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Since its initial release in 2022, ggClusterNet has become a vital tool for microbiome research, enabling microbial co‐occurrence network analysis and visualization in over 300 studies. To address emerging challenges, including multi‐factor experimental designs, multi‐treatment conditions, and multi‐omics data, we present a comprehensive upgrade with four key components: (1) A microbial co‐occurrence network pipeline integrating network computation (Pearson/Spearman/SparCC correlations), visualization, topological characterization of network and node properties, multi‐network comparison with statistical testing, network stability (robustness) analysis, and module identification and analysis; (2) Network mining functions for multi‐factor, multi‐treatment, and spatiotemporal‐scale analysis, including Facet.Network() and module.compare.m.ts(); (3) Transkingdom network construction using microbiota, multi‐omics, and other relevant data, with diverse visualization layouts such as MatCorPlot2() and cor_link3(); and (4) Transkingdom and multi‐omics network analysis, including corBionetwork.st() and visualization algorithms tailored for complex network exploration, including model_maptree2(), model_Gephi.3(), and cir.squ(). The updates in ggClusterNet 2 enable researchers to explore complex network interactions, offering a robust, efficient, user‐friendly, reproducible, and visually versatile tool for microbial co‐occurrence networks and indicator correlation patterns. The ggClusterNet 2R package is open‐source and available on GitHub (https://github.com/taowenmicro/ggClusterNet). ggClusterNet 2 drives the evolution of network analysis, offering researchers an accurate, efficient, convenient, reproducible, and visually compelling tool. Highlights The ggClusterNet 2 introduces a comprehensive microbial co‐occurrence network analysis pipeline. Enhanced network analysis workflow tailored for complex experimental designs and diverse data types. Enhanced visualization of microbiomes and their correlated environmental or host‐associated indicators. Introduced various visualization algorithms for transkingdom and multi‐omics interaction networks.

Plant microbiomes play an important role in plant health, growth, disease and stress resistance, but interacting drivers and ecological processes shaping microbiome diversity remain elusive. In this study, we examined bacterial communities using 16S rRNA gene sequencing in bulk soil, rhizosphere, rhizoplane and endosphere of three salt‐tolerant date palm cultivars. We found the highest diversity in the rhizosphere and bulk soil, while lower diversity in the rhizoplane and endophyte compartments across the three cultivars. Furthermore, the bacterial microbiome exhibited genotype and compartment‐specificity, with significant differences (p < 0.05) noted in community composition between compartments of the same date palm cultivar and among cultivars. Bacterial diversity and co‐occurrence network complexity progressively decreased as host selection pressure increased from the soil to epiphytes, then to endophytes. Specialist microorganisms dominate the community composition and play a major role in microbial interactions in each compartment. The ecological model showed that stochastic processes, primarily drift (37%), predominantly shaped microbial community assembly in bulk soil, whereas deterministic processes, mainly homogenous selection, governed microbial assembly in the rhizosphere, rhizoplane, and endosphere, contributed 59%, 60%, and 64%, respectively. Notably, the heatmap based on PICRUSt2 analysis showed that functional profiles clustered distinctly by compartment, with significant differences (p < 0.05) in differentially abundant metabolic pathways, reflecting the functional specialisation of plant‐associated compartments. Our findings provide strong empirical support for the theoretical model of host selection and niche occupation in date palm microbiome assembly, with significant implications for sustainable agriculture in arid ecosystems through improved crop management and microbiome manipulation. Summary Bulk and rhizosphere soil have a higher diversity than rhizoplane and endophytes. Date palm genotype and compartment shape their microbiome. Specialist microorganisms dominate community composition in each compartment. Network complexity decreases from soil to epiphytes to endophytes. Deterministic processes govern microbial assembly in plant‐associated niche.

