Travel-related malaria is regularly encountered in the United States, and the U.S. Centers for Disease Control and Prevention (CDC) characterizes Plasmodium falciparum drug-resistance genotypes routinely for travel-related cases. An important aspect of antimalarial drug resistance is understanding its geographic distribution. However, specimens submitted to CDC laboratories may have missing, incomplete, or inaccurate travel data. To complement genotyping for drug-resistance markers Pfcrt, Pfmdr1, Pfk13, Pfdhps, Pfdhfr, and PfcytB at CDC, amplicons of Pfs47 and Pfcpmp are also sequenced as markers of geographic origin. Here, a bi-allele likelihood (BALK) classifier was trained using Pfs47 and Pfcpmp sequences from published P. falciparum genomes of known geographic origin to classify clinical genotypes to a continent. Among P. falciparum-positive blood samples received at CDC for drug-resistance genotyping from 2018 to 2021 (n = 380), 240 included a travel history with the submission materials, though 6 were excluded due to low sequence quality. Classifications obtained for the remaining 234 were compared to their travel histories. Classification results were over 96% congruent with reported travel for clinical samples, and with collection sites for field isolates. Among travel-related samples, only two incongruent results occurred; a specimen submitted citing Costa Rican travel classified to Africa, and a specimen with travel referencing Sierra Leone classified to Asia. Subsequently, the classifier was applied to specimens with unreported travel histories (n = 140; 5 were excluded due to low sequence quality). For the remaining 135 samples, geographic classification data were paired with results generated using CDC's Malaria Resistance Surveillance (MaRS) protocol, which detects single-nucleotide polymorphisms in and generates haplotypes for Pfcrt, Pfmdr1, Pfk13, Pfdhps, Pfdhfr, and PfcytB. Given the importance of understanding the geographic distribution of antimalarial drug resistance, this work will complement domestic surveillance efforts by expanding knowledge on the geographic origin of drug-resistant P. falciparum entering the USA.

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.

All errors found in this paper are due to six samples included in the paper incorrectly assigned as being from Queensland, Australia. The report of Strongyloides fuelleborni infections from Australia made in this paper was incorrect, as those samples in fact originated in Guinea-Bissau and Senegal. | | There is an error in Table 4. Specimen Human 333_Au from Queensland (Australia) should be listed as Human 333_GuBi from Guinea-Bissau. Specimen Human 368_16_Au from Queensland (Australia) should be listed as Human 368_16_Se from Senegal. Specimen Human 378_Au from Queensland (Australia) should be listed as Human 378_Bo from Bolivia. Specimen Human 507_Au from Queensland (Australia) should be listed as Human 507_Ni from Nigeria. Specimen Human 524_Au from Queensland (Australia) should be listed as Human 524_Ni from Nigeria. Specimen Human 563_Au from Queensland (Australia) should be listed as Human 563_GuBi from Guinea-Bissau. The authors have provided a corrected Table 4 with the corrected specimens and locations in red.

In a 2018 report, an unusual case of cutaneous leishmaniasis was described in a 72-year-old female patient residing in Arizona, United States of America (USA). Preliminary analysis of the 18S rDNA and glyceraldehyde-3-phosphate dehydrogenase genes supported the conclusion that the Leishmania strain (strain 218-L139) isolated from this case was a novel species, though a complete taxonomic description was not provided. Identification of Leishmania at the species level is critical for clinical management and epidemiologic investigations so it is important that novel human-infecting species are characterized taxonomically and assigned a unique scientific name compliant with the ICZN code. Therefore, we sought to provide a complete taxonomic description of Leishmania strain 218-L139. Phylogenetic analysis of several nuclear loci and partial maxicircle genome sequences supported its position within the subgenus Leishmania and further clarified the distinctness of this new species. Morphological characterization of cultured promastigotes and amastigotes from the original case material is also provided. Thus, we conclude that Leishmania (Leishmania) ellisi is a new cause of autochthonous cutaneous leishmaniasis in 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. © 2023

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.

