Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes

Interplay between children's cognitive profiles and within-school social interactions is nuanced and differs across ages

Studies investigating the link between school achievement and social networks have shown that both cognitive and non-cognitive factors are integral to academic success. However, these investigations have predominantly been confined by two limitations: 1) they rarely combine cognitive and social data from the same individuals, and 2) when incorporating social data, it is often unidimensional, focusing only on a single type of relationship among children, such as friendship networks or time spent together. This research builds on prior findings by considering cognitive and social data, including preferences for schoolwork relations, leisure/play relations, and friendships, of nearly 5,000 students from Catalonia (Spain) aged 6 through 15. Our findings indicate that children prefer to interact with those who exhibit similar cognitive profiles, but that their preferences diverge between schoolwork and play-related relations during both primary and secondary school. The diverging preferences of children of older ages suggest a greater understanding of the different purposes and expectations of various social interactions.

A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection

Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated microbiome studies. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility. Taxonomic abundance data generated from metagenomic sequencing technologies are high-dimensional and compositional, suffering from uneven sampling depth, over-dispersion, and zero-inflation. These characteristics often challenge the reliability of the current methods for microbiome community detection. To study the microbiome co-occurrence network and perform community detection, we propose a generalized Bayesian stochastic block model that is tailored for microbiome data analysis where the data are transformed using the recently developed modified centered-log ratio transformation. Our model also allows us to leverage taxonomic tree information using a Markov random field prior. The model parameters are jointly inferred by using Markov chain Monte Carlo sampling techniques. Our simulation study showed that the proposed approach performs better than competing methods even when taxonomic tree information is non-informative. We applied our approach to a real urinary microbiome dataset from postmenopausal women. To the best of our knowledge, this is the first time the urinary microbiome co-occurrence network structure in postmenopausal women has been studied. In summary, this statistical methodology provides a new tool for facilitating advanced microbiome studies.

Nested patterns of commensals and endosymbionts in microbial communities of mosquito vectors

BACKGROUND

Mosquitoes serve as vectors for numerous pathogens, posing significant health risks to humans and animals. Understanding the complex interactions within mosquito microbiota is crucial for deciphering vector-pathogen dynamics and developing effective disease management strategies. Here, we investigated the nested patterns of Wolbachia endosymbionts and Escherichia-Shigella within the microbiota of laboratory-reared Culex pipiens f. molestus and Culex quinquefasciatus mosquitoes. We hypothesized that Wolbachia would exhibit a structured pattern reflective of its co-evolved relationship with both mosquito species, while Escherichia-Shigella would display a more dynamic pattern influenced by environmental factors.

RESULTS

Our analysis revealed different microbial compositions between the two mosquito species, although some microorganisms were common to both. Network analysis revealed distinct community structures and interaction patterns for these bacteria in the microbiota of each mosquito species. Escherichia-Shigella appeared prominently within major network modules in both mosquito species, particularly in module P4 of Cx. pipiens f. molestus, interacting with 93 nodes, and in module Q3 of Cx. quinquefasciatus, interacting with 161 nodes, sharing 55 nodes across both species. On the other hand, Wolbachia appeared in disparate modules: module P3 in Cx. pipiens f. molestus and a distinct module with a single additional taxon in Cx. quinquefasciatus, showing species-specific interactions and no shared taxa. Through computer simulations, we evaluated how the removal of Wolbachia or Escherichia-Shigella affects network robustness. In Cx. pipiens f. molestus, removal of Wolbachia led to a decrease in network connectivity, while Escherichia-Shigella removal had a minimal impact. Conversely, in Cx. quinquefasciatus, removal of Escherichia-Shigella resulted in decreased network stability, whereas Wolbachia removal had minimal effect.

CONCLUSIONS

Contrary to our hypothesis, the findings indicate that Wolbachia displays a more dynamic pattern of associations within the microbiota of Culex pipiens f. molestus and Culex quinquefasciatus mosquitoes, than Escherichia-Shigella. The differential effects on network robustness upon Wolbachia or Escherichia-Shigella removal suggest that these bacteria play distinct roles in maintaining community stability within the microbiota of the two mosquito species.

Differential nested patterns of Anaplasma marginale and Coxiella-like endosymbiont across Rhipicephalus microplus ontogeny

Understanding the intricate ecological interactions within the microbiome of arthropod vectors is crucial for elucidating disease transmission dynamics and developing effective control strategies. In this study, we investigated the ecological roles of Coxiella-like endosymbiont (CLE) and Anaplasma marginale across larval, nymphal, and adult stages of Rhipicephalus microplus. We hypothesized that CLE would show a stable, nested pattern reflecting co-evolution with the tick host, while A. marginale would exhibit a more dynamic, non-nested pattern influenced by environmental factors and host immune responses. Our findings revealed a stable, nested pattern characteristic of co-evolutionary mutualism for CLE, occurring in all developmental stages of the tick. Conversely, A. marginale exhibited variable occurrence but exerted significant influence on microbial community structure, challenging our initial hypotheses of its non-nested dynamics. Furthermore, in silico removal of both microbes from the co-occurrence networks altered network topology, underscoring their central roles in the R. microplus microbiome. Notably, competitive interactions between CLE and A. marginale were observed in nymphal network, potentially reflecting the impact of CLE on the pathogen transstadial-transmission. These findings shed light on the complex ecological dynamics within tick microbiomes and have implications for disease management strategies.

