A manifold-based framework for studying the dynamics of the vaginal microbiome.

The vaginal microbiome plays a crucial role in our health. The composition of this community can be classified into five community state types (CSTs), four of which are primarily consisted of Lactobacillus species and considered healthy, while the fifth features non-Lactobacillus populations and signifies a disease state termed Bacterial vaginosis (BV), which is associated with various symptoms and increased susceptibility to diseases. Importantly, however, the exact mechanisms and dynamics underlying BV development are not yet fully understood, including specifically possible routes from a healthy to a BV state. To address this gap, this study set out to characterize the progression from healthy- to BV-associated compositions by analyzing 8026 vaginal samples and using a manifold-detection framework. This approach, inspired by single-cell analysis, aims to identify low-dimensional trajectories in the high-dimensional composition space. It further orders samples along these trajectories and assigns a score (pseudo-time) to each analyzed or new sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV states for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. Several BV indicators can also be successfully predicted based on pseudo-time scores, and key taxa involved in BV development can be identified using this approach. Taken together, these findings demonstrate how manifold detection can be used to successfully characterize the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.

A Manifold-Based Framework for Studying the Dynamics of the Vaginal Microbiome.

The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) - a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsel's test, and several Amsel's criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.

Rapid starch degradation in the wood of olive trees under heat and drought is permitted by three stress-specific beta amylases.

Carbon reserve use is a major drought response in trees, enabling tree survival in conditions prohibiting photosynthesis. However, regulation of starch metabolism under drought at the whole-tree scale is still poorly understood. To this end, we combined measurements of nonstructural carbohydrates (NSCs), tree physiology and gene expression. The experiment was conducted outside on olive trees in pots under 90 d of seasonal spring to summer warming. Half of the trees were also subjected to limited water conditions for 28 d. Photosynthesis decreased in dehydrating trees from 19 to 0.5 µmol m-2  s-1 during the drought period. Starch degradation and mannitol production were a major drought response, with mannitol increasing to 71% and 41% out of total NSCs in shoots and roots, respectively. We identified the gene family members potentially relevant either to long-term or stress-induced carbon storage. Partitioning of expression patterns among ß amylase and starch synthase family members was observed, with three ß amylases possibly facilitating the rapid starch degradation under heat and drought. Our results suggest a group of stress-related, starch metabolism genes, correlated with NSC fluctuations during drought and recovery. The daily starch metabolism gene expression was different from the stress-mode starch metabolism pattern, where some genes are uniquely expressed during the stress-mode response.