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 of the population) as efficiently as core genes (e.g., shared by >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.

c Taxonomic classification of Clostridium botulinum is based on the production of botulinum neurotoxin (BoNT), while closely related, nontoxic organisms are classified as Clostridium sporogenes. However, this taxonomic organization does not accurately mirror phylogenetic relationships between these species. A phylogenetic reconstruction using 2,016 orthologous genes shared among strains of C. botulinum group I and C. sporogenes clearly separated these two species into discrete clades which showed ϳ93% average nucleotide identity (ANI) between them. Clustering of strains based on the presence of variable orthologs revealed 143 C. sporogenes clade-specific genetic signatures, a subset of which were further evaluated for their ability to correctly classify a panel of presumptive C. sporogenes strains by PCR. Genome sequencing of several C. sporogenes strains lacking these signatures confirmed that they clustered with C. botulinum strains in a core genome phylogenetic tree. Our analysis also identified C. botulinum strains that contained C. sporogenes clade-specific signatures and phylogenetically clustered with C. sporogenes strains. The genome sequences of two bont/B2-containing strains belonging to the C. sporogenes clade contained regions with similarity to a bont-bearing plasmid (pCLD), while two different strains belonging to the C. botulinum clade carried bont/B2 on the chromosome. These results indicate that bont/B2 was likely acquired by C. sporogenes strains through horizontal gene transfer. The genome-based classification of these species used to identify candidate genes for the development of rapid assays for molecular identification may be applicable to additional bacterial species that are challenging with respect to their classification. Botulinum neurotoxins (BoNT) cause neuromuscular paralysis associated with botulism and are produced by various clostridia, most notably Clostridium botulinum. C. botulinum is a Gram-positive, anaerobic, endospore-forming bacillus that can be classified on the basis of metabolic properties into four separate groups (groups I to IV). Group I C. botulinum strains are proteolytic, saccharolytic, and capable of producing BoNT types A, B, and F. Group II C. botulinum strains are nonproteolytic and can produce BoNT types B, E, and F, while group III strains produce BoNT types C and D. Group IV strains, which are also identified as Clostridium argentinense, produce BoNT type G.The nontoxic species Clostridium sporogenes shares nearly identical metabolic properties with group I C. botulinum, including the formation of lipase-positive colonies when grown on egg yolk agar. Because of these similarities and the comparable heat resistance of its spores, C. sporogenes has been used as a surrogate organism for C. botulinum in the study of thermal processing for foods (1). Previous studies have shown that some strains of C. sporogenes and group I C. botulinum can be differentiated phenotypically by soluble protein expression, measured by polyacrylamide gel electrophoresis (2) and ga...

Diagnostic testing for foodborne pathogens relies on culture-based techniques that are not rapid enough for real-time disease surveillance and do not give a quantitative picture of pathogen abundance or the response of the natural microbiome. Powerful sequence-based culture-independent approaches, such as shotgun metagenomics, could sidestep these limitations and potentially reveal a pathogenspecific signature on the microbiome that would have implications not only for diagnostics but also for better understanding disease progression and pathogen ecology. However, metagenomics have not yet been validated for foodborne pathogen detection. Toward closing these gaps, we applied shotgun metagenomics to stool samples collected from two geographically isolated (Alabama and Colorado) foodborne outbreaks, where the etiologic agents were identified by culture-dependent methods as distinct strains of Salmonella enterica subsp. enterica serovar Heidelberg. Metagenomic investigations were consistent with the culture-based findings and revealed, in addition, the in situ abundance and level of intrapopulation diversity of the pathogen, the possibility of coinfections with Staphylococcus aureus, overgrowth of commensal Escherichia coli, and significant shifts in the gut microbiome during infection relative to reference healthy samples. Additionally, we designed our bioinformatics pipeline to deal with several challenges associated with the analysis of clinical samples, such as the high frequency of coeluting human DNA sequences and assessment of the virulence potential of pathogens. Comparisons of these results to those of other studies revealed that in several, but not all, cases of diarrheal outbreaks, the disease and healthy states of the gut microbial community might be distinguishable, opening new possibilities for diagnostics.IMPORTANCE Diagnostic testing for enteric pathogens has relied for decades on culture-based techniques, but a total of 38.4 million cases of foodborne illness per year cannot be attributed to specific causes. This study describes new cultureindependent metagenomic approaches and the associated bioinformatics pipeline to detect and type the causative agents of microbial disease with unprecedented accuracy, opening new possibilities for the future development of health technologies and diagnostics. Our tools and approaches should be applicable to other microbial diseases in addition to foodborne diarrhea.KEYWORDS Salmonella, diagnostics, diarrhea, human gut, metagenomics D iagnostic testing for enteric pathogens has relied for decades on culture-based techniques, and culture-derived isolates currently form the foundation for public health surveillance. However, the clinical landscape is rapidly changing as culture-

