PMID- 36658630
OWN - NLM
STAT- MEDLINE
DCOM- 20230123
LR  - 20230123
IS  - 1756-3305 (Electronic)
IS  - 1756-3305 (Linking)
VI  - 16
IP  - 1
DP  - 2023 Jan 19
TI  - Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for 
      differential identification of adult Schistosoma worms.
PG  - 20
LID - 10.1186/s13071-022-05604-0 [doi]
LID - 20
AB  - BACKGROUND: Schistosomiasis is a major neglected tropical disease that affects up 
      to 250 million individuals worldwide. The diagnosis of human schistosomiasis is 
      mainly based on the microscopic detection of the parasite's eggs in the feces 
      (i.e., for Schistosoma mansoni or Schistosoma japonicum) or urine (i.e., for 
      Schistosoma haematobium) samples. However, these techniques have limited 
      sensitivity, and microscopic expertise is waning outside endemic areas. 
      Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass 
      spectrometry (MS) has become the gold standard diagnostic method for the 
      identification of bacteria and fungi in many microbiological laboratories. 
      Preliminary studies have recently shown promising results for parasite 
      identification using this method. The aims of this study were to develop and 
      validate a species-specific database for adult Schistosoma identification, and to 
      evaluate the effects of different storage solutions (ethanol and RNAlater) on 
      spectra profiles. METHODS: Adult worms (males and females) of S. mansoni and S. 
      japonicum were obtained from experimentally infected mice. Species identification 
      was carried out morphologically and by cytochrome oxidase 1 gene sequencing. 
      Reference protein spectra for the creation of an in-house MALDI-TOF MS database 
      were generated, and the database evaluated using new samples. We employed 
      unsupervised (principal component analysis) and supervised (support vector 
      machine, k-nearest neighbor, Random Forest, and partial least squares 
      discriminant analysis) machine learning algorithms for the identification and 
      differentiation of the Schistosoma species. RESULTS: All the spectra were 
      correctly identified by internal validation. For external validation, 58 new 
      Schistosoma samples were analyzed, of which 100% (58/58) were correctly 
      identified to genus level (log score values >/= 1.7) and 81% (47/58) were reliably 
      identified to species level (log score values >/= 2). The spectra profiles showed 
      some differences depending on the storage solution used. All the machine learning 
      algorithms classified the samples correctly. CONCLUSIONS: MALDI-TOF MS can 
      reliably distinguish adult S. mansoni from S. japonicum.
CI  - (c) 2022. The Author(s).
FAU - Ebersbach, Jurena Christiane
AU  - Ebersbach JC
AD  - Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, 
      Germany.
FAU - Sato, Marcello Otake
AU  - Sato MO
AD  - Laboratory of Tropical Medicine and Parasitology, Dokkyo Medical University, 
      Mibu, Tochigi, Japan.
FAU - de Araujo, Matheus Pereira
AU  - de Araujo MP
AD  - Laboratory of Tropical Medicine and Parasitology, Dokkyo Medical University, 
      Mibu, Tochigi, Japan.
FAU - Sato, Megumi
AU  - Sato M
AD  - Graduate School of Health Sciences, Niigata University, Niigata, Japan.
FAU - Becker, Soren L
AU  - Becker SL
AD  - Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, 
      Germany.
AD  - Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
AD  - University of Basel, Basel, Switzerland.
FAU - Sy, Issa
AU  - Sy I
AD  - Institute of Medical Microbiology and Hygiene, Saarland University, Homburg, 
      Germany. issa.sy@uks.eu.
LA  - eng
PT  - Journal Article
DEP - 20230119
PL  - England
TA  - Parasit Vectors
JT  - Parasites & vectors
JID - 101462774
SB  - IM
MH  - Female
MH  - Adult
MH  - Humans
MH  - Animals
MH  - Mice
MH  - *Fungi/chemistry
MH  - Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
MH  - *Schistosoma japonicum
MH  - Bacteria
MH  - Lasers
PMC - PMC9854196
OTO - NOTNLM
OT  - Helminth
OT  - Identification
OT  - Machine learning
OT  - Matrix-assisted laser desorption/ionization-time of flight mass spectrometry
OT  - Schistosoma japonicum
OT  - Schistosoma mansoni
OT  - Storage media
OT  - Trematode
COIS- The authors declare no competing interests.
EDAT- 2023/01/20 06:00
MHDA- 2023/01/24 06:00
PMCR- 2023/01/19
CRDT- 2023/01/19 23:50
PHST- 2022/09/23 00:00 [received]
PHST- 2022/11/30 00:00 [accepted]
PHST- 2023/01/19 23:50 [entrez]
PHST- 2023/01/20 06:00 [pubmed]
PHST- 2023/01/24 06:00 [medline]
PHST- 2023/01/19 00:00 [pmc-release]
AID - 10.1186/s13071-022-05604-0 [pii]
AID - 5604 [pii]
AID - 10.1186/s13071-022-05604-0 [doi]
PST - epublish
SO  - Parasit Vectors. 2023 Jan 19;16(1):20. doi: 10.1186/s13071-022-05604-0.