@article{fdi:010087617,
  title = {{MALDI}-{TOF} : a new tool for the identification of {S}chistosoma cercariae and detection of hybrids},
  author = {{H}uguenin, {A}. and {K}incaid-{S}mith, {J}ulien and {D}epaquit, {J}. and {B}oissier, {J}. and {F}erte, {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}chistosomiasis is a neglected water-born parasitic disease caused by {S}chistosoma affecting more than 200 million people. {I}ntrogressive hybridization is common among these parasites and raises issues concerning their zoonotic transmission. {M}orphological identification of {S}chistosoma cercariae is difficult and does not permit hybrids detection. {O}ur objective was to assess the performance of {MALDI}-{TOF} ({M}atrix {A}ssistated {L}aser {D}esorption-{I}onization-{T}ime {O}f {F}light) mass spectrometry for the specific identification of cercariae in human and non-human {S}chistosoma and for the detection of hybridization between {S}. bovis and {S}. haematobium. {S}pectra were collected from laboratory reared molluscs infested with strains of {S}. haematobium, {S}. mansoni, {S}. bovis, {S}. rodhaini and {S}. bovis x {S}. haematobium natural ({C}orsican hybrid) and artificial hybrids. {C}luster analysis showed a clear separation between {S}. haematobium, {S}. bovis, {S}. mansoni and {S}. rodhaini. {C}orsican hybrids are classified with those of the parental strain of {S}. haematobium whereas other hybrids formed a distinct cluster. {I}n blind test analysis the developed {MALDI}-{TOF} spectral database permits identification of {S}chistosoma cercariae with high accuracy (94%) and good specificity ({S}. bovis: 99.59%, {S}. haematobium 99.56%, {S}. mansoni and {S}. rodhaini: 100%). {M}ost misidentifications were between {S}. haematobium and the {C}orsican hybrids. {T}he use of machine learning permits to improve the discrimination between these last two taxa, with accuracy, {F}1 score and {S}ensitivity/{S}pecificity > 97%. {I}n multivariate analysis the factors associated with obtaining a valid identification score (> 1.7) were absence of ethanol preservation (p < 0.001) and a number of 2-3 cercariae deposited per well (p < 0.001). {A}lso, spectra acquired from {S}. mansoni cercariae are more likely to obtain a valid identification score than those acquired from {S}. haematobium (p<0.001). {MALDI}-{TOF} is a reliable technique for high-throughput identification of {S}chistosoma cercariae of medical and veterinary importance and could be useful for field survey in endemic areas. {A}uthor summary{S}chistosomiases are neglected tropical diseases, affecting approximately 200 million people worldwide. {T}hey are transmitted during contact with water contaminated with the infesting stage of the parasite (the cercaria stage). {S}pecies-level recognition of cercariae present in water has important implications for field campaigns aimed at eradicating schistosomiasis. {I}n addition, {S}chistosomes are able to hybridize between different species. {I}dentification of {S}chistosomes cercariae on microscopy is difficult because of their similarity, and it does not allow hybrids to be distinguished. {M}olecular biology techniques allow a reliable diagnosis but are expensive. {MALDI}-{TOF} is a recent technique that permits an inexpensive identification of micro-organisms in a few minutes. {I}n this paper, we evaluate {MALDI}-{TOF} identification of {S}chistosomes cercariae.{W}e have implemented a database of {MALDI}-{TOF} cercariae spectra obtained from parental strains and hybrids of species of medical or veterinary interest, allowing reliable identification with an accuracy of 94%. {T}he identification errors mainly come from confusion between the natural {C}orsican hybrid ({S}. haematobium x {S}. bovis) and {S}. haematobium. {T}he use of machine learning algorithms permits to obtain an accuracy of more than 97% in the recognition of these two parasites. {I}n conclusion, {MALDI}-{TOF} is a promising tool for the identification of {S}chistosome cercariae.},
  keywords = {},
  booktitle = {},
  journal = {{PL}o{S} {N}eglected {T}ropical {D}iseases},
  volume = {17},
  numero = {3},
  pages = {e0010577 [18 ]},
  ISSN = {1935-2735},
  year = {2023},
  DOI = {10.1371/journal.pntd.0010577},
  URL = {https://www.documentation.ird.fr/hor/fdi:010087617},
}