Identifying keystone species in microbial communities using deep learning

Role of sn-2 palmitate on the development of the infant gut microbiome: A metagenomic insight

The infant gut microbiome, which develops from birth, has profound and lasting effects on human health. Its establishment in early life is influenced by events such as delivery mode and feeding type. This study examined the effects of formula milk enriched with sn-2 palmitate on the gut microbiota of healthy term infants. We conducted a 16-week comparative analysis of three feeding groups: infants receiving high sn-2 palmitate formula (n = 30), regular vegetable oil formula (n = 32), and breast milk (n = 30). Using shotgun metagenomic sequencing of fecal samples, we performed a comprehensive assessment of the gut microbiota. While overall microbial composition and diversity were comparable across groups, the functional profile of the microbiome in infants receiving sn-2 palmitate-enriched formula more closely resembled that of breastfed infants compared to the control formula group. This similarity extended to microbial species interactions, virulence gene abundance, and metabolic pathway expression patterns. In addition, sn-2 palmitate promoted the proliferation of Bifidobacterium breve and enhanced the robustness of the gut microbial ecology. Notably, the phylogenetic analysis of B. breve strains in the sn-2 palmitate group showed closer alignment with the breastfed group compared to the control group. These findings suggest that sn-2 palmitate-enriched formula may confer gut microbiota functional benefits that more closely resemble those of breast milk compared to control formula milk. This study provides scientific evidence for the development of future functional infant formulas.

Linking plant genes to arthropod community dynamics: current progress and future challenges

Plant genetic variation can play a key role in shaping ecological communities. Prior work investigated the effects of coarse-grain variation among plant genotypes on their diverse arthropod communities. Several recent studies, however, have leveraged the boom of genomic resources to study how genome-wide plant variation influences associated communities. These studies have demonstrated that the effects of plant genomic variation are not just detectable but can be important drivers of arthropod communities in natural ecosystems. Field common gardens and lab-based mesocosm experiments are also revealing candidate genes that have large effects on arthropod communities. While we highlight these exciting results, we also discuss key challenges to address in future research. We argue that a major hurdle lies in the integration of genomic tools with hierarchical models of species communities (HMSCs). HMSCs are generative models that provide the opportunity to not only better understand the processes underlying community change but to also predict community dynamics. We also advocate for future research to apply models of genomic prediction to explore the genetic architecture of arthropod community phenotypes. We hypothesize that this genetic architecture will follow an exponential distribution, where a few genes of large effect, but also many genes of small effect, contribute to variation in arthropod communities. The next generation of studies linking plant genes to community dynamics will require interdisciplinary collaborations to build truly predictive models of plant genetic and arthropod community change.

Keystone microbial taxa identified by deep learning reveal mechanisms of phosphorus stoichiometric homeostasis in submerged macrophytes under different hydrodynamic states

Phosphorus (P) pollution in aquatic ecosystems triggers eutrophication, disrupting ecological processes. Although phytoremediation using submerged macrophytes is promising, its efficacy depends on plant-microbe interactions and stoichiometric homeostasis. A significant knowledge gap exists regarding the assembly and impact of key microbial communities on stoichiometric homeostasis under fluctuating environmental conditions, hindering the optimization of phytoremediation strategies. Given that hydrodynamic fluctuations are a primary source of environmental variability in aquatic systems, this study explored the intricate relationships among stoichiometric homeostasis, microbial community structure, and ecosystem stability, with a specific focus on their impact on rhizosphere P metabolism in Vallisneria natans and Myriophyllum spicatum under different hydrodynamic states. A Deep Learning-based Keystoneness Taxa Identification (DLKTI) framework was developed to identify key microbial taxa. Microbial community stability analysis preceded key taxa determination to enhance result reliability and ecological relevance based on the premise that distinct states provide a more dependable baseline for attributing observed changes to specific perturbations rather than to inherent fluctuations. These findings indicate that the key taxa identified by the DLKTI framework adequately characterized the overall ecological features of the microbial community (average ρ = 0.39, p<0.05). Moreover, including microbial pools and diversity indices of the screened key microbial taxa improved the explanatory power for submerged macrophyte traits (5% and 6%, respectively) and rhizosphere oxidative stress responses (25% and 4%, respectively). Partial least squares path modeling demonstrated the crucial role of stoichiometric homeostasis for P in ecosystem functioning (path coefficient of inhibition of phytoplankton growth = 0.58, p<0.001). The findings elucidating plant-microbe interaction patterns under different hydrodynamic states allow for the development of targeted interventions to enhance rhizosphere P metabolism, thereby increasing the efficiency of phytoremediation for eutrophication management and aquatic ecosystem restoration.

