Use and abuse of correlation analyses in microbial ecology

Unravelling microbial interactions in a synthetic broad bean paste microbial community

The biotic factors governing the assembly and functionality of broad bean paste microbiota remain largely unexplored due to its highly complex fermentation ecosystem. This study constructed a synthetic community comprising Zygosaccharomyces rouxii, Staphylococcus carnosus, Bacillus subtilis, Bacillus amyloliquefaciens, Tetragenococcus halophilus and Weissella confusa, representing key microorganisms involved in broad bean paste fermentation. The generalized Lotka-Volterra (gLV) model revealed that the microbial interaction network among the six species was dominated by pairwise interactions. The abundances of most species in the multi-species communities at 2 and 4 days were accurately predicted using the gLV model, based on pairwise species combinations outcomes. Among pairwise interactions, negative interactions (57 %) were significantly more prevalent than positive interactions (37 %), with the former generally being stronger. Subsequent investigations demonstrated that the tested Z. rouxii inhibited acid accumulation by acid-producing bacteria, while the two strains belonging to the genus Bacillus stimulated lactic acid bacteria growth and lactic acid accumulation. The sequential inoculation strategy, informed by the interaction network, enhanced the synthetic community's bioaugmentation in broad bean paste, significantly improving ester and mellow flavors, reducing unpleasant odors, and increasing volatile flavor substances to 9.43 times that of natural fermentation. Overall, this study revealed the interaction network of six key microorganisms in broad bean paste using the gLV model and guided the application of the synthetic community in its fermentation, significantly enhancing flavor quality.

Drivers of spatiotemporal community variations in estuarine ecosystems: A case study of the waters adjacent to the Yangtze Estuary

Understanding the processes and mechanisms driving species distribution and spatiotemporal variations in communities is a crucial theme in community ecology and conservation biology. Due to its complex geographical features and natural environmental gradients, the unique conditions of the waters adjacent to the Yangtze Estuary facilitate further research into estuarine community aggregation and diversity patterns. We used Variation Partitioning Analysis (VPA) to link community composition with environmental and spatiotemporal factors, quantifying the contributions of stochastic and deterministic processes to community spatiotemporal variations. Employing Canonical Correspondence Analysis (CCA), multi-variable regression, Mantel tests, and Spearman's rank correlation, we identified the main drivers for different species and communities. The results indicate that the community structure of fish and invertebrates in the waters adjacent to the Yangtze Estuary shows significant spatiotemporal variations. Temporal community changes are mainly driven by environmental factors, with significant biomass declines over years and seasonal β-diversity shifts. Despite the long time series of this study (2004-2022), the degree of seasonal variability in the community remains greater than interannual variability. Spatial variations in the community result from the combined effects of stochastic (random dispersal) and deterministic processes (environmental filtering), with non-demersal communities showing greater spatial changes. Temperature, chemical oxygen demand (COD), and pH are environmental factors with significant driving effects. This study quantitatively analyzed the significant impacts of environmental factors on fish and invertebrate communities by integrating neutral processes, specifically random dispersal, with environmental filtering. It thereby provides crucial information for systematic biodiversity conservation and water environment management planning.

Construction of a synthetic microbial community and its application in salt-reduced soy sauce fermentation

High salt conditions negatively affect the fermentation efficiency of soy sauce and human health. This study aimed to construct a synthetic microbial community based on dominant functional microorganisms for salt-reduced soy sauce fermentation by investigating the succession and function of the microbial community during factory soy sauce fermentation. The findings revealed that the interplay between salinity and microorganisms influenced the dynamic changes of microbial communities. Furthermore, Aspergillus, Wickerhamomyces, Zygosaccharomyces, Staphylococcus, Weissella, and Tetragenococcus were analyzed to play key roles during soy sauce fermentation. Subsequently, the core strains were isolated and their strains and metabolic characteristics were evaluated. Finally, six strains (Aspergillus oryzae JQ09, Wickerhamomyces anomalus HJ07, Zygosaccharomyces rouxii JZ11, Staphylococcus carnosus QJ26, Weissella paramesenteroides ZJ19, and Tetragenococcus halophilus GY03) were employed to reconstruct the synthetic microbial community and conduct salt-reduced soy sauce fermentation. Biofortification increased the accumulation of metabolites in salt-reduced soy sauce. When the salt content was reduced to 14%, the sensory characteristics of soy sauce were closest to those of traditional soy sauce. Overall, this research presents a bottom-up approach to establish a simplified microbial community model with desired functions through deconstructing and reconstructing microbial structure and function. It has the potential to enhance the fermentation efficiency and realize the fermentation of salt-reduced traditional fermented food.

Testing for Consistency in Co-occurrence Patterns Among Bacterial Taxa Across the Microbiomes of Four Different Trematode Parasites

Elucidating the specific processes and drivers of community assembly in the host microbiome is essential to fully understand host biology. Toward this goal, an important first step is to describe co-occurrence patterns among different microbial taxa, which can be driven by numerous factors, such as host identity. While host identity can be an important influential factor on co-occurrence patterns, a limited number of studies have explored the relative importance of host identity after controlling for other environmental factors. Here, we examined microbial co-occurrence patterns in four phylogenetically distinct trematode species living within the same snail species, collected concomitantly from the same habitat. Our previous study determined that all these trematodes shared some bacterial taxa, and the relative abundance of microbial taxa differed among trematodes, possibly due to differences in their eco-physiological traits. Here, we specifically predict that pairwise microbial co-occurrence patterns also vary among trematode host species. Our results showed that co-occurrence patterns among eight microbial families varied greatly among the four trematode hosts, with some microbial families co-occurring in some trematode species, whereas no such patterns were observed in other trematodes. Our study suggests that the habitat identity (trematode species) and its associated biotic characteristics, such as physiological and ecological traits, can determine co-occurrence patterns among microbial taxa, with substantial effects on local community composition.

MIMIC: a Python package for simulating, inferring, and predicting microbial community interactions and dynamics

SUMMARY

The study of microbial communities is vital for understanding their impact on environmental, health, and technological domains. The Modelling and Inference of MICrobiomes Project (MIMIC) introduces a Python package designed to advance the simulation, inference, and prediction of microbial community interactions and dynamics. Addressing the complex nature of microbial ecosystems, MIMIC integrates a suite of mathematical models, including previously used approaches such as Generalized Lotka-Volterra (gLV), Gaussian Processes (GP), and Vector Autoregression (VAR) plus newly developed models for integrating multi-omic data, to offer a versatile framework for analyzing microbial dynamics. By leveraging Bayesian inference and machine learning techniques, MIMIC provides the ability to infer the dynamics of microbial communities from empirical data, facilitating a deeper understanding of their complex biological processes, unveiling possible unknown ecological interactions, and enabling the design of microbial communities. Such insights could help to advance microbial ecology research, optimizing biotechnological applications, and contribute to environmental sustainability and public health strategies. MIMIC is designed for flexibility and ease of use, aiming to support researchers and practitioners in microbial ecology and microbiome research.

AVAILABILITY AND IMPLEMENTATION

MIMIC is freely available under the MIT License at https://github.com/ucl-cssb/MIMIC. It is implemented in Python (version 3.7 or higher) and is compatible with Windows, macOS, and Linux operating systems. MIMIC depends on standard Python libraries including NumPy, SciPy, and PyMC. Comprehensive examples and tutorials (including the main text demonstrations) are provided as Jupyter notebooks in the examples/directory and at the MIMIC Docs website, along with detailed installation instructions and real-world data use cases. The software will remain freely available for at least two years following publication. A code snapshot for this publication is also available at Zenodo: https://doi.org/10.5281/zenodo.15149003.

