Seasonal cyclosporiasis outbreaks occur in the United States every year. To better understand the disease, the Centers for Disease Control and Prevention developed a novel genotyping system that successfully clusters nonclonal eukaryotes. We examined temporal-geographic distributions of Cyclospora cluster consensus genotypes (CCGs) and applied regression analyses to identify correlations between Cyclospora spp. parasites and clinical manifestations or epidemiologic risk factors, using data collected during 2018-2021. No CCG was uniquely associated with or consistently detected in a state during the study, suggesting that cyclosporiasis in the United States is likely caused by frequent parasite introductions. We identified positive associations between infection with C. ashfordi and C. cayetanensis and consumption of specific produce items: cilantro, mango, and onion for C. ashfordi and iceberg lettuce, carrot, and cauliflower for C. cayetanensis. Our findings can guide future research into public health interventions aimed at reducing the burden of cyclosporiasis in the United States.

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.

Recent cyclosporiasis outbreaks associated with fresh produce grown in the United States highlight the need to better understand C. cayetanensis prevalence in U.S. agricultural environments. In this study, C. cayetanensis occurrence was assessed in municipal wastewater sludge, on-farm portable toilets, irrigation pond water, and spent packing house dump tank water in a Southeastern Georgia growing region over two years. Detection of the C. cayetanensis 18S rRNA qPCR gene target in pond samples was 0%, 28%, and 42% (N=217) depending on the detection definition used, and ≤ 1% in dump tank samples (N=46). However, no qPCR detections were confirmed by sequencing, suggesting false detection occurred due to cross-reactions. C. cayetanensis qPCR detections were confirmed in 9% of wastewater sludge samples (N=76). The human-specific fecal markers HF183 and crAssphage were detected in 33% and 6% of pond samples, respectively and 4% and 0% of dump tank samples, respectively. Despite community Cyclospora shedding and evidence of human fecal contamination in irrigation water, there was no correlation between C. cayetanensis and HF183 qPCR detections, further supporting that 18S gene target qPCR amplifications were due to cross reactions. When evaluating C. cayetanensis qPCR environmental detection data, the impact of assay specificity and detection criteria should be considered. Moreover, additional sequence-based testing may be needed to appropriately interpret Cyclospora qPCR environmental data.

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.

We recently described a targeted amplicon deep sequencing (TADS) strategy that utilizes a nested PCR targeting the 18S rDNA gene of blood-borne parasites. The assay facilitates selective digestion of host DNA by targeting enzyme restriction sites present in vertebrates but absent in parasites. This enriching of parasite-derived amplicon drastically reduces the proportion of host-derived reads during sequencing and results in the sensitive detection of several clinically important blood parasites including Plasmodium spp., Babesia spp., kinetoplastids, and filarial nematodes. Despite these promising results, high costs and the laborious nature of metagenomics sequencing are prohibitive to the routine use of this assay in most laboratories. We describe and evaluate a new metagenomic approach that utilizes a set of primers modified from our original assay that incorporates Illumina barcodes and adapters during the PCR steps. This modification makes amplicons immediately compatible with sequencing on the Illumina MiSeq platform, removing the need for a separate library preparation, which is expensive and time-consuming. We compared this modified assay to our previous nested TADS assay in terms of preparation speed, limit of detection (LOD), and cost. Our modifications reduced assay turnaround times from 7 to 5 days. The cost decreased from approximately $40 per sample to $11 per sample. The modified assay displayed comparable performance in the detection and differentiation of human-infecting Plasmodium spp., Babesia spp., kinetoplastids, and filarial nematodes in clinical samples. The LOD of this modified approach was determined for malaria parasites and remained similar to that previously reported for our earlier assay (0.58 Plasmodium falciparum parasites/µL of blood). These modifications markedly reduced costs and turnaround times, making the assay more amenable to routine diagnostic applications.