Characteristics and environmental driving mechanisms of bacterial communities in the Bohai Sea

The Bohai Sea, a semi-enclosed marginal sea, hosts a diverse array of bacterial communities that play pivotal roles in marine biogeochemical cycles. However, understanding of bacterial communities remains fragmented in the Bohai Sea, with unclear links between environmental factors and key species, and limited insights into the roles of environment and space in shaping the bacterial communities. In this study, we compiled a series of data, and investigated how spatial and environmental factors influence the region's distribution, assembly, and function of bacterial communities using high-throughput sequencing and statistical analyses. The results revealed that the bacterial communities in the Bohai Sea exhibited a high heterogeneity of spatial and environmental factors. Major drivers of community assembly included geographic location, nutrient availability (NO2-N, NO3-N, and NH4-N), temperature, and dissolved oxygen. Additionally, we found that the bacterial community structure in the nearshore waters of the Bohai Sea was distinctly different from that in the distant seas. Furthermore, we identified key bacterial species, including Marinimicrobia, Proteobacteria, Lentisphaerae, and Cyanobacteria that significantly contributed to community structure and function by random forest analysis. Notably, the abundance of Cyanobacteria was strongly correlated with environmental factors (NO2-N, NO3-N, and NH4-N), suggesting their potential as bioindicators of environmental change in marine ecosystems. More importantly, deterministic processes in the assembly of bacterial communities played a greater role than stochastic processes in highly polluted regions (BS3). The results of this research enhanced our understanding of the ecological processes governing bacterial community dynamics and provided valuable insights for monitoring and management marine ecosystem.

Harnessing microbial division of labor for biomanufacturing: a review of laboratory and formal modeling approaches

Bioprocess industries aim to meet the increasing demand for product complexity by designing enhanced cellular and metabolic capabilities for the host. Monocultures, standard biomanufacturing workhorses, are often restricted in their capability to meet these demands, and the solution often involves the genetic modification of the host. Synthetic microbial communities are a promising alternative to monocultures because they exhibit division of labor, enabling efficient resource utilization and pathway modularity. This specialization minimizes metabolic burden and enhances robustness to perturbations, providing a competitive advantage. Despite this potential, their utilization in biotechnological or bioprocessing applications remains limited. The recent emergence of new and innovative community design tools and strategies, particularly those harnessing the division of labor, holds promise to change this outlook. Understanding the microbial interactions governing natural microbial communities can be used to identify complementary partners, informing synthetic community design. Therefore, we particularly consider engineering division of labor in synthetic microbial communities as a viable solution to accelerate progress in the field. This review presents the current understanding of how microbial interactions enable division of labor and how this information can be used to design synthetic microbial communities to perform tasks otherwise unfeasible to individual organisms. We then evaluate laboratory and formal modeling approaches specifically developed to: elucidate microbial community physiology, guide experimental design, and improve our understanding of complex community interactions assisting synthetic community design. By synthesizing these insights, we aim to present a comprehensive framework that advances the use of microbial communities in biomanufacturing applications.

Small molecule metabolites drive plant rhizosphere microbial community assembly patterns