Strongyloidiasis is caused by the human infective nematodes Strongyloides stercoralis, Strongyloides fuelleborni subsp. fuelleborni and Strongyloides fuelleborni subsp. kellyi. The zoonotic potential of S. stercoralis and the potential role of dogs in the maintenance of strongyloidiasis transmission has been a topic of interest and discussion for many years. In Australia, strongyloidiasis is prevalent in remote socioeconomically disadvantaged communities in the north of the continent. Being an isolated continent that has been separated from other regions for a long geological period, description of the diversity of Australian Strongyloides genotypes adds to our understanding of the genetic diversity within the genus. Using PCR enrichment combined with Illumina sequencing technology, we sequenced the Strongyloides SSU 18S rDNA hyper-variable I and hyper-variable IV regions using Strongyloides-specific primers, and a fragment of the mtDNA cox1 gene using primers that are broadly specific for Strongyloides sp. and hookworms. These loci were amplified from DNA extracted from Australian human and dog faeces, and one human sputum sample. Using this approach, we confirm for the first time that potentially zoonotic S. stercoralis genotypes are present in Australia, suggesting that dogs represent a potential reservoir of human strongyloidiasis in remote Australian communities.Author summary Strongyloides stercoralis is a soil-transmitted nematode that causes the disease strongyloidiasis. Due to the autoinfective nature of this parasite, it can re-infect a host causing chronic infection. If not diagnosed and treated it can be highly detrimental to human health and has a high mortality rate. Strongyloidiasis is common in remote communities in the north of Australia and has been an issue for decades. Despite various successful intervention programs to treat human strongyloidiasis, the disease remains endemic in those communities. Here for the first time we looked at the Australian dogs’ potential to infect humans and found that they carry two genetically distinct strains of Strongyloides spp., one of which also infects humans. This supports the hypothesis that dogs are a potential source for human strongyloidiasis. We also found that dogs in Australia might be carrying unique haplotypes. Whether these new haplotypes are also human infective is to be confirmed by further research.

The apicomplexan parasite Cyclospora cayetanensis causes seasonal foodborne outbreaks of the gastrointestinal illness cyclosporiasis. Prior to the coronavirus disease-2019 pandemic, annually reported cases were increasing in the USA, leading the US Centers for Disease Control and Prevention to develop a genotyping tool to complement cyclosporiasis outbreak investigations. Thousands of US isolates and 1 from China (strain CHN_HEN01) were genotyped by Illumina amplicon sequencing, revealing 2 lineages (A and B). The allelic composition of isolates was examined at each locus. Two nuclear loci (CDS3 and 360i2) distinguished lineages A and B. CDS3 had 2 major alleles: 1 almost exclusive to lineage A and the other to lineage B. Six 360i2 alleles were observed 2 exclusive to lineage A (alleles A1 and A2), 2 to lineage B (B1 and B2) and 1 (B4) was exclusive to CHN_HEN01 which shared allele B3 with lineage B. Examination of heterozygous genotypes revealed that mixtures of A- and B-type 360i2 alleles occurred rarely, suggesting a lack of gene flow between lineages. Phylogenetic analysis of loci from whole-genome shotgun sequences, mitochondrial and apicoplast genomes, revealed that CHN_HEN01 represents a distinct lineage (C). Retrospective examination of epidemiologic data revealed associations between lineage and the geographical distribution of US infections plus strong temporal associations. Given the multiple lines of evidence for speciation within human-infecting Cyclospora, we provide an updated taxonomic description of C. cayetanensis, and describe 2 novel species as aetiological agents of human cyclosporiasis: Cyclospora ashfordi sp. nov. and Cyclospora henanensis sp. nov. (Apicomplexa: Eimeriidae).

Human strongyloidiasis is an important neglected tropical disease primarily caused by the nematode Strongyloides stercoralis, and to a lesser extent Strongyloides fuelleborni which mainly infects non-human primates. Zoonotic sources of infection have important implications for control and prevention of morbidity and mortality caused by strongyloidiasis. Recent molecular evidence suggests that for S. fuelleborni, primate host specificity is variable among genotypes across the Old World, and consequently that these types likely vary in their capacity for human spillover infections. Populations of free-roaming vervet monkeys (Chlorocebus aethiops sabaeus), introduced to the Caribbean Island of Staint Kitts from Africa, live in close contact with humans, and concern has arisen regarding their potential to serve as reservoirs of zoonotic infections. In this study, we sought to determine the genotypes of S. fuelleborni infecting St. Kitts vervets to explore whether they are potential reservoirs for human-infecting S. fuelleborni types. Fecal specimens were collected from St. Kitts vervets and S. fuelleborni infections were confirmed microscopically and by PCR. Strongyloides fuelleborni genotypes were determined from positive fecal specimens using an Illumina amplicon sequencing-based genotyping approach targeting the mitochondrial cox1 locus and 18S rDNA hypervariable regions I and IV of Strongyloides species. Phylogenetic analysis of resultant genotypes supported that S. fuelleborni from St. Kitts vervets is of an exclusively African variety, falling within the same monophyletic group as an isolate which has been detected previously in a naturally infected human from Guinea-Bissau. This observation highlights that St. Kitts vervets may serve as potential reservoirs for zoonotic S. fuelleborni infection, which warrants further exploration.

