COVID-19 mRNA vaccine-mediated antibodies in human breast milk and their association with breast milk microbiota composition

doi:10.21203/rs.3.rs-2786723/v1

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COVID-19 mRNA vaccine-mediated antibodies in human breast milk and their association with breast milk microbiota composition

https://doi.org/10.21203/rs.3.rs-2786723/v1

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published 05 Oct, 2023

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Newborns can acquire immunological protection to SARS-CoV-2 through vaccine-conferred antibodies in human breast milk. However, there are some concerns around lactating mothers with regards to potential short- and long-term adverse events and vaccine-induced changes to their breast milk microbiome composition, which helps shape the early-life microbiome. Here, we recruited 49 lactating mothers from Hong Kong who received two doses of BNT162b2 vaccine between June 2021 and August 2021. Breast milk samples were self-collected by participating mothers pre-vaccination, one week post-first dose, one week post-second dose, and one month post-second dose. The levels of SARS-CoV-2 spike-specific IgA and IgG in breast milk peaked at one week post-second dose. Subsequently, the levels of both antibodies rapidly waned in breast milk, with IgA levels returning to baseline levels one month post-second dose. The richness and composition of human breast milk microbiota changed dynamically throughout the vaccination regimen, but the abundances of beneficial microbes such as Bifidobacterium species did not significantly change after vaccination. In addition, we found that baseline breast milk bacterial composition can predict spike-specific IgA levels at one week post-second dose (Area Under Curve: 0.72, 95% confidence interval: 0.58–0.85). Taken together, our results suggest that infants may acquire immunological protection from breast milk from SARS-CoV-2-vaccinated mothers by both the vertical transmission of antibodies and beneficial microbiota.

Biological sciences/Microbiology/Vaccines/RNA vaccines

Biological sciences/Immunology/Vaccines/RNA vaccines

Variants of the SARS-CoV-2 virus are causing significant disease burden worldwide, especially for unvaccinated children^1,2. The vaccination of breastfeeding women against SARS-CoV-2 is recommended by health agencies to protect both the mother and infant^3–5; SARS-CoV-2-specific antibodies can be found in both breast milk and infant stool post-vaccination^6,7. However, in several countries, vaccination rates among lactating mothers are relatively low due to fears of unpredictable adverse effects on their infants^8–11. This reluctance is a cause of concern, as breast milk antibodies not only confer protection to invading pathogens but can also shape the infant’s immune system development by facilitating the establishment and maintenance of the infants’ gut microbiome^12,13. The early-life gut microbiome has a profound impact on long-term health^14,15, including the establishment of T- and B-cell immunity¹⁶. Besides immune development, we previously found that the gut microbiota also plays a significant role in modulating the immune response to SARS-CoV-2 vaccines. Bifidobactrium adolescentis was found to be abundant in CoronaVac high-responders and associated with enriched carbohydrate metabolic pathways with immune-potentiating effects. Prevotella copri and two Megamonas species were suggested to play an anti-inflammatory role in reducing adverse events following BNT162b2 and CoronaVac vaccination¹⁷. However, the changes, if any, in breast milk microbiota composition post-vaccination and their influence on the immune responses to SARS-CoV-2 vaccination in humans remains unelucidated. We therefore conducted a prospective longitudinal study in Hong Kong to investigate anti-SARS-CoV-2 antibody kinetics in the breast milk of lactating women and compared the change of microbiome composition before and after mRNA COVID-19 vaccination.

