iNAP: An integrated network analysis pipeline for microbiome studies

iMeta, Journal Year: 2022, Volume and Issue: 1(2)

Published: March 16, 2022

Abstract Integrated network analysis pipeline (iNAP) is an online for generating and analyzing comprehensive ecological networks in microbiome studies. It implemented two sections, that is, construction analysis, integrates many open‐access tools. Network contains multiple feasible alternatives, including correlation‐based approaches (Pearson's correlation Spearman's rank along with random matrix theory, sparse correlations compositional data) conditional dependence‐based methods (extended local similarity inverse covariance estimation association inference), while provides topological structures at different levels the potential effects of environmental factors on structures. Considering full workflow, from data set to result, iNAP molecular interdomain (IDENAP), which correspond intradomain associations microbial species taxonomic levels. Here, we describe detailed workflow by taking IDENAP as example show steps assist researchers conduct relevant analyses using their own sets. Afterwards, some auxiliary tools facilitating are introduced effectively aid switch operations. Therefore, iNAP, easy‐to‐use platform network‐associated approaches, can enable better understand organization communities. available http://mem.rcees.ac.cn:8081 free registration.

Language: Английский

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Microbial interactions for nutrient acquisition in soil: Miners, scavengers, and carriers

Soil Biology and Biochemistry, Journal Year: 2023, Volume and Issue: 188, P. 109215 - 109215

Published: Oct. 24, 2023

Language: Английский

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Modeling ecosystem-scale carbon dynamics in soil: The microbial dimension

Soil Biology and Biochemistry, Journal Year: 2023, Volume and Issue: 178, P. 108948 - 108948

Published: Jan. 4, 2023

Language: Английский

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Soil and Phytomicrobiome for Plant Disease Suppression and Management under Climate Change: A Review

Plants, Journal Year: 2023, Volume and Issue: 12(14), P. 2736 - 2736

Published: July 23, 2023

The phytomicrobiome plays a crucial role in soil and ecosystem health, encompassing both beneficial members providing critical goods services pathogens threatening food safety security. potential benefits of harnessing the power for plant disease suppression management are indisputable interest agriculture but also forestry landscaping. Indeed, diseases can be mitigated by situ manipulations resident microorganisms through agronomic practices (such as minimum tillage, crop rotation, cover cropping, organic mulching, etc.) well applying microbial inoculants. However, numerous challenges, such lack standardized methods microbiome analysis difficulty translating research findings into practical applications at stake. Moreover, climate change is affecting distribution, abundance, virulence many pathogens, while altering functioning, further compounding strategies. Here, we will first review literature demonstrating how agricultural have been found effective promoting health enhancing suppressiveness mitigation shift phytomicrobiome. Challenges barriers to identification use then discussed before focusing on impacts functioning outcome.

Language: Английский

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Positive associations fuel soil biodiversity and ecological networks worldwide

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(6)

Published: Jan. 29, 2024

Microbial interactions are key to maintaining soil biodiversity. However, whether negative or positive associations govern the microbial system at a global scale remains virtually unknown, limiting our understanding of how microbes interact support biodiversity and functions. Here, we explored ecological networks among multitrophic organisms involving bacteria, protists, fungi, invertebrates in survey across 20 regions planet found that both pairs triads taxa governed networks. We further revealed with greater levels supported larger resulted lower network fragility withstand potential perturbations species losses. Our study provides unique evidence widespread between their crucial role structure worldwide.

Language: Английский

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Fungi stabilize multi‐kingdom community in a high elevation timberline ecosystem

iMeta, Journal Year: 2022, Volume and Issue: 1(4)

Published: Aug. 15, 2022

Microbes dominate terrestrial ecosystems via their great species diversity and vital ecosystem functions, such as biogeochemical cycling mycorrhizal symbiosis. Fungi other organisms form diverse association networks. However, the roles of belonging to different kingdoms in multi-kingdom community networks have remained largely elusive. In light integrative microbiome initiative, we inferred multiple-kingdom biotic associations from high elevation timberline soils using SPIEC-EASI method. Biotic interactions among plants, nematodes, fungi, bacteria, archaea were surveyed at network levels. Compared single-kingdom networks, increased within-kingdom cross-kingdom edge numbers by 1012 10,772, respectively, well mean connectivity negative proportion 15.2 0.8%, respectively. Fungal involvement stability (i.e., resistance node loss) connectivity, but reduced modularity, when compared with those archaea. entire network, fungal nodes characterized significantly higher degree betweenness than bacteria. more often played role connector, linking modules. Consistently, structural equation modeling multiple regression on matrices corroborated "bridge" fungi level, plants soil biota. Overall, our findings suggest that can stabilize self-organization process The facilitate initiation carrying out studies natural reveal complex above- belowground linkages.