Comparing parasite genotypes to inform parasitic disease outbreak investigations involves computation of genetic distances that are typically analyzed by hierarchical clustering to identify related isolates, indicating a common source. A limitation of hierarchical clustering is that hierarchical clusters are not discrete, they are nested. Consequently, small groups of similar isolates exist within larger groups that get progressively larger as relationships become increasingly distant. Investigators must dissect hierarchical trees at a partition number ensuring grouped isolates belong to the same strain; a process typically performed subjectively, introducing bias into resultant groupings. We describe an unbiased, probabilistic framework for partition number selection that ensures partitions comprise isolates that are statistically likely to belong to the same strain. We compute distances and establish a normalized distribution of background distances that is used to demarcate a threshold below which the closeness of relationships is unlikely to be random. Distances are hierarchically clustered and the dendrogram dissected at a partition number where most within-partition distances fall below the threshold. We evaluated this framework by partitioning 1,137 clustered Cyclospora cayetanensis genotypes including 552 isolates epidemiologically linked to various outbreaks. The framework was 91% sensitive and 100% specific in assigning epidemiologically-linked isolates to the same partition.

Multi-locus sequence typing (MLST) is widely used to investigate genetic relationships among eukaryotic taxa, including parasitic pathogens. MLST analysis workflows typically involve construction of alignment-based phylogenetic trees - i.e., where tree structures are computed from nucleotide differences observed in a multiple sequence alignment (MSA). Notably, alignment-based phylogenetic methods require that all isolates/taxa are represented by a single sequence. When multiple loci are sequenced these sequences may be concatenated to produce one tree that includes information from all loci. Alignment-based phylogenetic techniques are robust and widely used yet possess some shortcomings, including how heterozygous sites are handled, intolerance for missing data (i.e., partial genotypes), and differences in the way insertions-deletions (indels) are scored/treated during tree construction. In certain contexts, 'haplotype-based' methods may represent a viable alternative to alignment-based techniques, as they do not possess the aforementioned limitations. This is namely because haplotype-based methods assess genetic similarity based on numbers of shared (i.e., intersecting) haplotypes as opposed to similarities in nucleotide composition observed in an MSA. For haplotype-based comparisons, choosing an appropriate distance statistic is fundamental, and several statistics are available to choose from. However, a comprehensive assessment of various available statistics for their ability to produce a robust haplotype-based phylogenetic reconstruction has not yet been performed. We evaluated seven distance statistics by applying them to extant MLST datasets from the gastrointestinal parasite Cyclospora cayetanensis and two species of pathogenic nematode of the genus Strongyloides. We compare the genetic relationships identified using each statistic to epidemiologic, geographic, and host metadata. We show that Barratt's heuristic definition of genetic distance was the most robust among the statistics evaluated. Consequently, it is proposed that Barratt's heuristic represents a useful approach for use in the context of challenging MLST datasets possessing features (i.e., high heterozygosity, partial genotypes, and indel or repeat-based polymorphisms) that confound or preclude the use of alignment-based methods.

Assessing genetic relatedness of Plasmodium falciparum genotypes is a key component of antimalarial efficacy trials. Previous methods have focused on determining a priori definitions of the level of genetic similarity sufficient to classify two infections as sharing the same strain. However, factors such as mixed-strain infections, allelic suppression, imprecise typing methods, and heterozygosity complicate comparisons of apicomplexan genotypes. Here, we introduce a novel method for nonparametric statistical testing of relatedness for P. falciparum parasites. First, the background distribution of genetic distance between unrelated strains is computed. Second, a threshold genetic distance is computed from this empiric distribution of distances to demarcate genetic distances that are unlikely to have arisen by chance. Third, the genetic distance between paired samples is computed, and paired samples with genetic distances below the threshold are classified as related. The method is designed to work with any arbitrary genetic distance definition. We validated this procedure using two independent approaches to calculating genetic distance. We assessed the concordance of the novel nonparametric classification with a gold-standard Bayesian approach for 175 pairs of recurrent P. falciparum episodes from previously published malaria efficacy trials with microsatellite data from five studies in Guinea and Angola. The novel nonparametric approach was 98% sensitive and 84-89% specific in correctly identifying related genotypes compared with a gold-standard Bayesian algorithm. The approach provides a unified and systematic method to statistically assess relatedness of P. falciparum parasites using arbitrary genetic distance methodologies.