Prospective cohort recruitment and sample collection

We recruited 49 lactating mothers who planned to receive the BNT162b2 mRNA vaccine, when more than 99% of the Hong Kong population was not infected by COVID-19 (late 2021)¹⁸. Mothers who were serologically negative for SARS-CoV-2 pre-vaccination were included, while those who failed to receive two doses of BNT162b2 were excluded. Breast milk was self-collected at the participants’ home in sterile milk bags at four timepoints (pre-vaccination, one week post-first dose, one week post-second dose, and one month post-second dose, Fig. 1A). Samples were sent to the laboratory within eight hours of collection, aliquoted, and preserved at -80°C until DNA extraction. Demographic and lifestyle factors including age, education, and breastfeeding habits, were collected through self-administered questionnaires at each timepoint. The study protocol was approved by the University of Hong Kong/Hospital Authority Hong Kong West Cluster Institutional review board (IRB Ref. No. UW-21-415). All participants provided written consent during enrolment. This study followed the STROBE reporting guideline.

ELISA for SARS-CoV-2 spike protein-specific IgA and IgG

SARS-CoV-2 spike protein-specific IgA and IgG levels in breast milk were measured to assess immune responses to BNT162b2 at different stages of the vaccination regimen. Breast milk samples were centrifuged for 15 minutes at 1000 x g at 2–8°C and the aqueous fraction was collected. This process was repeated twice. The samples were then immediately tested for SARS-CoV-2 spike receptor-binding domain-specific (RBD-specific) IgG or IgA as described previously¹⁹ and horseradish peroxidase (HRP)-conjugated goat anti-human IgA (1:5000, GE Healthcare) was used instead to detect human IgA. Each sample was diluted at a ratio of 1:100 in Chonblock blocking buffer. The cutoff point of antibody levels was derived from the mean optical density (OD) plus three standard deviations of the negative controls (Fig. 1A). IgA levels were used as the main outcome for subsequent analyses due to its predominance and significant immune functionality in breast milk²⁰.

Breast milk microbiota analysis

DNA was extracted from breast milk using QIAamp PowerFecal Pro DNA Kit (QIAGEN, Hilden, Germany). V3-V4 regions of the 16S rRNA genes were amplified and sequenced by Illumina MiSeq. The QIIME2 pipeline²¹ was used to generate amplicon sequence variants (ASVs) (eAppendix). ASV profiles were summarized into phylum- to species-level taxonomic profiles. Sequencing reads were deposited at Sequence Read Archive under accession PRJNA917338. The same pipeline was applied to the validation cohort²², which consists of 110 Chinese lactating mothers without COVID19-vaccination. Their breast milk samples were collected at 4, 8, and 12 weeks (T1, T2, and T3) postpartum. Microbial functional profiles of the breast milk samples were predicted using PICRUSt2²³.

Statistical analyses

We primarily explored the association between breast milk microbiota composition and levels of SARS-CoV-2 spike receptor-binding domain-specific antibodies. Detailed statistical analyses can be found in eAppendix.

Recruitment of lactating mothers receiving BNT162b2

Between June and Dec 2021, 49 lactating mothers planning on receiving BNT162b2 were recruited and followed up at four timepoints (Fig. 1A). After removing samples without available sequencing data and one outlier (positive ELISA result pre-vaccination), 175 samples from 44 participants remained for downstream analyses. Participants’ ages ranged from 25 to 42 years, with a median age of 36 years (interquartile range (IQR): 31, 39). Nearly half (44.2%) of the participants gave birth via planned cesarean section. The intervals between two vaccine doses ranged between 20–42 days (median: 21 days, IQR: 21, 27) (Table 1).

