGs in metagenomic data. / Master of Science / Antibiotics are the drug to fight against the infection of bacteria. Since the first antibiotic was discovered in 1928, many antibiotic drugs have been developed. At the same time, scientists discovered many genes responsible for the resistance of antibiotic drugs. Nowadays, antibiotic resistance is a global threat. Detecting antibiotic resistance genes in the environment is the first step toward monitoring and controlling antibiotic resistance. In recent years, next-generation sequencing has been widely used to get the DNA sequence from the environment. Metagenomics analysis has been used over the years to detect and analyze ARGs. In the literature, it has been reported that a single gene could carry two parts of sequences corresponding to two different ARGs, thus conferring resistance to two different antibiotics. This fusion might have some evolutionary advantages. In this study, we developed a novel computational tool to detect multi-functional ARGs. We collected data from three sources: the treatment plant water, the hospital wastewater, and the reclaimed water, and identified 19, 57, and 8 potential bi-functional ARGs in each source, respectively. After we manually inspected the result, we found three most likely bi-functional ARGs. We also found one bi-functional ARG that appears in all three datasets. The gene is responsible for aminoglycoside and tetracycline resistance. The tool will serve as the initial screening step to detect multi-functional ARGs.

The advent of new methods to detect animal drug residues has resulted in a need to independently validate them. The effects of processing milk on the performance of these methods was evaluated. Antibiotic-free milk samples were spiked with sulfamethazine, penicillin G, and chlortetracycline at levels of 10, 10 and 30 ppb, respectively. Spiked milk and negative control milk was heat-treated, homogenized or heat-treated and homogenized. The procedures evaluated for penicillin detection were Bacillus stearothermophilus disk assay, a HPLC described by Barker et al., Charm II microbial receptor assay, and CITE Probe and LacTek enzyme immunoassays. The procedures evaluated for sulfonamide detection were an HPLC method described by Long et al., Charm II microbial receptor assay, CITE Probe, LacTek and Signal enzyme immunoassays. The methods evaluated for tetracycline detection were a HPLC method described by Long et al., Charm II microbial receptor assay, and LacTek and CITE Probe enzyme immunoassays. The results indicate that the commercial tests and the disk assay were not adversely affected by processing treatments. Significant treatment differences were found when testing raw Charm II data by analysis of variance but these differences did not effect the overall results of the test. Results of the HPLC method were inconclusive for the three drugs tested. / Master of Science

A study was carried out to investigate the effects of a drug-free feeding program on broiler performances. A total of 2,496 Cobb 500 chicks were randomly assigned to one of four dietary treatments with each group replicated 13 times. The four diets evaluated were: 1) negative control (NC): basal diet without growth promoter or coccidiostat; 2) positive control (PC): diet 1 + Lincomycin; 3) Program 1 (PG1): diet 1 + Bio-Mos®, Vegpro®, MTB-100®, Acid Pak 4-Way®, and All-Lac XCL®; 4) Program 2 (PG2): diet 1 + Bio-Mos® and All-Lac XCL®. Additives were used at commercially recommended rates. All chicks were vaccinated with a live oocyst coccidia vaccine on d 0 at the hatchery. Four phases of feeding were used during the trial with changes occurring at d 14, 28, and 35. Performance values measured were body weight, feed intake, yield, and mortality, while body weight gain and feed conversation rate (FCR) were calculated. Chicks were challenged with coccidia at d 14 to evaluate the protective effect of the feeding programs and coccidia vaccination. Segments of duodenum, ileum, and ceca were removed to measure intestinal morphology. Final body weight gains of birds on PC (2.736 kg) were greater (P < 0.05) compared to NC (2.650 kg), while birds on PG1 (2.681 kg) and PG2 (2.710 kg) were similar to positive and negative control. Overall, feed intake was similar across the treatments with the exception of period 2 (15 to 28 days) when birds consumed more (P < 0.05) of PC and PG1 compared to NC. Cumulative FCR at d 35 and 49 was improved (P < 0.05) in birds consuming PC and PG2 when compared to NC. Overall, birds consuming NC had greater mortality (P < 0.05; 12%) compared to PC (7.6 %), PG1 (4.6%) and PG2 (6.7 %) with most of the mortality occurring from d 0 to d 28. Mortality for birds consuming PG1 was also lower (P < 0.05) compared to the PC. There were no dietary effects on lesion scores or yields of processed products at d 42 (females) or d 49 (males). Interaction of dietary treatments with age and days of age alone showed effects (P < 0.0001) on the morphology of duodenum, ileum, and ceca. Lamina propria in ceca was thicker (P < 0.008) in birds consuming NC compared to PG1 and PG2. This study indicated that feeding birds without growth promoters resulted in greater mortality and decreased performance compared to using an antibiotic, while Bio-Mos® in combination with All-Lac XCL® helped to reduce the negative effects. / Master of Science

