The recovery of metagenome-assembled genomes (MAGs) from metagenomic data has recently become a common task for microbial studies. The strengths and limitations of the underlying bioinformatics algorithms are well appreciated by now based on performance tests with mock datasets of known composition. However, these mock datasets do not capture the complexity and diversity often observed within natural populations, since their construction typically relies on only a single genome of a given organism. Further, it remains unclear if MAGs can recover population variable (e.g., shared by >10% but 90% of the members). To address these issues, we compared the gene variability of pathogenic Escherichia coli isolates from eight diarrheal samples, for which the isolate was the causative agent, against their corresponding MAGs recovered from the companion metagenomic dataset. Our analysis revealed that MAGs with completeness estimates near 95% captured only 77% of the population core genes and 50% of the variable genes, on average. Further, about 5% of the genes of these MAG were conservatively identified as missing in the isolate and were of different (non-Enterobacteriaceae) taxonomic origin, suggesting errors at the genome binning step, even though contamination estimates based on commonly used pipelines were only 1.5%. Therefore, the quality of MAGs may oftentimes be worse than estimated, and we offer examples of how to recognize and improve such MAGs to sufficient quality by -for instance- employing only contigs longer than 1,000bp for binning. IMPORTANCE Metagenome assembly and recovery of metagenome-assembled genomes (MAGs) have recently become common tasks for microbiome studies across environmental and clinical settings. However, to what extent MAGs can capture the genes of the population they represent remains speculative. Current approaches to evaluate MAG quality are limited to the recovery and copy number of universal, housekeeping genes but these genes represent a small fraction of the total genome, leaving the majority of the genome essentially inaccessible. If MAG quality in reality is lower than these approaches would estimate, this could have dramatic consequences for all downstream analyses and interpretations. In this study, we evaluated this issue using a novel approach that employs comparisons of MAGs to isolate genomes derived from the same samples. Further, our samples originated from a diarrhea case-control study, and thus our results are relevant for recovering the virulence factors of pathogens from metagenomic datasets.

The impact of heavy rainfall events on waterborne diarrheal diseases is uncertain. We conducted weekly, active surveillance for diarrhea in 19 villages in Ecuador from February 2004 to April 2007 in order to evaluate whether biophysical and social factors modify vulnerability to heavy rainfall events. A heavy rainfall event was defined as 24-hour rainfall exceeding the 90th percentile value (56 mm) in a given 7-day period within the study period. Mixed-effects Poisson regression was used to test the hypothesis that rainfall in the prior 8 weeks, water and sanitation conditions, and social cohesion modified the relationship between heavy rainfall events and diarrhea incidence. Heavy rainfall events were associated with increased diarrhea incidence following dry periods (incidence rate ratio = 1.39, 95% confidence interval: 1.03, 1.87) and decreased diarrhea incidence following wet periods (incidence rate ratio = 0.74, 95% confidence interval: 0.59, 0.92). Drinking water treatment reduced the deleterious impacts of heavy rainfall events following dry periods. Sanitation, hygiene, and social cohesion did not modify the relationship between heavy rainfall events and diarrhea. Heavy rainfall events appear to affect diarrhea incidence through contamination of drinking water, and they present the greatest health risks following periods of low rainfall. Interventions designed to increase drinking water treatment may reduce climate vulnerability.

Animals found in close proximity to humans in low-and middle-income countries (LMICs) harbor many pathogens capable of infecting humans, transmissible via their feces. Contact with animal feces poses a currently unquantified-though likely substantial-risk to human health. In LMIC settings, human exposure to animal feces may explain some of the limited success of recent water, sanitation, and hygiene interventions that have focused on limiting exposure to human excreta, with less attention to containing animal feces. We conducted a review to identify pathogens that may substantially contribute to the global burden of disease in humans through their spread in animal feces in the domestic environment in LMICs. Of the 65 potentially pathogenic organisms considered, 15 were deemed relevant, based on burden of disease and potential for zoonotic transmission. Of these, five were considered of highest concern based on a substantial burden of disease for which transmission in animal feces is potentially important: Campylobacter, non-typhoidal Salmonella (NTS), Lassa virus, Cryptosporidium, and Toxoplasma gondii. Most of these have a wide range of animal hosts, except Lassa virus, which is spread through the feces of rats indigenous to sub-Saharan Africa. Combined, these five pathogens cause close to one million deaths annually. More than half of these deaths are attributed to invasive NTS. We do not estimate an overall burden of disease from improperly managed animal feces in LMICs, because it is unknown what proportion of illnesses caused by these pathogens can be attributed to contact with animal feces. Typical water quantity, water quality, and handwashing interventions promoted in public health and development address transmission routes for both human and animal feces; however, sanitation interventions typically focus on containing human waste, often neglecting the residual burden of disease from pathogens transmitted via animal feces. This review compiles evidence on which pathogens may contribute to the burden of disease through transmission in animal feces; these data will help prioritize intervention types and regions that could most benefit from interventions aimed at reducing human contact with animal feces.

Background. Positive associations have been noted between temperature and diarrhea incidence, but considerable uncertainty surrounds quantitative estimates of this relationship because of pathogen-specific factors and a scarcity of data on the influence of meteorological factors on the risk of disease. Quantifying these relationships is important for disease prevention and climate change adaptation. Methods. To address these issues, we performed a systematic literature review of studies in which at least 1 full year of data on the monthly incidence of diarrheagenic Escherichia coli were reported. We characterized seasonal patterns of disease incidence from 28 studies. In addition, using monthly time- and location-specific weather data for 18 studies, we performed univariate Poisson models on individual studies and a meta-analysis, using a generalized estimating equation, on the entire data set. Results. We found an 8% increase in the incidence of diarrheagenic E. coli (95% confidence interval, 5%–11%; P < .0001) for each 1°C increase in mean monthly temperature. We found a modest positive association between 1-month-lagged mean rainfall and incidence of diarrheagenic E. coli, which was not statistically significant when we controlled for temperature. Conclusions. These results suggest that increases in ambient temperature correspond to an elevated incidence of diarrheagenic E. coli and underscore the need to redouble efforts to prevent the transmission of these pathogens in the face of increasing global temperatures.