Table 1

Baseline characteristics of the study population in different immunity response group at one week after second dose
Variables	Overall (N = 43)	Low-IgA subjects (N = 25)	High-IgA subjects (N = 18)	P value
Maternal age (years)				0.250
Mean (SD)	35.0 (4.50)	35.6 (4.20)	34.2 (4.89)
Median (IQR)	36.0 (31.0,39.0)	37.0 (32.0,39.0)	32.0 (30.3,37.8)
Gestational age (weeks)				0.960
Mean (SD)	38.9 (1.03)	38.9 (1.05)	38.8 (1.03)
Median (IQR)	38.9 (38.0, 39.7)	38.9 (38.0, 39.7)	38.9 (38.0, 39.8)
Infant's birth weight (kg)				0.658
Mean (SD)	3.3 (0.41)	3.3 (0.48)	3.2 (0.29)
Median (IQR)	3.2 (3.1, 3.6)	3.2 [3.0, 3.6]	3.2 [3.0, 3.6]
Total number of previous children				0.213
0	3 (7.0)	3 (12.0)	0 (0)
1	20 (46.5)	12 (48.0)	8 (44.4)
2	15 (34.9)	6 (24.0)	9 (50.0)
3	5 (11.6)	4 (16.0)	1 (5.6)
Delivery type				0.414
Spontaneous vaginal delivery	13 (30.2)	5 (20.0)	8 (44.4)
Assisted vaginal delivery	3 (7.0)	2 (8.0)	1 (5.6)
Planned C-section	19 (44.2)	13 (52.0)	6 (33.3)
Emergency C- section	8 (18.6)	5 (20.0)	3 (16.7)
Labor induced				1
No	28 (65.1)	16 (64.0)	12 (66.7)
Yes	15 (34.9)	9 (36.0)	6 (33.3)
Epidural anesthesia use				0.057
No	25 (58.1)	11 (44.0)	14 (77.8)
Yes	17 (39.5)	13 (52.0)	4 (22.2)
Missing	1 (2.3)	1 (4.0)	0 (0)
Intramuscular analgesia use				0.516
No	31 (72.1)	19 (76.0)	12 (66.7)
Yes	12 (27.9)	6 (24.0)	6 (33.3)
Infant's sex				0.765
Female	25 (58.1)	14 (56.0)	11 (61.1)
Male	18 (41.9)	11 (44.0)	7 (38.9)
Smoked previously				0.419
No	42 (97.7)	25 (100)	17 (94.4)
Yes	1 (2.3)	0 (0)	1 (5.6)
Partner smokes				1
No	38 (88.4)	22 (88.0)	16 (88.9)
Yes	5 (11.6)	3 (12.0)	2 (11.1)
Marital status				0.419
Married	42 (97.7)	25 (100)	17 (94.4)
Not married	1 (2.3)	0 (0)	1 (5.6)
Place of birth				0.766
Hong Kong SAR	33 (76.7)	19 (76.0)	14 (77.8)
Mainland, China	6 (14.0)	3 (12.0)	3 (16.7)
Others	4 (9.3)	3 (12.0)	1 (5.6)
Years living in HK (customized)				0.455
< 15 years	9 (20.9)	4 (16.0)	5 (27.8)
>=15 years	34 (79.1)	21 (84.0)	13 (72.2)
Educational level (customized)				0.47
Postgraduate degree or above	14 (32.6)	9 (36.0)	5 (27.8)
Below university level	8 (18.6)	3 (12.0)	5 (27.8)
University degree	21 (48.8)	13 (52.0)	8 (44.4)
Family income per month (customized)				0.144
HK$40,000 or more	38 (88.4)	24 (96.0)	14 (77.8)
Under HK$40,000	5 (11.6)	1 (4.0)	4 (22.2)
Categorical data are presented as number (percentage) and continuous variables as median (interquartile range). Within-group valid percentages are shown. Only subjects with both available antibody and 16S data (at one week after second dose) were summarized in the table.

Levels of anti-SARS-CoV-2 IgA and IgG in breast milk were boosted after two vaccine doses

After the first dose, levels of SARS-CoV-2 spike protein-specific IgA and IgG in breast milk remained unchanged compared with pre-vaccination. Levels were boosted one week post-second dose (median (IQR), IgA OD: 0.116 (0.096, 0.137) vs. 0.198 (0.164, 0.280), Fig. 1B; IgG OD: 0.058 (0.054, 0.062) vs. 0.216 (0.132, 0.310), p < 0.001, Fig. 1C). Although IgA levels returned to baseline levels one month post-second dose (p < 0.001), IgG levels remained significantly higher compared to baseline (IgG OD: 0.058 vs. 0.164, p < 0.001). Additionally, there was a significant moderate association between the two antibodies in breast milk (Spearman’s rho coefficient: 0.46, p < 0.001 Supplementary Fig. 1A).