The Subgingival Microbial Composition in Health and Periodontitis with Different Probing Depths

The differences in microbiota between periodontitis and health have been extensively studied; however, knowledge about how the microbiota shifts from shallow to deep periodontal pockets remains limited despite its clinical importance in disease progres-sion and management. Patients diagnosed with stage III periodontitis commonly pre-sent varied probing depths (PD) within the same oral cavity, reflecting localized disease severity. This study aims to analyze the microbiome of subgingival plaques at various PDs in periodontitis patients. Subgingival plaques were collected from sixteen healthy subjects (health group) and periodontal pockets of sixteen stage III periodontitis pa-tients (PD 0-3 mm, PD 4-5 mm and PD 6-9 mm groups). A total of 64 subgingival plaque samples underwent 16S rRNA gene sequencing. The PD 6-9 mm group exhib-ited significantly higher alpha diversity than the health group, and distinct subgingival microbial community structures were observed in periodontitis patients, regardless of probing depth. The relative abundance of specific genera differed notably between health and periodontitis states; Corynebacterium and Cardiobacterium decreased, whereas Schaalia increased in shallow pockets (PD 0-3 mm) of periodontitis relative to the health group. Co-occurrence network analysis on the species level revealed that the PD 4-5 mm group had the most complex interspecies interactions, followed by the PD 6-9 mm and PD 0-3 mm groups. These findings indicate significant variations in mi-crobial diversity, composition, and interspecies interactions associated with periodon-tal health and periodontitis severity, highlighting their potential relevance for clinical diagnosis and targeted therapeutic strategies.

Differentiation and Interconnection of the Bacterial Community Associated with Silene nigrescens Along the Soil-To-Plant Continuum in the Sub-Nival Belt of the Qiangyong Glacier

Plant microbiomes provide significant fitness advantages to their plant hosts, especially in the sub-nival belt. Studies to date have primarily focused on belowground communities in this region. Here, we utilized high-throughput DNA sequencing to quantify bacterial communities in the rhizosphere soil as well as in the root and leaf endosphere compartments of Silene nigrescens to uncover the differentiation and interconnections of these bacterial communities along the soil-to-plant continuum. Our findings reveal that the bacterial communities exhibit notable variation across different plant compartment niches: the rhizosphere soil, root endosphere, and leaf endosphere. There was a progressive decline in diversity, network complexity, network modularity, and niche breadth from the rhizosphere soil to the root endosphere, and further to the leaf endosphere. Conversely, both the host plant selection effect and the stability of these communities showed an increasing trend. Total nitrogen and total potassium emerged as crucial factors accounting for the observed differences in diversity and composition, respectively. Additionally, 3.6% of the total amplicon sequence variants (ASVs) were shared across the rhizosphere soil, root endosphere, and leaf endosphere. Source-tracking analysis further revealed bacterial community migration among these compartments. The genera Pseudomonas, IMCC26256, Mycobacterium, Phyllobacterium, and Sphingomonas constituted the core of the bacterial microbiome. These taxa are shared across all three compartment niches and function as key connector species. Notably, Pseudomonas stands out as the predominant taxon among these bacteria, with nitrogen being the most significant factor influencing its relative abundance. These findings deepen our understanding of the assembly principles and ecological dynamics of the plant microbiome in the sub-nival belt, offering an integrated framework for its study.

A blueprint for contemporary studies of microbiomes

This editorial piece co-authored by the Senior Editors at Microbiome aims to highlight current challenges in the field of environmental and host-associated microbiome research. We also take the opportunity to clarify our expectations for the articles submitted to the journal. At Microbiome, we are seeking studies that provide either new mechanistic insights into the role of microbiomes in health and environmental systems or substantial conceptual or technical advances. Manuscripts need to meet high standards of language accuracy, quality of microbiome analyses, and data and protocol availability, including detailed reporting of wet-lab and in silico protocols, all of which can critically enhance transparency and reproducibility. We think that such efforts are essential to push the boundaries of our knowledge on microbiomes in a concerted, international effort.

Age-related dynamics of predominant methanogenic archaea in the human gut microbiome

BACKGROUND

The reciprocal relationship between aging and alterations in the gut microbiota is a subject of ongoing research. While the role of bacteria in the gut microbiome is well-documented, specific changes in the composition of methanogens during extreme aging and the impact of high methane production in general on health remain unclear. This study was designed to explore the association of predominant methanogenic archaea within the human gut and aging.

METHODS

Shotgun metagenomic data from the stool samples of young adults (n = 127, Age: 19-59 y), older adults (n = 86, Age: 60-99 y), and centenarians (n = 34, age: 100-109 years) were analyzed.

RESULTS

Our findings reveal a compelling link between age and the prevalence of high methanogen phenotype, while overall archaeal diversity diminishes. Surprisingly, the archaeal composition of methanogens in the microbiome of centenarians appears more akin to that of younger adults, showing an increase in Methanobrevibacter smithii, rather than Candidatus Methanobrevibacter intestini. Remarkably, Ca. M. intestini emerged as a central player in the stability of the archaea-bacteria network in adults, paving the way for M. smithii in older adults and centenarians. Notably, centenarians exhibit a highly complex and stable network of these two methanogens with other bacteria. The mutual exclusion between Lachnospiraceae and these methanogens throughout all age groups suggests that these archaeal communities may compensate for the age-related drop in Lachnospiraceae by co-occurring with Oscillospiraceae.

CONCLUSIONS

This study underscores the dynamics of archaeal microbiome in human physiology and aging. It highlights age-related shifts in methanogen composition, emphasizing the significance of both M. smithii and Ca. M. intestini and their partnership with butyrate-producing bacteria for potential enhanced health.

The Same Source of Microbes has a Divergent Assembly Trajectory Along a Hot Spring Flowing Path

Hot spring microbial mats represent intricate biofilms that establish self-sustaining ecosystems, hosting diverse microbial communities which facilitate a range of biochemical processes and contribute to the structural and functional complexity of these systems. While community structuring across mat depth has received substantial attention, mechanisms shaping horizontal spatial composition and functional structure of these communities remain understudied. Here, we explored the contributions of species source, local environment and species interaction to microbial community assembly processes in six microbial mat regions following a flow direction with a temperature decreasing from 73.3°C to 52.8°C. Surprisingly, we found that despite divergent community structures and potential functions across different microbial mats, large proportions of the community members (45.50%-80.29%) in the recipient mat communities originated from the same source community at the upper limit of temperature for photosynthetic life. This finding indicated that the source species were dispersed with water and subsequently filtered and shaped by local environmental factors. Furthermore, critical species with specific functional attributes played a pivotal role in community assembly by influencing potential interactions with other microorganisms. Therefore, species dispersal via water flow, environmental variables, and local species interaction jointly governed microbial assembly, elucidating assembly processes in the horizontal dimension of hot spring microbial mats and providing insights into microbial community assembly within extreme biospheres.

