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An improved mechanistic understanding of microbial interactions can guide targeted interventions or inform the rational design of microbial communities to optimize them for applications such as pathogen control, food fermentation, and various biochemical processes. Existing methodologies for inferring the mechanisms behind microbial interactions often rely on complex model-building and are, therefore, sensitive to the introduction of biases from the incorporated existing knowledge and model-building assumptions. We highlight the microbial interaction prediction potential of cross-species co-expression analysis, which contrasts with these methods by its data-driven nature. We describe the utility of cross-species co-expression for various types of interactions and thereby inform future studies on use-cases of the approach and the opportunities and pitfalls that can be expected in its application.

MNetClass provides a valuable tool for microbiome network analysis, enabling the identification of key microbial subcommunities across diverse ecological niches. Implemented as an R package ( https://github.com/YihuaWWW/MNetClass ), it offers broad accessibility for researchers. Here, we systematically benchmarked MNetClass against existing microbial clustering methods on synthetic data using various performance metrics, demonstrating its superior efficacy. Notably, MNetClass operates without the need for control groups and effectively identifies central microbes, highlighting its potential as a robust framework for advancing microbiome research.

Background Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation. 2NmSq7Q5noeLTtkrbKwa4L Video abstract Conclusion This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.

Microbes are omnipresent in the biosphere and perform biological and chemical processes critical to ecosystem function, nutrient cycling, and global climate regulation. In the ocean, microbes constitute more than two-thirds of biomass with abundances reaching over one million microbial cells per milliliter of seawater. Microbes are omnipresent in the biosphere and perform biological and chemical processes critical to ecosystem function, nutrient cycling, and global climate regulation. In the ocean, microbes constitute more than two-thirds of biomass with abundances reaching over one million microbial cells per milliliter of seawater. Our understanding of the marine microbial world has rapidly expanded with use of innovative molecular and chemical ‘omics tools to uncover previously hidden taxonomic diversity, spatiotemporal distributions, and novel metabolic functions. Recognition that specific microbial taxa cooccur in consistent patterns in the ocean has implicated microbe-microbe interactions as important, but poorly constrained, regulators of microbial activity. Here, I examine cooperative interactions among marine plankton, with a focus on the metabolic “currencies” that establish microbial partnerships in the surface-ocean trade economy. I discuss current and future directions to study microbial metabolic interactions in order to strengthen our understanding of ecosystem interdependencies and their impact on ocean biogeochemistry.

Background The plant microbiome is an integral part of the host and increasingly recognized as playing fundamental roles in plant growth and health. Increasing evidence indicates that plant rhizosphere recruits beneficial microbes to the plant to suppress soil-borne pathogens. However, the ecological processes that govern plant microbiome assembly and functions in the below- and aboveground compartments under pathogen invasion are not fully understood. Here, we studied the bacterial and fungal communities associated with 12 compartments (e.g., soils, roots, stems, and fruits) of chili pepper ( Capsicum annuum L.) using amplicons (16S and ITS) and metagenomics approaches at the main pepper production sites in China and investigated how Fusarium wilt disease (FWD) affects the assembly, co-occurrence patterns, and ecological functions of plant-associated microbiomes. Results The amplicon data analyses revealed that FWD affected less on the microbiome of pepper reproductive organs (fruit) than vegetative organs (root and stem), with the strongest impact on the upper stem epidermis. Fungal intra-kingdom networks were less stable and their communities were more sensitive to FWD than the bacterial communities. The analysis of microbial interkingdom network further indicated that FWD destabilized the network and induced the ecological importance of fungal taxa. Although the diseased plants were more susceptible to colonization by other pathogenic fungi, their below- and aboveground compartments can also recruit potential beneficial bacteria. Some of the beneficial bacterial taxa enriched in the diseased plants were also identified as core taxa for plant microbiomes and hub taxa in networks. On the other hand, metagenomic analysis revealed significant enrichment of several functional genes involved in detoxification, biofilm formation, and plant-microbiome signaling pathways (i.e., chemotaxis) in the diseased plants. Conclusions Together, we demonstrate that a diseased plant could recruit beneficial bacteria and mitigate the changes in reproductive organ microbiome to facilitate host or its offspring survival. The host plants may attract the beneficial microbes through the modulation of plant-microbiome signaling pathways. These findings significantly advance our understanding on plant-microbiome interactions and could provide fundamental and important data for harnessing the plant microbiome in sustainable agriculture. DwWQb6Dg7ZT1-tarvfq632 Video abstract

milk*Microbes play a critical role in soil global biogeochemical circulation and microbe–microbe interactions have also evoked enormous interests in recent years. Utilization of green manures can stimulate microbial activity and affect microbial composition and diversity. However, few studies focus on the microbial interactions or detect the key functional members in communities. With the advances of metagenomic technologies, network analysis has been used as a powerful tool to detect robust interactions between microbial members. Here, random matrix theory-based network analysis was used to investigate the microbial networks in response to four different green manure fertilization regimes (Vicia villosa, common vetch, milk vetch, and radish) over two growth cycles from October 2012 to September 2014. The results showed that the topological properties of microbial networks were dramatically altered by green manure fertilization. Microbial network under milk vetch amendment showed substantially more intense complexity and interactions than other fertilization systems, indicating that milk vetch provided a favorable condition for microbial interactions and niche sharing. The shift of microbial interactions could be attributed to the changes in some major soil traits and the interactions might be correlated to plant growth and production. With the stimuli of green manures, positive interactions predominated the network eventually and the network complexity was in consistency with maize productivity, which suggested that the complex soil microbial networks might benefit to plants rather than simple ones, because complex networks would hold strong the ability to cope with environment changes or suppress soil-borne pathogen infection on plants. In addition, network analyses discerned some putative keystone taxa and seven of them had directly positive interactions with maize yield, which suggested their important roles in maintaining environmental functions and in improving plant growth.