We also found that mothers who received epidural anesthesia during delivery had a lower IgA levels one week post-second dose (p = 0.020, Supplementary Table 1).

Changes in breast milk bacterial richness and composition were observed post-vaccination

We conducted 16S rRNA amplicon sequencing analysis to determine if microbiome composition was associated with levels of vaccine-induced anti-RBD antibodies in breast milk. Breast milk microbial composition (Supplementary Fig. 2A) and diversity (alpha and beta diversities) (Fig. 1D-H) were compared over different timepoints. We observed a significant increase in bacterial Chao1 richness between pre-vaccine and one week post-first dose samples (p = 0.044), but not in terms of Shannon diversity (p = 0.440, Fig. 1D-E). Microbiome richness returned to baseline levels one week post-second dose. To determine whether this change in alpha diversity is associated with vaccination, we analyzed longitudinal breast milk microbiota data from an independent, unvaccinated cohort²², in which we observed a continuous decline of Chao1 diversity (Supplementary Fig. 1B). Regarding beta-diversity, a significant shift was observed between pre-vaccination and all post-second dose vaccination timepoints (adjusted p = 0.030, 0.006, and 0.006 for one week post-first dose and one week and one month post-second dose, respectively, Fig. 1F). However, no significant changes in microbiome beta diversity were observed in the independent cohort (Supplementary Fig. 1C; adjusted p = 0.240 0.360, 1.000, respectively). We also found that baseline breast milk microbiome composition was significantly associated with intramuscular analgesia use during labor (p = 0.012, Supplementary Table 2).

We then conducted LEfSe to identify differentially abundant bacterial taxa between timepoints. In total, 109 differentially abundant species were identified between pre- and post-vaccination timepoints, ten of which remained differentially abundant throughout all three post-vaccination timepoints. Fifty-three species were differentially abundant between baseline levels and post-second dose, including Anaerococcus octavius, Arthrobacter russicus, Bacteroides caecimuris, Clostridiaceae bacterium, Helicobacter rodentium, Lactobacillus aviarius, and Rothia sp. (LEfSe, LDA score > 1.5 and p value < 0.05, Fig. 1G, Supplementary Table 3). The 12 most abundant species varied across samples and timepoints (Supplementary Fig. 2).

Baseline breast milk bacterial composition is associated with anti-SARS-CoV-2 antibody levels in breast milk following vaccination

We then investigated whether breast milk bacterial composition was associated with its antibody levels post-vaccination. Since immune parameters peaked at one week post-second dose (Fig. 1B-C), we focused our subsequent analyses on this timepoint, thereby dichotomizing the cohort into those with high- and low-IgA levels using the prior-specified cutoff (High, 20; Low, 28; Fig. 1A).

The Chao1 richness and beta diversity of baseline microbiota were not significantly different between high- and low-IgA participants (p = 0.464 and 0.071, respectively, Fig. 2A). However, the baseline Shannon richness in high-IgA subjects was significantly higher than that of low-IgA subjects (p = 0.046, Fig. 2B).

We then investigated whether there were taxonomic differences in baseline microbiota between high- and low-IgA subjects. For both groups, the most abundant phylum was Firmicutes, followed by Proteobacteria and Actinobacteria. The relative abundance of Firmicutes was higher in low-IgA subjects (median relative abundance of 61.78% and 74.62% for high- and low-IgA group, respectively), while levels of Proteobacteria were depleted (18.64% and 9.01% for high- and low-IgA group, respectively, Supplementary Fig. 2A).

