Microbiome Understanding in Maternity Study (MUMS), an Australian prospective longitudinal cohort study of maternal and infant microbiota: study protocol

Change in microbiota profile after vaginal estriol cream in postmenopausal women with stress incontinence

Introduction

Vaginal estrogen is a treatment for genitourinary symptoms of menopause (GSM), which comprises vaginal atrophy and urinary dysfunction, including incontinence. Previous studies show that estrogen therapy promotes lactobacilli abundance and is associated with reduced GSM symptoms, including reduction of stress incontinence. However, detailed longitudinal studies that characterize how the microbiome changes in response to estrogen are scarce. We aimed to compare the vaginal microbiota of postmenopausal women, before and 12 weeks after vaginal estrogen cream.

Methods

A total of 44 paired samples from 22 postmenopausal women with vaginal atrophy and stress incontinence were collected pre-vaginal estrogens and were compared to 12 weeks post-vaginal estrogen. Microbiota was characterized by 16S rRNA amplicon sequencing and biodiversity was investigated by comparing the alpha- and beta-diversity and potential markers were identified using differential abundance analysis.

Results

Vaginal estrogen treatment was associated with a reduction in vaginal pH and corresponded with a significant reduction in alpha diversity of the microbiota. Healthy vaginal community state type was associated with lower mean pH 4.89 (SD = 0.6), in contrast to dysbiotic state which had a higher mean pH 6.4 (SD = 0.74). Women with lactobacilli dominant community pre-treatment, showed stable microbiota and minimal change in their pH. Women with lactobacilli deficient microbiome pre-treatment improved markedly (p = 0.004) with decrease in pH -1.31 and change to heathier community state types.

Conclusion

In postmenopausal women with stress incontinence, vaginal estrogen promotes Lactobacillus and Bifidobacterium growth and lowers vaginal pH. Maximum response is seen in those with a dysbiotic vaginal microbiota pre-treatment.

The P4 Study: Postpartum Maternal and Infant Faecal Microbiome 6 Months After Hypertensive Versus Normotensive Pregnancy

Objective/Hypothesis

To explore potential differences in faecal microbiome between women, and their infants, who had normotensive pregnancies (NP) and those who had a hypertensive pregnancy (HP), either gestational hypertension (GH) or preeclampsia (PE).

Methods

This is a sub study of P4 (Postpartum Physiology, Psychology, and Paediatrics Study) and includes 18 mother-infant pairs: 10 NP and 8 HP (HP as defined by blood pressure > 140/90mmHg; of which 6 had PE, and 2 GH), six months postpartum. The participating mothers collected stool samples from themselves and their infants. 16S rRNA V3-V4 amplicons were used to study the faecal microbiome.

Results

The sample of women and their infants were mostly primiparous (n =16) with vaginal birth (n = 14). At the time of faecal sampling 8 women were using hormonal contraception, and one HP woman remained on an antihypertensive. All women had blood pressure < 130/80mmHg, and 10 had high BMI (> 30). All infants had started solids, 8 were exclusively breastfed, 1 exclusively formula fed and 9 both. Three infants had been exposed to a course of antibiotics. Six months postpartum, there were no significant differences in alpha or beta diversity between the gut microbiota of HP and NP women (P > 0.05). However, a statistically significant difference was detected in alpha diversity between infants following HP and NP, with lower diversity levels in HP infants (P < 0.05). It was also found that at a genus and species level, the gut microbiota of HP women was enriched with Bifidobacterium and Bifidobacterium sp. and depleted in Barnesiella and Barnesiella intestinihominis when compared to NP women (P < 0.05). Similarly, the gut microbiota of infants born from HP was enriched in Streptococcus infantis and depleted in Sutterella, Sutterella sp., Bacteroides sp. and Clostridium aldenense compared to infants born from NP (P < 0.05).

Discussion

While our findings are at best preliminary, due to the very small sample size, they do suggest that the presence of hypertension in pregnancy may adversely affect the maternal microbiota postpartum, and that of their infants. Further analysis of postpartum microbiome data from future studies will be important to validate these early findings and provide further evidence about the changes in the microbiota in the offspring of women following hypertensive disorders of pregnancy (HDP), including possible links to the causes of long-term cardiovascular disease, the prevalence of which is increased in women who have experienced HDP.

Microbial enrichment and storage for metagenomics of vaginal, skin, and saliva samples

Few validated protocols are available for large-scale collection, storage, and analysis of microbiome samples from the vagina, skin, and mouth. To prepare for a large-scale study on the female microbiome by remote self-sampling, we investigated the impact of sample collection, storage, and host DNA depletion on microbiome profiling. Vaginal, skin, and saliva samples were analyzed using 16S rRNA gene amplicon and metagenomic shotgun sequencing, and qPCR. Of the two tested storage buffers, the eNAT buffer could keep the microbial composition stable during various conditions. All three tested host DNA-depletion approaches showed a bias against Gram-negative taxa. However, using the HostZERO Microbial DNA and QIAamp DNA Microbiome kits, samples still clustered according to body site and not by depletion approach. Therefore, our study showed the effectiveness of these methods in depleting host DNA. Yet, a suitable approach is recommended for each habitat studied based on microbial composition.

•

Lysis buffer keeps the microbial composition stable during various storage conditions

•

Host DNA depletion introduces a larger bias toward Gram-negative taxa

•

The HostZERO Microbial DNA kit performed best in human DNA depletion for metagenomics

•

Body site-specific approach based on microbial composition is needed to minimize bias

Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).