Microbial populations hardly ever grow logistically and never sublinearly

We investigate the growth dynamics of microbial populations, challenging the conventional logistic model. By analyzing empirical data from various biomes, we demonstrate that microbial growth is better described by a generalized logistic model, the θ-logistic model. This accounts for different growth mechanisms and environmental fluctuations, leading to a generalized gamma distribution of abundance fluctuations. Our findings reveal that microbial growth is never sublinear, so they cannot endorse-at least in the microbial world-the recent proposal of this mechanism as a stability enhancer of highly diverse communities. These results have significant implications for understanding macroecological patterns and the stability of microbial ecosystems.

Archaeal diversity in the microbiomes of four wild bird species

Archaea are generally low-abundance members of the vertebrate microbiota that require specific PCR primers to be detected in metabarcoding studies, and the robust intraspecific sample size is necessary for well-supported conclusions about archaeal diversity. Using 16S rRNA gene amplicons generated using both Archaea-Specific and Universal primers, we investigated prokaryotic diversity in 110 fecal samples from four wild bird species from four different orders: Anna's Hummingbird (Calypte anna), Saltmarsh Sparrow (Ammospiza caudacuta), Ruddy Turnstone (Arenaria interpres), and Canada Goose (Branta canadensis). Our aim was to test the hypotheses that Archaea-Specific primers would offer higher resolution of archaeal diversity and that the four ecologically distinct host species would have distinct archaeal communities. Archaea-Specific primers resulted in increases in archaeal richness and detection of Archaea in all four birds compared to the Universal primers. The ammonia-oxidizing archaeal order Nitrososphaerales was detected in all four host species, and methanogenic orders were enriched in samples from Canada Geese. In Bacteria-Archaea co-occurrence networks, Archaea-Specific primers found many more significant interactions than the Universal primers alone. Methanogenic archaeal orders dominated the microbiota in Canada Geese and were found to a lesser extent in the other host species, suggesting an important functional role of methanogens in Canada Geese. Overall, this study advances our knowledge of the archaeal component of the microbiome in wild birds and provides insight into the potential functional roles Archaea play in studies of avian gastrointestinal microbiota.

IMPORTANCE

Archaea may be persistent members of host-associated microbiomes across diverse host taxa; their detection has been limited due to their low abundance and the inadequacy of Universal primers. Large-scale studies of Archaea in vertebrate microbiomes have historically had low intraspecific sample sizes for bird species and had conflicting results. This study demonstrates the improved capability of the Archaea-Specific primers to detect archaeal diversity in diverse avian host species compared to the widely used Universal primers. We also identified both shared and species-specific archaeal taxa across four ecologically distinct avian host species from four different orders with implications for functional importance. Future studies interested in comprehensively cataloging prokaryotic diversity in avian microbiomes using amplicon-based sequencing methods should include Archaea-Specific primers to adequately probe archaeal diversity.

Soil biodiversity and function under global change

Soil organisms represent the most abundant and diverse organisms on the planet and support almost every ecosystem function we know, and thus impact our daily lives. Some of these impacts have been well-documented, such as the role of soil organisms in regulating soil fertility and carbon sequestration; processes that have direct implications for essential ecosystem services including food security and climate change mitigation. Moreover, soil biodiversity also plays a critical role in supporting other aspects from One Health-the combined health of humans, animals, and the environment-to the conservation of historic structures such as monuments. Unfortunately, soil biodiversity is also highly vulnerable to a growing number of stressors associated with global environmental change. Understanding how and when soil biodiversity supports these functions, and how it will adapt to changing environmental conditions, is crucial for conserving soils and maintaining soil processes for future generations. In this Essay, we discuss the fundamental importance of soil biodiversity for supporting multiple ecosystem services and One Health, and further highlight essential knowledge gaps that need to be addressed to conserve soil biodiversity for the next generations.

Soil organic matter composition affects ecosystem multifunctionality by mediating the composition of microbial communities in long-term restored meadows

BACKGROUND

Soil organic matter composition and microbial communities are key factors affecting ecosystem multifunctionality (EMF) during ecosystem restoration. However, there is little information on their interacting mechanisms in degraded and restored meadows. To fill this knowledge gap, plant, root and soil samples from alpine swamp meadows, alpine Kobresia meadows, severely degraded alpine meadows, short-term restored meadows (< 5 years) and long-term restored meadows (6-14 years) were collected. We leveraged high-throughput sequencing, liquid chromatography and mass spectrometry to characterize soil microbial communities and soil organic matter composition, measured microbial carbon metabolism and determined EMF.

RESULTS

It emerged that the similarity of soil microorganisms in meadows decreased with increasing heterogeneity of soil properties. Dispersal limitation and ecological drift led to the homogenization of the bacterial community. Based on co-occurrence network analysis, an increase in microbial network complexity promoted EMF. Root total phosphorus and soil organic matter components were the key predictors of EMF, while organic acids and phenolic acids increased the stability of the microbial network in long-term restored meadows. Carbon metabolism did not increase in restored meadows, but the niche breadth of soil microorganisms and the utilization efficiency of small molecular carbon sources such as amino acids did increase.

CONCLUSIONS

These findings emphasize the importance of soil organic matter composition in ecological restoration and that the composition should be considered in management strategies aimed at enhancing EMF.

Together throughout the year: seasonal patterns of bacterial and eukaryotic microbial communities in a macrotidal estuary

BACKGROUND

Estuaries are complex ecosystems linking river and marine environments, where microorganisms play a key role in maintaining ecosystem functions. In the present study, we investigated monthly 8 sites at two depth layers and over a one-year period the bacterial and eukaryotic community dynamics along the Seine macrotidal estuary (Normandy, France). To date, the taxonomy of the microbial diversity present in this anthropized estuary remains elusive and the drivers of the microbial community structure are still unknown.

RESULTS

The metabarcoding analysis of 147 samples revealed both a high bacterial and eukaryotic diversity, dominated by Proteobacteria, Bacteriodota, Actinobacteriota and Bacillariophyta, Spirotrichea, Dinophyceae, respectively. Along the estuary we only detected significant spatial patterns in the bacterial and eukaryotic community compositions for three and two months out of twelve, respectively. However, we found a clear seasonal effect on the diversity of both microbial communities driven by physical and chemical variables that were fluctuating over the year (temperature, irradiance, river flow). Biotic associations were also significant drivers of both alpha and beta diversity. Throughout the year, we identified a diverse and abundant core microbiota composed of 74 bacterial and 41 eukaryotic OTUs. These regionally abundant species include habitat generalists encompassing heterotrophs, phototrophs and consumers. Yet, many of these core OTUs remain taxonomically and functionally poorly assigned.

CONCLUSIONS

This molecular survey represents a milestone in the understanding of macrotidal estuary dynamics and the Seine ecosystem, through the identification of putative markers of ecosystem functioning. It also identifies seasons and biotic associations as main drivers of the Seine estuary microbiota and reveals the importance of a core microbiota throughout the year.

The impact of spray-induced gene silencing on cereal phyllosphere microbiota

BACKGROUND

Fusarium head blight (FHB) is a major disease affecting cereal crops including wheat, barley, rye, oats and maize. Its predominant causal agent is the ascomycete fungus Fusarium graminearum, which infects the spikes and thereby reduces grain yield and quality. The frequency and severity of FHB epidemics has increased in recent years, threatening global food security. Spray-induced gene silencing (SIGS) is an alternative technique for tackling this devastating disease through foliar spraying with exogenous double-stranded RNA (dsRNA) to silence specific pathogen genes via RNA interference. This has the advantage of avoiding transgenic approaches, but several aspects of the technology require further development to make it a viable field-level management tool. One such existing knowledge gap is how dsRNA spraying affects the microbiota of the host plants.