Language: Английский

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Microbial habitat specificity largely affects microbial co-occurrence patterns and functional profiles in wetland soils

Geoderma, Journal Year: 2022, Volume and Issue: 418, P. 115866 - 115866

Published: April 5, 2022

Language: Английский

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

The ISME Journal, Journal Year: 2023, Volume and Issue: 17(11), P. 1798 - 1807

Published: Sept. 2, 2023

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

Language: Английский

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A guide for comparing microbial co‐occurrence networks

iMeta, Journal Year: 2023, Volume and Issue: 2(1)

Published: Jan. 10, 2023

The article provides a pipeline for comparing microbial co-occurrence networks based on the R microeco package and meconetcomp package. It has high flexibility expansibility can help users efficiently compare built from different groups of samples or construction approaches. Microorganisms are ubiquitous in diverse environments earth play important roles ecosystem functions ranging biogeochemical cycles [1] to maintenance host health [2, 3]. Microbial assemblages generally comprised large number species, which is represented as "microbial community" defined context spatiotemporal scales. Identifying members their abundance community basic task ecology studies. Over past two decades, giant leap forward sequencing techniques made this possible, leading rapid increase data size. Furthermore, advances bioinformatic softwares (e.g., QIIME2 [4]) have profoundly improved speed convenience sequence analysis. After obtaining operational taxonomic units (OTU), amplicon variants (ASV) species abundances (sequence counts estimated abundance) analysis process, following statistics visualizations be performed using language [5] related packages, grown up cutting-edge system recent decades [6]. Many statistical approaches used microbiome benefiting similarities between macro- micro-ecosystems. However, there also some dissimilarities research methods routes these ecosystems [2]. invisibility microbes, proportion uncultured huge diversity lead difficulty hypothesis-driven studies, especially those referring interactions functions. Researchers usually need try series tools find suitable one verify hypothesis. To maximize accessibility provide good user experience, code must organized manner that adheres both conciseness functionality. Based background, [7] was developed R6 class make customized easier faster. In addition, file2meco [8] facilitate conversion files other software, such [4] HUMAnN [9]. network often decipher hidden patterns complex consortia wide range studies (see [10] references therein). contrast macro-organisms (mainly observations [11, 12]), mostly constructed count tables obtained metagenomic data. several general issues rendering challenge, including compositionality denote proportions instead absolute abundances), sparsity (a zeros), inference direct associations paired taxa. Another challenge how explain edges signs given not recommended interpreted cross-sectional [13]. best our knowledge, correlation-based (Pearson Spearman correlation) may earliest widely approach studied habitats, soil [14]. address existed correlation network, been developed, Sparse Correlations Compositional (SparCC) [15], Compositionally Corrected by REnormalization PErmutation (CCREPE) [16], Correlation through Lasso (CCLasso) [17]. Further, graphical model created robustly infer taxa optimize structure. For example, SParse InversE Covariance Estimation Ecological Association Inference (SPIEC-EASI) [18] combines transformations compositional algorithms sparse neighborhood inverse covariance selection reconstruct network. FlashWeave [19] adopts local-to-global learning framework directly associated neighbors (i.e., Markov blanket) taxon scalability heterogenous sets. comparisons [20] reviews thoroughly discussed robustness particularly depending upon challenges. BEEM-static method [21] dedicated seek out with generalized Lotka-Volterra (gLV) an expectation-maximization algorithm, offering directed gain insight into communities. Along development, controversial voices worry about misuse network-related (especially network) biotic [13, 22, 23]. main reason hold hub answering many questions ecology. that, no matter used, contain more less information approaches, parameters, features themselves. There another case actual captured because itself biological characteristics higher-order interaction [24, 25]). Broadly speaking, interpretations represent largely overlooked mainly due its difficulty. Although recently reveal among microbes [19, 21], applicability still clear when heterogeneous sets applied. So it 