We identified 23 differentially abundant species between the high- and low-IgA subjects (p = 0.004, Fig. 2D). Unclassified Neisseria and Corynebacterium kroppenstedti showed the largest effect size in enrichment for high- and low-IgA subjects, respectively (LDA score: 3.69 and 4.12, respectively). The mixed effects model showed that unclassified Neisseria and Neisseria elongata were persistently higher in subjects with high breast milk IgA levels (p = 0.045 and 0.031, respectively, Supplementary Table 4).

Besides differences in baseline microbiota, taxonomic differences in microbiota one week post-second dose between high- and low-IgA subjects were identified by LEfSe. Higher abundances of Bifidobacterium bifidum and unclassified Bifidobacterium were found in the high-IgA subjects (LDA score: 2.59 and 2.87, respectively, Fig. 3).

We then used random forest to investigate the power of these distinct microbial species in predicting levels of anti-SARS-CoV-2 antibodies in breast milk one week post-second dose. We constructed a model based on five microbial features including Neisseria elongata, Pseudomonas balearica, unclassified Pseudopropionibacterium, unclassified Neisseria, and unclassified Aggregatibacter. The model based solely on microbial features [ AUC (95% CI): 0.72 (0.58–0.85)] performed better than the model which combined microbial and demographic predictors [AUC (95% CI): 0.66 (0.53–0.80), Fig. 4A].

To investigate the potential mechanisms by which pre-vaccination breast milk microbiota affects antibody levels, we estimated the association between predicted microbial functional pathways and anti-SARS-CoV-2 IgA levels at one week post-second dose. A total of 437 pathways were predicted by PICRUSt2, among which nine were significantly enriched in high-IgA subjects (Wilcoxon rank-sum, adjusted p < 0.05, Fig. 4). The correlation network showed that higher abundances of selected bacterial markers were associated with sugar metabolism pathways (e.g., sucrose degradation IV, glucose and glucose-1-phosphate degradation, fucose degradation). For example, fucose degradation was positively correlated with all bacterial markers. Additionally, superpathways for arginine and polyamine biosynthesis were correlated with all markers expect for Neisseria elongate (Fig. 4B).

Impact of vaccination on the abundance of Lactobacilli and Bifidobacteria in breast milk

Lactobacilli and Bifidobacteria constitute a core microbiota of shared taxa between breast milk and infant stool^24,25. Given those genera play a beneficial role in the healthy development of the infant’s gut microbiota and immune system^26,27, we examined the association between these taxa and immune responses to BNT162b2 in breast milk. We detected the presence of Lactobacilli (L. aviarius, L. fermentum, L. gasseri, L. iners, L. intestinalis, and unclassified Lactobacillus) and Bifidobacteria (B. bifidum, B. breve, B. dentium, B. longum, and unclassified Bifidobacterium). Vaccination did not significantly change the abundance of Lactobacillus and Bifidobacterium in breast milk, except for L. aviarius and L. fermentum (Fig. 5, Supplementary Table 5). However, these two genera were not persistently high in subjects with high IgA levels (Supplementary Table 6).

Breast milk vertically transmits maternal antibodies and microbes to infants^12,28. To the best of our knowledge, this is the first longitudinal human study to show that baseline breast milk microbiota composition and its post-vaccination changes reflect vaccine-conferred anti-SARS-CoV-2 antibody levels in breast milk. The post-vaccination changes in bacterial richness differ from those in the external longitudinal cohort of unvaccinated lactating mothers, suggesting that these changes are attributable to BNT162b2 vaccination. Vaccination did not significantly impact the relative abundances of the beneficial Bifidobacteria. We also identified differential abundant bacterial species at the baseline timepoint which were associated with higher immune responses. For example, unclassified Neisseria and Neisseria elongata were enriched at pre-vaccination in participants with higher anti-SARS-CoV-2 antibody levels in their breast milk post-vaccination.