RESULTS

We found that the diversity, structure and composition of the bacterial microbiota are subject to changes depending on dsRNA targeted and host studied, while the fungal microbiota in the phyllosphere remained relatively unchanged upon spraying with dsRNA. Analyses of fungal co-occurrence patterns also showed that F. graminearum established itself among the fungal communities through negative interactions with neighbouring fungi. Through these analyses, we have also found bacterial and fungal genera ubiquitous in the phyllosphere, irrespective of dsRNA treatment. These results suggest that although rarer and less abundant microbial species change upon dsRNA spray, the ubiquitous bacterial and fungal components of the phyllosphere in wheat and barley remain unchanged.

CONCLUSION

We show for the first time the effects of exogenous dsRNA spraying on bacterial and fungal communities in the wheat and barley phyllospheres using a high-throughput amplicon sequencing approach. The results obtained further validate the safety and target-specificity of SIGS and emphasize its potential as an environmentally friendly option for managing Fusarium head blight in wheat and barley.

PyNetCor: a high-performance Python package for large-scale correlation analysis

The development of multi-omics technologies has generated an abundance of biological datasets, providing valuable resources for investigating potential relationships within complex biological systems. However, most correlation analysis tools face computational challenges when dealing with these high-dimensional datasets containing millions of features. Here, we introduce pyNetCor, a fast and scalable tool for constructing correlation networks on large-scale and high-dimensional data. PyNetCor features optimized algorithms for both full correlation coefficient matrix computation and top-k correlation search, outperforming other tools in the field in terms of runtime and memory consumption. It utilizes a linear interpolation strategy to rapidly estimate P-values and achieve false discovery rate control, demonstrating a speedup of over 110 times compared to existing methods. Overall, pyNetCor supports large-scale correlation analysis, a crucial foundational step for various bioinformatics workflows, and can be easily integrated into downstream applications to accelerate the process of extracting biological insights from data.

Response of soil microbial community structure to temperature and nitrogen fertilizer in three different provenances of Pennisetum alopecuroides

Soil microorganisms are key indicators of soil health, and it is crucial to investigate the structure and interactions of soil microbial communities among three different provenances of Pennisetum alopecuroides under varying nitrogen fertilizer and temperature levels in Northwest China. This study aims to provide theoretical support for the sustainable use of artificial grassland in this region. Employing a two-factor pot-control experiment with three nitrogen fertilizer treatments and three temperature treatments, a total of all treatments was utilized to examine the composition and abundance of soil microbial communities associated with Pennisetum alopecuroides using high-throughput sequencing, PCR technology, and molecular ecological network analysis. The results revealed that Proteobacteria was the dominant bacterial phylum while Ascomycota was the dominant fungal phylum in the soil samples from three provenances of Pennisetum. Specifically, Proteobacteria exhibited higher abundance in the N3T2 treatment compared to other treatments under N3T2 (25-30°C, 3 g/pot) treatment conditions in Shaanxi and Gansu provinces; similarly, Proteobacteria was more abundant in the N1T2 (25-30°C, 1 g/pot) treatment in Inner Mongolia under N1T2. Moreover, Ascomycota displayed higher abundance than other treatments in both Inner Mongolia and Gansu provinces. Additionally, Pennisetum Ascomycota demonstrated greater prevalence under (25-30°C, 3 g/pot) treatment compared to other treatments; furthermore, Shaanxi's Pennisetum Ascomycota exhibited increased prevalence under N3T1 (18-23°C, 3 g/pot) treatment compared to other treatments. The richness and diversity of soil microbial communities were significantly influenced by nitrogen fertilizer and temperature changes, leading to notable alterations in their structure. Molecular ecological network analyses revealed strong collaborative relationships among microbial species in Shaanxi Pennisetum and Inner Mongolia Pennisetum under high nitrogen and high temperature treatments, while competitive relationships were observed among microbial species in Gansu Pennisetum under similar conditions. Redundancy analysis indicated that soil pH, total potassium, and total phosphorus were the primary environmental factors influencing microorganisms. In summary, this study offers a theoretical foundation for assessing the sustainable utilization of Pennisetum artificial grasslands in Northwest China by investigating the shifts in soil microbial communities and the driving factors under varying nitrogen fertilizer and temperature levels.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Seafood Wholesale Market. Here, we analyze environmental qPCR and sequencing data collected in the Huanan market in early 2020. We demonstrate that market-linked severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic diversity is consistent with market emergence and find increased SARS-CoV-2 positivity near and within a wildlife stall. We identify wildlife DNA in all SARS-CoV-2-positive samples from this stall, including species such as civets, bamboo rats, and raccoon dogs, previously identified as possible intermediate hosts. We also detect animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them with those from farms and other markets. This analysis provides the genetic basis for a shortlist of potential intermediate hosts of SARS-CoV-2 to prioritize for serological and viral sampling.

A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data

Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a "target of interest" within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG's ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray-Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG's natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.

From nets to networks: tools for deciphering phytoplankton metabolic interactions within communities and their global significance

Our oceans are populated with a wide diversity of planktonic organisms that form complex dynamic communities at the base of marine trophic networks. Within such communities are phytoplankton, unicellular photosynthetic taxa that provide an estimated half of global primary production and support biogeochemical cycles, along with other essential ecosystem services. One of the major challenges for microbial ecologists has been to try to make sense of this complexity. While phytoplankton distributions can be well explained by abiotic factors such as temperature and nutrient availability, there is increasing evidence that their ecological roles are tightly linked to their metabolic interactions with other plankton members through complex mechanisms (e.g. competition and symbiosis). Therefore, unravelling phytoplankton metabolic interactions is the key for inferring their dependency on, or antagonism with, other taxa and better integrating them into the context of carbon and nutrient fluxes in marine trophic networks. In this review, we attempt to summarize the current knowledge brought by ecophysiology, organismal imaging, in silico predictions and co-occurrence networks using 'omics data, highlighting successful combinations of approaches that may be helpful for future investigations of phytoplankton metabolic interactions within their complex communities.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Lactate-mediated medium-chain fatty acid production from expired dairy and beverage waste

Fruits, vegetables, and dairy products are typically the primary sources of household food waste. Currently, anaerobic digestion is the most used bioprocess for the treatment of food waste with concomitant generation of biogas. However, to achieve a circular carbon economy, the organics in food waste should be converted to new chemicals with higher value than energy. Here we demonstrate the feasibility of medium-chain carboxylic acid (MCCA) production from expired dairy and beverage waste via a chain elongation platform mediated by lactate. In a two-stage fermentation process, the first stage with optimized operational conditions, including varying temperatures and organic loading rates, transformed expired dairy and beverage waste into lactate at a concentration higher than 900 mM C at 43 °C. This lactate was then used to produce >500 mM C caproate and >300 mM C butyrate via microbial chain elongation. Predominantly, lactate-producing microbes such as Lactobacillus and Lacticaseibacillus were regulated by temperature and could be highly enriched under mesophilic conditions in the first-stage reactor. In the second-stage chain elongation reactor, the dominating microbes were primarily from the genera Megasphaera and Caproiciproducens, shaped by varying feed and inoculum sources. Co-occurrence network analysis revealed positive correlations among species from the genera Caproiciproducens, Ruminococcus, and CAG-352, as well as Megasphaera, Bacteroides, and Solobacterium, indicating strong microbial interactions that enhance caproate production. These findings suggest that producing MCCAs from expired dairy and beverage waste via lactate-mediated chain elongation is a viable method for sustainable waste management and could serve as a chemical production platform in the context of building a circular bioeconomy.

