Human mitochondrial DNA provides a promising target for fecal source tracking because it is unique and intrinsic to humans. We developed a TaqMan chemistry assay, hCYTB484, targeting the cytochrome b gene of the human mitochondrial genome on a droplet digital PCR (ddPCR) platform and compared the performance of hCYTB484 with the HF183/BacR287 assay, a widely used assay targeting human-associated Bacteroides . For both assays, we defined the analytical limit of detection and analytical lower limit of quantification using frequency of detection and imprecision goals, respectively. We then established these analytical limits using empirical ddPCR data, presenting a novel approach to determining the analytical lower limit of quantification. We evaluated assay sensitivity using individual human feces from US, Bangladesh, and Mozambique and evaluated assay specificity using cow, pig, chicken, and goat samples collected from the US. To compare assay performance across a range of thresholds, we utilized receiver operating characteristic curves. The hCYTB484 marker was detected and quantifiable in 100% of the human feces from the 3 geographical distant regions whereas the HF183/BacR287 marker was detectable and quantifiable in 51% and 31% (respectively) of human feces samples. The hCYTB484 marker also was more specific (97%), having fewer detections in pig, chicken, and goat samples than the HF183/BacR287 marker (80%). The higher performance of the hCYTB484 marker in individual feces from geographically distant regions is desirable in the detection of fecal pollution from sources to which fewer individuals contribute, such as the non-sewered forms of sanitation (e.g. pit latrines and septic tanks) that serve most of Earth’s population and carry the highest risk of exposure to fecal-oral pathogens.

Little is known about the public health risks associated with natural creek sediments that are affected by runoff and fecal pollution from agricultural and livestock practices. For instance, the persistence of foodborne pathogens such as Shiga toxin-producing Escherichia coli (STEC) originating from these practices remains poorly quantified. Towards closing these knowledge gaps, the water-sediment interface of two creeks in the Salinas River Valley of California was sampled over a 9-month period using metagenomics and traditional culture-based tests for STEC. Our results revealed that these sediment communities are extremely diverse and have functional and taxonomic diversity comparable to that observed in soils. With our sequencing effort (∼4 Gbp per library), we were unable to detect any pathogenic E. coli in the metagenomes of 11 samples that had tested positive using culture-based methods, apparently due to relatively low abundance. Furthermore, there were no significant differences in the abundance of human- or cow-specific gut microbiome sequences in the downstream impacted sites compared to that in upstream more pristine (control) sites, indicating natural dilution of anthropogenic inputs. Notably, the high number of metagenomic reads carrying antibiotic resistance genes (ARGs) found in all samples was significantly higher than ARG reads in other available freshwater and soil metagenomes, suggesting that these communities may be natural reservoirs of ARGs. The work presented here should serve as a guide for sampling volumes, amount of sequencing to apply, and what bioinformatics analyses to perform when using metagenomics for public health risk studies of environmental samples such as sediments. IMPORTANCE Current agricultural and livestock practices contribute to fecal contamination in the environment and the spread of food- and waterborne disease and antibiotic resistance genes (ARGs). Traditionally, the level of pollution and risk to public health are assessed by culture-based tests for the intestinal bacterium Escherichia coli. However, the accuracy of these traditional methods (e.g., low accuracy in quantification, and false-positive signal when PCR based) and their suitability for sediments remain unclear. We collected sediments for a time series metagenomics study from one of the most highly productive agricultural regions in the United States in order to assess how agricultural runoff affects the native microbial communities and if the presence of Shiga toxin-producing Escherichia coli (STEC) in sediment samples can be detected directly by sequencing. Our study provided important information on the potential for using metagenomics as a tool for assessment of public health risk in natural environments.

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Background Standing genetic variation is important especially in immune response-related genes because of threats to wild populations like the emergence of novel pathogens. Genetic variation at the major histocompatibility complex (MHC), which is crucial in activating the adaptive immune response, is influenced by both natural selection and historical population demography, and their relative roles can be difficult to disentangle. To provide insight into the influences of natural selection and demography on MHC evolution in large populations, we analyzed geographic patterns of variation at the MHC class II DRB exon 2 locus in mule deer (Odocoileus hemionus) using sequence data collected across their entire broad range. Results We identified 31 new MHC-DRB alleles which were phylogenetically similar to other cervid MHC alleles, and one allele that was shared with white-tailed deer (Odocoileus virginianus). We found evidence for selection on the MHC including high dN/dS ratios, positive neutrality tests, deviations from Hardy–Weinberg Equilibrium (HWE) and a stronger pattern of isolation-by-distance (IBD) than expected under neutrality. Historical demography also shaped variation at the MHC, as indicated by similar spatial patterns of variation between MHC and microsatellite loci and a lack of association between genetic variation at either locus type and environmental variables. Conclusions Our results show that both natural selection and historical demography are important drivers in the evolution of the MHC in mule deer and work together to shape functional variation and the evolution of the adaptive immune response in large, well-connected populations.

Fecal material in the environment is a primary source of pathogens that cause waterborne diseases and affect over a billion people worldwide. Microbial source tracking (MST) assays based on single genes (e.g., 16S rRNA) do not always provide the resolution needed to attribute fecal contamination sources. In this work, we used dialysis bag mesocosms simulating a freshwater habitat that were spiked separately with cow, pig, or human feces to monitor the decay of host-specific fecal signals over time with metagenomics, traditional qPCR, and culture-based methods. Sequencing of the host fecal communities used as inocula recovered 79 non-redundant metagenome-assembled genomes (MAGs) whose abundance patterns showed that the majority of the fecal community signal was not detectable in the mesocosm metagenomes after four days. Several MAGs showed high host specificity, and thus are promising candidates for biomarkers for their respective host type. Traditional qPCR methods varied in their correlation with MAG decay kinetics. Notably, the human-specific Bacteroides assay, HF183/BFDRev, consistently under-estimated fecal pollution due to not being present in all hosts and/or primer mismatches. This work provides new insights on the persistence and decay kinetics of host-specific gut microbes in the environment and identifies several MAGs as putative biomarkers for improved MST.