The assembly of rhizosphere microbial communities is essential for maintaining plant health, yet it is influenced by a wide range of biotic and abiotic factors. The key drivers shaping the composition of these communities, however, remain poorly understood. In this study, we analyzed 108 plant samples and evaluated root traits, plant growth characteristics, soil enzyme activities, rhizosphere metabolites, and soil chemical properties to identify the primary determinants of rhizosphere community assembly. Across 36 soil samples, we obtained 969,634 high-quality sequences, clustering into 6,284 ASVs predominantly classified into Proteobacteria (57.99%), Actinobacteria (30%), and Bacteroidetes (5.13%). Our findings revealed that rhizosphere metabolites accounted for more variance in microbial community composition compared to chemical properties (ANOVA, F = 1.53, p = 0.04), enzyme activities, or root traits (ANOVA, F = 1.04, p = 0.001). Seven small molecule metabolites, including glycerol, sorbitol, phytol, and alpha-ketoglutaric acid, were significantly correlated with βNTI, underscoring their role as critical drivers of microbial community assembly. The genus Rhizobium, significantly associated with βNTI (R = 0.25, p = 0.009), emerged as a keystone taxon shaping community structure. Soil culture experiments further validated that small molecule metabolites can modulate microbial community assembly. The ST treatment, enriched with these metabolites, produced 1,032,205 high-quality sequences and exhibited significant shifts in community composition (Adonis, p = 0.001, R = 0.463), with Rhizobium showing higher abundance compared to the control (CK). Variable selection (βNTI >2) drove phylogenetic turnover in ST, while stochastic processes (|βNTI| < 2) dominated in CK. This study provides quantitative insights into the role of rhizosphere metabolites in shaping microbial community assembly and highlights their potential for targeted modulation of rhizosphere microbiomes.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21 567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles and FR profiles, and identified 14 636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Differences in oral microbiota associated with type 2 diabetes mellitus between the Dai and Han populations

BACKGROUND

Type 2 diabetes mellitus (T2DM) development is closely linked to microbiota, influenced by geography, ethnicity, gender, and age. While the relationship between oral microbiota and T2DM has been explored, specific microbiota associated with T2DM in the Dai and Han populations remains unclear. This study aims to compare oral microbiota differences and identify keystone species between these populations, both with and without T2DM.

METHODS

We recruited 28 han participants (6 healthy children, 10 healthy adults, 12 adults with T2DM) and 34 Dai participants (11 healthy children, 10 healthy adults, 13 adults with T2DM). Blood samples were collected for biochemical analysis, and saliva samples underwent DNA extraction and 16S rRNA sequencing.

RESULTS

Age significantly influenced oral microbiota differences between the Dai and Han populations, overshadowing the effects of diabetes. In the Dai population with T2DM, notable increases in Alistipes putredinis, Lactobacillus spp., Faecalibacterium prausnitzii, and Akkermansia muciniphila were observed compared to the Han population. Keystone genera differed, with Fusibacter central to the Dai population's microbial network, while the Han network was more scattered.

CONCLUSION

This is the first comparative analysis of oral microbiota in the Dai and Han populations with T2DM, highlighting age and ethnicity's influence on microbial composition.

A metagenome-assembled genome inventory for children reveals early-life gut bacteriome and virome dynamics

Existing microbiota databases are biased toward adult samples, hampering accurate profiling of the infant gut microbiome. Here, we generated a metagenome-assembled genome inventory for children (MAGIC) from a large collection of bulk and viral-like particle-enriched metagenomes from 0 to 7 years of age, encompassing 3,299 prokaryotic and 139,624 viral species-level genomes, 8.5% and 63.9% of which are unique to MAGIC. MAGIC improves early-life microbiome profiling, with the greatest improvement in read mapping observed in Africans. We then identified 54 candidate keystone species, including several Bifidobacterium spp. and four phages, forming guilds that fluctuated in abundance with time. Their abundances were reduced in preterm infants and were associated with childhood allergies. By analyzing the B. longum pangenome, we found evidence of phage-mediated evolution and quorum sensing-related ecological adaptation. Together, the MAGIC database recovers genomes that enable characterization of the dynamics of early-life microbiomes, identification of candidate keystone species, and strain-level study of target species.