Rare biosphere drives deterministic community assembly, co-occurrence network stability, and system performance in industrial wastewater treatment system

Bacterial community is strongly associated with activated sludge performance, but there still remains a knowledge gap regarding the rare bacterial community assembly and their influence on the system performance in industrial wastewater treatment plants (IWWTPs). Here, we investigated bacterial communities in 11 full-scale IWWTPs with similar process designs, aiming to uncover ecological processes and functional traits regulating abundant and rare communities. Our findings indicated that abundant bacterial community assembly was governed by stochastic processes; thereby, abundant taxa are generally present in wastewater treatment compartments across different industrial types. On the contrary, rare bacterial taxa were primarily driven by deterministic processes (homogeneous selection 61.9%-79.7%), thus they only exited in specific IWWTPs compartments and wastewater types. The co-occurrence networks analysis showed that the majority of keystone taxa were rare bacterial taxa, with rare taxa contributing more to network stability. Furthermore, rare bacteria rather than abundant bacteria in the oxic compartment contributed more to the degradation of xenobiotics compounds, and they were main potential drivers of pollutant removal. This study demonstrated the irreplaceable roles of rare bacterial taxa in maintaining system performance of IWWTPs, and called for environmental engineers and microbial ecologists to increase their attention on rare biosphere.

Aridity shapes distinct biogeographic and assembly patterns of forest soil bacterial and fungal communities at the regional scale

Climate change is exacerbating drought in arid and semi-arid forest ecosystems worldwide. Soil microorganisms play a key role in supporting forest ecosystem services, yet their response to changes in aridity remains poorly understood. We present results from a study of 84 forests at four south-to-north Loess Plateau sites to assess how increases in aridity level (1- precipitation/evapotranspiration) shapes soil bacterial and fungal diversity and community stability by influencing community assembly. We showed that soil bacterial diversity underwent a significant downward trend at aridity levels >0.39, while fungal diversity decreased significantly at aridity levels >0.62. In addition, the relative abundance of Actinobacteria and Ascomycota increased with higher aridity level, while the relative abundance of Acidobacteria and Basidiomycota showed the opposite trend. Bacterial communities also exhibited higher similarity-distance decay rates across geographic and environmental gradients than did fungal communities. Phylogenetic bin-based community assembly analysis revealed homogeneous selection and dispersal limitation as the two dominant processes in bacterial and fungal assembly. Dispersal limitation of bacterial communities monotonically increased with aridity levels, whereas homogeneous selection of fungal communities monotonically decreased. Importantly, aridity also increased the sensitivity of microbial communities to environmental disturbance and potentially decreased community stability, as evidenced by greater community similarity-environmental distance decay rates, narrower habitat niche breadth, and lower microbial network stability. Our study provides new insights into soil microbial drought response, with implications on the sustainability of ecosystems under environmental stress.

Quantifying microbial interactions: concepts, caveats, and applications

Microbial communities are fundamental to every ecosystem on Earth and hold great potential for biotechnological applications. However, their complex nature hampers our ability to study and understand them. A common strategy to tackle this complexity is to abstract the community into a network of interactions between its members - a phenomenological description that captures the overall effects of various chemical and physical mechanisms that underpin these relationships. This approach has proven useful for numerous applications in microbial ecology, including predicting community dynamics and stability and understanding community assembly and evolution. However, care is required in quantifying and interpreting interactions. Here, we clarify the concept of an interaction and discuss when interaction measurements are useful despite their context-dependent nature. Furthermore, we categorize different approaches for quantifying interactions, highlighting the research objectives each approach is best suited for.

Global Marine Cold Seep Metagenomes Reveal Diversity of Taxonomy, Metabolic Function, and Natural Products

Cold seeps in the deep sea are closely linked to energy exploration as well as global climate change. The alkane-dominated chemical energy-driven model makes cold seeps an oasis of deep-sea life, showcasing an unparalleled reservoir of microbial genetic diversity. Here, by analyzing 113 metagenomes collected from 14 global sites across 5 cold seep types, we present a comprehensive Cold Seep Microbiomic Database (CSMD) to archive the genomic and functional diversity of cold seep microbiomes. The CSMD includes over 49 million non-redundant genes and 3175 metagenome-assembled genomes, which represent 1895 species spanning 105 phyla. In addition, beta diversity analysis indicates that both the sampling site and cold seep type have a substantial impact on the prokaryotic microbiome community composition. Heterotrophic and anaerobic metabolisms are prevalent in microbial communities, accompanied by considerable mixotrophs and facultative anaerobes, highlighting the versatile metabolic potential in cold seeps. Furthermore, secondary metabolic gene cluster analysis indicates that at least 98.81% of the sequences potentially encode novel natural products, with ribosomally synthesized and post-translationally modified peptides being the predominant type widely distributed in archaea and bacteria. Overall, the CSMD represents a valuable resource that would enhance the understanding and utilization of global cold seep microbiomes.

Genetic architecture and correlations between the gut microbiome and gut gene transcription in Chinook salmon (Oncorhynchus tshawytscha)

Population divergence through selection can drive local adaptation in natural populations which has implications for the effective restoration of declining and extirpated populations. However, adaptation to local environmental conditions is complicated when both the host and its associated microbiomes must respond via co-evolutionary change. Nevertheless, for adaptation to occur through selection, variation in both host and microbiome traits should include additive genetic effects. Here we focus on host immune function and quantify factors affecting variation in gut immune gene transcription and gut bacterial community composition in early life-stage Chinook salmon (Oncorhynchus tshawytscha). Specifically, we utilized a replicated factorial breeding design to determine the genetic architecture (sire, dam and sire-by-dam interaction) of gut immune gene transcription and microbiome composition. Furthermore, we explored correlations between host gut gene transcription and microbiota composition. Gene transcription was quantified using nanofluidic qPCR arrays (22 target genes) and microbiota composition using 16 S rRNA gene (V5-V6) amplicon sequencing. We discovered limited but significant genetic architecture in gut microbiota composition and transcriptional profiles. We also identified significant correlations between gut gene transcription and microbiota composition, highlighting potential mechanisms for functional interactions between the two. Overall, this study provides support for the co-evolution of host immune function and their gut microbiota in Chinook salmon, a species recognized as locally adapted. Thus, the inclusion of immune gene transcription profile and gut microbiome composition as factors in the development of conservation and commercial rearing practices may provide new and more effective approaches to captive rearing.

Distance-decay equations of antibiotic resistance genes across freshwater reservoirs

Distance-decay (DD) equations can discern the biogeographical pattern of organisms and genes in a better way with advanced statistical methods. Here, we developed a data Compilation, Arrangement, and Statistics framework to advance quantile regression (QR) into the generation of DD equations for antibiotic resistance genes (ARGs) across various spatial scales using freshwater reservoirs as an illustration. We found that QR is superior at explaining dissemination potential of ARGs to the traditionally used least squares regression (LSR). This is because our model is based on the 'law of limiting factors', which reduces influence of unmeasured factors that reduce the efficacy of the LSR method. DD equations generated from the 99th QR model for ARGs were 'Sall = 90.03e-0.01Dall' in water and 'Sall = 92.31e-0.011Dall' in sediment. The 99th QR model was less impacted by uneven sample sizes, resulting in a better quantification of ARGs dissemination. Within an individual reservoir, the 99th QR model demonstrated that there is no dispersal limitation of ARGs at this smaller spatial scale. The QR method not only allows for construction of robust DD equations that better display dissemination of organisms and genes across ecosystems, but also provides new insights into the biogeography exhibited by key parameters, as well as the interactions between organisms and environment.

