Human milk antibodies to global pathogens reveal geographic and interindividual variations in IgA and IgG
Bibliographic record
Abstract
BACKGROUNDThe use of high-throughput technologies has enabled rapid advancement in the knowledge of host immune responses to pathogens. Our objective was to compare the repertoire, protection, and maternal factors associated with human milk antibodies to infectious pathogens in different economic and geographic locations.METHODSUsing multipathogen protein microarrays, 878 milk and 94 paired serum samples collected from 695 women in 5 high and low-to-middle income countries (Bangladesh, Finland, Peru, Pakistan, and the United States) were assessed for specific IgA and IgG antibodies to 1,607 proteins from 30 enteric, respiratory, and bloodborne pathogens.RESULTSThe antibody coverage across enteric and respiratory pathogens was highest in Bangladeshi and Pakistani cohorts and lowest in the U.S. and Finland. While some pathogens induced a dominant IgA response (Campylobacter, Klebsiella, Acinetobacter, Cryptosporidium, and pertussis), others elicited both IgA and IgG antibodies in milk and serum, possibly related to the invasiveness of the infection (Shigella, enteropathogenic E. coli "EPEC", Streptococcus pneumoniae, Staphylococcus aureus, and Group B Streptococcus). Besides the differences between economic regions and decreases in concentrations over time, human milk IgA and IgG antibody concentrations were lower in mothers with high BMI and higher parity, respectively. In Bangladeshi infants, a higher specific IgA concentration in human milk was associated with delayed time to rotavirus infection, implying protective properties of antirotavirus antibodies, whereas a higher IgA antibody concentration was associated with greater incidence of Campylobacter infection.CONCLUSIONThis comprehensive assessment of human milk antibody profiles may be used to guide the development of passive protection strategies against infant morbidity and mortality.FUNDINGBill and Melinda Gates Foundation grant OPP1172222 (to KMJ); Bill and Melinda Gates Foundation grant OPP1066764 funded the MDIG trial (to DER); University of Rochester CTSI and Environmental Health Sciences Center funded the Rochester Lifestyle study (to RJL); and R01 AI043596 funded PROVIDE (to WAP).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".