10.5061/DRYAD.WPZGMSBKX
Aitken, Elizabeth
0000-0002-2677-6208
University of Melbourne
Ortega-Pajares, Amaya
University of Melbourne
Alemu, Agersew
University of Melbourne
Hasang, Wina
University of Melbourne
Dini, Saber
University of Melbourne
Unger, Holger
University of Tasmania
Ome-Kaius, Maria
Walter and Eliza Hall Institute of Medical Research
Nielsen, Morten
University of Copenhagen
Salanti, Ali
University of Copenhagen
Smith, Joe
Seattle Children's Hospital
Kent, Stephen
University of Melbourne
Hogarth, P Mark
Burnet Institute
Wines, Bruce
Burnet Institute
Simpson, Julie
University of Melbourne
Damelang, Timon
0000-0002-6150-4435
University of Melbourne
Chung, Amy
University of Melbourne
Rogerson, Stephen
University of Melbourne
Antibody features towards VAR2CSA and CSA binding infected erythrocytes in
a cohort of pregnant women from PNG
Dryad
dataset
2021
National Health and Medical Research Council
https://ror.org/011kf5r70
APP1143946
Bill & Melinda Gates Foundation
https://ror.org/0456r8d26
46099
National Health and Medical Research Council
https://ror.org/011kf5r70
GNT1145303
National Health and Medical Research Council
https://ror.org/011kf5r70
APP1092789
National Health and Medical Research Council
https://ror.org/011kf5r70
APP1140509
National Health and Medical Research Council
https://ror.org/011kf5r70
APP1104975
2021-06-25T00:00:00Z
2021-06-25T00:00:00Z
en
https://doi.org/10.7554/eLife.65776
323263 bytes
5
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Plasmodium falciparum causes placental malaria, which results in adverse
outcomes for mother and child. P. falciparum infected erythrocytes that
express the parasite protein VAR2CSA on their surface can bind to
placental chondroitin sulfate-A. It has been hypothesized that naturally
acquired antibodies towards VAR2CSA protect against placental infection,
but it has proven difficult to use measures of antibody to identify
individuals protected from disease. We used a systems serology approach to
identify naturally acquired antibody features mid pregnancy that were
associated with protection from placental malaria at delivery. Machine
learning techniques selected six out of 169 measured antibody features
towards VAR2CSA that could predict (with 86% accuracy) whether a woman
would subsequently have active placental malaria infection at delivery.
Selected features were associated with inhibition of placental binding
and/or opsonic phagocytosis of infected erythrocytes, and network analysis
indicated that there are not one but multiple pathways to protection from
placental malaria.
167 features towards chondroitin sulfate-A binding Plasmodium falciparum
infected erythrocytes and individual domains of VAR2CSA recombinant
protein were measured in 127 women at 14-26 gestation weeks. Women were
also grouped based on infection status at delivery. After acquisition of
antibody feature measurements the data was processed. The right-skewness
of the distribution of the features was reduced by log-transformation
(log(x+1)). Four antibody features that had negative values were
right-shifted to have their minimum at zero prior to log-transformation.
Next, the distributions of the features were centered and scaled to have
zero mean and unit standard deviation. 0.82% of observations
were missing and were imputed, multivariate Imputations by Chained
Equations (van Buuren and Groothuis-Oudshoorn, 2011) with predictive mean
matching was used to impute any missing values. The imputation process was
repeated five times and the median of the imputed values across the five
generated imputed datasets was finally used for each missing value.
A detailed description of all variables is included in the ReadMe files.