Antimicrobial resistance in rural rivers: Comparative study of the Coquet (Northumberland) and Eden (Cumbria) River catchments

Many studies have characterised resistomes in river microbial communities. However, few have compared resistomes in parallel rural catchments that have few point-source inputs of antimicrobial genes (ARGs) and organisms (i.e., AMR) - catchments where one can contrast more nebulous drivers of AMR in rural rivers. Here, we used quantitative microbial profiling (QMP) to compare resistomes and microbiomes in two rural river catchments in Northern England, the Coquet and Eden in Northumberland and Cumbria, respectively, with different hydrological and geographical conditions. The Eden has higher flow rates, higher annual surface runoff, and longer periods of soil saturation, whereas the Coquet is drier and has lower flowrates. QMP analysis showed the Eden contained significantly more abundant microbes associated with soil sources, animal faeces, and wastewater than the Coquet, which had microbiomes like less polluted rivers (Wilcoxon test, p < 0.01). The Eden also had greater ARG abundances and resistome diversity (Kruskal Wallis, p < 0.05), and higher levels of potentially clinically relevant ARGs. The Eden catchment had greater and flashier runoff and more extensive agricultural land use in its middle reach, which explains higher levels of AMR in the river. Hydrological and geographic factors drive AMR in rural rivers, which must be considered in environmental monitoring programmes.

The Co-occurrence Matrix and the Correlation Network of Phytophagous Insects Are Driven by Abiotic and Biotic Variables: the Case of Canola

Co-occurrence a correlation profiles are driven by different factors (exogenous and endogenous) and drawing a profile of association between species based on co-occurrence, without assessing how these species vary in terms of ecological niche can lead to wrong conclusions. The objective was to determine the co-occurrence and correlation patterns of phytophagous insects in canola crop and to evaluate how these patterns varied according to the crop stage (phenology-biotic) and sowing times (agricultural practice-abiotic). We found that the patterns of co-occurrence and correlation between species were reflections of population variations due to the phenology and sowing times of canola. Variations in the multi-species abundance matrix were influenced by mean air temperature and accumulated rainfall. The main species associated with canola in southern Brazil, in terms of abundance, were P. xylostella, D. speciosa, and N. viridula. These species were mostly negatively associated. When evaluating their population variations, we found that they explore different temporal niches, whether in terms of phenology or sowing times. Finally, we demonstrate empirically that despite being important, association patterns based on co-occurrence and correlation should be interpreted in light of the understanding of patterns of niche exploitation and temporal variation of species.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Microbial community assembly in engineered bioreactors

Microbial community assembly (MCA) processes that shape microbial communities in environments are being used to analyze engineered bioreactors such as activated sludge systems and anaerobic digesters. The goal of studying MCA is to be able to understand and predict the effect of design and operation procedures on bioreactor microbial composition and function. Ultimately, this can lead to bioreactors that are more efficient, resilient, or resistant to perturbations. This review summarizes the ecological theories underpinning MCA, evaluates MCA analysis methods, analyzes how these MCA-based methods are applied to engineered bioreactors, and extracts lessons from case studies. Furthermore, we suggest future directions in MCA research in engineered bioreactor systems. The review aims to provide insights and guidance to the growing number of environmental engineers who wish to design and understand bioreactors through the lens of MCA.

Phage predation, disease severity, and pathogen genetic diversity in cholera patients

Despite an increasingly detailed picture of the molecular mechanisms of bacteriophage (phage)-bacterial interactions, we lack an understanding of how these interactions evolve and impact disease within patients. In this work, we report a year-long, nationwide study of diarrheal disease patients in Bangladesh. Among cholera patients, we quantified Vibrio cholerae (prey) and its virulent phages (predators) using metagenomics and quantitative polymerase chain reaction while accounting for antibiotic exposure using quantitative mass spectrometry. Virulent phage (ICP1) and antibiotics suppressed V. cholerae to varying degrees and were inversely associated with severe dehydration depending on resistance mechanisms. In the absence of antiphage defenses, predation was "effective," with a high predator:prey ratio that correlated with increased genetic diversity among the prey. In the presence of antiphage defenses, predation was "ineffective," with a lower predator:prey ratio that correlated with increased genetic diversity among the predators. Phage-bacteria coevolution within patients should therefore be considered in the deployment of phage-based therapies and diagnostics.

Microbial community structures and bacteria-Cylindrospermopsis raciborskii interactions in Yilong Lake

Cylindrospermopsis raciborskii-dominated harmful algae blooms have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii-blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world.

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

The Potential of Co-Evolution and Interactions of Gut Bacteria-Phages in Bamboo-Eating Pandas: Insights from Dietary Preference-Based Metagenomic Analysis

Bacteria and phages are two of the most abundant biological entities in the gut microbiome, and diet and host phylogeny are two of the most critical factors influencing the gut microbiome. A stable gut bacterial community plays a pivotal role in the host's physiological development and immune health. A phage is a virus that directly infects bacteria, and phages' close associations and interactions with bacteria are essential for maintaining the stability of the gut bacterial community and the entire microbial ecosystem. Here, we utilized 99 published metagenomic datasets from 38 mammalian species to investigate the relationship (diversity and composition) and potential interactions between gut bacterial and phage communities and the impact of diet and phylogeny on these communities. Our results highlight the co-evolutionary potential of bacterial-phage interactions within the mammalian gut. We observed a higher alpha diversity in gut bacteria than in phages and identified positive correlations between bacterial and phage compositions. Furthermore, our study revealed the significant influence of diet and phylogeny on mammalian gut bacterial and phage communities. We discovered that the impact of dietary factors on these communities was more pronounced than that of phylogenetic factors at the order level. In contrast, phylogenetic characteristics had a more substantial influence at the family level. The similar omnivorous dietary preference and closer phylogenetic relationship (family Ursidae) may contribute to the similarity of gut bacterial and phage communities between captive giant panda populations (GPCD and GPYA) and omnivorous animals (OC; including Sun bear, brown bear, and Asian black bear). This study employed co-occurrence microbial network analysis to reveal the potential interaction patterns between bacteria and phages. Compared to other mammalian groups (carnivores, herbivores, and omnivores), the gut bacterial and phage communities of bamboo-eating species (giant pandas and red pandas) exhibited a higher level of interaction. Additionally, keystone species and modular analysis showed the potential role of phages in driving and maintaining the interaction patterns between bacteria and phages in captive giant pandas. In sum, gaining a comprehensive understanding of the interaction between the gut microbiota and phages in mammals is of great significance, which is of great value in promoting healthy and sustainable mammals and may provide valuable insights into the conservation of wildlife populations, especially endangered animal species.

