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Human milk microbiome studies are currently hindered by low milk bacterial/human cell ratios and often rely on 16S rRNA gene sequencing, which limits downstream analyses. Here, we aimed to find a method to study milk bacteria and assess bacterial sharing between maternal and infant microbiota. We tested four DNA isolation methods, two bacterial enrichment methods and three sequencing methods on mock communities, milk samples and negative controls. Of the four DNA isolation kits, the DNeasy PowerSoil Pro (PS) and MagMAX Total Nucleic Acid Isolation (MX) kits provided consistent 16S rRNA gene sequencing results with low contamination. Neither enrichment method substantially decreased the human metagenomic sequencing read-depth. Long-read 16S-ITS-23S rRNA gene sequencing biased the mock community composition but provided consistent results for milk samples, with little contamination. In contrast to 16S rRNA gene sequencing, 16S-ITS-23S rRNA gene sequencing of milk, infant oral, infant faecal and maternal faecal DNA from 14 mother-infant pairs provided sufficient resolution to detect significantly more frequent sharing of bacteria between related pairs compared to unrelated pairs. In conclusion, PS or MX kit-DNA isolation followed by 16S rRNA gene sequencing reliably characterises human milk microbiota, and 16S-ITS-23S rRNA gene sequencing enables studies of bacterial transmission in low-biomass samples.
A network analysis of postpartum depression and mother-to-infant bonding shows common and unique symptom-level connections across three postpartum periods
Postpartum depression and mother-to-infant bonding difficulties (MIBD), two issues crucial to maternal and infant mental health, often coexist and affect each other. Our study aims to dissect their complex relationship through a graphical LASSO network analysis of individual symptoms in 5594 Japanese postpartum women, whose geographical distribution was nationally representative. We identified ‘fear’, ‘enjoyment’, ‘overwhelm’, and ‘insomnia’ as common bridge symptoms linking postpartum depression and MIBD across three distinct postpartum periods. Moreover, ‘self-harm’ emerged as a bridge symptom in the first 6 months and the 7–12 month period, while ‘laugh’ was a bridge symptom in the first 6 months and the 13–24 month period. Notably, ‘self-blame’ was identified as a unique bridge symptom specific to the 13–24 month period. Our analysis highlights the complexities of symptom connectivity across postpartum stages and underscores the critical need for interventions that address both common and stage-specific bridge symptoms to effectively support maternal mental health and strengthen mother-to-infant bonding.
Young infants’ exposure to parabens: lotion use as a potential source of exposure
Parabens are widely used as antimicrobials in personal care products and pharmaceuticals. While previous studies demonstrate paraben exposure is ubiquitous, data investigating infants’ exposure is limited.
Associations between maternal pre-pregnancy BMI and mean diffusivity of the hippocampus and amygdala in infants
Maternal pre-pregnancy obesity may negatively affect offspring outcomes, including neurodevelopment. This study examined the relationship between maternal pre-pregnancy body mass index (MBMI) and the microstructure of the hippocampus and amygdala in neonates.
The rise to power of the microbiome: power and sample size calculation for microbiome studies
A priori power and sample size calculations are crucial to appropriately test null hypotheses and obtain valid conclusions from all clinical studies. Statistical tests to evaluate hypotheses in microbiome studies need to consider intrinsic features of microbiome datasets that do not apply to classic sample size calculation. In this review, we summarize statistical approaches to calculate sample sizes for typical microbiome study scenarios, including those that hypothesize microbiome features to be the outcome, the exposure or the mediator, and provide relevant R scripts to conduct some of these calculations. This review is intended to be a resource to facilitate the conduct of sample size calculations that are based on testable hypotheses across several dimensions of the microbiome. Implementation of these methods will improve the quality of human or animal microbiome studies, enabling reliable conclusions that will generalize beyond the study sample.
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