Evaluation of the Increased Genetic Resolution and Utility for Source Tracking of a Recently Developed Method for Genotyping Cyclospora cayetanensis.

Cyclospora cayetanensis is a foodborne parasite that causes cyclosporiasis, an enteric illness in humans. Genotyping methods are used to genetically discriminate between specimens from cyclosporiasis cases and can complement source attribution investigations if the method is sufficiently sensitive for application to food items. A very sensitive targeted amplicon sequencing (TAS) assay for genotyping C. cayetanensis encompassing 52 loci was recently designed. In this study, we analyzed 66 genetically diverse clinical specimens to assess the change in phylogenetic resolution between the TAS assay and a currently employed eight-marker scheme. Of the 52 markers, ≥50 were successfully haplotyped for all specimens, and these results were used to generate a hierarchical cluster dendrogram. Using a previously described statistical approach to dissect hierarchical trees, the 66 specimens resolved into 24 and 27 distinct genetic clusters for the TAS and an 8-loci scheme, respectively. Although the specimen composition of 15 clusters was identical, there were substantial differences between the two dendrograms, highlighting the importance of both inclusion of additional genome coverage and choice of loci to target for genotyping. To evaluate the ability to genetically link contaminated food samples with clinical specimens, C. cayetanensis was genotyped from DNA extracted from raspberries inoculated with fecal specimens. The contaminated raspberry samples were assigned to clusters with the corresponding clinical specimen, demonstrating the utility of the TAS assay for traceback efforts.

Novel insights on the genetic population structure of human-infecting Cyclospora spp. and evidence for rapid subtype selection among isolates from the USA.

Human-infecting Cyclospora was recently characterized as three species, two of which (C. cayetanensis and C. ashfordi) are currently responsible for all known human infections in the USA, yet much remains unknown about the genetic structure within these two species. Here, we investigate Cyclospora genotyping data from 2018 through 2022 to ascertain if there are temporal patterns in the genetic structure of Cyclospora parasites that cause infections in US residents from year to year. First, we investigate three levels of genetic characterization: species, subpopulation, and strain, to elucidate annual trends in Cyclospora infections. Next, we determine if shifts in genetic diversity can be linked to any of the eight loci used in our Cyclospora genotyping approach. We observed fluctuations in the abundance of Cyclospora types at the species and subpopulation levels, but no significant temporal trends were identified; however, we found recurrent and sporadic strains within both C. ashfordi and C. cayetanensis. We also uncovered major shifts in the mitochondrial genotypes in both species, where there was a universal increase in abundance of a specific mitochondrial genotype that was relatively abundant in 2018 but reached near fixation (was observed in over 96% of isolates) in C. ashfordi by 2022. Similarly, this allele jumped from 29% to 82% relative abundance of isolates belonging to C. cayetanensis. Overall, our analysis uncovers previously unknown temporal-genetic patterns in US Cyclospora types from 2018 through 2022 and is an important step to presenting a clearer picture of the factors influencing cyclosporiasis outbreaks in the USA.

An improved framework for detecting discrete epidemiologically meaningful partitions in hierarchically clustered genetic data.

Motivation: Hierarchical clustering of microbial genotypes has the limitation that hierarchical clusters are nested, where smaller groups of related isolates exist within larger groups that get progressively larger as relationships become increasingly distant. In an epidemiologic context, investigators must dissect hierarchical trees into discrete groupings that are epidemiologically meaningful. We recently described a statistical framework (Method A) for dissecting hierarchical trees that attempts to minimize investigator bias. Here, we apply a modified version of that framework (Method B) to a hierarchical tree constructed from 2111 genotypes of the foodborne parasite Cyclospora, including 639 genotypes linked to epidemiologically defined outbreaks. To evaluate Method B's performance, we examined the concordance between these epidemiologically defined groupings and the genetic partitions identified. We also used the same epidemiologic clusters to evaluate the performance of Method A, plus two tree-dissection methods (cutreeHybrid and cutreeDynamic) available within the Dynamic Tree Cut R package, in addition to the TreeCluster method and PARNAS. Results: Compared to the other methods, Method B, TreeCluster, and PARNAS were the most accurate (99.4%) in identifying genetic groups that reflected the epidemiologic groupings, noting that TreeCluster and PARNAS performed identically on our dataset. CutreeHybrid identified groups reflecting patterns in the wider Cyclospora population structure but lacked finer, strain-level discrimination (Simpson's D: cutreeHybrid=0.785). CutreeDynamic displayed good strain discrimination (Simpson's D = 0.933), though lacked sensitivity (77%). At two different threshold/radius settings TreeCluster/PARNAS displayed similar utility to Method B. However, Method B computes a tree-dissection threshold automatically, and the threshold/radius settings used when executing TreeCluster/PARNAS here were computed using Method B. Using a TreeCluster threshold of 0.045 as recommended in the TreeCluster documentation, epidemiologic utility dropped markedly below that of Method B. Availability and implementation: Relevant code and data are publicly available. Source code (Method B) and instructions for its use are available here: https://github.com/Joel-Barratt/Hierarchical-tree-dissection-framework.