Metagenomics has become a reliable tool for the analysis of the microbial diversity and the molecular mechanisms carried out by microbial communities. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. Interpretation of specific information from metagenomic data is especially a challenge for environmental samples as current annotation systems only offer broad classification of microbial diversity and function. Therefore, I developed MetaStorm, a public web-service that facilitates customization of computational analysis for metagenomic data. The identification of antibiotic resistance genes (ARGs) from metagenomic data is carried out by searches against curated databases producing a high rate of false negatives. Thus, I developed DeepARG, a deep learning approach that uses the distribution of sequence alignments to predict over 30 antibiotic resistance categories with a high accuracy. Curation of ARGs is a labor intensive process where errors can be easily propagated. Thus, I developed ARGminer, a web platform dedicated to the annotation and inspection of ARGs by using crowdsourcing. Effective environmental monitoring tools should ideally capture not only ARGs, but also mobile genetic elements and indicators of co-selective forces, such as metal resistance genes. Here, I introduce NanoARG, an online computational resource that takes advantage of the long reads produced by nanopore sequencing technology to provide insights into mobility, co-selection, and pathogenicity. Sequence alignment has been one of the preferred methods for analyzing metagenomic data. However, it is slow and requires high computing resources. Therefore, I developed MetaMLP, a machine learning approach that uses a novel representation of protein sequences to perform classifications over protein functions. The method is accurate, is able to identify a larger number of hits compared to sequence alignments, and is >50 times faster than sequence alignment techniques. / Doctor of Philosophy / Antimicrobial resistance (AMR) is one of the biggest threats to human public health. It has been estimated that the number of deaths caused by AMR will surpass the ones caused by cancer on 2050. The seriousness of these projections requires urgent actions to understand and control the spread of AMR. In the last few years, metagenomics has stand out as a reliable tool for the analysis of the microbial diversity and the AMR. By the use of next generation sequencing, metagenomic studies can generate millions of short sequencing reads that are processed by computational tools. However, with the rapid adoption of metagenomics, a large amount of data has been generated. This situation requires the development of computational tools and pipelines to manage the data scalability, accessibility, and performance. In this thesis, several strategies varying from command line, web-based platforms to machine learning have been developed to address these computational challenges. In particular, by the development of computational pipelines to process metagenomics data in the cloud and distributed systems, the development of machine learning and deep learning tools to ease the computational cost of detecting antibiotic resistance genes in metagenomic data, and the integration of crowdsourcing as a way to curate and validate antibiotic resistance genes.

The use of antibiotics in dairy cattle has the potential to stimulate the development and subsequent fecal dissemination of antibiotic resistance genes (ARGs) in bacteria. The objectives were to use metagenomic techniques to evaluate the effect of antibiotic treatment on ARG prevalence in the fecal microbiome of the dairy cow and to determine the temporal excretion pattern of ARGs. Twelve Holstein cows were assigned to one of four antibiotic treatments: control, pirlimycin, ceftiofur, or cephapirin. Fecal samples were collected on d -1, 1, 3, 5, 7, 14, 21, and 28. Samples were freeze-dried and subjected to DNA extraction followed by Illumina paired-end HiSeq sequencing and quantitative polymerase chain reaction (qPCR). Illumina sequences were analyzed using MG-RAST and the Antibiotic Resistance Gene Database (ARDB) via BLAST. Abundance of ampC, ermB, tetO, tetW, and 16S rRNA genes were determined using qPCR. All data were statistically analyzed with PROC GLIMMIX in SAS. Antibiotic treatment resulted in a shift in bacterial cell functions. Sequences associated with 'resistance to antibiotics and toxic compounds' were higher in ceftiofur-treated cows than control cows. Ceftiofur-treated cows had a higher abundance of 𝛽-lactam and multidrug resistance sequences than control cows. There was no effect of treatment or day on fecal tetO and ermB excretion. The relative abundances of tetW and ampC were higher on d 3 post-treatment than d 5 and d 28. In conclusion, antibiotic use in dairy cattle shifted bacterial cell functions and temporarily increased antibiotic resistance in the fecal microbiome. / Master of Science