Characteristics of putative keystones in the healthy adult human gut microbiota as determined by correlation network analysis

Keystone species are thought to play a critical role in determining the structure and function of microbial communities. As they are important candidates for microbiome-targeted interventions, the identification and characterization of keystones is a pressing research goal. Both empirical as well as computational approaches to identify keystones have been proposed, and in particular correlation network analysis is frequently utilized to interrogate sequencing-based microbiome data. Here, we apply an established method for identifying putative keystone taxa in correlation networks. We develop a robust workflow for network construction and systematically evaluate the effects of taxonomic resolution on network properties and the identification of keystone taxa. We are able to identify correlation network keystone species and genera, but could not detect taxa with high keystone potential at lower taxonomic resolution. Based on the correlation patterns observed, we hypothesize that the identified putative keystone taxa have a stabilizing effect that is exerted on correlated taxa. Correlation network analysis further revealed subcommunities present in the dataset that are remarkably similar to previously described patterns. The interrogation of available metatranscriptomes also revealed distinct transcriptional states present in all putative keystone taxa. These results suggest that keystone taxa may have stabilizing properties in a subset of community members rather than global effects. The work presented here contributes to the understanding of correlation network keystone taxa and sheds light on their potential ecological significance.

A cross-systems primer for synthetic microbial communities

The design and use of synthetic communities, or SynComs, is one of the most promising strategies for disentangling the complex interactions within microbial communities, and between these communities and their hosts. Compared to natural communities, these simplified consortia provide the opportunity to study ecological interactions at tractable scales, as well as facilitating reproducibility and fostering interdisciplinary science. However, the effective implementation of the SynCom approach requires several important considerations regarding the development and application of these model systems. There are also emerging ethical considerations when both designing and deploying SynComs in clinical, agricultural or environmental settings. Here we outline current best practices in developing, implementing and evaluating SynComs across different systems, including a focus on important ethical considerations for SynCom research.

Cadmium contamination decreased bacterial network complexity and stability in coastal reclamation areas

Cadmium(Cd) contamination can exert significantly adverse effects on soil microbiota in reclaimed areas, however, its effects on bacterial network structure are still limitedly understood. Here we collected soil samples from typical reclaimed wetlands (RW) and ditch wetlands (DW) in coastal reclamation areas and examined the effects of Cd contamination on the bacterial network complexity and stability. The results showed that the bacterial networks were destabilized by the Cd contamination, while bacteria in DW soils showed robust invulnerability characterized by higher node constancy and compositional stability compared with RW soils. Soil bacteria resisted Cd stress by forming a network with intensive connections in the module but sparser connections among the modules. Especially, network modularity was higher in DW soils than in RW soils, but made it more vulnerable to nodes removal. In addition, Cd contamination promoted bacterial positive cohesion but decreased negative cohesion in RW soils. Flavobacteriaceae, Xanthomonadaceae, and Alcaligenaceae were identified as core phylotypes, which played pivotal roles in regulating interspecies interactions due to higher contributions to cohesion and significant correlations with soil nutrients. The findings of this work indicate the changes of bacterial network structure and the indispensable role of core phylotypes in regulating interactions and maintaining network sustainability under Cd contamination.

Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

Effects of glucose and lactate on Streptococcus mutans abundance in a novel multispecies oral biofilm model

The oral microbiome plays an important role in protecting oral health. Here, we established a controlled mixed-species in vitro biofilm model and used it to assess the impact of glucose and lactate on the ability of Streptococcus mutans, an acidogenic and aciduric species, to compete with commensal oral bacteria. A chemically defined medium was developed that supported the growth of S. mutans and four common early colonizers of dental plaque: Streptococcus gordonii, Actinomyces oris, Neisseria subflava, and Veillonella parvula. Biofilms containing the early colonizers were developed in a continuous flow bioreactor, exposed to S. mutans, and incubated for up to 7 days. The abundance of bacteria was estimated by quantitative polymerase chain reaction (qPCR). At high glucose and high lactate, the pH in bulk fluid rapidly decreased to approximately 5.2, and S. mutans outgrew other species in biofilms. In low glucose and high lactate, the pH remained above 5.5, and V. parvula was the most abundant species in biofilms. By contrast, in low glucose and low lactate, the pH remained above 6.0 throughout the experiment, and the microbial community in biofilms was relatively balanced. Fluorescence in situ hybridization confirmed that all species were present in the biofilm and the majority of cells were viable using live/dead staining. These data demonstrate that carbon source concentration is critical for microbial homeostasis in model oral biofilms. Furthermore, we established an experimental system that can support the development of computational models to predict transitions to microbial dysbiosis based on metabolic interactions.IMPORTANCEWe developed a controlled (by removing host factor) dynamic system metabolically representative of early colonization of Streptococcus mutans not measurable in vivo. Hypotheses on factors influencing S. mutans colonization, such as community composition and inoculation sequence and the effect of metabolite concentrations, can be tested and used to predict the effect of interventions such as dietary modifications or the use of toothpaste or mouthwash on S. mutans colonization. The defined in vitro model (species and medium) can be simulated in an in silico model to explore more of the parameter space.