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

Human-infecting Cyclospora spp. currently include three coccidian parasites that cause the gastrointestinal disease cyclosporiasis in humans. They are often spread through contaminated produce, including leafy greens and berries. The increased availability of sensitive molecular tests for the diagnosis of cyclosporiasis is an important advancement, allowing public health agencies to better understand the scope and source of cyclosporiasis outbreaks. To improve the diagnosis of infected patients, rapidly detect outbreaks, and keep the food supply safe, it is important to continue to develop sensitive, reliable, and inexpensive tests to detect human-infecting Cyclospora spp. In this report, we describe the development and evaluation of two novel one-tube nested qPCR assays for the detection of human-infecting Cyclospora spp. in clinical stool samples, one targeting cytb and the other targeting coxI. Of these, the assay targeting the cytb mitochondrial locus possessed strong performance characteristics compared to a routinely used 18S assay, including a markedly improved (approximately 10-fold lower) relative detection limit of 0.613 oocysts per gram of feces. This is compared to coxI that has a relative detection limit equal to that of the 18S assay. Given the strong performance characteristics of the cytb assay, we propose that it may be useful to diagnostic laboratories wishing to screen clinical fecal specimens suspected of containing human-infecting Cyclospora spp.IMPORTANCEHuman-infecting Cyclospora spp. cause gastrointestinal distress among healthy individuals contributing to morbidity and putting stress on the economics of countries and companies in the form of produce recalls. Accessible and easy-to-use diagnostic tools available to a wide variety of laboratories would aid in the early detection of possible outbreaks of cyclosporiasis. This, in turn, will assist in the timely traceback investigation to the suspected source of an outbreak by informing the smallest possible recall and protecting consumers from contaminated produce. This manuscript describes two novel detection methods with improved performance for the causative agents of cyclosporiasis when compared to the currently used 18S assay.

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.

Cyclosporiasis results from an infection of the small intestine by Cyclospora parasites after ingestion of contaminated food or water, often leading to gastrointestinal distress. Recent developments in temporally linking genetically related Cyclospora isolates demonstrated effectiveness in supporting epidemiological investigations. We used 'temporal-genetic clusters' (TGCs) to investigate reported cyclosporiasis cases in the United States during the 2021 peak-period (1 May - 31 August 2021). Our approach split 655 genotyped isolates into 55 genetic clusters and 31 TGCs. We linked two large multi-state epidemiological clusters (Epidemiologic Cluster 1 [n = 136 cases, 54 genotyped] and Epidemiologic Cluster 2 [n = 42 cases, 15 genotyped]) to consumption of lettuce varieties; however, product traceback did not identify a specific product for either cluster due to the lack of detailed product information. To evaluate the utility of TGCs, we performed a retrospective case study comparing investigation outcomes of outbreaks first detected using epidemiological methods with those of the same outbreaks had TGCs been used to first detect them. Our study results indicate that adjustments to routine epidemiological approaches could link additional cases to epidemiological clusters of cyclosporiasis. Overall, we show that CDC's integrated genotyping and epidemiological investigations provide valuable insights into cyclosporiasis outbreaks in the United States.

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).

Hierarchical clustering of pathogen genotypes is widely used to complement epidemiologic investigations of outbreaks. Investigators must dissect trees to obtain genetic partitions that provide epidemiologists with meaningful information. Statistical approaches to tree dissection often require a user-defined parameter to predict the optimal partition number and augmenting this parameter can drastically impact resultant partition memberships. Here, we demonstrate how to optimize a given tree dissection parameter to maximize accuracy irrespective of the tree dissection method used. We hierarchically clustered 1,873 genotypes of the foodborne pathogen Cyclospora spp., including 587 possessing links to historic outbreaks. We dissected the resulting tree using a statistical method requiring users to select the value of a 'stringency parameter' (s), with a recommended value of 95% to 99.5%. We dissected this hierarchical tree across s-values from 94% to 99.5% (at increments of 0.25%), to identify a value that maximized partitioning accuracy, defined as the degree to which genetic partitions conform to known epidemiologic groupings. We show that s-values of 96.5% and 96.75% yield the highest accuracy (> 99.9%) when clustering Cyclospora sp. isolates with known epidemiologic linkages. In practice, the optimized s-value will generate robust genetic partitions comprising isolates likely derived from a common food source, even when the epidemiologic grouping is not known prior to genetic clustering. While the s-value is specific to the tree dissection method used here, the optimization approach described could be applied to any parameter/method used to dissect hierarchical trees.

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.