Exposure to antibiotics has the potential to increase the incidence and proliferation of antibiotic resistance genes (ARG) in the gut and fecal microbiome. Non-saleable, antibiotic-containing milk from cows treated with antibiotics (waste milk) is commonly fed to dairy calves but the effects of ingestion of antibiotics at an early age on the gut microbiome and the development of ARG in the naive gut are not well understood. Pirlimycin, a lincosamide antibiotic acting against Gram positive bacteria through inhibiting protein synthesis by binding to the 50S ribosome, is commonly used as mastitis therapy. Lincosamides are also considered highly important in human medicine, often used against Staphylococcus aureus and Clostridium difficile infections. Emerging microbial resistance to pirlimycin is of concern for both animal and human health. The objective of this study was to determine the effect of early lincosamide antibiotic exposure on the abundance of ARG in feces of milk-fed calves. Eight female Holstein calves were blocked by age, paired by block, and randomly assigned to pasteurized whole milk (control; n = 4) or milk containing 0.2 mg/L of pirlimycin (treatment; n = 4). Calves were enrolled after receiving two colostrum feedings and were fed 5.68 L of pasteurized whole milk, treatment, or control, divided into two daily feedings, from d 1 to d 50 of age. After weaning calves were fed non-medicated starter grain ad libitum. Fecal samples were collected weekly until 85 d of age and freeze-dried. DNA was extracted using QiaAmp® Fast DNA Stool Mini Kit and qPCR was used to quantify the absolute abundance (gene copies/g of wet feces) and relative abundance (gene copies/copies of 16S rRNA genes) of erm(B), tet(O), tet(W) and 16S rRNA genes. Data was analyzed using PROC GLIMMIX in SAS. Abundance of 16S rRNA genes, tet(O) and tet(W) were not different between control and pirlimycin-fed calves nor were the relative abundance of tet(O) (mean = 0.050 tet(O) copies/16S rRNA genes) or tet(W) (0.561 tet(W) copies/16S rRNA genes). While abundance of erm(B) was higher in pirlimycin-fed calves compared to control calves (6.46 and 6.04 log gene copies/g wet feces; P = 0.04) the relative abundance of erm(B) (0.273 gene copies/16S rRNA genes) in feces of calves was not influenced by treatment. There was an effect of day (P < 0.10) for absolute abundance of tet(O), tet(W), and erm(B) indicating that the levels change with time as the fecal microbiome develops. This study suggests that feeding pirlimycin-containing non-saleable milk to growing calves may increase environmental loading of erm(B), which codes for resistance to highly important macrolide and lincosamide antibiotics. Additional research is needed on effects of feeding waste milk to calves on other fecal ARG and on the post-excretion and post-application fate of these genes. / Master of Science

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antibiotics, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens, thus serving as key point of interest for the surveillance of antibiotic resistance (AR) dissemination. This thesis advances knowledge about the fate of AR indicators throughout treatment and reuse. The field study informs approaches for monitoring AR at a WWTP by characterizing the resistome (i.e., full profile of ARGs) and microbiome across eight sampling events via metagenomic sequencing, complemented by antibiotic data. The WWTP significantly reduced the total load of ARGs and antibiotics, although correlations between ARGs and antibiotics were generally weak. Quantitative polymerase chain reaction was applied to validate the quantitative capacity of metagenomics, whereby we found strong correlations. The influent and effluent to the WWTP were remarkably stable with time, providing further insight into the sampling frequency necessary for adequate surveillance. The laboratory study examined the effects of commonly applied disinfection processes (chlorination, chloramination, and ultraviolet irradiation [UV]) on the inactivation of antibiotic resistant pathogens and corresponding susceptible pathogens in recycled and potable water. Further, we evaluated their regrowth following disinfection by simulating distribution. Acinetobacter baumannii, an environmental opportunistic pathogen, regrew especially well following UV disinfection, although not when a disinfectant residual was present. Enterococcus faecium, a fecal pathogen, did not regrow following any disinfection process. There were no significant differences between water types. The findings of this study emphasize a need to move beyond the framework of assessing treatment efficacy based on the attenuation of fecal pathogens. / Master of Science / Wastewater treatment plants (WWTPs) have traditionally been designed and further enhanced to minimize environmental contamination caused by solid waste, fecal pathogens, nutrients (e.g., nitrogen), and organic matter. However, treatment has not been optimized to remove the contaminants of emerging concern (CECs) investigated in this thesis: antibiotic resistant bacteria (ARB), antibiotic resistance genes (ARGs), and antibiotics. WWTPs are key point of interest for local and global surveillance of antibiotic resistance as they can receive the aforementioned CECs (via human excretion or improper disposal) from various sources (e.g., residences, hospitals). Antibiotic resistant bacteria have caused 2.8 million infections and subsequently 35,000 deaths in the United States each year. Considering treated wastewater can serve as a route of exposure for humans, potential spread of antibiotic resistance by WWTPs is of high priority to mitigate from a public health perspective. In the first study utilizing a technology to assess the full complement of ARGs in a given sample, we observed that the total load of ARGs was removed by approximately 50% across wastewater treatment, on average; total antibiotic load exhibited a similar reduction. The second study demonstrated that antibiotic resistant environmental opportunistic pathogen (i.e., pathogens which take advantage of the "opportunity" to infect an immunocompromised host, especially thriving in low nutrient engineered systems), Acinetobacter baumannii, possesses the ability to regrow following disinfection in the absence of a disinfectant residual. In contrast, antibiotic resistant Enterococcus faecium, an opportunistic pathogen of fecal origin, was successfully inactivated and unable to regrow. The findings of this study emphasize a need to move beyond the framework of assessing treatment efficacy based on the attenuation of fecal pathogens.