Soil microbial communities are involved in and contribute to several processes in soil ecosystems. Nonetheless, how various forest management approaches and their timeframes influence soil microbial community composition and network complexity is poorly understood. Hence, in this study, a time‐series method examined how microbial populations in the soil of Carya cathayensis var. dabeishansis forests varied across different management practices (no management, extensive management, and intensive management) and over periods of 0, 3, 8, 15, and 20 years. High‐throughput sequencing determined the species composition of soil microbial communities, co‐occurrence network analysis assessed interrelationships between communities, and null model theory elucidated deterministic and stochastic processes governing community assembly. The results indicated that under both treatment methods, soil bacterial diversity indices increased compared to the control during short‐term management (3 years), but subsequently declined with further prolonged management duration. Moreover, soil acid phosphatase activity and total potassium levels primarily shaped the bacterial species in the soil, with Acidobacteriota (21.96%–31.45%), Proteobacteria (22.82%–31.12%), Actinobacteria (6.81%–13.05%), and Chloroflexi (6.68%–9.67%) representing the most prevalent bacterial taxa. Interactions between soil bacterial and fungal communities were predominantly cooperative across both management strategies (79.88%–100%). However, the degree of cooperation fluctuated throughout the duration. Stochastic processes, particularly diffusion limitation, played a key role in shaping the assembly of these microbial communities. The diffusion limitation of soil microorganisms was smaller in extensively managed forests than in intensively managed forests. These results highlight the need for balanced forest management strategies, where short‐term intensive practices could help preserve soil microbial diversity and sustain ecosystem functions. Therefore, we strongly recommend adopting an intermittent forest management approach, particularly in intensively managed forests, where it is necessary to allow the ecosystem adequate time for autonomous recovery. The interspecific relationships between soil bacterial and fungal communities were mainly collaborative in both extensive management and intensive management; Sustained forest management negatively affects soil microbial diversity, composition, and network complexity; Soil microbial community assembly processes diverge under different management methods.

Warming is altering the functioning of desert ecosystems in global drylands. Microbial communities are crucial for maintaining these ecosystems, yet how their co-occurrence networks and assembly mechanisms respond to warming remains unclear. Using 16 S and ITS rRNA amplicon sequencing, we examined bacterial and fungal community composition and structure. Further, we investigated cross-trophic bacterial-fungal interactions via inter-domain ecological network analysis. Warming significantly altered the diversity, composition, and structure of both bacterial and fungal communities. It increased bacterial network complexity but simplified the fungal network. Notably, warming enhanced cross-trophic interactions between bacteria and fungi, facilitating the maintenance of microbial hierarchical interactions, particularly bacterial network complexity. However, microbial keystone taxa declined dramatically under warming, 41.18% of these belonged to Ascomycota. Neutral community models and normalized stochastic ratio-based analyses revealed that deterministic processes dominated community assembly, with warming increasing their relative importance by 8–46%. This suggests a potential deterministic environmental filtering induced by warming. Collectively, these findings advance our understanding of the ecological mechanisms and microbial interactions underpinning rhizospheric communities in drylands under future climate change.

Background Microbiome recruitment is influenced by plant host, but how host plant impacts the assembly, functions, and interactions of perennial plant root microbiomes is poorly understood. Here we examined prokaryotic and fungal communities between rhizosphere soils and the root endophytic compartment in two native Miscanthus species ( Miscanthus sinensis and Miscanthus floridulus ) of Taiwan and further explored the roles of host plant on root-associated microbiomes. Results Our results suggest that host plant genetic variation, edaphic factors, and site had effects on the root endophytic and rhizosphere soil microbial community compositions in both Miscanthus sinensis and Miscanthus floridulus , with a greater effect of plant genetic variation observed for the root endophytic communities. Host plant genetic variation also exerted a stronger effect on core prokaryotic communities than on non-core prokaryotic communities in each microhabitat of two Miscanthus species. From rhizosphere soils to root endophytes, prokaryotic co-occurrence network stability increased, but fungal co-occurrence network stability decreased. Furthermore, we found root endophytic microbial communities in two Miscanthus species were more strongly driven by deterministic processes rather than stochastic processes. Root-enriched prokaryotic OTUs belong to Gammaproteobacteria, Alphaproteobacteria, Betaproteobacteria, Sphingobacteriia, and [Saprospirae] both in two Miscanthus species, while prokaryotic taxa enriched in the rhizosphere soil are widely distributed among different phyla. Conclusions We provide empirical evidence that host genetic variation plays important roles in root-associated microbiome in Miscanthus . The results of this study have implications for future bioenergy crop management by providing baseline data to inform translational research to harness the plant microbiome to sustainably increase agriculture productivity. 7yeh-8ahY_T_UpG1ho_dZs Video Abstract

Acid rain (AR), as a global environmental threat, has profoundly adverse effects on natural soil ecosystems. Microorganisms involved in the nitrogen (N) cycle regulate the global N balance and climate stabilization, but little is known whether and how AR influences the structure and complexity of these microbial communities. Herein, we conducted an intact soil core experiment by manipulating the acidity of simulated rain (pH 7.5 (control, CK) vs. pH 4.0 (AR)) in subtropical agricultural soil, to reveal the differences in the structure and complexity of soil nitrifying and denitrifying microbiota using Illumina amplicon sequencing of functional genes ( , , and ). Networks of ammonia-oxidizing archaea (AOA) and -carrying denitrifiers in AR treatment were less complex with fewer nodes and lower connectivity, while network of -carrying denitrifiers in AR treatment had higher complexity and connectivity relative to CK. Supporting this, AR reduced the abundance of keystone taxa in networks of AOA and -carrying denitrifiers, but increased the abundance of keystone taxa in -carrying denitrifiers network. However, AR did not alter the community structure of AOA, ammonia-oxidizing bacteria (AOB), -, and -carrying denitrifiers. Moreover, AR did not change soil N O emissions during the experimental period. AOB community structure significantly correlated with content of soil available phosphorus (P), while the community structures of - and -carrying denitrifiers both correlated with soil pH and available P content. Soil N O emission was mainly driven by the -carrying denitrifiers. Our results present new perspective on the impacts of AR on soil N-cycle microbial network complexity and keystone taxa in the context of global changes.