A previously described universal parasite diagnostic (nUPDx) based on PCR amplification of the 18S rDNA and deep-amplicon sequencing, can detect human blood parasites with a sensitivity comparable to real-time PCR. To date, the efficacy of this assay has only been assessed on human blood. This study assessed the utility of nUPDx for the detection of parasitic infections in animals using blood, tissues, and other biological sample types from mammals, birds, and reptiles, known to be infected with helminth, apicomplexan, or pentastomid parasites (confirmed by microscopy or PCR), as well as negative samples. nUPDx confirmed apicomplexan and/or nematode infections in 24 of 32 parasite-positive mammals, while also identifying several undetected coinfections. nUPDx detected infections in 6 of 13 positive bird and 1 of 2 positive reptile samples. When applied to 10 whole parasite specimens (worms and arthropods), nUPDx identified all to the genus or family level, and detected one incorrect identification made by morphology. Babesia sp. infections were detected in 5 of the 13 samples that were negative by other diagnostic approaches. While nUPDx did not detect PCR/microscopy-confirmed trichomonads or amoebae in cloacal swabs/tissue from 8 birds and 2 reptiles due to primer template mismatches, 4 previously undetected apicomplexans were detected in these samples. Future efforts to improve the utility of the assay should focus on validation against a larger panel of tissue types and animal species. Overall, nUPDx shows promise for use in both veterinary diagnostics and wildlife surveillance, especially because species-specific PCRs can miss unknown or unexpected pathogens.

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.

For complex clinical cases where a parasitic infection is suspected, it can be difficult for clinicians to recommend an appropriate laboratory test. These tests are usually pathogen-specific and require a certain degree of suspicion for the precise etiology. Recently, Flaherty et al. (2021) described an assay, the universal parasite diagnostic (UPDx) that can potentially provide a diagnosis of any parasite present in a specimen. Using primers that amplify DNA from all eukaryotes, UPDx differentiates several parasitic infections in blood by amplicon-based next-generation sequencing (NGS) of the 18S rDNA locus. As the state's public health reference laboratory, the Parasitology Laboratory at the Wadsworth Center (New York, NY) receives specimens from patients who have potentially encountered a wide variety of parasites. As such, the ability to differentiate several blood parasites using a single assay is of interest. We assessed UPDx for its ability to confirm parasitic infections for 20 specimens that were previously identified by real-time PCR (RT-PCR). This included specimens positive for Babesia microti, Trypanosoma cruzi, Leishmania tropica, various Plasmodium species, and specimens comprising mixed Plasmodium sp. infections. Results obtained using UPDx were largely concordant with the RT-PCR assays. A T. cruzi positive specimen was negative by UPDx and for two mixed Plasmodium sp. infections only one species was detected. The results obtained for other specimens were concordant. We conclude that UPDx shows promise for the detection of blood parasites in diagnostic laboratories. As NGS becomes cheaper, assays like UPDx will become increasingly amenable to use in clinical settings.

Cyclosporiasis is a diarrheal illness caused by the food-borne parasite Cyclospora cayetanensis. Annually reported cases have been increasing in the United States prompting development of genotyping tools to aid cluster detection. A recently developed Cyclospora genotyping system based on eight genetic markers was applied to clinical samples collected during the cyclosporiasis peak-period of 2020, facilitating assessment of its epidemiologic utility. While the system performed well and helped inform epidemiological investigations, inclusion of additional markers to improve cluster detection was supported. Consequently, investigations have commenced to identify additional markers to enhance performance.

Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens.

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.

BACKGROUND: Targeted amplicon deep sequencing (TADS) has enabled characterization of diverse bacterial communities, yet the application of TADS to communities of parasites has been relatively slow to advance. The greatest obstacle to this has been the genetic diversity of parasitic agents, which include helminths, protozoa, arthropods, and some acanthocephalans. Meanwhile, universal amplification of conserved loci from all parasites without amplifying host DNA has proven challenging. Pan-eukaryotic PCRs preferentially amplify the more abundant host DNA, obscuring parasite-derived reads following TADS. Flaherty et al. (2018) described a pan-parasitic TADS method involving amplification of eukaryotic 18S rDNA regions possessing restriction sites only in vertebrates. Using this method, host DNA in total DNA extracts could be selectively digested prior to PCR using restriction enzymes, thereby increasing the number of parasite-derived reads obtained following NGS. This approach showed promise though was only as sensitive as conventional PCR. RESULTS: Here, we expand on this work by designing a second set of pan-eukaryotic primers flanking the priming sites already described, enabling nested PCR amplification of the established 18S rDNA target. This nested approach facilitated introduction of a second restriction digestion between the first and second PCR, reducing the proportional mass of amplifiable host-derived DNA while increasing the number of PCR amplification cycles. We applied this method to blood specimens containing Babesia, Plasmodium, various kinetoplastids, and filarial nematodes and confirmed its limit of detection (LOD) to be approximately 10-fold lower than previously described, falling within the range of most qPCR methods. CONCLUSIONS: The assay detects and differentiates the major malaria parasites of humans, along with several other clinically important blood parasites. This represents an important step towards a TADS-based universal parasite diagnostic (UPDx) test with a sufficient LOD for routine applications. Video Abstract.

