Evidence map›Paper›PMID 35757771›Full record

SynthesisFrontiers in immunology2022

Meta-Analysis of Human Antibodies Against

Eizo Takashima, Bernard N Kanoi, Hikaru Nagaoka, Masayuki Morita, Ifra Hassan, Nirianne M Q Palacpac, Thomas G Egwang, Toshihiro Horii, Jesse Gitaka, Takafumi Tsuboi

Abstract readMeta-Analysis
In one paragraph

Synthesis in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Exploring theFrontiers in immunology · 2025
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Eizo TakashimaDivision of Malaria Research, Proteo-Science Center, Ehime University, Matsuyama, Japan.
Bernard N KanoiCentre for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University, Thika, Kenya.
Hikaru NagaokaDivision of Malaria Research, Proteo-Science Center, Ehime University, Matsuyama, Japan.
Masayuki MoritaDivision of Malaria Research, Proteo-Science Center, Ehime University, Matsuyama, Japan.
Ifra HassanDivision of Malaria Research, Proteo-Science Center, Ehime University, Matsuyama, Japan.
Nirianne M Q PalacpacDepartment of Malaria Vaccine Development, Research Institute for Microbial Diseases, Osaka University, Suita, Japan.
Thomas G EgwangMed Biotech Laboratories, Kampala, Uganda.
Toshihiro HoriiDepartment of Malaria Vaccine Development, Research Institute for Microbial Diseases, Osaka University, Suita, Japan.
Jesse GitakaCentre for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University, Thika, Kenya.
Takafumi TsuboiDivision of Cell-Free Sciences, Proteo-Science Center, Ehime University, Matsuyama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Concerted efforts to fight malaria have caused significant reductions in global malaria cases and mortality. Sustaining this will be critical to avoid rebound and outbreaks of seasonal malaria. Identifying predictive attributes that define clinical malaria will be key to guide development of second-generation tools to fight malaria. Broadly reactive antibodies against variable surface antigens that are expressed on the surface of infected erythrocytes and merozoites stage antigens are targets of naturally acquired immunity and prime candidates for anti-malaria therapeutics and vaccines. However, predicting the relationship between the antigen-specific antibodies and protection from clinical malaria remains unresolved. Here, we used new datasets and multiple approaches combined with re-analysis of our previous data to assess the multi-dimensional and complex relationship between antibody responses and clinical malaria outcomes. We observed 22 antigens (17 PfEMP1 domains, 3 RIFIN family members, merozoite surface protein 3 (PF3D7_1035400), and merozoites-associated armadillo repeats protein (PF3D7_1035900) that were selected across three different clinical malaria definitions (1,000/2,500/5,000 parasites/µl plus fever). In addition, Principal Components Analysis (PCA) indicated that the first three components (Dim1, Dim2 and Dim3 with eigenvalues of 306, 48, and 29, respectively) accounted for 66.1% of the total variations seen. Specifically, the Dim1, Dim2 and Dim3 explained 52.8%, 8.2% and 5% of variability, respectively. We further observed a significant relationship between the first component scores and age with antibodies to PfEMP1 domains being the key contributing variables. This is consistent with a recent proposal suggesting that there is an ordered acquisition of antibodies targeting PfEMP1 proteins. Thus, although limited, and further work on the significance of the selected antigens will be required, these approaches may provide insights for identification of drivers of naturally acquired protective immunity as well as guide development of additional tools for malaria elimination and eradication.

Indexed as

MalariaMalaria, FalciparumAnimalsAntibodiesHumansMerozoitesPlasmodium falciparumProtozoan ProteinsAntibodiesProtozoan Proteinsmalaria immunityPCAPfEMP1Plasmodium falciparumRIFINvariable surface antigens

Identifiers

PMID35757771
PMCPMC9218060

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.