Global Population Genomics of Two Subspecies of Cryptosporidium hominis during 500 Years of Evolution
Open accessPublished version of a preprint
Abstract
Cryptosporidiosis is a major global health problem and a primary cause of diarrhea, particularly in young children in low- and middle-income countries (LMICs). The zoonotic Cryptosporidium parvum and anthroponotic Cryptosporidium hominis cause most human infections. Here, we present a comprehensive whole-genome study of C. hominis, comprising 114 isolates from 16 countries within five continents. We detect two lineages with distinct biology and demography, which diverged circa 500 years ago. We consider these lineages two subspecies and propose the names C. hominis hominis and C. hominis aquapotentis (gp60 subtype IbA10G2). In our study, C. h. hominis is almost exclusively represented by isolates from LMICs in Africa and Asia and appears to have undergone recent population contraction. In contrast, C. h. aquapotentis was found in high-income countries, mainly in Europe, North America, and Oceania, and appears to be expanding. Notably, C. h. aquapotentis is associated with high rates of direct human-to-human transmission, which may explain its success in countries with well-developed environmental sanitation infrastructure. Intriguingly, we detected genomic regions of introgression following secondary contact between the subspecies. This resulted in high diversity and divergence in genomic islands of putative virulence genes, including muc5 (CHUDEA2_430) and a hypothetical protein (CHUDEA6_5270). This diversity is maintained by balancing selection, suggesting a co-evolutionary arms race with the host. Finally, we find that recent gene flow from C. h. aquapotentis to C. h. hominis, likely associated with increased human migration, maybe driving the evolution of more virulent C. hominis variants.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Flagged first, then by citations
Research works printed but not linked: 78
- Abrahamsen, 2004, Complete genome sequence of the apicomplexan, Cryptosporidium parvum, Science, 304, 441, 10.1126/science.1094786 · DOI
- Assefa, 2014, estMOI: estimating multiplicity of infection using parasite deep sequencing data, Bioinformatics, 30, 1292, 10.1093/bioinformatics/btu005 · DOI
- Bolger, 2014, Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics, 30, 2114, 10.1093/bioinformatics/btu170 · DOI · Paper page
- Bouckaert, 2014, DensiTree 2: seeing trees through the forest, bioRxiv, 012401
- Bouckaert, 2014, BEAST 2: a software platform for Bayesian evolutionary analysis, PLoS Comput Biol., 10, e1003537, 10.1371/journal.pcbi.1003537 · DOI · Paper page
- Bouzid, 2013, Cryptosporidium pathogenicity and virulence, Clin Microbiol Rev., 26, 115, 10.1128/CMR.00076-12 · DOI
- Cacciò, 2016, Human cryptosporidiosis in Europe, Clin Microbiol Infect., 22, 471, 10.1016/j.cmi.2016.04.021 · DOI
- Cama, 2008, Cryptosporidium species and subtypes and clinical manifestations in children, Peru, Emerg Infect Dis., 14, 1567, 10.3201/eid1410.071273 · DOI
- Carmicheal, 2020, Presence and structure-activity relationship of intrinsically disordered regions across mucins, FASEB J., 34, 1939, 10.1096/fj.201901898RR · DOI
- Cevallos, 2000, Mediation of Cryptosporidium parvum infection in vitro by mucin-like glycoproteins defined by a neutralizing monoclonal antibody, Infect Immun., 68, 5167, 10.1128/IAI.68.9.5167-5175.2000 · DOI
- Chalmers, 2019, Analysis of the Cryptosporidium spp. and gp60 subtypes linked to human outbreaks of cryptosporidiosis in England and Wales, 2009 to 2017, Parasit Vectors, 12, 95, 10.1186/s13071-019-3354-6 · DOI
- Chalmers, 2010, Detection of Cryptosporidium species and sources of contamination with Cryptosporidium hominis during a waterborne outbreak in north west Wales, J Water Health, 8, 311, 10.2166/wh.2009.185 · DOI
- Chatterjee, 2010, Evidence for mucin-like glycoproteins that tether sporozoites of Cryptosporidium parvum to the inner surface of the oocyst wall, Eukaryot Cell, 9, 84, 10.1128/EC.00288-09 · DOI
- Corsi, 2021, Evolutionary epidemiology of a zoonosis, bioRxiv