Escherichia coli is a leading contributor to infectious diarrhea and child mortality worldwide, but it remains unknown how alterations in the gut microbiome vary for distinct E. coli pathotype infections and whether these signatures can be used for diagnostic purposes. Further, the majority of enteric diarrheal infections are not diagnosed with respect to their etiological agent(s) due to technical challenges. To address these issues, we devised a novel approach that combined traditional, isolate-based and molecular-biology techniques with metagenomics analysis of stool samples and epidemiological data. Application of this pipeline to children enrolled in a case-control study of diarrhea in Ecuador showed that, in about half of the cases where an E. coli pathotype was detected by culture and PCR, E. coli was likely not the causative agent based on the metagenome-derived low relative abundance, the level of clonality, and/or the virulence gene content. Our results also showed that diffuse adherent E. coli (DAEC), a pathotype that is generally underrepresented in previous studies of diarrhea and thus, thought not to be highly virulent, caused several small-scale diarrheal outbreaks across a rural to urban gradient in Ecuador. DAEC infections were uniquely accompanied by coelution of large amounts of human DNA and conferred significant shifts in the gut microbiome composition relative to controls or infections caused by other E. coli pathotypes. Our study shows that diarrheal infections can be efficiently diagnosed for their etiological agent and categorized based on their effects on the gut microbiome using metagenomic tools, which opens new possibilities for diagnostics and treatment. IMPORTANCE E. coli infectious diarrhea is an important contributor to child mortality worldwide. However, diagnosing and thus treating E. coli infections remain challenging due to technical and other reasons associated with the limitations of the traditional culture-based techniques and the requirement to apply Koch’s postulates. In this study, we integrated traditional microbiology techniques with metagenomics and epidemiological data in order to identify cases of diarrhea where E. coli was most likely the causative disease agent and evaluate specific signatures in the disease-state gut microbiome that distinguish between diffuse adherent, enterotoxigenic, and enteropathogenic E. coli pathotypes. Therefore, our methodology and results should be highly relevant for diagnosing and treating diarrheal infections and have important applications in public health.

Background: While the importance of commensal microbes in vaginal health is well appreciated, little is known about the effects of gynecological cancer (GynCa) and radiation therapy (RT) on the vaginal microbiome (VM) of postmenopausal women. Methods: We studied women with GynCa, pre-(N = 65) and post-RT (N = 25) and a group of healthy controls (N = 67) by sequencing the V4 region of the 16S rRNA gene from vaginal swabs and compared the diversity and composition of VMs between the three groups accounting for potential confounding factors in multivariate analysis of variance. Results: Comparisons of cancer vs healthy groups revealed that Lactobacillus andBifidobacterium have significantly higher relative abundance in the healthy group, while the cancer group was enriched in 16 phylogroups associated with bacterial vaginosis (BV) and inflammation, including Sneathia, Prevotella, Peptoniphilus, Fusobacterium, Anaerococcus, Dialister, Moryella, and Peptostreptococcus. In our sample, RT affected the α-diversity and correlated with higher abundance of typically rare VM species, including several members of the Lacnospiraceae family, a taxon previously linked to vaginal dysbiosis. In addition to cancer and treatment modalities, age and vaginal pH were identified as significant parameters that structure the VM. Conclusions: This is among the first reports identifying VM changes among postmenopausal women with cancer. RT alone seems to affect several phylogroups (12 bacterial genera), while gynecological cancer and its treatment modalities are associated with even greater significant shifts in the vaginal microbiota including the enrichment of opportunistic bacterial pathogens, which warrants further attention. K E Y W O R D S16S rRNA gene, gynecologic cancer, postmenopausal women, radiation therapy, vaginal microbiota | 3715 TSEMENTZI ET al.