Changes in breast milk post SARS-CoV-2 vaccination may confer immunological protection to infants via two distinct mechanisms: through antibodies and microbiota. Regarding protection conferred by the former, elevated levels of anti-SARS-CoV-2 antibodies in breast milk, could provide barrier immunity and protect infants by coating the surface of their gastrointestinal tract^29,30. We found increased levels of anti-SARS-CoV-2 IgA and IgG in the breast milk of mothers after two doses of BNT62b2-vaccination. However, IgA levels decreased one week post-second dose while IgG levels remained high, which is consistent with prior studies³¹. Since SARS-CoV-2 antibodies can be transferred to infants through breast milk⁷, the waning of IgA levels over time may decrease levels of breast milk-borne anti-SARS-CoV-2 antibodies transferred to infants from breast milk and, by extension, immunological protection to SARS-CoV-2.

Aside from antibodies in breast milk, its microbiota could have immunological benefits for infants. We found at least three ways in which microbiota are associated with anti-SARS-CoV-2 antibody levels. Firstly, unclassified Neisseria and Neisseria elongata were consistently enriched at pre-vaccination in mothers with higher IgA levels in their breast milk post-vaccination. These immunomodulatory Neisseria sp. may increase anti-SARS-CoV-2 antibody levels by interacting with T- and B-cells³². These species can also mediate inflammatory responses through toll-like receptor 2³². The outer membrane vesicles of these species could therefore act as adjuvants to boost BNT162b2 immunogenicity by enhancing IgG immune responses³³. However, the safety profile of these species and causality need to be established in further experiments.

The interaction between breast milk microbiota and antibody levels could be a result of the microbiota’s influence on immunomodulatory metabolic pathways. Our data indicated that higher abundances of selected bacterial markers are associated with metabolic pathways (e.g., fucose degradation), the latter of which may enhance vaccine-induced immunity levels in breast milk. Nine microbial metabolic pathways were enriched in high-IgA subjects, including fucose degradation and super pathways responsible for arginine and polyamine biosynthesis. Most of these pathways, especially the sugar metabolism-related pathways, could play immunomodulatory roles (e.g., inflammation and T-cell modulation) once transmitted from breast milk to the infant gut^34–37. For example, sucrose degradation IV (sucrose phosphorylase) (PWY-5384) and glucose and glucose-1-phosphate degradation (GLUCOSE1PMETAB-PWY) pathways were the most significant pathways enriched in newborn feces compared to breast milk, with the former pathway being found in the Bifidobacterium genus²⁵. Glucose and glucose-1-phosphate degradation metabolites are used by Enterobacteriaceae for immediate energy and may induce an inflammatory response³⁶. As such, the breast milk microbiota could favor the immune response to BNT162b2 by modulating sugar metabolism-related pathways.

The enrichment of fucose degradation pathways, which are immunomodulatory³⁸, in high-IgA subjects indicates the importance of breast milk metabolite composition in shaping the immune response. Fucose, a key component of human milk oligosaccharides (HMOs) which plays a vital role in infant gut colonization and development of innate immunity during early life^39–41, may be utilized by gut commensals to facilitate colonization⁴² and regulate SCFA production by modulating mucosal immune components^38,43−45. This increased SCFA production has been linked with enhanced systemic immune responses after cholera and influenza vaccination in mice⁴⁵. Additionally, fucosylation of sIgA contributes to intestinal proteolytic resistance and binding to pathogenic bacteria^40,46. Therefore, SARS-CoV-2 vaccination may also affect immune responses through breast milk metabolite composition.

The increased abundances of the probiotic Bifidobacteria may also be linked to increased anti-SARS-CoV-2 antibody levels. Both the abundances of unclassified Bifidobacterium and B. bifidum at the one-week post-second dose timepoint were significantly higher in subjects with higher IgA levels. But their abundances were not affected by vaccination as they were not significantly different between pre- and post-vaccination. As such, these taxa might have potential immunological benefits, which have also been observed in prior intervention studies. Previous studies used Bifidobacteria probiotics to successfully enhance vaccine efficacy (e.g., against influenza, diphtheria, rotavirus, and polio), with increased humoral responses following the vaccination of neonates and young children^45,47. Infant gut Bifidobacteria abundance is also associated with CD4⁺ T-cell responses and increased antibody responses to Bacillus Calmette-Guérin, tetanus toxoid, and hepatitis B virus vaccines^48,49. These Bifidobacteria-associated effects may therefore extend to the SARS-CoV-2 vaccine. Further downstream longitudinal studies focused on infants and vaccine efficacy are required to assess how the probiotic microbiota in breast milk influence immune responses in infants.