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Human strongyloidiasis is a serious disease mostly attributable to Strongyloides stercoralis and to a lesser extent Strongyloides fuelleborni, a parasite mainly of non-human primates. The role of animals as reservoirs of human-infecting Strongyloides is ill-defined, and whether dogs are a source of human infection is debated. Published multi-locus sequence typing (MLST) studies attempt to elucidate relationships between Strongyloides genotypes, hosts, and distributions, but typically examine relatively few worms, making it difficult to identify population-level trends. Combining MLST data from multiple studies is often impractical because they examine different combinations of loci, eliminating phylogeny as a means of examining these data collectively unless hundreds of specimens are excluded. A recently-described machine learning approach that facilitates clustering of MLST data may offer a solution, even for datasets that include specimens sequenced at different combinations of loci. By clustering various MLST datasets as one using this procedure, we sought to uncover associations among genotype, geography, and hosts that remained elusive when examining datasets individually. Multiple datasets comprising hundreds of S. stercoralis and S. fuelleborni individuals were combined and clustered. Our results suggest that the commonly proposed 'two lineage' population structure of S. stercoralis (where lineage A infects humans and dogs, lineage B only dogs) is an over-simplification. Instead, S. stercoralis seemingly represents a species complex, including two distinct populations over-represented in dogs, and other populations vastly more common in humans. A distinction between African and Asian S. fuelleborni is also supported here, emphasizing the need for further resolving these taxonomic relationships through modern investigations.

Strongyloidiasis is a neglected tropical disease caused by the human infective nematodes Strongyloides stercoralis, Strongyloides fuelleborni fuelleborni and Strongyloides fuelleborni kellyi. Previous large-scale studies exploring the genetic diversity of this important genus have focused on Southeast Asia, with a small number of isolates from the USA, Switzerland, Australia and several African countries having been genotyped. Consequently, little is known about the global distribution of geographic sub-variants of these nematodes and the genetic diversity that exists within the genus Strongyloides generally. We extracted DNA from human, dog and primate feces containing Strongyloides, collected from several countries representing all inhabited continents. Using a genotyping assay adapted for deep amplicon sequencing on the Illumina MiSeq platform, we sequenced the hyper-variable I and hyper-variable IV regions of the Strongyloides 18S rRNA gene and a fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene from these specimens. We report several novel findings including unique S. stercoralis and S. fuelleborni genotypes, and the first identifications of a previously unknown S. fuelleborni infecting humans within Australia. We expand on an existing Strongyloides genotyping scheme to accommodate S. fuelleborni and these novel genotypes. In doing so, we compare our data to all 18S and cox1 sequences of S. fuelleborni and S. stercoralis available in GenBank (to our knowledge), that overlap with the sequences generated using our approach. As this analysis represents more than 1,000 sequences collected from diverse hosts and locations, representing all inhabited continents, it allows a truly global understanding of the population genetic structure of the Strongyloides species infecting humans, non-human primates, and domestic dogs.

Strongyloidiasis is caused by the human infective nematodes Strongyloides stercoralis, Strongyloides fuelleborni subsp. fuelleborni and Strongyloides fuelleborni subsp. kellyi. The zoonotic potential of S. stercoralis and the potential role of dogs in the maintenance of strongyloidiasis transmission has been a topic of interest and discussion for many years. In Australia, strongyloidiasis is prevalent in remote socioeconomically disadvantaged communities in the north of the continent. Being an isolated continent that has been separated from other regions for a long geological period, description of diversity of Australian Strongyloides genotypes adds to our understanding of the genetic diversity within the genus. Using PCR and amplicon sequencing (Illumina sequencing technology), we sequenced the Strongyloides SSU rDNA hyper-variable I and hyper-variable IV regions using Strongyloides-specific primers, and a fragment of the mtDNA cox1 gene using primers that are broadly specific for Strongyloides sp. and hookworms. These loci were amplified from DNA extracted from Australian human and dog faeces, and one human sputum sample. Using this approach, we confirm for the first time that potentially zoonotic S. stercoralis populations are present in Australia, suggesting that dogs represent a potential reservoir of human strongyloidiasis in remote Australian communities.

Sexually reproducing pathogens such as Cyclospora cayetanensis often produce genetically heterogeneous infections where the number of unique sequence types detected at any given locus varies depending on which locus is sequenced. The genotypes assigned to these infections quickly become complex when additional loci are analysed. This genetic heterogeneity confounds the utility of traditional sequence-typing and phylogenetic approaches for aiding epidemiological trace-back, and requires new methods to address this complexity. Here, we describe an ensemble of two similarity-based classification algorithms, including a Bayesian and heuristic component that infer the relatedness of C. cayetanensis infections. The ensemble requires a set of haplotypes as input and assigns arbitrary distances to specimen pairs reflecting their most likely relationships. The approach was applied to data generated from a test cohort of 88 human fecal specimens containing C. cayetanensis, including 30 from patients whose infections were associated with epidemiologically defined outbreak clusters of cyclosporiasis. The ensemble assigned specimens to plausible clusters of genetically related infections despite their complex haplotype composition. These relationships were corroborated by a significant number of epidemiological linkages (P < 0.0001) suggesting the ensemble's utility for aiding epidemiological trace-back investigations of cyclosporiasis.