Evidence map›Paper›PMID 25922409›Full record

ArticleMolecular & cellular proteomics : MCP2015

Towards High-throughput Immunomics for Infectious Diseases: Use of Next-generation Peptide Microarrays for Rapid Discovery and Mapping of Antigenic Determinants.

Santiago J Carmona, Morten Nielsen, Claus Schafer-Nielsen, Juan Mucci, Jaime Altcheh, Virginia Balouz, Valeria Tekiel, Alberto C Frasch, Oscar Campetella, Carlos A Buscaglia and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
7.4field-weighted citation impact, top 2% of its field
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

49 citing papers in PubMed, 86 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. B-EPIC: A Transformer-Based Language Model for Decoding B Cell Immunodominance Patterns.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  5. Identification of highly conservedEmerging microbes & infections · 2024
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. AssessingPathogens (Basel, Switzerland) · 2021
    Article
  18. Article
  19. Article
  20. Review
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

11 authors at 5 institutions in 2 countries.

Santiago J CarmonaFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Morten NielsenFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina; §Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Lyngby, Denmark;
Claus Schafer-Nielsen¶Schafer-N ApS, 2100 Copenhagen, Denmark;
Juan MucciFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Jaime Altcheh‖Servicio de Parasitología y Chagas, Hospital de Niños Ricardo Gutiérrez, Ciudad de Buenos Aires, Argentina.
Virginia BalouzFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Valeria TekielFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Alberto C FraschFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Oscar CampetellaFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Carlos A BuscagliaFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina;
Fernán AgüeroFrom the ‡Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús, Universidad de San Martín - CONICET, Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina; fernan@unsam.edu.ar.
National University of General San Martín · ARInstituto Tecnológico de Chascomús · ARConsejo Nacional de Investigaciones Científicas y Técnicas · ARHospital General de Niños Ricardo Gutierrez · ARSchafer-N (Denmark) · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15 mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15 mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼ threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens.

Indexed as

Antigens, ProtozoanB-LymphocytesChagas DiseaseDatabases, ProteinEnzyme-Linked Immunosorbent AssayEpitope MappingEpitopes, B-LymphocyteHigh-Throughput Screening AssaysHumansPeptidesProtein Array AnalysisProteomicsReproducibility of ResultsAntigens, ProtozoanEpitopes, B-LymphocytePeptides

Identifiers

PMID25922409
PMCPMC4587317
OpenAlexW2124226000

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.