Phage predation, disease severity and pathogen genetic diversity in cholera patients

Despite an increasingly detailed picture of the molecular mechanisms of phage-bacterial interactions, we lack an understanding of how these interactions evolve and impact disease within patients. Here we report a year-long, nation-wide study of diarrheal disease patients in Bangladesh. Among cholera patients, we quantified Vibrio cholerae (prey) and its virulent phages (predators) using metagenomics and quantitative PCR, while accounting for antibiotic exposure using quantitative mass spectrometry. Virulent phage (ICP1) and antibiotics suppressed V. cholerae to varying degrees and were inversely associated with severe dehydration depending on resistance mechanisms. In the absence of anti-phage defenses, predation was 'effective,' with a high predator:prey ratio that correlated with increased genetic diversity among the prey. In the presence of anti-phage defenses, predation was 'ineffective,' with a lower predator:prey ratio that correlated with increased genetic diversity among the predators. Phage-bacteria coevolution within patients should therefore be considered in the deployment of phage-based therapies and diagnostics.

Microbial community interactions determine the mineralization of soil organic phosphorus in subtropical forest ecosystems

In subtropical forest ecosystems with few phosphorus (P) inputs, P availability and forest productivity depend on soil organic P (Po) mineralization. However, the mechanisms by which the microbial community determines the status and fate of soil Po mineralization remain unclear. In the present study, soils were collected from three typical forest types: secondary natural forest (SNF), mixed planting, and monoculture forest of Chinese fir. The P fractions, Po-mineralization ability, and microbial community in the soils of different forest types were characterized. In addition, we defined Po-mineralizing taxa with the potential to interact with the soil microbial community to regulate Po mineralization. We found that a higher labile P content persisted in SNF and was positively associated with the Po-mineralization capacity of the soil microbial community. In vitro cultures of soil suspensions revealed that soil Po mineralization of three forest types was distinguished by differences in the composition of fungal communities. We further identified broad phylogenetic lineages of Po-mineralizing fungi with a high intensity of positive interactions with the soil microbial community, implying that the facilitation of Po-mineralizing taxa is crucial for soil P availability. Our dilution experiments to weaken microbial interactions revealed that in SNF soil, which had the highest interaction intensity of Po-mineralizing taxa with the community, Po-mineralization capacity was irreversibly lost after dilution, highlighting the importance of microbial diversity protection in forest soils. In summary, this study demonstrates that the interactions of Po-mineralizing microorganisms with the soil microbial community are critical for P availability in subtropical forests.IMPORTANCEIn subtropical forest ecosystems with few phosphorus inputs, phosphorus availability and forest productivity depend on soil organic phosphorus mineralization. However, the mechanisms by which the microbial community interactions determine the mineralization of soil organic phosphorus remain unclear. In the present study, soils were collected from three typical forest types: secondary natural forest, mixed planting, and monoculture forest of Chinese fir. We found that a higher soil labile phosphorus content was positively associated with the organic phosphorus mineralization capacity of the soil microbial community. Soil organic phosphorus mineralization of three forest types was distinguished by the differences in the composition of fungal communities. The positive interactions between organic phosphorus-mineralizing fungi and the rest of the soil microbial community facilitated organic phosphorus mineralization. This study highlights the importance of microbial diversity protection in forest soils and reveals the microbial mechanism of phosphorus availability maintenance in subtropical forest ecosystems.

Microbiologic surveys for Baijiu fermentation are affected by experimental design

The traditional Chinese alcoholic beverage Baijiu is produced by spontaneous fermentation of grains under anaerobic conditions. While numerous studies have used metagenomic technology to investigate the microbiome of Baijiu brewing, the microbial succession mechanism of Baijiu brewing has not been fully clarified, and metagenomic strategies for microecology surveys have not been comprehensively evaluated. Using the fermentation process of strong-flavor Baijiu as a model, we compared the data for bacterial communities based on short read 16S rRNA variable regions, V3-V4, and full-length 16S regions, V1-V9, to whole metagenomic shotgun sequencing (WMS) to measure the effect of technology selection on phylogenetic and functional profiles. The results showed differences in bacterial compositions and their relation to volatiles and physicochemical variables between sequencing methods. Furthermore, the percentage of V3-V4 sequences assigned to species level was higher than the percentage of V1-V9 sequences according to SILVA taxonomy, but lower according to NCBI taxonomy (P < 0.05). In both SILVA and NCBI taxonomies, the relative abundances of bacterial communities at both the genus and family levels were more positively correlated with WMS data in the V3-V4 dataset than in the V1-V9 dataset. The WMS identified changes in abundances of multiple metabolic pathways during fermentation (P < 0.05), including "starch and sucrose metabolism," "galactose metabolism," and "fatty acid biosynthesis." Although functional predictions derived from 16S data show similar patterns to WMS, most metabolic pathway changes are uncorrelated (P > 0.05). This study provided new technical and biological insights into Baijiu production that may assist in selection of methodologies for studies of fermentation systems.

Top-down and bottom-up microbiome engineering approaches to enable biomanufacturing from waste biomass

Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted.

ONE-SENTENCE SUMMARY

This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.

Segmental patterning of microbiota and immune cells in the murine intestinal tract

The intestine exhibits distinct characteristics along its length, with a substantial immune cell reservoir and diverse microbiota crucial for maintaining health. This study investigates how anatomical location and regional microbiota influence intestinal immune cell abundance. Using conventionally colonized and germ-free mice, segment-specific immune cell composition and microbial communities were assessed. Metagenomic sequencing analyzed microbiome variations, while flow cytometry and immunofluorescence examined immune cell composition. Microbiome composition varied significantly along the intestine, with diversity and abundance increasing from upper to lower segments. Immune cells showed distinct segment-specific patterning influenced by microbial colonization and localization. T cell subsets displayed varied dependence on microbiome presence and anatomical location. This study highlights locoregional differences in intestinal immune cell and microbiome composition, identifying immune subsets susceptible to microbiota presence. The findings provide context for understanding immune cell alterations in disease models.

Land use conversion increases network complexity and stability of soil microbial communities in a temperate grassland

Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species "interactions", possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.

On the De Novo Emergence of Ecological Interactions during Evolutionary Diversification: A Conceptual Framework and Experimental Test

AbstractEcological interactions are crucial to the structure and function of biological communities, but we lack a causal understanding of the forces shaping their emergence during evolutionary diversification. Here we provide a conceptual framework linking different modes of diversification (e.g., ecological diversification), which depend on environmental characteristics, to the evolution of different forms of ecological interactions (e.g., resource partitioning) in asexual lineages. We tested the framework by examining the net interactions in communities of Pseudomonas aeruginosa produced via experimental evolution in nutritionally simple (SIM) or complex (COM) environments by contrasting the productivity and competitive fitness of whole evolved communities relative to their component isolates. As expected, we found that nutritional complexity drove the evolution of communities with net positive interactions whereas SIM communities had similar performance as their component isolates. A follow-up experiment revealed that high fitness in two COM communities was driven by rare variants (frequency <0.1%) that antagonized PA14, the ancestral strain and common competitor used in fitness assays. Our study suggests that the evolution of de novo ecological interactions in asexual lineages is predictable at a broad scale from environmental conditions. Further, our work demonstrates that rare variants can disproportionately impact the function of relatively simple microbial communities.