Mining strategies for isolating plastic-degrading microorganisms

Plastic waste is a growing global pollutant. Plastic degradation by microorganisms has captured attention as an earth-friendly tactic. Although the mechanisms of plastic degradation by bacteria, fungi, and algae have been explored over the past decade, a large knowledge gap still exists regarding the identification, sorting, and cultivation of efficient plastic degraders, primarily because of their uncultivability. Advances in sequencing techniques and bioinformatics have enabled the identification of microbial degraders and related enzymes and genes involved in plastic biodegradation. In this review, we provide an outline of the situation of plastic degradation and summarize the methods for effective microbial identification using multidisciplinary techniques such as multiomics, meta-analysis, and spectroscopy. This review introduces new strategies for controlling plastic pollution in an environmentally friendly manner. Using this information, highly efficient and colonizing plastic degraders can be mined via targeted sorting and cultivation. In addition, based on the recognized rules and plastic degraders, we can perform an in-depth analysis of the associated degradation mechanism, metabolic features, and interactions.

The geomicrobiology of limestone, sulfuric acid speleogenetic, and volcanic caves: basic concepts and future perspectives

Caves are ubiquitous subterranean voids, accounting for a still largely unexplored surface of the Earth underground. Due to the absence of sunlight and physical segregation, caves are naturally colonized by microorganisms that have developed distinctive capabilities to thrive under extreme conditions of darkness and oligotrophy. Here, the microbiomes colonizing three frequently studied cave types, i.e., limestone, sulfuric acid speleogenetic (SAS), and lava tubes among volcanic caves, have comparatively been reviewed. Geological configurations, nutrient availability, and energy flows in caves are key ecological drivers shaping cave microbiomes through photic, twilight, transient, and deep cave zones. Chemoheterotrophic microbial communities, whose sustenance depends on nutrients supplied from outside, are prevalent in limestone and volcanic caves, while elevated inorganic chemical energy is available in SAS caves, enabling primary production through chemolithoautotrophy. The 16S rRNA-based metataxonomic profiles of cave microbiomes were retrieved from previous studies employing the Illumina platform for sequencing the prokaryotic V3-V4 hypervariable region to compare the microbial community structures from different cave systems and environmental samples. Limestone caves and lava tubes are colonized by largely overlapping bacterial phyla, with the prevalence of Pseudomonadota and Actinomycetota, whereas the co-dominance of Pseudomonadota and Campylobacterota members characterizes SAS caves. Most of the metataxonomic profiling data have so far been collected from the twilight and transient zones, while deep cave zones remain elusive, deserving further exploration. Integrative approaches for future geomicrobiology studies are suggested to gain comprehensive insights into the different cave types and zones. This review also poses novel research questions for unveiling the metabolic and genomic capabilities of cave microorganisms, paving the way for their potential biotechnological applications.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Strategies for tailoring functional microbial synthetic communities

Natural ecosystems harbor a huge reservoir of taxonomically diverse microbes that are important for plant growth and health. The vast diversity of soil microorganisms and their complex interactions make it challenging to pinpoint the main players important for the life support functions microbes can provide to plants, including enhanced tolerance to (a)biotic stress factors. Designing simplified microbial synthetic communities (SynComs) helps reduce this complexity to unravel the molecular and chemical basis and interplay of specific microbiome functions. While SynComs have been successfully employed to dissect microbial interactions or reproduce microbiome-associated phenotypes, the assembly and reconstitution of these communities have often been based on generic abundance patterns or taxonomic identities and co-occurrences but have only rarely been informed by functional traits. Here, we review recent studies on designing functional SynComs to reveal common principles and discuss multidimensional approaches for community design. We propose a strategy for tailoring the design of functional SynComs based on integration of high-throughput experimental assays with microbial strains and computational genomic analyses of their functional capabilities.