26], producing dilemma what extent interactions. appealed inferred background knowledge previous 26]. Even macroecology empirical information, study revealed rarely matched net [27], highlighting inadvisable practice coupling co-occurrences terms ecological hypothesis, currently frequently null constrained ordination conditional deterministic consortia, random (such drift dispersal neutral process) cannot generate strong patterns. understood new application classic "checkerboard distribution" approach. derived joint distribution historical legacy. Various layers complexity inherent systems variability multiple abiotic factors factors) blur whether association real influence association. Moreover, correspond dependent models underlying algorithms, generating problem interpretation. prevalence boosted burst various fields, far enough hypotheses mechanisms behind descriptive metrics. implemented software "black-box" lack combine types methods. reasonably use researchers thorough understanding flows details construction. practice, improve edge finding. complicated experimental design, treatments [28], urgency user-friendly comparison. already integrated online MetagenoNets [29] iNAP [30]) packages igraph [31], NetCoMi [32], ggClusterNet [33]) devoted visualizations. But comparison daunting challenge. protocol, comparison, (https://github.com/ChiLiubio/meconetcomp) (Figure 1A). protocol introduces usage trans_network much possible classes show power 1B). A characterized edge-weighted graph G = (V, E), where V (node) represents feature (ASV/OTU/species) E (edge) encodes connection weight denotes strength connection. (+ −) positive negative associations. set soil_amp stool_met microtable objects prepared. How import own users? Please read document tutorial (https://chiliubio.github.io/microeco_tutorial/basic-class.html#microtable-class). Pearson datasets, adding parameter "use_WGCNA_pearson_spearman TRUE" calculation. Note requires WGCNA installed (https://cran.r-project.org/web/packages/WGCNA/). parameters construction, please see function cal_network command: help(trans_network). Module hubs (nodes highly links within module, Zi > 2.5 Pi ≤ 0.62); (2) Connectors connect modules, (3) Network act module connectors, (4) Peripherals only few almost always nodes < 0.62). we introduced Different take full advantage list class, perform each part most noteworthy limitation practice. applied covered protocol. date, standards available Nor "one size fits all" tool automatically match user's set. Similar results benchmark detection strategies vary sensitivity precision [20], found across at aspect 2B saved figures stool_met_network set). We argued except selections [10, 20, 25], feasible valuable learn reflect integrating model) packages. Disentangling environmental effects attractive topic [39]. Yet gold standard diminish feasibility studying questions. report shows rare likely caused metabolic cross-feeding [40]. habitats like soil, should interpretation interacts. though determinate impede methodological development limit ability broadly link patterns, assembly processes combining instance, differential phylogenetic distance 2C) showed (strong correlations) might varying processes. affect topological structure soils categories Chinese wetlands 2E). With increasing cultured metagenome-assembled genomes, meaning shared 2B) figure interact [41] explaining results. While all beyond scope examples demonstrated generated combination list, "for" loop, easy use, did consider too manipulations, rarefaction, filtering, parameters. These operations critical sense they expected. since object special attribute binding objects, loop package, classes. All authors contributed development. initial idea conceived Chi Liu, Minjie Yao, Xiangzhen Li. During test improvements. original manuscript written Li, revised Chaonan Yanqiong Jiang, Raymond J. Zeng. This work supported National Natural Science Foundation China (42077206 32070548). thank four anonymous reviewers helpful suggestions manuscript. reporting bugs, suggestions, usability problems. declare conflict interest. Besides datasets loaded deposited GitHub (https://github.com/ChiLiubio/network_protocol) Gitee (https://gitee.com/chiliubio/network_protocol). Supporting Information materials (figures, tables, scripts, abstracts, slides, videos, translated versions, update materials) DOI iMeta http://www.imeta.science/.

Language: Английский

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Environmental DNA metabarcoding reveals the influence of human activities on microeukaryotic plankton along the Chinese coastline

Water Research, Journal Year: 2023, Volume and Issue: 233, P. 119730 - 119730

Published: Feb. 12, 2023

Language: Английский

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