Community assembly of the human piercing microbiome

Predicting how biological communities respond to disturbance requires understanding the forces that govern their assembly. We propose using human skin piercings as a model system for studying community assembly after rapid environmental change. Local skin sterilization provides a 'clean slate' within the novel ecological niche created by the piercing. Stochastic assembly processes can dominate skin microbiomes due to the influence of environmental exposure on local dispersal, but deterministic processes might play a greater role within occluded skin piercings if piercing habitats impose strong selection pressures on colonizing species. Here we explore the human ear-piercing microbiome and demonstrate that community assembly is predominantly stochastic but becomes significantly more deterministic with time, producing increasingly diverse and ecologically complex communities. We also observed changes in two dominant and medically relevant antagonists (Cutibacterium acnes and Staphylococcus epidermidis), consistent with competitive exclusion induced by a transition from sebaceous to moist environments. By exploiting this common yet uniquely human practice, we show that skin piercings are not just culturally significant but also represent ecosystem engineering on the human body. The novel habitats and communities that skin piercings produce may provide general insights into biological responses to environmental disturbances with implications for both ecosystem and human health.

Natural restoration exhibits better soil bacterial network complexity and stability than artificial restoration on the Loess Plateau, China

Natural restoration (NR, e.g., secondary succession) and artificial restoration (AR, e.g., afforestation) are key approaches for rehabilitating degraded land; however, a comparative assessment of microbial network between these approaches is lacking. We compared bacterial networks under NR and AR in two different watersheds on the Loess Plateau. Our findings revealed significantly heightened network complexity under NR compared to AR, including metrics such as node, edge, modularity, degree, centrality, and keystone nodes. NR's network robustness exceeded AR by 19.45-35.9% and 7.79-17.74% in the two watersheds, aligning with the ecological principle that complexity begets stability. The significantly higher negative/positive cohesion and natural connectivity under NR also support its better network stability than AR. Integrated analysis of paired sequencing data from five Loess Plateau studies conducted on the Loess Plateau further confirmed the higher complexity and stability of bacterial networks under NR. Further analysis unveiled "biological interactions" as primary drivers of bacterial co-occurrence (on average 84.21% of links), surpassing the influence of environmental filtering (5.17%) or dispersal limitation (4.2%). Importantly, networked communities under NR exhibited generally stronger linkages with various ecosystem function than AR. Overall, our study provides insights into vegetation restoration strategies from the perspective of microbial network, underscoring natural regeneration's potential as a superior remedy for degraded-land restoration.

Competition-cooperation mechanism between Escherichia coli and Staphylococcus aureus based on systems mapping

Introduction

Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging.

Methods

In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions.

Results

By designing and implementing a co-culture experiment for 100 pairs of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), we mapped 244 significant QTL combinations in the interaction process of the two bacteria using this model, including 69 QTLs from E. coli and 59 QTLs from S. aureus, respectively. Through gene annotation, we obtained 57 genes in E. coli, among which the genes with higher frequency were ypdC, nrfC, yphH, acrE, dcuS, rpnE, and ptsA, while we obtained 43 genes in S. aureus, among which the genes with higher frequency were ebh, SAOUHSC_00172, capF, gdpP, orfX, bsaA, and phnE1.

Discussion

By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.

Severe Prolonged Drought Favours Stress-Tolerant Microbes in Australian Drylands

Drylands comprise one-third of Earth's terrestrial surface area and support over two billion people. Most drylands are projected to experience altered rainfall regimes, including changes in total amounts and fewer but larger rainfall events interspersed by longer periods without rain. This transition will have ecosystem-wide impacts but the long-term effects on microbial communities remain poorly quantified. We assessed belowground effects of altered rainfall regimes (+ 65% and -65% relative to ambient) at six sites in arid and semi-arid Australia over a period of three years (2016-2019) coinciding with a significant natural drought event (2017-2019). Microbial communities differed significantly among semi-arid and arid sites and across years associated with variation in abiotic factors, such as pH and carbon content, along with rainfall. Rainfall treatments induced shifts in microbial community composition only at a subset of the sites (Milparinka and Quilpie). However, differential abundance analyses revealed that several taxa, including Acidobacteria, TM7, Gemmatimonadates and Chytridiomycota, were more abundant in the wettest year (2016) and that their relative abundance decreased in drier years. By contrast, the relative abundance of oligotrophic taxa such as Actinobacteria, Alpha-proteobacteria, Planctomycetes, and Ascomycota and Basidiomycota, increased during the prolonged drought. Interestingly, fungi were shown to be more sensitive to the prolonged drought and to rainfall treatment than bacteria with Basidiomycota mostly dominant in the reduced rainfall treatment. Moreover, correlation network analyses showed more positive associations among stress-tolerant dominant taxa following the drought (i.e., 2019 compared with 2016). Our result indicates that such stress-tolerant taxa play an important role in how whole communities respond to changes in aridity. Such knowledge provides a better understanding of microbial responses to predicted increases in rainfall variability and the impact on the functioning of semi-arid and arid ecosystems.

Impact of intraspecific variation in insect microbiomes on host phenotype and evolution

Microbes can be an important source of phenotypic plasticity in insects. Insect physiology, behaviour, and ecology are influenced by individual variation in the microbial communities held within the insect gut, reproductive organs, bacteriome, and other tissues. It is becoming increasingly clear how important the insect microbiome is for insect fitness, expansion into novel ecological niches, and novel environments. These investigations have garnered heightened interest recently, yet a comprehensive understanding of how intraspecific variation in the assembly and function of these insect-associated microbial communities can shape the plasticity of insects is still lacking. Most research focuses on the core microbiome associated with a species of interest and ignores intraspecific variation. We argue that microbiome variation among insects can be an important driver of evolution, and we provide examples showing how such variation can influence fitness and health of insects, insect invasions, their persistence in new environments, and their responses to global environmental changes. A and B are two stages of an individual or a population of the same species. The drivers lead to a shift in the insect associated microbial community, which has consequences for the host. The complex interplay of those consequences affects insect adaptation and evolution and influences insect population resilience or invasion.

Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

Integration of Untargeted Metabolomics and Microbial Community Analyses to Characterize Distinct Deep-Sea Methane Seeps

Deep-sea methane seeps host highly diverse microbial communities whose biological diversity is distinct from other marine habitats. Coupled with microbial community analysis, untargeted metabolomics of environmental samples using high resolution tandem mass spectrometry provides unprecedented access to the unique specialized metabolisms of these chemosynthetic microorganisms. In addition, the diverse microbial natural products are of broad interest due to their potential applications for human and environmental health and well-being. In this exploratory study, sediment cores were collected from two methane seeps (-1000 m water depth) with very different gross geomorphologies, as well as a non-seep control site. Cores were subjected to parallel metabolomic and microbial community analyses to assess the feasibility of representative metabolite detection and identify congruent patterns between metabolites and microbes. Metabolomes generated using high resolution liquid chromatography tandem mass spectrometry were annotated with predicted structure classifications of the majority of mass features using SIRIUS and CANOPUS. The microbiome was characterized by analysis of 16S rRNA genes and analyzed both at the whole community level, as well as the small subgroup of Actinobacteria, which are known to produce societally useful compounds. Overall, the younger Dagorlad seep possessed a greater abundance of metabolites while there was more variation in abundance, number, and distribution of metabolites between samples at the older Emyn Muil seep. Lipid and lipid-like molecules displayed the greatest variation between sites and accounted for a larger proportion of metabolites found at the older seep. Overall, significant differences in composition of the microbial community mirrored the patterns of metabolite diversity within the samples; both varied greatly as a function of distance from methane seep, indicating a deterministic role of seepage. Interdisciplinary research to understand microbial and metabolic diversity is essential for understanding the processes and role of ubiquitous methane seeps in global systems and here we increase understanding of these systems by visualizing some of the chemical diversity that seeps add to marine systems.