Outbreaks of cyclosporiasis, a food-borne illness caused by the coccidian parasite Cyclospora cayetanensis have increased in the USA in recent years, with approximately 2300 laboratory-confirmed cases reported in 2018. Genotyping tools are needed to inform epidemiological investigations, yet genotyping Cyclospora has proven challenging due to its sexual reproductive cycle which produces complex infections characterized by high genetic heterogeneity. We used targeted amplicon deep sequencing and a recently described ensemble-based distance statistic that accommodates heterogeneous (mixed) genotypes and specimens with partial genotyping data, to genotype and cluster 648 C. cayetanensis samples submitted to CDC in 2018. The performance of the ensemble was assessed by comparing ensemble-identified genetic clusters to analogous clusters identified independently based on common food exposures. Using these epidemiologic clusters as a gold standard, the ensemble facilitated genetic clustering with 93.8% sensitivity and 99.7% specificity. Hence, we anticipate that this procedure will greatly complement epidemiologic investigations of cyclosporiasis.

The number of Asian migrants working in sub-Saharan developing countries like Angola has been increasing. Their malaria risk, prevention, and care-seeking practices have not been characterized. A cross-sectional survey was conducted in 733 Chinese and Southeast Asian migrants in Angola. Respondents were interviewed and provided blood samples. Samples were analyzed to detect Plasmodium antigen and characterize host anti-Plasmodium response. Positive samples were genotyped using the pfs47 marker. Most respondents (72%; 95% CI: 68-75) reported using bed nets, but less than 1% reported using chemoprophylaxis. Depending on the assay, 1-4% of respondents had evidence of active malaria infection. By contrast, 55% (95% CI: 52-59) were seropositive for Plasmodium antibodies. Most infections were Plasmodium falciparum, but infection and/or exposure to Plasmodium vivax and Plasmodium malariae was also detected. Seroprevalence by time in Angola showed most exposure occurred locally. One respondent had sufficiently high parasitemia for pfs47 genotyping, which showed that the infection was likely locally acquired despite recent travel to home country. Asian migrants to Angola are at substantial risk of malaria. Employers should consider enhanced malaria prevention programs, including chemoprophylaxis; embassies should encourage prevention practices. Angolan healthcare workers should be aware of high malaria exposure in Asian migrants.

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.

Cyclospora cayetanensis is an intestinal parasite responsible for the diarrheal illness, cyclosporiasis. Molecular genotyping, using targeted amplicon sequencing, provides a complementary tool for outbreak investigations, especially when epidemiological data are insufficient for linking cases and identifying clusters. The goal of this study was to identify candidate genotyping markers using a novel workflow for detection of segregating single nucleotide polymorphisms (SNPs) in C. cayetanensis genomes. Four whole C. cayetanensis genomes were compared using this workflow and four candidate markers were selected for evaluation of their genotyping utility by PCR and Sanger sequencing. These four markers covered 13 SNPs and resolved parasites from 57 stool specimens, differentiating C. cayetanensis into 19 new unique genotypes.

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.

Cyclosporiasis is an infection caused by Cyclospora cayetanensis, which is acquired by consumption of contaminated fresh food or water. In the United States, cases of cyclosporiasis are often associated with foodborne outbreaks linked to imported fresh produce or travel to disease-endemic countries. Epidemiologic investigation has been the primary method for linking outbreak cases. A molecular typing marker that can identify genetically related samples would be helpful in tracking outbreaks. We evaluated the mitochondrial junction region as a potential genotyping marker. We tested stool samples from 134 laboratory-confirmed cases in the United States by using PCR and Sanger sequencing. All but 2 samples were successfully typed and divided into 14 sequence types. Typing results were identical among samples within each epidemiologically defined case cluster for 7 of 10 clusters. These findings suggest that this marker can distinguish between distinct case clusters and might be helpful during cyclosporiasis outbreak investigations.