A strength of this study was the prospective assessment of breast milk microbiota and complete clinical data collection pre- and post-vaccination. However, the study was limited by its small sample size and lack of a longitudinal control cohort without COVID-vaccination, due to difficulties in recruiting subjects during the pandemic. Nevertheless, this limitation was made up through including an external validation cohort, albeit samples were collected pre-pandemic. Ideally, larger cohorts across different populations, as well as a control cohort collected during the pandemic are needed to validate our findings. We did not measure neutralizing antibody levels in breastmilk. However, our findings are still valid, as prior studies reported a moderate positive correlation between levels of neutralizing antibody and SARS-CoV-2-specific antibodies at one week post-second dose⁵⁰. Besides, further investigations are necessary to elucidate the safety profile, the immunomodulatory role of Neisseria sp. as a BNT162b2 adjunct, and the mechanisms by which fucosylation shapes the breast milk microbiome. Moreover, as clinical and metagenomic data from infants were not collected, we could not determine the infants’ immunity to SARS-CoV-2 and how breast milk microbiota was transmitted to the infant gut. The role of this transmission in immunomodulation could not be explained quantitively by our study. Additionally, the effects of vaccines based on different mechanisms, such as CoronaVac, remains to be investigated.

In summary, we observed several changes in breast milk microbiota post-BNT162b2 vaccination, except for Bifidobacteria abundances. The abundances of probiotic species were significantly associated with vaccine-induced immunity levels in breast milk. Vaccination of lactating mothers may therefore impart additional immunological protection to infants through breast milk by both the vertical transmission of antibodies and beneficial microbiota.

Author contributions

KYWL and HMT conceived and designed the study. LCHT, SSMC and LLMP carried out antibodies testing and analysis. SZ, ZYS, XZ, and HKFW extracted DNA from breast milk samples. HMT, SZ, ZYS, YP, and JZ performed bioinformatics and statistical analyses. HSLF, NN, MSLC, JYYK, and EPHC recruited participants. SZ, YWL, ZYS, CKPM, FKLC and HMT wrote the manuscript with input from all co-authors. HMT acts as the guarantor for this study and publication.

Acknowledgements

We thank all study participants for providing specimens and devoting time to our research. This study was funded by InnoHK, We also appreciate Zerong Lu, Max Gu and Erin Liu for sharing metadata for the control cohort. The Government of Hong Kong Special Administrative Region of the People’s Republic of China, the Theme-based Research Scheme of the Research Grants Council of the Hong Kong Special Administrative Region (T11-705/21-N) and Emergency Key Program of Guangzhou Laboratory (Grant No. EKPG22-30-6).

Competing interests

All authors declare no financial or non-financial competing interests.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Sequencing reads were deposited at Sequence Read Archive under accession PRJNA917338. Additional datasets generated and/or analysed in this study are available from the corresponding author on reasonable request.

Code availability

The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.

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COVID-19 mRNA vaccine-mediated antibodies in human breast milk and their association with breast milk microbiota composition

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Journal Publication

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Abstract

Figures

Introduction

Methodology

Prospective cohort recruitment and sample collection

ELISA for SARS-CoV-2 spike protein-specific IgA and IgG

Breast milk microbiota analysis

Statistical analyses

Results

Recruitment of lactating mothers receiving BNT162b2

Levels of anti-SARS-CoV-2 IgA and IgG in breast milk were boosted after two vaccine doses

Changes in breast milk bacterial richness and composition were observed post-vaccination

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1