- Craun, 1998, Waterborne outbreaks of cryptosporidiosis, J AWWA, 90, 81, 10.1002/j.1551-8833.1998.tb08500.x · DOI
- Danecek, 2011, The variant call format and VCFtools, Bioinformatics, 27, 2156, 10.1093/bioinformatics/btr330 · DOI
- Drummond, 2005, Bayesian coalescent inference of past population dynamics from molecular sequences, Mol Biol Evol., 22, 1185, 10.1093/molbev/msi103 · DOI
- Efstratiou, 2017, Waterborne transmission of protozoan parasites: review of worldwide outbreaks-an update 2011–2016, Water Res., 114, 14, 10.1016/j.watres.2017.01.036 · DOI
- Excofffier, 2021, fastsimcoal2: demographic inference under complex evolutionary scenarios, Bioinformatics, 10.1093/bioinformatics/btab468 · DOI
- Feng, 2018, Genetic diversity and population structure of Cryptosporidium, Trends Parasitol, 10.1016/j.pt.2018.07.009 · DOI
- Feng, 2014, Multilocus sequence typing of an emerging Cryptosporidium hominis subtype in the United States, J Clin Microbiol., 52, 524, 10.1128/JCM.02973-13 · DOI
- Guo, 2015, Comparative genomic analysis reveals occurrence of genetic recombination in virulent Cryptosporidium hominis subtypes and telomeric gene duplications in Cryptosporidium parvum, BMC Genomics, 16, 320, 10.1186/s12864-015-1517-1 · DOI
- Gupta, 2002, Prediction of glycosylation across the human proteome and the correlation to protein function, Pac Symp Biocomput., :310
- Hadfield, 2015, Generation of whole genome sequences of new Cryptosporidium hominis and Cryptosporidium parvum isolates directly from stool samples, BMC Genomics, 16, 650, 10.1186/s12864-015-1805-9 · DOI
- Heiges, 2006, CryptoDB: a Cryptosporidium bioinformatics resource update, Nucleic Acids Res., 34, D419, 10.1093/nar/gkj078 · DOI
- Herges, 2012, Evidence that Cryptosporidium parvum populations are panmictic and unstructured in the Upper Midwest of the United States, Appl Environ Microbiol., 78, 8096, 10.1128/AEM.02105-12 · DOI
- Horton, 2007, WoLF PSORT: protein localization predictor, Nucleic Acids Res., 35, W585, 10.1093/nar/gkm259 · DOI
- Huson, 2006, Application of phylogenetic networks in evolutionary studies, Mol Biol Evol., 23, 254, 10.1093/molbev/msj030 · DOI
- Isaza, 2015, Revisiting the reference genomes of human pathogenic Cryptosporidium species: reannotation of C. parvum Iowa and a new C. hominis reference, Sci Rep., 5, 16324, 10.1038/srep16324 · DOI
- Jex, 2010, Genetic richness and diversity in Cryptosporidium hominis and C. parvum reveals major knowledge gaps and a need for the application of “next generation” technologies–research review, Biotechnol Adv., 28, 17, 10.1016/j.biotechadv.2009.08.003 · DOI
- Khalil, 2018, Morbidity, mortality, and long-term consequences associated with diarrhoea from Cryptosporidium infection in children younger than 5 years: a meta-analyses study, Lancet Glob Health, 6, e758, 10.1016/S2214-109X(18)30283-3 · DOI
- King, 2017, Prevalence and epidemiology of human Cryptosporidium parvum IIc infections in England and Wales, Lancet, 389, S56, 10.1016/S0140-6736(17)30452-X · DOI
- King, 2019, Anthroponotic transmission of Cryptosporidium parvum predominates in countries with poorer sanitation: a systematic review and meta-analysis, Parasit Vectors, 12, 16, 10.1186/s13071-018-3263-0 · DOI
- Kopelman, 2015, Clumpak: a program for identifying clustering modes and packaging population structure inferences across K, Mol Ecol Resour., 15, 1179, 10.1111/1755-0998.12387 · DOI
- Kotloff, 2013, Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study, Lancet, 382, 209, 10.1016/S0140-6736(13)60844-2 · DOI · Paper page
- Leigh, 2015, popart: full-feature software for haplotype network construction, Methods Ecol Evolut., 6, 1110, 10.1111/2041-210X.12410 · DOI
- Letunic, 2016, Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees, Nucleic Acids Res., 44, W242, 10.1093/nar/gkw290 · DOI
- Li, 2009, Fast and accurate short read alignment with Burrows-Wheeler transform, Bioinformatics, 25, 1754, 10.1093/bioinformatics/btp324 · DOI · Paper page
- Li, 2013, Genetic recombination and Cryptosporidium hominis virulent subtype IbA10G2, Emerg Infect Dis., 19, 1573, 10.3201/eid1910.121361 · DOI