  Upwelling may generate unique hydrological and environmental heterogeneity, leading to enhanced diffusion to reshape microbial communities. However, it remains largely unknown how different microbial taxa respond to highly complex and dynamic upwelling systems. In the present study, geographic patterns and co-occurrence network of different microbial communities in response to upwelling were examined. Our results showed that coastal upwelling shaped prokaryotic and eukaryotic microbial community and decreased their diversity. In addition, bacteria and microeukaryote had similar biogeographical patterns with distinct assembly mechanisms. The impact of stochastic processes on bacteria was significantly stronger compared with microeukaryote in upwelling. Lower network complexity but more frequent interaction was found in upwelling microbial co-occurrence. However, the upwelling environment increased the robustness and modularity of bacterial network, while eukaryotic network was just the opposite. Co-occurrence networks of bacteria and microeukaryote showed significant distance-decay patterns, while the bacterial network had a stronger spatial variation. Temperature and salinity were the strongest environmental factors affecting microbial coexistence, whereas the topological characteristics of bacterial and eukaryotic networks had different responses to the upwelling environment. These findings expanded our understanding of biogeographic patterns of microbial community and ecological network and the underlying mechanisms of different microbial taxa in upwelling.

Periodontitis progression is accompanied by a succession of the oral microbiome. However, the dynamic microbial transitions that link different disease stages and contribute to disease progression remain incompletely understood. This study aims to identify microbial taxa that may serve as potential drivers underlying increasing severity of periodontitis. Subgingival sample 16S rRNA gene sequencing data were reprocessed for quality control and taxonomic annotation. MaAsLin2 was used to identify microbial differences between groups, and co-occurrence networks were built based on the differential taxa. NetMoss algorithm was applied to identify key microbes driving the transition from health to periodontitis. Correlation and mediation analyses were used to assess the associations between these driver taxa and periodontal clinical indicators. Putative novel pathogens (e.g. , ) were markedly enriched in periodontitis, whereas potential protective taxa (e.g. and ) had higher relative abundance in the health group. The microbial co-occurrence networks in the periodontitis groups were progressively disrupted, characterized by reduced network robustness and heightened vulnerability in the Stage III and IV groups. The driver taxa probably influenced the severity of periodontitis through modulation of periodontal clinical indicators, with positive or negative correlations observed between these taxa and periodontal clinical indicators. , , , and are proposed as key driver taxa across different periodontal health conditions, and exhibited significant correlations with periodontal clinical indicators.

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.

ObjectivesThe microbial community structure of the saccharifying starter, Nongxiangxing Daqu(Daqu), is a crucial factor in determining Baijiu’s quality. Lactic acid bacteria (LAB), are the dominant microorganisms in the Daqu. The present study investigated the effects of LAB on the microbial community structure and its contribution to microbial community function during the fermentation of Daqu.MethodsThe effect of LAB on the structure and function of the microbial community of Daqu was investigated using high-throughput sequencing technology combined with multivariate statistical analysis.ResultsLAB showed a significant stage-specific evolution pattern during Daqu fermentation. The LEfSe analysis and the random forest learning algorithm identified LAB as vital differential microorganisms during Daqu fermentation. The correlation co-occurrence network showed aggregation of LAB and Daqu microorganisms, indicating LAB’s significant position in influencing the microbial community structure, and suggests that LAB showed negative correlations with Bacillus, Saccharopolyspora, and Thermoactinomyces but positive correlations with Issatchenkia, Candida, Acetobacter, and Gluconobacter. The predicted genes of LAB enriched 20 functional pathways during Daqu fermentation, including Biosynthesis of amino acids, Alanine, aspartate and glutamate metabolism, Valine, leucine and isoleucine biosynthesis and Starch and sucrose metabolism, which suggested that LAB had the functions of polysaccharide metabolism and amino acid biosynthesis.ConclusionLAB are important in determining the composition and function of Daqu microorganisms, and LAB are closely related to the production of nitrogenous flavor substances in Daqu. The study provides a foundation for further exploring the function of LAB and the regulation of Daqu quality.