Uterine Microbiome: Does the Sampling Technique Matter?

Studies have proven the significance of microbial communities in various parts of the human body for health. In recent years it has been discovered that the uterine cavity is not sterile, and endometrium has its own microbiome which appears to have an impact on female fertility and gynecological pathologies. Lactobacillus has shown to dominate the microbial profile in the uterus and is considered an indicator of a healthy uterine environment. Yet, many argue that the Lactobacillus dominance is due to vaginal contamination during the sampling process. To date there is no clearly defined healthy endometrial microbial profile, which is largely due to the fact that determining the microbial community from the endometrium is complicated, and there is currently no consensus on sampling methods for the endometrial microbiome. As a result, this restricts ability to replicate discoveries made in other cohorts. Here we aim to give an overview of the sampling methods used and discuss what impedes the endometrial microbiome studies as well as how to reach a consensus on the study design. This knowledge could be incorporated into the future research and the knowledge on endometrial microbiome could be included into the diagnostics and treatment of female reproductive health.

Differences in microbial profile of endometrial fluid and tissue samples in women with in vitro fertilization failure are driven by Lactobacillus abundance.

INTRODUCTION: The endometrial microbiota has been linked to several gynecological disorders, including infertility. It has been shown that the microbial profile of endometrium could have a role in fertilization and pregnancy outcomes. In this study we aim to assess the microbial community of endometrial tissue (ET) and endometrial fluid (EF) samples in women receiving in vitro fertilization (IVF) treatment. We also search for possible associations between chronic endometritis (CE) and endometrial microbiota. MATERIAL AND METHODS: This was a cohort study involving 25 women aged between 28 and 42 years with both primary and secondary infertility and with at least one IVF failure. The ET and EF sample collection was carried out between September 2016 and November 2018. Each of the participants provided two types of samples-tissue and fluid samples (50 samples in total). A 16S rRNA sequencing was performed on both of the sample types for microbial profile evaluation. CE was diagnosed based on a CD138 immunohistochemistry where CE diagnosis was confirmed in the presence of one or more plasma cells. Microbial profiles of women with and without CE were compared in both sample types separately. RESULTS: We report no differences in the microbial composition and alpha diversity (pObserved  = 0.07, pShannon  = 0.65, pInverse Simpson  = 0.59) between the EF and ET samples of IVF patients. We show that the abundance of the genus Lactobacillus influences the variation in microbial beta diversity between and fluid samples (r2  = 0.34; false discovery rate [FDR] <9.9 × 10-5 ). We report that 32% (8/25) of the participants had differences in Lactobacillus dominance in the paired samples and these samples also present a different microbial diversity (pShannon  = 0.06, FDRweighted UniFrac  = 0.01). These results suggest that the microbial differences between ET and fluid samples are driven by the abundance of genus Lactobacillus. The microbiome of CE and without CE (ie non-CE) women in our sample set of IVF patients was similar. CONCLUSIONS: Our findings show that genus Lactobacillus dominance is an important factor influencing the microbial composition of ET and fluid samples.

Using fecal immunochemical tubes for the analysis of the gut microbiome has the potential to improve colorectal cancer screening.

Colorectal cancer (CRC) is a challenging public health problem which successful treatment depends on the stage at diagnosis. Recently, CRC-specific microbiome signatures have been proposed as a marker for CRC detection. Since many countries have initiated CRC screening programs, it would be useful to analyze the microbiome in the samples collected in fecal immunochemical test (FIT) tubes for fecal occult blood testing. Therefore, we investigated the impact of FIT tubes and stabilization buffer on the microbial community structure evaluated in stool samples from 30 volunteers and compared the detected communities to those of fresh-frozen samples, highlighting previously published cancer-specific communities. Altogether, 214 samples were analyzed by 16S rRNA gene sequencing, including positive and negative controls. Our results indicated that the variation between individuals was greater than the differences introduced by the collection strategy. The vast majority of the genera were stable for up to 7 days. None of the changes observed between fresh-frozen samples and FIT tube specimens were related to previously identified CRC-specific bacteria. Overall, we show that FIT tubes can be used for profiling the microbiota in CRC screening programs. This circumvents the need to collect additional samples and can possibly improve the sensitivity of CRC detection.

The Gut Microbiome in Polycystic Ovary Syndrome and Its Association with Metabolic Traits.

CONTEXT: Despite the gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. OBJECTIVE: Compare the gut microbiome in late fertile age women with and without PCOS and investigate whether changes in the gut microbiome correlate with PCOS-related metabolic parameters. DESIGN: Prospective, case-control study using the Northern Finland Birth Cohort 1966. SETTING: General community. PARTICIPANTS: A total of 102 PCOS women and 201 age- and body mass index (BMI)-matched non-PCOS control women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. INTERVENTION: (s): None. MAIN OUTCOME MEASURE(S): Bacterial diversity, relative abundance, and correlations with PCOS-related metabolic measures. RESULTS: Bacterial diversity indices did not differ significantly between PCOS and controls (Shannon diversity P = .979, unweighted UniFrac P = .175). Four genera whose balance helps to differentiate between PCOS and non-PCOS were identified. In the whole cohort, the abundance of 2 genera from Clostridiales, Ruminococcaceae UCG-002, and Clostridiales Family XIII AD3011 group, were correlated with several PCOS-related markers. Prediabetic PCOS women had significantly lower alpha diversity (Shannon diversity P = .018) and markedly increased abundance of genus Dorea (false discovery rate = 0.03) compared with women with normal glucose tolerance. CONCLUSION: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles. However, there are significant microbial changes in PCOS individuals depending on their metabolic health.