Back

Degree of immunoglobulin kappa light chain glycosylation of anti-spike SARS CoV-2 antibodies correlates with COVID-19 severity.

Grigaite, R.; Iles, J. K.; Harding, S.; Patel, R.; Wallis, G.; Iles, R. K.

2023-01-09 infectious diseases
10.1101/2023.01.06.23284259
Show abstract

Glycosylation of antibodies and the effects this has on inflammatory responses has concentrated predominately on the study of glycosylation moieties found in the Fc region of heavy chains. Light chain glycosylation and their ratios are relatively understudied. Nevertheless, variable glycosylation and ratio of {kappa} and {lambda} light chains have been associated with worse prognosis in myeloma and in tissue deposition - amyloidosis. The {kappa} & {lambda} light chains, of antibodies binding to SARS-CoV2 nucleocapsid and spike protein were analysed, using MALDI-ToF MS, in respect to their intensity, ratios, glycosylation patterns and any pattern changes correlating with COVID-19 severity. The molecular masses and signal intensity of {kappa} and {lambda} glycosylated and non-glycosylated light chains were measured for immunoglobulins isolated from plasma of sero-positive and sero-negative health care workers (HCW), and convalescent patients who had suffered from acute respiratory distress syndrome (ARDS). Overall, there was no significant changes in {kappa} to {lambda} ratio of total IgG (via protein G capture) antibodies between the groups. A non-statistically significant trend towards {lambda} light chains was found in antibodies against SARS CoV-2 Nucleocapsid and Spike proteins. However, detailed analysis of the molecular forms found a significant increase and bias towards un-glycosylated light chains and in particular un-glycosylated {kappa} light chains, in antibodies against SAR-CoV-2 spike protein, from convalescent COVID-ARDS patients. Here we have demonstrated a bias towards un-glycosylated {kappa} chains in anti-spike antibodies in those who suffered from ARDS as a result of SARS-CoV2 infection 3 months after recovery. How this relates to the immunopathology of COVID-19 requires further study.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Frontiers in Immunology
based on 140 papers
Top 0.3%
12.2%
2
PLOS ONE
based on 1737 papers
Top 52%
9.9%
3
Life Science Alliance
based on 11 papers
Top 0.1%
7.4%
4
Viruses
based on 79 papers
Top 0.3%
5.7%
5
International Journal of Molecular Sciences
based on 39 papers
Top 0.2%
5.2%
6
Scientific Reports
based on 701 papers
Top 35%
5.2%
7
The Journal of Immunology
based on 19 papers
Top 0.3%
4.4%
50% of probability mass above
8
Clinical & Translational Immunology
based on 14 papers
Top 0.1%
2.7%
9
EMBO Molecular Medicine
based on 15 papers
Top 0.5%
1.8%
10
JCI Insight
based on 63 papers
Top 3%
1.8%
11
PLOS Pathogens
based on 35 papers
Top 0.8%
1.7%
12
Journal of Medical Virology
based on 95 papers
Top 5%
1.7%
13
eBioMedicine
based on 82 papers
Top 3%
1.5%
14
Nature Communications
based on 483 papers
Top 31%
1.5%
15
EBioMedicine
based on 21 papers
Top 0.8%
1.3%
16
Biomedicines
based on 21 papers
Top 2%
1.3%
17
iScience
based on 74 papers
Top 5%
1.3%
18
Journal of Virological Methods
based on 20 papers
Top 2%
1.2%
19
Frontiers in Medicine
based on 99 papers
Top 16%
1.2%
20
Frontiers in Cellular and Infection Microbiology
based on 22 papers
Top 4%
0.8%
21
BMC Infectious Diseases
based on 110 papers
Top 21%
0.7%
22
Frontiers in Microbiology
based on 36 papers
Top 4%
0.7%
23
Pathogens
based on 16 papers
Top 0.9%
0.7%
24
Nature Immunology
based on 14 papers
Top 2%
0.7%
25
Journal of Infection
based on 64 papers
Top 9%
0.7%