Integration of metataxonomic data sets into microbial association networks highlights shared bacterial community dynamics in fermented vegetables

The management of food fermentation is still largely based on empirical knowledge, as the dynamics of microbial communities and the underlying metabolic networks that produce safe and nutritious products remain beyond our understanding. Although these closed ecosystems contain relatively few taxa, they have not yet been thoroughly characterized with respect to how their microbial communities interact and dynamically evolve. However, with the increased availability of metataxonomic data sets on different fermented vegetables, it is now possible to gain a comprehensive understanding of the microbial relationships that structure plant fermentation. In this study, we applied a network-based approach to the integration of public metataxonomic 16S data sets targeting different fermented vegetables throughout time. Specifically, we aimed to explore, compare, and combine public 16S data sets to identify shared associations between amplicon sequence variants (ASVs) obtained from independent studies. The workflow includes steps for searching and selecting public time-series data sets and constructing association networks of ASVs based on co-abundance metrics. Networks for individual data sets are then integrated into a core network, highlighting significant associations. Microbial communities are identified based on the comparison and clustering of ASV networks using the "stochastic block model" method. When we applied this method to 10 public data sets (including a total of 931 samples) targeting five varieties of vegetables with different sampling times, we found that it was able to shed light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages.

IMPORTANCE

Within the growing body of research on the bacterial communities involved in the fermentation of vegetables, there is particular interest in discovering the species or consortia that drive different fermentation steps. This integrative analysis demonstrates that the reuse and integration of public microbiome data sets can provide new insights into a little-known biotope. Our most important finding is the recurrent but transient appearance, at the beginning of vegetable fermentation, of amplicon sequence variants (ASVs) belonging to Enterobacterales and their associations with ASVs belonging to Lactobacillales. These findings could be applied to the design of new fermented products.

Ecological network analysis reveals cancer-dependent chaperone-client interaction structure and robustness

Cancer cells alter the expression levels of metabolic enzymes to fuel proliferation. The mitochondrion is a central hub of metabolic reprogramming, where chaperones service hundreds of clients, forming chaperone-client interaction networks. How network structure affects its robustness to chaperone targeting is key to developing cancer-specific drug therapy. However, few studies have assessed how structure and robustness vary across different cancer tissues. Here, using ecological network analysis, we reveal a non-random, hierarchical pattern whereby the cancer type modulates the chaperones' ability to realize their potential client interactions. Despite the low similarity between the chaperone-client interaction networks, we highly accurately predict links in one cancer type based on another. Moreover, we identify groups of chaperones that interact with similar clients. Simulations of network robustness show that this group structure affects cancer-specific response to chaperone removal. Our results open the door for new hypotheses regarding the ecology and evolution of chaperone-client interaction networks and can inform cancer-specific drug development strategies.

Evaluating the prebiotic effect of oligosaccharides on gut microbiome wellness using in vitro fecal fermentation

We previously proposed the Gut Microbiome Wellness Index (GMWI), a predictor of disease presence based on a gut microbiome taxonomic profile. As an application of this index for food science research, we applied GMWI as a quantitative tool for measuring the prebiotic effect of oligosaccharides. Mainly, in an in vitro anaerobic batch fermentation system, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2'-fucosyllactose (2FL), were mixed separately with fecal samples obtained from healthy adult volunteers. To find out how 24 h prebiotic fermentation influenced the GMWI values in their respective microbial communities, changes in species-level relative abundances were analyzed in the five prebiotics groups, as well as in two control groups (no substrate addition at 0 h and for 24 h). The GMWI of fecal microbiomes treated with any of the five prebiotics (IN (0.48 ± 0.06) > FOS (0.47 ± 0.03) > XOS (0.33 ± 0.02) > GOS (0.26 ± 0.02) > 2FL (0.16 ± 0.06)) were positive, which indicates an increase of relative abundances of microbial species previously found to be associated with a healthy, disease-free state. In contrast, the GMWI of samples without substrate addition for 24 h (-0.60 ± 0.05) reflected a non-healthy, disease-harboring microbiome state. Compared to the original prebiotic index (PI) and α-diversity metrics, GMWI provides a more data-driven, evidence-based indexing system for evaluating the prebiotic effect of food components. This study demonstrates how GMWI can be applied as a novel PI in dietary intervention studies, with wider implications for designing personalized diets based on their impact on gut microbiome wellness.

TaxiBGC: a Taxonomy-Guided Approach for Profiling Experimentally Characterized Microbial Biosynthetic Gene Clusters and Secondary Metabolite Production Potential in Metagenomes

Biosynthetic gene clusters (BGCs) in microbial genomes encode bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read assembly, predicting a narrow breadth of BGC classes, and not providing the SM product. To overcome these limitations, we developed taxonomy-guided identification of biosynthetic gene clusters (TaxiBGC), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score, 0.56; mean PPV score, 0.80) compared with directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score, 0.49; mean PPV score, 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple diseases, including Crohn's disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques. IMPORTANCE Currently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared among multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.