Microdiversity of the vaginal microbiome is associated with preterm birth

Targeting Megasphaera species to promote cervicovaginal health

Maintaining a healthy cervicovaginal microbiome (CVM) is vital for women's wellbeing; it is dependent primarily on Lactobacillus dominance. Microbiome imbalances, driven by Megasphaera species, contribute to infections and disease. Comprehensive research into Megasphaera biology and interventions is crucial for personalized women's healthcare, and additional efforts are required to mitigate the risks posed by cervicovaginal dysbiosis.

Data-driven insights can transform women's reproductive health

This perspective explores the transformative potential of data-driven insights to understand and address women's reproductive health conditions. Historically, clinical studies often excluded women, hindering comprehensive research into conditions such as adverse pregnancy outcomes and endometriosis. Recent advances in technology (e.g., next-generation sequencing techniques, electronic medical records (EMRs), computational power) provide unprecedented opportunities for research in women's reproductive health. Studies of molecular data, including large-scale meta-analyses, provide valuable insights into conditions like preterm birth and preeclampsia. Moreover, EMRs and other clinical data sources enable researchers to study populations of individuals, uncovering trends and associations in women's reproductive health conditions. Despite these advancements, challenges such as data completeness, accuracy, and representation persist. We emphasize the importance of holistic approaches, greater inclusion, and refining and expanding on how we leverage data and computational integrative approaches for discoveries so that we can benefit not only women's reproductive health but overall human health.

Vaginal microbiomes show ethnic evolutionary dynamics and positive selection of Lactobacillus adhesins driven by a long-term niche-specific process

The vaginal microbiome's composition varies among ethnicities. However, the evolutionary landscape of the vaginal microbiome in the multi-ethnic context remains understudied. We perform a systematic evolutionary analysis of 351 vaginal microbiome samples from 35 multi-ethnic pregnant women, in addition to two validation cohorts, totaling 462 samples from 90 women. Microbiome alpha diversity and community state dynamics show strong ethnic signatures. Lactobacillaceae have a higher ratio of non-synonymous to synonymous polymorphism and lower nucleotide diversity than non-Lactobacillaceae in all ethnicities, with a large repertoire of positively selected genes, including the mucin-binding and cell wall anchor genes. These evolutionary dynamics are driven by the long-term evolutionary process unique to the human vaginal niche. Finally, we propose an evolutionary model reflecting the environmental niches of microbes. Our study reveals the extensive ethnic signatures in vaginal microbial ecology and evolution, highlighting the importance of studying the host-microbiome ecosystem from an evolutionary perspective.

Preterm birth, a consequence of immune deviation mediated hyperinflammation

Preterm birth represents a multifaceted syndrome with intricacies still present in our comprehension of its etiology. In the context of a semi-allograft, the prosperity from implantation to pregnancy to delivery hinges on the establishment of a favorable maternal-fetal immune microenvironment and a successful trilogy of immune activation, immune tolerance and then immune activation transitions. The occurrence of spontaneous preterm birth could be related to abnormalities within the immune trilogy, stemming from deviation in maternal and fetal immunity. These immune deviations, characterized by insufficient immune tolerance and early immune activation, ultimately culminated in an unsustainable pregnancy. In this review, we accentuated the role of both innate and adaptive immune reason in promoting spontaneous preterm birth, reviewed the risk of preterm birth from vaginal microbiome mediated by immune changes and the potential of vaginal microbiomes and metabolites as a new predictive marker, and discuss the changes in the role of progesterone and its interaction with immune cells in a preterm birth population. Our objective was to contribute to the growing body of knowledge in the field, shedding light on the immunologic reason of spontaneous preterm birth and effective biomarkers for early prediction, providing a roadmap for forthcoming investigations.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

Depth-dependent microbial metagenomes sampled in the northeastern Indian Ocean

The northeastern Indian Ocean exhibits distinct hydrographic characteristics influenced by various local and remote forces. Variations in these driving factors may alter the physiochemical properties of seawater, such as dissolved oxygen levels, and affect the diversity and function of microbial communities. How the microbial communities change across water depths spanning a dissolved oxygen gradient has not been well understood. Here we employed both 16S rDNA amplicon and metagenomic sequencing approaches to study the microbial communities collected from different water depths along the E87 transect in the northeastern Indian Ocean. Samples were collected from the surface, Deep Chlorophyll Maximum (DCM), Oxygen Minimum Zone (OMZ), and bathypelagic layers. Proteobacteria were prevalent throughout the water columns, while Thermoproteota were found to be abundant in the aphotic layers. A total of 675 non-redundant metagenome-assembled genomes (MAGs) were constructed, spanning 21 bacterial and 5 archaeal phyla. The community structure and genomic information provided by this dataset offer valuable resources for the analysis of microbial biogeography and metabolism in the northeastern Indian Ocean.