Evidence map›Paper›PMID 38266555›Full record

ArticleEBioMedicine2024

Host glycosylation of immunoglobulins impairs the immune response to acute Lyme disease.

Benjamin S Haslund-Gourley, Jintong Hou, Kyra Woloszczuk, Elizabeth J Horn, George Dempsey, Elias K Haddad, Brian Wigdahl, Mary Ann Comunale

Open access · goldAbstract read
In one paragraph

Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. N-Glycan-Dependent Proinflammatory Effects of IgM in Anti-MAG Neuropathy.Neurology(R) neuroimmunology & neuroinflammation · 2025
    Article
  3. 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

8 authors at 1 institution in 1 country.

Benjamin S Haslund-GourleyDepartment of Microbiology and Immunology and the Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
Jintong HouDepartment of Microbiology and Immunology and the Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
Kyra WoloszczukDepartment of Microbiology and Immunology and the Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
Elizabeth J HornLyme Disease Biobank, Portland, Oregon, USA.
George DempseyEast Hampton Family Medicine, East Hampton North, New York, USA.
Elias K HaddadDepartment of Microbiology and Immunology and the Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
Brian WigdahlDepartment of Microbiology and Immunology and the Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
Mary Ann ComunaleDepartment of Microbiology and Immunology and the Institute for Molecular Medicine and Infectious Disease, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA. Electronic address: mc375@drexel.edu.
Institute for Molecular Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLyme disease is caused by the bacteria Borreliella burgdorferi sensu lato (Bb) transmitted to humans from the bite of an infected Ixodes tick. Current diagnostics for Lyme disease are insensitive at the early disease stage and they cannot differentiate between active infections and people with a recent history of antibiotic-treated Lyme disease.

methodsMachine learning technology was utilized to improve the prediction of acute Lyme disease and identify sialic acid and galactose sugar structures (N-glycans) on immunoglobulins associated specifically at time points during acute Lyme disease time. A plate-based approach was developed to analyze sialylated N-glycans associated with anti-Bb immunoglobulins. This multiplexed approach quantitates the abundance of Bb-specific IgG and the associated sialic acid, yielding an accuracy of 90% in a powered study.

findingsIt was demonstrated that immunoglobulin sialic acid levels increase during acute Lyme disease and following antibiotic therapy and a 3-month convalescence, the sialic acid level returned to that found in healthy control subjects (p < 0.001). Furthermore, the abundance of sialic acid on Bb-specific IgG during acute Lyme disease impaired the host's ability to combat Lyme disease via lymphocytic receptor FcγRIIIa signaling. After enzymatically removing the sialic acid present on Bb-specific antibodies, the induction of cytotoxicity from acute Lyme disease patient antigen-specific IgG was significantly improved.

interpretationTaken together, Bb-specific immunoglobulins contain increased sialylation which impairs the host immune response during acute Lyme disease. Furthermore, this Bb-specific immunoglobulin sialyation found in acute Lyme disease begins to resolve following antibiotic therapy and convalescence.

fundingFunding for this study was provided by the Coulter-Drexel Translational Research Partnership Program as well as from a Faculty Development Award from the Drexel University College of Medicine Institute for Molecular Medicine and Infectious Disease and the Department of Microbiology and Immunology.

Indexed as

Borrelia burgdorferiLyme DiseaseAnti-Bacterial AgentsConvalescenceGlycosylationHumansImmunityImmunoglobulin GN-Acetylneuraminic AcidPolysaccharidesAnti-Bacterial AgentsImmunoglobulin GN-Acetylneuraminic AcidPolysaccharidesAcute immune responseHost glycosylationIgG N-glycansLyme diseaseMachine learningMultiplex lectin assay

Identifiers

PMID38266555
PMCPMC10818078
OpenAlexW4391155137

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.