- Lighten, 2017, Evolutionary genetics of immunological supertypes reveals two faces of the Red Queen, Nat Commun., 8, 1294, 10.1038/s41467-017-01183-2 · DOI
- Lin, 2020, Role of protein glycosylation in host-pathogen interaction, Cells, 9, 1022, 10.3390/cells9041022 · DOI
- Lippuner, 2018, RNA-Seq analysis during the life cycle of Cryptosporidium parvum reveals significant differential gene expression between proliferating stages in the intestine and infectious sporozoites, Int J Parasitol., 48, 413, 10.1016/j.ijpara.2017.10.007 · DOI
- Manske, 2012, Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing, Nature, 487, 375, 10.1038/nature11174 · DOI
- Martin, 2015, RDP4: detection and analysis of recombination patterns in virus genomes, Virus Evol., 1, 10.1093/ve/vev003 · DOI
- Martin, 2000, RDP: detection of recombination amongst aligned sequences, Bioinformatics, 16, 562, 10.1093/bioinformatics/16.6.562 · DOI
- McKenna, 2010, The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data, Genome Res., 20, 1297, 10.1101/gr.107524.110 · DOI · Paper page
- McKerr, 2022, Cross-sectional household transmission study of Cryptosporidium shows that C. hominis infections are a key risk factor for spread, BMC Infect Dis., 22, 114, 10.1186/s12879-022-07086-y · DOI
- Mészáros, 2018, IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding, Nucleic Acids Res., 46, W329, 10.1093/nar/gky384 · DOI
- Morrison, 2008, The population structure of the Cryptosporidium parvum population in Scotland: a complex picture, Infect Genet Evol., 8, 121, 10.1016/j.meegid.2007.10.010 · DOI
- Nader, 2019, Evolutionary genomics of anthroponosis in Cryptosporidium, Nat Microbiol, 4, 826, 10.1038/s41564-019-0377-x · DOI
- Nguyen, 2014, IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies, Mol Biol Evol., 32, 268, 10.1093/molbev/msu300 · DOI · Paper page
- Nichols, 2014, Cryptosporidium: parasite and disease, 81, 10.1007/978-3-7091-1562-6_3 · DOI
- O'Connor, 2009, Polymorphic mucin antigens CpMuc4 and CpMuc5 are integral to Cryptosporidium parvum infection in vitro, Eukaryotic Cell, 8, 461, 10.1128/EC.00305-08 · DOI
- Pavlidis, 2013, SweeD: likelihood-based detection of selective sweeps in thousands of genomes, Mol Biol Evol., 30, 2224, 10.1093/molbev/mst112 · DOI
- Perez-Vilar, 1999, The structure and assembly of secreted mucins, J Biol Chem., 274, 31751, 10.1074/jbc.274.45.31751 · DOI
- Pfeifer, 2014, PopGenome: an efficient Swiss army knife for population genomic analyses in R, Mol Biol Evol., 31, 1929, 10.1093/molbev/msu136 · DOI
- Pond, 2005, Datamonkey: rapid detection of selective pressure on individual sites of codon alignments, Bioinformatics, 21, 2531, 10.1093/bioinformatics/bti320 · DOI
- Posada, 2001, Evaluation of methods for detecting recombination from DNA sequences: computer simulations, Proc Natl Acad Sci USA., 98, 13757, 10.1073/pnas.241370698 · DOI
- Pritchard, 2000, Inference of population structure using multilocus genotype data, Genetics, 155, 945, 10.1093/genetics/155.2.945 · DOI
- Putignani, 2010, Global distribution, public health and clinical impact of the protozoan pathogen Cryptosporidium, Interdiscip Perspect Infect Dis., 2010:753512
- Rambaut, 2018, Posterior summarization in Bayesian phylogenetics using Tracer 1.7, Syst Biol., 67, 901, 10.1093/sysbio/syy032 · DOI · Paper page
- Razakandrainibe, 2018, Common occurrence of Cryptosporidium hominis in asymptomatic and symptomatic calves in France, PLoS Negl Trop Dis., 12, e0006355, 10.1371/journal.pntd.0006355 · DOI
- Robinson, 2020, Cryptosporidium diagnostic assays: molecular detection, Methods Mol Biol., 2052, 11, 10.1007/978-1-4939-9748-0_2 · DOI
- Ryan, 2016, Cryptosporidium in humans and animals—a one health approach to prophylaxis, Parasite Immunol., 38, 535, 10.1111/pim.12350 · DOI
- Salminen, 1995, Identification of breakpoints in intergenotypic recombinants of HIV type 1 by bootscanning, AIDS Res Hum Retroviruses, 11, 1423, 10.1089/aid.1995.11.1423 · DOI
- Santin, 2020, Cryptosporidium and Giardia in Ruminants, Vet Clin North Am Food Anim Pract., 36, 223, 10.1016/j.cvfa.2019.11.005 · DOI