Oral metagenomes from Native American Ancestors reveal distinct microbial lineages in the pre-contact era.

OBJECTIVES: Limited studies have focused on how European contact and colonialism impacted Native American oral microbiomes, specifically, the diversity of commensal or opportunistically pathogenic oral microbes, which may be associated with oral diseases. Here, we studied the oral microbiomes of pre-contact Wichita Ancestors, in partnership with the Descendant community, The Wichita and Affiliated Tribes, Oklahoma, USA. MATERIALS AND METHODS: Skeletal remains of 28 Wichita Ancestors from 20 archeological sites (dating approximately to 1250-1450 CE) were paleopathologically assessed for presence of dental calculus and oral disease. DNA was extracted from calculus, and partial uracil deglycosylase-treated double-stranded DNA libraries were shotgun-sequenced using Illumina technology. DNA preservation was assessed, the microbial community was taxonomically profiled, and phylogenomic analyzes were conducted. RESULTS: Paleopathological analysis revealed signs of oral diseases such as caries and periodontitis. Calculus samples from 26 Ancestors yielded oral microbiomes with minimal extraneous contamination. Anaerolineaceae bacterium oral taxon 439 was found to be the most abundant bacterial species. Several Ancestors showed high abundance of bacteria typically associated with periodontitis such as Tannerella forsythia and Treponema denticola. Phylogenomic analyzes of Anaerolineaceae bacterium oral taxon 439 and T. forsythia revealed biogeographic structuring; strains present in the Wichita Ancestors clustered with strains from other pre-contact Native Americans and were distinct from European and/or post-contact American strains. DISCUSSION: We present the largest oral metagenome dataset from a pre-contact Native American population and demonstrate the presence of distinct lineages of oral microbes specific to the pre-contact Americas.

Analysis of global human gut metagenomes shows that metabolic resilience potential for short-chain fatty acid production is strongly influenced by lifestyle.

High taxonomic diversity in non-industrial human gut microbiomes is often interpreted as beneficial; however, it is unclear if taxonomic diversity engenders ecological resilience (i.e. community stability and metabolic continuity). We estimate resilience through genus and species-level richness, phylogenetic diversity, and evenness in short-chain fatty acid (SCFA) production among a global gut metagenome panel of 12 populations (n = 451) representing industrial and non-industrial lifestyles, including novel metagenomic data from Burkina Faso (n = 90). We observe significantly higher genus-level resilience in non-industrial populations, while SCFA production in industrial populations is driven by a few phylogenetically closely related species (belonging to Bacteroides and Clostridium), meaning industrial microbiomes have low resilience potential. Additionally, database bias obfuscates resilience estimates, as we were 2-5 times more likely to identify SCFA-encoding species in industrial microbiomes compared to non-industrial. Overall, we find high phylogenetic diversity, richness, and evenness of bacteria encoding SCFAs in non-industrial gut microbiomes, signaling high potential for resilience in SCFA production, despite database biases that limit metagenomic analysis of non-industrial populations.

Functional diversity of microbial ecologies estimated from ancient human coprolites and dental calculus.

Human microbiome studies are increasingly incorporating macroecological approaches, such as community assembly, network analysis and functional redundancy to more fully characterize the microbiome. Such analyses have not been applied to ancient human microbiomes, preventing insights into human microbiome evolution. We address this issue by analysing published ancient microbiome datasets: coprolites from Rio Zape (n = 7; 700 CE Mexico) and historic dental calculus (n = 44; 1770-1855 CE, UK), as well as two novel dental calculus datasets: Maya (n = 7; 170 BCE-885 CE, Belize) and Nuragic Sardinians (n = 11; 1400-850 BCE, Italy). Periodontitis-associated bacteria (Treponema denticola, Fusobacterium nucleatum and Eubacterium saphenum) were identified as keystone taxa in the dental calculus datasets. Coprolite keystone taxa included known short-chain fatty acid producers (Eubacterium biforme, Phascolarctobacterium succinatutens) and potentially disease-associated bacteria (Escherichia, Brachyspira). Overlap in ecological profiles between ancient and modern microbiomes was indicated by similarity in functional response diversity profiles between contemporary hunter-gatherers and ancient coprolites, as well as parallels between ancient Maya, historic UK, and modern Spanish dental calculus; however, the ancient Nuragic dental calculus shows a distinct ecological structure. We detected key ecological signatures from ancient microbiome data, paving the way to expand understanding of human microbiome evolution. This article is part of the theme issue 'Insights into health and disease from ancient biomolecules'.