To understand the effects of agricultural land use change and management on soil carbon (C) cycling, it is crucial to examine how these changes can influence microbial soil C cycling. Network analysis can offer insights into the structure, complexity, and stability of the soil microbiome in response to environmental disturbances, including land use change. Using SparCC-based co-occurrence networks, we studied how land use change impacts the connectivity, complexity, and stability of microbial C-cycling gene networks across an agricultural mosaic landscape in Canterbury, New Zealand. The most densely connected networks were found in land uses that were under the most intensive agricultural management, or under naturally regenerating vegetation. The microbial C-cycling gene networks from both land uses presented high network connectivity, low modularity, and a low proportion of negative gene interactions. In contrast, microbial C-cycling genes from native forests, which had the most stable and undisturbed plant cover, had the lowest network connectivity, highest modularity, and a greater proportion of negative gene interactions. Although the differences in total soil C content between land uses were small, the large effects of land use on the network structure of microbial C-cycling genes may have important implications for long-term microbial soil C cycling. Furthermore, this research highlights the value of using microbial network analysis to study the metabolic gene interactions shaping the functional structure of soil microbial communities in a manner not typically captured by more traditional forms of microbial diversity analysis

Characterizing ecological relationships between viruses, bacteria and protists in the ocean are critical to understanding ecosystem function, yet these relationships are infrequently investigated together. We evaluated these relationships through microbial association network analysis of samples collected approximately monthly from March 2008 to January 2011 in the surface ocean (0–5 m) at the San Pedro Ocean Time series station. Bacterial, T4-like myoviral and protistan communities were described by Automated Ribosomal Intergenic Spacer Analysis and terminal restriction fragment length polymorphism of the gene encoding the major capsid protein (g23) and 18S ribosomal DNA, respectively. Concurrent shifts in community structure suggested similar timing of responses to environmental and biological parameters. We linked T4-like myoviral, bacterial and protistan operational taxonomic units by local similarity correlations, which were then visualized as association networks. Network links (correlations) potentially represent synergistic and antagonistic relationships such as viral lysis, grazing, competition or other interactions. We found that virus–bacteria relationships were more cross-linked than protist–bacteria relationships, suggestive of increased taxonomic specificity in virus–bacteria relationships. We also found that 80% of bacterial–protist and 74% of bacterial–viral correlations were positive, with the latter suggesting that at monthly and seasonal timescales, viruses may be following their hosts more often than controlling host abundance.

Bacteriophages (phages) are viruses that infect bacteria. As the natural regulators of microbial communities, it plays a crucial role in microbiome turnover. Predicting phage–bacteria interactions (PBIs), as well as bacteria–bacteria interactions (BBIs) is essential for advancing microbiome research. Given the high cost and risk of wet-lab techniques, computational biology and bioinformatics methods are reasonable alternatives. However, existing approaches suffer from the low predictive accuracy and poor efficiency. In this study, we proposed a multi learning-based model named MVPHI for predicting complex microbial interactions. More specifically, we first construct a heterogeneous multi-attributed microbial network (MAMN) based on pathogenic bacteria and associated phages. Next, MVPHI introduces three different view microbial characteristics for training the model, including the statistical-view, textual-view and topology-view features. Experimental results on seven benchmark datasets indicated that MVPHI achieves superior performance compared with the six variant models and eight baseline algorithms. Moreover, case study and protein docking experiments further demonstrated the robustness and generalization of our model. In conclusion, the proposed MVPHI model has potential ability to predict novel PBIs and BBIs, and can also provide valuable insights for phages screening and bacterial community research.

Abstract Large amounts of carbon sequestered in permafrost on the Tibetan Plateau (TP) are becoming vulnerable to microbial decomposition in a warming world. However, knowledge about how the responsible microbial community responds to warming-induced permafrost thaw on the TP is still limited. This study aimed to conduct a comprehensive comparison of the microbial communities and their functional potential in the active layer of thawing permafrost on the TP. We found that the microbial communities were diverse and varied across soil profiles. The microbial diversity declined and the relative abundance of Chloroflexi, Bacteroidetes, Euryarchaeota, and Bathyarchaeota significantly increased with permafrost thawing. Moreover, warming reduced the similarity and stability of active layer microbial communities. The high-throughput qPCR results showed that the abundance of functional genes involved in liable carbon degradation and methanogenesis increased with permafrost thawing. Notably, the significantly increased mcrA gene abundance and the higher methanogens to methanotrophs ratio implied enhanced methanogenic activities during permafrost thawing. Overall, the composition and functional potentials of the active layer microbial community in the Tibetan permafrost region are susceptible to warming. These changes in the responsible microbial community may accelerate carbon degradation, particularly in the methane releases from alpine permafrost ecosystems on the TP. Warming-induced permafrost thawing increased the abundance of anaerobic microorganisms and functional genes involved in labile carbon degradation and methane cycles, which could accelerate soil carbon degradation on TP.