- Segura, 2015, Predominant virulent IbA10G2 subtype of Cryptosporidium hominis in human isolates in Barcelona: a five-year study, PLoS One, 10, e0121753, 10.1371/journal.pone.0121753 · DOI
- Smith, 1992, Analyzing the mosaic structure of genes, J Mol Evol., 34, 126, 10.1007/BF00182389 · DOI
- Tichkule, 2021, Comparative genomics revealed adaptive admixture in Cryptosporidium hominis in Africa, Microb Genom., 7
- Van der Auwera, 2013, From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline, Curr Protoc Bioinform., 43, 11.10.11, 10.1002/0471250953.bi1110s43 · DOI · Paper page
- Van Oosterhout, 2021, Mitigating the threat of emerging infectious diseases; a coevolutionary perspective, Virulence, 12, 1288, 10.1080/21505594.2021.1920741 · DOI
- Wang, 2014, Cryptosporidium parvum IId family: clonal population and dispersal from Western Asia to other geographical regions, Sci Rep., 4, 4208, 10.1038/srep04208 · DOI
- Ward, 2016, HYBRIDCHECK: software for the rapid detection, visualization and dating of recombinant regions in genome sequence data, Mol Ecol Resour., 16, 534, 10.1111/1755-0998.12469 · DOI
- Widerström, 2014, Large outbreak of Cryptosporidium hominis infection transmitted through the public water supply, Sweden, Emerg Infect Dis., 20, 581, 10.3201/eid2004.121415 · DOI
- Yang, 2021, Molecular epidemiology of human cryptosporidiosis in low-and middle-income countries, Clin Microbiol Rev., 34, e00087-19, 10.1128/CMR.00087-19 · DOI
- Zhang, 2006, KaKs_Calculator: calculating Ka and Ks through model selection and model averaging, Genom Proteom Bioinform., 4, 259, 10.1016/S1672-0229(07)60007-2 · DOI
- Zheng, 2012, A high-performance computing toolset for relatedness and principal component analysis of SNP data, Bioinformatics, 28, 3326, 10.1093/bioinformatics/bts606 · DOI
- Zhou, 2003, Molecular surveillance of Cryptosporidium spp. in raw wastewater in Milwaukee: implications for understanding outbreak occurrence and transmission dynamics, J Clin Microbiol., 41, 5254, 10.1128/JCM.41.11.5254-5257.2003 · DOI
- Authors
- Tichkule, S., Cacciò, S. M., Robinson, G., Chalmers, R. M., Mueller, I., Emery-Corbin, S. J., Eibach, D., Tyler, K. M., van Oosterhout, C., & Jex, A. R.
- Affiliations
- The University of Melbourne (AU)
- Istituto Superiore di Sanità (IT)
- Singleton Hospital (GB)
- Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
- German Center for Infection Research (DE)
- University of East Anglia (GB)
- Corresponding author
- Tichkule, Swapnil
- Cacciò, Simone M.
- van Oosterhout, Cock
- Jex, Aaron R.
- ORCID
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-03-18
- Volume
- 39
- Issue
- 4
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- PMC
- PMC9004413
- OpenAlex
- W4220870828
- Cited by
- 23
- References
- 1
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 6 · 2022: 6
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Biology and Evolution (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
- Funders
- Australian Government
- European Commission (2014‐2020, 643476, HORIZON2020)
- Australian Society for Parasitology
- Monash University
- Earth and Life Systems Alliance
- Medical Research Council
- National Health and Medical Research Council (1194330)
- European Regional Development Fund (2014-2020’, Interreg 2 Seas programme 2014-2020, 2S05-043)
- Interreg (2S05-043 H4DC, 2014-2020)
- Topics
- Life Sciences › Immunology and Microbiology › Parasitology › Parasitic Infections and Diagnostics
- Health Sciences › Medicine › Infectious Diseases › Viral gastroenteritis research and epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- Cryptosporidium hominis, cryptosporidiosis, subspecies differentiation, population genomics, population contraction, human-to-human transmission, introgression, virulence genes, balancing selection, host-pathogen coevolution, gene flow, low- and middle-income countries
- MeSH terms
- Child, Child, Preschool, Cryptosporidiosis, Cryptosporidium, Genotype, Humans, DNA, Protozoan, Genome, Metagenomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review